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He J, Liu X, Zhu C, Zha J, Li Q, Zhao M, Wei J, Li M, Wu C, Wang J, Jiao Y, Ning S, Zhou J, Hong Y, Liu Y, He H, Zhang M, Chen F, Li Y, He X, Wu J, Lu S, Song K, Lu X, Zhang J. ASD2023: towards the integrating landscapes of allosteric knowledgebase. Nucleic Acids Res 2024; 52:D376-D383. [PMID: 37870448 PMCID: PMC10767950 DOI: 10.1093/nar/gkad915] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2023] [Revised: 09/22/2023] [Accepted: 10/06/2023] [Indexed: 10/24/2023] Open
Abstract
Allosteric regulation, induced by perturbations at an allosteric site topographically distinct from the orthosteric site, is one of the most direct and efficient ways to fine-tune macromolecular function. The Allosteric Database (ASD; accessible online at http://mdl.shsmu.edu.cn/ASD) has been systematically developed since 2009 to provide comprehensive information on allosteric regulation. In recent years, allostery has seen sustained growth and wide-ranging applications in life sciences, from basic research to new therapeutics development, while also elucidating emerging obstacles across allosteric research stages. To overcome these challenges and maintain high-quality data center services, novel features were curated in the ASD2023 update: (i) 66 589 potential allosteric sites, covering > 80% of the human proteome and constituting the human allosteric pocketome; (ii) 748 allosteric protein-protein interaction (PPI) modulators with clear mechanisms, aiding protein machine studies and PPI-targeted drug discovery; (iii) 'Allosteric Hit-to-Lead,' a pioneering dataset providing panoramic views from 87 well-defined allosteric hits to 6565 leads and (iv) 456 dualsteric modulators for exploring the simultaneous regulation of allosteric and orthosteric sites. Meanwhile, ASD2023 maintains a significant growth of foundational allosteric data. Based on these efforts, the allosteric knowledgebase is progressively evolving towards an integrated landscape, facilitating advancements in allosteric target identification, mechanistic exploration and drug discovery.
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Affiliation(s)
- Jixiao He
- State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Medicinal Chemistry and Bioinformatics Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Xinyi Liu
- State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Medicinal Chemistry and Bioinformatics Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Chunhao Zhu
- College of Pharmacy, Ningxia Medical University, 1160 Shengli Street, Yinchuan, Ningxia 750004, China
| | - Jinyin Zha
- State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Qian Li
- State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Medicinal Chemistry and Bioinformatics Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Mingzhu Zhao
- State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Medicinal Chemistry and Bioinformatics Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Jiacheng Wei
- State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Medicinal Chemistry and Bioinformatics Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Mingyu Li
- State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Medicinal Chemistry and Bioinformatics Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Chengwei Wu
- State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Medicinal Chemistry and Bioinformatics Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
- Department of Assisted Reproduction, Shanghai Ninth People's Hospital, Shanghai Jiao-Tong University School of Medicine (SJTU-SM), Shanghai 200011, China
| | - Junyuan Wang
- State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Department of Assisted Reproduction, Shanghai Ninth People's Hospital, Shanghai Jiao-Tong University School of Medicine (SJTU-SM), Shanghai 200011, China
| | - Yonglai Jiao
- State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Medicinal Chemistry and Bioinformatics Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Shaobo Ning
- State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Medicinal Chemistry and Bioinformatics Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Jiamin Zhou
- State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Medicinal Chemistry and Bioinformatics Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
- Department of Assisted Reproduction, Shanghai Ninth People's Hospital, Shanghai Jiao-Tong University School of Medicine (SJTU-SM), Shanghai 200011, China
| | - Yue Hong
- State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Medicinal Chemistry and Bioinformatics Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Yonghui Liu
- State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Medicinal Chemistry and Bioinformatics Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Hongxi He
- State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Department of Assisted Reproduction, Shanghai Ninth People's Hospital, Shanghai Jiao-Tong University School of Medicine (SJTU-SM), Shanghai 200011, China
| | - Mingyang Zhang
