RCSB Protein Data Bank: Enabling biomedical research and drug discovery

Discovery and characterization of novel FAK inhibitors for breast cancer therapy via hybrid virtual screening, biological evaluation and molecular dynamics simulations

Focal adhesion kinase (FAK) is a critical drug target implicated in various disease pathways, including hematological malignancies and breast cancer. Therefore, identifying FAK inhibitors with novel scaffolds could offer new opportunities for developing effective therapeutic compounds. Herein, we disclosed the discovery of a new backbone inhibitor of FAK using an "internal" database, employing a structure-based high-transparency permeability virtual screening (HTVS) and a DeepDock algorithm based on geometric deep learning. Subsequently, molecular docking was conducted at different precisions to identify 10 compounds for further evaluation of biological activity. Ultimately, compound 4, a pyrimidin-4-amine derivative, demonstrated inhibitory activity against FAK and breast cancer cells, further supporting its potential as a FAK inhibitor. Moreover, molecular dynamics simulations were carried out to gain more detailed insights into the binding mechanism between compound 4 and FAK to guide subsequent structural optimization.

Restorative mechanisms of Shugan Yiyang capsule on male infertility through 'pharmaco-metabo-net' tripartite correlation analysis

BACKGROUND

Shugan Yiyang capsule (SGYY), a commonly used traditional Chinese medicine formulation, is primarily indicated for the treatment of erectile dysfunction, yet existing studies on the therapeutic effects on male infertility (MI) are insufficient and the specific mechanisms remain poorly understood. Given the close relationship between MI, sperm quality, and erectile function, this study aims to investigate the role of SGYY in the restoration of MI and explore the underlying mechanisms.

METHODS

The efficacy of SGYY is comprehensively evaluated through pharmacodynamic, metabolomic, and network pharmacology. Sperm parameters, reproductive hormones, sexual behavior, neural enzymes, oxidative stress markers, pro-inflammatory cytokines, and testicular histopathology are measured to reveal the restorative effects of MI. Furthermore, urine and serum metabolomics, along with network pharmacology and surface plasmon resonance, are employed to explore the molecular mechanisms and predict core targets.

RESULTS

SGYY significantly improved overall health parameters, including body weight, water intake, urine output, food consumption, and spontaneous activity. Specifically, SGYY prominently recovered sexual behavior, ameliorated sperm quality, increased mitochondrial membrane potential, normalized reproductive hormones, upregulated endothelial nitric oxide synthase, attenuated oxidative stress markers, and pro-inflammatory cytokines, and repaired testicular pathological damage. Metabolomic analysis identified 47 candidate biomarkers, among which SGYY significantly modulated 39 potential biomarkers, encompassing 8 main metabolic pathways such as histidine metabolism, cysteine and methionine metabolism, propanoate metabolism, and taurine and hypotaurine metabolism. Additionally, network pharmacology predicted 8 core targets, comprising HSP90AA1, ESR1, MAPK1, CASP3, IL6, TNF, BCL2, and MAPK8.

CONCLUSION

SGYY improves sperm quality and erectile function by regulating oxidative stress, energy metabolism, and neurological function, thereby exerting a restorative effect on MI, as evidenced by the modulation of 8 main metabolic pathways, 39 potential biomarkers, and 8 core targets. Pharmacodynamic provides foundational validation, metabolomic uncovers intrinsic metabolic changes, and network pharmacology predicts therapeutic targets, with findings from the 'Pharmaco-Metabo-Net' tripartite correlation analysis providing a solid theoretical strategy and scientific evidence to support the clinical application of SGYY in restoring MI.

Single-nucleus RNA sequencing and network pharmacology reveal the mediation of fisetin on neuroinflammation in Alzheimer's disease

BACKGROUND

Alzheimer's Disease (AD) is a neurodegenerative disorder characterized by a progressive decline in cognitive function and memory. This study explores cellular subgroups in AD using single-nucleus RNA sequencing (snRNA-seq). It integrates the pharmacological network of traditional Chinese medicine (TCM) to identify potential therapeutic targets, providing a theoretical basis for the development of clinical AD.

METHODS

We obtained data information from the Gene Expression Omnibus (GEO) for snRNA-seq analysis. Enrichment and pseudotime analysis were performed to explore the functions and differentiation pathways of cellular subgroups. Cellular communication networks were mapped to reveal subgroup interactions. Additionally, a pharmacological network for AD was constructed using the TCM pharmacology database.

RESULTS

We identified several cell subgroups associated with AD pathology, contributing to disease progression in various ways. Notably, the TNC+ CD44+ astrocyte subgroup activated the I-kappa B kinase/ NF-κB signaling pathway, leading to increased expression of inflammatory cytokines. In the pharmacological network, fisetin was identified as a promising compound with the potential to bind to the CD44 protein, mitigating the inflammatory response and preventing further neuronal damage.

CONCLUSIONS

By exploring the ecological landscape of various cellular subgroups in AD and investigating the roles and mechanisms, combined with molecular docking and pharmacological network screening, our findings provide new insights and therapeutic possibilities for AD treatment.

Discovery of Novel DDR1 Inhibitors through a Hybrid Virtual Screening Pipeline, Biological Evaluation and Molecular Dynamics Simulations

Acute myeloid leukemia (AML) is a heterogeneous hematopoietic malignancy with limited therapeutic options for many patients. Discoidin domain receptor 1 (DDR1), a transmembrane tyrosine kinase receptor, has been implicated in AML progression and represents a promising therapeutic target. In this study, we employed a hybrid virtual screening workflow that integrates deep learning-based binding affinity predictions with molecular docking techniques to identify potential DDR1 inhibitors. A multistage screening process involving PSICHIC, KarmaDock, Vina-GPU, and similarity-based scoring was conducted, leading to the selection of seven candidate compounds. The biological evaluation identified Compound 4 as a novel DDR1 inhibitor, demonstrating significant DDR1 inhibitory activity with an IC50 of 46.16 nM and a 99.86% inhibition rate against Z-138 cells at 10 μM. Molecular dynamics simulations and binding free energy calculations further validated the stability and strong binding interactions of Compound 4 with DDR1. This study highlights the utility of combining deep learning models with traditional molecular docking techniques to accelerate the discovery of potent and selective DDR1 inhibitors. The identified compounds hold promise for further development as targeted therapies for AML.

Subtle Changes at the RBD/hACE2 Interface During SARS-CoV-2 Variant Evolution: A Molecular Dynamics Study

The SARS-CoV-2 Omicron variants show different behavior compared to the previous variants, especially with respect to the Delta variant, which promotes a lower morbidity despite being much more contagious. In this perspective, we performed molecular dynamics (MD) simulations of the different spike RBD/hACE2 complexes corresponding to the WT, Delta and four Omicron variants. Carrying out a comprehensive analysis of residue interactions within and between the two partners allowed us to draw the profile of each variant by using complementary methods (PairInt, hydrophobic potential, contact PCA). PairInt calculations highlighted the residues most involved in electrostatic interactions, which make a strong contribution to the binding with highly stable interactions between spike RBD and hACE2. Apolar contacts made a substantial and complementary contribution in Omicron with the detection of two hydrophobic patches. Contact networks and cross-correlation matrices were able to detect subtle changes at point mutations as the S375F mutation occurring in all Omicron variants, which is likely to confer an advantage in binding stability. This study brings new highlights on the dynamic binding of spike RBD to hACE2, which may explain the final persistence of Omicron over Delta.

Biochemical and molecular docking-based strategies of Acalypha indica and Boerhavia diffusa extract by targeting bacterial strains and cancer proteins

Antibiotic-resistant microbes have emerged around the world, presenting a risk to health. Plant-derived drugs have become a potential source for the production of antibiotic-resistant drugs and cancer therapies. In this study, we investigated the antibacterial, cytotoxic and antioxidant properties of Acalypha indica and Boerhavia diffusa, and conducted in silico molecular docking experiments against EGFR and VEGFR-2 proteins. The metabolic extract of A. indica inhibited Streptococcus iniae and Staphylococcus sciuri with inhibition zones of 21.66 ± 0.57 mm and 20.33 ± 0.57 mm, respectively. The B. diffusa leaf extract produced inhibition zones of 20.3333 ± 0.5773 mm and 20.33 ± 0.57 mm against Streptococcus iniae and Edwardsiella anguillarum, respectively. A. indica and B. diffusa extracts had toxicities of 162.01 μg/ml and 175.6 μg/ml, respectively. Moreover, B. diffusa (IC50 =154.42 µg/ml) leaf extract exhibited moderately higher antioxidant activity compared with the A. indica (IC50 = 218.97 µg/ml) leaf extract. Multiple interactions were observed at Leu694, Met769 and Leu820 sites for EGFR and at Asp1046 and Cys1045 sites for VEGFR during the molecular docking study. CID-235030, CID-70825 and CID-156619353 had binding energies of -7.6 kJ/mol, -7.5 kJ/mol and -7.6 kJ/mol, respectively, with EGFR protein. VEGFR-2 protein had docking energies of -7.5 kJ/mol, -7.6 kJ/mol and -7.3 kJ/mol, respectively, for CID-6420353, CID-156619353 and CID-70825 compounds. The MD simulation trajectories revealed the hit compound; CID-235030 and EGFR complex, CID-6420353 and VEGFR-2 exhibit stable profile in the root mean square deviation (RMSD), radius of gyration (Rg), solvent accessible surface area (SASA), hydrogen bond and root mean square fluctuation (RMSF) and the binding free energy by MM-PBSA method. This study indicates that methanol extracts of A. indica and B. diffusa may play a crucial role in developing antibiotic-resistant and cancer drugs.

