Next Article in Journal
Germanium in Carbon Fullerenes: Quantum-Chemical Insights into Substitution, Adsorption, and Encapsulation Phenomena
Previous Article in Journal
Carbonic Anhydrase 3 Overexpression Modulates Signalling Pathways Associated with Cellular Stress Resilience and Proteostasis
Previous Article in Special Issue
Hsp70 Peptides Induce TREM-1-Dependent and TREM-1-Independent Activation of Cytotoxic Lymphocytes
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
IJMS
Volume 26
Issue 24
10.3390/ijms262412065
ijms-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Plasmodium knowlesi Heat Shock Protein 90s: In Silico Analysis Reveals Unique Druggable Structural Features

by
Michael O. Daniyan
1,
Harpreet Singh
2 and
Gregory L. Blatch
3,4,*
1
Department of Pharmacology, Faculty of Pharmacy, Obafemi Awolowo University, Ile-Ife 220005, Nigeria
2
Department of Bioinformatics, Hans Raj Mahila Maha Vidyalaya, Jalandhar 144008, India
3
The Vice Chancellery, The University of Notre Dame Australia, Fremantle, WA 6959, Australia
4
Biomedical Biotechnology Research Unit, Department of Biochemistry, Microbiology and Bioinformatics, Rhodes University, Makhanda 6140, South Africa
*
Author to whom correspondence should be addressed.
Int. J. Mol. Sci. 2025, 26(24), 12065; https://doi.org/10.3390/ijms262412065
Submission received: 6 November 2025 / Revised: 9 December 2025 / Accepted: 11 December 2025 / Published: 15 December 2025
(This article belongs to the Special Issue Heat Shock Proteins (HSPs): Biological Functions and Mechanisms of Actions)
Browse Figures
Versions Notes

Abstract

The increasing threat of zoonotic malaria parasites of humans, such as Plasmodium knowlesi, make the search for improved pharmacotherapy imperative. Using protein sequence and structural analyses, phylogenetics, protein network mapping, protein–ligand interaction, and small molecule docking studies, we have identified for the first time the predicted structure, function, and druggability of the P. knowlesi heat shock protein 90s (PkHsp90s). Four isoforms were identified (in the cytosol, endoplasmic reticulum, mitochondrion, and apicoplast), and key structural differences were elucidated compared to human Hsp90s. In particular, the glycine-rich helix loop (GHL) motif of cytosolic PkHsp90 was predicted to have a straight conformation that forms a plasmodial-specific hydrophobic extension of the lid domain of the ATP-binding site, which was not observed for the cytosolic human Hsp90s, HSPC1 (Hsp90α), and HSPC3 (Hsp90β). Virtual screening identified for the first time a number of compounds from the ZINC database (ZINC22007970, ZINC724661072, and ZINC724661078) that were predicted to bind strongly to the GHL-associated pocket of PkHsp90, with weak or no binding to HSPC1. This study has provided a molecular framework in support of rational drug design, targeting PkHsp90s as a promising route for antimalarial drug development in the fight against zoonotic malaria.

1. Introduction

The predominant nature of malaria infection as a leading global health burden remains unabated. This is especially concerning with an estimated 263 million cases and approximately 600,000 deaths reported in 2024, mainly in tropical and subtropical regions [1]. Among the five Plasmodium species that infect humans, Plasmodium knowlesi, which was originally identified as a simian malaria species, has gained significant attention as an emerging zoonotic parasite, causing major human malaria in Southeast Asia (Malaysia, Indonesia, and Thailand) [2,3]. Its rapid upsurge in parasitaemia and severe disease progression within a 24 h erythrocytic cycle, coupled with multi-organ failure and death in untreated cases, distinguishes P. knowlesi from P. falciparum or P. vivax [2,4,5,6,7]. However, the need for accurate diagnosis remains a challenge that should be addressed as a matter of urgency to avoid misidentification because of the close resemblance of P. knowlesi and P. malariae when viewed under light microscopy [6,8,9].
Plasmodium parasites have evolved robust survival mechanisms in the face of hostile physiological stresses encountered in the human host. A critical component of the parasite’s adaptive response is the chaperones of the heat shock protein (Hsp) family, particularly heat shock protein 90 (Hsp90), a highly conserved molecular chaperone [10,11]. Hsp90 is essential for the proper folding and unfolding, stability, and function of a broad range of client proteins, many of which are involved in signal transduction, cell cycle control, and the stress response [12,13,14]. The functions of plasmodial Hsp90 that are crucial to parasite survival include sporozoite maturation, erythrocyte invasion, and the regulation of gene expression in response to environmental cues [10,15,16]. Structurally, Hsp90 comprises three functional domains: the N-terminal domain (NTD), which possesses ATP-binding and hydrolysis activity; the middle domain (MD), responsible for client protein interaction; and the C-terminal domain (CTD), which facilitates dimerization and interactions with co-chaperones [17]. The ATPase-driven conformational changes enable Hsp90 to cycle through different states required for client protein maturation and stabilization [18]. This dynamic chaperone cycle is modulated in close cooperation with Hsp70 chaperones via Hsp70/Hsp90 organizing protein (Hop) [19,20]. The functional interplay of plasmodial Hsp90 and associated chaperones and co-chaperones has been presented [21,22,23], revealing that these proteins operate in networks by partnering with each other and with the host chaperones.
Several reports have highlighted the potential of plasmodial Hsp90s as novel therapeutic targets by exploiting the unique structural differences between P. falciparum Hsp90s (PfHsp90s) and their human counterparts for selective drug targeting [24,25,26,27,28]. For instance, the cytosolic PfHsp90 ATP-binding site has a unique hydrophobic extension (encompassing the glycine-rich hinge loop or GHL motif) that could potentially be exploited for the design of novel inhibitors [29,30]. Several Hsp90 inhibitors, including those originally developed for cancer therapy, have shown potent antimalarial activity, including geldanamycin (GDM) and its derivative 17-AAG (17-allylamino-17-demethoxygeldanamycin), as well as radicicol and its analogues [31,32,33]. Efforts to develop more selective inhibitors have yielded promising candidates such as PU-H71, a purine-scaffold Hsp90 inhibitor that exhibits nanomolar potency against P. falciparum, with reduced cytotoxicity in mammalian cells, and shows potential for improved activity when combined with chloroquine [34]. The challenges of anticancer drug discovery studies have indicated that CTD inhibitors [35] and disruptors of protein–protein interactions (particularly those inhibiting Hsp90 association with co-chaperones and client proteins) [36] have the potential to address the drug resistance associated with activation of the heat shock response (HSR) by NTD pan-inhibitors. Furthermore, isoform-specific NTD inhibitors have been identified, particularly those selectively targeting cytosolic human HSPC3 (Hsp90β) over cytosolic human HSPC1 (Hsp90α), which appear to be both safe and effective, boding well for future clinical trials assessment as anticancer drugs [37]. Indeed, it is becoming increasingly evident that the historical clinical issues associated with NTD pan-inhibitors were primarily due to the inhibition of HSPC1 [37]. In summary, future drug repurposing for the development of novel PfHsp90-based antimalarial drugs should focus on isoform selectivity, CTD inhibitors, and modulators of protein–protein interactions.
However, despite these advances in PfHsp90 research, little is known about the Hsp90 orthologs in P. knowlesi, apart from the recent identification of four members: cytosolic P. knowlesi Hsp90, PkHsp90, PKNH_0107000; endoplasmic reticulum (ER) P. knowlesi glucose-regulated protein 94 kDa, PkGRP94, PKNH_1441400; mitochondrial P. knowlesi TNF receptor-associated protein 1, PkTRAP1, PKNH_0915900; and apicoplast P. knowlesi Hsp90_A, PkHsp90_A, PKNH_1238400 [38]. Although the P. knowlesi genome has been sequenced and annotated [39,40,41], comprehensive structural and functional characterization of its Hsp90 proteins is lacking. Understanding the molecular features of P. knowlesi Hsp90s is essential for assessing their functional roles and potential as drug targets. Therefore, this study aims to elucidate the structural and functional landscape of P. knowlesi Hsp90s through bioinformatics analyses, including sequence and structural alignments, domain annotation, homology modelling, phylogenetics, and protein network mapping. Also, using protein–ligand interaction analyses, small molecule docking studies, and virtual screening, we aim to predict potential small molecule inhibitors of cytosolic PkHsp90 from known or predicted inhibitors of cytosolic PfHsp90. This study will fill a critical knowledge gap and support rational drug design targeting these essential chaperones in the fight against zoonotic malaria.

2. Results

2.1. Identification and Characterization of Plasmodium knowlesi Hsp90 Isoforms

The BLASTp search analysis using P. falciparum Hsp90 orthologs as queries identified four putative Hsp90 isoforms in the genome of all Plasmodium species used in this study (P. knowlesi, P. vivax, P. berghei, and P. yoeli). In P. knowlesi, these isoforms are (i) cytosolic PkHsp90 (PKNH_0107000); (ii) ER-localized PkGRP94 (PKNH_1441400); (iii) mitochondrial-localized PkTRAP1 (PKNH_0915900); and (iv) apicoplast-localized PkHsp90_A (PKNH_1238400) (Table S1). All isoforms exhibited the conserved signature Hsp90 domain organization, having an NTD (ATPase, co-chaperone binding), charged linker region (interdomain communication), MD (client binding, co-chaperone binding, ATPase activation), and CTD (dimerization, co-chaperone binding) (Figure 1 and Figure 2). The GHL motif “GxxGxG” was present in all Hsp90 isoforms of all Plasmodium species, forming part of the conserved sequence “IGQFGVGFYS”, similar to human HSPC1 (Figure S1), thereby confirming the identity of these proteins as belonging to the Hsp90 family. The MEEVD C-terminal motif required for co-chaperone interaction [13,14,21] was found in cytosolic PkHsp90 (Figure 1 and Figure 2), similar to cytosolic PfHsp90, suggesting that PkHsp90 has canonical co-chaperone–chaperone interactions [22,23]. In addition, the mitochondrial, ER and apicoplast signal sequences were identified in the respective isoforms for targeting to their sub-cellular organelles, as well as the C-terminal ER retention sequence (NDEL) in PkGRP94 (Figure 1 and Figure 2). The InterPro [42,43] analysis confirmed the presence of signature Hsp90 domains in all isoforms (Figure 2). Notably, the cytosolic PkHsp90 contained potential co-chaperone binding motifs and nuclear localization signals, suggesting that it may shuttle between the cytosolic and nuclear compartments under stress conditions. The predicted structures of all the P. knowlesi Hsp90 isoforms shared similar overall 3D domain architectures as the human cytosolic Hsp90s (HSPC1 and HSPC3) (Figure 3). However, noticeable differences included the longer charged linker region of cytosolic PkHsp90 (Figure 3C) and the presence of a major insertion in the NTD of apicoplast PkHsp90_A (Figure 3F), which both appeared as disordered coils on the dimeric structures.

