Journal of Molecular Graphics and Modelling

Metastatic melanoma is an aggressive cutaneous malignancy frequently driven by the oncogenic V600E mutation within the BRAF kinase. While first-generation Type IS BRAF inhibitors, such as dabrafenib, are currently prescribed to target this specific molecular vulnerability, paradoxical MAPK pathway activation, and acquired drug resistance necessitate the continuous development of structurally optimized lead molecules. In this study, chemical intuition, bioisosteric replacement, and computational molecular docking were employed to propose two novel BRAFV600E drug candidates. The proposed therapeutics, engineered to incorporate constrained sp3-hybridized aliphatic rings and a sulfoximine bioisostere, demonstrated thermodynamically superior docking scores within the mutant catalytic cleft compared to dabrafenib. Lastly, a homology analysis determined that Mus musculus is a suitable model organism for future preclinical studies and confirmed crucial structural selectivity against microbial off-target kinases.

A priori assessment of target proteins' druggability remains an unsolved problem in the field of drug development. The empirical approaches widely used to solve this problem demonstrate low efficiency. In this work, we investigated the factor of hydration of a representative set of 65 evolutionarily and structurally unrelated human enzymes in a water environment. This factor depends only on the structure of the proteins, and not on the physical and chemical properties of any potential ligands. The results show that, unlike the widely used approaches based on calculations of the accessible surface area (ASA), the content of low-entropy water molecules (LEW) in the active sites of human enzymes is systematically higher than that in other areas of their surface, including inactive cavities. Optimal criteria and a step-by-step procedure for identifying protein ligand binding sites are proposed. The proposed approach, based on the calculation of the LEW content in the first hydration layer of potentially interesting target proteins, makes it possible to evaluate their medicinal suitability even before the development of any ligands. The article also presents the results of a comparative analysis of experimental Raman spectroscopy data and the results of molecular dynamics simulations of water hydrogen bonds using three widely used water models (TIP3P, OPC3, and TIP5P) and standard algorithms for calculating hydrogen bond networks.

Numerous advances in quantum and computational chemistry over the last decades, well as the development of computer science, allowed utilisation of more precise and complex models, which can be now applied to much bigger systems than in the past. The authors used Gaussian, coupled with theoretical methods, to predict a new way of peptide bond formation, which could have taken place in prebiotic conditions. To better tackle this difficult task, the properties of substrates (glycine-derived radicals) were extensively analysed, using the aforementioned tool - Gaussian, paired with taking resonance and hybridisation into account, to better understand the stereochemistry and the very nature of processes taking place. The result is a series of reactions, which without any sophisticated catalysts and with relatively low energy thresholds ({inverted exclamation}20 kcal/mol) can lead to formation of dipeptides (and further, oligopeptides). The authors also hope, the other predicted properties of the investigated molecules can be of use to any researcher, who would like to utilise them in their experiments. Author summaryOur goal was to investigate a way first peptide bonds in prebiotic conditions could have been formed. This is an extremely important step in research into the beginning of life on Earth. We found a very promising series of reactions, which uses atomic hydrogen as its only catalyst and confirmed our expectations with theoretical calculations, using Gaussian. There are two radicals derived from glycine, which perform major roles in the process, so we investigated their properties with Gaussian and verified that the results are in agreement with our own theoretical considerations. This involved checking for possible geometric isomers and conformers and creating models which could explain their properties. We are well aware that such calculations have limitations and there is no model, which is 100% accurate, so our results should be further confirmed by empirical data in the future. However, we still to be as thorough as possible in how we approached the subject.