Elucidating the protective mechanism of ganoderic acid DM on breast cancer based on network pharmacology and in vitro experimental validation

Ganoderma lucidum, a popular medicinal fungus, has been utilized to treat a variety of diseases. It possesses a unique therapeutic and pharmacological reputation in suppressing cancer/tumor progression, especially breast cancer, due to its embedded rich bioactive chemical constituents, mainly triterpenoids (ganoderic acids). The most prevalent malignant tumor in women with a high mortality and morbidity rate is breast cancer. Ganoderic acids A, D, DM, F, and H are evidenced in previous research to have breast cancer-preventive properties by exhibiting autophagic and apoptosis, anti-proliferative, and anti-angiogenesis effects. However, the anti-breast cancer mechanism remains unclear. The putative targets of the ganoderic acids were further determined using bioinformatics techniques and molecular docking calculation. Finally, the key targets were verified in vitro. A total of 53 potential target proteins associated with 202 pathways were predicted to be related to breast cancer. The potential targets were narrowed down to six key targets (AKT1, PIK3CA, epidermal growth factor receptor [EGFR], STAT1, ESR1, and CTNNB1), using different algorithms of the CytoHubba plugin, which were further validated using molecular docking analysis. The ganoderic acid DM (GADM) and the targets (PIK3CA and EGFR) with the strongest interactions were validated via MDA-MB-231 and MCF7 cells. The expression level of PIK3CA in both MDA-MB-231 and MCF7 cells was dose-dependently suppressed by GADM, whereas EGFR expression was unexpectedly increased, which warrants further investigation. These data indicated that the network pharmacology-based prediction of GADM targets for treating human breast cancer could be reliable.

Membrane protein structure determination from Paramagnetic Relaxation Enhancement and internuclear distance restraints

Magic angle spinning nuclear magnetic resonance (MAS NMR) is well suited for the determination of protein structure. The key structural information is obtained in the form of spectral cross peaks between spatially close nuclear spins, but assigning these cross peaks unambiguously to unique spin pairs is often a tedious task because of spectral overlap. Here, we use a seven-helical membrane protein Anabaena Sensory Rhodopsin (ASR) as a model system to demonstrate that transverse Paramagnetic Relaxation Enhancements (PRE) extracted from 2D MAS NMR spectra could be used to obtain a protein structural model. Starting with near complete assignments (93%) of ASR residues, TALOS + predicted backbone dihedral angles and secondary structure restraints in the form of backbone hydrogen bonds are combined with PRE-based restraints and used to generate a coarse model. This model is subsequently utilized as a template reference to facilitate automated assignments of highly ambiguous internuclear correlations. The template is used in an iterative cross peak assignment process and is progressively improved through the inclusion of disambiguated restraints, thereby converging to a low root-mean-square-deviation structural model. In addition to improving structure calculation conversion, the inclusion of PREs also improves packing between helices within an alpha-helical bundle.

A Hybrid Energy-Based and AI-Based Screening Approach for the Discovery of Novel Inhibitors of AXL

AXL, part of the TAM receptor tyrosine kinase family, plays a significant role in the growth and survival of various tissues and tumors, making it a critical target for cancer therapy. This study introduces a novel high-throughput virtual screening (HTVS) methodology that merges an AI-enhanced graph neural network, PLANET, with a geometric deep learning algorithm, DeepDock. Using this approach, we identified potent AXL inhibitors from our database. Notably, compound 9, with an IC50 of 9.378 nM, showed excellent inhibitory activity, suggesting its potential as a candidate for further research. We also performed molecular dynamics simulations to explore the interactions between compound 9 and AXL, providing insights for future enhancements. This hybrid screening method proves effective in finding promising AXL inhibitors, and advancing the development of new cancer therapies.

Genome-wide analyses of Mycobacterium tuberculosis complex isolates reveal insights into circulating lineages and drug resistance mutations in The Gambia

Tuberculosis (TB), caused by the Mycobacterium tuberculosis complex (MTBC), remains a pressing global health challenge, with the West African region, including The Gambia, experiencing a substantial burden. This study explores the genetic diversity of MTBC strains circulating in The Gambia for nearly two decades (2002-2021) to enhance understanding of drug resistance dynamics and inform targeted diagnostic and treatment strategies. Using whole-genome sequencing (WGS) data from 1,803 TB isolates, we identified the predominance of lineage 4 (L4, 67.2%) and lineage 6 (L6, 26.6%) strains, with L4 showing more significant genetic variability over time. Drug susceptibility analysis of these isolates revealed that 78% (1421 isolates) were drug-susceptible, while 6.5% (119 isolates) exhibited resistance, primarily to isoniazid, rifampicin, and their combination. Additionally, 15.5% (282 isolates) were classified as Other, having potential drug-resistance mutations of uncertain significance by the WHO catalogue. Interestingly, our resistance-associated analysis showed the lineage 6 specific ethambutol uncertain significance (by WHO catalogue) mutation (embC Ala307Thr) more prevalent in The Gambia than in West Africa and globally. Structural analysis showed that first-line drug resistance mutations frequently occur in solvent-inaccessible and conserved regions of proteins, often impacting protein stability and reflecting a balance between resistance, fitness, and evolutionary adaptation. This study highlights the coexistence of globally prevalent and regionally restricted MTBC lineages, underscoring the importance of region-specific TB control measures. Integrating bioinformatic and structural analyses revealed many uncertain significant mutations by the WHO catalogue in The Gambian isolates compared to West Africa and globally. These findings reinforce the necessity of continuous genomic surveillance to address the evolving challenges of TB in high-burden settings like West Africa.

Exploring the mechanism of action of Phyllanthus emblica in the treatment of epilepsy based on network pharmacology and molecular docking

This study explores the mechanism of Phyllanthus emblica in treating epilepsy (EP) through network pharmacology and molecular docking. The Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform identified the chemical composition of P emblica, Swiss ADME screened active components, and Swiss Target Prediction predicted targets. EP-related targets were identified using Gene Cards, OMIM, Drug Bank, TTD, and DisGeNET, and Venny 2.1.0 was used to find intersecting targets. Protein-protein interaction network analysis was conducted with STRING and Cytoscape. Chem 3D and Pymol were used for structural optimization and molecular docking was performed with AutoDock Tools 1.5.7 and Vina. Fifty-three active components and 126 intersecting targets were identified. Gene Ontology analysis revealed 3416 biological processes, 287 cellular components, and 457 molecular functions. Kyoto Encyclopedia of Genes and Genomes pathways showed neuroactive ligand-receptor interactions, nitrogen metabolism, and serotonergic synapses as key pathways. Molecular docking indicated strong binding energies between P emblica core components and targets, especially 2-ethylhexyl ester with MAPK3, luteolin with SRC, and kaempferol with MAPK1. This study explores the therapeutic potential of P emblica in treating EP through network pharmacology and molecular docking. A total of 53 active components were identified, with key compounds like 2-ethylhexyl ester, phyllanthin, luteolin, and kaempferol targeting critical proteins such as SRC, AKT1, APP, MAPK3, and MAPK1. These targets are involved in pathways related to synaptic transmission, oxidative stress, and inflammation, indicating potential neuroprotective and anti-inflammatory effects. Gene Ontology analysis highlighted the regulation of synaptic activity, while Kyoto Encyclopedia of Genes and Genomes pathway analysis emphasized pathways like neuroactive ligand-receptor interactions and serotonergic synapses. Molecular docking demonstrated strong binding affinities between active components and core targets, supporting the effectiveness of P emblica in modulating neuronal excitability and reducing neuroinflammation. These findings provide a theoretical basis for its clinical application in EP management.

AlphaMut: A Deep Reinforcement Learning Model to Suggest Helix-Disrupting Mutations

Helices are important secondary structural motifs within proteins and are pivotal in numerous physiological processes. While amino acids (AA) such as alanine and leucine are known to promote helix formation, proline and glycine disfavor it. Helical structure formation, however, also depends on its environment, and hence, prior prediction of a mutational effect on a helical structure is difficult. Here, we employ a reinforcement learning algorithm to develop a predictive model for helix-disrupting mutations. We start with a model to disrupt helices independent of their protein environment. Our results show that only a few mutations lead to a drastic disruption of the target helix. We further extend our approach to helices in proteins and validate the results using rigorous free energy calculations. Our strategy identifies amino acids crucial for maintaining structural integrity and predicts key mutations that could alter protein structure. Through our work, we present a new use case for reinforcement learning in protein structure disruption.