2.2. Sequence Alignment and Phylogenetic Analysis

Multiple sequence alignments of full-length protein revealed that P. knowlesi Hsp90 isoforms shared >62% identity with their predicted orthologs in other Plasmodium species used in this study (P. falciparum, P. knowlesi, P. vivax, P. berghei, and P. yoeli) and less than 40% sequence identity with their paralogs (Table S1 and Figure S1). The cytosolic orthologs shared very high sequence identities (>93%) with one another and were the only plasmodial Hsp90 proteins with >67% sequence identities with cytosolic human HSPC1 (Figure S2 and Table S1). The next highest sequence identities were found with the apicoplast and ER orthologs, with >79% sequence identities (Figures S3 and S4, Table S1). The lowest sequence identities were found among the mitochondria orthologs (with as low as 62.56%) (Figure S5 and Table S1). As expected, the NTD encompassing the highly conserved ATPase domain exhibited the greatest level of sequence identity (Figure 1 and Table S1). The conservation of the overall Hsp90 domain architecture and signature motifs, especially the ATPase domain and co-chaperone binding sites, suggests that the P. knowlesi Hsp90 isoforms are functionally similar to their homologues and, like the P. falciparum Hsp90 isoforms, may perform essential roles in parasite stress management and life cycle progression [44,45]. It is also possible that known small molecule inhibitors of the P. falciparum Hsp90 isoforms may similarly modulate P. knowlesi Hsp90 isoforms.
The phylogenetic tree clustered each isoform distinctly, with P. knowlesi Hsp90s grouping closely with P. vivax Hsp90s in all isoforms, and P. berghei proteins grouping with P. yoeli in ER and mitochondria isoforms, consistent with known evolutionary proximity [46] (Figure 4). Arising off the clusters or in-between are the P. falciparum Hsp90 isoforms, suggesting a slightly greater evolutionary distance [46,47]. An even more distant relationship with human Hsp90 isoforms correlated with the lower level of sequence similarity and structural differences that could be exploited for drug intervention (Figure 4). It should be noted that human ER HSPC4 clustered closer to the cytosolic Hsp90 isoforms instead of the ER Hsp90 isoforms but arose from the same root. To probe this further, two additional apicomplexan Hsp90 proteins (from Theileria anniculata and Babesia ovata), as well as a chloroplast Hsp90 from Nicotiana tabacum, were included in the phylogenetic analysis. The results showed that the two additional apicomplexan Hsp90s shared similar clustering with the plasmodial isoforms (Figure 4). Interestingly, HSPC4 appears to be evolutionarily closer to chloroplast Hsp90 and may serve as an intermediate between cytosolic and ER Hsp90 isoforms.
Ijms 26 12065 g001
Figure 1. Domain organization of human (HSPC1/Hsp90α and HSPC3/Hsp90β) and P. knowlesi (PkHSP90; PkGRP94; PkTRAP1; and PkHsp90_A) Hsp90s. Abbreviations: NTD: N-terminal domain; L: charged linker region; MD: middle domain; and CTD: C-terminal domain. Textured boxes represent: ER signal peptide (light blue); mitochondrial targeting peptide (magenta); and apicoplast targeting peptide (grey). The name, localization and accession numbers of the protein sequences used to construct the linear domain organization layout are HSPC1/Hsp90α, cytosolic, NP_001017963.2; HSPC3/Hsp90β, cytosolic, NP_031381.2; PkHsp90, cytosolic, PKNH_0107000; PkGRP94, ER, PKNH_1441400; PkTRAP1, mitochondrion, PKNH_0915900; and PkHsp90_A, apicoplast, PKNH_1238400. The numbers at the end of each domain schematic represent the total number of amino acids in each protein. This linear domain layout was created using the standalone version of the IBS 1.0 (IBS: an illustrator for the presentation and visualization of biological sequence; available online: http://ibs.biocuckoo.org/; accessed on 28 September 2025).
Figure 1. Domain organization of human (HSPC1/Hsp90α and HSPC3/Hsp90β) and P. knowlesi (PkHSP90; PkGRP94; PkTRAP1; and PkHsp90_A) Hsp90s. Abbreviations: NTD: N-terminal domain; L: charged linker region; MD: middle domain; and CTD: C-terminal domain. Textured boxes represent: ER signal peptide (light blue); mitochondrial targeting peptide (magenta); and apicoplast targeting peptide (grey). The name, localization and accession numbers of the protein sequences used to construct the linear domain organization layout are HSPC1/Hsp90α, cytosolic, NP_001017963.2; HSPC3/Hsp90β, cytosolic, NP_031381.2; PkHsp90, cytosolic, PKNH_0107000; PkGRP94, ER, PKNH_1441400; PkTRAP1, mitochondrion, PKNH_0915900; and PkHsp90_A, apicoplast, PKNH_1238400. The numbers at the end of each domain schematic represent the total number of amino acids in each protein. This linear domain layout was created using the standalone version of the IBS 1.0 (IBS: an illustrator for the presentation and visualization of biological sequence; available online: http://ibs.biocuckoo.org/; accessed on 28 September 2025).
Ijms 26 12065 g001
Ijms 26 12065 g002
Figure 2. Multiple sequence alignment of P. knowlesi Hsp90 isoforms. Blue annotation indicates degree of conservation and sequence identity with a threshold of not less than 100%. Identified with arrows indicating the range of sequences for the protein domains are orange for the signal peptide/pre-NTD region; black for the NTD; red for the charged linker region; green for the MD; and purple for the CTD. In the yellow and blue boxes are the GHL motif “GxxGxG” and the C-terminal motifs, respectively. The red and green boxes identified critical sequences that make up the ATP-binding pocket and lid domains in PkHsp90, respectively, and their corresponding sequences in other isoforms. In between the red boxes is the region of very low sequence similarity. The P. knowlesi Hsp90 isoforms used are as follows: PkHsp90, PKNH_0107000; PkGRP94, PKNH_1441400; PkHsp90_A, PKNH_1238400; and PkTRAP1, PKNH_0915900. Alignment and annotation of sequence identity and conservation were generated using Jalview v2.11.5.0 [48]. The other annotation was conducted using Microsoft PowerPoint, and the image was processed with GIMP v2.10.14 software [49].
Figure 2. Multiple sequence alignment of P. knowlesi Hsp90 isoforms. Blue annotation indicates degree of conservation and sequence identity with a threshold of not less than 100%. Identified with arrows indicating the range of sequences for the protein domains are orange for the signal peptide/pre-NTD region; black for the NTD; red for the charged linker region; green for the MD; and purple for the CTD. In the yellow and blue boxes are the GHL motif “GxxGxG” and the C-terminal motifs, respectively. The red and green boxes identified critical sequences that make up the ATP-binding pocket and lid domains in PkHsp90, respectively, and their corresponding sequences in other isoforms. In between the red boxes is the region of very low sequence similarity. The P. knowlesi Hsp90 isoforms used are as follows: PkHsp90, PKNH_0107000; PkGRP94, PKNH_1441400; PkHsp90_A, PKNH_1238400; and PkTRAP1, PKNH_0915900. Alignment and annotation of sequence identity and conservation were generated using Jalview v2.11.5.0 [48]. The other annotation was conducted using Microsoft PowerPoint, and the image was processed with GIMP v2.10.14 software [49].
Ijms 26 12065 g002
Ijms 26 12065 g003
Figure 3. Predicted 3D structures of human ((A): HSPC1; (B): HSPC3) and P. knowlesi ((C): cytosolic PkHsp90; (D): ER PkGRP94; (E): mitochondrial PkTRAP1; and (F): apicoplast PkHsp90_A) Hsp90s as full-length dimers. The modelling was conducted with SWISS-MODEL (SWISS-MODEL: homology modelling of protein structures and complexes; available online: https://swissmodel.expasy.org/; accessed on 7 October 2025, homodimer mode), taking 5FWK, 8FFV, 7Y04 (electron microscopic structures), 5ULS, 4YIN, and 5ULS (X-ray crystallographic structures), respectively, as templates. The NTD, charged linker region, MD, and CTD are depicted in red, purple, green, and blue colours for both the monomers, respectively. The boundaries of the NTD and MD of the representative HSPC3 were derived from the X-ray crystallographic structures solved for these domains (NTD: PDB ID, 5UCJ and MD: PDB ID, 3PRY). Images for the predicted structures were rendered using UCSF Chimera 1.18 (UCSF Chimera: a visualization system for exploratory research and analysis; available online: https://www.cgl.ucsf.edu/chimera/; accessed on 7 October 2025).
Figure 3. Predicted 3D structures of human ((A): HSPC1; (B): HSPC3) and P. knowlesi ((C): cytosolic PkHsp90; (D): ER PkGRP94; (E): mitochondrial PkTRAP1; and (F): apicoplast PkHsp90_A) Hsp90s as full-length dimers. The modelling was conducted with SWISS-MODEL (SWISS-MODEL: homology modelling of protein structures and complexes; available online: https://swissmodel.expasy.org/; accessed on 7 October 2025, homodimer mode), taking 5FWK, 8FFV, 7Y04 (electron microscopic structures), 5ULS, 4YIN, and 5ULS (X-ray crystallographic structures), respectively, as templates. The NTD, charged linker region, MD, and CTD are depicted in red, purple, green, and blue colours for both the monomers, respectively. The boundaries of the NTD and MD of the representative HSPC3 were derived from the X-ray crystallographic structures solved for these domains (NTD: PDB ID, 5UCJ and MD: PDB ID, 3PRY). Images for the predicted structures were rendered using UCSF Chimera 1.18 (UCSF Chimera: a visualization system for exploratory research and analysis; available online: https://www.cgl.ucsf.edu/chimera/; accessed on 7 October 2025).
Ijms 26 12065 g003
Ijms 26 12065 g004
Figure 4. Phylogenetic analysis of plasmodial and human Hsp90 isoforms. Pb, Pf, Pk, Pv, and Py are P. berghei, P. falciparum, P. knowlesi, P. vivax, and P. yoeli. Hsp90, Hsp90_A, TRAP1, and GRP94 are the cytosolic, apicoplast, mitochondrial, and ER Hsp90 isoforms, respectively. HSPC1 (cytosolic), HSPC3 (cytosolic), HSPC4 (ER), and HSPC5 (mitochondrial) are the human Hsp90 isoforms. Control proteins included in the phylogenetic analysis were additional apicomplexan Hsp90s (Bo and Ta are Babesia ovata and Theileria anniculata), namely, TaHsp90 (TA12105), TaTRAP1 (TA06845), TaGRP94 (TA06470), TaHsp90_A (TA10720), BoHsp90 (BOVATA_007200), BoTRAP1 (BOVATA_017520), BoGRP94 (BOVATA_020650), and BoHsp90_A (BOVATA_003160), as well as NtHsp90 (LOC107797756), the chloroplast Hsp90 from Nicotiana tabacum. Phylogenetic analyses were performed using the NGPhylogeny.fr online resource as detailed in the Materials and Methods Section 4.3. Final image processing was conducted with GIMP v2.10.14 [49].
Figure 4. Phylogenetic analysis of plasmodial and human Hsp90 isoforms. Pb, Pf, Pk, Pv, and Py are P. berghei, P. falciparum, P. knowlesi, P. vivax, and P. yoeli. Hsp90, Hsp90_A, TRAP1, and GRP94 are the cytosolic, apicoplast, mitochondrial, and ER Hsp90 isoforms, respectively. HSPC1 (cytosolic), HSPC3 (cytosolic), HSPC4 (ER), and HSPC5 (mitochondrial) are the human Hsp90 isoforms. Control proteins included in the phylogenetic analysis were additional apicomplexan Hsp90s (Bo and Ta are Babesia ovata and Theileria anniculata), namely, TaHsp90 (TA12105), TaTRAP1 (TA06845), TaGRP94 (TA06470), TaHsp90_A (TA10720), BoHsp90 (BOVATA_007200), BoTRAP1 (BOVATA_017520), BoGRP94 (BOVATA_020650), and BoHsp90_A (BOVATA_003160), as well as NtHsp90 (LOC107797756), the chloroplast Hsp90 from Nicotiana tabacum. Phylogenetic analyses were performed using the NGPhylogeny.fr online resource as detailed in the Materials and Methods Section 4.3. Final image processing was conducted with GIMP v2.10.14 [49].
Ijms 26 12065 g004

