Journal of Molecular Graphics and Modelling

Molecular mimicry between pathogen-derived and self-peptides shown by MHC molecules is one of the critical mechanisms in the pathophysiology of autoimmune diseases. Numerous studied has been conducted in this field to identify sequence similarity, but evaluating structural and dynamic similarity, systematic computational frameworks remain limited. Therefore, we created an automated multi-parameter molecular dynamics analysis workflow and used it to compare three peptides (KP1, KP2, and KP3) generated from Klebsiella pneumoniae bound to HLA-B class protein with one human self-peptide (Annexin-derived, ANX). We assessed six complementing parameters using one microsecond-scale MD simulation: radius of gyration (Rg), solvent-accessible surface area (SASA), hydrogen bonding dynamics, MM-GBSA binding free energy, root mean square fluctuation (RMSF), and root mean square deviation (RMSD) to understand time-dependent structural and dynamic behaviour of all the peptide-HLA-B complex. Additionally, hydrogen bond occupancy and molecular mechanics generalised Born surface area (MM-GBSA) binding free energy calculations were performed to provide a more comprehensive assessment of complex stability. Our analysis suggests that KP1 exhibits structural features consistent with molecular mimicry, maintaining conformational stability, surface exposure, and interaction patterns comparable to ANX. In contrast, KP2 showed reduced stability, characterised by higher RMSD values and substantial hydrogen bond loss, whereas KP3 displayed intermediate behaviour, with relatively favourable energetics but noticeable conformational variability. Overall, the multi-parameter framework enabled differentiation among the candidate peptides based on combined structural, dynamic, and energetic properties. The workflow can be adapted for the analysis of larger peptide datasets and may provide a systematic approach for investigating potential autoimmune-relevant molecular mimics in microbial proteomes, with required adjustments according to the system.

Marburg virus (MARV) is a highly pathogenic filovirus that causes hemorrhagic fever with a high mortality rate, with very limited treatment options. The urgent need for targeted antiviral agents emphasizes the importance of structure-based drug discovery approaches. The present study aimed to evaluate the antiviral potential of Withaferin A (PubChem CID-265237) against three key proteins of MARV: viral protein 35 (VP35), and nucleoproteins (NP). Three-dimensional structures of these proteins were retrieved from RCSB-Protein Data Bank and docked with Withaferin A using AutoDock Vina. The ligand demonstrated favourable binding affinities towards all three viral targets, indicating strong interaction potential at functionally relevant sites. Drug-likeness and pharmacokinetic properties predicted using SwissADME and pkCSM indicated acceptable ADMET profiles that comply with key drug-like criteria. To validate the stability of the docking, molecular dynamics simulations (GROMACS, 100 nanoseconds) were conducted. The protein-ligand complexes exhibited stable root mean square deviation (RMSD), root mean square fluctuation (RMSF), and consistent hydrogen bonding patterns throughout the simulation. The MM-GBSA binding free energy analysis further supported favorable binding energetics, predominantly driven by van der Waals and electrostatic interactions. Altogether, these findings demonstrate that Withaferin A exhibits promising multi-target inhibitory potential against key MARV proteins. This study provides molecular insights into ligand-protein interactions and supports further experimental validation of Withaferin A as a potential therapeutic candidate against Marburg virus.

