Search Results (48)

Marinesco–Sjögren syndrome (MSS) is a rare autosomal recessive disorder characterized by cerebellar ataxia, congenital cataracts, developmental delay, hypotonia, and progressive myopathy. Most reported cases are linked to pathogenic variants in SIL1, a gene encoding a co-chaperone essential for protein folding in the endoplasmic reticulum. Here, we present a comprehensive case study of a Turkish pediatric patient diagnosed with MSS, supported by genetic, bioinformatic, and structural modeling analyses. Whole-exome sequencing revealed a homozygous splice-site variant (SIL1 c.453+1G>T), confirmed by Sanger sequencing and segregation analysis. In silico annotation using Genomize, InterVar, Franklin, VarSome, ClinVar, OMIM, and PubMed classified the variant as pathogenic according to ACMG guidelines. Structural modeling by Phyre2 and I-TASSER demonstrated that the variant abolishes the intron 5 donor site, leading to truncation of the wild-type 461-amino-acid protein into a shortened ~189-amino-acid polypeptide. This truncation results in the loss of critical Armadillo (ARM) repeats required for HSPA5 interaction, explaining the observed instability and impaired chaperone function. Clinically, the patient presented with congenital cataracts, ataxia, developmental delay, and progressive muscle weakness, consistent with previously reported MSS cases. Comparison with the literature confirmed that splice-site variants frequently correlate with severe phenotypes, including early-onset ataxia and cataracts. This report highlights the importance of integrating genomic, structural, and clinical data to better understand genotype–phenotype correlations in MSS. Our findings expand the mutational spectrum of SIL1, reinforce the role of splicing defects in disease pathogenesis, and emphasize the necessity of comprehensive molecular diagnostics for rare neurogenetic syndromes. Full article

The objective of this study was to identify the AlkB family genes in poultry using bioinformatics, and to explore their molecular characteristics, evolutionary relationships, and expression patterns to clarify their potential functions in poultry. (1) Methods: The study utilized the NCBI database to obtain chicken genome data, and screened and validated AlkB family members (ALKBH1-5, ALKBH8, and FTO) by hmmsearch and TBtools. MEGA 11.0 was used for phylogenetic analysis, PHYRE2 and I-TASSER predicted protein structures, and the String database was used to construct an interoperability network. Finally, the tissue expression profiles were analyzed by using The Human Protein Atlas online database and qRT-PCR. (2) Results: Phylogenetic analysis revealed distinct avian and mammalian clusters, with chicken AlkB proteins exhibiting low sequence homology but conserved 3D structures compared to mammals. Chromosomal synteny and conserved domains highlighted evolutionary divergence, with ALKBH4 lacking typical AlkB structural motifs. Protein interaction networks linked ALKBH1/2/3/5/8/FTO, underscoring functional coordination in poultry adaptation. Tissue-specific expression showed high AlkB levels in brain tissues, while ALKBH5 dominated in muscle. During differentiation, ALKBH3, ALKBH5, and FTO expression significantly increased during myoblast differentiation. (3) Conclusions: This study identified seven AlkB family genes in poultry, revealing their phylogenetic classification into two subfamilies, conserved structural domains, chromosomal synteny, and tissue-specific expression patterns. Full article

