LigTMap: ligand and structure-based target identification and activity prediction for small molecular compounds.
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ABSTRACT:
SUBMITTER: Shaikh F
PROVIDER: S-EPMC8194164 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Discovery and optimization of small molecule inhibitors as therapeutic drugs have immensely benefited from rational structure-based drug design. With recent advances in high-resolution structure determination, computational power, and machine learning methodology, it is becoming more tractable to elucidate the structural basis of drug potency. However, the applicability of machine learning models to drug design is limited by the interpretability of the resulting models in terms of feature importance. Here, we take advantage of the large number of available inhibitor-bound HIV-1 protease structures and associated potencies to evaluate inhibitor diversity and machine learning models to predict ligand affinity. First, using a hierarchical clustering approach, we grouped HIV-1 protease inhibitors and identified distinct core structures. Explicit features including protein-ligand interactions were extracted from high-resolution cocrystal structures as 3D-based fingerprints. We found that a gradient boosting machine learning model with this explicit feature attribution can predict binding affinity with high accuracy. Finally, Shapley values were derived to explain local feature importance. We found specific van der Waals (vdW) interactions of key protein residues are pivotal for the predicted potency. Protein-specific and interpretable prediction models can guide the optimization of many small molecule drugs for improved potency.
| S-EPMC6940596 | biostudies-literature
Project description:Cyclophilin A acts as protein folding chaperones and intracellular transports in many cellular processes. Previous studies have shown that cyclophilin A can interact with HIV-1 (human immunodeficiency virus type 1) gag protein and enhance viral infectivity. Many cyclophilin A inhibitors such as cyclosporin A can inhibit HIV-1 replication in vitro. Here, we report a structure-based identification of novel non-peptidic cyclophilin A inhibitors as anti-HIV lead compounds. Following a computer-aided virtual screening and subsequent surface plasmon resonance (SPR) analysis, 12 low molecular weight cyclophilin A ligands were selected for further evaluation of their in vitro inhibition of peptidyl-prolyl cis-trans isomerase (PPIase) activity of cyclophilin A and HIV-1 replication. Five of these compounds (FD5, FD8, FD9, FD10 and FD12) exhibited inhibition against both PPIase activity and HIV-1 infection. These active compounds will be used as leads for structure and activity relationship (SAR) and optimization studies in order to design more effective anti-HIV-1 therapeutics, and as probes for investigating the effect of cyclophilins on HIV-1 replication.
| S-EPMC3033441 | biostudies-literature
Project description:Baby Boom (BBM) is a key transcription factor that triggers embryogenesis, enhances transformation and regeneration efficiencies, and regulates developmental pathways in plants. Triggering or activating BBM in non-model crops could overcome the bottlenecks in plant breeding. Understanding BBM's structure is critical for functional characterization and determination of interacting partners and/or ligands. The current in silico study aimed to study BBM's sequence and conservation across all plant proteomes, predict protein-protein and protein-ligand interactions, and perform molecular docking and molecular dynamics (MD) simulation to specifically determine the binding site amino acid residues. In addition, peptide sequences that interact with BBM have also been predicted, which provide avenues for altered functional interactions and the design of peptide mimetics that can be experimentally validated for their role in tissue culture or transformation media. This novel data could pave the way for the exploitation of BBM's potential as the master regulator of specialized plant processes such as apomixes, haploid embryogenesis, and CRISPR/Cas9 transgenic development.
| S-EPMC9687316 | biostudies-literature
Project description:MotivationOur knowledge of the metabolites in cells and their reactions is far from complete as revealed by metabolomic measurements that detect many more small molecules than are documented in metabolic databases. Here, we develop an approach for predicting the reactivity of small-molecule metabolites in enzyme-catalyzed reactions that combines expert knowledge, computational chemistry and machine learning.ResultsWe classified 4843 reactions documented in the KEGG database, from all six Enzyme Commission classes (EC 1-6), into 80 reaction classes, each of which is marked by a characteristic functional group transformation. Reaction centers and surrounding local structures in substrates and products of these reactions were represented using SMARTS. We found that each of the SMARTS-defined chemical substructures is widely distributed among metabolites, but only a fraction of the functional groups in these substructures are reactive. Using atomic properties of atoms in a putative reaction center and molecular properties as features, we trained support vector machine (SVM) classifiers to discriminate between functional groups that are reactive and non-reactive. Classifier accuracy was assessed by cross-validation analysis. A typical sensitivity [TP/(TP+FN)] or specificity [TN/(TN+FP)] is ?0.8. Our results suggest that metabolic reactivity of small-molecule compounds can be predicted with reasonable accuracy based on the presence of a potentially reactive functional group and the chemical features of its local environment.AvailabilityThe classifiers presented here can be used to predict reactions via a web site (http://cellsignaling.lanl.gov/Reactivity/). The web site is freely available.