2.3. Functional Annotation and Protein–Protein Interaction (PPI) Analysis

Network analysis was conducted with the four identified P. knowlesi Hsp90 isoforms in comparison with P. falciparum Hsp90 isoforms using STRING v11 [50,51,52] and ShinyGO v0.82 [53] online resources. While all the four P. falciparum Hsp90 isoforms were successfully mapped by STRING v11, only three (PKNH_0915900/PkTRAP1, PKNH_1238400/PkHsp90_A, and PKNH_1441400/PkGRP94) were successfully mapped in P. knowlesi (Tables S2 and S3). The Gene Ontology (GO) enrichment analysis revealed similar biological processes and molecular functions associated with all the Hsp90 isoforms in P. falciparum and P. knowlesi (Table 1). Of the top 20 GO-enriched terms associated with P. falciparum Hsp90 proteins, 12 can be identified with P. knowlesi Hsp90 isoforms. With all the GO functional terms associated with the three mapped P. knowlesi Hsp90s also found in the four mapped P. falciparum Hsp90s, it can be inferred that the unmapped P. knowlesi protein (PKNH_0107000/PkHsp90) will possess similar functional terms with its corresponding P. falciparum ortholog. Meanwhile, GO annotation indicated very strong involvement in biological processes, including protein folding (GO:0006457), response to stress (GO:0006950), and ATPase activities (GO:0016887 and GO:0140657), as well as molecular functions, such as, unfolded protein binding (GO:0051082), ATP binding (GO:0005524), adenyl ribonucleotide binding (GO:0032559), and adenyl nucleotide binding (GO:0030554) (Table 1 and Figure S6). These functions are critical during parasite development in the febrile host environment, particularly during schizogony and erythrocyte invasion. The prominent GO cellular components identified were sarcoplasmic reticulum lumen (GO:0033018), melanosome (GO:0042470), and the perinuclear region of the cytoplasm (GO:0048471). These are associated with Hsp90 orthologs in apicoplast (PKNH_1238400/PkHsp90_A and PF3D7_1443900/PfHsp90_A) and ER (PKNH_1441400/PkGRP94 and PF3D7_1222300/PfGRP94) in P. knowlesi and P. falciparum, suggesting similar roles for P. knowlesi Hsp90s.
In addition, we predicted a STRING-based PPI network for cytosolic PkHsp90 (Figure 5) using the interacting partners of cytosolic PfHsp90 (Figure S7) and their orthologs in P. knowlesi as templates (retrieved from PlasmoDB [54] and confirmed with BLASTp searches [55]). The PkHsp90 interaction network revealed expected predicted interactions with certain key parasite-resident co-chaperone proteins previously reported for PfHsp90 [56], including PkHOP (PKNH_0420900) and a type III J-domain protein (JDP; also called Hsp40) (PKNH_1207800). The predicted interactions with a number of other parasite-resident proteins aligned well with known Hsp90 chaperone functions associated with important intracellular processes, such as the regulation of transcription (e.g., the transcription factor pre-binding protein, PkPREBP/PKNH_0811600), translation (e.g., the translation factor pelota-like protein, PkPelo/PKNH_0317500), and protein folding and the stress response (e.g., the cytosolic Hsp110, PkHsp70-z/PKNH_0107400) (Figure 5). Furthermore, the PkHsp90 network appears to extend beyond the boundaries of the parasite, suggesting an association with components of the protein export machinery (e.g., exported protein 3, PkEXP3/PKNH_0609300) (Figure 5).
Ijms 26 12065 g005
Figure 5. Protein network interaction scheme of cytosolic PkHsp90. Target nodes are represented as octagons bordered in red and filled in yellow for all predicted interacting proteins. The source node, cytosolic PkHsp90, is shown as a rectangle bordered in red and filled in purple. Arrows (black) point from the source to the target nodes and serve as indicators of predicted functional interactions. In brackets are other known common names. Shown in a blue-bordered rectangular box are the BioGrid-identified interacting partners that are not found in the STRING v11 database, where the network was generated. The network was rendered using Cytoscape v3.10.4 [57,58]. Known putative functions are shown in oval shapes pointed by block arrows [59,60,61,62,63,64,65,66,67,68]. Images were prepared using PowerPoint and GIMP v2.10.14 software [49].
Figure 5. Protein network interaction scheme of cytosolic PkHsp90. Target nodes are represented as octagons bordered in red and filled in yellow for all predicted interacting proteins. The source node, cytosolic PkHsp90, is shown as a rectangle bordered in red and filled in purple. Arrows (black) point from the source to the target nodes and serve as indicators of predicted functional interactions. In brackets are other known common names. Shown in a blue-bordered rectangular box are the BioGrid-identified interacting partners that are not found in the STRING v11 database, where the network was generated. The network was rendered using Cytoscape v3.10.4 [57,58]. Known putative functions are shown in oval shapes pointed by block arrows [59,60,61,62,63,64,65,66,67,68]. Images were prepared using PowerPoint and GIMP v2.10.14 software [49].
Ijms 26 12065 g005

2.4. Sequence and Structural Analyses of the ATP Binding Pockets and Lid Domains

A structural analysis of the ATP binding pocket and lid domain of PkHsp90 in comparison with those of PfHsp90 and human Hsp90 (HSPC1) was conducted, with the aim of identifying structural and functional similarities and differences that may be applicable in drug targeting. The lid domain consists of the GHL motif, a N-terminal α-helix, and the characteristic IXXSG (plasmodial) and LXXGA (human) motifs located in the cap region. To this end, a model of the NTD of PkHsp90 was generated using the SWISS MODEL server [69,70]. The model showed good quality, with a GMQE score of 0.82, MolProbity score of 0.83, Clash score of 0.30, a high Ramachandran favoured percentage (96.62%), no Ramachandran and Rotamer outliers (0.00%), and a good QMEANDisCo Global score (0.80 ± 0.06). Also, to assess the degree of sequence conservation, a multiple sequence alignment of the ATP binding pockets and lid domains was conducted for all P. knowlesi Hsp90 isoforms, cytosolic PfHsp90, and the human cytosolic isoforms (HSPC1 and HSPC3) using Jalview v2.11.5.0 [48] (Figure 6A). The cytosolic PkHsp90 and PfHsp90 showed >97% sequence similarity between Q14 and S49 and between D79 and L129 with the human Hsp90 isoforms. These are the regions containing residues that interact with known NTD ligands of cytosolic Hsp90s. From I50 to E78, there are increasing substitutions with different amino acids in the human Hsp90 isoforms (Figure 6A). Further structural analysis of the NTDs revealed three loops (Figure 6B). The lid domains (R98–L129; Figure 6B, cyan) and ATP interacting pockets (Q14–S49 and D79–A97; Figure 6B, blue) are known to be functionally relevant as many known inhibitors interact with residues within these loops [24,30,71]. The third loop (Figure 6B, brown) is the joining loop of the ATP interacting pocket and has very low levels of sequence similarity. Specifically, L64/L59 to V92/V88 of HSPC1/HSPC3 are essentially different from the plasmodial cytosolic Hsp90s (I50 to E78), showing about 34% sequence similarity (Figure 6A,B). Also, F63 in PfHsp90 is replaced with Y63 in PkHsp90 (Figure 6A), providing a potential target for selective inhibition of PkHsp90. When compared with other P. knowlesi Hsp90 isoforms, this region in PkHsp90 also showed low sequence similarity. Therefore, this region may constitute a novel loop that could be exploited for isoform-selective drug targeting. Interestingly, both ADP and GDM do not show any interaction with residues of this loop in both human and plasmodial cytosolic Hsp90s (Figure 6B).
Comparative structural analysis of the lid domain of cytosolic PkHsp90 revealed that the GHL motif had a straight conformation that formed a hydrophobic extension or pocket, which was not observed for human HSPC1 where the GHL was curved (Figure 6C). This key difference was highlighted when the lid domains of PkHsp90 and HSPC1 were superimposed on one another (Figure 6C). Furthermore, in HSPC1 (1BYQ), ADP did not show any interaction with K112 or the closely linked G136 and G137, despite interacting with F138. On the other hand, in PkHsp90, ADP did show an interaction with R98 (equivalent to K112 in HSPC1). Cytosolic PfHsp90 has previously been shown to have a hydrophobic extension of the lid domain [30]; hence, the findings for PkHsp90 reinforce the existence of a unique hydrophobic pocket in the lid domains of cytosolic plasmodial Hsp90s. Furthermore, while most of the residues in the lid domains are conserved in the P. knowlesi Hsp90 isoforms, there are certain key differences between PkHsp90 and the other isoforms (e.g., R98 in PkHsp90 was replaced with K160, Q226 and K174 in PkGRP94, PkHsp90_A and PkTRAP1, respectively) that are likely to cause the plasmodial-specific hydrophobic pocket within the lid domains to have different structural features. To further interrogate the observed differences in the lid domains of P. knowlesi Hsp90 isoforms, a comparative structural analysis of the lid domains in all isoforms was conducted using their full-length predicted structures (Figure S8). Although they show similar structural features, PkHsp90 appears to be in a more open conformation then the other isoforms. Also, with a longer insertion (S188–Q210), the lid domain of PkTRAP1 was more hydrophilic compared to the other isoforms. PkHsp90 is the most hydrophobic, followed by PkGRP94 and PkHsp90_A. The increasing change toward hydrophilicity from PkHsp90 to PkGRP94, followed by PkHsp90_A to PkTRAP1, may be an indication of potential functional differences.

2.5. Comparative Docking Analysis of the ATP Binding Pocket and Lid Domains

The modelled NTD in complex with ADP for PkHsp90 and the experimentally determined NTDs co-crystallized with ADP of PfHsp90 (PDB: 3K60) and human HSPC1 (PDB: 1BYQ) were used to assess the binding potentials of our local database of PfHsp90 small molecule inhibitors. The DoGSiteScorer v2.0.0 predicted multiple druggable pockets on the PkHsp90 model, with a high druggability score (0.81) comparable to PfHsp90 (3K60: 0.83) and HSPC1 (1BYQ: 0.83). The ADP-binding site is a highly druggable site, with ligand coverage of greater than 90% in all protein complexes. A comparative protein–ligand interaction analysis was conducted on the three proteins with ADP and GDM (Figure S9). The interaction analysis revealed that PkHsp90 and PfHsp90 shared some amino acids in common, such as A41, K44, D79, M84, and R98. It also revealed hydrogen bonding with critical ADP binding cleft residues, like D79, M84, and R98. Importantly, while human HSPC1 shared hydrogen bonding with two equivalent residues of the plasmodial Hsp90s (D93 and M98 equivalent to D79 and M84), one critical contact was missing (K112 equivalent to R98), as alluded to in the previous section. The lid domain has been reported to possess the potential for highly selective targeting of PfHsp90 in antimalarial drug discovery [29,30], and structural analyses in this report suggest that the lid domain of PkHsp90 could also be selectively targeted. In this report, we conducted virtual screening of our local database of small molecule inhibitors against the ADP-binding pocket and lid domains of PkHsp90, PfHsp90, and HSPC1 (Table S4). The ranked scores of the interactions of each ligand with the proteins revealed their potential for possible preferential functional binding with the plasmodial Hsp90 proteins. Specifically, the ranking showed that all the small molecule compounds considered in this study had very weak or no binding affinity with the lid domain of HSPC1, suggesting selective targeting of the plasmodial Hsp90s. Further analysis revealed that of the top-rated consensus ADP-binding site interacting molecules, only two out of ten bound to HSPC1 (Table 2). Similarly, of the top-rated lid domain interacting molecules, only four compounds bound to HSPC1 (Table 2). To further evaluate this potential, we constructed a box and whisker plot of the screening scores (Figure 7). The plot confirmed the preferential binding to the plasmodial Hsp90s over HSPC1, suggesting that these small molecule compounds might be useful leads in drug discovery efforts against P. knowlesi malaria. It should be noted, however, that although some level of selectivity might be achieved with the ADP-binding site, interaction with the lid domains showed a distinctive selective interaction with PkHsp90 and PfHsp90, indicating a higher potential for selective targeting.
Furthermore, we performed a comparative analysis of the docking pose and ligand interactions of the top-rated ZINC compounds (ZINC22007970, ZINC724661072, and ZINC724661078) in comparison with N-CBZ-5B, an amino-alcohol carbazoles compound, which was previously identified as a unique binder of the lid domain of PfHsp90 [29,30] (Figure 8). The compounds showed little (ZINC724661078 and N-CBZ-5B) or no (ZINC22007970 and ZINC724661072) interactions within the lid domain of HSPC1. Those that showed low affinity interaction with HSPC1 did not interact with the critical residues (e.g., K112). However, for PkHsp90 and PfHsp90, all the compounds docked within the lid domain and showed interaction with the critical residues, sharing many residues with N-CBZ-5B, including R98, G100, T101, A107, F120, V122, and G123. The interaction analysis also showed that PkHsp90 and PfHsp90 shared similar interacting residues within the lid domains. Therefore, it can be inferred that PkHsp90 is structurally and functionally amenable to inhibition by small molecule compounds known to bind to plasmodial Hsp90s, with the potential for lead optimization based on its unique structural features.