Dengue Virus Serotype 2 is a human pathogenic flavivirus that encodes a non-structural protein 3 (DEN2-NS3) containing a helicase domain essential for viral replication. DEN2-NS3 utilizes energy derived from NTP hydrolysis to unwind dsRNA and dsDNA. A galloylated catechin, (-)-epigallocatechin gallate (EGCG), was previously reported to be highly potent against the Zika Virus NS3 helicase, with an IC50 value observed at 295.7 nM. This prompted an investigation to determine if three catechins, namely, (-)-epigallocatechin (EGC), (-)-epicatechin gallate (ECG), and EGCG, would act as potent inhibitors of DEN2-NS3. Enzyme-inhibition assays revealed that the helicase catalytic domain, DEN2-NS3(S171-K618), is strongly inhibited by these galloylated catechins. We observed Ki values of 400 {+/-} 86.6 nM for EGCG (mixed-mode inhibition with respect to ATP) and 550 {+/-} 250 nM for ECG (uncompetitive inhibition with respect to ATP). Furthermore, using a computational workflow starting with SiteMap, we provide evidence that a highly druggable pocket exists within the RNA-binding cavity, involving residues ASP290, ARG387, ASP409, MET429, HIS487, ASP541, ARG599, and ASP603. These catechins were each analyzed through 200-ns molecular dynamics (MD) simulations to evaluate the binding stability within the target DEN2-NS3 binding pocket. Computational results revealed that EGCG and ECG maintained high stability, forming shared, highly persistent amino acid contacts (>45% occupancy) with ASP603, ARG599, ASP541, and ARG387. In conclusion, we have demonstrated that EGCG and ECG achieve strong binding and allosteric disruption of the critical RNA-binding channel. We suggest that future structural optimization of these compounds into stable prodrug derivatives could yield promising antiviral therapies. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=99 SRC="FIGDIR/small/733882v1_ufig1.gif" ALT="Figure 1"> View larger version (41K): org.highwire.dtl.DTLVardef@2db363org.highwire.dtl.DTLVardef@5c2fdaorg.highwire.dtl.DTLVardef@49bf8eorg.highwire.dtl.DTLVardef@1bf31f1_HPS_FORMAT_FIGEXP M_FIG C_FIG

The phenotypic plasticity, slow replication, and complex, hydrophobic cell envelope of Mycobacterium tuberculosis contribute to its successful survival as a pathogen and its drug tolerance. Consequently, the global threat of multidrug-resistant Tuberculosis (MDR-TB), coupled with lengthy and highly toxic treatment regimens, necessitates the development of innovative treatment solutions. Mycobacteriophages are natural viruses of mycobacteria that typically encode two endolysins, which cooperatively facilitate host cell lysis at the end of the lytic life cycle: LysA, a peptidoglycan hydrolase, and LysB, a lipolytic enzyme, targeting the mycolylarabinogalactan-peptidoglycan complex. Their precise and efficient lytic activity, along with their low propensity to induce resistance, make them, particularly LysBs, promising candidates for new treatment solutions. In this study, we report MTB-LysB1, a novel LysB enzyme from an F1 sub-cluster mycobacteriophage isolated from our laboratory collection. While studying its structural features by comparing the modelled structure with representative mycobacteriophage LysB homologues, we found that the /{beta}-hydrolase fold and key motifs are conserved. Also, we identified putative membrane-interaction motifs that may play a role in LysB1s cell permeation. Significantly, we found MTB-LysB1 to be active against both drug-susceptible and multidrug-resistant (MDR) M. tuberculosis strains at nanomolar concentrations, comparable to the well-characterised D29 LysB reference enzyme. Beyond its standalone activity, MTB-LysB1 exhibits an additive effect when combined with the TB drugs rifampicin and moxifloxacin, and co-administration reduces the drugs minimum inhibitory concentrations (MICs), which holds clinical significance. By structurally damaging the mycobacterial cell wall, the enzyme appears to act as a permeability enhancer for the chemotherapeutic drugs, thereby improving antibiotic efficacy. Collectively, our findings position the enzyme not only as a novel antimycobacterial agent but also provide a structural framework for its rational engineering as a promising next-generation adjunct to TB drug regimens. HighlightsO_LIA novel F1 sub-cluster phage-derived LysB is discovered and characterised using integrated computational, biochemical and microbiological methods. C_LIO_LIAlphaFold2 modelling, molecular dynamics simulations and comparative structural analyses revealed an /{beta}-hydrolase fold with conserved catalytic and membrane-interaction features. C_LIO_LIThe enzyme exhibited high esterase activity, thermal stability and potent lytic activity against Mycobacterium tuberculosis. C_LIO_LIAn additive effect with TB drugs rifampicin and moxifloxacin highlights MTB-LysB1s potential as an adjunct therapeutic. C_LI

