Journal of Molecular Graphics and Modelling

Human African trypanosomiasis, caused by a protozoan parasite Trypanosoma brucei, is a neglected tropical disease for which well-tolerated, conveniently administered, and highly efficacious medicines are still missing. Previously, T. brucei Phosphofructokinase was targeted by small-molecule inhibitor development efforts. This approach has shown promise both in vitro and in vivo. In this study, we have used these wet-lab results, evaluated the compounds already characterised by Molecular Dynamics simulations, found relationships between in silico and wet-lab data and used these observations to evaluate compounds that we selected through several different approaches of virtual screens. We observed that inhibitor-ATP interactions are highly predictive of the inhibitory activity. Several compounds selected through virtual screens have outperformed previously characterised compounds.

Chagas disease, caused by Trypanosoma cruzi, demands novel therapeutic strategies that overcome the toxicity and limited efficacy of current treatments. To address this need, herein we report an integrative, target-centric strategy that combines parasite proteome mining, structural modeling, and experimental validation. Functional enrichment and druggability analyses identified phosphopyruvate hydratase (PPH) as a promising candidate due to its essential metabolic role and limited similarity to human homologs. Notably, proteome mining revealed the presence and conservation of PPH across kinetoplastid parasites, including Leishmania donovani, supporting its evaluation beyond T. cruzi. For the selected PPH sequences, AlphaFold-derived three-dimensional models underwent extensive molecular dynamics refinement, yielding stable conformational ensembles suitable for structure-based studies. Using this validated model, virtual screening of the Latin American Natural Products Database - LANaPDB - identified aptosimon as a top-ranked compound candidate. Molecular dynamics simulations further showed ligand-dependent binding behavior, suggesting alternative binding modes distinct from the canonical substrate configuration. In vitro assays demonstrated consistent antiparasitic activity against intracellular T. cruzi amastigotes (IC = 3.52 {+/-} 0.023 {micro}g/mL) and Leishmania donovani promastigotes (IC = 13.06 {+/-} 0.018 {micro}g/mL), supporting the biological relevance of the aptosimon-related lignan chemotype, hinokinin, across two kinetoplastid parasite models. Together, these results support PPH as a structurally tractable and biologically relevant candidate target, while identifying an aptosimon-related lignan chemotype, represented experimentally by hinokinin, as a cross-species antiparasitic scaffold that warrants further biochemical target-validation studies.

Cancer is a serious public health problem, and is becoming more common, with a projected increase in deaths and more than 25 million new cases in coming decades. A number of molecular mechanisms are involved in the tumoral process, with one of them, never in mitosis A-related kinase 2 (NEK2), a serine/threonine protein kinase, being a frequent target of amplification in various malignancies that is responsible for chromosomal instability, aneuploidy and activation of several oncogenic pathways. Available kinase inhibitors are not yet optimized in terms of their pharmacokinetic properties for clinical use, and current therapies, such as chemotherapeutic agents or immunotherapies are often limited by their resistance. In silico methods represent an effective tool to search for novel potent inhibitors, before testing in animals, with time constraints and limited resources. To find new inhibitors of NEK2, we used E pharmacophore-based modeling and structure based virtual screening in this study. NEK2 was chosen as the target for therapeutic intervention and an energy optimized pharmacophore model was employed to screen the Enamine REAL library of millions of compounds. Pharmacodynamic and Pharmacokinetic properties of the Top hits were tested using ADMET profiling. These were further screened using molecular docking (standard precision and extra precision) and virtual screening to obtain three lead compounds 1, 2, and 3 which have docking score of -7.414, -8.037 and -7.562 respectively. MM-GBSA calculations were used to estimate the binding free energies for these complexes, which were determined to be -54.92, -54.18 and -49.23 kcal/mol. Lastly, 100 ns molecular dynamics simulations have been run to evaluate complex stability in dynamic situations. The overall results of the MD showed the overall stability of the NEK2-ligand complexes, and thus these three compounds are promising NEK2 inhibitor candidates and could be further validated in vitro and in vivo for clinical application. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=133 SRC="FIGDIR/small/742111v1_ufig1.gif" ALT="Figure 1"> View larger version (70K): org.highwire.dtl.DTLVardef@14e9cfeorg.highwire.dtl.DTLVardef@24ec78org.highwire.dtl.DTLVardef@20be75org.highwire.dtl.DTLVardef@1b80c02_HPS_FORMAT_FIGEXP M_FIG C_FIG