3. Discussion

This study is the first comprehensive bioinformatic analysis to provide structural and functional insight into the Hsp90 proteins of P. knowlesi, with a view to assessing their suitability for selective drug targeting. While the cytosolic PkHsp90 interaction network revealed the expected predicted interactions with certain key parasite-resident co-chaperones (PkHOP and a type III JDP) [56], other predicted interactions were less expected, but nevertheless consistent with the role of the major molecular chaperones in proteostasis, such as the regulation of transcription (e.g., PkPREBP) [65], translation (e.g., PkPelo) [67], and protein folding and the stress response (e.g., PkHsp70-z) [59,60,61]. Interestingly, the PkHsp90 network appears to include proteins beyond the boundaries of the parasite (e.g., PkEXP3) [63].
While the predicted structures of all the P. knowlesi Hsp90 isoforms shared similar overall 3D domain architectures as the human cytosolic Hsp90s (HSPC1 and HSPC3), key differences were identified (e.g., the longer charged linker region of cytosolic PkHsp90 and a large insertion in the NTD of apicoplast PkHsp90_A). Protein sequence and structural analyses, protein–ligand interaction, and small molecule docking studies revealed that cytosolic PkHsp90 harbours distinct druggable features that differ from human HSPC1, suggesting the feasibility for species-selective inhibitor development. In particular, we have confirmed the presence of a second plasmodial-specific GHL-associated hydrophobic pocket extending from the ATP-binding pocket, which was not observed for human HSPC1. Virtual screening against this pocket, using a database of small molecules known or predicted to inhibit PfHp90, revealed preferential small molecule binding to PkHsp90 over human HSPC1. This virtual screening identified for the first time a number of compounds from the ZINC database (ZINC22007970, ZINC724661072, and ZINC724661078) that bound to the GHL-associated pocket of PkHsp90, with greater apparent affinity than previously identified amino-alcohol carbazoles [30]. While targeting the ATP pocket remains a valid option, our virtual screening suggests that greater selectivity for plasmodial Hsp90s over human Hsp90s can be achieved by targeting the GHL-associated pocket. In addition, we have highlighted a key loop in PkHsp90 and PfHsp90 (I50 to E78) that has very low levels of sequence similarity with HSPC1. While the GHL-associated pocket lies downstream of the ATP pocket, this loop connects the two coils that make up the residues interacting with ATP. This loop also harbours the replacement of F63 in PfHsp90 with Y63 in PkHsp90, suggesting its potential for selective targeting of PkHsp90. However, further analysis will be needed to ascertain the functional and drug targeting potential of this loop.

4. Materials and Methods

4.1. Sequence Retrieval and Identification of Hsp90 Isoforms

The protein sequences of P. falciparum Hsp90 family members were retrieved from the PlasmoDB [54,73,74] and NCBI protein databases [75]. The BLASTp searches [55] in the PlasmoDB platform were conducted using the retrieved known P. falciparum Hsp90 sequences as queries to identify orthologs in P. knowlesi, P. vivax, P. berghei, and P. yoeli. Identified sequences were validated based on conserved Hsp90 domain architecture. Domain annotation was conducted using Interpro online resource [42,43]. With respect to the human Hsp90 sequences, the HSPC nomenclature proposed by Kampinga et al. (2009) [76] was adopted, where HSPC1 (gene ID 3320) refers to the inducible cytosolic isoform (also called Hsp90α), HSPC3 (gene ID 3326) refers to the constitutively expressed cytosolic isoform (also called Hsp90β), HSPC4 (gene ID 7184) refers to the ER isoform (also called GRP94), and HSPC5 (gene ID 10131) refers to the mitochondrial isoform (also called TRAP1). Also retrieved, were the control proteins included in the phylogenetic analysis, namely, TaHsp90 (TA12105), TaTRAP1 (TA06845), TaGRP94 (TA06470), and TaHsp90_A (TA10720) from Theileria anniculata; BoHsp90 (BOVATA_007200), BoTRAP1 (BOVATA_017520), BoGRP94 (BOVATA_020650), and BoHsp90_A (BOVATA_003160) from Babesia ovata; as well as NtHsp90 (LOC107797756), the chloroplast Hsp90 from Nicotiana tabacum. The complete list of all proteins used in this study and their Uniprot and NCBI identifiers are contained in Table S5.

4.2. Three-Dimensional Protein Structure Retrieval and Modelling

Where indicated, the three-dimensional (3D) structures of proteins for alignment and docking were retrieved from the RCSB PDB or AlphaFold databases [77,78]. The following proteins were retrieved: NTD of human Hsp90 bound to GDM (PDB: 1YET) and ADP (PDB: 1BYQ); full-length human Hsp90 bound to ATP (PDB: 5FWK); NTD of PfHsp90 bound to ADP (PDB: 3K60); cytosolic PkHsp90 (PKNH_0107000; AlphaFold model: A0A679KRE8), ER PkGRP94 (PKNH_1441400; AlphaFold model: A0A384LP92), apicoplast PkHsp90_A (PKNH_1238400; AlphaFold model: A0A384K893), and mitochondrial PkTRAP1 (PKNH_0915900; AlphaFold model: A0A384K8H3). The modelling for the full-length Hsp90 dimers was conducted with SWISS-MODEL [69,70] (SWISS-MODEL: homology modelling of protein structures and complexes; available online: https://swissmodel.expasy.org/; accessed on 7 October 2025; homodimer mode), taking 5FWK, 8FFV, 7Y04 (electron microscopic structures), 5ULS, 4YIN, and 5ULS (X-ray crystallographic structures), respectively, as templates. For the detailed structural analysis and docking studies of the ATP-binding site and associated lid domain, a model of the NTD of PkHsp90 was generated using SWISS-MODEL server, with chain A of the NTD of PfHsp90 bound to ADP (PDB: 3K60) as template (97.16% sequence coverage). The NTD PkHsp90–ADP complex model was generated via superimposition with the template, as earlier described [24].

4.3. Multiple Sequence and Structural Alignments and Phylogenetic Analysis

The retrieved Hsp90 sequences from 4.1 above were aligned using the mcoffee mode of TCoffee v13.46.2.7c9e712d [79] and Clustal Omega v1.2.4 [80,81,82] as implemented in UniProt online resource [83]. The mcoffee mode of TCoffee computes a consensus sequence alignment from separate alignments obtained from ClustalW v2.0 [84], TCoffee v13.46.2.7c9e712d [79], abPOA v1.5.5 [85], Muscle v5 [86], Mafft v7.526 [87], DIALIGN-T v1.0.2-15 [88], PCMA v2.0 [89], and ProbCons v1.12 [90]. The alignments were conducted on the full-length sequences, checked manually, and annotated using Jalview v2.11.5.0 [48]. The Clustal Omega v1.2.4 from the Uniprot resource was used to compute pairwise percentage identity using the full-length sequences. Protein structure alignments were performed using TM-Align as implemented in the RCSB PDB online resource [77,91]. Structural alignment of each AlphaFold downloaded structure was performed with multiple control PDB structures (1YET, 5FWK and 3K60). The aligned structures were viewed using Discovery Studio Visualizer v25.1.0.24284 [72]. Phylogenetic trees were constructed using the NGPhylogeny.fr online resource [92] with its PhyML/OneClick default settings, involving sequence alignment using Muscle v5 [86], curation to eliminate poorly aligned positions and divergent regions using Gblocks 0.91b, phylogeny using PhyML v3.1/3.0 aLRT, and tree rendering using TreeDyn 198.3 [92]. Phylogenetic tree annotated was conducted using the iTOL v6 online resource [93]. Final image processing was conducted with GIMP v2.10.14 [49,94].

4.4. Protein–Protein Interaction (PPI) Network and Functional Analysis

Protein interaction networks were constructed for P. knowlesi and P. falciparum Hsp90 protein families using the STRING v11 database [50,52]. High-confidence (0.70) interactions within the query proteins and not more than 10 additional proteins as determined by the STRING v11 database were selected based on all active interaction sources (text-mining, experiments, databases, co-expression, neighbourhood, gene fusion, and co-occurrence) for the predictions. Protein interaction networks for known interacting partners of PfHsp90 were retrieved from the BioGRID database [95,96]. The protein sequence of each of the retrieved PfHsp90 partners was used as a query sequence for BLASTp searches [55] in the PlasmoDB platform [54] to identify orthologs in P. knowlesi. Functional networks of PfHsp90 and associated partners, as well as those of the identified orthologs of P. knowlesi, were constructed using the STRING v11 database [50,52]. The two networks were used as templates for the construction of a predicted network of PkHsp90 with identified orthologs. Functional annotations were inferred using Gene Ontology (GO) terms and KEGG pathway enrichment analysis through ShinyGO v0.82 online resource [53].

4.5. Sequence and Structural Analyses of the ATP-Binding Pocket and Lid Domains

The structural analysis of the ATP-binding pocket and lid domain of PkHsp90 was performed in comparison with those of PfHsp90 and human Hsp90 (HSPC1/Hsp90α) as previously reported [30]. Multiple sequence alignment of the NTD sequence was conducted for all P. knowlesi Hsp90 isoforms, HSPC1 (Hsp90α), HSPC3 (Hsp90β), and PfHsp90 using the mcoffee mode of TCoffee v13.46.2.7c9e712d [79], as detailed in Section 4.3. The alignment was checked manually and annotated using Jalview v2.11.5.0 [48]. The structural features of the ATP-binding pockets and lid domains (consisting of the GHL motif, the N-terminal α-helix of the lid domains, and the characteristic IXXSG and LXXGA motifs located in the cap of the lid domains of plasmodial and human Hsp90s, respectively) were generated for analysis using Discovery Studio Visualizer v25.1.0.24284 [72].

4.6. Drug Target Analysis and Ligand Docking

Ligands reported to have potential for interaction with the plasmodial Hsp90 were sourced from the literature [27,31,97,98,99] to create a local database. The 3D structures of ligands were either downloaded from the ZINC database [100,101] or PubChem [75]. Druggability of the modelled complex was evaluated using DoGSiteScorer v2.0.0 [102,103]. Preparation of protein structures and ligands for docking using the VEGA ZZ v3.2.4.29 platform [104] and the validation of the docking procedure were performed as previously reported [24]. Two controls—the experimentally determined NTDs co-crystallized with ADP of HSPC1 (PDB: 1BYQ) and PfHsp90 (PDB: 3K60) and the test, the modelled NTD of PkHsp90 with ADP (PKNH_0107000)—were used for screening against the database of sourced Hsp90 inhibitors. Molecular docking was conducted, targeting the ATP-binding pocket and lid domain binding pocket, using AutoDock Vina v1.2.7 [105]. For each docking interaction, exhaustiveness and binding modes were set at 8 and 10, respectively, leading to the generation of ten poses per ligand. Docking interaction scores were analyzed for the identification of the top-rated interacting compounds of PkHsp90 in comparison with PfHsp90 and HSPC1. The coordinates (PDB files) for the protein structures used in the docking (PkHsp90, PfHsp90, and HSPC1) and the coordinates (SDF files) for the top-rated poses of the compounds (ZINC22007970, ZINC724661072, ZINC724661078, and N-CBZ-5B) are lodged in the Supplementary Materials. Also, a comparative analysis of the scores for the identification of selective inhibition was performed using box and whisker plots. Binding affinities and interaction profiles were analyzed to assess the potential for selective inhibition using Discovery Studio Visualizer v25.1.0.24284 [72].

5. Conclusions

In conclusion, this study represents the first comprehensive bioinformatics analysis of the Hsp90 isoforms of P. knowlesi, a zoonotic malaria parasite of growing clinical concern. Through sequence analysis, domain annotation, phylogenetic analysis, network analysis, protein–ligand interaction, and docking simulations, the predicted structure, function, and druggability of the PkHsp90s were clearly revealed. These findings will inform the development of hypotheses for future experimental testing of the role of this important family of proteins in knowlesi malaria. All isoforms demonstrated high conservation with other Plasmodium species, supporting their fundamental role in parasite survival and stress adaptation, while the network analysis revealed that these chaperones played a role in key cellular processes within and beyond the parasite, making them attractive multi-pathway targets. The findings from the molecular docking and virtual screening are very promising; however, further experiments are required to validate the novel potential PkHsp90 inhibitor compounds, through biochemical assays (using recombinant protein, ATPase, and chaperone activity inhibition assays), parasite growth inhibition assays, and cytotoxicity profiling using human cell lines. Given the adaptability of P. knowlesi and its potential for severe human infections, characterizing and targeting essential stress response proteins like Hsp90 offers a promising route for antimalarial drug development.

Supplementary Materials

The following supporting information can be downloaded at https://www.mdpi.com/article/10.3390/ijms262412065/s1. References [30,48,49,53,78] are cited in the Supplementary Materials.

Author Contributions

Conceptualization, G.L.B. and M.O.D.; validation, M.O.D., H.S. and G.L.B.; formal analysis, M.O.D., H.S. and G.L.B.; writing—original draft preparation, M.O.D.; writing—review and editing, M.O.D., H.S. and G.L.B. All authors have read and agreed to the published version of the manuscript.

Funding

Research conducted by G.L.B. is supported by the Incentive Funding for Rated Researchers Programme of the NRF, South Africa (Grant Numbers RA22111973834). H.S. acknowledges institutional funding from the Department of Science and Technology, Ministry of Science and Technology, Government of India, for the development of computational resources (Fund for Improvement of Science and Technology Infrastructure, FIST, grant D. O. No. SR/FST/PG College/2009), and the Department of Biotechnology, Ministry of Science and Technology, Government of India, for providing support under the DBT Star College Scheme (grant no. HRD-11011/28/2021-HRD-DBT). M.O.D acknowledges the Institutional Based Research (IBR) Intervention support through the Tertiary Education Trust Fund (TETFund) Intervention in Research Project, Government of Nigeria.