Plexin-B1 is a transmembrane receptor that integrates signals from Rho-family and Ras-family (Rap1b) GTPases to regulate cellular processes. While ligand simulated activation of the receptor is largely understood, the role of membrane composition and GTPase allosteric effects on plexin structure, internal protein dynamics, and function is still to be elucidated. Here, we performed multi-replica, 1 s all-atom simulations of Plexin-B1-GTPase complexes on PIP2- and PIP3-containing membranes to investigate the effects of these two signaling lipids, as well as on the GTPases. We found that both Rap1b and Rnd1 stably associate with the membrane, with PIP2 promoting broader lipid engagement and stronger Rap1b-Plexin-B1 interactions, whereas PIP3 enhances Rnd1-Plexin contacts and induces a membrane proximal orientation of Plexins juxtamembrane helix and makes contacts with a previously discovered activation switch loop. Contact map and network analyses revealed lipid-dependent shifts in allosteric communication, with PIP2 favoring Rap1b-centric hotspots and PIP3 favoring Rnd1-centric pathways. These predictions allow us to suggest a model for plexin intracellular region activation where both the identity of phosphoinositides and GTPase context synergistically stabilize Plexin-B1 membrane engagement, alter structural dynamics, and allosteric networks. Thus, we propose that the membrane is an active modulator of plexin receptor signaling.

Antimycin A (AA) is widely used as an inhibitor of the mitochondrial respiratory chain, targeting the Qi site of cytochrome bc1 (complex III). In photosynthetic organisms, AA is also well known to inhibit the photosynthetic PROTON GRADIENT REGULATION 5 (PGR5)-dependent cyclic electron flow around photosystem I (CEF-PSI). Although AA is frequently used as a specific inhibitor of PGR5-dependent CEF-PSI in photosynthetic reactions, we recently clarified that some of the major components of AA, which is typically a mixture of closely related compounds, also exert direct inhibitory effects on photosystem II (PSII). Nevertheless, the binding site and binding mode of AA in PSII remain largely unexplored. Structurally, AA consists of a salicylic acid moiety connected via an amide bond to a hydrophobic dilactone ring moiety. To identify important structural factors of AA for exhibiting inhibitory effects on PSII (assessed by QA- reoxidation measurements), we here investigated the relationship between structure and inhibitory potency using 38 AA-like compounds (AALCs), including commercial compounds and a series of synthetic AA analogs. Some AALCs exhibited substantially stronger impacts on PSII than natural AA. High acidity of the phenolic OH and the presence of a free amide NH of the salicylamide moiety were critical for the effects on PSII. In contrast, while the dilactone ring moiety also affected the inhibitory activity, this was replaceable with certain hydrophobic structures. Based on our results, together with the known structure-activity relationship and binding mode of AA in complex III, we propose tentative binding models for AA in PSII. HighlightsO_LIStructure-activity relationship of AA-like compounds on PSII is examined C_LIO_LISeveral AA-like compounds more potent than AA against PSII are identified C_LIO_LIPhenolic OH acidity and free amide NH of salicylamide moiety are key for AA effects C_LIO_LIThe dilactone ring moiety is replaceable with certain hydrophobic structures C_LIO_LITentative binding models for AA in PSII are proposed C_LI

Molecular dynamics simulations of nucleic acids are performed using a solvent-buffer distance of 10 [A] between the solute surface and the simulation box boundary. Although this cell size has been extensively explored in protein simulations, its implications for nucleic acid dynamics are not well understood. Nucleic acids are elongated, highly charged, and flexible structures with hydration and dynamical properties distinct from those of proteins and therefore, they may require different solvent-layer considerations in simulations. In this study, we investigated the effect of simulation cell size on nucleic acid dynamics by simulating a 30-base-pair double-helical nucleic acid structure and its two single-stranded forms using solvent-buffer distances of 3, 5, 10, 15, and 20 [A]. Smaller cells may impose restricted hydration, molecular crowding, and periodic image interactions. However, larger cells provide solvent space for conformational relaxation. A total of 45 s of molecular dynamics simulations were performed (3 structures x 5 cell sizes x 3 replicates x 1 s). Our results show that while the commonly used 10 [A] buffer may be sufficient to maintain the stability of the double-stranded nucleic acid, larger cells are required to capture the conformational dynamics of single-stranded structures. In both, increasing the cell size to 15 or 20 [A] enables broader conformational sampling. The first hydration shell exhibits reduced crowding in the 20 [A] cell, consistent with more relaxed conformations. At larger cell sizes, single-stranded nucleic acids adopt compact, self-associated conformations for stability. Together, this study presents physical insight into how simulation cell size and solvent environment influence nucleic acid dynamics.

