GRaSP-web: a machine learning strategy to predict binding sites based on residue neighborhood graphs

PUResNetV2.0: a deep learning model leveraging sparse representation for improved ligand binding site prediction

Accurate ligand binding site prediction (LBSP) within proteins is essential for drug discovery. We developed ProteinUNetResNetV2.0 (PUResNetV2.0), leveraging sparse representation of protein structures to improve LBSP accuracy. Our training dataset included protein complexes from 4729 protein families. Evaluations on benchmark datasets showed that PUResNetV2.0 achieved an 85.4% Distance Center Atom (DCA) success rate and a 74.7% F1 Score on the Holo801 dataset, outperforming existing methods. However, its performance in specific cases, such as RNA, DNA, peptide-like ligand, and ion binding site prediction, was limited due to constraints in our training data. Our findings underscore the potential of sparse representation in LBSP, especially for oligomeric structures, suggesting PUResNetV2.0 as a promising tool for computational drug discovery.

Thermostable bacterial L-asparaginase for polyacrylamide inhibition and in silico mutational analysis

The L-asparaginase (ASPN) enzyme has received recognition in various applications including acrylamide degradation in the food industry. The synthesis and application of thermostable ASPN enzymes is required for its use in the food sector, where thermostable enzymes can withstand high temperatures. To achieve this goal, the bacterium Bacillus subtilis was isolated from the hot springs of Tapovan for screening the production of thermostable ASPN enzyme. Thus, ASPN with a maximal specific enzymatic activity of 0.896 U/mg and a molecular weight of 66 kDa was produced from the isolated bacteria. The kinetic study of the enzyme yielded a Km value of 1.579 mM and a Vmax of 5.009 µM/min with thermostability up to 100 min at 75 °C. This may have had a positive indication for employing the enzyme to stop polyacrylamide from being produced. The current study has also been extended to investigate the interaction of native and mutated ASPN enzymes with acrylamide. This concluded that the M10 (with 10 mutations) has the highest protein and thermal stability compared to the wild-type ASPN protein sequence. Therefore, in comparison to a normal ASPN and all other mutant ASPNs, M10 is the most favorable mutation. This research has also demonstrated the usage of ASPN in food industrial applications.

BioLiP2: an updated structure database for biologically relevant ligand-protein interactions

With the progress of structural biology, the Protein Data Bank (PDB) has witnessed rapid accumulation of experimentally solved protein structures. Since many structures are determined with purification and crystallization additives that are unrelated to a protein's in vivo function, it is nontrivial to identify the subset of protein-ligand interactions that are biologically relevant. We developed the BioLiP2 database (https://zhanggroup.org/BioLiP) to extract biologically relevant protein-ligand interactions from the PDB database. BioLiP2 assesses the functional relevance of the ligands by geometric rules and experimental literature validations. The ligand binding information is further enriched with other function annotations, including Enzyme Commission numbers, Gene Ontology terms, catalytic sites, and binding affinities collected from other databases and a manual literature survey. Compared to its predecessor BioLiP, BioLiP2 offers significantly greater coverage of nucleic acid-protein interactions, and interactions involving large complexes that are unavailable in PDB format. BioLiP2 also integrates cutting-edge structural alignment algorithms with state-of-the-art structure prediction techniques, which for the first time enables composite protein structure and sequence-based searching and significantly enhances the usefulness of the database in structure-based function annotations. With these new developments, BioLiP2 will continue to be an important and comprehensive database for docking, virtual screening, and structure-based protein function analyses.

In silico investigation of Komaroviquinone as a potential inhibitor of SARS-CoV-2 main protease (Mpro): Molecular docking, molecular dynamics, and QM/MM approaches

COVID-19 has highlighted the urgent need for new therapeutic agents to combat the spread of the virus. The main protease of SARS-CoV-2 (Mpro) has emerged as a promising target. In this study, we conducted an in silico investigation to explore the potential of Komaroviquinone, an icetexane diterpene, as a therapeutic agent against COVID-19. We employed molecular docking, molecular dynamics, and QM/MM methodologies to compare the binding affinity, molecular interactions, and stability of Komaroviquinone and the FDA-approved antiviral drug Nirmatrelvir with the SARS-CoV-2 Mpro protein. The study demonstrated that Komaroviquinone exhibits strong interaction with Mpro, with a binding energy comparable to Nirmatrelvir. The ADMET analysis revealed that Barbatusol, Brussonol, and Komaroviquinone possess superior solubility, permeability, and intestinal absorption compared to Nirmatrelvir, as well as more favorable distribution properties and lower toxicity profiles. Notably, Nirmatrelvir displayed toxicity and hepatotoxicity, which were not present in the natural compounds. Thus, it is suggested that Komaroviquinone may be a promising candidate for the development of effective and safer therapeutic agents against COVID-19. Experimental validation is necessary to confirm its potential as a treatment for the disease.