Kinase family proteins constitute the second largest protein class targeted in drug development efforts, most prominently to treat cancer, but also several other diseases associated with kinase dysfunction. In this work we focus on type II kinase inhibitors which bind to the "classical" inactive conformation of the protein kinase catalytic domain where the DFG motif has a "DFG-out" orientation and the activation loop is folded. Many Tyrosine kinases (TKs) exhibit strong binding affinity with a wide spectrum of type II inhibitors while serine/threonine kinases (STKs) often bind more weakly. Recent work suggests this difference is largely due to differences in the folded to extended conformational equilibrium of the activation loop between TKs vs. STKs. The binding affinity of a type II inhibitor to its kinase target can be decomposed into a sum of two contributions: (1) the free energy cost to reorganize the protein from the active to inactive state, and (2) the binding affinity of the type II inhibitor to the inactive kinase conformation. In previous work we used a Potts statistical energy potential based on sequence co-variation to thread sequences over ensembles of active and inactive kinase structures. The threading function was used to estimate the free energy cost to reorganize kinases from the active to classical inactive conformation, and we showed that this estimator is consistent with the results of molecular dynamics free energy simulations for a small set of STKs and TKs. In the current study, we analyze the results of a large-scale study of the binding affinities of 50 type II inhibitors to 348 kinases, of which the results for 16 of the 50 type II inhibitors were reported in an earlier study (the "Davis dataset"); the binding data for the remaining 34 type II inhibitors to the panel of 348 kinases were recently obtained (the "Schrodinger dataset"). We use the Potts statistical energy model to investigate the contribution of protein reorganization to the selectivity of the large kinase panel against the set of 50 type II inhibitors, and find that protein reorganization makes a significant contribution to the selectivity. The AUC of the receiver-operator characteristic curve is [~]0.8. We report the results of an internal "blind test", that shows how Potts threading energies can provide more accurate estimates of kinase selectivity than corresponding predictions using experimental results of small sample size. We discuss why two STK phylogenetic kinase families, STE and CMGC, appear to contain many outliers, and how to improve the ability to predict kinase selectivity with a more complete analysis of the kinase conformational landscape. We compare the performance of Potts threading for predicting binding properties of the large set of (50) Type II inhibitors to 348 kinases, with those of a sequence-based purely machine learning model, DeepDTAGen, a publicly available machine learning model that was trained on the complete Davis dataset, including both Type I and Type II kinase inhibitors. We observe that DeepDTAGen performs well on binding predictions for the 16 type II inhibitors in the Davis dataset, but performs poorly on binding predictions for the 34 type II inhibitors against 348 kinases in the Schrodinger dataset.

Signaling through the insulin receptor (IR) and the type 1 insulin-like growth factor receptor (IGF1R) is modulated by secreted hormones and growth factor ligands (e.g. insulin and insulin-like growth factor 1, IGF1). Impaired signaling in these receptors often leads to diabetes and oncogenic diseases. The discovery of entirely novel viral insulin/IGF-like peptides (VILPs) that can stimulate receptors from the insulin family has raised questions about their structures and binding modes to receptors. These peptides exist in a single-chain (sc) or a double-chain (dc) configuration with folds likely similar to IGF1 and insulin, respectively. The interactions of VILPs with the human receptors are beginning to be mapped but little is known about their interactions with the receptors in fish-the host organism for viruses known to carry these peptide sequences. We have previously reported [Chuard et al., Cell Rep. 2025 44(8):116149] structural models of several VILPs from the Iridoviridae virus family bound to their cognate receptors in Zebrafish (Zeb). In this work, we conducted all-atom molecular dynamics (MD) simulations of these peptides and their receptor-bound complexes along with free energy calculations to assess the energetic contributions of VILP residues for their binding to Zebrafish receptors. Most of the observed Zeb insulin/Zeb {micro}IR and Zeb IGF1/Zeb {micro}IGF1R site 1 interactions are consistent with previously known interactions of human peptides with their receptors, highlighting similarities in their binding modes. However, we also report some non-conserved residues in VILPs that establish significant and unique interactions with residues in Zeb receptors. Furthermore, we identified residues in each VILP which can be potentially mutated into conserved insulin/IGF1 residues to possibly enhance the binding affinity of these peptides.

The process of DNA transcription leads to the generation of torsional stress, which must be resolved for smooth progression of the transcription machinery. In Saccharomyces cerevisiae, DNA topoisomerase I (Top1), a type IB topoisomerase, plays a critical role in relaxing supercoils and mitigating the topological strain associated with transcription. While several proteins from the transcription machinery have been reported to interact with yeast Top1, detailed characterization and functional relevance of these interactions have remained underexplored. This gap is partly due to the absence of a complete three-dimensional structure of the full-length enzyme, which hinders structure-based computational analyses of its interactome. In this study, we present a template-based model of full-length yeast Top1. Leveraging this model, we investigated its molecular interaction with Rpc82, a key subunit of RNA polymerase III enzyme, responsible for transcribing small non-coding RNAs such as tRNAs and 5S rRNA. Through molecular docking and molecular dynamics simulations, critical residues at the Top1-Rpc82 interface were identified that likely mediate their interaction. Our findings provide new insights into the structural basis of Top1s association with RNA polymerase III and its potential role in regulating Pol III-mediated transcription. The Top1 model developed here offers a valuable framework for future in silico studies aimed at elucidating the broader interactome and regulatory mechanisms of this essential enzyme.