The Protein Data Bank (PDB) is an ever-growing, open-access repository of structural data of biological molecules. This international database has been instrumental in the development of artificial intelligence and deep learning models for protein structure prediction and design. The PDB growth is a crucially important factor influencing further development of these models. Therefore, after analyzing the growth trend in PDB depositions since the archive's launch, we found that it is well fitted by the Gompertz function, a growth curve used across various disciplines. Furthermore, we observed that the function captures the "discovery of novel folds", i.e., the cumulative number of distinct folds among protein domains that constitute most of the PDB. Consequently, based on the fitting results, we estimated the likely numbers of PDB entries and protein folds. These findings provide insights into deceleration of growth in recent years and enable us to assess anticipated trends.

Human aquaporins (AQPs) are essential membrane channels, yet their inherent hydrophobicity complicates structural and functional studies. We present the systematic application of the QTY code to human AQPs, integrating it with AlphaFold 3 structure prediction to design and validate that four-representative human AQPs (AQP1, AQP3, AQP4, AQP7) can be converted into water-soluble analogs while maintaining their conformation. This approach features a novel platform for editing challenging membrane proteins. The QTY code was applied to the transmembrane regions of the selected four AQPs. Subsequently, the water-soluble QTY analogs of the four AQPs were predicted using AlphaFold 3. The predicted structures were superposed with CyroEM- or X-ray-determined native structures in PyMOL. Further analyses included root-mean-square deviation (RMSD) calculations, visualization of hydrophobic surface reduction, and inspection of conserved protein-ligand binding ability. After applying the QTY code, sequence changes between native AQPs and their QTY analogs was significant (42.86-48.80%). Nevertheless, their structures superposed well in analyses, with only slight deviations (RMSD < 0.6 [A]). In addition, the surface hydrophobicity of all QTY-edited AQPs was significantly reduced. Importantly, molecular contacts between the cholesterol ligand and protein were largely preserved for both native AQP1 and its QTY analog. Finally, all AlphaFold3-predicted structures for AQPs have high confidence values (pLDDT > 90; pTM ~0.83), supporting the reliability of the predicted structures. The findings demonstrate that membrane protein hydrophobicity can be edited and reduced without compromising fold integrity or functional architecture. Integration of the QTY code with AlphaFold 3 affords a high-throughput platform for designing water-soluble, structurally faithful analogs of challenging membrane proteins. Such a strategy can provide a potent platform for detergent-free biochemical studies and water-soluble analogs for therapeutic monoclonal antibody discoveries, thus advancing research of this pharmacologically important protein family.

Structure-based drug discovery is known to apply computational methods in a tiered hierarchy, with each layer narrowing the candidate set and refining the binding picture before committing to the next, more expensive step. We present a four-tiered computational benchmarking study evaluating five engines against a panel of 36 compounds targeting B-secretase 1 (BACE1), a validated Alzheimer's disease target with extensive co-crystal ground truth. This study evaluates Flexible Docking and Boltz2 Cofolding as the primary tier, followed by Ensemble Docking, and then Protein-Ligand MD with MM/PBSA and MM/GBSA post-processing. This is then concluded with Relative Binding Free Energy Perturbation (RevFEP) as the terminal refinement layer. Each method was benchmarked against the experimental binding free energies derived from the co-crystal structures spanning -7.85 to -11.35 kcal/mol. Our findings revealed that Flexible Docking reproduced the co-crystal binding mode for 35 of 36 ligands (97.2% within 2.0 A RMSD) but did not rank potency at this resolution. Boltz2 CoFolding provided an orthogonal structural cross-check with a receptor backbone RMSD of 0.293 A against the experimental co-crystal structure. Ensemble Docking identified the optimal receptor conformation for downstream FEP setup. MD with MM/GBSA decomposition identified van der Waals complementarity as the primary potency driver (Pearson r = +0.855, R2 = 0.732 on a 10-compound subset). RevFEP delivered the highest affinity correlation of any method (Pearson r = +0.662, R2 = 0.438, Spearman p = +0.624, mean absolute error 1.02 kcal/mol across all 36 ligands), resolving potency differences within a narrow 3.5 kcal/mol congeneric window that no other engine could discriminate. We characterize what each engine contributes independently and where RevFEP delivers signals no other engine achieves.