| S-EPMC3102224 | biostudies-literature
Project description:BackgroundMany computational approaches have been used for target prediction, including machine learning, reverse docking, bioactivity spectra analysis, and chemical similarity searching. Recent studies have suggested that chemical similarity searching may be driven by the most-similar ligand. However, the extent of bioactivity of most-similar ligands has been oversimplified or even neglected in these studies, and this has impaired the prediction power.ResultsHere we propose the MOst-Similar ligand-based Target inference approach, namely MOST, which uses fingerprint similarity and explicit bioactivity of the most-similar ligands to predict targets of the query compound. Performance of MOST was evaluated by using combinations of different fingerprint schemes, machine learning methods, and bioactivity representations. In sevenfold cross-validation with a benchmark Ki dataset from CHEMBL release 19 containing 61,937 bioactivity data of 173 human targets, MOST achieved high average prediction accuracy (0.95 for pKi ≥ 5, and 0.87 for pKi ≥ 6). Morgan fingerprint was shown to be slightly better than FP2. Logistic Regression and Random Forest methods performed better than Naïve Bayes. In a temporal validation, the Ki dataset from CHEMBL19 were used to train models and predict the bioactivity of newly deposited ligands in CHEMBL20. MOST also performed well with high accuracy (0.90 for pKi ≥ 5, and 0.76 for pKi ≥ 6), when Logistic Regression and Morgan fingerprint were employed. Furthermore, the p values associated with explicit bioactivity were found be a robust index for removing false positive predictions. Implicit bioactivity did not offer this capability. Finally, p values generated with Logistic Regression, Morgan fingerprint and explicit activity were integrated with a false discovery rate (FDR) control procedure to reduce false positives in multiple-target prediction scenario, and the success of this strategy it was demonstrated with a case of fluanisone. In the case of aloe-emodin's laxative effect, MOST predicted that acetylcholinesterase was the mechanism-of-action target; in vivo studies validated this prediction.ConclusionsUsing the MOST approach can result in highly accurate and robust target prediction. Integrated with a FDR control procedure, MOST provides a reliable framework for multiple-target inference. It has prospective applications in drug repurposing and mechanism-of-action target prediction.
| S-EPMC5346209 | biostudies-literature
Project description:Activity cliffs (ACs) are formed by two structurally similar compounds with a large difference in potency. Accurate AC prediction is expected to help researchers' decisions in the early stages of drug discovery. Previously, predictive models based on matched molecular pair (MMP) cliffs have been proposed. However, the proposed methods face a challenge of interpretability due to the black-box character of the predictive models. In this study, we developed interpretable MMP fingerprints and modified a model-specific interpretation approach for models based on a support vector machine (SVM) and MMP kernel. We compared important features highlighted by this SVM-based interpretation approach and the SHapley Additive exPlanations (SHAP) as a major model-independent approach. The model-specific approach could capture the difference between AC and non-AC, while SHAP assigned high weights to the features not present in the test instances. For specific MMPs, the feature weights mapped by the SVM-based interpretation method were in agreement with the previously confirmed binding knowledge from X-ray co-crystal structures, indicating that this method is able to interpret the AC prediction model in a chemically intuitive manner.
| S-EPMC8401777 | biostudies-literature
Project description:This work was a structured virtual screening for marine bioactive compounds with reported antiviral activities which were subjected to structure-based studies against SARS-CoV-2 co-crystallized proteins. The molecular docking of marine bioactive compounds against the main protease (Mpro, PDB ID: 6lu7 and 6y2f), the spike glycoprotein (PDB ID: 6vsb), and the RNA polymerase (PDB ID: 6m71) of SARS-CoV-2 was performed. Ligand-based approach with the inclusion of rapid overlay chemical structures (ROCS) was also addressed in order to examine the probability of these marine compounds sharing relevance and druggability with the reported drugs. Among the examined marine library, the highest scores in different virtual screening aspects were displayed by compounds with flavonoids core, acyl indole, and pyrrole carboxamide alkaloids. Moreover, a complete overlay with the co-crystallized ligands of Mpro was revealed by sceptrin and debromo-sceptrin. Thalassoilin (A-B) which was found in the Red Sea exhibited the highest binding and similarity outcomes among all target proteins. These data highlight the importance of marine natural metabolites in regard to further studies for discovering new drugs to combat the COVID-19 pandemic.