Institutional Review Board Statement

Not applicable. This article reports the findings of a bioinformatics study, as such, no animal or human studies were performed.

Informed Consent Statement

Not applicable. This article reports the findings of a bioinformatics study, as such, no human participants were involved in the research.

Data Availability Statement

All the data associated with this article can be found in the body of the article and in the Supplementary Materials.

Conflicts of Interest

All authors declare that they have no conflicts of interest.

References

  1. World Health Organization. World Malaria Report 2025; WHO: Geneva, Switzerland, 2025. [Google Scholar]
  2. Antinori, S.; Galimberti, L.; Milazzo, L.; Corbellino, M. Plasmodium knowlesi: The Emerging Zoonotic Malaria Parasite. Acta Trop. 2013, 125, 191–201. [Google Scholar] [CrossRef]
  3. Ahebwa, A.; Sukkanon, C.; Hii, J.; Ngoen-Klan, R.; Arunyawat, U.; Manguin, S.; Chareonviriyaphap, T. Prevalence and Determinants of Zoonotic Malaria in the Greater Mekong Subregion (GMS) and Malaysia: Systematic Review and Meta-Analysis. Malar. J. 2025, 24, 218. [Google Scholar] [CrossRef]
  4. Kotepui, M.; Kotepui, K.U.; Milanez, G.D.; Masangkay, F.R. Prevalence of Severe Plasmodium knowlesi Infection and Risk Factors Related to Severe Complications Compared with Non-Severe P. knowlesi and Severe P. falciparum Malaria: A Systematic Review and Meta-Analysis. Infect. Dis. Poverty 2020, 9, 106. [Google Scholar] [CrossRef] [PubMed]
  5. Cox-Singh, J.; Hiu, J.; Lucas, S.B.; Divis, P.C.; Zulkarnaen, M.; Chandran, P.; Wong, K.T.; Adem, P.; Zaki, S.R.; Singh, B.; et al. Severe Malaria—A Case of Fatal Plasmodium knowlesi Infection with Post-Mortem Findings: A Case Report. Malar. J. 2010, 9, 10. [Google Scholar] [CrossRef] [PubMed]
  6. Anstey, N.M.; Grigg, M.J.; Rajahram, G.S.; Cooper, D.J.; William, T.; Kho, S.; Barber, B.E. Knowlesi Malaria: Human Risk Factors, Clinical Spectrum, and Pathophysiology. Adv. Parasitol. 2021, 113, 1–43. [Google Scholar] [CrossRef] [PubMed]
  7. Barber, B.E.; William, T.; Grigg, M.J.; Parameswaran, U.; Piera, K.A.; Price, R.N.; Yeo, T.W.; Anstey, N.M. Parasite Biomass-Related Inflammation, Endothelial Activation, Microvascular Dysfunction and Disease Severity in Vivax Malaria. PLoS Pathog. 2015, 11, e1004558. [Google Scholar] [CrossRef]
  8. Marchand, R.P.; Culleton, R.; Maeno, Y.; Quang, N.T.; Nakazawa, S. Co-Infections of Plasmodium knowlesi, P. falciparum, and P. vivax among Humans and Anopheles Dirus Mosquitoes, Southern Vietnam. Emerg. Infect. Dis. 2011, 17, 1232–1239. [Google Scholar] [CrossRef]
  9. Lee, K.S.; Divis, P.C.S.; Zakaria, S.K.; Matusop, A.; Julin, R.A.; Conway, D.J.; Cox-Singh, J.; Singh, B. Plasmodium knowlesi: Reservoir Hosts and Tracking the Emergence in Humans and Macaques. PLoS Pathog. 2011, 7, e1002015. [Google Scholar] [CrossRef]
  10. Banumathy, G.; Singh, V.; Pavithra, S.R.; Tatu, U. Heat Shock Protein 90 Function Is Essential for Plasmodium falciparum Growth in Human Erythrocytes. J. Biol. Chem. 2003, 278, 18336–18345. [Google Scholar] [CrossRef]
  11. Picard, D. Heat-Shock Protein 90, a Chaperone for Folding and Regulation. Cell. Mol. Life Sci. 2002, 59, 1640–1648. [Google Scholar] [CrossRef]
  12. Pratt, W.B. The Role of the Hsp90-Based Chaperone System in Signal Transduction by Nuclear Receptors and Receptors Signaling via MAP Kinase. Annu. Rev. Pharmacol. Toxicol. 1997, 37, 297–326. [Google Scholar] [CrossRef]
  13. Li, J.; Soroka, J.; Buchner, J. The Hsp90 Chaperone Machinery: Conformational Dynamics and Regulation by Co-Chaperones. Biochim. Biophys. Acta—Mol. Cell Res. 2012, 1823, 624–635. [Google Scholar] [CrossRef]
  14. Taipale, M.; Jarosz, D.F.; Lindquist, S. HSP90 at the Hub of Protein Homeostasis: Emerging Mechanistic Insights. Nat. Rev. Mol. Cell Biol. 2010, 11, 515–528. [Google Scholar] [CrossRef] [PubMed]
  15. Acharya, P.; Kumar, R.; Tatu, U. Chaperoning a Cellular Upheaval in Malaria: Heat Shock Proteins in Plasmodium falciparum. Mol. Biochem. Parasitol. 2007, 153, 85–94. [Google Scholar] [CrossRef]
  16. Shahinas, D.; Pillai, D.R. Role of Hsp90 in Plasmodium falciparum Malaria; Advances in Experimental Medicine and Biology; Springer: Cham, Switzerland, 2021; Volume 1340, pp. 125–139. [Google Scholar] [CrossRef]
  17. Verba, K.A.; Wang, R.Y.-R.; Arakawa, A.; Liu, Y.; Shirouzu, M.; Yokoyama, S.; Agard, D.A. Atomic Structure of Hsp90-Cdc37-Cdk4 Reveals That Hsp90 Traps and Stabilizes an Unfolded Kinase. Science 2016, 352, 1542–1547. [Google Scholar] [CrossRef]
  18. Krukenberg, K.A.; Street, T.O.; Lavery, L.A.; Agard, D.A. Conformational Dynamics of the Molecular Chaperone Hsp90. Q. Rev. Biophys. 2011, 44, 229–255. [Google Scholar] [CrossRef]
  19. Scheufler, C.; Brinker, A.; Bourenkov, G.; Pegoraro, S.; Moroder, L.; Bartunik, H.; Hartl, F.U.; Moarefi, I. Structure of TPR Domain-Peptide Complexes: Critical Elements in the Assembly of the Hsp70-Hsp90 Multichaperone Machine. Cell 2000, 101, 199–210. [Google Scholar] [CrossRef]
  20. Schwarz, K.; Baindur-Hudson, S.; Blatch, G.L.; Edkins, A.L. Hsp70/Hsp90 Organising Protein (Hop): Coordinating Much More than Chaperones; Subcellular Biochemistry; Springer: Cham, Switzerland, 2023; Volume 101, pp. 81–125. [Google Scholar] [CrossRef]
  21. Daniyan, M.O.; Przyborski, J.M.; Shonhai, A. Partners in Mischief: Functional Networks of Heat Shock Proteins of Plasmodium falciparum and Their Influence on Parasite Virulence. Biomolecules 2019, 9, 295. [Google Scholar] [CrossRef] [PubMed]
  22. Chua, C.-S.; Low, H.; Sim, T.-S. Co-Chaperones of Hsp90 in Plasmodium falciparum and Their Concerted Roles in Cellular Regulation. Parasitology 2014, 141, 1177–1191. [Google Scholar] [CrossRef] [PubMed]
  23. Dutta, T.; Singh, H.; Edkins, A.L.; Blatch, G.L. Hsp90 and Associated Co-Chaperones of the Malaria Parasite. Biomolecules 2022, 12, 1018. [Google Scholar] [CrossRef]
  24. Daniyan, M.O.; Ojo, O.T. In Silico Identification and Evaluation of Potential Interaction of Azadirachta indica Phytochemicals with Plasmodium falciparum Heat Shock Protein 90. J. Mol. Graph. Model. 2019, 87, 144–164. [Google Scholar] [CrossRef]
  25. Li, Y.; Dong, J.; Qin, J.J. Small Molecule Inhibitors Targeting Heat Shock Protein 90: An Updated Review. Eur. J. Med. Chem. 2024, 275, 116562. [Google Scholar] [CrossRef]
  26. Shonhai, A. Plasmodial Heat Shock Proteins: Targets for chemotherapy. FEMS Immulogy Med. Microbiol. 2010, 58, 61–74. [Google Scholar] [CrossRef]
  27. Ahmad, T.; Alhammadi, B.A.; Almaazmi, S.Y.; Arafa, S.; Blatch, G.L.; Dutta, T.; Gestwicki, J.E.; Keyzers, R.A.; Shonhai, A.; Singh, H. Plasmodium falciparum Heat Shock Proteins as Antimalarial Drug Targets: An Update. Cell Stress Chaperones 2024, 29, 326–337. [Google Scholar] [CrossRef]
  28. Stofberg, M.L.; Caillet, C.; de Villiers, M.; Zininga, T. Inhibitors of the Plasmodium falciparum Hsp90 towards Selective Antimalarial Drug Design: The Past, Present and Future. Cells 2021, 10, 2849. [Google Scholar] [CrossRef]
  29. Wang, T.; Picard, D.; Mäser, P.; Picard, D. Inhibition of Plasmodium falciparum Hsp90 Contributes to the Antimalarial Activities of Aminoalcohol-Carbazoles. J. Med. Chem. 2016, 59, 6344–6352. [Google Scholar] [CrossRef]
  30. Wang, T.; Bisson, W.H.; Mäser, P.; Scapozza, L.; Picard, D. Differences in Conformational Dynamics between Plasmodium falciparum and Human Hsp90 Orthologues Enable the Structure-Based Discovery of Pathogen-Selective Inhibitors. J. Med. Chem. 2014, 57, 2524–2535. [Google Scholar] [CrossRef]
  31. Kitson, R.R.A.; Kitsonova, D.; Siegel, D.; Ross, D.; Moody, C.J. Geldanamycin, a Naturally Occurring Inhibitor of Hsp90 and a Lead Compound for Medicinal Chemistry. J. Med. Chem. 2024, 67, 17946–17963. [Google Scholar] [CrossRef] [PubMed]
  32. Tabassum, W.; Bhattacharyya, S.; Varunan, S.M.; Bhattacharyya, M.K. Febrile Temperature Causes Transcriptional Downregulation of Plasmodium falciparum Sirtuins through Hsp90-Dependent Epigenetic Modification. Mol. Microbiol. 2021, 115, 1025–1038. [Google Scholar] [CrossRef] [PubMed]
  33. Wider, D.; Péli-Gulli, M.P.; Briand, P.A.; Tatu, U.; Picard, D. The Complementation of Yeast with Human or Plasmodium falciparum Hsp90 Confers Differential Inhibitor Sensitivities. Mol. Biochem. Parasitol. 2009, 164, 147–152. [Google Scholar] [CrossRef] [PubMed]
  34. Shahinas, D.; Folefoc, A.; Taldone, T.; Chiosis, G.; Crandall, I.; Pillai, D.R. A Purine Analog Synergizes with Chloroquine (CQ) by Targeting Plasmodium falciparum Hsp90 (PfHsp90). PLoS ONE 2013, 8, e75446. [Google Scholar] [CrossRef] [PubMed]
  35. Pugh, K.W.; Zhang, Z.; Wang, J.; Xu, X.; Munthali, V.; Zuo, A.; Blagg, B.S.J. From Bacteria to Cancer: A Benzothiazole-Based DNA Gyrase B Inhibitor Redesigned for Hsp90 C-Terminal Inhibition. ACS Med. Chem. Lett. 2020, 11, 1535–1538. [Google Scholar] [CrossRef]
  36. Serwetnyk, M.A.; Blagg, B.S.J. The Disruption of Protein-Protein Interactions with Co-Chaperones and Client Substrates as a Strategy towards Hsp90 Inhibition. Acta Pharm. Sin. B 2021, 11, 1446–1468. [Google Scholar] [CrossRef]
  37. Reynolds, T.S.; Mishra, S.J.; Blagg, B.S.J. Assessment of Hsp90β-Selective Inhibitor Safety and on-Target Effects. Sci. Rep. 2025, 15, 3692. [Google Scholar] [CrossRef]
  38. Daniyan, M.O.; Singh, H.; Blatch, G.L. The J Domain Proteins of Plasmodium knowlesi, a Zoonotic Malaria Parasite of Humans. Int. J. Mol. Sci. 2024, 25, 12302. [Google Scholar] [CrossRef]
  39. Benavente, E.D.; Gomes, A.R.; De Silva, J.R.; Grigg, M.; Walker, H.; Barber, B.E.; William, T.; Yeo, T.W.; de Sessions, P.F.; Ramaprasad, A.; et al. Whole Genome Sequencing of Amplified Plasmodium knowlesi DNA from Unprocessed Blood Reveals Genetic Exchange Events between Malaysian Peninsular and Borneo Subpopulations. Sci. Rep. 2019, 9, 9873. [Google Scholar] [CrossRef] [PubMed]
