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Kumar S, Teli MK, Kim MH. GPCR-IPL score: multilevel featurization of GPCR-ligand interaction patterns and prediction of ligand functions from selectivity to biased activation. Brief Bioinform 2024; 25:bbae105. [PMID: 38517694 PMCID: PMC10959162 DOI: 10.1093/bib/bbae105] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2023] [Revised: 02/12/2024] [Accepted: 02/27/2024] [Indexed: 03/24/2024] Open
Abstract
G-protein-coupled receptors (GPCRs) mediate diverse cell signaling cascades after recognizing extracellular ligands. Despite the successful history of known GPCR drugs, a lack of mechanistic insight into GPCR challenges both the deorphanization of some GPCRs and optimization of the structure-activity relationship of their ligands. Notably, replacing a small substituent on a GPCR ligand can significantly alter extracellular GPCR-ligand interaction patterns and motion of transmembrane helices in turn to occur post-binding events of the ligand. In this study, we designed 3D multilevel features to describe the extracellular interaction patterns. Subsequently, these 3D features were utilized to predict the post-binding events that result from conformational dynamics from the extracellular to intracellular areas. To understand the adaptability of GPCR ligands, we collected the conformational information of flexible residues during binding and performed molecular featurization on a broad range of GPCR-ligand complexes. As a result, we developed GPCR-ligand interaction patterns, binding pockets, and ligand features as score (GPCR-IPL score) for predicting the functional selectivity of GPCR ligands (agonism versus antagonism), using the multilevel features of (1) zoomed-out 'residue level' (for flexible transmembrane helices of GPCRs), (2) zoomed-in 'pocket level' (for sophisticated mode of action) and (3) 'atom level' (for the conformational adaptability of GPCR ligands). GPCR-IPL score demonstrated reliable performance, achieving area under the receiver operating characteristic of 0.938 and area under the precision-recall curve of 0.907 (available in gpcr-ipl-score.onrender.com). Furthermore, we used the molecular features to predict the biased activation of downstream signaling (Gi/o, Gq/11, Gs and β-arrestin) as well as the functional selectivity. The resulting models are interpreted and applied to out-of-set validation with three scenarios including the identification of a new MRGPRX antagonist.
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Affiliation(s)
- Surendra Kumar
- Gachon Institute of Pharmaceutical Science & Department of Pharmacy, College of Pharmacy, Gachon University, 191 Hambakmoeiro, Yeonsu-gu, Incheon, Republic of Korea
| | - Mahesh K Teli
- Gachon Institute of Pharmaceutical Science & Department of Pharmacy, College of Pharmacy, Gachon University, 191 Hambakmoeiro, Yeonsu-gu, Incheon, Republic of Korea
| | - Mi-hyun Kim
- Gachon Institute of Pharmaceutical Science & Department of Pharmacy, College of Pharmacy, Gachon University, 191 Hambakmoeiro, Yeonsu-gu, Incheon, Republic of Korea
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Prediction of chemical warfare agents based on cholinergic array type meta-predictors. Sci Rep 2022; 12:16709. [PMID: 36203081 PMCID: PMC9537167 DOI: 10.1038/s41598-022-21150-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2022] [Accepted: 09/23/2022] [Indexed: 11/08/2022] Open
Abstract
Molecular insights into chemical safety are very important for sustainable development as well as risk assessment. This study considers how to manage future upcoming harmful agents, especially potentially cholinergic chemical warfare agents (CWAs). For this purpose, the structures of known cholinergic agents were encoded by molecular descriptors. And then each drug target interaction (DTI) was learned from the encoded structures and their cholinergic activities to build DTI classification models for five cholinergic targets with reliable statistical validation (ensemble-AUC: up to 0.790, MCC: up to 0.991, accuracy: up to 0.995). The collected classifiers were transformed into 2D or 3D array type meta-predictors for multi-task: (1) cholinergic prediction and (2) CWA detection. The detection ability of the array classifiers was verified under the imbalanced dataset between CWAs and none CWAs (area under the precision-recall curve: up to 0.997, MCC: up to 0.638, F1-score of none CWAs: up to 0.991, F1-score of CWAs: up to 0.585).
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Ahn S, Lee SE, Kim MH. Random-forest model for drug-target interaction prediction via Kullbeck-Leibler divergence. J Cheminform 2022; 14:67. [PMID: 36192818 PMCID: PMC9531514 DOI: 10.1186/s13321-022-00644-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2022] [Accepted: 09/11/2022] [Indexed: 12/04/2022] Open
Abstract
Virtual screening has significantly improved the success rate of early stage drug discovery. Recent virtual screening methods have improved owing to advances in machine learning and chemical information. Among these advances, the creative extraction of drug features is important for predicting drug–target interaction (DTI), which is a large-scale virtual screening of known drugs. Herein, we report Kullbeck–Leibler divergence (KLD) as a DTI feature and the feature-driven classification model applicable to DTI prediction. For the purpose, E3FP three-dimensional (3D) molecular fingerprints of drugs as a molecular representation allow the computation of 3D similarities between ligands within each target (Q–Q matrix) to identify the uniqueness of pharmacological targets and those between a query and a ligand (Q–L vector) in DTIs. The 3D similarity matrices are transformed into probability density functions via kernel density estimation as a nonparametric estimation. Each density model can exploit the characteristics of each pharmacological target and measure the quasi-distance between the ligands. Furthermore, we developed a random forest model from the KLD feature vectors to successfully predict DTIs for representative 17 targets (mean accuracy: 0.882, out-of-bag score estimate: 0.876, ROC AUC: 0.990). The method is applicable for 2D chemical similarity.