Exploring the Mechanisms of Sanguinarine in the Treatment of Osteoporosis by Integrating Network Pharmacology Analysis and Deep Learning Technology

BACKGROUND

Sanguinarine (SAN) has been reported to have antioxidant, antiinflammatory, and antimicrobial activities with potential for the treatment of osteoporosis (OP).

OBJECTIVE

This work purposed to unravel the molecular mechanisms of SAN in the treatment of OP.

METHODS

OP-related genes and SAN-related targets were predicted from public databases. Differential expression analysis and VennDiagram were adopted to detect SAN-related targets against OP. Protein-protein interaction (PPI) network was served for core target identification. Molecular docking and DeepPurpose algorithm were further adopted to investigate the binding ability between core targets and SAN. Gene pathway scoring of these targets was calculated utilizing gene set variation analysis (GSVA). Finally, we explored the effect of SAN on the expressions of core targets in preosteoblastic MC3T3-E1 cells.

RESULTS

A total of 21 candidate targets of SAN against OP were acquired. Furthermore, six core targets were identified, among which CASP3, CTNNB1, and ERBB2 were remarkably differentially expressed in OP and healthy individuals. The binding energies of SAN with CASP3, CTNNB1, and ERBB2 were -6, -6.731, and -7.162 kcal/mol, respectively. Moreover, the GSVA scores of the Wnt/calcium signaling pathway were significantly lower in OP cases than in healthy individuals. In addition, the expression of CASP3 was positively associated with Wnt/calcium signaling pathway. CASP3 and ERBB2 were significantly lower expressed in SAN group than in DMSO group, whereas the expression of CTNNB1 was in contrast.

CONCLUSION

CASP3, CTNNB1, and ERBB2 emerge as potential targets of SAN in OP prevention and treatment.

Insight into Protein Engineering: From In silico Modelling to In vitro Synthesis

Protein engineering alters the polypeptide chain to obtain a novel protein with improved functional properties. This field constantly evolves with advanced in silico tools and techniques to design novel proteins and peptides. Rational incorporating mutations, unnatural amino acids, and post-translational modifications increases the applications of engineered proteins and peptides. It aids in developing drugs with maximum efficacy and minimum side effects. Currently, the engineering of peptides is gaining attention due to their high stability, binding specificity, less immunogenic, and reduced toxicity properties. Engineered peptides are potent candidates for drug development due to their high specificity and low cost of production compared with other biologics, including proteins and antibodies. Therefore, understanding the current perception of designing and engineering peptides with the help of currently available in silico tools is crucial. This review extensively studies various in silico tools available for protein engineering in the prospect of designing peptides as therapeutics, followed by in vitro aspects. Moreover, a discussion on the chemical synthesis and purification of peptides, a case study, and challenges are also incorporated.

Advances and Challenges in Antiviral Development for Respiratory Viruses

The development of antivirals for respiratory viruses has advanced markedly in response to the growing threat of pathogens such as Influenzavirus (IAV), respiratory syncytial virus (RSV), and SARS-CoV-2. This article reviews the advances and challenges in this field, highlighting therapeutic strategies that target critical stages of the viral replication cycle, including inhibitors of viral entry, replication, and assembly. In addition, innovative approaches such as inhibiting host cellular proteins to reduce viral resistance and repurposing existing drugs are explored, using advanced bioinformatics tools that optimize the identification of antiviral candidates. The analysis also covers emerging technologies such as nanomedicine and CRISPR gene editing, which promise to improve the stability and efficacy of treatments. While current antivirals offer valuable options, they face challenges such as viral evolution and the need for accessible treatments for vulnerable populations. This article underscores the importance of continued innovation in biotechnology to overcome these limitations and provide safe and effective treatments. Combining traditional and advanced approaches in developing antivirals is essential in order to address respiratory viral diseases that affect global health.

RankMHC: Learning to Rank Class-I Peptide-MHC Structural Models

The binding of peptides to class-I Major Histocompability Complex (MHC) receptors and their subsequent recognition downstream by T-cell receptors are crucial processes for most multicellular organisms to be able to fight various diseases. Thus, the identification of peptide antigens that can elicit an immune response is of immense importance for developing successful therapies for bacterial and viral infections, even cancer. Recently, studies have demonstrated the importance of peptide-MHC (pMHC) structural analysis, with pMHC structural modeling methods gradually becoming more popular in peptide antigen identification workflows. Most of the pMHC structural modeling tools provide an ensemble of candidate peptide poses in the MHC-I cleft, each associated with a score stemming from a scoring function, with the top scoring pose assumed to be the most representative of the ensemble. However, identifying the binding mode, that is, the peptide pose from the ensemble that is closer to an unavailable native structure, is not trivial. Oftentimes, the peptide poses characterized as best by a protein-ligand scoring function are not the ones that are the most representative of the actual structure. In this work, we frame the peptide binding pose identification problem as a Learning-to-Rank (LTR) problem. We present RankMHC, an LTR-based pMHC binding mode identification predictor, which is specifically trained to predict the most accurate ranking of an ensemble of pMHC conformations. RankMHC outperforms classical peptide-ligand scoring functions, as well as previous Machine Learning (ML)-based binding pose predictors. We further demonstrate that RankMHC can be used with many pMHC structural modeling tools that use different structural modeling protocols.

Exploring host epigenetic enzymes as targeted therapies for visceral leishmaniasis: in silico design and in vitro efficacy of KDM6B and ASH1L inhibitors

In order to combat various infectious diseases, the utilization of host-directed therapies as an alternative to chemotherapy has gained a lot of attention in the recent past, since it bypasses the existing limitations of conventional therapies. The use of host epigenetic enzymes like histone lysine methyltransferases and lysine demethylases as potential drug targets has successfully been employed for controlling various inflammatory diseases like rheumatoid arthritis and acute leukemia. In our earlier study, we have already shown that the functional knockdown of KDM6B and ASH1L in the experimental model of visceral leishmaniasis has resulted in a significant reduction of organ parasite burden. Herein, we performed a high throughput virtual screening against KDM6B and ASH1L using > 53,000 compounds that were obtained from the Maybridge library and PubChem Database, followed by molecular docking to evaluate their docking score/Glide Gscore. Based on their docking scores, the selected inhibitors were later assessed for their in vitro anti-leishmanial efficacy. Out of all inhibitors designed against KDM6B and ASH1L, HTS09796, GSK-J4 and AS-99 particularly showed promising in vitro activity with IC50 < 5 µM against both extracellular promastigote and intracellular amastigote forms of L. donovani. In vitro drug interaction studies of these inhibitors further demonstrated their synergistic interaction with amphotericin-B and miltefosine. However, GSK-J4 makes an exception by displaying an in different mode of interaction with miltefosine. Collectively, our in silico and in vitro studies acted as a platform to identify the applicability of these inhibitors targeted against KDM6B and ASH1L for anti-leishmanial therapy.

Molecular mechanisms underlying Tao-Hong-Si-Wu decoction treating hyperpigmentation based on network pharmacology, Mendelian randomization analysis, and experimental verification

CONTEXT

Hyperpigmentation, a common skin condition marked by excessive melanin production, currently has limited effective treatment options.

OBJECTIVE

This study explores the effects of Tao-Hong-Si-Wu decoction (THSWD) on hyperpigmentation and to elucidate the underlying mechanisms.

MATERIALS AND METHODS

We employed network pharmacology, Mendelian randomization, and molecular docking to identify THSWD's hub targets and mechanisms against hyperpigmentation. The Cell Counting Kit-8 (CCK-8) assay determined suitable THSWD treatment concentrations for PIG1 cells. These cells were exposed to graded concentrations of THSWD-containing serum (2.5%, 5%, 10%, 15%, 20%, 30%, 40%, and 50%) and treated with α-MSH (100 nM) to induce an in vitro hyperpigmentation model. Assessments included melanin content, tyrosinase activity, and Western blotting.

RESULTS

ALB, IL6, and MAPK3 emerged as primary targets, while quercetin, apigenin, and luteolin were the core active ingredients. The CCK-8 assay indicated that concentrations between 2.5% and 20% were suitable for PIG1 cells, with a 50% cytotoxicity concentration (CC50) of 32.14%. THSWD treatment significantly reduced melanin content and tyrosinase activity in α-MSH-induced PIG1 cells, along with downregulating MC1R and MITF expression. THSWD increased ALB and p-MAPK3/MAPK3 levels and decreased IL6 expression in the model cells.

DISCUSSION AND CONCLUSION

THSWD mitigates hyperpigmentation by targeting ALB, IL6, and MAPK3. This study paves the way for clinical applications of THSWD as a novel treatment for hyperpigmentation and offers new targeted therapeutic strategies.