| S-EPMC7904452 | biostudies-literature
Project description:BackgroundDuring the second wave of the COVID-19 pandemic, an unusual increase in cases of mucormycosis was observed in India, owing to immunological dysregulation caused by the SARS-CoV-2 and the use of broad-spectrum antibiotics, particularly in patients with poorly controlled diabetes with ketoacidosis to have contributed to the rise, and it has been declared an epidemic in several states of India. Because of the black colouring of dead and dying tissue caused by the fungus, it was dubbed "black fungus" by several Indian media outlets. In this study, attempts were taken to unmask novel therapeutic options to treat mucormycosis disease. Rhizopus species is the primary fungi responsible for 70% of mucormycosis cases.ResultsWe chose three important proteins from the Rhizopus delemar such as CotH3, Lanosterol 14 alpha-demethylase and Mucoricin which plays a crucial role in the virulence of Mucorales. Initially, we explored the physiochemical, structural and functional insights of proteins and later using AutoDock Vina, we applied computational protein-ligand binding modelling to perform a virtual screening around 300 selected compounds against these three proteins, including FDA-approved drugs, FDA-unapproved drugs, investigational-only drugs and natural bioactive compounds. ADME parameters, toxicity risk and biological activity of those compounds were approximated via in silico methods. Our computational studies identified six ligands as potential inhibitors against Rhizopus delemar, including 12,28-Oxamanzamine A, vialinin B and deoxytopsentin for CotH3; pramiconazole and saperconazole for Lanosterol 14 alpha-demethylase; and Hesperidin for Mucoricin. Interestingly, 12,28-Oxamanzamine A showed a maximum binding affinity with all three proteins (CotH3: - 10.2 kcal/mol Lanosterol 14 alpha-demethylase: - 10.9 kcal/mol Mucoricin: - 8.6 kcal/mol).ConclusionsIn summary, our investigation identified 12,28-Oxamanzamine A, vialinin B, deoxytopsentin, pramiconazole, saperconazole and hesperidin as potent bioactive compounds for treating mucormycosis that may be considered for further optimisation techniques and in vitro and in vivo studies.Supplementary informationThe online version contains supplementary material available at 10.1186/s42269-022-00704-4.
| S-EPMC8802264 | biostudies-literature
Project description:3DLigandSite is a web tool for the prediction of ligand-binding sites in proteins. Here, we report a significant update since the first release of 3DLigandSite in 2010. The overall methodology remains the same, with candidate binding sites in proteins inferred using known binding sites in related protein structures as templates. However, the initial structural modelling step now uses the newly available structures from the AlphaFold database or alternatively Phyre2 when AlphaFold structures are not available. Further, a sequence-based search using HHSearch has been introduced to identify template structures with bound ligands that are used to infer the ligand-binding residues in the query protein. Finally, we introduced a machine learning element as the final prediction step, which improves the accuracy of predictions and provides a confidence score for each residue predicted to be part of a binding site. Validation of 3DLigandSite on a set of 6416 binding sites obtained 92% recall at 75% precision for non-metal binding sites and 52% recall at 75% precision for metal binding sites. 3DLigandSite is available at https://www.wass-michaelislab.org/3dligandsite. Users submit either a protein sequence or structure. Results are displayed in multiple formats including an interactive Mol* molecular visualization of the protein and the predicted binding sites.
| S-EPMC9252821 | biostudies-literature
Project description:Background Atrial fibrillation (AF) is the most common sustained cardiac arrhythmia and engenders significant global health care burden. The underlying mechanisms of AF is remained to be revealed and current treatment options for AF have limitations. Besides, a detection system can help identify those at risk of developing AF and will enable personalized management. Materials and Methods In this study, we utilized the robust rank aggregation method to integrate six AF microarray datasets from the Gene Expression Omnibus database, and identified a set of differentially expressed genes between patients with AF and controls. Potential compounds were identified by mining the Connectivity Map database. Functional modules and closely-interacted clusters were identified using weighted gene co-expression network analysis and protein–protein interaction network, respectively. The overlapped hub genes were further filtered. Subsequent analyses were performed to analyze the function, biological features, and regulatory networks. Moreover, a reliable Machine Learning-based diagnostic model was constructed and visualized to clarify the diagnostic features of these genes. Results A total of 156 upregulated and 34 downregulated genes were identified, some of which had not been previously investigated. We showed that mitogen-activated protein kinase and epidermal growth factor receptor inhibitors were likely to mitigate AF based on Connectivity Map analysis. Four genes, including CXCL12, LTBP1, LOXL1, and IGFBP3, were identified as hub genes. CXCL12 was shown to play an important role in regulation of local inflammatory response and immune cell infiltration. Regulation of CXCL12 expression in AF was analyzed by constructing a transcription factor-miRNA-mRNA network. The Machine Learning-based diagnostic model generated in this study showed good efficacy and reliability. Conclusion Key genes involving in the pathogenesis of AF and potential therapeutic compounds for AF were identified. The biological features of CXCL12 in AF were investigated using integrative bioinformatics tools. The results suggested that CXCL12 might be a biomarker that could be used for distinguishing subsets of AF, and indicated that CXCL12 might be an important intermediate in the development of AF. A reliable Machine Learning-based diagnostic model was constructed. Our work improved understanding of the mechanisms of AF predisposition and progression, and identified potential therapeutic avenues for treatment of AF.
| S-EPMC9329605 | biostudies-literature