The existence of s-DAPK-1, an alternatively spliced variant of DAPK-1, adds complexity to our understanding of the proteins involved in the regulation of cell survival, apoptosis, and autophagy. DAPK-1 has been implicated in the regulation of these processes; however, it remains unclear whether s-DAPK-1 also plays a similar role or a separate function; thus, determining its involvement in these processes is challenging due to the limited understanding of its regulation, interacting partners, function, and three-dimensional (3D) structure. Hence, this study was aimed at (1) understanding the regulation of s-DAPK-1 by predicting its microRNA targets, (2) predicting the 3D structure of s-DAPK-1, (3) its physicochemical and thermodynamic properties, (4) its interacting partners, and (5) molecular functions using computational methods. To achieve this aim, various bioinformatics tools and in silico webservers, such as ProteinPrompt, ProtParam, ProtScale, ScooP, Hawkdock, Phyre2, I-TASSER, PSIPRED, SAVES, and PROCHECK, along with user-friendly databases, such as NCBI, TarBase, and Protein Data Bank (PDB), were employed. For miRNA prediction, we used TarBase, and identified the specific microRNAs targeting s-DAPK-1. Furthermore, the Phyre2 database demonstrated that s-DAPK-1 possesses 40% alpha helices and 4% beta strands, forming a stable 3D structure. Additionally, s-DAPK-1 demonstrated stability to withstand high temperatures, suggesting that it is a thermostable protein. Moreover, s-DAPK-1 was found to interact with a variety of proteins involved in tumor progression and gene regulation, including a prion protein and histone H2B type 2-E (H2B2E). This suggests that s-DAPK-1 may perform diverse molecular functions such as regulation of metabolic processes, nucleic acid binding, and mRNA splicing by interacting with different proteins. Full article

The SCN9A gene, a critical regulator of pain perception, encodes the voltage-gated sodium channel Nav1.7, a key mediator of pain signal transmission. This study conducts a multimodal assessment of SCN9A, integrating genetic variation, structural architecture, and molecular dynamics to elucidate its role in pain regulation. Using advanced computational methods, I-TASSER simulations generated structural decoys of the SCN9A homology domain, producing an ensemble of conformational states. SPICKER clustering identified five representative models with a C-score of −3.19 and TM-score of 0.36 ± 0.12, reflecting moderate structural similarity to experimental templates while highlighting deviations that may underpin functional divergence. Validation via ProSA-web supported model reliability, yielding a Z-score of −1.63, consistent with native-like structures. Central to the analysis was the R1150W non-synonymous variant, a potential pathogenic variant. Structural modeling revealed localized stability in the mutant conformation but disrupted hydrogen bonding and altered charge distribution. Its pathogenicity was underscored by a high MetaRNN score (0.7978498) and proximity to evolutionarily conserved regions, suggesting functional importance. Notably, the variant lies within the Sodium-Ion-Transport-Associated Domain, where perturbations could impair ion conductance and channel gating—mechanisms critical for neuronal excitability. These findings illuminate how SCN9A variants disrupt pain signaling, linking genetic anomalies to molecular dysfunction. While computational insights advance mechanistic understanding, experimental validation is essential to confirm the variant’s impact on Nav1.7 dynamics and cellular physiology. By refining SCN9A’s molecular blueprint and highlighting its therapeutic potential as a target for precision analgesics, this work provides a roadmap for mitigating pain-related disorders through channel-specific modulation. Integrating structural bioinformatics with functional genomics, this study deciphers SCN9A’s role in pain biology, laying the groundwork for novel strategies to manage pathological pain. Full article

This study employed an integrated in silico approach to identify and characterize anticancer peptides (ACPs) derived from Arca species. Using a comprehensive bioinformatics pipeline (BIOPEP, ToxinPred, ProtParam, ChemDraw, SwissTargetPrediction, and I-TASSER), we screened hydrolyzed bioactive peptides from Arca species, identifying seventeen novel peptide candidates. Subsequent in vitro validation revealed three peptides (KW, WQIWYK, KGKWQIWYKSL) with significant anticancer activity, demonstrating both high biosafety and clinical potential. Our findings highlight Arca species proteins as a valuable source of therapeutic ACPs and establish bioinformatics as an efficient strategy for rapid discovery of bioactive peptides. This approach combines computational prediction with experimental validation, offering a robust framework for developing novel peptide-based therapeutics. Full article