DigFrag as a digital fragmentation method used for artificial intelligence-based drug design

Fragment-Based Drug Design (FBDD) plays a pivotal role in the field of drug discovery and development. The construction of high-quality fragment libraries is a critical step in FBDD. Conventional fragmentation approaches often rely on rigid rules and chemical intuition, limiting their adaptability to diverse molecular structures. The rapid development of Artificial Intelligence (AI) technology offers a transformative opportunity to rethink traditional methods. Here, we present DigFrag, a digital fragmentation method that highlights important substructures by focusing locally within the molecular graph. In addition, we feed the fragments segmented by machine intelligence and human expertise into the deep generative model to compare the preference for data from different sources. Experimental results show that the structural diversity of fragments segmented by DigFrag is higher, and more desirable compounds are generated based on these fragments. These results also demonstrate that data generated based on AI methods may be more suitable for AI models. Moreover, a user-friendly platform called MolFrag ( https://dpai.ccnu.edu.cn/MolFrag/ ) is developed based on various fragmentation techniques to support molecular segmentation.

Discovery of Therapeutic Antibodies Targeting Complex Multi-Spanning Membrane Proteins

Complex integral membrane proteins, which are embedded in the cell surface lipid bilayer by multiple transmembrane spanning polypeptides, encompass families of proteins that are important target classes for drug discovery. These protein families include G protein-coupled receptors, ion channels, transporters, enzymes, and adhesion molecules. The high specificity of monoclonal antibodies and the ability to engineer their properties offers a significant opportunity to selectively bind these target proteins, allowing direct modulation of pharmacology or enabling other mechanisms of action such as cell killing. Isolation of antibodies that bind these types of membrane proteins and exhibit the desired pharmacological function has, however, remained challenging due to technical issues in preparing membrane protein antigens suitable for enabling and driving antibody drug discovery strategies. In this article, we review progress and emerging themes in defining discovery strategies for a generation of antibodies that target these complex membrane protein antigens. We also comment on how this field may develop with the emerging implementation of computational techniques, artificial intelligence, and machine learning.

The mechanism of quercetin in treating intracerebral hemorrhage was investigated by network pharmacology and molecular docking

BACKGROUND

The aim of this study was to explore the molecular mechanism of quercetin in the treatment of intracerebral hemorrhage.

METHODS

Quercetin target genes and intracerebral hemorrhage target genes were collected from 5 databases. After standardized conversion of the obtained target genes through uniprot database, cross genes of the 2 were obtained using Venny 2.1 online tool. Further, protein interaction relationships were obtained in the String database, and then core target genes were screened and visualized by Cytoscape software, and cross genes were enriched by GO and KEGG pathways. Finally, the active drug ingredients and target proteins were verified and visualized by computer.

RESULTS

In this study, 197 quercetin targets were identified as potential targets for the treatment of intracerebral hemorrhage, and 7 core target genes (TP53, STAT3, AKT1, SRC, JUN, TNF, and IL6) were screened. The GO and KEGG analyses further shed light on the molecular mechanisms underlying quercetin's treatment of intracerebral hemorrhage, involving multiple biological processes and signaling pathways (such as cancer pathways, lipids, and atherosclerosis). The stable binding of quercetin to these 7 key targets was confirmed by molecular docking simulation.

CONCLUSION

Quercetin may treat intracerebral hemorrhage through multi-target-multi-pathway mechanisms, including regulating apoptosis, inhibiting inflammatory response, inhibiting iron death, and regulating angiogenesis, which can help alleviate nerve damage caused by intracerebral hemorrhage.

Comprehensive detection and characterization of human druggable pockets through binding site descriptors

Druggable pockets are protein regions that have the ability to bind organic small molecules, and their characterization is essential in target-based drug discovery. However, deriving pocket descriptors is challenging and existing strategies are often limited in applicability. We introduce PocketVec, an approach to generate pocket descriptors via inverse virtual screening of lead-like molecules. PocketVec performs comparably to leading methodologies while addressing key limitations. Additionally, we systematically search for druggable pockets in the human proteome, using experimentally determined structures and AlphaFold2 models, identifying over 32,000 binding sites across 20,000 protein domains. We then generate PocketVec descriptors for each site and conduct an extensive similarity search, exploring over 1.2 billion pairwise comparisons. Our results reveal druggable pocket similarities not detected by structure- or sequence-based methods, uncovering clusters of similar pockets in proteins lacking crystallized inhibitors and opening the door to strategies for prioritizing chemical probe development to explore the druggable space.

Advancing rational pesticide development against Drosophila suzukii: bioinformatics tools and applications-a systematic review

CONTEXT

Drosophila suzukii (Matsumura, 1931) is a widespread agricultural pest responsible for significant damage to various soft-skinned fruit hosts. The revolutionary potential of bioinformatics in agriculture emerges from its ability to provide extensive information on pests, fungi, chemical resistance, implications of non-target species, and other critical aspects. This wealth of information allows researchers to engage in projects and applied research in diverse agricultural domains that face these challenges. In this context, bioinformatics tools play a fundamental role. The negative impact of pests on crops, resulting in substantial economic losses, has highlighted the importance of in silico methods.

METHODS

To achieve this, we conducted a systematic search in scientific databases using as keywords "Drosophila suzukii," "biopesticides," "simulations computational," and "in-silico." After applying the filters of relevance and publication date, we organized the articles and prioritized those that directly addressed that matched the keywords and the use of bioinformatics tools. Additionally, we included studies focusing on in silico assays of biopesticides, such as molecular docking. Our review aimed to present a collection of recent literature on biopesticides against Drosophila suzukii, emphasizing bioinformatics methods. Through this work, we strive to contribute to the literature of new perspectives on the development and efficiency of biopesticides, along with to advance research that may improve pest control strategies.

RESULTS

In the results of the systematic review, we found 2734 articles related to the selected keywords. Six of these articles directly address Drosophila suzukii and the use of bioinformatics tools in the search for alternatives in pest control. In the selected studies, we observed that two articles tend to focus on phylogenetic approaches, searching for gene sequences, amino acids, and constructing phylogenetic trees. The other three articles used molecular modeling and docking of receptors such as GABA and TRP with plant-derived and synthetic compounds to study intermolecular interactions. However, we identified gaps in these studies that could lead to further research in the biorational development of biopesticides using bioinformatics tools.

Integrated network pharmacology and molecular docking to investigate the potential mechanism of Tufuling on Alzheimer's disease

Objective

This study aimed to investigate the mechanism of Tu Fu Ling in treating Alzheimer's disease (AD) using network pharmacology and molecular docking.

Methods

The TCMSP and Swiss target prediction databases were utilized to confirm the active components of Tu Fu Ling and their corresponding targets, with target gene names converted using the UniProt database. Genes related to AD were collected from DisGeNET, GeneCards, and the Open Target Platform databases. Common target genes between the disease and the drug were obtained using Venny 2.1 tools and visualized using Cytoscape software. Protein-protein interaction (PPI) data were further analyzed to determine correlations between common target genes, and GO and KEGG pathway enrichment analyses were performed for intersecting genes. Finally, PYmol, AutoDock Tool, Discovery Studio 2020, and PyRx software were used for preliminary computer virtual verification and visualization of active drug ingredients and target proteins.

Results

Nine active ingredients meeting the screening criteria yielded a total of 168 genes after removing duplicates. A total of 3833 target genes were collected, with 129 overlapping target genes identified. GO enrichment analysis identified 643 biological processes, 82 cellular components, and 147 molecular functions. KEGG pathway enrichment analysis also revealed a pathway closely related to AD (hsa05010: Alzheimer's disease). In molecular docking analysis, the binding affinity between the 9 active ingredients and 10 core targets ranged from -3.5 to -12.3 kcal/mol, indicating strong binding.

Conclusion

This study preliminarily verified the combination of Tu Fu Ling's screened active ingredient and the calculated core target, suggesting a potential mechanism of action to improve the symptoms of AD patients through multi-target and multi-pathway approaches. This provides a valuable reference for further exploration of the pharmacological mechanism of AD and the formulation of drug therapy.

GTalign: spatial index-driven protein structure alignment, superposition, and search

With protein databases growing rapidly due to advances in structural and computational biology, the ability to accurately align and rapidly search protein structures has become essential for biological research. In response to the challenge posed by vast protein structure repositories, GTalign offers an innovative solution to protein structure alignment and search-an algorithm that achieves optimal superposition at high speeds. Through the design and implementation of spatial structure indexing, GTalign parallelizes all stages of superposition search across residues and protein structure pairs, yielding rapid identification of optimal superpositions. Rigorous evaluation across diverse datasets reveals GTalign as the most accurate among structure aligners while presenting orders of magnitude in speedup at state-of-the-art accuracy. GTalign's high speed and accuracy make it useful for numerous applications, including functional inference, evolutionary analyses, protein design, and drug discovery, contributing to advancing understanding of protein structure and function.

Investigation of the mechanism of baicalein in the treatment of periodontitis based on network pharmacology, molecular docking and experimental validation

PURPOSE

To verify the effect and mechanism of baicalein in the treatment of periodontitis through network pharmacology, molecular docking and in vitro experiments.