Toll-like receptors (TLRs) are vital components of the innate immune system, recognizing both exogenous pathogens signals (PAMPs) and internal stress signals (DAMPs). TLR2 is unique among the human (Homo sapiens) TLR family members, as it contains a large cavity for binding hydrophobic ligands, such as lipoteichoic acid (LTA) and di/triacyl lipopeptides (Pam2/3CSK4). This study analyzed the structural phylogeny of cavity presence in the TLR2 lineage in vertebrates (vTLR) enabled by AI protein structure predictions and explored the potential convergent evolution of similar features in invertebrates (iTLRs). Analysis of AI models of TLR2s shows that this cavity is consistently present in TRL2 orthologs across jawed vertebrates (Gnathostomata). In jawless vertebrates (Cyclostomatha), these cavities were found in lamprey (Petromyzon marinus) TLR2 model, but only in some extant hagfish (Myxini), suggesting an ancestral origin in basal vertebrates followed by lineage-specific losses. TLR2 paralogs were found in several species, with a similar central cavity but potentially different ligand specificities. In silico ligand docking showed Pam2CSK4 binds to this cavity in all TLRs and paralogs consistently, demonstrating the conserved function of the ligand-binding pocket in gram-positive bacteria recognition across TLR2 branches. Changes in the TLR2 cavity size and shape in some vertebrate groups show the evolution of this DAMP recognition mechanism adapted to its respective pathogens. iTLRs form a separate phylogenetic branch with distinct structural features, but in literature some are considered to be TLR2 orthologs. Indeed, TLRs from some species of Helobdella and Ciona, contain a cavity with some similarity to that in the vTLR2 lineage. However, detailed structural comparisons of their location in the LRR domain and the structural details of the models suggest that their cavities have developed independently from that in TLR2s. Smaller cavities are present in other branches of the LRR family, but show different locations, shapes, and features, indicating that the binding of small ligands in the internal cavities within the LRR domains evolved multiple times in the LRR domain family history.

Brucellosis is a globally important zoonotic disease caused by Brucella melitensis, the most virulent and clinically significant species affecting both humans and livestock. Unlike many Gram-negative pathogens, B. melitensis, a facultative intracellular pathogen, lacks conventional virulence factors and instead relies on specialized systems such as the Type IV Secretion System (T4SS) for secretion of effector proteins. In this study, an integrated computational pipeline was implemented to identify, model, and assemble the T4SS components, encoded by virB operon, from the complete B. melitensis proteome. Template-based modeling strategies were employed to generate structures of T4SS subcomplexes, referencing crystallographic data from E. coli T4SS. Structural superposition with E. coli homologs revealed highly conserved architecture despite only 30-50% sequence identity. Stereochemical validation confirmed high model quality and favorable interactions among most VirB protein pairs. Membrane insertion analysis of the membrane-embedded assemblies further corroborated the spatial orientation of the modeled T4SS. Potential of T4SS as a drug target was explored by targeting dimeric interface of VirB11 ATPase to disrupt protein-protein interactions that could disarm the pathogen. Virtual screening of compounds from DrugBank database revealed compounds with docking score [≤] -7.0 kcal/mol that were screened based on ADMET properties, yielding three promising candidates - Ezetimibe (Drug Id: DB00973), Chlordiazepoxide (Drug Id: DB00475), and Alloin (Drug Id: DB15477). MM-GBSA analysis estimated favorable binding free energies for these compounds and molecular dynamics simulation for 200 ns further confirmed the protein-ligand interaction stability. Collectively, these findings provide new insights into the architecture of B. melitensis T4SS and identify three potential drug molecules targeting T4SS. This supports FDA - approved drug repurposing as an effective strategy for anti-virulence therapy against Brucellosis. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=103 SRC="FIGDIR/small/706537v1_ufig1.gif" ALT="Figure 1"> View larger version (46K): org.highwire.dtl.DTLVardef@847c4borg.highwire.dtl.DTLVardef@1fc2551org.highwire.dtl.DTLVardef@f62a7corg.highwire.dtl.DTLVardef@15f3468_HPS_FORMAT_FIGEXP M_FIG C_FIG