This study evaluates the performance of various structure prediction tools and molecular docking platforms for therapeutic peptides targeting coronary artery disease (CAD). Structure prediction tools, including AlphaFold 3, I-TASSER 5.1, and PEP-FOLD 4, were employed to generate accurate peptide conformations. These methods, ranging from deep-learning-based (AlphaFold) to template-based (I-TASSER 5.1) and fragment-based (PEP-FOLD), were selected for their proven capabilities in predicting reliable structures. Molecular docking was conducted using four platforms (HADDOCK 2.4, HPEPDOCK 2.0, ClusPro 2.0, and HawDock 2.0) to assess binding affinities and interactions. A 100 ns molecular dynamics (MD) simulation was performed to evaluate the stability of the peptide–receptor complexes, along with Molecular Mechanics/Poisson–Boltzmann Surface Area (MM/PBSA) calculations to determine binding free energies. The results demonstrated that Apelin, a therapeutic peptide, exhibited superior binding affinities and stability across all platforms, making it a promising candidate for CAD therapy. Apelin’s interactions with key receptors involved in cardiovascular health were notably stronger and more stable compared to the other peptides tested. These findings underscore the importance of integrating advanced computational tools for peptide design and evaluation, offering valuable insights for future therapeutic applications in CAD. Future work should focus on in vivo validation and combination therapies to fully explore the clinical potential of these therapeutic peptides. Full article

Feline calicivirus (FCV), an important model for studying the biology of the Caliciviridae family, encodes the leader of the capsid (LC) protein, a viral factor known to induce apoptosis when expressed in a virus-free system. Our research has shown that the FCV LC protein forms disulfide bond-dependent homo-oligomers and exhibits intrinsic toxicity; however, it lacked a polybasic region and a transmembrane domain (TMD); thus, it was initially classified as a non-classical viroporin. The unique nature of the FCV LC protein, with no similarity to other proteins beyond the Vesivirus genus, has posed challenges for bioinformatic analysis reliant on sequence similarity. In this study, we continued characterizing the LC protein using the AlphaFold 2 and the recently released AlphaFold 3 artificial intelligence tools to predict the LC protein tertiary structure. We compared it to other molecular modeling algorithms, such as I-Tasser’s QUARK, offering new insights into its putative TMD. Through exogenous interaction, we found that the recombinant LC protein associates with the CrFK plasmatic membrane and can permeate cell membranes in a disulfide bond-independent manner, suggesting that this interaction might occur through a TMD. Additionally, we examined its potential to activate the intrinsic apoptosis pathway in murine and human ovarian cancer cell lines, overexpressing survivin, an anti-apoptotic protein. All these results enhance our understanding of the LC protein’s mechanism of action and suggest its role as a class-I viroporin. Full article

Protein structure prediction is important for understanding their function and behavior. This review study presents a comprehensive review of the computational models used in predicting protein structure. It covers the progression from established protein modeling to state-of-the-art artificial intelligence (AI) frameworks. The paper will start with a brief introduction to protein structures, protein modeling, and AI. The section on established protein modeling will discuss homology modeling, ab initio modeling, and threading. The next section is deep learning-based models. It introduces some state-of-the-art AI models, such as AlphaFold (AlphaFold, AlphaFold2, AlphaFold3), RoseTTAFold, ProteinBERT, etc. This section also discusses how AI techniques have been integrated into established frameworks like Swiss-Model, Rosetta, and I-TASSER. The model performance is compared using the rankings of CASP14 (Critical Assessment of Structure Prediction) and CASP15. CASP16 is ongoing, and its results are not included in this review. Continuous Automated Model EvaluatiOn (CAMEO) complements the biennial CASP experiment. Template modeling score (TM-score), global distance test total score (GDT_TS), and Local Distance Difference Test (lDDT) score are discussed too. This paper then acknowledges the ongoing difficulties in predicting protein structure and emphasizes the necessity of additional searches like dynamic protein behavior, conformational changes, and protein–protein interactions. In the application section, this paper introduces some applications in various fields like drug design, industry, education, and novel protein development. In summary, this paper provides a comprehensive overview of the latest advancements in established protein modeling and deep learning-based models for protein structure predictions. It emphasizes the significant advancements achieved by AI and identifies potential areas for further investigation. Full article

