Search Results (3,041)

Protein enzymes are highly efficient catalysts that exhibit adaptability and selectivity under diverse biological conditions. In some organisms, such as bacteria, structurally similar enzymes, for instance, shikimate kinase (SK) and adenylate kinase (AK), coexist and act on chemically related ligands. This raises the question of whether these enzymes can accommodate and potentially react with each other’s ligands. In this study, we investigate the stability of non-cognate ligand binding in SK and explore whether external electric fields (EFs) can modulate this interaction, leading to cross-reactivity in SK. Using molecular dynamics simulations, we assess the structural integrity of SK and the binding behavior of ATP and AMP under EF-off and EF-on cases. Our results show that EFs enhance protein structure stability, stabilize non-cognate ligands in the binding pocket, and reduce local energetic frustration near the R116 residue located in the binding site. In addition to this, dimensionality reduction analyses reveal that EFs induce more coherent protein motions and reduce the number of metastable states. Together, these findings suggest that external EFs can reshape enzyme–ligand interactions and may serve as a tool to modulate enzymatic specificity and functional promiscuity. Thus, we provide computational evidence that supports the concept of using an EF as a tunable parameter in enzyme engineering and synthetic biology. However, further experimental investigation would be valuable to assess the reliability of our computational predictions. Full article

Background: Drug–drug interactions (DDIs) may occur when two or more drugs are taken together, leading to undesired side effects or potential synergistic effects. Most clinical effects of drug combinations have not been assessed in clinical trials. Therefore, predicting DDIs can provide better patient management, avoid drug combinations that can negatively affect patient care, and exploit potential synergistic combinations to improve current therapies in women’s healthcare. Methods: A DDI prediction model was built to describe relevant drug combinations affecting reproductive treatments. Approved drug features (chemical structure of drugs, side effects, targets, enzymes, carriers and transporters, pathways, protein–protein interactions, and interaction profile fingerprints) were obtained. A unified predictive score revealed unknown DDIs between reproductive and commonly used drugs and their associated clinical effects on reproductive health. The performance of the prediction model was validated using known DDIs. Results: This prediction model accurately predicted known interactions (AUROC = 0.9876) and identified 2991 new DDIs between 192 drugs used in different female reproductive conditions and other drugs used to treat unrelated conditions. These DDIs included 836 between drugs used for in vitro fertilization. Most new DDIs involved estradiol, acetaminophen, bupivacaine, risperidone, and follitropin. Follitropin, bupivacaine, and gonadorelin had the highest discovery rate (42%, 32%, and 25%, respectively). Some were expected to improve current therapies (n = 23), while others would cause harmful effects (n = 11). We also predicted twelve DDIs between oral contraceptives and HIV drugs that could compromise their efficacy. Conclusions: These results show the importance of DDI studies aimed at identifying those that might compromise or improve their efficacy, which could lead to personalizing female reproductive therapies. Full article

Background: Amyotrophic lateral sclerosis is a systemic, complex, multifactorial, and fatal neurodegenerative disease with various factors involved in its etiology. This study aimed to understand the effects of SNPs in the MTHFR, MTR, SLC19A1, and VAPB genes on protein functionality and structure and their influence on ALS susceptibility. Methods: The dbSNP and ClinVar databases were used for SNP data annotation, while UniProt and PDB provided protein sequences. We performed functional and structural predictions of SNPs using PolyPhen-2 and SNAP2. We modeled mutant proteins using AlphaFold 2 and visualized them in PyMOL to compare native and mutant forms. Results: Our results identified SNP rs74315431 as pathogenic, inducing structural and functional changes and exhibiting visible alterations in the three-dimensional structure. Although predicted as non-pathogenic, SNPs rs1801131, rs1805087, and rs1051266 caused protein structural alterations, a finding confirmed by three-dimensional visualization. SNP rs1801133 diverged from the others, being predicted as pathogenic but without causing changes in protein structure or function. Conclusions: Our study found a strong correlation between SNAP2-predicted alterations and those predicted by AlphaFold 2, whereas PolyPhen-2 results did not directly correlate with three-dimensional structure changes. Full article