METHODS

Firstly, multiple databases were used to predict targets of baicalein and periodontitis. And the screened key target genes of baicalein for treating periodontitis were subjected to GO and KEGG analysis; then these targets were analyzed by molecular docking techniques. In vitro experiments including CCK-8, RT-qPCR, ELISA and Immunofluorescence were conducted to validate the efficacy of baicalein in treating periodontitis.

RESULTS

Seventeen key targets were screened from the databases, GO and KEGG analysis of these targets revealed that baicalein may exert therapeutic effects through regulating TNF, PI3K-Akt, HIF-1 and other signaling pathways. Molecular docking analysis showed that baicalein has good binding potential to several targets. In vitro cellular assays showed that baicalein inhibited the expression of TNF-α, MMP-9, IL-6 and MCP1 in P.g-LPS-induced macrophages at both the mRNA and protein level. And the immunofluorescence intensity of iNOS, a marker of M1 type macrophages, which mainly secretes inflammatory factors, was significantly reduced.

CONCLUSION

Baicalein has the characteristics and advantages of "multicomponent, multitarget, and multipathway" in the treatment of periodontitis. In vitro cellular assays further confirmed the inhibitory effect of baicalein on the secretion of inflammatory factors of macrophages in periodontitis models, providing a theoretical basis for further study of the material basis and molecular mechanism of baicalein in the treatment of periodontal diseases.

Structural characterization of tyrosinases and an update on human enzymes

Tyrosinase, a pivotal enzyme in melanin biosynthesis, orchestrates the pigmentation process in humans, affecting skin, hair, and eye color. This chapter examines the three-dimensional structure and functional aspects of tyrosinases from various sources, highlighting their di-metal ion coordination crucial for catalytic activity. I explore the biochemical pathwayscheme catalyzed by tyrosinase, specifically the oxidation of L-tyrosine to L-dopaquinone, a precursor in melanin synthesis. Detailed structural analyses, including 3D structures obtained from X-ray crystallography and computational modeling, reveal key insights into the enzyme's active site, variations among tyrosinases, and substrate binding mechanisms. Furthermore, the chapter investigates the role of human tyrosinase variants, their inhibitors, essential for developing therapeutic and cosmetic applications targeting hyperpigmentation disorders. Structural characterizations of tyrosinase-inhibitor complexes provide a foundation for designing effective inhibitors, with compounds like kojic acid, L-mimosine, and (S)-3-amino-tyrosine demonstrating significant inhibitory potential. This comprehensive examination of the structure, function, and inhibition mechanisms of tyrosinase offers avenues for innovative treatments in biotechnology, health, and beyond.

Deep learning large-scale drug discovery and repurposing

Large-scale drug discovery and repurposing is challenging. Identifying the mechanism of action (MOA) is crucial, yet current approaches are costly and low-throughput. Here we present an approach for MOA identification by profiling changes in mitochondrial phenotypes. By temporally imaging mitochondrial morphology and membrane potential, we established a pipeline for monitoring time-resolved mitochondrial images, resulting in a dataset comprising 570,096 single-cell images of cells exposed to 1,068 United States Food and Drug Administration-approved drugs. A deep learning model named MitoReID, using a re-identification (ReID) framework and an Inflated 3D ResNet backbone, was developed. It achieved 76.32% Rank-1 and 65.92% mean average precision on the testing set and successfully identified the MOAs for six untrained drugs on the basis of mitochondrial phenotype. Furthermore, MitoReID identified cyclooxygenase-2 inhibition as the MOA of the natural compound epicatechin in tea, which was successfully validated in vitro. Our approach thus provides an automated and cost-effective alternative for target identification that could accelerate large-scale drug discovery and repurposing.

Exploration of Berberine Against Ulcerative Colitis via TLR4/NF-κB/HIF-1α Pathway by Bioinformatics and Experimental Validation

Purpose

This study aimed to delineate the molecular processes underlying the therapeutic effects of berberine on UC by employing network pharmacology tactics, molecular docking, and dynamic simulations supported by empirical validations both in vivo and in vitro.

Patients and Methods

We systematically screened potential targets and relevant pathways affected by berberine for UC treatment from comprehensive databases, including GeneCards, DisGeNET, and GEO. Molecular docking and simulation protocols were used to assess the interaction stability between berberine and its principal targets. The predictions were validated using both a DSS-induced UC mouse model and a lipopolysaccharide (LPS)-stimulated NCM460 cellular inflammation model.

Results

Network pharmacology analysis revealed the regulatory effect of the TLR4/NF-κB/HIF-1α pathway in the ameliorative action of berberine in UC. Docking and simulation studies predicted the high-affinity interactions of berberine with pivotal targets: TLR4, NF-κB, HIF-1α, and the HIF inhibitor KC7F2. Moreover, in vivo analyses demonstrated that berberine attenuates clinical severity, as reflected by decreased disease activity index (DAI) scores, reduced weight loss, and mitigated intestinal inflammation in DSS-challenged mice. These outcomes include suppression of the proinflammatory cytokines IL-6 and TNF-α and downregulation of TLR4/NF-κB/HIF-1α mRNA and protein levels. Correspondingly, in vitro findings indicate that berberine decreases cellular inflammatory injury and suppresses TLR4/NF-κB/HIF-1α signaling, with notable effectiveness similar to that of the HIF-1α inhibitor KC7F2.

Conclusion

Through network pharmacology analysis and experimental substantiation, this study confirmed that berberine enhances UC treatment outcomes by inhibiting the TLR4/NF-κB/HIF-1α axis, thereby mitigating inflammatory reactions and improving colonic pathology.

Impact of structural biology and the protein data bank on us fda new drug approvals of low molecular weight antineoplastic agents 2019-2023

Open access to three-dimensional atomic-level biostructure information from the Protein Data Bank (PDB) facilitated discovery/development of 100% of the 34 new low molecular weight, protein-targeted, antineoplastic agents approved by the US FDA 2019-2023. Analyses of PDB holdings, the scientific literature, and related documents for each drug-target combination revealed that the impact of structural biologists and public-domain 3D biostructure data was broad and substantial, ranging from understanding target biology (100% of all drug targets), to identifying a given target as likely druggable (100% of all targets), to structure-guided drug discovery (>80% of all new small-molecule drugs, made up of 50% confirmed and >30% probable cases). In addition to aggregate impact assessments, illustrative case studies are presented for six first-in-class small-molecule anti-cancer drugs, including a selective inhibitor of nuclear export targeting Exportin 1 (selinexor, Xpovio), an ATP-competitive CSF-1R receptor tyrosine kinase inhibitor (pexidartinib,Turalia), a non-ATP-competitive inhibitor of the BCR-Abl fusion protein targeting the myristoyl binding pocket within the kinase catalytic domain of Abl (asciminib, Scemblix), a covalently-acting G12C KRAS inhibitor (sotorasib, Lumakras or Lumykras), an EZH2 methyltransferase inhibitor (tazemostat, Tazverik), and an agent targeting the basic-Helix-Loop-Helix transcription factor HIF-2α (belzutifan, Welireg).

Foundation models in molecular biology

Determining correlations between molecules at various levels is an important topic in molecular biology. Large language models have demonstrated a remarkable ability to capture correlations from large amounts of data in the field of natural language processing as well as image generation, and correlations captured from data using large language models can also be applicable to solving a wide range of specific tasks, hence large language models are also referred to as foundation models. The massive amount of data that exists in the field of molecular biology provides an excellent basis for the development of foundation models, and the recent emergence of foundation models in the field of molecular biology has really pushed the entire field forward. We summarize the foundation models developed based on RNA sequence data, DNA sequence data, protein sequence data, single-cell transcriptome data, and spatial transcriptome data respectively, and further discuss the research directions for the development of foundation models in molecular biology.

PDB NextGen Archive: centralizing access to integrated annotations and enriched structural information by the Worldwide Protein Data Bank

The Protein Data Bank (PDB) is the global repository for public-domain experimentally determined 3D biomolecular structural information. The archival nature of the PDB presents certain challenges pertaining to updating or adding associated annotations from trusted external biodata resources. While each Worldwide PDB (wwPDB) partner has made best efforts to provide up-to-date external annotations, accessing and integrating information from disparate wwPDB data centers can be an involved process. To address this issue, the wwPDB has established the PDB Next Generation (or NextGen) Archive, developed to centralize and streamline access to enriched structural annotations from wwPDB partners and trusted external sources. At present, the NextGen Archive provides mappings between experimentally determined 3D structures of proteins and UniProt amino acid sequences, domain annotations from Pfam, SCOP2 and CATH databases and intra-molecular connectivity information. Since launch, the PDB NextGen Archive has seen substantial user engagement with over 3.5 million data file downloads, ensuring researchers have access to accurate, up-to-date and easily accessible structural annotations. Database URL: http://www.wwpdb.org/ftp/pdb-nextgen-archive-site.