Azospirillum brasilense Sp7 produces PHB, which is covered by granule-associated proteins (GAPs). Phasins are the main GAPs. Previous studies have shown phasins can regulate PHB synthesis. When A. brasilense grows under stress conditions, it uses sigma factors to transcribe genes for survival. One of these factors is the σ²⁴ factor. This study determined the possible interaction between phasins and the σ²⁴ factor or phasin-σ²⁴ factor complex and DNA. Three-dimensional structures of phasins and σ²⁴ factor structures were predicted using the I-TASSER and SWISS-Model servers, respectively. Subsequently, a molecular docking between phasins and the σ²⁴ factor was performed using the ClusPro 2.0 server, followed by molecular docking between protein complexes and DNA using the HDOCK server. Evaluation of the types of ligand–receptor interactions was performed using the BIOVIA Discovery Visualizer for three-dimensional diagrams, as well as the LigPlot server to obtain bi-dimensional diagrams. The results showed the phasins (Pha4_Abs7 or Pha5_Abs7)-σ²⁴ factor complex was bound near the −35 box of the promoter region of the phaC gene. However, in the individual interaction of PhaP5_Abs7 and the σ²⁴ factor, with DNA, both proteins were bound to the −35 box. This did not occur with PhaP4_Abs7, which was bound to the −10 box. This change could affect the transcription level of the phaC gene and possibly affect PHB synthesis. Full article

The prognosis of mixed-lineage leukemia (MLL) has remained a significant health concern, especially for infants. The minimal treatments available for this aggressive type of leukemia has been an ongoing problem. Chromosomal translocations of the KMT2A gene are known as MLL, which expresses MLL fusion proteins. A protein called menin is an important oncogenic cofactor for these MLL fusion proteins, thus providing a new avenue for treatments against this subset of acute leukemias. In this study, we report results using the structure-based drug design (SBDD) approach to discover potential novel MLL-mediated leukemia inhibitors from natural products against menin. The three-dimensional (3D) protein model was derived from Protein Databank (Protein ID: 4GQ4), and EasyModeller 4.0 and I-TASSER were used to fix missing residues during rebuilding. Out of the ten protein models generated (five from EasyModeller and I-TASSER each), one model was selected. The selected model demonstrated the most reasonable quality and had 75.5% of residues in the most favored regions, 18.3% of residues in additionally allowed regions, 3.3% of residues in generously allowed regions, and 2.9% of residues in disallowed regions. A ligand library containing 25,131 ligands from a Chinese database was virtually screened using AutoDock Vina, in addition to three known menin inhibitors. The top 10 compounds including ZINC000103526876, ZINC000095913861, ZINC000095912705, ZINC000085530497, ZINC000095912718, ZINC000070451048, ZINC000085530488, ZINC000095912706, ZINC000103580868, and ZINC000103584057 had binding energies of −11.0, −10.7, −10.6, −10.2, −10.2, −9.9, −9.9, −9.9, −9.9, and −9.9 kcal/mol, respectively. To confirm the stability of the menin–ligand complexes and the binding mechanisms, molecular dynamics simulations including molecular mechanics Poisson–Boltzmann surface area (MM/PBSA) computations were performed. The amino acid residues that were found to be potentially crucial in ligand binding included Phe243, Met283, Cys246, Tyr281, Ala247, Ser160, Asn287, Asp185, Ser183, Tyr328, Asn249, His186, Leu182, Ile248, and Pro250. MI-2-2 and PubChem CIDs 71777742 and 36294 were shown to possess anti-menin properties; thus, this justifies a need to experimentally determine the activity of the identified compounds. The compounds identified herein were found to have good pharmacological profiles and had negligible toxicity. Additionally, these compounds were predicted as antileukemic, antineoplastic, chemopreventive, and apoptotic agents. The 10 natural compounds can be further explored as potential novel agents for the effective treatment of MLL-mediated leukemia. Full article