TasA gene, encoding a functional amyloid protein critical for biofilm formation and antimicrobial activity, was cloned from the endophytic strain Bacillus amyloliquefaciens BS-3, isolated from rubber tree roots. This study identified the shortest functional TasA variant (483 bp, 160 aa) reported to date, featuring unique amino acid substitutions in conserved domains. Bioinformatics analysis predicted a signal peptide (1–27 aa) and transmembrane domain (7–29 aa), which were truncated to optimize heterologous expression. Two prokaryotic vectors (pET28a and pCZN1) were constructed, with pCZN1-TasA expressed solubly in Escherichia coli Arctic Express at 15 °C, while pET28a-TasA formed inclusion bodies at 37 °C. Purified recombinant TasA exhibited potent antifungal activity, achieving 98.6% ± 1.09 inhibition against Colletotrichum acutatum, 64.77% ± 1.34 against Alternaria heveae. Notably, TasA completely suppressed spore germination in C. acutatum and Oidium heveae Steinmannat 60 μg/mL. Structural analysis via AlphaFold3 revealed that truncation enhanced protein stability. These findings highlight BS-3-derived TasA as a promising biocontrol agent, providing molecular insights for developing protein-based biopesticides against rubber tree pathogens. Full article

Background: Antibacterial resistance (ABR) poses a major challenge to global health, with methicillin-resistant Staphylococcus aureus (MRSA) being one of the prominent multidrug-resistant strains. MRSA has developed resistance through the expression of Penicillin-Binding Protein 2a (PBP2a), a key transpeptidase enzyme involved in bacterial cell wall biosynthesis. Objectives: The objective was to design and characterize a novel small-molecule inhibitor targeting PBP2a as a strategy to combat MRSA. Methods: We synthesized a new indole triazole conjugate (ITC) using eco-friendly and click chemistry approaches. In vitro antibacterial tests were performed against a panel of strains to evaluate the ITC antibacterial potential. Further, a series of in silico evaluations like molecular docking, MD simulations, free energy landscape (FEL), and principal component analysis (PCA) using the crystal structure of PBP2a (PDB ID: 4CJN), in order to predict the mechanism of action, binding mode, structural stability, and energetic profile of the 4CJN-ITC complex. Results: The compound ITC exhibited noteworthy antibacterial activity, which effectively inhibited the selected strains. Binding score and energy calculations demonstrated high affinity of ITC for the allosteric site of PBP2a and significant interactions responsible for complex stability during MD simulations. Further, FEL and PCA provided insights into the conformational behavior of ITC. These results gave the structural clues for the inhibitory action of ITC on the PBP2a. Conclusions: The integrated in vitro and in silico studies corroborate the potential of ITC as a promising developmental lead targeting PBP2a in MRSA. This study demonstrates the potential usage of rational drug design approaches in addressing therapeutic needs related to ABR. Full article

Recent advancements have been made in the precise prediction of protein structures within the Protein Folding Problem (PFP), particularly in relation to minimizing the energy function to achieve stable and biologically relevant protein structures. This problem is classified as NP-hard within computational theory, necessitating the development of various techniques and algorithms. Bio-inspired algorithms have proven effective in addressing NP-hard challenges in practical applications. This study introduces a novel hybrid algorithm, termed GRSABio, which integrates the strategies of Jumping Spider Algorithm (JSOA) with the Golden Ratio Simulated Annealing (GRSA) for peptide prediction. Furthermore, the GRSABio algorithm incorporates a Convolutional Neural Network for fragment prediction (FCNN), forms an enhanced methodology called GRSABio-FCNN. This integrated framework achieves improved structure refinement based on energy for protein prediction. The proposed enhanced GRSABio-FCNN approach was applied to a dataset of 60 peptides. The Wilcoxon and Friedman statistics test were employed to compare the GRSABio-FCNN results against recent state-of-the-art-approaches. The results of these tests indicate that the GRSABio-FCNN approach is competitive with state-of-the-art methods for peptides up to 50 amino acids in length and surpasses leading PFP algorithms for peptides with up to 30 amino acids. Full article