  40. Pinheiro, M.M.; Ahmed, M.A.; Millar, S.B.; Sanderson, T.; Otto, T.D.; Lu, W.C.; Krishna, S.; Rayner, J.C.; Cox-Singh, J. Plasmodium knowlesi Genome Sequences from Clinical Isolates Reveal Extensive Genomic Dimorphism. PLoS ONE 2015, 10, e0121303. [Google Scholar] [CrossRef] [PubMed]
  41. Elsworth, B.; Ye, S.; Dass, S.; Tennessen, J.A.; Sultana, Q.; Thommen, B.T.; Paul, A.S.; Kanjee, U.; Grüring, C.; Ferreira, M.U.; et al. The Essential Genome of Plasmodium knowlesi Reveals Determinants of Antimalarial Susceptibility. Science 2025, 387, eadq6241. [Google Scholar] [CrossRef]
  42. Jones, P.; Binns, D.; Chang, H.Y.; Fraser, M.; Li, W.; McAnulla, C.; McWilliam, H.; Maslen, J.; Mitchell, A.; Nuka, G.; et al. InterProScan 5: Genome-Scale Protein Function Classification. Bioinformatics 2014, 30, 1236–1240. [Google Scholar] [CrossRef]
  43. Blum, M.; Andreeva, A.; Florentino, L.C.; Chuguransky, S.R.; Grego, T.; Hobbs, E.; Pinto, B.L.; Orr, A.; Paysan-Lafosse, T.; Ponamareva, I.; et al. InterPro: The Protein Sequence Classification Resource in 2025. Nucleic Acids Res. 2025, 53, D444–D456. [Google Scholar] [CrossRef]
  44. Corbett, K.D.; Berger, J.M. Structure of the ATP-Binding Domain of Plasmodium falciparum Hsp90. Proteins 2010, 78, 2738–2744. [Google Scholar] [CrossRef] [PubMed]
  45. Garnier, C.; Lafitte, D.; Tsvetkov, P.O.; Barbier, P.; Leclerc-Devin, J.; Millot, J.-M.; Briand, C.; Makarov, A.A.; Catelli, M.G.; Peyrot, V. Binding of ATP to Heat Shock Protein 90: Evidence for an ATP-Binding Site in the C-Terminal Domain. J. Biol. Chem. 2002, 277, 12208–12214. [Google Scholar] [CrossRef]
  46. Silva, J.C.; Egan, A.; Friedman, R.; Munro, J.B.; Carlton, J.M.; Hughes, A.L. Genome Sequences Reveal Divergence Times of Malaria Parasite Lineages. Parasitology 2010, 138, 1737. [Google Scholar] [CrossRef]
  47. Datta, N.; Singh Chauhan, V. Origin and Evolution of Human Malaria Parasite, P. falciparum and P. vivax. In Nature at Work: Ongoing Saga of Evolution; Springer: New Delhi, India, 2010; pp. 307–317. ISBN 978-81-8489-992-4. [Google Scholar]
  48. Waterhouse, A.M.; Procter, J.B.; Martin, D.M.A.; Clamp, M.; Barton, G.J. Jalview Version 2-A Multiple Sequence Alignment Editor and Analysis Workbench. Bioinformatics 2009, 25, 1189–1191. [Google Scholar] [CrossRef]
  49. Wolf, J. GIMP 2.6; Galileo Press: Cambridge, UK, 2010; ISBN 9783836216104. [Google Scholar]
  50. Szklarczyk, D.; Gable, A.L.; Lyon, D.; Junge, A.; Wyder, S.; Huerta-Cepas, J.; Simonovic, M.; Doncheva, N.T.; Morris, J.H.; Bork, P.; et al. STRING V11: Protein-Protein Association Networks with Increased Coverage, Supporting Functional Discovery in Genome-Wide Experimental Datasets. Nucleic Acids Res. 2019, 47, D607–D613. [Google Scholar] [CrossRef] [PubMed]
  51. Snel, B.; Lehmann, G.; Bork, P.; Huynen, M.A. String: A Web-Server to Retrieve and Display the Repeatedly Occurring Neighbourhood of a Gene. Nucleic Acids Res. 2000, 28, 3442–3444. [Google Scholar] [CrossRef] [PubMed]
  52. von Mering, C.; Huynen, M.; Jaeggi, D.; Schmidt, S.; Bork, P.; Snel, B. STRING: A Database of Predicted Functional Associations between Proteins. Nucleic Acids Res. 2003, 31, 258–261. [Google Scholar] [CrossRef]
  53. Ge, S.X.; Jung, D.; Jung, D.; Yao, R. ShinyGO: A Graphical Gene-Set Enrichment Tool for Animals and Plants. Bioinformatics 2019, 36, 2628. [Google Scholar] [CrossRef]
  54. Aurrecoechea, C.; Chea, C.; Brestelli, J.; Brunk, B.P.; Dommer, J.; Fischer, S.; Gajria, B.; Gao, X.; Gingle, A.; Grant, G.; et al. PlasmoDB: A Functional Genomic Database for Malaria Parasites. Nucleic Acids Res. 2009, 37, D539–D543. [Google Scholar] [CrossRef]
  55. Altschul, S.F.; Madden, T.L.; Schäffer, A.A.; Zhang, J.; Zhang, Z.; Miller, W.; Lipman, D.J. Gapped BLAST and PSI-BLAST: A New Generation of Protein Database Search Programs. Nucleic Acids Res. 1997, 25, 3389–3402. [Google Scholar] [CrossRef]
  56. LaCount, D.J.; Vignali, M.; Chettier, R.; Phansalkar, A.; Bell, R.; Hesselberth, J.R.; Schoenfeld, L.W.; Ota, I.; Sahasrabudhe, S.; Kurschner, C.; et al. A Protein Interaction Network of the Malaria Parasite Plasmodium falciparum. Nature 2005, 438, 103–107. [Google Scholar] [CrossRef] [PubMed]
  57. Shannon, P.; Markiel, A.; Ozier, O.; Baliga, N.S.; Wang, J.T.; Ramage, D.; Amin, N.; Schwikowski, B.; Ideker, T. Cytoscape: A Software Environment for Integrated Models of Biomolecular Interaction Networks. Genome Res. 2003, 13, 2498–2504. [Google Scholar] [CrossRef]
  58. Ono, K.; Fong, D.; Gao, C.; Churas, C.; Pillich, R.; Lenkiewicz, J.; Pratt, D.; Pico, A.R.; Hanspers, K.; Xin, Y.; et al. Cytoscape Web: Bringing Network Biology to the Browser. Nucleic Acids Res. 2025, 53, W203–W212. [Google Scholar] [CrossRef]
  59. Zininga, T.; Achilonu, I.; Hoppe, H.; Prinsloo, E.; Dirr, H.W.; Shonhai, A. Overexpression, Purification and Characterisation of the Plasmodium falciparum Hsp70-z (PfHsp70-z) Protein. PLoS ONE 2015, 10, e0129445. [Google Scholar] [CrossRef]
  60. Muralidharan, V.; Oksman, A.; Pal, P.; Lindquist, S.; Goldberg, D.E. Plasmodium falciparum Heat Shock Protein 110 Stabilizes the Asparagine Repeat-Rich Parasite Proteome during Malarial Fevers. Nat. Commun. 2012, 3, 1310. [Google Scholar] [CrossRef]
  61. Zininga, T.; Achilonu, I.; Hoppe, H.; Prinsloo, E.; Dirr, H.W.; Shonhai, A. Plasmodium falciparum Hsp70-z, an Hsp110 Homologue, Exhibits Independent Chaperone Activity and Interacts with Hsp70-1 in a Nucleotide-Dependent Fashion. Cell Stress Chaperones 2016, 21, 499–513. [Google Scholar] [CrossRef] [PubMed]
  62. Gitau, G.W.; Mandal, P.; Blatch, G.L.; Przyborski, J.; Shonhai, A. Characterisation of the Plasmodium falciparum Hsp70-Hsp90 Organising Protein (PfHop). Cell Stress Chaperones 2012, 17, 191–202. [Google Scholar] [CrossRef] [PubMed]
  63. Batinovic, S.; McHugh, E.; Chisholm, S.A.; Matthews, K.; Liu, B.; Dumont, L.; Charnaud, S.C.; Schneider, M.P.; Gilson, P.R.; De Koning-Ward, T.F.; et al. An Exported Protein-Interacting Complex Involved in the Trafficking of Virulence Determinants in Plasmodium-Infected Erythrocytes. Nat. Commun. 2017, 8, 16044. [Google Scholar] [CrossRef]
  64. Muzenda, F.L.; Stofberg, M.L.; Mthembu, W.; Achilonu, I.; Strauss, E.; Zininga, T. Characterization and Inhibition of the Chaperone Function of Plasmodium falciparum Glucose-Regulated Protein 94KDa (PfGrp94). Proteins 2025, 93, 957–971. [Google Scholar] [CrossRef]
  65. Komaki-Yasuda, K.; Okuwaki, M.; Nagata, K.; Kawazu, S.; Kano, S. Identification of a Novel and Unique Transcription Factor in the Intraerythrocytic Stage of Plasmodium falciparum. PLoS ONE 2013, 8, e74701. [Google Scholar] [CrossRef]
  66. Gill, J.; Kumar, A.; Yogavel, M.; Belrhali, H.; Jain, S.K.; Rug, M.; Brown, M.; Maier, A.G.; Sharma, A. Structure, Localization and Histone Binding Properties of Nuclear-Associated Nucleosome Assembly Protein from Plasmodium falciparum. Malar. J. 2010, 9, 90. [Google Scholar] [CrossRef]
  67. Pavlovic Djuranovic, S.; Erath, J.; Andrews, R.J.; Bayguinov, P.O.; Chung, J.J.; Chalker, D.L.; Fitzpatrick, J.A.; Moss, W.N.; Szczesny, P.; Djuranovic, S. Plasmodium falciparum Translational Machinery Condones Polyadenosine Repeats. eLife 2020, 9, e57799. [Google Scholar] [CrossRef]
  68. Dutta, T.; Pesce, E.R.; Maier, A.G.; Blatch, G.L. Role of the J Domain Protein Family in the Survival and Pathogenesis of Plasmodium falciparum; Advances in Experimental Medicine and Biology; Springer: Cham, Switzerland, 2021; Volume 1340, pp. 97–123. [Google Scholar] [CrossRef]
  69. Guex, N.; Peitsch, M.C.; Schwede, T. Automated comparative protein structure modeling with SWISS-MODEL and Swiss-PdbViewer: A historical perspective. Electrophoresis 2009, 30 (Suppl. 1), S162–S173. [Google Scholar] [CrossRef] [PubMed]
  70. Waterhouse, A.; Bertoni, M.; Bienert, S.; Studer, G.; Tauriello, G.; Gumienny, R.; Heer, F.T.; de Beer, T.A.P.; Rempfer, C.; Bordoli, L.; et al. SWISS-MODEL: Homology modelling of protein structures and complexes. Nucleic Acids Res. 2018, 46, W296–W303. [Google Scholar] [CrossRef]
  71. Stofberg, M.L.; Muzenda, F.L.; Achilonu, I.; Strauss, E.; Zininga, T. In Silico Screening of Selective ATP Mimicking Inhibitors Targeting the Plasmodium falciparum Grp94. J. Biomol. Struct. Dyn. 2024, 43, 6214–6225. [Google Scholar] [CrossRef]
  72. Dassault Systèmes. BIOVIA Discovery Studio Modeling Environment, Release 4.5; Dassault Systèmes: San Diego, CA, USA, 2015. [Google Scholar]
  73. Amos, B.; Aurrecoechea, C.; Barba, M.; Barreto, A.; Basenko, E.Y.; Bażant, W.; Belnap, R.; Blevins, A.S.; Böhme, U.; Brestelli, J.; et al. VEuPathDB: The Eukaryotic Pathogen, Vector and Host Bioinformatics Resource Center. Nucleic Acids Res. 2022, 50, D898–D911. [Google Scholar] [CrossRef]
  74. Alvarez-Jarreta, J.; Amos, B.; Aurrecoechea, C.; Bah, S.; Barba, M.; Barreto, A.; Basenko, E.Y.; Belnap, R.; Blevins, A.; Böhme, U.; et al. VEuPathDB: The Eukaryotic Pathogen, Vector and Host Bioinformatics Resource Center in 2023. Nucleic Acids Res. 2024, 52, D808–D816. [Google Scholar] [CrossRef]
  75. Sayers, E.W.; Bolton, E.E.; Brister, J.R.; Canese, K.; Chan, J.; Comeau, D.C.; Farrell, C.M.; Feldgarden, M.; Fine, A.M.; Funk, K.; et al. Database Resources of the National Center for Biotechnology Information in 2023. Nucleic Acids Res. 2022, 51, D29. [Google Scholar] [CrossRef] [PubMed]
  76. Kampinga, H.H.; Hageman, J.; Vos, M.J.; Kubota, H.; Tanguay, R.M.; Bruford, E.A.; Cheetham, M.E.; Chen, B.; Hightower, L.E. Guidelines for the Nomenclature of the Human Heat Shock Proteins. Cell Stress Chaperones 2009, 14, 105–111. [Google Scholar] [CrossRef]
  77. Bittrich, S.; Segura, J.; Duarte, J.M.; Burley, S.K.; Rose, Y. RCSB Protein Data Bank: Exploring Protein 3D Similarities via Comprehensive Structural Alignments. Bioinformatics 2024, 40, btae370. [Google Scholar] [CrossRef] [PubMed]