The challenge of rapidly diagnosing myocardial ischemia in unstable angina (UA) patients presenting to the Emergency Department (ED) is due to a lack of sensitive blood biomarkers. This has prompted an investigation into microRNAs (miRNAs) related to cardiac-derived Nourin for potential diagnostic application. The Nourin protein is rapidly expressed in patients with acute coronary syndrome (ACS) (UA and acute myocardial infarction (AMI)). MicroRNAs regulate gene expression through mRNA binding and, thus, may represent potential biomarkers. We initially identified miR-137 and miR-106b and conducted a clinical validation, which demonstrated that they were highly upregulated in ACS patients, but not in healthy subjects and non-ACS controls. Using integrated comprehensive bioinformatics analysis, the present study confirms that the Nourin protein targets miR-137 and miR-106b, which are linked to myocardial ischemia and inflammation associated with ACS. Molecular docking demonstrated robust interactions between the Nourin protein and miR137/hsa-miR-106b, involving hydrogen bonds and hydrophobic interactions, with −10 kcal/mol binding energy. I-TASSER generated Nourin analogs, with the top 10 chosen for structural insights. Antigenic regions and MHCII epitopes within the Nourin SPGADGNGGEAMPGG sequence showed strong binding to HLA-DR/DQ alleles. The Cytoscape network revealed interactions of -miR137/hsa-miR--106b and Phosphatase and tensin homolog (PTEN) in myocardial ischemia. RNA Composer predicted the secondary structure of miR-106b. Schrödinger software identified key Nourin-RNA interactions critical for complex stability. The study identifies miR-137 and miR-106b as potential ACS diagnostic and therapeutic targets. This research underscores the potential of miRNAs targeting Nourin for precision ACS intervention. The analysis leverages RNA Composer, Schrödinger, and I-TASSER tools to explore interactions and structural insights. Robust Nourin-miRNA interactions are established, bolstering the case for miRNA-based interventions in ischemic injury. In conclusion, the study contributes to UA and AMI diagnosis strategies through bioinformatics-guided exploration of Nourin-targeting miRNAs. Supported by comprehensive molecular analysis, the hypoxia-induced miR-137 for cell apoptosis (a marker of cell damage) and the inflammation-induced miR-106b (a marker of inflammation) confirmed their potential clinical use as diagnostic biomarkers. This research reinforces the growing role of miR-137/hsa-miR-106b in the early diagnosis of myocardial ischemia in unstable angina patients. Full article

(1) Background: Mutations in NFκB1, a transcriptional regulator of immunomodulating proteins, are a known cause of inborn errors of immunity. Our proband is a 22-year-old male with a diagnosis of common variable immunodeficiency (CVID), cytopenias with massive splenomegaly, and nodular regenerative hyperplasia of the liver. Genetic studies identified a novel, single-point mutation variant in NFκB1, c. T638A p. V213E. (2) Methods: Next-generation panel sequencing of the patient uncovered a novel single-point mutation in the NFκB1 gene that was modeled using the I-TASSER homology-modeling software, and molecular dynamics were assessed using the YASARA2 software (version 20.14.24). (3) Results: This variant replaces valine with glutamic acid at position 213 in the NFκB1 sequence. Molecular modeling and molecular dynamic studies showed altered dynamics in and around the rel homology domain, ankyrin regions, and death domain of the protein. We postulate that these changes alter overall protein function. (4) Conclusions: This case suggests the pathogenicity of a novel variant using protein-modeling techniques and molecular dynamic simulations. Full article

The structural analysis of proteins is a major domain of biomedical research. Such analysis requires resolved three-dimensional structures of proteins. Advancements in computer technology have led to progress in biomedical research. In silico prediction and modeling approaches have facilitated the construction of protein structures, with or without structural templates. In this study, we used three neural network-based de novo modeling approaches—AlphaFold2 (AF2), Robetta-RoseTTAFold (Robetta), and transform-restrained Rosetta (trRosetta)—and two template-based tools—the Molecular Operating Environment (MOE) and iterative threading assembly refinement (I-TASSER)—to construct the structure of a viral capsid protein, hepatitis C virus core protein (HCVcp), whose structure have not been fully resolved by laboratory techniques. Templates with sufficient sequence identity for the homology modeling of complete HCVcp are currently unavailable. Therefore, we performed domain-based homology modeling for MOE simulations. The templates for each domain were obtained through sequence-based searches on NCBI and the Protein Data Bank. Then, the modeled domains were assembled to construct the complete structure of HCVcp. The full-length structure and two truncated forms modeled using various computational tools were compared. Molecular dynamics (MD) simulations were performed to refine the structures. The root mean square deviation of backbone atoms, root mean square fluctuation of Cα atoms, and radius of gyration were calculated to monitor structural changes and convergence in the simulations. The model quality was evaluated through ERRAT and phi–psi plot analysis. In terms of the initial prediction for protein modeling, Robetta and trRosetta outperformed AF2. Regarding template-based tools, MOE outperformed I-TASSER. MD simulations resulted in compactly folded protein structures, which were of good quality and theoretically accurate. Thus, the predicted structures of certain proteins must be refined to obtain reliable structural models. MD simulation is a promising tool for this purpose. Full article