The unfolding or melting temperature (TM) is a central quantity to characterize the stability of proteins and other biopolymers. The accurate prediction of protein melting temperatures by molecular mechanics force field simulations is highly desirable for many biophysical and biotechnological applications. Since the time scales for protein (un-)folding are hardly accessible in conventional MD (cMD) simulations, enhanced sampling techniques such as Temperature Replica Exchange Molecular Dynamics (TREMD) are typically employed. However, TREMD simulations are computationally very demanding especially if large temperature ranges need to be covered. Additionally, if the TM is initially unknown, setting up TREMD simulations is often challenging. To find the optimal initial conditions for such simulations, we describe their performance based on a theoretical model, which we validate on a minimalistic Markov Chain Monte Carlo (MCMC) simulation setup. In an effort to reduce the computational demand, we have investigated the possibility to use small sets of TREMD temperature ladders placed iteratively in the vicinity of a TM estimate. Different TREMD setups were extensively tested on the fast-folding protein Chignolin. We found that appropriate starting conformations lead to significantly faster convergence. Furthermore, we found that, in practice, combining multiple small temperature ladders can be advantageous in comparison to one single temperature ladder. Based on our findings, we formulate practical recommendations on how to set up TREMD for protein melting with optimal efficiency.

Synaptic vesicle glycoproteins 2 (SV2) are integral membrane proteins essential for neurotransmitter release and are implicated in neurological disorders including epilepsy and Parkinsons disease. In the attempt to develop a ligand selective for SV2C, and in collaboration with UCB, UCB-F was identified as a potential candidate. However, the affinity of UCB-F to SV2C was found to be temperature dependent, decreasing by about 10-fold from +4 to 37 degrees. UCB1A was subsequently identified as SV2C ligand displaying in vitro a 100-fold selectivity for SV2C compared with SV2A. In this study we investigated whether the binding of UCB-1A to SV2A and SV2C was affected by the temperature. A combination of experimental binding assay data and molecular dynamics (MD) simulations were used. The binding studies revealed that UCB1A affinity for SV2A decreased significantly at 37 {degrees}C compared with 4 {degrees}C, whereas binding to SV2C remained largely unchanged. MD simulations reproduced these observations, namely that ligand RMSD values at 310 K showed that UCB1A binding fluctuated markedly in the SV2A complex, with many trajectories exceeding the 3.0 [A] stability cutoff, whereas UCB1A remained relatively well-anchored in SV2C under the same conditions. Structural analysis showed that, while UCB1A adopts a conserved binding pose across all isoforms stabilized by {pi}- {pi} stacking and a hydrogen bond with Asp, SV2C possesses a unique stabilizing feature. In SV2C, Tyr298 is less exposed to the solvent and engages in a persistent hydrogen bond with Asparagine, a structural feature that reinforces pocket stability and limits temperature-induced destabilization. This interaction is absent in SV2A, consistent with its greater temperature sensitivity. Together, these findings provide a mechanistic explanation for the experimentally observed temperature independence of UCB1A binding to SV2C. More broadly, the results highlight the importance of incorporating physiologically relevant temperatures into SV2 ligand evaluation and demonstrate how combining experiments with simulations can uncover isoform-specific mechanisms of ligand recognition and stability.