Heteromeric amino acid transporters (HATs) mediate essential amino acid flux across membranes, but the molecular dynamics of substrate translocation remain poorly defined for many family members. Here, using conventional and adaptive steered molecular dynamics (cMD and ASMD) simulations, we identify residue W230 in the b0,+AT transport channel as a dynamic gate that regulates arginine (Arg) influx through side chain flipping. By integrating dynamic network analysis with dynamical cross-correlation of residue motions, we show that regulatory signals propagate from the Arg binding site through transmembrane helix 5 (TM5), a connecting loop, and TM6 to reach W230. We propose a dynamic gating mechanism for b0,+AT - mediated amino acid transport. Arg binding at V186 triggers signal propagation that enhances cooperative interactions between W230 and Arg, driving the side chain flipping of W230. Our findings reveal a dynamic gating mechanism underlying b0,+AT - dependent Arg transport and suggest that residue-triggered side chain reorientation may represent a conserved and efficient strategy in transporter function. Author SummaryAmino acids are the essential building blocks of life, and their transport across cell membranes is vital for nutrition and cellular signaling. Heteromeric amino acid transporters (HATs) mediate this process, yet how they physically move substrates through the protein at the atomic level remains poorly understood. In this study, we used advanced computer simulations to observe, in unprecedented detail, how b0,+AT--a key HAT member--transports the amino acid arginine. Our simulations revealed that a single residue, tryptophan 230 (W230), functions as a molecular gate: its side chain flips open to allow arginine to pass and then closes behind it, ensuring one-way traffic into the cell. We further discovered that the initial binding of arginine sends a signal through specific structural elements (helices and loops) to trigger this gate opening. This work not only uncovers a dynamic gating mechanism for b0,+AT but also suggests that similar side-chain flipping events may represent a common and efficient strategy used by other transporters to control substrate movement. Our findings provide a new framework for understanding transporter function and could inform future drug design targeting these critical membrane proteins.

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.

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/).

Engulfment and Cell Motility protein 1 (ELMO1) regulates cell migration, phagocytosis, and cytoskeletal remodeling, positioning it as a compelling therapeutic target across kidney diseases, oncology, enteric infections and inflammation. Despite this potential, no approved therapeutics or clinically validated small-molecule modulators of ELMO1 currently exist. ELMO1 functions by forming a complex with DOCK180 (or DOCK2) to activate the small GTPase Rac1, and the recent structural resolution of the ELMO1/DOCK2 complex now provides an opportunity to target this protein-protein interface directly. Here, we present the first investigation into the druggability of the ELMO1/DOCK2 complex and report the initial virtual screening to identify small-molecule inhibitors of this interaction. Molecular dynamics (MD) and free energy level (FEL) studies were carried out to validate the potential of the predicted hits. This work establishes a computational framework for the development of the first generation of ELMO1-targeted therapeutics. In addition to demonstrating the drugability of ELMO1, this work introduces two open-source Python tools for the rapid analysis and visualization of protein-protein interaction and ligand-protein MD trajectories from DESMOND output files. These tools are designed to be broadly accessible, offering practical utility to the wider DESMOND user community.

The Epstein-Barr virus (EBV) is a highly prevalent virus worldwide that is associated with several lymphoid and epithelial malignancies. However, extensive research on EBV integral membrane proteins BILF1, LMP1 and LMP2, has been scarce due to their hydrophobic transmembrane domains. Our study applies the QTY code (glutamine, threonine, tyrosine) to design water-soluble analogs of BILF1, LMP1 and LMP2 with reduced hydrophobicity, where we systematically replaced hydrophobic amino acid residues leucine (L), isoleucine (I), valine (V), and phenylalanine (F) with structurally similar polar residues glutamine (Q), threonine (T), and tyrosine (Y). We retrieved their native sequences from UniProt, identified transmembrane domains using Protter, then performed QTY design through the Protein Solubilizing Server (PSS). We then predicted native and QTY structures using in silico prediction tools AlphaFold3, ColabFold, and Boltz-2. Our analyses demonstrate that despite significant protein sequence replacements in their transmembrane domains (54.15%-61.59%) and increased intrinsic solubility, the QTY analogs exhibited minimal changes in isoelectric point (0.00-0.15 decrease) and molecular weight (0.7-1.2 kDa increase). Additionally, structural superpositions between QTY analogs and native structures using PyMOL yield low RMSD values (0.217[A] -1.202[A]). Our results demonstrate the QTY codes ability to design detergent-free analogs of BILF1, LMP1 and LMP2 with substantially reduced hydrophobicity and aggregation propensity whilst preserving native-like structures. Our results may facilitate protein characterization studies, therapeutic research on EBV, and other protocols that typically require protein solubilization.

In a recent study of a mice model it was suggested that after myocardial infarction Serum Amyloid A (SAA) aggregates are formed that contribute to the long-term complications of the infarct, and that a similar mechanism may exist for humans. Motivated by this hypothesis we have designed four peptide candidates that may interfere with formation of SAA3 fibrils, and using all-atom molecular dynamics have evaluated their ability to destabilize SAA fibrils. As the lifetime of peptide drugs can be increased by replacing L-amino acids with their mirror D-amino acids, we have built the peptides from D-amino acids. We identify two of these peptides, DRI-R5S and DRI-H6A, as promising drug candidates.

Amylose contributes to starch crystallinity, but the stability of packed amylose double helices in water at elevated temperature remains insufficiently characterized. Here, we used molecular dynamics simulations to test whether chain length affects the short-timescale stability of A-type amylose oligomers in water. Six systems differing in chain length (6, 12, or 24 glucose units per chain) and oligomer size (isolated double strand or dodecamer of six double strands) were simulated, and five independent 1-s production runs were analyzed for each simulated condition. Oligomers with six glucose units showed structural collapse accompanied by increased water penetration. By contrast, dodecamers with 12 or 24 glucose units largely retained packed double-helical organization over the simulated timescale, although fraying was observed at their ends. These results indicate that chain length and lateral packing strongly affect the early structural response of amylose-like crystalline segments in hot water. The present simulations do not establish the ultimate fate of longer oligomers at longer timescales, but they identify a relative stability difference that is relevant to molecular interpretations of hydration-driven disordering in starch.

Protein behavior inside cells is dominated by the crowded nature of the intracellular environment. Progress in structure determination of proteins and protein complexes, based on advances in Artificial Intelligence, provides an opportunity for structure-based modeling of cellular phenomena. Such modeling at the atomic resolution has been advanced by the traditional simulation techniques, e.g. molecular dynamics. A recently developed docking-based approach implements Markov Chain Monte Carlo sampling of intermolecular energy landscapes, offering several orders of magnitude faster simulation protocols. The approach allows addressing much longer trajectories of macromolecular systems in the crowded intracellular environment at atomic resolution. The sampling by design avoids low-probability (high-energy) states, which greatly accelerates the simulation process. A notable feature of this docking-based approach is the rigid body approximation of protein structures. The rigid-body approximation had been the primary direction in the protein docking field up until recent developments in deep learning. The rigid-body approach should be quite robust for the higher energy transient interactions that dominate the highly crowded cellular environment, as they likely involve relatively small conformational change. However, it is less applicable to the low-energy protein-protein complexes, especially those involving flexible regions. We addressed this problem by incorporating AlphaFold3 top models of the protein complexes in the mapping of the intermolecular energy landscape, as representative of the low-energy configurations of the protein assembly. By the nature of the AlphaFold predictions, these models involve appropriate conformational change between unbound and bound structures. These low-energy docking poses are combined with the rigid-body docking predictions that cover the multiplicity of the transient interactions. Such combination directly addresses the conformational flexibility of proteins upon binding along with the multiplicity of the transient protein encounters in the crowded cellular environment. SIGNIFICANCEProtein behavior inside cells is dominated by the crowded nature of intracellular environment. A recently developed approach allowed addressing long simulation trajectories of macromolecular systems in such environment at atomic resolution. A notable feature of this approach is the rigid body approximation in representation of the protein structures, which had been popular in the field up until the recent developments in artificial intelligence. However, such approximation is less applicable to stable protein-protein complexes, especially those involving flexible regions. We addressed this problem head-on by incorporating top deep learning-generated models of protein complexes. The new approach directly accounts for the flexibility of protein structures upon binding, along with the multiplicity of the transient protein encounters in the crowded cellular environment.

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.

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.

Kinema is a traditional fermented soybean food indigenous to the eastern Himalayan regions of Nepal and India. The fermentation process is primarily mediated by the bacterium Bacillus subtilis, which produces several bioactive compounds and enzymes with potential therapeutic applications. Considering the growing burden of cardiovascular diseases and the need for effective fibrinolytic agents for thrombolytic therapy, this study aimed to extract, partially purify, and evaluate the thrombolytic potential of kinemakinase derived from kinema prepared from white soybeans. Partial purification of the enzyme was achieved using ammonium sulfate precipitation. Thrombolytic activity was assessed in vitro using human blood clots, where three enzyme dilutions demonstrated clot lysis ranging from 66% to 68%, indicating considerable fibrinolytic potential. In silico analyses were also performed to investigate the structural and functional characteristics of the enzyme. The tertiary structure obtained from UniProt was modeled using the Robetta server and refined with GalaxyRefine. Docking with fibrin using ClusPro 2.0 and molecular dynamics simulations using iMODS confirmed favorable interaction and structural stability, while disulfide engineering enhanced protein stability. The findings suggest that kinema-derived kinemakinase may serve as a promising alternative thrombolytic agent, warranting further biochemical characterization and dosage optimization.

Interleukin-13 (IL-13) is an immunomodulatory cell signaling cytokine that has been implicated in neurodegenerative disease and chronic inflammation. IL-13 binds to its low and high affinity receptors, IL-13 receptor 1 (IL-13R1) and IL-13 receptor 2 (IL-13R2), respectively, with residence times that vary accordingly. As the binding kinetics of the cytokine-receptor complexes influence cellular responses, we employed the molecular dynamics (MD) simulation-based{tau} -random acceleration molecular dynamics method ({tau}RAMD) to compute relative residence times for wild-type (WT) IL-13 and 19 IL-13 mutants to the two receptors. Comparison with experimental kinetic data shows that the{tau} RAMD computations capture the trends in residence times. Analysis of simulated dissociation trajectories of the cytokine-receptor complexes reveals two distinct dissociation pathways of IL-13 from each of the receptors. This study thus pinpoints key determinants of the interaction of IL-13 with its receptors which could be targeted for therapeutic design. Statement of SignificanceCytokines are regulatory proteins that bind to cell surface receptors and thereby send signals to the cellular interior. Interleukin-13 (IL-13) is a cytokine that has a low and a high affinity receptor. It has important physiological roles, and its deregulation is involved in diseases such as atopic dermatitis and asthma. We employed a molecular dynamics simulation-based method to compute the effects of changes in the sequence of IL-13 on the lifetimes of complexes of IL-13 and its receptors. Comparison with experiments supports the validity of the computational approach and analysis of the simulations reveals two distinct ways in which IL-13 dissociates from each receptor. These results thus provide a map for targeting IL-13 - receptor interactions for the design of therapeutics.

The ability of small molecules to cross the blood-brain barrier (BBB) remains a major bottleneck in neurotherapeutic development. While experimental assays and machine learning approaches provide approximate permeability estimates, they lack atomistic insight into the underlying transport mechanisms. Here, we employ all-atom molecular dynamics simulations of a compositionally realistic BBB lipid bilayer to characterize the passive permeation of two bioactive propolis-derived compounds, Caffeic Acid Phenethyl Ester (CAPE) and Artepillin-C (ARC). Using steered molecular dynamics and umbrella sampling, we computed free energy profiles, diffusion coefficients, and permeability metrics across the membrane. CAPE encounters a modest barrier at the lipid headgroup region but minimal resistance within the hydrophobic core, resulting in a low free energy barrier ([~]2-3 kcal/mol) and favorable permeability (logP_eff {approx} 0.28). In contrast, ARC exhibits a substantial energetic barrier within the membrane core, leading to high resistivity and strongly unfavorable permeability (logP_eff {approx} -10.91). The heterogeneous lipid model reproduces experimentally consistent membrane properties and reveals how lipid composition modulates transport energetics. These findings provide mechanistic insight into BBB permeability and demonstrate the utility of atomistic simulations for guiding the design of neuroactive therapeutics.

Plant-derived small molecules possess highly diverse physicochemical properties, and the computational design of their protein recognition elements depends not only on the global structural quality of candidate backbones, but also on whether the local binding pocket, ligand-contact pattern, and predefined recognition conformation can be consistently retained after sequence design and structural back-prediction. To explore pocket-design strategies for different types of natural-product small molecules, this study selected capsaicin, (4R)-limonene, and quercetin as model ligands, representing a flexible amphipathic molecule, a compact hydrophobic monoterpene, and a rigid polyphenolic flavonoid scaffold, respectively, and covering the dimensions of pungent sensory flavor, volatile aroma, and flavonoid functional constituents. A ligand- physicochemical-property-guided computational design and multi-stage prioritization framework was established for candidate protein binders. The results showed that candidates with favorable initial global structural scores did not necessarily form reasonable local small-molecule binding pockets, indicating that evaluation of the local ligand environment is essential for candidate prioritization. After screening, 31 partial- pocket candidate backbones for capsaicin, 75 buried hydrophobic-pocket candidate backbones for (4R)-limonene, and 56 pocket-qualified candidate backbones for quercetin were obtained. Further sequence design and structural back-prediction analyses indicated that a subset of candidates could maintain the original pocket geometry and major ligand-contact patterns after sequence realization. Overall, these results suggest that the physicochemical properties of different plant-derived small molecules substantially influence the efficiency of de novo protein pocket formation, with compact hydrophobic ligands being more compatible with buried hydrophobic- pocket strategies, whereas flexible or multipolar ligands require a more refined balance between hydrophobic burial and polar exposure. This study provides a pre- experimental computational prioritization framework for natural-product small- molecule-recognizing proteins and offers candidate resources for subsequent protein expression, in vitro binding validation, active-constituent enrichment, and development of small-molecule biorecognition tools. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=107 SRC="FIGDIR/small/743643v1_ufig1.gif" ALT="Figure 1"> View larger version (50K): org.highwire.dtl.DTLVardef@8fe6c2org.highwire.dtl.DTLVardef@176cef2org.highwire.dtl.DTLVardef@10c8201org.highwire.dtl.DTLVardef@2b28cf_HPS_FORMAT_FIGEXP M_FIG C_FIG

Mechanistic elucidation and inhibition of the pathogenic aberrant mitochondrial localization of the RNA/DNA-binding protein, TDP-43, can help in the therapeutics of the neurodegenerative disease amyotrophic lateral sclerosis (ALS). A mitochondrial localization sequence of TDP-43, M1, is largely solvent inaccessible, therefore, how it interacts with the mitochondrial import machinery to facilitate TDP-43s transit to mitochondria is unclear. Towards this, we examined the unfolding TDP-43s N-terminal domain (NTD) that hosts M1, using equilibrium all-atom molecular dynamics (MD) simulations, and observed an early loss of the hydrogen-bonded interactions between {beta}4-{beta}5 bridge and the interactions involving residues Phe-35 and Gly-40 of M1, indicating structural lability of M1 to become solvent-accessible that may enhance its interaction with the mitochondrial receptor(s) for import. Furthermore, via virtual screening of 2,115 FDA-approved and 515,545 non-FDA-approved small molecules from ZINC15 database towards binding to M1 and inhibiting TDP-43s mitochondrial import, we identified a molecule, ZINC73240059, that was previously characterized as an inhibitor of MAP kinase-activating protein kinase 2 (MAPKAPK2). ZINC73240059 remains stably bound to M1 of NTD during MD simulations manifesting negative Gibbs free energy ({Delta}G) with significant contribution from Pro-36 of M1. Overall, ZINC73240059 can be a molecule of interest towards thwarting TDP-43s pathogenic mitochondrial localization in ALS.

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.