Study on therapeutic mechanism of total salvianolic acids against myocardial ischemia-reperfusion injury based on network pharmacology, molecular docking, and experimental study

ETHNOPHARMACOLOGICAL RELEVANCE

Radix Salviae miltiorrhizae, also known as Danshen in Chinese, effectively activates the blood and resolves stasis. Total salvianolic acids (SA) is the main active ingredient of Danshen, and related preparations, such as salvianolate injection are commonly used clinically to treat myocardial ischemia-reperfusion injury (MIRI). However, the potential targets and key active ingredients of SA have not been sufficiently investigated.

AIM OF THE STUDY

This study aimed to investigate the mechanism of action of SA in treating MIRI.

MATERIALS AND METHODS

Network pharmacology and molecular docking techniques were used to predict SA targets against MIRI. The key acting pathway of SA were validated by performing experiments in a rat MIRI model.

RESULTS

Twenty potential ingredients and 54 targets of SA in treating MIRI were identified. Ingredient-target-pathway network analysis revealed that salvianolic acid B and rosmarinic acid had the highest degree value. Pathway enrichment analysis showed that SA may regulate MIRI through the IL-17 signaling pathway, and this result was confirmed in the rat MIRI experiment.

CONCLUSION

The results of this study indicate that SA may protect MIRI by regulating the IL-17 pathway.

Structural and mechanistic insights into Quinolone Synthase to address its functional promiscuity

Quinolone synthase from Aegle marmelos (AmQNS) is a type III polyketide synthase that yields therapeutically effective quinolone and acridone compounds. Addressing the structural and molecular underpinnings of AmQNS and its substrate interaction in terms of its high selectivity and specificity can aid in the development of numerous novel compounds. This paper presents a high-resolution AmQNS crystal structure and explains its mechanistic role in synthetic selectivity. Additionally, we provide a model framework to comprehend structural constraints on ketide insertion and postulate that AmQNS's steric and electrostatic selectivity plays a role in its ability to bind to various core substrates, resulting in its synthetic diversity. AmQNS prefers quinolone synthesis and can accommodate large substrates because of its wide active site entrance. However, our research suggests that acridone is exclusively synthesized in the presence of high malonyl-CoA concentrations. Potential implications of functionally relevant residue mutations were also investigated, which will assist in harnessing the benefits of mutations for targeted polyketide production. The pharmaceutical industry stands to gain from these findings as they expand the pool of potential drug candidates, and these methodologies can also be applied to additional promising enzymes.

AlphaFold2 modeling and molecular dynamics simulations of an intrinsically disordered protein

We use AlphaFold2 (AF2) to model the monomer and dimer structures of an intrinsically disordered protein (IDP), Nvjp-1, assisted by molecular dynamics (MD) simulations. We observe relatively rigid dimeric structures of Nvjp-1 when compared with the monomer structures. We suggest that protein conformations from multiple AF2 models and those from MD trajectories exhibit a coherent trend: the conformations of an IDP are deviated from each other and the conformations of a well-folded protein are consistent with each other. We use a residue-residue interaction network (RIN) derived from the contact map which show that the residue-residue interactions in Nvjp-1 are mainly transient; however, those in a well-folded protein are mainly persistent. Despite the variation in 3D shapes, we show that the AF2 models of both disordered and ordered proteins exhibit highly consistent profiles of the pLDDT (predicted local distance difference test) scores. These results indicate a potential protocol to justify the IDPs based on multiple AF2 models and MD simulations.

Discovery of the small molecular inhibitors against sclerostin loop3 as potential anti-osteoporosis agents by structural based virtual screening and molecular design

Sclerostin is a secreted glycoprotein that expresses predominantly in osteocytes and inhibits bone formation by antagonizing the Wnt/β-catenin signaling pathway, and the loop3 region of sclerostin has recently discovered as a novel therapeutic target for bone anabolic treatment without increasing cardiovascular risk. Herein, we used a structural based virtual screening to search for small molecular inhibitors selectively targeting sclerostin loop3. A novel natural product hit ZINC4228235 (THFA) was identified as the sclerostin loop3-selective inhibitor with a Kd value of 42.43 nM against sclerostin loop3. The simplification and derivation of THFA using molecular modeling-guided modification allowed the discovery of an effective and loop3-selective small molecular inhibitor, compound (4-(3-acetamidoprop-1-yn-1-yl)benzoyl)glycine (AACA), with improved binding affinity (Kd = 15.4 nM) compared to the hit THFA. Further in-vitro experiment revealed that compound AACA could attenuate the suppressive effect of transfected sclerostin on Wnt signaling and bone formation. These results make AACA as a potential candidate for development of anti-osteoporosis agents without increasing cardiovascular risk.

Investigating the Protein-Based Therapeutic Relationship between Honey Protein SHP-60 and Bevacizumab on Angiogenesis: Exploring the Synergistic Effect through In Vitro and In Silico Analysis

Honey is a natural product produced by honeybees, which has been used not only as food but also as a medicine by humans for thousands of years. In this study, 60 kDa protein was purified from Pakistani Sidr honey named as SHP-60 (Sidr Honey Protein-60), and its antioxidant potential and the effect of Bevacizumab with purified protein on in vitro angiogenesis using human umbilical vein endothelial cells (HUVEC) were investigated. We further validated the molecular protein-protein (SHP-60 with Bevacizumab) interactions through in silico analysis. It showed very promising antioxidant activity by reducing 2,2-diphenyl-1-picrylhydrazyl free radicals with a maximum of 83% inhibition at 50 μM and an IC50 of 26.45 μM statistically significant (**p < 0.01). Angiogenesis is considered a hallmark of cancer, and without it, the tumor cannot grow or metastasize. Bevacizumab, SHP-60, and both in combination were used to treat HUVEC, and the MTT assay was used to assess cell viability. To demonstrate in vitro angiogenesis, HUVEC was grown on Geltrex, and the formation of endotubes was examined using a tube formation assay. HUVEC viability was dose-dependently decreased by Bevacizumab, SHP-60, and both together. Bevacizumab and SHP-60 both inhibited angiogenesis in vitro, and their combination displayed levels of inhibition even higher than those of Bevacizumab alone. We investigated the protein-protein molecular docking interactions and molecular dynamics simulation analysis of MRJP3 (major royal jelly protein 3) similar to SHP-60 in molecular weight with both the heavy chain (HC) and light chain (LC) of Bevacizumab. We found significant interactions between the LC and MRJP3, indicating that ASN468, GLN470, and ASN473 of MRJP3 interact with SER156, SER159, and GLU161 of LC of Bevacizumab. The integration of experimental data and computational techniques is believed to improve the reliability of the findings and aid in future drug design.

In silico drug design strategies for discovering novel tuberculosis therapeutics

INTRODUCTION

Tuberculosis remains a significant concern in global public health due to its intricate biology and propensity for developing antibiotic resistance. Discovering new drugs is a protracted and expensive endeavor, often spanning over a decade and incurring costs in the billions. However, computer-aided drug design (CADD) has surfaced as a nimbler and more cost-effective alternative. CADD tools enable us to decipher the interactions between therapeutic targets and novel drugs, making them invaluable in the quest for new tuberculosis treatments.

AREAS COVERED

In this review, the authors explore recent advancements in tuberculosis drug discovery enabled by in silico tools. The main objectives of this review article are to highlight emerging drug candidates identified through in silico methods and to provide an update on the therapeutic targets associated with Mycobacterium tuberculosis.

EXPERT OPINION

These in silico methods have not only streamlined the drug discovery process but also opened up new horizons for finding novel drug candidates and repositioning existing ones. The continued advancements in these fields hold great promise for more efficient, ethical, and successful drug development in the future.

In silico investigation of potential phytoconstituents against ligand- and voltage-gated ion channels as antiepileptic agents

The most promising anticonvulsant phytocompounds were explored in this work using docking, molecular dynamic (MD) simulation, and Molecular Mechanics-Poisson-Boltzmann Surface Area (MM-PBSA) approaches. A total of 70 phytochemicals were screened against α-amino-3-hydroxyl-5-methyl-4-isoxazole propionic acid (AMPA), N-methyl-d-aspartate (NMDA), voltage-gated sodium ion channels (VGSC), and carbonic anhydrase enzyme II (CA II) receptors, and the docking results were compared to the reference drug phenytoin. Amentoflavone displayed the highest affinity for AMPA and VGSC receptors, with docking scores of - 10.4 and - 10.1 kcal/mol, respectively. Oliganthin H-NMDA and epigallocatechin-3-gallate-CA II complexes showed docking scores of - 10.9 and - 6.9 kcal/mol, respectively. All four complexes depicted a high dock score compared to the phenytoin complex at the binding site of the corresponding proteins. The MD simulation investigated the stabilities and favorable conformation of apoproteins and ligand/reference-bound complexes. The results revealed that proteins AMPA, VGSC, and CA II were more efficiently stabilized by lead phytochemicals than phenytoin binding. Additionally, principal component analysis and MM-PBSA results suggested that these lead phytocompounds have good compactness and strong binding free energy. Further, physicochemical and pharmacokinetic studies revealed that these final lead phytochemicals would be suitable for oral intake, have sufficient intestinal permeability, and have the ability to cross the blood-brain barrier (BBB). Comprehensively, this study predicted amentoflavone as the best lead phytochemical out of the 70 anticonvulsant phytocompounds that can be used to treat epilepsy.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13205-024-03948-1.