The molecular mechanics-generalized Born surface area method (MMGBSA) is one of the most commonly used end state approaches used for the calculation of the binding free energy towards computational drug design and screening studies. It is customary to break up the free energy into van der Waals, electrostatic, polar solvation (GB), and nonpolar solvation (SA) terms and then either correlate these terms with experiment or assign physical meaning to each term. Here, we demonstrate that this assumption of independent fitting coefficients for decomposed energy terms could be invalid. Through analytic derivation and large-scale molecular dynamics simulations, we show that (i) the protein and ligand Coulomb interaction energy and the GB solvation correction are almost perfectly collinear (R2[&ge;]0.99) reflecting their designed role as vacuum electrostatics plus solvent screening, and (ii) the van der Waals interaction and SA term likewise exhibit strong correlation, as both depend primarily on buried surface area. Interaction entropy and C2 entropy corrections are also found to be strongly dependent on underlying electrostatic fluctuations, further reinforcing redundancy. These findings hold both at the level of instantaneous trajectory fluctuations and when averaged across a diverse set of 139 protein-protein complexes and persist in both single-trajectory and three trajectory MMGBSA protocols. Our results caution against using decomposed MMGBSA terms as independent predictors in regression models and suggest instead combining correlated terms into effective polar, nonpolar, and entropic contributions. Our study provides a systematic diagnosis of collinearity in MMGBSA and highlights pathways toward more interpretable and statistically robust predictive modeling.

BackgroundTuberculosis remains a leading global infectious killer, with BCG offering inconsistent adult protection and rising drug-resistant strains demanding novel vaccine strategies. We report the first multi-epitope vaccine construct simultaneously targeting three previously unexplored Mycobacterium tuberculosis virulence proteins; EccB3, MycP, and polyketide synthase which collectively govern nutrient acquisition, ESX secretion integrity, and innate immune evasion. MethodsUsing a reverse vaccinology pipeline, B-cell, CTL, and HTL epitopes were predicted, filtered for allergenicity, toxicity, and IFN-{gamma} induction, then assembled into an 823-residue chimeric construct incorporating beta-defensin and PADRE adjuvants with AAY/GPGPG linkers, covering [~]90% global HLA diversity. The construct underwent AlphaFold structure prediction, 3DRefine refinement, disulfide engineering, PROCHECK/ProSA validation, ClusPro 2.0 docking against TLR1/TLR2, and C-IMMSIM immune simulation. ResultsThe construct (82.3 kDa, instability index 32.48) showed strong structural quality (94.7% favoured Ramachandran residues), stable TLR1/TLR2 binding (weighted energy: -1,371.0 kcal/mol), and robust in silico immune responses and durable memory cell formation following booster simulation. ConclusionThis computationally validated construct represents a promising multi-target TB vaccine candidate warranting experimental advancement.

"Mirror life", self-replicating organisms composed of non-natural-chirality biomacromolecules, presents a future threat with potentially global consequences. Consequently, there is strong agreement among experts that it should not be created. However, there is some disagreement over how effective existing medical countermeasures might prove against mirror bacteria in the event that they were created. Here, we leverage computational chemistry methods including docking and molecular dynamics to determine the likely binding efficacy of existing antibiotics against natural and mirror bacterial protein targets. We find that most existing antibiotics fail to bind to mirror bacterial protein targets, unlike their natural-chirality targets. This suggests altered binding of current medical countermeasures, which may impact the antimicrobial activity against mirror bacteria were the latter were created.