  78. Jumper, J.; Evans, R.; Pritzel, A.; Green, T.; Figurnov, M.; Ronneberger, O.; Tunyasuvunakool, K.; Bates, R.; Žídek, A.; Potapenko, A.; et al. Highly Accurate Protein Structure Prediction with AlphaFold. Nature 2021, 596, 583–589. [Google Scholar] [CrossRef]
  79. Notredame, C.; Higgins, D.G.; Heringa, J. T-Coffee: A Novel Method for Fast and Accurate Multiple Sequence Alignment. J. Mol. Biol. 2000, 302, 205–217. [Google Scholar] [CrossRef]
  80. Sievers, F.; Wilm, A.; Dineen, D.; Gibson, T.J.; Karplus, K.; Li, W.; Lopez, R.; McWilliam, H.; Remmert, M.; Söding, J.; et al. Fast, Scalable Generation of High-Quality Protein Multiple Sequence Alignments Using Clustal Omega. Mol. Syst. Biol. 2011, 7, 539. [Google Scholar] [CrossRef]
  81. Sievers, F.; Higgins, D.G. Clustal Omega for Making Accurate Alignments of Many Protein Sequences. Protein Sci. 2018, 27, 135–145. [Google Scholar] [CrossRef]
  82. Sievers, F.; Higgins, D.G. The Clustal Omega Multiple Alignment Package. Methods Mol. Biol. 2021, 2231, 3–16. [Google Scholar] [CrossRef] [PubMed]
  83. Bateman, A.; Martin, M.J.; Orchard, S.; Magrane, M.; Ahmad, S.; Alpi, E.; Bowler-Barnett, E.H.; Britto, R.; Bye-A-Jee, H.; Cukura, A.; et al. UniProt: The Universal Protein Knowledgebase in 2023. Nucleic Acids Res. 2023, 51, D523–D531. [Google Scholar] [CrossRef]
  84. Larkin, M.A.; Blackshields, G.; Brown, N.P.; Chenna, R.; Mcgettigan, P.A.; McWilliam, H.; Valentin, F.; Wallace, I.M.; Wilm, A.; Lopez, R.; et al. Clustal W and Clustal X Version 2.0. Bioinformatics 2007, 23, 2947–2948. [Google Scholar] [CrossRef]
  85. Gao, Y.; Liu, Y.; Ma, Y.; Liu, B.; Wang, Y.; Xing, Y. AbPOA: An SIMD-Based C Library for Fast Partial Order Alignment Using Adaptive Band. Bioinformatics 2021, 37, 2209–2211. Erratum in Bioinformatics 2021, 37, 3384. https://doi.org/10.1093/bioinformatics/btab587. [CrossRef]
  86. Edgar, R.C.; Drive, R.M.; Valley, M. MUSCLE: Multiple Sequence Alignment with High Accuracy and High Throughput. Nucleic Acids Res. 2004, 32, 1792–1797. [Google Scholar] [CrossRef]
  87. Katoh, K.; Misawa, K.; Kuma, K.I.; Miyata, T. MAFFT: A Novel Method for Rapid Multiple Sequence Alignment Based on Fast Fourier Transform. Nucleic Acids Res. 2002, 30, 3059–3066. [Google Scholar] [CrossRef]
  88. Subramanian, A.R.; Weyer-Menkhoff, J.; Kaufmann, M.; Morgenstern, B. DIALIGN-T: An Improved Algorithm for Segment-Based Multiple Sequence Alignment. BMC Bioinform. 2005, 6, 66. [Google Scholar] [CrossRef] [PubMed]
  89. Pei, J.; Sadreyev, R.; Grishin, N.V. PCMA: Fast and Accurate Multiple Sequence Alignment Based on Profile Consistency. Bioinformatics 2003, 19, 427–428. [Google Scholar] [CrossRef]
  90. Do, C.B.; Mahabhashyam, M.S.P.P.; Brudno, M.; Batzoglou, S. ProbCons: Probabilistic Consistency-Based Multiple Sequence Alignment. Genome Res. 2005, 15, 330–340. [Google Scholar] [CrossRef] [PubMed]
  91. Rose, P.W.; Beran, B.; Bi, C.; Bluhm, W.F.; Dimitropoulos, D.; Goodsell, D.S.; Prlić, A.; Quesada, M.; Quinn, G.B.; Westbrook, J.D.; et al. The RCSB Protein Data Bank: Redesigned Web Site and Web Services. Nucleic Acids Res. 2011, 39, D392–D401. [Google Scholar] [CrossRef]
  92. Lemoine, F.; Correia, D.; Lefort, V.; Doppelt-Azeroual, O.; Mareuil, F.; Cohen-Boulakia, S.; Gascuel, O. NGPhylogeny.Fr: New Generation Phylogenetic Services for Non-Specialists. Nucleic Acids Res. 2019, 47, W260–W265. [Google Scholar] [CrossRef]
  93. Letunic, I.; Bork, P. Interactive Tree of Life (ITOL) v6: Recent Updates to the Phylogenetic Tree Display and Annotation Tool. Nucleic Acids Res. 2024, 52, W78–W82. [Google Scholar] [CrossRef]
  94. Kimball, S.; Mattis, P. GIMP—GNU Image Manipulation Program. 1996. Available online: https://www.gimp.org (accessed on 28 September 2025).
  95. Oughtred, R.; Rust, J.; Chang, C.; Breitkreutz, B.-J.; Stark, C.; Willems, A.; Boucher, L.; Leung, G.; Kolas, N.; Zhang, F.; et al. The BioGRID Database: A Comprehensive Biomedical Resource of Curated Protein, Genetic, and Chemical Interactions. Protein Sci. 2021, 30, 187–200. [Google Scholar] [CrossRef]
  96. Stark, C.; Breitkreutz, B.-J.; Reguly, T.; Boucher, L.; Breitkreutz, A.; Tyers, M. BioGRID: A General Repository for Interaction Datasets. Nucleic Acids Res. 2006, 34, D535–D539. [Google Scholar] [CrossRef]
  97. Schulte, T.W.; Akinaga, S.; Soga, S.; Sullivan, W.; Stensgard, B.; Toft, D.; Neckers, L.M. Antibiotic Radicicol Binds to the N-Terminal Domain of Hsp90 and Shares Important Biologic Activities with Geldanamycin. Cell Stress Chaperones 1998, 3, 100–108. [Google Scholar] [CrossRef]
  98. Donnelly, A.; Blagg, B.S.J. Novobiocin and Additional Inhibitors of the Hsp90 C-Terminal Nucleotide-Binding Pocket. Curr. Med. Chem. 2008, 15, 2702–2717. [Google Scholar] [CrossRef] [PubMed]
  99. Everson, N.; Bach, J.; Hammill, J.T.; Falade, M.O.; Rice, A.L.; Guy, R.K.; Eagon, S. Identification of Plasmodium falciparum Heat Shock 90 Inhibitors via Molecular Docking. Bioorganic Med. Chem. Lett. 2021, 35, 127818. [Google Scholar] [CrossRef]
  100. Irwin, J.J.; Shoichet, B.K. ZINC—A Free Database of Commercially Available Compounds for Virtual Screening. J. Chem. Inf. Model. 2005, 45, 177–182. [Google Scholar] [CrossRef]
  101. Irwin, J.J.; Sterling, T.; Mysinger, M.M.; Bolstad, E.S.; Coleman, R.G. ZINC: A Free Tool to Discover Chemistry for Biology. J. Chem. Inf. Model. 2012, 52, 1757–1768. [Google Scholar] [CrossRef]
  102. Volkamer, A.; Kuhn, D.; Grombacher, T.; Rippmann, F.; Rarey, M. Combining Global and Local Measures for Structure-Based Druggability Predictions. J. Chem. Inf. Model. 2012, 52, 360–372. [Google Scholar] [CrossRef]
  103. Volkamer, A.; Griewel, A.; Grombacher, T.; Rarey, M. Analyzing the Topology of Active Sites: On the Prediction of Pockets and Subpockets. J. Chem. Inf. Model. 2010, 50, 2041–2052. [Google Scholar] [CrossRef] [PubMed]
  104. Pedretti, A.; Villa, L.; Vistoli, G. VEGA: A Versatile Program to Convert, Handle and Visualize Molecular Structure on Windows-Based PCs. J. Mol. Graph. Model. 2002, 21, 47–49. [Google Scholar] [CrossRef] [PubMed]
  105. Trott, O.; Olson, A.J. AutoDock Vina: Improving the Speed and Accuracy of Docking with a New Scoring Function, Efficient Optimization and Multithreading Oleg. J. Comput. Chem. 2010, 31, 455–461. [Google Scholar] [CrossRef] [PubMed]
Ijms 26 12065 g006
Figure 6. Comparative analyses of the ATP-binding pockets and lid domains of human and plasmodial Hsp90s, highlighting that the GHL has a straight conformation in PkHsp90 forming a hydrophobic extension, which was not observed for human HSPC1 where the GHL was curved. (A) Sequence analysis: Highlighted with double-faced arrows are ATP/ADP binding regions in black, regions of low sequence similarity in red, and lid domains of plasmodial (PfHsp90 and P. knowlesi Hsp90 isoforms) as compared with human Hsp90s (HSPC1 and HSPC3) in rose. Sequences within the low similarity region that are identical in PfHsp90, PkHsp90, HSPC1, and HSPC3 are identified with asterisks. A key amino acid residue difference between PfHsp90 (F63) and PkHsp90 (Y63) is highlighted in red text on a yellow background. Blue background colour indicates sequence identity and conservation with a threshold of not less than 100%. Amino acids identified with coloured balls on the alignment represent glycine-rich hinge loop (GHL, red), N-terminal α-helix of the lid domain (green), and the characteristic IXXSG and LXXGA motifs of cytosolic plasmodial and human Hsp90s, respectively (yellow). (B) Functional loops analysis: Loops are rendered as solid ribbons coloured cyan for the lid domains, brown for the regions of low sequence similarity, and blue for the ATP/ADP interacting loop. A key amino acid residue difference between PfHsp90 (F63) and PkHsp90 (Y63) and the corresponding HSPC1 residue (H77) are rendered as sticks coloured by element. (C) Structural analysis: For HSPC1 and PkHsp90, the lid domains are rendered as solid grey ribbons, with the GHL motifs in red, the N-terminal α-helix of the lid domains in green, and IXXSG and LXXGA motifs in yellow, as previously reported [30]. Some corresponding amino acids are rendered as sticks with parent colour. Other regions are rendered as Ca wire in light grey. Hydrophobicity is rendered as transparent surfaces, with a colour range from blue (most hydrophilic) to orange (most hydrophobic). PkHsp90/HSPC1 represents the superimposition of the lid domains of PkHsp90 (blue) and HSPC1 (cyan), with some corresponding amino acids rendered as sticks with parent colour. HSPC1 (1BYQ) and PfHsp90 (3K60) are the experimentally determined structures of the NTDs co-crystallized with ADP. PkHsp90 is the modelled NTD with ADP (PKNH_0107000). Images were prepared using Discovery Studio Visualizer v25.1.0.24264 [72], arranged on PowerPoint, and processed with GIMP v2.10.14 software [49].