Structural highlights of macromolecular complexes and assemblies

The structures of macromolecular assemblies have given us deep insights into cellular processes and have profoundly impacted biological research and drug discovery. We highlight the structures of macromolecular assemblies that have been modeled using integrative and computational methods and describe how open access to these structures from structural archives has empowered the research community. The arsenal of experimental and computational methods for structure determination ensures a future where whole organelles and cells can be modeled.

GPCR-BERT: Interpreting Sequential Design of G Protein-Coupled Receptors Using Protein Language Models

With the rise of transformers and large language models (LLMs) in chemistry and biology, new avenues for the design and understanding of therapeutics have been opened up to the scientific community. Protein sequences can be modeled as language and can take advantage of recent advances in LLMs, specifically with the abundance of our access to the protein sequence data sets. In this letter, we developed the GPCR-BERT model for understanding the sequential design of G protein-coupled receptors (GPCRs). GPCRs are the target of over one-third of Food and Drug Administration-approved pharmaceuticals. However, there is a lack of comprehensive understanding regarding the relationship among amino acid sequence, ligand selectivity, and conformational motifs (such as NPxxY, CWxP, and E/DRY). By utilizing the pretrained protein model (Prot-Bert) and fine-tuning with prediction tasks of variations in the motifs, we were able to shed light on several relationships between residues in the binding pocket and some of the conserved motifs. To achieve this, we took advantage of attention weights and hidden states of the model that are interpreted to extract the extent of contributions of amino acids in dictating the type of masked ones. The fine-tuned models demonstrated high accuracy in predicting hidden residues within the motifs. In addition, the analysis of embedding was performed over 3D structures to elucidate the higher-order interactions within the conformations of the receptors.

Chinese herb couple against diabetes: integrating network pharmacology and mechanism study

Cassia twig is a dry twig of Cinnamomum cassia Presl, a Lauraceae plant. Astragalus L is one of the largest genuses of flowering plants in the Leguminosae family. Roots of A. membranaceus Bge. var. mongholicus (Bge.) Hsiao, A. membranaceus (Fisch.) Bge. Chinese herb couple refers to the matching of two herbs in pairs, mostly with synergistic effects or toxicity reduction. This Chinese herb couple (Cassia twig-Astragalus) come from the classic famous book "Zhang Xichun's book on Chinese herb couple", which is widely used to treat diabetes. Moreover, both Cassia twig and Astragalus belong to the homology of medicine and food. However, its mechanism is still unclear. The study identified the effective components of Cassia twig-Astragalus by UPLC-Q-TOF-MS/MS and investigated the mechanism of Cassia twig-Astragalus in treating diabetes by virtue of network pharmacology, molecular docking and experimental verification. Firstly, based on UPLC-Q-TOF-MS/MS and network pharmacology, a total of 10 active ingredients of Astragalus and 6 active ingredients of Cassia twig were screened, and a total of 13 key targets were obtained. There were 64 targets at the intersection of Cassia twig-Astragalus with diabetes, mainly including IL-17, TNF, NF-κβ, AGE-RAGE signaling pathway, etc. It mainly involves the response of cells to insulin stimulation, the response to insulin and the positive regulation of cell adhesion. Secondly, molecular docking results showed that quercetin has good binding activities with AKT1 and TNF. Calycosin has good binding activities with AKT1, TNF and CAV1. Formononetin has good binding activities with TNF and IL-6. Isorhamnetin has good binding activities with AKT1, TNF and IL-6. Finally, the animal experiments showed that Cassia twig-Astragalus could improve the body weight, blood glucose and glucose tolerance in diabetic rats. After the intervention with Cassia twig-Astragalus, the inflammatory factors (IL-10, TNF-α, IL-6) were significantly improved in diabetic rats, which also effectively reduced TG and TC.

Jianpi Huayu Decoction suppresses cellular senescence in colorectal cancer via p53-p21-Rb pathway: Network pharmacology and in vivo validation

ETHNOPHARMACOLOGICAL RELEVANCE

Jianpi Huayu Decoction (JHD) is an herbal prescription in traditional Chinese medicine based on Sijunzi Decoction to treat patients with colorectal cancer (CRC). Its effects on the inhibition of CRC cell proliferation and tumor growth are promising; however, its multicomponent nature makes a complete understanding of its mechanism challenging.

AIM OF THE STUDY

To explore the therapeutic targets and underlying molecular pathways of JHD in CRC treatment using network pharmacology techniques and in vivo validation.

MATERIALS AND METHODS

The active ingredients and targets of JHD were identified, compound-target interactions were mapped, and enrichment analyses were conducted. We identified the hub targets of JHD-induced cellular senescence in CRC. The binding affinities between compounds and targets were evaluated through molecular docking. Subsequently, we conducted bioinformatic analyses to compare the expression of hub targets between colorectal tissue and normal tissue. Finally, in vivo experiments were carried out utilizing a xenograft model to assess the effects of JHD on cellular senescence biomarkers.

RESULTS

Network pharmacology revealed 129 active ingredients in JHD that were associated with 678 targets, leading to the construction of compound-target and target-pathway networks. Enrichment analyses highlighted key pathways including cellular senescence. Based on this, hub targets associated with cellular senescence were determined and validated. Molecular docking indicated favorable interactions between the active components and hub targets. Gene expression and survival analysis in CRC tissue were consistent with the potential roles of hub genes. Animal experiments showed that JHD triggered cellular senescence and suppressed the growth of CRC by regulating the p53-p21-Rb signaling pathway.

CONCLUSIONS

This research adopted network pharmacology, bioinformatics, and animal experiments to unveil that JHD induces cellular senescence in CRC by influencing the p53-p21-Rb pathway and senescence-associated secretory phenotypes, highlighting its potential as a CRC treatment.

The natural breakthrough: phytochemicals as potent therapeutic agents against spinocerebellar ataxia type 3

There is no FDA-approved drug for neurological disorders like spinocerebellar ataxia type 3. CAG repeats mutation in the ATXN3 gene, causing spinocerebellar ataxia type 3 disease. Symptoms include sleep cycle disturbance, neurophysiological abnormalities, autonomic dysfunctions, and depression. This research focuses on drug discovery against ATXN3 using phytochemicals of different plants. Three phytochemical compounds (flavonoids, diterpenoids, and alkaloids) were used as potential drug candidates and screened against the ATXN3 protein. The 3D structure of ATXN3 protein and phytochemicals were retrieved and validation of the protein was 98.1% Rama favored. The protein binding sites were identified for the interaction by CASTp. ADMET was utilized for the pre-clinical analysis, including solubility, permeability, drug likeliness and toxicity, and chamanetin passed all the ADMET properties to become a lead drug candidate. Boiled egg analysis attested that the ligand could cross the gastrointestinal tract. Pharmacophore analysis showed that chamanetin has many hydrogen acceptors and donors which can form interaction bonds with the receptor proteins. Chamanetin passed all the screening analyses, having good absorption, no violation of Lipinski's rule, nontoxic properties, and good pharmacophore properties. Chamanetin was one of the lead compounds with a - 7.2 kcal/mol binding affinity after screening the phytochemicals. The stimulation of ATXN3 showed stability after 20 ns of interaction in an overall 50 ns MD simulation. Chamanetin (Flavonoid) was predicted to be highly active against ATXN3 with good drug-like properties. In-silico active drug against ATXN3 from a plant source and good pharmacokinetics parameters would be excellent drug therapy for SC3, such as flavonoids (Chamanetin).

New perspectives on the potential of tetrandrine in the treatment of non-small cell lung cancer: bioinformatics, Mendelian randomization study and experimental investigation

BACKGROUND

Although there are numerous treatment methods for NSCLC, long-term survival remains a challenge for patients. The objective of this study is to investigate the role and causal relationship between the target of tetrandrine and non-small cell lung cancer (NSCLC) through transcriptome and single-cell sequencing data, summary-data-based Mendelian Randomization (SMR) and basic experiments. The aim is to provide a new perspective for the treatment of NSCLC.

METHODS

We obtained the drug target gene of tetrandrine through the drug database, and then used the GSE19188 data set to obtain the NSCLC pathogenic gene, established a drug-disease gene interaction network, screened out the hub drug-disease gene, and performed bioinformatics and tumor cell immune infiltration analysis. Single-cell sequencing data (GSE148071) to determine gene location, SMR to clarify causality and drug experiment verification.