Chronic hepatitis B virus (HBV) infection contributes to hepatocellular carcinoma by disrupting host transcription, cell-cycle control, and apoptotic signaling. Isochlorogenic acid A (ICAA), a natural compound with antiviral and hepatoprotective properties, was previously shown to inhibit HBV replication by interfering with multiple steps of the viral life cycle. Because chronic HBV often reflects an imbalance between proliferation and cell death, we investigated how ICAA affects gene expression related to these processes in the presence or absence of HBV. We performed transcriptome analysis using RNA sequencing (RNA-seq) in HepAD38 cells (a HepG2-derived stable HBV-expressing line) and HepG2 control cells (HBV-negative) treated with ICAA or DMSO. HBV caused major differences in gene expression in HepAD38 cells compared with HBV-negative HepG2 cells. Principal component analysis showed that ICAA significantly altered HBV-dependent expression patterns, resulting in 189 differentially expressed genes (DEGs) that were regulated in opposite directions by both HBV and ICAA. Functional enrichment analysis highlighted pathways in viral carcinogenesis, apoptosis, MAPK signaling, and p53 signaling. Annexin V/propidium iodide assays showed apoptotic cells in both treated and untreated HepAD38 cultures, with only minor pattern changes. Mechanistically, in untreated HBV-positive cells caspase-9 cleavage failed to activate PARP, suggesting that induction of intrinsic apoptosis is followed by blocked execution. In contrast, ICAA inhibits caspase-9 cleavage in a dose-dependent manner, while activating PARP. Consistent with this, ICAA treatment increased apoptotic DNA fragmentation in HepAD38, reflecting the proapoptotic potential of ICAA under these conditions facilitating the elimination of HBV-positive cells by apoptosis. These findings highlight the potential therapeutic relevance of this compound in processes associated with HBV pathogenesis, together with its antiviral effect. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=185 SRC="FIGDIR/small/733975v1_ufig1.gif" ALT="Figure 1"> View larger version (47K): org.highwire.dtl.DTLVardef@38d107org.highwire.dtl.DTLVardef@235a13org.highwire.dtl.DTLVardef@ee988aorg.highwire.dtl.DTLVardef@60cb13_HPS_FORMAT_FIGEXP M_FIG C_FIG

In an era of increasing drug resistance, exploring alternative molecules is crucial for the efficient management and treatment of viral diseases. Nucleic acid aptamers have emerged as highly promising candidates due to their exceptional target specificity, low immunogenicity, and versatile mechanisms for viral blocking. This manuscript describes AptViralDB, a manually curated database providing comprehensive information on experimentally validated antiviral aptamers. It contains 1,768 entries of antiviral aptamers against 40 viral species and 104 molecular targets, compiled from literature and existing databases. Each entry provides detailed annotations, including sequence, aptamer type, target, chemical modifications, binding affinity, antiviral activity, stability, and cytotoxicity. We also provide predicted secondary structures and their corresponding minimum free energy (MFE) values. Additionally, a knowledge graph created using ArcadeDB/openCypher enables users to seamlessly explore connections among aptamers, viruses, molecular targets, and biological activities. Finally, the platform offers advanced search and browsing tools, BLAST-based sequence similarity searches, GC-content analysis, downloadable datasets, and REST API access to support computational applications. (https://webs.iiitd.edu.in/raghava/aptviraldb/).

Predicting the location of metal-binding sites in proteins is crucial for fundamental biological questions and biotechnological applications. Over the past decade, the rise in metal-bound protein structures in the Protein Data Bank, combined with advanced statistical models such as deep learning, has accelerated the development of metal-binding site prediction tools. Several approaches are now available, offering high-quality benchmarks and predictive performance. Our initial development in this area is BioMetAll, whose first version was based on backbone pre-organization. Here, we introduce its second version, featuring two major updates: 1) metal-specific scoring functions and 2) prediction using backbone geometry alone or in combination with first coordination sphere descriptors. Apart from demonstrating metal sensitivity and yielding better benchmarking results, this new version allows the assessment of the influence of considering the metals first coordination sphere versus backbone pre-organization on how metallic species bind to proteins.

While most existing methods are limited to predicting the tertiary structures of proteins containing only canonical residues, the PEPstrMOD server (developed in 2015) pioneered structure prediction for chemically modified and non-natural peptides. Despite its widespread use, the original framework was restricted to peptides of 7 to 25 residues and relied on older backbone-prediction algorithms. To address these limitations, we present PEPstrMOD2, which introduces three major advancements over its predecessor. First, it replaces the original in-house coordinate generation with state-of-the-art deep learning (DL) algorithms, leveraging AlphaFold2 and ESMFold for highly accurate initial structure prediction. Secondly, it greatly expands the accessible chemical space through incorporation of new, AMBER force-field compatible library of 257 post-translational modifications (PTMs), 428 non-canonical amino acids (NCAAs), and 243 terminal modifications. Lastly, through the application of native scalability of AlphaFold2 (AF2) and ESMFold (EF), PEPstrMOD2 eliminates the original restrictions of the length, enabling the structural modeling of longer, complex therapeutic peptides and small proteins. We evaluated the performance of PEPstrMOD2 against state-of-the-art methods across three distinct peptide datasets. For the AfCyc dataset consisting of 80 cyclic peptides, PEPstrMOD2 obtained a competitive average atom-level Root Mean Square Deviation (RMSD) of 2.05 angstroms, compared to 1.13 angstroms by AlphaFold3 (AF3) and 1.82 angstroms by AfCycDesign. Remarkably, for the modified peptide ModPep433 dataset, PEPstrMOD2 outperformed AF3, achieving the lower average RMSD score of 4.49 angstroms against 4.67 angstroms of AF3. Furthermore, in the case of the ModPep16 benchmark, PEPstrMOD2 achieved 2.50 angstroms average RMSD value, which is two times more accurate than that of the original PEPstrMOD (5.84 angstroms). In summary, PEPstrMOD2 provides a powerful, high-throughput, and highly accurate platform to facilitate peptide-based drug development and structural biology research. While the original PEPstrMOD was restricted to a web server interface, PEPstrMOD2 is available as both an intuitive webserver and a standalone command-line tool via GitHub, featuring Docker support for easy deployment and reproducible, large-scale modeling pipelines (https://webs.iiitd.edu.in/raghava/pepstrmod/).