Figure 6. Comparative analyses of the ATP-binding pockets and lid domains of human and plasmodial Hsp90s, highlighting that the GHL has a straight conformation in PkHsp90 forming a hydrophobic extension, which was not observed for human HSPC1 where the GHL was curved. (A) Sequence analysis: Highlighted with double-faced arrows are ATP/ADP binding regions in black, regions of low sequence similarity in red, and lid domains of plasmodial (PfHsp90 and P. knowlesi Hsp90 isoforms) as compared with human Hsp90s (HSPC1 and HSPC3) in rose. Sequences within the low similarity region that are identical in PfHsp90, PkHsp90, HSPC1, and HSPC3 are identified with asterisks. A key amino acid residue difference between PfHsp90 (F63) and PkHsp90 (Y63) is highlighted in red text on a yellow background. Blue background colour indicates sequence identity and conservation with a threshold of not less than 100%. Amino acids identified with coloured balls on the alignment represent glycine-rich hinge loop (GHL, red), N-terminal α-helix of the lid domain (green), and the characteristic IXXSG and LXXGA motifs of cytosolic plasmodial and human Hsp90s, respectively (yellow). (B) Functional loops analysis: Loops are rendered as solid ribbons coloured cyan for the lid domains, brown for the regions of low sequence similarity, and blue for the ATP/ADP interacting loop. A key amino acid residue difference between PfHsp90 (F63) and PkHsp90 (Y63) and the corresponding HSPC1 residue (H77) are rendered as sticks coloured by element. (C) Structural analysis: For HSPC1 and PkHsp90, the lid domains are rendered as solid grey ribbons, with the GHL motifs in red, the N-terminal α-helix of the lid domains in green, and IXXSG and LXXGA motifs in yellow, as previously reported [30]. Some corresponding amino acids are rendered as sticks with parent colour. Other regions are rendered as Ca wire in light grey. Hydrophobicity is rendered as transparent surfaces, with a colour range from blue (most hydrophilic) to orange (most hydrophobic). PkHsp90/HSPC1 represents the superimposition of the lid domains of PkHsp90 (blue) and HSPC1 (cyan), with some corresponding amino acids rendered as sticks with parent colour. HSPC1 (1BYQ) and PfHsp90 (3K60) are the experimentally determined structures of the NTDs co-crystallized with ADP. PkHsp90 is the modelled NTD with ADP (PKNH_0107000). Images were prepared using Discovery Studio Visualizer v25.1.0.24264 [72], arranged on PowerPoint, and processed with GIMP v2.10.14 software [49].
Ijms 26 12065 g006
Ijms 26 12065 g007
Figure 7. Box and whisker plot of virtual screening of small molecule inhibitors against the ADP-binding pockets and lid domains of plasmodial and human Hsp90s. Plots with filled and unfilled boxes indicate ligand interaction with lid domains (GHL) or ADP-binding pockets (ADP), respectively. The black dots represent data points that fall outside the main data distribution range, and the crosses represent the average of the data. Human HSPC1 (1BYQ) and PfHsp90 (3K60) are the experimentally determined NTDs co-crystallized with ADP. PkHsp90 is the modelled NTD with ADP (PKNH_0107000). Image was prepared on Microsoft Excel and processed with GIMP v2.10.14 software [49].
Figure 7. Box and whisker plot of virtual screening of small molecule inhibitors against the ADP-binding pockets and lid domains of plasmodial and human Hsp90s. Plots with filled and unfilled boxes indicate ligand interaction with lid domains (GHL) or ADP-binding pockets (ADP), respectively. The black dots represent data points that fall outside the main data distribution range, and the crosses represent the average of the data. Human HSPC1 (1BYQ) and PfHsp90 (3K60) are the experimentally determined NTDs co-crystallized with ADP. PkHsp90 is the modelled NTD with ADP (PKNH_0107000). Image was prepared on Microsoft Excel and processed with GIMP v2.10.14 software [49].
Ijms 26 12065 g007
Ijms 26 12065 g008
Figure 8. Docking pose and interaction analysis of top-rated compounds with the lid domains of HSPC1, PkHsp90, and PfHsp90. The lid domains are shown as solid grey ribbons, with the amino acids typifying critical motifs coloured red (GHL), green (N-terminal α-helix), and yellow (IXXSG in PkHsp90/PfHsp90 and LXXGA in HSPC1), as previously reported [30]. All other regions are rendered as Ca wire in light grey to allow for visibility of the interacting residues. The docked compounds are rendered as sticks and coloured by atoms with carbon as cyan, chloride as green, nitrogen as blue, and oxygen as red. Interacting residues are represented as sticks with parent colours. Broken lines represent hydrogen bond interactions (blue), hydrophobic interactions (purple), and electrostatic interactions (black). Hydrophobicity is rendered as transparent surfaces with a colour range from blue (most hydrophilic) to orange (most hydrophobic). Human HSPC1 (1BYQ) and PfHsp90 (3K60) are the experimentally determined NTDs co-crystallized with ADP. PkHsp90 is the modelled NTD with ADP (PKNH_0107000). Images were prepared using Discovery Studio Visualizer v25.1.0.24284 [72], arranged on PowerPoint, and processed with GIMP v2.10.14 software [49].
Figure 8. Docking pose and interaction analysis of top-rated compounds with the lid domains of HSPC1, PkHsp90, and PfHsp90. The lid domains are shown as solid grey ribbons, with the amino acids typifying critical motifs coloured red (GHL), green (N-terminal α-helix), and yellow (IXXSG in PkHsp90/PfHsp90 and LXXGA in HSPC1), as previously reported [30]. All other regions are rendered as Ca wire in light grey to allow for visibility of the interacting residues. The docked compounds are rendered as sticks and coloured by atoms with carbon as cyan, chloride as green, nitrogen as blue, and oxygen as red. Interacting residues are represented as sticks with parent colours. Broken lines represent hydrogen bond interactions (blue), hydrophobic interactions (purple), and electrostatic interactions (black). Hydrophobicity is rendered as transparent surfaces with a colour range from blue (most hydrophilic) to orange (most hydrophobic). Human HSPC1 (1BYQ) and PfHsp90 (3K60) are the experimentally determined NTDs co-crystallized with ADP. PkHsp90 is the modelled NTD with ADP (PKNH_0107000). Images were prepared using Discovery Studio Visualizer v25.1.0.24284 [72], arranged on PowerPoint, and processed with GIMP v2.10.14 software [49].
Ijms 26 12065 g008
Table 1. Comparative assessment of the Gene Ontology enrichments associated with mapped P. falciparum and P. knowlesi Hsp90 proteins.
Table 1. Comparative assessment of the Gene Ontology enrichments associated with mapped P. falciparum and P. knowlesi Hsp90 proteins.
P. falciparum EnrichmentGO TermsDescriptionP. knowlesi EnrichmentFunctional Terms
FDRFoldFDRFold
2.04 × 10−783.72GO:0051082unfolded protein binding1.65 × 10−6255.10MF
1.06 × 10−652.53GO:0006457protein folding1.03 × 10−5113.38BP
2.39 × 10−641.86GO:0016887ATP hydrolysis activity2.97 × 10−563.78BP
1.31 × 10−526.26GO:0017111nucleoside-triphosphatase activity9.59 × 10−536.71BP
1.48 × 10−524.69GO:0006950response to stress2.31 × 10−576.15BP
1.48 × 10−524.24GO:0016462pyrophosphatase activity9.59 × 10−534.24BP
1.48 × 10−523.81GO:0016818hydrolase activity acting on acid anhydrides in phosphorus-containing anhydrides9.59 × 10−533.35BP
1.48 × 10−523.71GO:0016817hydrolase activity acting on acid anhydrides9.59 × 10−533.13BP
3.71 × 10−518.6GO:0140657ATP-dependent activity0.0002224.30BP
0.0001812.32GO:0005524ATP binding0.0010313.43MF
0.0002111.67GO:0032559adenyl ribonucleotide binding0.0010313.36MF
0.0002111.62GO:0030554adenyl nucleotide binding0.0010313.32MF
0.0002910.26GO:0035639purine ribonucleoside triphosphate binding0.0012211.19MF
0.000329.8GO:0032555purine ribonucleotide binding0.0012211.14MF
0.000329.74GO:0017076purine nucleotide binding0.0012211.12MF
0.000329.71GO:0032553ribonucleotide binding0.0012211.04MF
0.000329.64GO:0050896response to stimulus7.92 × 10−542.87BP
0.000339.43GO:0097367carbohydrate derivative binding0.0012210.93MF
0.000339.4GO:0043168anion binding0.0013110.50MF
0.000398.8GO:1901265nucleoside phosphate binding0.002068.77MF
0.000398.8GO:0000166nucleotide binding0.002068.77MF
0.000438.52GO:0036094small molecule binding0.002138.55MF
0.000627.71GO:0016787hydrolase activity0.0012211.52BP
0.001076.63GO:0005515protein binding0.0011812.44MF
0.001536.02GO:0043167ion binding0.003966.86MF
0.005194.34GO:1901363heterocyclic compound binding0.006715.61MF
0.005194.33GO:0097159organic cyclic compound binding0.006715.61MF
0.012673.43GO:0003824catalytic activity0.014844.25BP
0.034492.55GO:0005488binding0.025423.49MF
0.076972.01GO:0009987cellular process0.025423.47BP
0.105661.82GO:0003674molecular function0.081762.30MF
0.106371.81GO:0008150biological process0.034373.11BP
Mapped genes: PfHsp90 (PF3D7_0708400), PfTRAP1 (PF3D7_1118200), PfGRP94 (PF3D7_1222300), PfHsp90_A (PF3D7_1443900), PkTRAP1 (PKNH_0915900), PkGRP94 (PKNH_1441400), and PkHsp90_A (PKNH_1238400); BP, biological process; MF, molecular function; GO, Gene Ontology; FDR, False Discovery Rate.
Table 2. Top consensus interactors of the ADP-binding pocket and lid domain after virtual screening.
Table 2. Top consensus interactors of the ADP-binding pocket and lid domain after virtual screening.
CompoundsADP Binding PocketLid Domain
PfHsp90PkHsp90HSPC1PfHsp90PkHsp90HSPC1
N-CBZ-3A−8.9−9.3 −8.4−8.5−5.2
N-CBZ-3B−8.7−9.5 −9−9.1
N-CBZ-3C−9−8.9 −8.4−8.2
N-CBZ-3E−9.2−9.3 −8.5−8.4
N-CBZ-3G−9.1−9.1 −8.5−8.5
N-CBZ-5B−8.9−9 −8.8−8.7−6.2
SNX-2112−9.7−9.7−9.2−8.6−8.2−6.2
ZINC22007970−11.4−11.3 −10.5−10.4
ZINC72461072−11.6−11.4 −10.7−10.4−7.4
ZINC72461078−11.6−11.4−11−10.2−10.3
N-CBZ-* are amino-alcohol carbazoles compounds. Binding energies are in kcal/mol.
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Daniyan, M.O.; Singh, H.; Blatch, G.L. Plasmodium knowlesi Heat Shock Protein 90s: In Silico Analysis Reveals Unique Druggable Structural Features. Int. J. Mol. Sci. 2025, 26, 12065. https://doi.org/10.3390/ijms262412065

AMA Style

Daniyan MO, Singh H, Blatch GL. Plasmodium knowlesi Heat Shock Protein 90s: In Silico Analysis Reveals Unique Druggable Structural Features. International Journal of Molecular Sciences. 2025; 26(24):12065. https://doi.org/10.3390/ijms262412065

Chicago/Turabian Style

Daniyan, Michael O., Harpreet Singh, and Gregory L. Blatch. 2025. "Plasmodium knowlesi Heat Shock Protein 90s: In Silico Analysis Reveals Unique Druggable Structural Features" International Journal of Molecular Sciences 26, no. 24: 12065. https://doi.org/10.3390/ijms262412065

APA Style

Daniyan, M. O., Singh, H., & Blatch, G. L. (2025). Plasmodium knowlesi Heat Shock Protein 90s: In Silico Analysis Reveals Unique Druggable Structural Features. International Journal of Molecular Sciences, 26(24), 12065. https://doi.org/10.3390/ijms262412065

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop