Protein Data Bank (PDB): database of three-dimensional structural information of biological macromolecules

Discovery of Protease-activated receptor 2 antagonists derived from phenylalanine for the treatment of breast cancer

Protease-activated receptor 2 (PAR2) has garnered attention as a potential therapeutic target in breast cancer. PAR2 is implicated in the activation of extracellular signal-regulated kinase 1/2 (ERK 1/2) via G protein and beta-arrestin pathways, contributing to the proliferation and metastasis of breast cancer cells. Despite the recognized role of PAR2 in breast cancer progression, clinically effective PAR2 antagonists remain elusive. To address this unmet clinical need, we synthesized and evaluated a series of novel compounds that target the orthosteric site of PAR2. Using in silico docking simulations, we identified compound 9a, an optimized derivative of compound 1a ((S)-N-(1-(benzylamino)-1-oxo-3-phenylpropan-2-yl)benzamide), which exhibited enhanced PAR2 antagonistic activity. Subsequent molecular dynamics simulations comparing 9a with the partial agonist 9d revealed that variations in ligand-induced conformational changes and interactions dictated whether the compound acted as an antagonist or agonist of PAR2. The results of this study suggest that further development of 9a could contribute to the advancement of PAR2 antagonists as potential therapeutic agents for breast cancer.

Efficient Generation of Protein Pockets with PocketGen

Designing small-molecule-binding proteins, such as enzymes and biosensors, is crucial in protein biology and bioengineering. Generating high-fidelity protein pockets areas where proteins interact with ligand molecules, is challenging due to complex interactions between ligand molecules and proteins, flexibility of ligand molecules and amino acid side chains, and complex sequence-structure dependencies. Here, we introduce PocketGen, a deep generative method for generating the residue sequence and the full-atom structure within the protein pocket region that leverages sequence structure consistency. PocketGen consists of a bilevel graph transformer for structural encoding and a sequence refinement module that uses a protein language model (pLM) for sequence prediction. The bilevel graph transformer captures interactions at multiple granularities (atom-level and residue/ligand-level) and aspects (intra-protein and protein-ligand) with bilevel attention mechanisms. For sequence refinement, a structural adapter using cross-attention is integrated into a pLM to ensure structure-sequence consistency. During training, only the adapter is fine-tuned, while the other layers of the pLM remain unchanged. Experiments show that PocketGen can efficiently generate protein pockets with higher binding affinity and validity than state-of-the-art methods. PocketGen is ten times faster than physics-based methods and achieves a 95% success rate (percentage of generated pockets with higher binding affinity than reference pockets) with over 64% amino acid recovery rate.

Cefiderocol susceptibility endows hope in treating carbapenem-resistant Pseudomonas aeruginosa: insights from in vitro and in silico evidence

'High-risk' hypermutable clones of Pseudomonas aeruginosa disseminating extensive drug-resistance (XDR) have raised global health concerns with escalating mortality rates in immunocompromised patients. Mutations in conventional drug-targets under antibiotic stress necessitate structural understanding to formulate sustainable therapeutics. In the present study, the major β-lactam antibiotic target, penicillin-binding protein-3 (PBP3) with mutations F533L and T91A, were identified in carbapenemase-positive P. aeruginosa isolates (n = 6) using whole genome sequencing. Antibiotic susceptibility tests showed susceptibility to cefiderocol (MIC ≤ 4 μg ml-1) despite pan-β-lactam resistance in the isolates. Both the mutations reduced local intra-chain interactions in PBP3 that marginally increased the local flexibility (∼1%) in the structures to affect antibiotic-interactions. Molecular dynamics simulations confirmed the overall stability of the PBP3 mutants through root-mean square deviations, radius of gyration, solvent-accessibility and density curves, which favored their selection. Docking studies unveiled that the mutations in PBP3 elicited unfavorable stereochemical clashes with the conventional antibiotics thereby increasing their inhibition constants (IC) up to ∼50 fold. It was deciphered that cefiderocol retained its susceptibility despite mutations in PBP3, due to its higher average binding affinity (ΔG: -8.2 ± 0.4 kcal mol-1) towards multiple PBP-targets and lower average binding affinity (ΔG: -6.7 ± 0.7 kcal mol-1) to β-lactamases than the other β-lactam antibiotics. The molecular dynamics simulations and molecular mechanics Poisson Boltzmann surface area calculations further indicated energetically favorable binding for cefiderocol with PBP3 proteins. The study gave structural insight into emerging non-polar amino acid substitutions in PBP3 causing XDR and recommends prioritizing available antibiotics based on multi-target affinities to overcome challenges imposed by target-protein mutations.

ISTransbase: an online database for inhibitor and substrate of drug transporters

Drug transporters, integral membrane proteins found throughout the human body, play critical roles in physiological and biochemical processes through interactions with ligands, such as substrates and inhibitors. The extensive and disparate data on drug transporters complicate understanding their complex relationships with ligands. To address this challenge, it is essential to gather and summarize information on drug transporters, inhibitors and substrates, and simultaneously develop a comprehensive and user-friendly database. Current online resources often provide fragmented information and have limited coverage of drug transporter substrates and inhibitors, highlighting the need for a specialized, comprehensive and openly accessible database. ISTransbase addresses this gap by amassing a substantial amount of data from literature, government documents and open databases. It includes 16 528 inhibitors and 4465 substrates of 163 drug transporters from 18 different species, resulting in a total of 93 841 inhibitor records and 51 053 substrate records. ISTransbase provides detailed insights into drug transporters and their inhibitors/substrates, encompassing transporter and molecule structure, transporter function and distribution, as well as experimental methods and results from transport or inhibition experiments. Furthermore, ISTransbase offers three search strategies that allow users to retrieve drugs and transporters based on multiple selectable constraints, as well as perform checks for drug-drug interactions. Users can also browse and download data. In summary, ISTransbase (https://istransbase.scbdd.com/) serves as a valuable resource for accurately and efficiently accessing information on drug transporter inhibitors and substrates, aiding researchers in exploring drug transporter mechanisms and assisting clinicians in mitigating adverse drug reactions Database URL: https://istransbase.scbdd.com/.

Protein-Protein Interaction Prediction via Structure-Based Deep Learning

Protein-protein interactions (PPIs) play an essential role in life activities. Many artificial intelligence algorithms based on protein sequence information have been developed to predict PPIs. However, these models have difficulty dealing with various sequence lengths and suffer from low generalization and prediction accuracy. In this study, we proposed a novel end-to-end deep learning framework, RSPPI, combining residual neural network (ResNet) and spatial pyramid pooling (SPP), to predict PPIs based on the protein sequence physicochemistry properties and spatial structural information. In the RSPPI model, ResNet was employed to extract the structural and physicochemical information from the protein three-dimensional structure and primary sequence; the SPP layer was used to transform feature maps to a single vector and avoid the fixed-length requirement. The RSPPI model possessed excellent cross-species performance and outperformed several state-of-the-art methods based either on protein sequence or gene ontology in most evaluation metrics. The RSPPI model provides a novel strategy to develop an AI PPI prediction algorithm.

Development of scoring-assisted generative exploration (SAGE) and its application to dual inhibitor design for acetylcholinesterase and monoamine oxidase B

De novo molecular design is the process of searching chemical space for drug-like molecules with desired properties, and deep learning has been recognized as a promising solution. In this study, I developed an effective computational method called Scoring-Assisted Generative Exploration (SAGE) to enhance chemical diversity and property optimization through virtual synthesis simulation, the generation of bridged bicyclic rings, and multiple scoring models for drug-likeness. In six protein targets, SAGE generated molecules with high scores within reasonable numbers of steps by optimizing target specificity without a constraint and even with multiple constraints such as synthetic accessibility, solubility, and metabolic stability. Furthermore, I suggested a top-ranked molecule with SAGE as dual inhibitors of acetylcholinesterase and monoamine oxidase B through multiple desired property optimization. Therefore, SAGE can generate molecules with desired properties by optimizing multiple properties simultaneously, indicating the importance of de novo design strategies in the future of drug discovery and development. SCIENTIFIC CONTRIBUTION: The scientific contribution of this study lies in the development of the Scoring-Assisted Generative Exploration (SAGE) method, a novel computational approach that significantly enhances de novo molecular design. SAGE uniquely integrates virtual synthesis simulation, the generation of complex bridged bicyclic rings, and multiple scoring models to optimize drug-like properties comprehensively. By efficiently generating molecules that meet a broad spectrum of pharmacological criteria-including target specificity, synthetic accessibility, solubility, and metabolic stability-within a reasonable number of steps, SAGE represents a substantial advancement over traditional methods. Additionally, the application of SAGE to discover dual inhibitors for acetylcholinesterase and monoamine oxidase B not only demonstrates its potential to streamline and enhance the drug development process but also highlights its capacity to create more effective and precisely targeted therapies. This study emphasizes the critical and evolving role of de novo design strategies in reshaping the future of drug discovery and development, providing promising avenues for innovative therapeutic discoveries.

GTE: a graph learning framework for prediction of T-cell receptors and epitopes binding specificity

The interaction between T-cell receptors (TCRs) and peptides (epitopes) presented by major histocompatibility complex molecules (MHC) is fundamental to the immune response. Accurate prediction of TCR-epitope interactions is crucial for advancing the understanding of various diseases and their prevention and treatment. Existing methods primarily rely on sequence-based approaches, overlooking the inherent topology structure of TCR-epitope interaction networks. In this study, we present $GTE$, a novel heterogeneous Graph neural network model based on inductive learning to capture the topological structure between TCRs and Epitopes. Furthermore, we address the challenge of constructing negative samples within the graph by proposing a dynamic edge update strategy, enhancing model learning with the nonbinding TCR-epitope pairs. Additionally, to overcome data imbalance, we adapt the Deep AUC Maximization strategy to the graph domain. Extensive experiments are conducted on four public datasets to demonstrate the superiority of exploring underlying topological structures in predicting TCR-epitope interactions, illustrating the benefits of delving into complex molecular networks. The implementation code and data are available at https://github.com/uta-smile/GTE.

Design and validation of a novel multi-epitopes vaccine against hantavirus

Hantavirus is a member of the order Bunyavirales and an emerging global pathogen. Hantavirus infections have affected millions of people globally based on available epidemiological data and research studies. Hemorrhagic fever with renal syndrome (HFRS) and hantavirus pulmonary syndrome (HPS) are the two main human diseases associated with hantavirus infections. Hence, efforts are required to develop a potent vaccine against the pathogen. The only vaccine that is in use for hantavirus is an inactivated virus vaccine, "Hantavax", but it failed to produce neutralizing antibodies. Vaccine development is of much importance in dealing with the surge of hantavirus globally. In this study, hantavirus five proteins (N protein, G1 and G2, L protein, and non-structural proteins) were used in NetCTL 1.2 program to predict T-cell epitopes. To predict major histocompatibility complex (MHC) binding alleles, an immune epitope database (IEDB) was used. All predicted epitopes were then investigated for different immunoinformatics analyses such as antigenicity and toxicity analyses. The good water-soluble, non-toxic, probable antigenic, and DRB*0101 binder was selected. A multi-epitopes-based vaccine designing was then done where linkers were used to connect the shortlisted epitopes. In addition, an adjuvant molecule was supplementary to the multi-epitopes peptide to improve the vaccine's immunogenic potential. The final vaccine construct's three-dimensional structure was modeled by ab initio method. The vaccine molecule was then evaluated for its binding potential with TLR-3 immune receptor, which is key for its recognition and processing by the host immune system. Docking studies were performed using HADDOCK software. The best-docked complex was selected and visualized for intermolecular binding and interactions using UCSF Chimera 1.16 software. The findings revealed that the designed vaccine might be a potential vaccine against hantavirus and can be used in experimental animal model testings.Communicated by Ramaswamy H. Sarma.

Norcantharidin Enhances the Antitumor Effect of 5-Fluorouracil by Inducing Apoptosis of Cervical Cancer Cells: Network Pharmacology, Molecular Docking, and Experimental Validation

The high recurrence rate of cervical cancer is a leading cause of cancer deaths in women. 5-Fluorouracil (5-FU) is an antitumor drug used to treat many types of cancer, but its diminishing effectiveness and side effects limit its use. Norcantharidin (NCTD), a demethylated derivative of cantharidin, exhibits various biological activities. Here, we investigated whether NCTD could potentiate 5-FU to induce cervical cancer cell death. To assess the cell viability and synergistic effects of the drugs, cell counting kit-8 and colony formation assays were performed using HR-HPV-positive cervical cancer cell lines. Annexin V-FITC/PI staining and TUNEL assays were performed to confirm the induction of apoptosis. The synergistic effect of NCTD on the antitumor activity of 5-FU was analyzed using network pharmacology, molecular docking, and molecular dynamics simulations. Apoptosis-related proteins were examined using immunoblotting. The combination of NCTD and 5-FU was synergistic in cervical cancer cell lines. Network pharmacological analysis identified 10 common targets of NCTD and 5-FU for cervical cancer treatment. Molecular docking showed the strong binding affinity of both compounds with CA12, CASP9, and PTGS1. Molecular dynamics simulations showed that the complex system of both drugs with caspase-9 could be in a stable state. NCTD enhanced 5-FU-mediated cytotoxicity by activating apoptosis-related proteins. NCTD acts synergistically with 5-FU to inhibit cervical cancer cell proliferation. NCTD enhances 5-FU-induced apoptosis in cervical cancer cell lines via the caspase-dependent pathway.

The antagonism mechanism of astilbin against cadmium-induced injury in chicken lungs via Treg/Th1 balance signaling pathway

The purpose of this study was to investigate the effect of Treg/Th1 imbalance in cadmium-induced lung injury and the potential protective effect of astilbin against cadmium-induced lung injury in chicken. Cadmium exposure significantly decreased T-AOC and GSH-Px levels and SOD activity in the chicken lung tissues. In contrast, it significantly increased the MDA and NO levels. These results indicate that cadmium triggers oxidative stress in lungs. Histopathological analysis revealed that cadmium exposure further induced infiltration of lymphocytes in the chicken lungs, indicating that cadmium causes pulmonary damage. Further analysis revealed that cadmium decreased the expression of IL-4 and IL-10 but increased those of IL-17, Foxp3, TNF-α, and TGF-β, indicating that the exposure of cadmium induced the imbalance of Treg/Th1. Moreover, cadmium adversely affected chicken lung function by activating the NF-kB pathway and inducing expression of genes downstream to these pathways (COX-2, iNOS), associated with inflammatory injury in the lung tissue. Astilbin reduced cadmium-induced oxidative stress and inflammation in the lungs by increasing antioxidant enzyme activities and restoring Treg/Th1 balance. In conclusion, our results suggest that astilbin treatment alleviated the effects of cadmium-mediated lung injury in chickens by restoring the Treg/Th1 balance.

ProInterVal: Validation of Protein-Protein Interfaces through Learned Interface Representations

Proteins are vital components of the biological world and serve a multitude of functions. They interact with other molecules through their interfaces and participate in crucial cellular processes. Disruption of these interactions can have negative effects on organisms, highlighting the importance of studying protein-protein interfaces for developing targeted therapies for diseases. Therefore, the development of a reliable method for investigating protein-protein interactions is of paramount importance. In this work, we present an approach for validating protein-protein interfaces using learned interface representations. The approach involves using a graph-based contrastive autoencoder architecture and a transformer to learn representations of protein-protein interaction interfaces from unlabeled data and then validating them through learned representations with a graph neural network. Our method achieves an accuracy of 0.91 for the test set, outperforming existing GNN-based methods. We demonstrate the effectiveness of our approach on a benchmark data set and show that it provides a promising solution for validating protein-protein interfaces.

Computational approach for identifying immunogenic epitopes and optimizing peptide vaccine through in-silico cloning against Mycoplasma genitalium

Mycoplasma genitalium is a pathogenic microorganism linked to a variety of severe health conditions including ovarian cancer, prostate cancer, HIV transmission, and sexually transmitted diseases. A more effective approach to address the challenges posed by this pathogen, given its high antibiotic resistance rates, could be the development of a peptide vaccine. In this study, we used experimentally validated 13 membrane proteins and their immunogenicity to identify suitable vaccine candidates. Thus, based on immunogenic properties and high conservation among other Mycoplasma genitalium sub-strains, the P110 surface protein is considered for further investigation. Later on, we identified T-cell epitopes and B-cell epitopes from the P110 protein to construct a multiepitope-based vaccine. As a result, the 'NIAPISFSFTPFTAA' T-cell epitope and 'KVKYESSGSNNISFDS' B-cell epitope have shown 99.53% and 87.50% population coverage along with 100% conservancy among the subspecies, and both epitopes were found to be non-allergenic. Furthermore, focusing on molecular docking analysis showed the lowest binding energy for MHC-I (-137.5 kcal/mol) and MHC-II (-183.3 kcal/mol), leading to a satisfactory binding strength between the T-cell epitopes and the MHC molecules. However, the constructed multiepitope vaccine (MEV) consisting of 54 amino acids demonstrates favorable characteristics for a vaccine candidate, including a theoretical pI of 4.25 with a scaled solubility of 0.812 and high antigenicity probabilities. Additionally, structural analyses reveal that the MEV displays substantial alpha helices and extended strands, vital for its immunogenicity. Molecular docking with the human Toll-like receptors TLR1/2 heterodimer shows strong binding affinity, reinforcing its potential to elicit an immune response. Our immune simulation analysis demonstrates immune memory development and robust immunity, while codon adaptation suggests optimal expression in E. coli using the pET-28a(+) vector. These findings collectively highlight the MEV's potential as a valuable vaccine candidate against M. genitalium.

QSAR Study, Molecular Docking and Molecular Dynamic Simulation of Aurora Kinase Inhibitors Derived from Imidazo[4,5-b]pyridine Derivatives

Cancer is a serious threat to human life and social development and the use of scientific methods for cancer prevention and control is necessary. In this study, HQSAR, CoMFA, CoMSIA and TopomerCoMFA methods are used to establish models of 65 imidazo[4,5-b]pyridine derivatives to explore the quantitative structure-activity relationship between their anticancer activities and molecular conformations. The results show that the cross-validation coefficients q2 of HQSAR, CoMFA, CoMSIA and TopomerCoMFA are 0.892, 0.866, 0.877 and 0.905, respectively. The non-cross-validation coefficients r2 are 0.948, 0.983, 0.995 and 0.971, respectively. The externally validated complex correlation coefficients r2pred of external validation are 0.814, 0.829, 0.758 and 0.855, respectively. The PLS analysis verifies that the QSAR models have the highest prediction ability and stability. Based on these statistics, virtual screening based on R group is performed using the ZINC database by the Topomer search technology. Finally, 10 new compounds with higher activity are designed with the screened new fragments. In order to explore the binding modes and targets between ligands and protein receptors, these newly designed compounds are conjugated with macromolecular protein (PDB ID: 1MQ4) by molecular docking technology. Furthermore, to study the nature of the newly designed compound in dynamic states and the stability of the protein-ligand complex, molecular dynamics simulation is carried out for N3, N4, N5 and N7 docked with 1MQ4 protease structure for 50 ns. A free energy landscape is computed to search for the most stable conformation. These results prove the efficient and stability of the newly designed compounds. Finally, ADMET is used to predict the pharmacology and toxicity of the 10 designed drug molecules.

Targeting XGHPRT enzyme to manage Helicobacter pylori induced gastric cancer: A multi-pronged machine learning, artificial intelligence and biophysics-based study

Helicobacter pylori infects the stomach mucosa of over half of the global population and can lead to gastric cancer. This pathogen has demonstrated resistance to many frequently prescribed antibiotics, thereby underscoring the pressing need to identify novel therapeutic targets. The inhibition or disruption of nucleic acid biosynthesis constitutes a promising avenue for either restraining or eradicating bacterial proliferation. The synthesis of RNA and DNA precursors (6-oxopurine nucleoside monophosphates) is catalyzed by the XGHPRT enzyme. In this study, using machine learning, artificial intelligence and biophysics-based software, CHEMBRIDGE-10000196, CHEMBRIDGE-10000295, and CHEMBRIDGE-10000955 were predicted as promising binders to the XGHPRT with a binding score of -14.20, -13.64, and -12.08 kcal/mol, respectively, compared to a control guanosine-5'-monophosphate exhibiting a docking score of -10.52 kcal/mol. These agents formed strong interactions with Met33, Arg34, Ala57, Asp92, Ser93, and Gly94 at short distance. The docked complexes of the lead compounds exhibited stable dynamics during the simulation time with no global changes noticed. The docked complexes demonstrate a significantly stable MM-GBSA and MM-PBSA net binding energy of -60.1 and -61.18 kcal/mol for the CHEMBRIDGE-10000196 complex. The MM-GBSA net energy value of the CHEMBRIDGE-10000295 complex and the CHEMBRIDGE-10000955 complex is -71.17 and -65.29 kcal/mol, respectively. The CHEMBRIDGE-10000295 and CHEMBRIDGE-10000955 complexes displayed a net value of -71.91 and -63.49 kcal/mol, respectively, as per the MM-PBSA. The major driving intermolecular interactions for the docked complexes were found to be the electrostatic and van der Waals. The three filtered molecules hold potential for experimental evaluation of their potency against the XGHPRT enzyme.

Neurogenesis-Associated Protein, a Potential Prognostic Biomarker in Anti-PD-1 Based Kidney Renal Clear Cell Carcinoma Patient Therapeutics

The transketolase 1 gene (TKTL1) is an essential factor that contributes to brain development. Some studies have shown the influence of TKTL1 in cancers, but it has been rarely reported in kidney cancer. Furthermore, the role of TKTL1 in the prognosis and tumor infiltration of immune cells in various cancers, particularly kidney cancer, remains unknown. In this study, TKTL1 expression and its clinical characteristics were investigated using a variety of databases. TIMER was used to investigate the relationship between TKTL1 and immune infiltrates in various types of cancer. We also studied the relationship between TKTL1 expression and response to PD-1 blocker immunotherapy in renal cancer. We conducted TKTL1 agonists virtual screening from 13,633 natural compounds (L6020), implemented secondary library construction according to the types of top results, and then conducted secondary virtual screening for 367 alkaloids. Finally, in vitro assays of cell viability assays and colony formation assays were performed to demonstrate the pharmacological potency of the screening of TKTL1 agonists. Using these methods, we determined that TKTL1 significantly affects the prognostic potential in different types of kidney cancer patients. The underlying mechanism might be that the TKTL1 expression level was positively associated with devious immunocytes in kidney renal clear cell carcinoma (KIRC) rather than in kidney renal papillary cell carcinoma (KIRP) and kidney chromophobe (KICH). This recruitment may result from the up-regulation of the mTOR signaling pathway affecting T cell metabolism. We also found that TKTL1 may act as an immunomodulator in KIRC patients' response to anti-PD-1 therapy. Moreover, we also found that piperine and glibenclamide are potent agonists of TKTL1. We have demonstrated, in vitro, that piperine and glibenclamide can inhibit the proliferation and clone formation of Caki-2 cell lines by agonizing the expression of TKTL1. In summary, our discovery implies that TKTL1 may be a promising prognostic biomarker for KIRC patients who respond to anti-PD-1 therapy. Piperine and glibenclamide may be effective therapeutic TKTL1 agonists, providing a theoretical basis for the clinical treatment of kidney cancer.

Molecular Property Diagnostic Suite for COVID-19 (MPDSCOVID-19): an open-source disease-specific drug discovery portal

Molecular Property Diagnostic Suite (MPDS) was conceived and developed as an open-source disease-specific web portal based on Galaxy. MPDSCOVID-19 was developed for COVID-19 as a one-stop solution for drug discovery research. Galaxy platforms enable the creation of customized workflows connecting various modules in the web server. The architecture of MPDSCOVID-19 effectively employs Galaxy v22.04 features, which are ported on CentOS 7.8 and Python 3.7. MPDSCOVID-19 provides significant updates and the addition of several new tools updated after six years. Tools developed by our group in Perl/Python and open-source tools are collated and integrated into MPDSCOVID-19 using XML scripts. Our MPDS suite aims to facilitate transparent and open innovation. This approach significantly helps bring inclusiveness in the community while promoting free access and participation in software development.

Availability & Implementation

The MPDSCOVID-19 portal can be accessed at https://mpds.neist.res.in:8085/.

Acceleration of Graph Neural Network-Based Prediction Models in Chemistry via Co-Design Optimization on Intelligence Processing Units

Atomic structure prediction and associated property calculations are the bedrock of chemical physics. Since high-fidelity ab initio modeling techniques for computing the structure and properties can be prohibitively expensive, this motivates the development of machine-learning (ML) models that make these predictions more efficiently. Training graph neural networks over large atomistic databases introduces unique computational challenges, such as the need to process millions of small graphs with variable size and support communication patterns that are distinct from learning over large graphs, such as social networks. We demonstrate a novel hardware-software codesign approach to scale up the training of atomistic graph neural networks (GNN) for structure and property prediction. First, to eliminate redundant computation and memory associated with alternative padding techniques and to improve throughput via minimizing communication, we formulate the effective coalescing of the batches of variable-size atomistic graphs as the bin packing problem and introduce a hardware-agnostic algorithm to pack these batches. In addition, we propose hardware-specific optimizations, including a planner and vectorization for the gather-scatter operations targeted for Graphcore's Intelligence Processing Unit (IPU), as well as model-specific optimizations such as merged communication collectives and optimized softplus. Putting these all together, we demonstrate the effectiveness of the proposed codesign approach by providing an implementation of a well-established atomistic GNN on the Graphcore IPUs. We evaluate the training performance on multiple atomistic graph databases with varying degrees of graph counts, sizes, and sparsity. We demonstrate that such a codesign approach can reduce the training time of atomistic GNNs and can improve their performance by up to 1.5× compared to the baseline implementation of the model on the IPUs. Additionally, we compare our IPU implementation with a Nvidia GPU-based implementation and show that our atomistic GNN implementation on the IPUs can run 1.8× faster on average compared to the execution time on the GPUs.

Molecular docking and metagenomics assisted mitigation of microplastic pollution

Microplastics, tiny, flimsy, and direct progenitors of principal and subsidiary plastics, cause environmental degradation in aquatic and terrestrial entities. Contamination concerns include irrevocable impacts, potential cytotoxicity, and negative health effects on mortals. The detection, recovery, and degradation strategies of these pollutants in various biota and ecosystems, as well as their impact on plants, animals, and humans, have been a topic of significant interest. But the natural environment is infested with several types of plastics, all having different chemical makeup, structure, shape, and origin. Plastic trash acts as a substrate for microbial growth, creating biofilms on the plastisphere surface. This colonizing microbial diversity can be glimpsed with meta-genomics, a culture-independent approach. Owing to its comprehensive description of microbial communities, genealogical evidence on unconventional biocatalysts or enzymes, genomic correlations, evolutionary profile, and function, it is being touted as one of the promising tools in identifying novel enzymes for the degradation of polymers. Additionally, computational tools such as molecular docking can predict the binding of these novel enzymes to the polymer substrate, which can be validated through in vitro conditions for its environmentally feasible applications. This review mainly deals with the exploration of metagenomics along with computational tools to provide a clearer perspective into the microbial potential in the biodegradation of microplastics. The computational tools due to their polymathic nature will be quintessential in identifying the enzyme structure, binding affinities of the prospective enzymes to the substrates, and foretelling of degradation pathways involved which can be quite instrumental in the furtherance of the plastic degradation studies.

Unveiling MurE ligase potential inhibitors for treating multi-drug resistant Acinetobacter baumannii

Acinetobacter baumannii is an opportunistic pathogen with ability to cause serious infection such as bacteremia, ventilator associated pneumonia, and wound infections. As strains of A. baumannii are resistant to almost all clinically used antibiotics and with the emergence of carbapenems resistant phenotypes warrants the search for novel antibiotics. Considering this, herein, a series of computer aided drug designing approach was utilized to search novel chemical scaffolds that bind stronger to MurE ligase enzyme of A. baumannii, which is involved peptidoglycan synthesis. The work identified LAS_22461675, LAS_34000090 and LAS_51177972 compounds as promising binding molecules with MurE enzyme having binding energy score of -10.5 kcal/mol, -9.3 kcal/mol and -8.6 kcal/mol, respectively. The compounds were found to achieve docked inside the MurE substrate binding pocket and established close distance chemical interactions. The interaction energies were dominated by van der Waals and less contributions were seen from hydrogen bonding energy. The dynamic simulation assay predicted the complexes stable with no major global and local changes noticed. The docked stability was also validated by MM/PBSA and MM/GBSA binding free energy methods. The net MM/GBSA binding free energy of LAS_22461675 complex, LAS_34000090 complex and LAS_51177972 complex is -26.25 kcal/mol, -27.23 kcal/mol and -29.64 kcal/mol, respectively. Similarly in case of MM-PBSA, the net energy value was in following order; LAS_22461675 complex (-27.67 kcal/mol), LAS_34000090 complex (-29.94 kcal/mol) and LAS_51177972 complex (-27.32 kcal/mol). The AMBER entropy and WaterSwap methods also confirmed stable complexes formation. Further, molecular features of the compounds were determined that predicted compounds to have good druglike properties and pharmacokinetic favorable. The study concluded the compounds to good candidates to be tested by in vivo and in vitro experimental assays.Communicated by Ramaswamy H. Sarma.

Discovery of novel inhibitor via molecular dynamics simulations against D-alanyl-D-alanine carboxypeptidase of Enterobacter cloacae

Antibiotics resistance by bacterial pathogens is a major concern to public health worldwide resulting in high health care costs and rising mortality. Subtractive proteomics prioritized D-alanyl-D-alanine carboxypeptidas (DacB) enzyme from Enterobacter cloacae ATCC 13047 as a potential candidate for drugs designing to block pathogen cell wall biosynthesis. Virtual screening of an antibacterial library against the target unraveled a hit compound (2-[(1-methylsulfonylpiperidin-3-yl)methyl]-6-(1H-pyrazol-4-yl) pyrazine) showing high affinity and stability with the target. The N-methyl-N-propyl-methanesulfonamide of the compound is seen as a closed affinity towards domain involving strong hydrogen bonds with Ser41, Lys44, Ser285, and Asn287. The 4-methyl-1H-pyrazole is posed towards the open cavity of domain I and II and formed hydrophobic and hydrophilic contacts. The system is highly stable with average carbon-alpha deviations of 1.69 Å over trajectories of 400-ns. Three vital residues projected: Arg437, Arg438 and Leu400 from enzyme pocket via Radial distribution function (RDF) assay, which actively engaged the inhibitor. Further confirmation is done by estimating binding free energies, which confirms the very low delta energy of -7.24 kcal/mol in Generalized Born (GB) method and -7.4363 kcal/mol in Poisson-Boltzmann (PB) method. WaterSwap calculations were performed that revealed the energies highly converged, an agreement on good system stability. Lastly, three DacB mutants were created to investigate the role of functional active residues and a decline in binding affinity of the residues was noticed. These computational results provide a gateway for experimentalists to further confirm their efficacy both in-vitro and in-vivo.Communicated by Ramaswamy H. Sarma.

Structure-Based Identification of Organoruthenium Compounds as Nanomolar Antagonists of Cannabinoid Receptors

Metal complexes exhibit a diverse range of coordination geometries, representing novel privileged scaffolds with convenient click types of preparation inaccessible for typical carbon-centered organic compounds. Herein, we explored the opportunity to identify biologically active organometallic complexes by reverse docking of a rigid, minimum-size octahedral organoruthenium scaffold against thousands of protein-binding pockets. Interestingly, cannabinoid receptor type 1 (CB1) was identified based on the docking scores and the degree of overlap between the docked organoruthenium scaffold and the hydrophobic scaffold of the cocrystallized ligand. Further structure-based optimization led to the discovery of organoruthenium complexes with nanomolar binding affinities and high selectivity toward CB2. Our work indicates that octahedral organoruthenium scaffolds may be advantageous for targeting the large and hydrophobic binding pockets and that the reverse docking approach may facilitate the discovery of novel privileged scaffolds, such as organometallic complexes, for exploring chemical space in lead discovery.

Insights into the crystal structure investigation and virtual screening approach of quinoxaline derivatives as potent against c-Jun N-terminal kinases 1

Quinoxaline derivatives are an important class of heterocyclic compounds in which N replaces one or more carbon atoms of the naphthalene ring and exhibit a wide spectrum of biological activities and therapeutic applications. As a result, we were encouraged to explore a new synthetic approach to quinoxaline derivatives. In this work, we synthesized two new derivatives namely, ethyl 4-(2-ethoxy-2-oxoethyl)-3-oxo-3,4-dihydroquinoxaline-2-carboxylate (2) and 3-oxo-3,4-dihydroquinoxaline-2-carbohydrazide (3) respectively. Their structures were confirmed by single-crystal X-ray analysis. Hirshfeld surface (HS) analysis is performed to understand the nature and magnitude of intermolecular interactions in the crystal packing. Density functional theory using the wb97xd/def2-TZVP method was chosen to explore their reactivity, electronic stability and optical properties. Charge transfer (CT) and orbital energies were analyzed via natural population analysis (NPA), and frontier molecular orbital (FMO) theory. The calculated excellent static hyperpolarizability (βo) indicates nonlinear optical (NLO) properties for 2 and 3. Both compounds show potent activity against c-Jun N-terminal kinases 1 (JNK 1) based on structural activity relationship studies, further subjected to molecular docking, molecular dynamics and ADMET analysis to understand their potential as drug candidates.Communicated by Ramaswamy H. Sarma.

Generative artificial intelligence in drug discovery: basic framework, recent advances, challenges, and opportunities

There are two main ways to discover or design small drug molecules. The first involves fine-tuning existing molecules or commercially successful drugs through quantitative structure-activity relationships and virtual screening. The second approach involves generating new molecules through de novo drug design or inverse quantitative structure-activity relationship. Both methods aim to get a drug molecule with the best pharmacokinetic and pharmacodynamic profiles. However, bringing a new drug to market is an expensive and time-consuming endeavor, with the average cost being estimated at around $2.5 billion. One of the biggest challenges is screening the vast number of potential drug candidates to find one that is both safe and effective. The development of artificial intelligence in recent years has been phenomenal, ushering in a revolution in many fields. The field of pharmaceutical sciences has also significantly benefited from multiple applications of artificial intelligence, especially drug discovery projects. Artificial intelligence models are finding use in molecular property prediction, molecule generation, virtual screening, synthesis planning, repurposing, among others. Lately, generative artificial intelligence has gained popularity across domains for its ability to generate entirely new data, such as images, sentences, audios, videos, novel chemical molecules, etc. Generative artificial intelligence has also delivered promising results in drug discovery and development. This review article delves into the fundamentals and framework of various generative artificial intelligence models in the context of drug discovery via de novo drug design approach. Various basic and advanced models have been discussed, along with their recent applications. The review also explores recent examples and advances in the generative artificial intelligence approach, as well as the challenges and ongoing efforts to fully harness the potential of generative artificial intelligence in generating novel drug molecules in a faster and more affordable manner. Some clinical-level assets generated form generative artificial intelligence have also been discussed in this review to show the ever-increasing application of artificial intelligence in drug discovery through commercial partnerships.

VSpipe-GUI, an Interactive Graphical User Interface for Virtual Screening and Hit Selection

Virtual screening of large chemical libraries is essential to support computer-aided drug development, providing a rapid and low-cost approach for further experimental validation. However, existing computational packages are often for specialised users or platform limited. Previously, we developed VSpipe, an open-source semi-automated pipeline for structure-based virtual screening. We have now improved and expanded the initial command-line version into an interactive graphical user interface: VSpipe-GUI, a cross-platform open-source Python toolkit functional in various operating systems (e.g., Linux distributions, Windows, and Mac OS X). The new implementation is more user-friendly and accessible, and considerably faster than the previous version when AutoDock Vina is used for docking. Importantly, we have introduced a new compound selection module (i.e., spatial filtering) that allows filtering of docked compounds based on specified features at the target binding site. We have tested the new VSpipe-GUI on the Hepatitis C Virus NS3 (HCV NS3) protease as the target protein. The pocket-based and interaction-based modes of the spatial filtering module showed efficient and specific selection of ligands from the virtual screening that interact with the HCV NS3 catalytic serine 139.

Antibacterial Potential of Symmetrical Twin-Drug 3,6-Diaminoxanthones

Global health faces a significant issue with the rise of infectious diseases caused by bacteria, fungi, viruses, and parasites. The increasing number of multi-drug resistant microbial pathogens severely threatens public health worldwide. Antibiotic-resistant pathogenic bacteria, in particular, present a significant challenge. Therefore, there is an urgent need to identify new potential antimicrobial targets and discover new chemical entities that can potentially reverse bacterial resistance. The main goal of this research work was to create and develop a library of 3,6-disubstituted xanthones based on twin drugs and molecular extension approaches to inhibit the activity of efflux pumps. The process involved synthesizing 3,6-diaminoxanthones through the reaction of 9-oxo-9H-xanthene-3,6-diyl bis(trifluoromethanesulfonate) with various primary and secondary amines. The resulting 3,6-disubstituted xanthone derivatives were then tested for their in vitro antimicrobial properties against a range of pathogenic strains and their efficacy in inhibiting the activity of efflux pumps, biofilm formation, and quorum-sensing. Several compounds have exhibited effective antibacterial properties against the Gram-positive bacterial species tested. Xanthone 16, in particular, has demonstrated exceptional efficacy with a remarkable MIC of 11 µM (4 µg/mL) against reference strains Staphylococcus aureus ATCC 25923 and Enterococcus faecalis ATCC 29212, and 25 µM (9 µg/mL) against methicillin-resistant S. aureus 272123. Furthermore, some derivatives have shown potential as antibiofilm agents in a crystal violet assay. The ethidium bromide accumulation assay pinpointed certain compounds inhibiting bacterial efflux pumps. The cytotoxic effect of the most promising compounds was examined in mouse fibroblast cell line NIH/3T3, and two monoamine substituted xanthone derivatives with a hydroxyl substituent did not exhibit any cytotoxicity. Overall, the nature of the substituent was critical in determining the antimicrobial spectra of aminated xanthones.

Characterizations and molecular docking mechanism of the interactions between peptide FDGDF (Phe-Asp-Gly-Asp-Phe) and SOD enzyme

In this study, we investigated the antioxidant properties of dry-cured beef crude peptide (BPH) at different storage periods. The combination characteristics of different concentrations of Phe-Asp-Gly-Asp-Phe (FDGDF) and superoxide dismutase (SOD) at different temperatures were analyzed by ultraviolet-visible spectroscopy, fluorescence spectroscopy, and FT-IR spectroscopy, combined with the detection of a SOD activity detection box. It was found that FDGDF could improve the activity of SOD by changing its secondary structure. Bonds were formed at O32/O40/O52 using quantum chemical simulation calculations, and the Fukui index was higher than that of most atoms, indicating that these atoms were more likely to participate in the reaction. SPR biological force analysis showed that FDGDF and SOD were in a fast binding and dissociation mode. This study revealed the theoretical basis for studying the antioxidant mechanism of dry-cured beef and provided ideas for developing new dry-cured beef products.

Phylogenomic analysis expands the known repertoire of single-stranded DNA viruses in benthic zones of the South Indian Ocean

Single-stranded (ss) DNA viruses are ubiquitous and constitute some of the most diverse entities on Earth. Most studies have focused on ssDNA viruses from terrestrial environments resulting in a significant deficit in benthic ecosystems including aphotic zones of the South Indian Ocean (SIO). Here, we assess the diversity and phylogeny of ssDNA in deep waters of the SIO using a combination of established viral taxonomy tools and a Hidden Markov Model based approach. Replication initiator protein-associated (Rep) phylogenetic reconstruction and sequence similarity networks were used to show that the SIO hosts divergent and as yet unknown circular Rep-encoding ssDNA viruses. Several sequences appear to represent entirely novel families, expanding the repertoire of known ssDNA viruses. Results suggest that a small proportion of these viruses may be circular genetic elements, which may strongly influence the diversity of both eukaryotes and prokaryotes in the SIO. Taken together, our data show that the SIO harbours a diverse assortment of previously unknown ssDNA viruses. Due to their potential to infect a variety of hosts, these viruses may be crucial for marine nutrient recycling through their influence of the biological carbon pump.

Novel sofosbuvir derivatives against SARS-CoV-2 RNA-dependent RNA polymerase: an in silico perspective

The human coronavirus, SARS-CoV-2, had a negative impact on both the economy and human health, and the emerging resistant variants are an ongoing threat. One essential protein to target to prevent virus replication is the viral RNA-dependent RNA polymerase (RdRp). Sofosbuvir, a uridine nucleotide analog that potently inhibits viral polymerase, has been found to help treat SARS-CoV-2 patients. This work combines molecular docking and dynamics simulation (MDS) to test 14 sofosbuvir-based modifications against SARS-CoV-2 RdRp. The results reveal comparable (slightly better) average binding affinity of five modifications (compounds 3, 4, 11, 12, and 14) to the parent molecule, sofosbuvir. Compounds 3 and 4 show the best average binding affinities against SARS-CoV-2 RdRp (- 16.28 ± 5.69 and - 16.25 ± 5.78 kcal/mol average binding energy compared to - 16.20 ± 6.35 kcal/mol for sofosbuvir) calculated by Molecular Mechanics Generalized Born Surface Area (MM-GBSA) after MDS. The present study proposes compounds 3 and 4 as potential SARS-CoV-2 RdRp blockers, although this has yet to be proven experimentally.

The Extent of Edgetic Perturbations in the Human Interactome Caused by Population-Specific Mutations

Until recently, efforts in population genetics have been focused primarily on people of European ancestry. To attenuate this bias, global population studies, such as the 1000 Genomes Project, have revealed differences in genetic variation across ethnic groups. How many of these differences can be attributed to population-specific traits? To answer this question, the mutation data must be linked with functional outcomes. A new "edgotype" concept has been proposed, which emphasizes the interaction-specific, "edgetic", perturbations caused by mutations in the interacting proteins. In this work, we performed systematic in silico edgetic profiling of ~50,000 non-synonymous SNVs (nsSNVs) from the 1000 Genomes Project by leveraging our semi-supervised learning approach SNP-IN tool on a comprehensive set of over 10,000 protein interaction complexes. We interrogated the functional roles of the variants and their impact on the human interactome and compared the results with the pathogenic variants disrupting PPIs in the same interactome. Our results demonstrated that a considerable number of nsSNVs from healthy populations could rewire the interactome. We also showed that the proteins enriched with interaction-disrupting mutations were associated with diverse functions and had implications in a broad spectrum of diseases. Further analysis indicated that distinct gene edgetic profiles among major populations could shed light on the molecular mechanisms behind the population phenotypic variances. Finally, the network analysis revealed that the disease-associated modules surprisingly harbored a higher density of interaction-disrupting mutations from healthy populations. The variation in the cumulative network damage within these modules could potentially account for the observed disparities in disease susceptibility, which are distinctly specific to certain populations. Our work demonstrates the feasibility of a large-scale in silico edgetic study, and reveals insights into the orchestrated play of population-specific mutations in the human interactome.

ZeroBind: a protein-specific zero-shot predictor with subgraph matching for drug-target interactions

Existing drug-target interaction (DTI) prediction methods generally fail to generalize well to novel (unseen) proteins and drugs. In this study, we propose a protein-specific meta-learning framework ZeroBind with subgraph matching for predicting protein-drug interactions from their structures. During the meta-training process, ZeroBind formulates training a protein-specific model, which is also considered a learning task, and each task uses graph neural networks (GNNs) to learn the protein graph embedding and the molecular graph embedding. Inspired by the fact that molecules bind to a binding pocket in proteins instead of the whole protein, ZeroBind introduces a weakly supervised subgraph information bottleneck (SIB) module to recognize the maximally informative and compressive subgraphs in protein graphs as potential binding pockets. In addition, ZeroBind trains the models of individual proteins as multiple tasks, whose importance is automatically learned with a task adaptive self-attention module to make final predictions. The results show that ZeroBind achieves superior performance on DTI prediction over existing methods, especially for those unseen proteins and drugs, and performs well after fine-tuning for those proteins or drugs with a few known binding partners.

FireProt 2.0: web-based platform for the fully automated design of thermostable proteins

Thermostable proteins find their use in numerous biomedical and biotechnological applications. However, the computational design of stable proteins often results in single-point mutations with a limited effect on protein stability. However, the construction of stable multiple-point mutants can prove difficult due to the possibility of antagonistic effects between individual mutations. FireProt protocol enables the automated computational design of highly stable multiple-point mutants. FireProt 2.0 builds on top of the previously published FireProt web, retaining the original functionality and expanding it with several new stabilization strategies. FireProt 2.0 integrates the AlphaFold database and the homology modeling for structure prediction, enabling calculations starting from a sequence. Multiple-point designs are constructed using the Bron-Kerbosch algorithm minimizing the antagonistic effect between the individual mutations. Users can newly limit the FireProt calculation to a set of user-defined mutations, run a saturation mutagenesis of the whole protein or select rigidifying mutations based on B-factors. Evolution-based back-to-consensus strategy is complemented by ancestral sequence reconstruction. FireProt 2.0 is significantly faster and a reworked graphical user interface broadens the tool's availability even to users with older hardware. FireProt 2.0 is freely available at http://loschmidt.chemi.muni.cz/fireprotweb.

Enhancing alphafold-multimer-based protein complex structure prediction with MULTICOM in CASP15

To enhance the AlphaFold-Multimer-based protein complex structure prediction, we developed a quaternary structure prediction system (MULTICOM) to improve the input fed to AlphaFold-Multimer and evaluate and refine its outputs. MULTICOM samples diverse multiple sequence alignments (MSAs) and templates for AlphaFold-Multimer to generate structural predictions by using both traditional sequence alignments and Foldseek-based structure alignments, ranks structural predictions through multiple complementary metrics, and refines the structural predictions via a Foldseek structure alignment-based refinement method. The MULTICOM system with different implementations was blindly tested in the assembly structure prediction in the 15th Critical Assessment of Techniques for Protein Structure Prediction (CASP15) in 2022 as both server and human predictors. MULTICOM_qa ranked 3rd among 26 CASP15 server predictors and MULTICOM_human ranked 7th among 87 CASP15 server and human predictors. The average TM-score of the first predictions submitted by MULTICOM_qa for CASP15 assembly targets is ~0.76, 5.3% higher than ~0.72 of the standard AlphaFold-Multimer. The average TM-score of the best of top 5 predictions submitted by MULTICOM_qa is ~0.80, about 8% higher than ~0.74 of the standard AlphaFold-Multimer. Moreover, the Foldseek Structure Alignment-based Multimer structure Generation (FSAMG) method outperforms the widely used sequence alignment-based multimer structure generation.

The impact of molecular variants, crystallization conditions and the space group on ligand-protein complexes: a case study on bacterial phosphotriesterase

A bacterial phosphotriesterase was employed as an experimental paradigm to examine the effects of multiple factors, such as the molecular constructs, the ligands used during protein expression and purification, the crystallization conditions and the space group, on the visualization of molecular complexes of ligands with a target enzyme. In this case, the ligands used were organophosphates that are fragments of the nerve agents and insecticides on which the enzyme acts as a bioscavenger. 12 crystal structures of various phosphotriesterase constructs obtained by directed evolution were analyzed, with resolutions of up to 1.38 Å. Both apo forms and holo forms, complexed with the organophosphate ligands, were studied. Crystals obtained from three different crystallization conditions, crystallized in four space groups, with and without N-terminal tags, were utilized to investigate the impact of these factors on visualizing the organophosphate complexes of the enzyme. The study revealed that the tags used for protein expression can lodge in the active site and hinder ligand binding. Furthermore, the space group in which the protein crystallizes can significantly impact the visualization of bound ligands. It was also observed that the crystallization precipitants can compete with, and even preclude, ligand binding, leading to false positives or to the incorrect identification of lead drug candidates. One of the co-crystallization conditions enabled the definition of the spaces that accommodate the substituents attached to the P atom of several products of organophosphate substrates after detachment of the leaving group. The crystal structures of the complexes of phosphotriesterase with the organophosphate products reveal similar short interaction distances of the two partially charged O atoms of the P-O bonds with the exposed β-Zn2+ ion and the buried α-Zn2+ ion. This suggests that both Zn2+ ions have a role in stabilizing the transition state for substrate hydrolysis. Overall, this study provides valuable insights into the challenges and considerations involved in studying the crystal structures of ligand-protein complexes, highlighting the importance of careful experimental design and rigorous data analysis in ensuring the accuracy and reliability of the resulting phosphotriesterase-organophosphate structures.

Virus diversity and activity is driven by snowmelt and host dynamics in a high-altitude watershed soil ecosystem

BACKGROUND

Viruses impact nearly all organisms on Earth, including microbial communities and their associated biogeochemical processes. In soils, highly diverse viral communities have been identified, with a global distribution seemingly driven by multiple biotic and abiotic factors, especially soil temperature and moisture. However, our current understanding of the stability of soil viral communities across time and their response to strong seasonal changes in environmental parameters remains limited. Here, we investigated the diversity and activity of environmental soil DNA and RNA viruses, focusing especially on bacteriophages, across dynamics' seasonal changes in a snow-dominated mountainous watershed by examining paired metagenomes and metatranscriptomes.

RESULTS

We identified a large number of DNA and RNA viruses taxonomically divergent from existing environmental viruses, including a significant proportion of fungal RNA viruses, and a large and unsuspected diversity of positive single-stranded RNA phages (Leviviricetes), highlighting the under-characterization of the global soil virosphere. Among these, we were able to distinguish subsets of active DNA and RNA phages that changed across seasons, consistent with a "seed-bank" viral community structure in which new phage activity, for example, replication and host lysis, is sequentially triggered by changes in environmental conditions. At the population level, we further identified virus-host dynamics matching two existing ecological models: "Kill-The-Winner" which proposes that lytic phages are actively infecting abundant bacteria, and "Piggyback-The-Persistent" which argues that when the host is growing slowly, it is more beneficial to remain in a dormant state. The former was associated with summer months of high and rapid microbial activity, and the latter with winter months of limited and slow host growth.

CONCLUSION

Taken together, these results suggest that the high diversity of viruses in soils is likely associated with a broad range of host interaction types each adapted to specific host ecological strategies and environmental conditions. As our understanding of how environmental and host factors drive viral activity in soil ecosystems progresses, integrating these viral impacts in complex natural microbiome models will be key to accurately predict ecosystem biogeochemistry. Video Abstract.

Cheminformatics approach for identification of N-HyMenatPimeMelly as a novel potential ligand against RAS and renal chloride channel

Some angiotensin receptor (AR) blockers interfere with the renal chloride channel (ClC-K), which plays an important role in urine concentration. Identifying ligands targeting this channel, whether activating or blocking, is highly desirable because it could open the way for interventions that modulate their activity. In this study, the Asinex (BioDesign) complete library was screened to identify a compound with favorable physicochemical and pharmacokinetic properties, which have both AR blocking and ClC-Ka-modulating activities to present it as a novel potential oral candidate which could be useful for treatment of salt-sensitive hypertension without major ClC-K affection. A compound, N-{[4-Hydroxy-1-(2-methyl-1,6-naphthyridin-4-yl)-4-piperidinyl]methyl}-N-methyl-L-lysinamide (N-HyMenatPimeMelly) (Chem Spider ID 68416221), was identified as a potent potential oral ligand of the renin-angiotensin system (RAS) and ClC-Ka with docking scores ranging from -10.978 to -7.324 with the four selected proteins (4YAY: AR type 1, 2PFI: Cytoplasmic domain of ClC-Ka, 6JOD: AR type 2 and 6M0J: Angiotensin-converting enzyme 2). The protein-ligand complex was used to perform molecular dynamics (MD) simulation for 100 ns. The QikProp and SwissADME tools' results showed that the compound has ADME/T and drug-likeness properties, which are within the permissible ranges for 95% of known drugs. The density functional theory (DFT) analysis and MD simulation extended the study toward computational validation. Throughout the study, N-HyMenatPimeMelly has shown good interactions and stable performance in MD simulation and DFT analysis. The whole analysis has produced promising results, and N-HyMenatPimeMelly can be treated as a novel potential RAS and ClC-K oral ligand, however, experimental validation is needed before human use.Communicated by Ramaswamy H. Sarma.

Evaluation of the Cytotoxic and Antiviral Effects of Small Molecules Selected by In Silico Studies as Inhibitors of SARS-CoV-2 Cell Entry

The spike protein of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) relies on host cell surface glycans to facilitate interaction with the angiotensin-converting enzyme 2 (ACE-2) receptor. This interaction between ACE2 and the spike protein is a gateway for the virus to enter host cells and may be targeted by antiviral drugs to inhibit viral infection. Therefore, targeting the interaction between these two proteins is an interesting strategy to prevent SARS-CoV-2 infection. A library of glycan mimetics and derivatives was selected for a virtual screening performed against both ACE2 and spike proteins. Subsequently, in vitro assays were performed on eleven of the most promising in silico compounds to evaluate: (i) their efficacy in inhibiting cell infection by SARS-CoV-2 (using the Vero CCL-81 cell line as a model), (ii) their impact on ACE2 expression (in the Vero CCL-81 and MDA-MB-231 cell lines), and (iii) their cytotoxicity in a human lung cell line (A549). We identified five synthetic compounds with the potential to block SARS-CoV-2 infection, three of them without relevant toxicity in human lung cells. Xanthene 1 stood out as the most promising anti-SARS-CoV-2 agent, inhibiting viral infection and viral replication in Vero CCL-81 cells, without causing cytotoxicity to human lung cells.

Pharmacological inhibition of cathepsin C (CatC) as a potential approach for cancer therapeutics

Studies have established that proteolytic enzyme inhibition holds significant promise in cancer prevention and treatment. Cathepsin C (CatC) is conserved lysosomal cysteine dipeptidyl aminopeptidase, which is the key for pro-inflammatory neutrophil serine protease activation and biological functioning. This makes CatC as a promising therapeutic drug target for the management of different cancer types. Considering this, using a wide range of computer aided drug-designing applications, several inhibitors are shortlisted against CatC active pocket, which interact with the enzyme with high affinity and form strong intermolecular interaction network. Compared to control, three molecules ASN_06916232, ASN_06917112 and ASN_06916892 are filtered as best binders of the CatC active pocket with binding energy value of -10.9 kcal/mol, -10.9 kcal/mol and -10.7 kcal/mol, respectively. These compounds interact with several important active side residues of CatC such as Ser233, Cys234, Gly277, Asn380 and His38. Furthermore, the complexes of these compounds with CatC reveal very stable dynamics with average RMSD value less than 3 Å. The binding energy analysis further indicates the compounds to have very stable van der Waals and electrostatic energies. In conclusion, these molecules are promising and require experimental validation to prove them as anti-CatC molecules.Communicated by Ramaswamy H. Sarma.

Screening of marine natural products for potential inhibitors targeting biotin biosynthesis pathway in Mycobacterium tuberculosis

Tuberculosis (TB) remains as one of the major public health concerns worldwide. A successful TB control and treatment is very challenging, due to continuing emergence of Mycobacterium tuberculosis strains resistant to known drugs. Therefore, the development of new drugs with different chemical and biological approaches is necessary to obtain more efficient anti-tubercular therapeutics. Biotin is an essential cofactor for lipid biosynthesis and gluconeogenesis in M. tuberculosis. M. tuberculosis relies on de novo biotin biosynthesis to obtain this vital cofactor since it cannot scavenge sufficient biotin from a mammalian host. In this study, comprehensive in silico methods including structure-based virtual screening, molecular docking, and molecular dynamic simulation analysis for ∼8000 marine natural products were performed against two essential enzymes involved in biotin synthesis and ligation of M. tuberculosis namely, pyridoxal 5'-phosphate-dependent transaminase (BioA) and mycobacterial biotin protein ligase (MtBPL). Two compounds; CMNPD10112 and CMNPD10113 are unveiled to bind the enzymes consistently and with high affinities. The binding pattern of compounds is further noticed in very stable binding modes as analyzed by molecular dynamics simulation and the mean RMSD of the complexes is within 4 Å. The intermolecular binding free energies validated complexes are less than -40 kcal/mol, which demonstrates strong and stable complexes formation. The identified hit compounds could be seeds for design of effective anti-mycobacterium therapeutics by inhibition of bacterial growth through blocking the biotin biosynthesis.Communicated by Ramaswamy H. Sarma.

Structure and dynamics of pteridine reductase 1: the key phenomena relevant to enzyme function and drug design

Pteridine reductase 1 (PTR1) is a folate and pterin pathway enzyme unique for pathogenic trypanosomatids. As a validated drug target, PTR1 has been the focus of recent research efforts aimed at finding more effective treatments against human parasitic diseases such as leishmaniasis or sleeping sickness. Previous PTR1-centered structural studies highlighted the enzyme characteristics, such as flexible regions around the active site, highly conserved structural waters, and species-specific differences in pocket properties and dynamics, which likely impacts the binding of natural substrates and inhibitors. Furthermore, several aspects of the PTR1 function, such as the substrate inhibition phenomenon and the level of ligand binding cooperativity in the enzyme homotetramer, likely related to the global enzyme dynamics, are poorly known at the molecular level. We postulate that future drug design efforts could greatly benefit from a better understanding of these phenomena through studying both the local and global PTR1 dynamics. This review highlights the key aspects of the PTR1 structure and dynamics relevant to structure-based drug design that could be effectively investigated by modeling approaches. Particular emphasis is given to the perspective of molecular dynamics, what has been accomplished in this area to date, and how modeling could impact the PTR1-targeted drug design in the future.

Structure-based virtual screening of natural compounds as inhibitors of HCV using molecular docking and molecular dynamics simulation studies

The hepatitis C virus (HCV), which causes hepatitis C, is a viral infection that damages the liver and causes inflammation in the liver. New potentially effective antiviral drugs are required for its treatment owing to various issues associated with the existing medications, including moderate to severe adverse effects, higher costs, and the emergence of drug-resistant strains. The objective of the current study was to utilize computational techniques to assess the anti-HCV efficacy of certain phytochemicals against tetraspanin (CD81) and claudin 1 (CLDN1) entry proteins. A 200-nanosecond molecular dynamics (MD) simulation was employed to examine the stability of the lead-protein complexes. Free binding energy and molecular docking calculations were conducted utilizing MM/GBSA method, and the selectivity of hit compounds for CD81 and CLDN1 was determined. Five significant CD81 and CLDN1 inhibitors were identified: Petasiphenone, Silibinin, Tanshinone IIA, Taxifolin, and Topaquinone. The MM/GBSA analysis of the compounds revealed high free binding energies. All the identified compounds were stable within the CD81 and CLDN1 binding pockets. This study indicated the promising inhibitory potential of the identified compounds against CD81 and CLDN1 receptors and might develop into potential viral entry inhibitors. However, to validate the chemotherapeutic capabilities of the discovered leads extensive preclinical research is required.Communicated by Ramaswamy H. Sarma.

SARS-CoV-2 antibodies recognize 23 distinct epitopic sites on the receptor binding domain

The COVID-19 pandemic and SARS-CoV-2 variants have dramatically illustrated the need for a better understanding of antigen (epitope)-antibody (paratope) interactions. To gain insight into the immunogenic characteristics of epitopic sites (ES), we systematically investigated the structures of 340 Abs and 83 nanobodies (Nbs) complexed with the Receptor Binding Domain (RBD) of the SARS-CoV-2 spike protein. We identified 23 distinct ES on the RBD surface and determined the frequencies of amino acid usage in the corresponding CDR paratopes. We describe a clustering method for analysis of ES similarities that reveals binding motifs of the paratopes and that provides insights for vaccine design and therapies for SARS-CoV-2, as well as a broader understanding of the structural basis of Ab-protein antigen (Ag) interactions.

Identification and dynamics of novel scaffolds against Enterococcus faecium serine hydroxymethyltransferase enzyme: a potential target for antibiotics development

Serine hydroxymethyltransferase enzyme is a significant player in purine, thymidylate, and L-serine biosynthesis and has been tagged as a potential target for cancer, viruses, and parasites. However, this enzyme as an anti-bacterial druggable target has not been explored much. Herein, in this work, different computational chemistry and biophysics techniques were applied to identify potential computational predicted inhibitory molecules against Enterococcus faecium serine hydroxymethyltransferase enzyme. By structure based virtual screening process of ASINEX antibacterial library against the enzyme two main compounds: Top-1_BDC_21204033 and Top-2_BDC_20700155 were reported as best binding molecules. The Top-1_BDC_21204033 and Top-2_BDC_20700155 binding energy value is -9.3 and -8.9 kcal/mol, respectively. The control molecule binding energy score is -6.55 kcal/mol. The mean RMSD of Top-1-BDC_21204033, Top-2-BDC_20700155 and control is 3.7 Å (maximum 5.03 Å), 1.7 Å (maximum 3.05 Å), and 3.84 Å (maximum of 6.7 Å), respectively. During the simulation time, the intermolecular docked conformation and interactions were seen stable despite of few small jumps by the compounds/control, responsible for high RMSD in some frames. The MM/GBSA and MM/PBSA binding free energy of lead Top-2-BDC_20700155 complex is -79.52 and -82.63 kcal/mol, respectively. This complex was seen as the most stable compared to the control. Furthermore, the lead molecules and control showed good druglikeness and pharmacokinetics profile. The lead molecules were non-toxic and non-mutagenic. In short, the compounds are promising in terms of binding to the serine hydroxymethyltransferase enzyme and need to be subjected to experimental studies.Communicated by Ramaswamy H. Sarma.

Improving AlphaFold2-based protein tertiary structure prediction with MULTICOM in CASP15

Since the 14th Critical Assessment of Techniques for Protein Structure Prediction (CASP14), AlphaFold2 has become the standard method for protein tertiary structure prediction. One remaining challenge is to further improve its prediction. We developed a new version of the MULTICOM system to sample diverse multiple sequence alignments (MSAs) and structural templates to improve the input for AlphaFold2 to generate structural models. The models are then ranked by both the pairwise model similarity and AlphaFold2 self-reported model quality score. The top ranked models are refined by a novel structure alignment-based refinement method powered by Foldseek. Moreover, for a monomer target that is a subunit of a protein assembly (complex), MULTICOM integrates tertiary and quaternary structure predictions to account for tertiary structural changes induced by protein-protein interaction. The system participated in the tertiary structure prediction in 2022 CASP15 experiment. Our server predictor MULTICOM_refine ranked 3rd among 47 CASP15 server predictors and our human predictor MULTICOM ranked 7th among all 132 human and server predictors. The average GDT-TS score and TM-score of the first structural models that MULTICOM_refine predicted for 94 CASP15 domains are ~0.80 and ~0.92, 9.6% and 8.2% higher than ~0.73 and 0.85 of the standard AlphaFold2 predictor respectively.

Identification of promising methionine aminopeptidase enzyme inhibitors: A combine study of comprehensive virtual screening and dynamics simulation study

Methionine aminopeptidase (MetAP) enzymes play a critical role in bacterial cell survival by cleaving formyl-methionine initiators at N-terminal of nascent protein, a process which is vital in proper protein folding. This makes MetAP an attractive and novel antibacterial target to unveil promising antibiotics. In this study, the crystal structure of R. prowazekii MetAP was used in structure-based virtual screening of drug libraries such as Asinex antibacterial library and Comprehensive Marine Natural Products Database (CMNPD) to identify promising lead molecules against the enzyme. This shortlisted three drug molecules; BDE-25098678, BDE-30686468 and BDD_25351157 as most potent leads that showed strong binding to the MetAP enzyme. The static docked conformation of the compounds to the MetAP was reevaluated in molecular dynamics simulation studies. The analysis observed the docked complexes as stable structure with no major local or global deviations noticed. These findings suggest the formation of strong intermolecular docked complexes, which showed stable dynamics and atomic level interactions network. The binding free energy analysis predicted net MMGBSA energy of complexes as: BDE-25098678 (-73.41 kcal/mol), BDE-30686468 (-59.93 kcal/mol), and BDD_25351157 (-75.39 kcal/mol). In case of MMPBSA, the complexes net binding energy was as; BDE-25098678 (-77.47 kcal/mol), BDE-30686468 (-69.47 kcal/mol), and BDD_25351157 (-75.6 kcal/mol). Further, the compounds were predicted to follow the famous Lipinski rule of five and have non-toxic, non-carcinogenic and non-mutagenic profile. The screened compounds might be used in experimental test to highlight the real anti- R. prowazekii MetAP activity.

Functional analysis, virtual screening, and molecular dynamics revealed potential novel drug targets and their inhibitors against cardiovascular disease in human

Cardiovascular disease (CVD) is a group of diseases, affecting the human heart and accounting for 30% of deaths worldwide. Major CVDs include heart failure, hypertension, stroke, etc. Various therapeutics are available against CVD, still there is a dire need to find out potential protein drug targets to reduce economic burden and mortality rate. Goal of the current study was to utilize sequential computational techniques to find the best cardiovascular drug targets and their inhibitors. Common human cardiovascular targets of both databases (GeneCards and Uniprot) were subjected to bioinformatics analyses. Purpose was to validate putative therapeutic targets employing the structure-based bioinformatics methods to determine their physiochemical properties and biological processes. Three stable proteins, that have 0 transmembrane helices, and possess biological processes were screened as potential protein-based therapeutic targets: Hemoglobin subunit beta (HBB), Gamma-enolase (ENO2), and Cholesteryl ester transfer protein (CETP). Tertiary structures of target proteins were retrieved from PDB, and molecular docking technique was utilized to evaluate a library of 5000 phytochemicals against the interacting residues of the target protein as well as their respective standard drugs through MOE and Pyrx software. Top five phytochemicals (d-Sesamin, 1,3-benzodioxole, Sativanone, Thiamine, and Cajanol) were identified based on their RMSD and docking scores as compared to their standard drugs. The docking studies were also validated by MM-GBSA binding free energy and molecular dynamics simulations. According to the study's findings, these phytochemicals may eventually be used as drugs to treat CVD. Further in vitro testing is required to confirm their efficacy and drug potency.Communicated by Ramaswamy H. Sarma.

Agonistic antibacterial potential of Loigolactobacillus coryniformis BCH-4 metabolites against selected human pathogenic bacteria: An in vitro and in silico approach

Lactic acid bacteria are known to produce numerous antibacterial metabolites that are active against various pathogenic microbes. In this study, bioactive metabolites from the cell free supernatant of Loigolactobacillus coryniformis BCH-4 were obtained by liquid-liquid extraction, using ethyl acetate, followed by fractionation, using silica gel column chromatography. The collected F23 fraction effectively inhibited the growth of pathogenic bacteria (Escherichia coli, Bacillus cereus, and Staphylococcus aureus) by observing the minimum inhibitory concentration (MIC) and minimum bactericidal concentrations (MBC). The evaluated values of MIC were 15.6 ± 0.34, 3.9 ± 0.59, and 31.2 ± 0.67 μg/mL and MBC were 15.6 ± 0.98, 7.8 ± 0.45, and 62.5 ± 0.23 μg/mL respectively, against the above-mentioned pathogenic bacteria. The concentration of F23 fraction was varying from 1000 to 1.9 μg/mL. Furthermore, the fraction also exhibited sustainable biofilm inhibition. Using the Electrospray Ionization Mass Spectrometry (ESI-MS/MS), the metabolites present in the bioactive fraction (F23), were identified as phthalic acid, myristic acid, mangiferin, 16-hydroxylpalmatic acid, apigenin, and oleandomycin. By using in silico approach, docking analysis showed good interaction of identified metabolites and receptor proteins of pathogenic bacteria. The present study suggested Loigolactobacillus coryniformis BCH-4, as a promising source of natural bioactive metabolites which may receive great benefit as potential sources of drugs in the pharmacological sector.

Recent Advances in Machine-Learning-Based Chemoinformatics: A Comprehensive Review

In modern drug discovery, the combination of chemoinformatics and quantitative structure-activity relationship (QSAR) modeling has emerged as a formidable alliance, enabling researchers to harness the vast potential of machine learning (ML) techniques for predictive molecular design and analysis. This review delves into the fundamental aspects of chemoinformatics, elucidating the intricate nature of chemical data and the crucial role of molecular descriptors in unveiling the underlying molecular properties. Molecular descriptors, including 2D fingerprints and topological indices, in conjunction with the structure-activity relationships (SARs), are pivotal in unlocking the pathway to small-molecule drug discovery. Technical intricacies of developing robust ML-QSAR models, including feature selection, model validation, and performance evaluation, are discussed herewith. Various ML algorithms, such as regression analysis and support vector machines, are showcased in the text for their ability to predict and comprehend the relationships between molecular structures and biological activities. This review serves as a comprehensive guide for researchers, providing an understanding of the synergy between chemoinformatics, QSAR, and ML. Due to embracing these cutting-edge technologies, predictive molecular analysis holds promise for expediting the discovery of novel therapeutic agents in the pharmaceutical sciences.

A cheminformatics-biophysics correlate to identify promising lead molecules against matrix metalloproteinase-2 (MMP-2) enzyme: A promising anti-cancer target

Matrix metalloproteinase-2 (MMP-2) is an endopeptidase enzyme that is devoted to extracellular matrix proteins degradation. The enzyme is warranted as promising drugs target for different light threating diseases such as arthritis, cancer and fibrosis. Herein, in this study, three drug molecules: CMNPD8322, CMNPD8320, and CMNPD8318 were filtered as high affinity binding compounds with binding energy score of -9.75 kcal/mol, -9.11 kcal/mol, -9.05 kcal/mol, respectively. The control binding energy score was -9.01 kcal/mol. The compounds docked deeply inside the pocket interacting with S1 pocket residues. The docked complexes dynamics in real time at cellular environment was then done to decipher the stable binding conformation and intermolecular interactions network. The compounds complexes achieved very stable dynamics with root mean square deviation (RMSD) with mean value of around 2-3 Å compared to control complex that showed higher fluctuations of 5 Å. The simulation trajectories frames based binding free energy demonstrated all the compounds-MMP-2 complexes reported highly stable energy, particularly the van der Waals energy dominate the overall net energy. Similarly, the complexes revalidation of WaterSwap based energies also disclosed the complexes highly stable in term docked conformation. Also, the compounds illustrated the compounds favorable pharmacokinetics and were non-toxic and non-mutagenic. Thus, the compounds might be used thorough experimental assays to confirm compounds selective biological potency against MMP-2 enzyme.

Vitamin B12 Auxotrophy in Isolates from the Deep Subsurface of the Iberian Pyrite Belt

Vitamin B₁₂ is an enzymatic cofactor that is essential for both eukaryotes and prokaryotes. The development of life in extreme environments depends on cofactors such as vitamin B₁₂ as well. The genomes of twelve microorganisms isolated from the deep subsurface of the Iberian Pyrite Belt have been analyzed in search of enzymatic activities that require vitamin B₁₂ or are involved in its synthesis and import. Results have revealed that vitamin B₁₂ is needed by these microorganisms for several essential enzymes such as ribonucleotide reductase, methionine synthase and epoxyqueosine reductase. Isolate Desulfosporosinus sp. DEEP is the only analyzed genome that holds a set core of proteins that could lead to the production of vitamin B₁₂. The rest are dependent on obtaining it from the subsurface oligotrophic environment in which they grow. Sought proteins involved in the import of vitamin B₁₂ are not widespread in the sample. The dependence found in the genomes of these microorganisms is supported by the production of vitamin B₁₂ by microorganisms such as Desulfosporosinus sp. DEEP, showing that the operation of deep subsurface biogeochemical cycles is dependent on cofactors such as vitamin B₁₂.

In silico mutagenesis-based designing of oncogenic SHP2 peptide to inhibit cancer progression

Cancer is among the top causes of death, accounting for an estimated 9.6 million deaths in 2018, it appeared that approximately 500,000 people die from cancer in the United States alone annually. The SHP2 plays a major role in regulation of cell growth, proliferation, and differentiation, and functional upregulation of this enzyme is linked to oncogenesis and developmental disorders. SHP2 activity has been linked to several cancer types for which no drugs are currently available. In our study, we aimed to design peptide inhibitors against the SHP2 mutant. The crystal structure of the human Src SH2-PQpYEEIPI peptide mutant was downloaded from the protein databank. We generated several peptides from the native wild peptide using an in silico mutagenesis method, which showed that changes (P302W, Y304F, E306Q, and Q303A) might boost the peptide's affinity for binding to SHP2. Furthermore, the dynamical stability and binding affinities of the mutated peptide were confirmed using Molecular dynamics simulation and Molecular Mechanics with Generalized Born and Surface Area Solvation free energy calculations. The proposed substitution greatly enhanced the binding affinity at the residue level, according to a study that decomposed energy into its component residues. Our proposed peptide may prevent the spread of cancer by inhibiting SHP2, according to our detailed analyses of binding affinities.