Background/ObjectivesThe unprecedented structural and binding data for antibodies to the SARS-COV2 virus taken together with the mutations for the spike protein allows for a broad simulation study of antibody-spike protein binding. This provides an understanding of the co-evolution of human immunity and viral immunity escape. MethodsWe utilized the YASARA molecular dynamics program to generate initial antibody-spike structures and simulate to equilibration for six SARS-COV2 variants and 10 different antibodies sampling two different binding regions to the receptor binding domain of the spike (especially for the Class I antibodies in the same part of the spike which attaches to the ACE2 receptor protein) and one to the N-terminal of the spike. Starting structures for antibody binding to variant spike proteins are perturbatively achieved through point mutations and insertions/deletions in the YASARA program. We employed YASARA to measure interfacial hydrogen bound counts between antibodies and variant spike proteins, and the HawkDock MMGBSA program to characterize trends in binding energies with mutation for four of the antibodies. We utilized the VMD program to analyze the time course of hydrogen bond populations. ResultsAs seen in previous studies, interfacial hydrogen bond counts serve as an excellent proxy for binding energies without the large systematic error inherent in the latter. We find that there is generally a decline in antibody binding strength, as measured by interfacial hydrogen bond counts, with viral evolution, but that a modest re-entrance of binding strength is present for most antibodies studied. Generically, the antibody heavy chain binds more strongly to the spike protein, through for approximately half the antibodies the light chain binding strength converges to the heavy chain strength with viral evolution. ConclusionsThe key conclusion is that the identified re-entrant immunity, speculatively arising from a balancing of maintenance of ACE2-spike binding while escaping antibodies through mutation, allows for some maintenance and even strengthening of immunity for later viral strains from early infection or vaccination.

The intracellular transport system is pivotal for cellular function and integrity, facilitated by cytoskeletal motor proteins such as dynein, which traverse along microtubules (MTs). The heterogeneity of the tubulin isotypes composing MTs introduces functional diversity, potentially affecting cytoskeletal motor proteins interactions with the MT. This in silico study investigated the influence of amino acid sequence variations in the C-terminal tails (CTTs) of six different Homo sapiens tubulin isotypes, TUBB2A, TUBB2B, TUBB2C, TUBB3, TUBB4A, and TUBB5, highly expressed in human brain tumors, and assessed the isotypes effect on the binding of motor protein dynein to MT. Among these isotypes, TUBB2A, TUBB2B, and TUBB2C were found to affect conformational motions of the dyneins microtubule-binding domain (MTBD) and stalk domain. The investigation highlighted the novel role of isotype-specific variations in lateral interactions between tubulin protofilaments (PFs) in determining the proximity of the {beta}-CTT of the adjacent PF to the MTBD, potentially affecting dyneins motility and suggesting how changes in isotype expression directly influence dyneins velocity and processivity and contribute to transport defects associated with neurological disorders and cancers. Isolating specific tubulin isotypes experimentally is challenging due to their high sequence similarity and complex interactions with other microtubule-associated proteins. This makes it challenging to distinguish between different tubulin isotypes and their effects, particularly in tissues where multiple isotypes are co-expressed. Additionally, these isotypes are heavily modified in vivo by post-translational modifications, which further complicate the isolation of a single, unmodified tubulin isotype. As a result, computational approaches have been necessary in this study for exploring these effects in a controlled, isotype-specific manner.

Glioblastoma Multiforme (GBM) is one of the most malignant forms of brain tumor in humans, with limited treatment options and poor overall survival rates. In the present study, we employed an in-silico workflow that integrated immunoinformatics and 3D structural modelling tools to design a multi-epitope vaccine against Podoplanin (PDPN), a transmembrane glycoprotein primarily involved in tumor invasion and metastasis. The differential expression of PDPN in tumor versus normal cells was investigated using transcriptomics datasets. Once the overexpression was confirmed, it was designated as a Tumor-Associated Antigen (TAA). B-cell, CTL, and HTL epitopes were predicted and screened for antigenicity, non-allergenicity, and non-toxicity. Selected epitopes were linked with appropriate adjuvant and linker sequences to construct a vaccine candidate. Codon optimization and in silico cloning was conducted to evaluate the constructs expression in a mammalian expression vector. The 3D structure of the vaccine candidate was modelled, refined, and validated before molecular docking with immune receptors and immune simulation studies. The results indicated that proposed polypeptide, RasIC-01v, could be a potential vaccine candidate for highly vigorous and dangerous cancer like GBM. Further experimental and immunological validations would be required to validate the commercial feasibility and development of RasIC-01v. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=116 SRC="FIGDIR/small/706629v1_ufig1.gif" ALT="Figure 1"> View larger version (35K): org.highwire.dtl.DTLVardef@7485b1org.highwire.dtl.DTLVardef@1f551c1org.highwire.dtl.DTLVardef@ca871eorg.highwire.dtl.DTLVardef@6cf53d_HPS_FORMAT_FIGEXP M_FIG C_FIG

Transition metal based compounds are promising therapeutic agents, particularly in cancer treatment. However, predicting their binding sites remains a major challenge. In this work, we investigate the applicability of two tools, Metal3D and Metal1D, for this purpose. Although originally trained to predict zinc ion binding sites only, both predictors successfully identify several experimentally observed binding sites for transition metal complexes directly from apo protein structures. At the same time, we highlight current limitations, such as the sensitivity to side-chain conformations, and discuss possible strategies for improvement. This work provides a first step toward establishing a robust computational pipeline in which rapid and low-cost predictors are able to identify putative hotspots for transition metal binding, which can then be refined using more accurate but computationally demanding methods. Author summaryTransition metals play a crucial role as therapeutic agents, especially in cancer therapy. However, the prediction of their binding site locations is challenging, as accurate computational methods often require time-consuming simulations, making them impractical when many possible binding sites must be explored. In this work, we explored the capability of two binding site predictors, originally developed to locate metal ions in proteins, to identify binding sites for more complex covalently-bound transition metal based agents. We found that these tools can often identify the experimentally-known binding regions, even when starting from the apo structure, in which the protein does not already contain the metal compound. At the same time, our results show clear limitations in more challenging cases, particularly when the binding involves only a single amino acid or when the binding site undergoes major structural rearrangements upon binding. Overall, our study shows that fast predictors can provide valuable early insights in the investigation of the binding sites of covalently-bound transition metal based compounds. When combined with more accurate simulation techniques, they can help focus computational efforts and ultimately support the rational design of transition metal based drugs.

We have conducted all atom molecular dynamics simulations of POPC and DPPC lipid bilayers using AMBER Lipid21 force field with eight different water models, including SPC/E, TIP3P, TIP3P-FB, TIP4P-FB, TIP4P-Ew, TIP4P/2005, TIP4P-D, and OPC, to identify the most compatible one without any modification. A number of parameters have been computed in order to understand the structure of the lipid bilayer: Area per lipid, Isothermal compressibility modulus, average Volume per lipid, electron density profile, bilayer thickness, X-ray and neutron scattering form factors, deuterium order parameter, and radial distribution function. The estimated Area per lipid, Isothermal compressibility factor, volume per lipid and bilayer thickness are highly consistent with experimental results for the SPC/E water model, indicating its suitability with the AMBER Lipid21 force field, insted of any modification. The bilayer electron density profiles of both the lipid bilayers demonstrate a little augmentation of water penetration with respect to the membrane surface for TIP4P-D water model. However, the experimental X-ray and neutron scattering form factors are aligning well with the simulated results for all studied water models, and TIP4P-D shows better for X-ray data. The deuterium order parameter for lipid acyl chains value less than 0.25 for all observed water models, depicting their disorderness for both the lipid bilayers. The lateral diffusion and reorientation autocorrelation function of the lipid molecules in both the bilayers are computed to reveal their dynamics across all water models. In comparison to other water models, the simulated trajectories predict better structure and reasonably fair dynamic properties for the SPC/E water model. The TIP4P-Ew water model reproduces the lateral diffusion co-efficient in close agreement with experiment. Reorientational dynamics for both the lipids in the bilayers for eight different water models are observed; the presence of slow and slowest time components corresponds to the lipid axial motion (wobble motion) and Twist/Splay motions. So, in view of the overall performance of the different water models with the AMBER Lipid21 all atom force field in reproducing membrane physical properties, the SPC/E water model appears to be an optimal choice.

Nuclear Pore Complexes (NPCs) are large protein complexes in eukaryotic cells that span the double-membrane of the nucleus and regulate bi-directional transport between nucleus and cytoplasm. T h e NPC core is lined by intrinsically disordered protein chains called nucleoporins (Nups) which form a selective barrier where large macromolecules (cargoes) need to bind to nuclear transport receptors (NTRs) such as Karyopherins (Kaps) to cross. Previous experimental results have suggested that not only Nups but Kaps, too, are important in the transport process of other NTRs/NTR-cargo complexes. In this work, we assess the role of Kaps in the transport of other NTRs (specifically, NTF2s) through the NPC, a process referred to as the "Kap-centric transport model". Here, using coarse-grained MD simulation we show that Kaps are able to direct NTF2s into the Nup meshwork, which leads to their increased flow. Our results also suggest that NTRs follow specific lanes inside the pore to maximize efficient transport.

Granulocyte macrophage-colony stimulating factor (GM-CSF) is a cytokine that plays a role in immune modulation. Its expression is associated with a multitude of different effects ranging from harmful, as in diseases such as rheumatoid arthritis and multiple sclerosis, to beneficial, as in the case of mitigation of diabetes type I and neutropenia. However, there is a large gap in knowledge explaining how GM-CSF toggles its structure for such physiological and pathological interactions. Our work describes an ongoing attempt to address this gap by focusing on a clustered histidine triad within -helices near the N-terminus, which prior studies have suggested play a role in binding ligands at an acidic pH. While GM-CSF is known to be highly flexible at a more acidic pH, several properties of its histidine triad remain unclear at the physiological pH at which GM-CSF would encounter its binding partners. We describe an effort to characterize the role of the GM-CSF histidines under physiological pH, specifically to determine if these histidines are key to GM-CSF structural integrity, and whether individual histidine residues modulate binding as they do at a lower pH. Our findings reveal that, while the histidine residues have an impact on GM-CSF structure, flexibility, and stability, they alone do not modulate the affinity for ligands at neutral pH. These data provide an initial explanation for the pleiotropic functions and interactions of GM-CSF within a biophysical context. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=82 SRC="FIGDIR/small/700583v1_ufig1.gif" ALT="Figure 1"> View larger version (24K): org.highwire.dtl.DTLVardef@a6fffcorg.highwire.dtl.DTLVardef@1f00c30org.highwire.dtl.DTLVardef@b04c50org.highwire.dtl.DTLVardef@6224d9_HPS_FORMAT_FIGEXP M_FIG C_FIG

Chikungunya virus (CHIKV) infection is one of the major public health concerns in several countries around the world. CHIKV non-structural protein 2 (nsP2) is a promising drug design target due to the enzymes multifunctional properties that facilities viral replication and propagation. To date, there is an evident lack of preventative and therapeutic developments that can be used against CHIKV. Drug repurposing is a time saving and cost-effective method used for the development of new drugs. In this study, drug repurposing was implemented with the use of HIV/HCV protease inhibitors to inhibit the active site of nsP2. Molecular dynamics simulations and analysis revealed the stability of two drugs, Indinavir and Paritaprevir. Indinavir forms a hydrogen bond with a major residue, which closes the flexible loop, situated in close proximity to the active site. This conformational shift in the orientation of the enzyme prevents accessibility to the active site thus disrupting the nsP2 protein from functioning effectively in viral replication. In conclusion, the findings of this study identified Indinavir was identified as a promising CHIKV nsP2 inhibitor. This study will provide the basis to further facilitate the drug repurposing strategy as an alternative approach for drug design of CHIKV inhibitors as well as other viral families.

Hyperpolarization-activated cyclic nucleotide-gated (HCN) channels are are a family of voltage-gated, cyclic-nucleotide modulated Na+/K+ channels that regulate spontaneous rhythmic electrical activity in both the heart and the brain. Understanding differences in the responsiveness to cyclic adenosine monophosphate (cAMP) modulation between HCN isoforms would offer insight into the specific binding interactions that drive channel activation. Using all-atom molecular dynamics (MD) simulations and the free-energy perturbation (FEP) approach, we determined the absolute binding free energy of cAMP to the the cyclicnucleotide-binding domain (CNBD) of HCN isoforms 1-4. By studying the free-energy of ligand binding to the various isoforms of HCN, our study advances the understanding of HCN channel activation and modulation mechanisms. Overall, our work offers insight into explaining differences in channel sensitivity across the isoforms of HCN.

Prasugrel is a prodrug, widely used in antiplatelet strategy for secondary prevention after acute coronary syndrome. The metabolism of prasugrel leads to the formation of the Prasugrel Active Metabolite (PAM), an irreversible P2Y12 receptor antagonist. Its mode of binding has not yet been fully established, although it is known that it binds covalently to P2Y12 by forming a disulfide bridge with cysteines and its sulfur moiety. PAM is a molecule with two chiral centers, resulting in four stereoisomers which appear to be stereoselective upon binding. A combination of different molecular modeling methods, such as molecular dynamics, ensemble docking, and Density Functional Theory (DFT), were used to rationalize these differences in antagonism observed in vitro and to elucidate the mode of binding of PAM to P2Y12. PAM is found to bind to the closed P2Y12 conformation in a preferential way. Although the four stereoisomers have comparable affinity, the location of the RS stereoisomer makes the formation of a disulfide bond with cysteines more favorable, particularly with cysteine 175. Compared to the RR stereoisomer, the RS stereoisomer interacts less deeply with the P2Y12 receptor, interacting in particular with the second and third extracellular loops, explaining the competition observed with cangrelor and an intermediate metabolite of prasugrel. Furthermore, DFT calculations have shown that the formation of a disulfide bridge is energetically more favorable with the RS stereoisomer than with the RR stereoisomer. The physical interactions and chemical reaction between the RS stereoisomer and the P2Y12 receptor are key factors in explaining the stereoselective binding of PAM to P2Y12.

Recombinant human Interleukin-2 (rhIL-2, Aldesleukin) is used in immunotherapy for metastatic melanoma and renal cell carcinoma. Low-dose IL-2 has been investigated for administration after adoptive T cell transfer to enhance CAR T expansion and sustain effector function. However, systemic IL-2 can cause severe toxicities and promote expansion of regulatory T cells (Tregs). Previous attempts at mitigating cytokine-mediated side effects involved isolating CAR T cell signaling from endogenous immune responses by developing IL-2/IL-2R{beta} based selective ligand-receptors systems. Expressing these variant orthogonal (ortho)IL2-R{beta} receptors in CAR T cells and supplying variant orthoIL-2, was shown to dramatically improve selectivity in CAR T cell expansion and anti-tumoral potency in a leukemia mouse model. This study describes the computational design of synthetic orthogonal cytokine receptor-ligand systems based on the scaffolds of the human canonical IL-2 and IL-2R{beta}. Leveraging state-of-the-art AlphaFold3 (AF3) structure prediction capabilities and a physics-informed constrained sequence generator (CSG), the pipeline generates, filters and ranks sets of putative orthoIL-2/orthoIL-2R{beta} mutant designs. Variants displaying minimal predicted off-target interactions and enhanced in target contacts are prioritized for structural modelling. Top designs showed outstanding AF3 structural and interfacial quality metrics ipTM and pTM, with averages between cognate pairs of 0.724{+/-}0.05 and 0.770{+/-}0.042, respectively. All in-silico hits showed ipTM <0.5 for non-cognates, indicating a good likelihood of orthogonality. Additionally, putative hits showed high levels of predicted structural fidelity to wild-type (WT) human IL-2/IL-2R{beta} (PDB: 2ERJ), with an average structural root-mean-square deviation (RMSD) of 0.843{+/-}0.375 [A]. These mutants incorporated 7-26 interfacial mutations derived from multiple interface selection strategies. Altogether, the results support the putative foldability and selective affinity of top-ranking mutants displaying metrics close-to or within experimental reference range. Finally, strengths and limitations are discussed, alongside the experimental implications of coupling a constrained protein design pipeline to the discovery and validation of selective binders based on naturally occurring scaffolds.