Exploring the structural and molecular interaction landscape of nirmatrelvir and Mpro complex: The study might assist in designing more potent antivirals targeting SARS-CoV-2 and other viruses

BACKGROUND

Several therapeutics have been developed and approved against SARS-CoV-2 occasionally; nirmatrelvir is one of them. The drug target of nirmatrelvir is Mpro, and therefore, it is necessary to comprehend the structural and molecular interaction of the Mpro-nirmatrelvir complex.

METHODS

Integrative bioinformatics, system biology, and statistical models were used to analyze the macromolecular complex.

RESULTS

Using two macromolecular complexes, the study illustrated the interactive residues, H-bonds, and interactive interfaces. It informed of six and nine H-bond formations for the first and second complex, respectively. The maximum bond length was observed as 3.33 Å. The ligand binding pocket's surface area and volume were noted as 303.485 Å2 and 295.456 Å3 for the first complex and 308.397 Å2 and 304.865 Å3 for the second complex. The structural proteome dynamics were evaluated by analyzing the complex's NMA mobility, eigenvalues, deformability, and B-factor. Conversely, a model was created to assess the therapeutic status of nirmatrelvir.

CONCLUSIONS

Our study reveals the structural and molecular interaction landscape of Mpro-nirmatrelvir complex. The study will guide researchers in designing more broad-spectrum antiviral molecules mimicking nirmatrelvir, which assist in fighting against SARS-CoV-2 and other infectious viruses. It will also help to prepare for future epidemics or pandemics.

Computational methods in glaucoma research: Current status and future outlook

Advancements in computational techniques have transformed glaucoma research, providing a deeper understanding of genetics, disease mechanisms, and potential therapeutic targets. Systems genetics integrates genomic and clinical data, aiding in identifying drug targets, comprehending disease mechanisms, and personalizing treatment strategies for glaucoma. Molecular dynamics simulations offer valuable molecular-level insights into glaucoma-related biomolecule behavior and drug interactions, guiding experimental studies and drug discovery efforts. Artificial intelligence (AI) technologies hold promise in revolutionizing glaucoma research, enhancing disease diagnosis, target identification, and drug candidate selection. The generalized protocols for systems genetics, MD simulations, and AI model development are included as a guide for glaucoma researchers. These computational methods, however, are not separate and work harmoniously together to discover novel ways to combat glaucoma. Ongoing research and progresses in genomics technologies, MD simulations, and AI methodologies project computational methods to become an integral part of glaucoma research in the future.

Layer-Wise Relevance Analysis for Motif Recognition in the Activation Pathway of the β2-Adrenergic GPCR Receptor

G-protein-coupled receptors (GPCRs) are cell membrane proteins of relevance as therapeutic targets, and are associated to the development of treatments for illnesses such as diabetes, Alzheimer's, or even cancer. Therefore, comprehending the underlying mechanisms of the receptor functional properties is of particular interest in pharmacoproteomics and in disease therapy at large. Their interaction with ligands elicits multiple molecular rearrangements all along their structure, inducing activation pathways that distinctly influence the cell response. In this work, we studied GPCR signaling pathways from molecular dynamics simulations as they provide rich information about the dynamic nature of the receptors. We focused on studying the molecular properties of the receptors using deep-learning-based methods. In particular, we designed and trained a one-dimensional convolution neural network and illustrated its use in a classification of conformational states: active, intermediate, or inactive, of the β2-adrenergic receptor when bound to the full agonist BI-167107. Through a novel explainability-oriented investigation of the prediction results, we were able to identify and assess the contribution of individual motifs (residues) influencing a particular activation pathway. Consequently, we contribute a methodology that assists in the elucidation of the underlying mechanisms of receptor activation-deactivation.