The traditional 'one drug, one target' paradigm assumes that drugs interact with a single specific binding site. Modern pharmacology has proven this definition overly simplistic and, instead, recognizes that drugs operate within complex biological systems and often interact with multiple targets. In this context, proteins cannot be viewed as possessing a single functional binding site, but rather as dynamic entities capable of accommodating ligands at multiple regions, including transient and cryptic pockets. Here, we review and repurpose representative pocket detection tools across geometry-based, energy-based, and machine/deep learning approaches, originally designed to work on static conformations, to evaluate their agreement on molecular dynamics-derived conformational ensembles. Using GLUT1 protein as a dynamic transporter model and Aldose reductase as a cryptic-pocket reference system, we combine inter-tool concordance, HDBSCAN-based spatial clustering, volumetric IoU analysis, and temporal persistence scoring. Our results show that different algorithmic classes capture complementary aspects of pocket dynamics, with energy-based methods showing stronger sensitivity to transient cryptic regions and geometry-based approaches depending more strongly on pre-formed cavities. This work proposes a consensus-oriented framework for identifying conserved and transient druggable pockets in dynamic protein systems.

Background: Cutaneous melanoma is the most aggressive malignant skin tumor, and metastasis represents the primary cause of patient mortality. Bisphenol S (BPS) has an unclear influence on melanoma metastasis and its underlying molecular mechanisms. Methods: Potential BPS targets were predicted using the SEA, SwissTargetPrediction, and SuperPred databases. Based on TCGA-SKCM transcriptomic data, differential expression analysis was performed, and Weighted Gene Co-expression Network Analysis (WGCNA) was employed to construct a gene co-expression network. Candidate genes were obtained by integrating BPS-related targets, differentially expressed genes (DEGs), module genes, and univariate Cox regression genes, followed by Gene Ontology (GO)/Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis and protein-protein interaction (PPI) network construction. Least Absolute Shrinkage and Selection Operator (LASSO)-Cox regression was applied to screen core prognostic genes and construct a risk prediction model. Further analyses included network construction, molecular docking, and 100 ns molecular dynamics (MD) simulation. Results: Integration of BPS-related targets, DEGs, WGCNA module genes, and Cox regression results yielded 13 candidate genes enriched in kinase activity regulation and melanoma-related pathways. LASSO-Cox regression ultimately identified three core prognostic genes--ABCB1, PIM2, and TSHR--all significantly upregulated in metastatic tissues, with area under the curve (AUC) values of approximately 0.7. High-expression patients exhibited significantly better overall survival than low-expression patients (P < 0.05). A nomogram incorporating the three genes and clinical parameters demonstrated good calibration performance. Within the ceRNA network, MALAT1 and hsa-miR-155-5p were identified as key regulatory molecules, and 37 potential transcription factors were predicted, including CEBPA, JUN, and STAT3. Molecular docking revealed strong binding affinities of BPS toward ABCB1 , PIM2, and TSHR, and MD simulations confirmed the structural stability of all three complexes. Conclusion: ABCB1, PIM2, and TSHR are the core target genes through which BPS influences melanoma metastasis via multidrug resistance, kinase signaling, and receptor-mediated signal transduction. The prognostic model based on these three genes demonstrates good clinical applicability, and the ceRNA and transcription factor regulatory networks provide a systematic molecular basis for understanding the association between BPS exposure and melanoma metastasis.