RESULTS

10 drug-disease genes were obtained from 213 drug targets and 529 disease genes. DO/GO/KEGG analysis showed that the above genes were all related to the progression and invasion of NSCLC. Four drug-disease genes were identified from a drug-disease PPI network. These four genes were highly expressed in tumors and positively correlated with plasma cells, T cells, and macrophages. Subsequent single-cell sequencing data confirmed that these four genes were distributed in epithelial cells, and SMR analysis revealed the causal relationship between CCNA2 and CCNB1 and the development of NSCLC. The final molecular docking and drug experiments showed that CCNA2 and CCNB1 are key targets for tetrandrine in the treatment of NSCLC.

In-silico Investigations for the Identification of Novel Inhibitors Targeting Hepatitis C Virus RNA-dependent RNA Polymerase

BACKGROUND

Hepatitis C is an inflammatory condition of the liver caused by the hepatitis C virus, exhibiting acute and chronic manifestations with severity ranging from mild to severe and lifelong illnesses leading to liver cirrhosis and cancer. According to the World Health Organization's global estimates, a population of about 58 million have chronic hepatitis C virus infection, with around 1.5 million new infections occurring every year.

OBJECTIVE

The present study aimed to identify novel molecules targeting the Hepatitis C viral RNA Dependent RNA polymerases, which play a crucial role in genome replication, mRNA synthesis, etc. Methods: Structure-based virtual screening of chemical libraries of small molecules was done using AutoDock/Vina. The top-ranking pose for every ligand was complexed with the protein and used for further protein-ligand interaction analysis using the Protein-ligand interaction Profiler. Molecules from virtual screening were further assessed using the pkCSM web server. The proteinligand interactions were further subjected to molecular dynamics simulation studies to establish dynamic stability.

RESULTS

Molecular docking-based virtual screening of the database of small molecules, followed by screening based on pharmacokinetic and toxicity parameters, yielded eight probable RNA Dependent RNA polymerase inhibitors. The docking scores for the proposed candidates ranged from - 8.04 to -9.10 kcal/mol. The potential stability of the ligands bound to the target protein was demonstrated by molecular dynamics simulation studies.

CONCLUSION

Data from exhaustive computational studies proposed eight molecules as potential anti-viral candidates, targeting Hepatitis C viral RNA Dependent RNA polymerases, which can be further evaluated for their biological potential.

Exploring the Molecular Mechanism of Niuxi-Mugua Formula in Treating Coronavirus Disease 2019 via Network Pharmacology, Computational Biology, and Surface Plasmon Resonance Verification

BACKGROUND

In China, Niuxi-Mugua formula (NMF) has been widely used to prevent and treat coronavirus disease 2019 (COVID-19). However, the mechanism of NMF for treating COVID-19 is not yet fully understood.

OBJECTIVE

This study aimed to explore the potential mechanism of NMF for treating COVID- 19 by network pharmacology, computational biology, and surface plasmon resonance (SPR) verification.

MATERIALS AND METHODS

The NMF-compound-target network was constructed to screen the key compounds, and the Molecular Complex Detection (MCODE) tool was used to screen the preliminary key genes. The overlapped genes (OGEs) and the preliminary key genes were further analyzed by enrichment analysis. Then, the correlation analysis of immune signatures and the preliminary key genes was performed. Molecular docking and molecular dynamic (MD) simulation assays were applied to clarify the interactions between key compounds and key genes. Moreover, the SPR interaction experiment was used for further affinity kinetic verification.

RESULTS

Lipid and atherosclerosis, TNF, IL-17, and NF-kappa B signaling pathways were the main pathways of NMF in the treatment of COVID-19. There was a positive correlation between almost the majority of immune signatures and all preliminary key genes. The key compounds and the key genes were screened out, and they were involved in the main pathways of NMF for treating COVID-19. Moreover, the binding affinities of most key compounds binding to key genes were good, and IL1B-Quercetin had the best binding stability. SPR analysis further demonstrated that IL1B-Quercetin showed good binding affinity.

CONCLUSION

Our findings provided theoretical grounds for NMF in the treatment of COVID-19.

Exploration of the effect and mechanism of Scutellaria barbata D. Don in the treatment of ovarian cancer based on network pharmacology and in vitro experimental verification

The mortality rate of ovarian cancer is the highest among gynecological cancers, posing a serious threat to women health and life. Scutellaria barbata D. Don (SBD) can effectively treat ovarian cancer. However, its mechanism of action is unclear. The aim of this study was to elucidate the mechanism of SBD in the treatment of ovarian cancer using network pharmacology, and to verify the experimental results using human ovarian cancer SKOV3 cells. The Herb and Disease Gene databases were searched to identify common targets of SBD and ovarian cancer. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis, and Protein-Protein Interaction (PPI) network analyses were performed to identify the potential molecular mechanisms behind SBD. Finally, the molecular docking and main possible pathways were verified by experimental studies. Cell proliferation, the mRNA expression level of key genes and signaling pathway were all investigated and evaluated in vitro. A total of 29 bioactive ingredients and 137 common targets in SBD were found to inhibit ovarian cancer development. The active ingredients identified include quercetin, luteolin, and wogonin. Analysis of the PPI network showed that AKT1, VEGFA, JUN, TNF, and Caspase-3 shared centrality among all target genes. The results of the KEGG pathway analysis indicated that the cancer pathway, PI3K-Akt signaling pathway, and MAPK signaling pathways mediated the effects of SBD against ovarian cancer progression. Cell experiments showed that quercetin, luteolin, and wogonin inhibited the proliferation and clone formation of SKOV3 cells and regulated mRNA expression of 5 key genes by inhibiting PI3K/Akt signaling pathway. Our results demonstrate that SBD exerted anti-ovarian cancer effects through its key components quercetin, luteolin and wogonin. Mechanistically, its anti-cancer effects were mediated by inhibition of the PI3K/Akt signaling pathways. Therefore, SBD might be a candidate drug for ovarian cancer treatment.

Mechanism of Valeriana Jatamansi Jones for the treatment of spinal cord injury based on network pharmacology and molecular docking

Spinal cord injury (SCI) is characterized by high rates of disability and death. Valeriana jatamansi Jones (VJJ), a Chinese herbal medicine, has been identified to improve motor function recovery in rats with SCI. The study aimed to analyze the potential molecular mechanisms of action of VJJ in the treatment of SCI. The main ingredients of VJJ were obtained from the literature and the SwissADME platform was used to screen the active ingredients. The Swiss TargetPrediction platform was used to predict the targets of VJJ, and the targets of SCI were obtained from the GeneCards and OMIM databases. The intersecting genes were considered potential targets of VJJ in SCI. The protein-protein interaction network was constructed using the STRING database and the hub genes of VJJ for SCI treatment were screened according to their degree values. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes enrichment analyses were performed using the Metascape database. Cytoscape software was used to construct the "herb-ingredient-target-pathway" network. Preliminary validation was performed using molecular docking via Auto Dock Vina software. A total of 56 active ingredients of VJJ, mainly iridoids, were identified. There were 1493 GO items (P < .01) and 173 signaling pathways (P < .01) obtained from GO and Kyoto Encyclopedia of Genes and Genomes enrichment, including the phosphoinositide-3-kinase (PI3K)-protein kinase B (Akt) signaling pathway, hypoxia-inducible factor 1 signaling pathway, and tumor necrosis factor signaling pathway. Molecular docking revealed that 12 hub genes enriched in the PI3K/Akt signaling pathway had a high binding affinity for the active ingredient of VJJ. VJJ may exert its therapeutic effects on SCI through the iridoid fraction, acting on signal transducer and activator of transcription 3, CASP3, AKT1, tumor necrosis factor, mammalian target of rapamycin, interleukin 6, and other hub genes, which may be related to the PI3K/Akt signaling pathway.

PDBminer to Find and Annotate Protein Structures for Computational Analysis

Computational methods relying on protein structure strongly depend on the structure selected for investigation. Typical sources of protein structures include experimental structures available at the Protein Data Bank (PDB) and high-quality in silico model structures, such as those available at the AlphaFold Protein Structure Database. Either option has significant advantages and drawbacks, and exploring the wealth of available structures to identify the most suitable ones for specific applications can be a daunting task. We provide an open-source software package, PDBminer, with the purpose of making structure identification and selection easier, faster, and less error prone. PDBminer searches the AlphaFold Database and the PDB for available structures of interest and provides an up-to-date, quality-ranked table of structures applicable for further use. PDBminer provides an overview of the available protein structures to one or more input proteins, parallelizing the runs if multiple cores are specified. The output table reports the coverage of the protein structures aligned to the UniProt sequence, overcoming numbering differences in PDB structures and providing information regarding model quality, protein complexes, ligands, and nucleic acid chain binding. The PDBminer2coverage and PDBminer2network tools assist in visualizing the results. PDBminer can be applied to overcome the tedious task of choosing a PDB structure without losing the wealth of additional information available in the PDB. Here, we showcase the main functionalities of the package on the p53 tumor suppressor protein. The package is available at http://github.com/ELELAB/PDBminer.