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Affiliation(s)
- Sangjin Ahn
- Gachon Institute of Pharmaceutical Science and Department of Pharmacy, College of Pharmacy, Gachon University, 191 Hambakmoeiro, Yeonsu-gu, Incheon, Republic of Korea.,Department of Artificial Intelligence, Ajou University, Suwon, 16499, Republic of Korea
| | - Si Eun Lee
- Gachon Institute of Pharmaceutical Science and Department of Pharmacy, College of Pharmacy, Gachon University, 191 Hambakmoeiro, Yeonsu-gu, Incheon, Republic of Korea
| | - Mi-Hyun Kim
- Gachon Institute of Pharmaceutical Science and Department of Pharmacy, College of Pharmacy, Gachon University, 191 Hambakmoeiro, Yeonsu-gu, Incheon, Republic of Korea.
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Zhou Y, Zhang Y, Lian X, Li F, Wang C, Zhu F, Qiu Y, Chen Y. Therapeutic target database update 2022: facilitating drug discovery with enriched comparative data of targeted agents. Nucleic Acids Res 2021; 50:D1398-D1407. [PMID: 34718717 PMCID: PMC8728281 DOI: 10.1093/nar/gkab953] [Citation(s) in RCA: 290] [Impact Index Per Article: 96.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2021] [Revised: 09/29/2021] [Accepted: 10/04/2021] [Indexed: 11/14/2022] Open
Abstract
Drug discovery relies on the knowledge of not only drugs and targets, but also the comparative agents and targets. These include poor binders and non-binders for developing discovery tools, prodrugs for improved therapeutics, co-targets of therapeutic targets for multi-target strategies and off-target investigations, and the collective structure-activity and drug-likeness landscapes of enhanced drug feature. However, such valuable data are inadequately covered by the available databases. In this study, a major update of the Therapeutic Target Database, previously featured in NAR, was therefore introduced. This update includes (a) 34 861 poor binders and 12 683 non-binders of 1308 targets; (b) 534 prodrug-drug pairs for 121 targets; (c) 1127 co-targets of 672 targets regulated by 642 approved and 624 clinical trial drugs; (d) the collective structure-activity landscapes of 427 262 active agents of 1565 targets; (e) the profiles of drug-like properties of 33 598 agents of 1102 targets. Moreover, a variety of additional data and function are provided, which include the cross-links to the target structure in PDB and AlphaFold, 159 and 1658 newly emerged targets and drugs, and the advanced search function for multi-entry target sequences or drug structures. The database is accessible without login requirement at: https://idrblab.org/ttd/.
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Affiliation(s)
- Ying Zhou
- State Key Laboratory for Diagnosis and Treatment of Infectious Disease, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, Zhejiang Provincial Key Laboratory for Drug Clinical Research and Evaluation, The First Affiliated Hospital, Zhejiang University, 79 QingChun Road, Hangzhou, Zhejiang 310000, China
| | - Yintao Zhang
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Xichen Lian
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Fengcheng Li
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Chaoxin Wang
- Department of Computer Science, Kansas State University, Manhattan 66506, USA
| | - Feng Zhu
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China.,Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare, Hangzhou 330110, China
| | - Yunqing Qiu
- State Key Laboratory for Diagnosis and Treatment of Infectious Disease, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, Zhejiang Provincial Key Laboratory for Drug Clinical Research and Evaluation, The First Affiliated Hospital, Zhejiang University, 79 QingChun Road, Hangzhou, Zhejiang 310000, China
| | - Yuzong Chen
- State Key Laboratory of Chemical Oncogenomics, Key Laboratory of Chemical Biology, The Graduate School at Shenzhen, Tsinghua University, Shenzhen 518055, China.,Qian Xuesen Collaborative Research Center of Astrochemistry and Space Life Sciences, Institute of Drug Discovery Technology, Ningbo University, Ningbo 315211, China
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Abstract
It is well established that medicinal chemists should depart from the flat, sp2-dominated nature of traditional drugs and incorporate complexities of bioactive natural products, such as sp3-richness, 3D topology and chirality. There is a gray area, however, in the relevance of newly developed chemical scaffolds that exhibit these complexities but do not correlate to anything observed in nature. This can leave synthetic methodologists searching for structural similarities between their newly developed products and known natural products in search of justification. This article offers a perspective on how these types of complex 'abiotic' scaffolds can be appreciated purely on the basis of their structural novelty, and identifies the unique advantages arising when a complex chemical entity unrecognized by nature is introduced to biological systems.
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