The worldwide increase in antifungal resistance, particularly in Candida sp., requires the exploration of novel therapeutic agents. Natural compounds have been a rich source of antimicrobial molecules, where peptides constitute the class of the most bioactive components. Therefore, this study looks into the target-specific binding efficacy of insect-derived antifungal peptides (n = 37) as possible alternatives to traditional antifungal treatments. Using computational methods, namely the HPEPDOCK and HDOCK platforms, molecular docking was performed to evaluate the interactions between selected key fungal targets, lanosterol 14-demethylase, or LDM (PDB ID: 5V5Z), secreted aspartic proteinase-5, or Sap-5 (PDB ID: 2QZX), N-myristoyl transferase, or NMT (PDB ID: 1NMT), and dihydrofolate reductase, or DHFR, of C. albicans. The three-dimensional peptide structure was modelled through the PEP-FOLD 3.5 tool. Further, we predicted the physicochemical properties of these peptides through the ProtParam and PEPTIDE 2.0 tools to assess their drug-likeness and potential for therapeutic applications. In silico results show that Blap-6 from Blaps rhynchopeter and Gomesin from Acanthoscurria gomesiana have the most antifungal potential against all four targeted proteins in Candida sp. Additionally, a molecular dynamics simulation study of LDM-Blap-6 was carried out at 100 nanoseconds. The overall predictions showed that both have strong binding abilities and are good candidates for drug development. In in vitro studies, Gomesin achieved complete biofilm eradication in three out of four Candida species, while Blap-6 showed moderate but consistent reduction across all species. C. tropicalis demonstrated relative resistance to complete eradication by both peptides. The present study provides evidence to support the antifungal activity of certain insect peptides, with potential to be used as alternative drugs or as a template for a new synthetic or modified peptide in pursuit of effective therapies against Candida spp. Full article

Membrane transporters play a crucial role in any cell. Identifying the substrates they translocate across membranes is important for many fields of research, such as metabolomics, pharmacology, and biotechnology. In this study, we leverage recent advances in deep learning, such as amino acid sequence embeddings with protein language models (pLMs), highly accurate 3D structure predictions with AlphaFold 2, and structure-encoding 3Di sequences from FoldSeek, for predicting substrates of membrane transporters. We test new deep learning features derived from both sequence and structure, and compare them to the previously best-performing protein encodings, which were made up of amino acid k-mer frequencies and evolutionary information from PSSMs. Furthermore, we compare the performance of these features either using a previously developed SVM model, or with a regularized feedforward neural network (FNN). When evaluating these models on sugar and amino acid carriers in A. thaliana, as well as on three types of ion channels in human, we found that both the DL-based features and the FNN model led to a better and more consistent classification performance compared to previous methods. Direct encodings of 3D structures with Foldseek, as well as structural embeddings with ProstT5, matched the performance of state-of-the-art amino acid sequence embeddings calculated with the ProtT5-XL model when used as input for the FNN classifier. Full article

Glutamate dehydrogenase (GDH) is ubiquitous in organisms and crucial for amino acid metabolism, energy production, and redox balance. The gdhA and gudB genes encoding GDH were identified in Bacillus altitudinis AS19 and shown to be regulated by iron. However, their functions remain unclear. In this study, gdhA and gudB were analyzed using bioinformatics tools, such as MEGA, Expasy, and SWISS-MODEL, expressed with a prokaryotic expression system, and the induction conditions were optimized to increase the yield of soluble proteins. Phylogenetic analysis revealed that GDH is evolutionarily conserved within the genus Bacillus. GdhA and GudB were identified as hydrophobic proteins, not secreted or membrane proteins. Their structures were primarily composed of irregular coils and α-helices. SWISS-MODEL predicts GdhA to be an NADP-specific GDH, whereas GudB is an NAD-specific GDH. SDS-PAGE analysis showed that GdhA was expressed as a soluble protein after induction with 0.2 mmol/L IPTG at 24 °C for 16 h. GudB was expressed as a soluble protein after induction with 0.1 mmol/L IPTG at 16 °C for 12 h. The proteins were confirmed by Western blot and mass spectrometry. The enzyme activity of recombinant GdhA was 62.7 U/mg with NADPH as the coenzyme. This study provides a foundation for uncovering the functions of two GDHs of B. altitudinis AS19. Full article

Background and objectives: Adducins are cytoskeletal proteins essential for membrane stability, actin–spectrin network organization, and cell signaling. Mutations in the genes ADD1, ADD2, and ADD3 have been linked to hypertension, neurodevelopmental disorders, and cancer. However, no comprehensive in silico saturation mutagenesis study has systematically evaluated the pathogenic potential and structural consequences of all possible missense mutations in adducins. This study aimed to identify high-risk variants and their potential impact on protein stability and function. Methods: We performed computational saturation mutagenesis for all possible single amino acid substitutions across the adducin proteins family. Pathogenicity predictions were conducted using four independent tools: AlphaMissense, Rhapsody, PolyPhen-2, and PMut. Predictions were validated against UniProt-annotated pathogenic variants. Predictive performance was assessed using Cohen’s Kappa, sensitivity, and precision. Mutations with a prediction probability ≥ 0.8 were further analyzed for structural stability using mCSM, DynaMut2, MutPred2, and Missense3D, with particular focus on functionally relevant domains such as phosphorylation and calmodulin-binding sites. Results: PMut identified the highest number of pathogenic mutations, while PolyPhen-2 yielded more conservative predictions. Several high-risk mutations clustered in known regulatory and binding regions. Substitutions involving glycine were consistently among the most destabilizing due to increased backbone flexibility. Validated variants showed strong agreement across multiple tools, supporting the robustness of the analysis. Conclusions: This study highlights the utility of multi-tool bioinformatic strategies for comprehensive mutation profiling. The results provide a prioritized list of high-impact adducin variants for future experimental validation and offer insights into potential therapeutic targets for disorders involving ADD1, ADD2, and ADD3 mutations. Full article