The genus Capripoxvirus belongs to the Poxviridae family. The sheeppox, goatpox, and lumpy skin disease viruses are three species of this genus with 96% identity in their genomes. These are financially devastating viral infections among cattle, which cause a reduction in animal products and lead to a loss in livestock industries. In the current study, the phylogenetic analysis was carried out to reveal the evolutionary relationships of Capripoxvirus species (i.e., sheeppox virus (SPPV), goatpox virus (GTPV), and lumpy skin disease virus (LSDV)) with other viruses from the Poxviridae family with >96% query coverage to find the similarity index among all members. The three viruses (i.e., SPPV, GTPV, and LSDV) joined the clade of Capripoxvirus of the Poxviridae family in the phylogenetic tree and exhibited close evolutionary relationships. The multiple sequence alignment using ClustalOmega revealed significant variations in the protein sequences of the DNA-dependent RNA polymerase of SPPV, GTPV, and LSDV. The three-dimensional structures of five selected bee peptides and DNA-directed RNA polymerase of SPPV, GTPV, and LSDV were predicted using trRosetta and I-TASSER and used for molecular docking and simulation studies. The protein–protein docking was carried out using HADDOCK server to explore the antiviral activity of peptides as honey bee proteins against SPPV, GTPV, and LSDV. In total, five peptides were docked to DNA-directed RNA polymerase of these viruses. The peptides mellitin and secapin-1 displayed the lowest binding scores (−106.9 +/− 7.2 kcal/mol and −101.4 +/− 11.3 kcal/mol, respectively) and the best patterns with stable complexes. The molecular dynamics simulation indicated that the complex of protein DNA-dependent RNA polymerase and the peptide melittin stayed firmly connected and the peptide binding to the receptor protein was stable. The findings of this study provide the evidence of bee peptides as potent antimicrobial agents against sheeppox, goatpox, and lumpy skin disease viruses with no complexity. Full article

Azurin is a natural protein produced by Pseudomonas aeruginosa that exhibits potential anti-tumor, anti-HIV, and anti-parasitic properties. The current study aimed to investigate the potential of azurin protein against breast cancer using both in silico and in vitro analyses. The amino acid sequence of Azurin was used to predict its secondary and tertiary structures, along with its physicochemical properties, using online software. The resulting structure was validated and confirmed using Ramachandran plots and ERRAT2. The mature azurin protein comprises 128 amino acids, and the top-ranked structure obtained from I-TASSER was shown to have a molecular weight of 14 kDa and a quality factor of 100% by ERRAT2, with 87.4% of residues in the favored region of the Ramachandran plot. Docking and simulation studies of azurin protein were conducted using HDOCK and Desmond servers, respectively. The resulting analysis revealed that Azurin docked against p53 and EphB2 receptors demonstrated maximum binding affinity, indicating its potential to cause apoptosis. The recombinant azurin gene was successfully cloned and expressed in a BL21 (DE3) strain using a pET20b expression vector under the control of the pelB ladder, followed by IPTG induction. The azurin protein was purified to high levels using affinity chromatography, yielding 70 mg/L. In vitro cytotoxicity assay was performed using MCF-7 cells, revealing the significant cytotoxicity of the azurin protein to be 105 µg/mL. These findings highlight the potential of azurin protein as an anticancer drug candidate. Full article