Prognostic markers such as overall survival (OS) and tertiary lymphoid structure (TLS) ratios, alongside diagnostic signatures like primary cancer-type classification, provide critical information for treatment selection, risk stratification, and longitudinal care planning across the oncology continuum. However, extracting these signals solely from sparse, high-dimensional multi-omics data remains a major challenge due to heterogeneity and frequent missingness in patient profiles. To address this challenge, we present SeNMo, a self-normalizing deep neural network trained on five heterogeneous omics layers—gene expression, DNA methylation, miRNA abundance, somatic mutations, and protein expression—along with the clinical variables, that learns a unified representation robust to missing modalities. Trained on more than 10,000 patient profiles across 32 tumor types from The Cancer Genome Atlas (TCGA), SeNMo provides a baseline that can be readily fine-tuned for diverse downstream tasks. On a held-out TCGA test set, the model achieved a concordance index of 0.758 for OS prediction, while external evaluation yielded 0.73 on the CPTAC lung squamous cell carcinoma cohort and 0.66 on an independent 108-patient Moffitt Cancer Center cohort. Furthermore, on Moffitt’s cohort, baseline SeNMo fine-tuned for TLS ratio prediction aligned with expert annotations (p < 0.05) and sharply separated high- versus low-TLS groups, reflecting distinct survival outcomes. Without altering the backbone, a single linear head classified primary cancer type with 99.8% accuracy across the 33 classes. By unifying diagnostic and prognostic predictions in a modality-robust architecture, SeNMo demonstrated strong performance across multiple clinically relevant tasks, including survival estimation, cancer classification, and TLS ratio prediction, highlighting its translational potential for multi-omics oncology applications. Full article

Sepsis is a fetal disease that requires a clear diagnostic biomarker for timely antibiotic treatment. Recent research has identified a pyroptosis-inducing epitope known as P5-5 in tetranectin (TN), a plasma protein produced by monocytes. Previously, we produced a 12F1 monoclonal antibody against the P5-5 and discovered that it could not only diagnose the presence but also monitor the progress of sepsis in the clinic. In the current study, we further investigated the structure site of the P5-5 and the recognition mechanism between the 12F1 mAb and the P5-5 epitope. To this end, 10 amino acids (NDALYEYLRQ) in the P5-5 were individually mutated to alanine, and their binding to the mAb was tested to confirm the most significant antigenic recognition sites. In the meanwhile, the spatial conformation of 12F1 mAb variable regions was modeled, and the molecular recognition mechanisms in detail of the mAb to the P5-5 epitope were further studied by molecular docking. Following epitope prediction and experimental verification, we demonstrated that the motif “DALYEYL” in the epitope sequence position 2−8 of TN-P5-5 is the major binding region for mAb recognition, in which two residues (4L and 8L) were essential for the interaction between the P5-5 epitope and the 12F1 mAb. Therefore, our study greatly narrowed down the previously reported motif from ten to seven amino acids and identified two Leu as critical contact residues. Finally, a single-chain variable fragment (scFv) from the 12F1 hybridoma was constructed, and it was confirmed that the identified motif and residues are prerequisites for the strong binding between P5-5 and 12F1. Altogether, the data of the present work could serve as a theoretic guide for the clinical design of biosynthetic drugs by artificial intelligence to treat sepsis. Full article

The potential of a protein to cause an allergic reaction is often assessed using a variety of computational techniques. Leveraging advances in high-throughput protein sequence data coupled with in silico or computational methods can be used to systematically analyze large proteomes for allergenic potential. Despite mango’s widespread consumption and growing clinical reports of hypersensitivity, the full extent of their allergenicity is yet unknown. In this study, for the first time, we conducted a genome-wide in silico analysis by analyzing a total of 54,010 protein sequences to identify the complete spectrum of potential mango allergens. These proteins were analyzed using various bioinformatics tools to predict their allergenic potential based on sequence similarity, structural features, and known allergen databases. In addition to the known mango allergens, including Man i 1, Man i 2, and Man i 3, our findings demonstrated that several isoforms of cysteine protease, non-specific lipid-transfer protein (LTP), legumin B-like, 11S globulin, vicilin, thaumatin-like protein, and ervatamin-B family proteins exhibited strong allergenic potential, with >80% 3D epitope identity, >70% linear 80 aa window identity, and matching with >80 known allergens. Thus, a genome-wide in silico study provided a comprehensive profile of the possible mango allergome, which could help identify the low-allergen-containing mango cultivars and aid in the development of accurate assays for variety-specific allergic reactions. Full article

The ZC4H2 gene is the site of congenital mutations linked to neurodevelopmental and musculoskeletal pathologies collectively termed ZARD (ZC4H2-Associated Rare Disorders). ZC4H2 consists of a coiled coil and a single novel zinc finger with four cysteines and two histidines, from which the protein obtains its name. Alpha Fold 3 confidently predicts a structure for the zinc finger but also for similarly sized random sequences, providing equivocal information on its folding status. We show using synthetic peptide fragments that the zinc finger of ZC4H2 is genuine and folds upon binding a zinc ion with picomolar affinity. NMR pH titration of histidines and UV–Vis of a cobalt complex of the peptide indicate its four cysteines coordinate zinc, while two histidines do not participate in binding. The experimental NMR structure of the zinc finger has a novel structural motif similar to RANBP2 zinc fingers, in which two orthogonal hairpins each contribute two cysteines to coordinate zinc. Most of the nine ZARD mutations that occur in the ZC4H2 zinc finger are likely to perturb this structure. While the ZC4H2 zinc finger shares the folding motif and cysteine-ligand spacing of the RANBP2 family, it is missing key substrate-binding residues. Unlike the NZF branch of the RANBP2 family, the ZC4H2 zinc finger does not bind ubiquitin. Since the ZC4H2 zinc finger occurs in a single copy, it is also unlikely to bind DNA. Based on sequence homology to the VAB-23 protein, the ZC4H2 zinc finger may bind RNA of a currently undetermined sequence or have alternative functions. Full article

Background: Pepper (Capsicum annuum L.) is highly susceptible to various abiotic stresses during their growth and development, leading to severe reductions in both yield and quality. β-Glucosidase (BGLU) is widely involved in plant growth and development, as well as in the response to abiotic stress. Methods: We performed a genome-wide identification of pepper BGLU (CaBGLU) genes. Phylogenetic analysis included BGLU proteins from Arabidopsis, tomato, and pepper. Gene structures, conserved motifs, and promoter cis-elements were analyzed bioinformatically. Synteny within the pepper genome was assessed. Protein-protein interaction potential was predicted. Gene expression patterns were analyzed across tissues and under abiotic stresses using transcriptomic data and qRT-PCR. Subcellular localization of a key candidate protein CaBGLU21 was confirmed experimentally. Results: We identified 32 CaBGLU genes unevenly distributed across eight chromosomes. Phylogenetic classification of 99 BGLU proteins into 12 subfamilies revealed an uneven distribution of CaBGLUs across six subfamilies. Proteins within subfamilies shared conserved motifs and gene structures. CaBGLU promoters harbored abundant light-, hormone- (MeJA, ABA, SA, GA), and stress-responsive elements (including low temperature). A duplicated gene pair (CaBGLU19/CaBGLU24) was identified. 27 CaBGLU proteins showed potential for interactions. Expression analysis indicated CaBGLU5 and CaBGLU30 were mesophyll-specific, while CaBGLU21 was constitutively high in non-leaf tissues. CaBGLU21 was consistently upregulated by cold, heat, and ABA. Subcellular localization confirmed CaBGLU21 resides in the tonoplast. Conclusions: This comprehensive analysis characterizes the pepper BGLU gene family. CaBGLU21, exhibiting constitutive expression in non-leaf tissues, strong upregulation under multiple stresses, and tonoplast localization, emerges as a prime candidate gene for further investigation into abiotic stress tolerance mechanisms in pepper. The findings provide a foundation for future functional studies and stress-resistant pepper breeding. Full article