- State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Medicinal Chemistry and Bioinformatics Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Feiying Chen
- State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Medicinal Chemistry and Bioinformatics Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Yanxiu Li
- State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Medicinal Chemistry and Bioinformatics Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Xinheng He
- State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Medicinal Chemistry and Bioinformatics Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Jing Wu
- State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Medicinal Chemistry and Bioinformatics Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Shaoyong Lu
- State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Medicinal Chemistry and Bioinformatics Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Kun Song
- Nutshell Therapeutics, Shanghai 201210, China
| | - Xuefeng Lu
- Department of Assisted Reproduction, Shanghai Ninth People's Hospital, Shanghai Jiao-Tong University School of Medicine (SJTU-SM), Shanghai 200011, China
| | - Jian Zhang
- State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Medicinal Chemistry and Bioinformatics Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
- College of Pharmacy, Ningxia Medical University, 1160 Shengli Street, Yinchuan, Ningxia 750004, China
- School of Pharmaceutical Sciences, Zhengzhou University, Zhengzhou 450001, China
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Manelfi C, Gemei M, Talarico C, Cerchia C, Fava A, Lunghini F, Beccari AR. "Molecular Anatomy": a new multi-dimensional hierarchical scaffold analysis tool. J Cheminform 2021; 13:54. [PMID: 34301327 PMCID: PMC8299179 DOI: 10.1186/s13321-021-00526-y] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2020] [Accepted: 06/13/2021] [Indexed: 11/10/2022] Open
Abstract
The scaffold representation is widely employed to classify bioactive compounds on the basis of common core structures or correlate compound classes with specific biological activities. In this paper, we present a novel approach called "Molecular Anatomy" as a flexible and unbiased molecular scaffold-based metrics to cluster large set of compounds. We introduce a set of nine molecular representations at different abstraction levels, combined with fragmentation rules, to define a multi-dimensional network of hierarchically interconnected molecular frameworks. We demonstrate that the introduction of a flexible scaffold definition and multiple pruning rules is an effective method to identify relevant chemical moieties. This approach allows to cluster together active molecules belonging to different molecular classes, capturing most of the structure activity information, in particular when libraries containing a huge number of singletons are analyzed. We also propose a procedure to derive a network visualization that allows a full graphical representation of compounds dataset, permitting an efficient navigation in the scaffold's space and significantly contributing to perform high quality SAR analysis. The protocol is freely available as a web interface at https://ma.exscalate.eu .
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Affiliation(s)
- Candida Manelfi
- Dompé Farmaceutici SpA, Via Campo di Pile, 67100, L'Aquila, Italy
| | - Marica Gemei
- Dompé Farmaceutici SpA, Via Campo di Pile, 67100, L'Aquila, Italy
| | - Carmine Talarico
- Dompé Farmaceutici SpA, Via Campo di Pile, 67100, L'Aquila, Italy
| | - Carmen Cerchia
- Department of Pharmacy, University of Naples "Federico II", 80131, Napoli, Italy
| | - Anna Fava
- Dompé Farmaceutici SpA, Via Campo di Pile, 67100, L'Aquila, Italy
| | - Filippo Lunghini
- Dompé Farmaceutici SpA, Via Campo di Pile, 67100, L'Aquila, Italy
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Amin SA, Banerjee S, Gayen S, Jha T. Protease targeted COVID-19 drug discovery: What we have learned from the past SARS-CoV inhibitors? Eur J Med Chem 2021; 215:113294. [PMID: 33618158 PMCID: PMC7880840 DOI: 10.1016/j.ejmech.2021.113294] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2020] [Revised: 02/06/2021] [Accepted: 02/08/2021] [Indexed: 12/25/2022]
Abstract
The fascinating similarity between the SARS-CoV and SARS-CoV-2, inspires scientific community to investigate deeper into the SARS-CoV proteases such as main protease (Mpro) and papain-like protease (PLpro) and their inhibitors for the discovery of SARS-CoV-2 protease inhibitors. Because of the similarity in the proteases of these two corona viruses, there is a greater chance for the previous SARS-CoV Mpro and PLpro inhibitors to provide effective results against SARS-CoV-2. In this context, the molecular fragments from the SARS-CoV protease inhibitors through the fragment-based drug design and discovery technique can be useful guidance for COVID-19 drug discovery. Here, we have focused on the structure-activity relationship studies of previous SARS-CoV protease inhibitors and discussed about crucial fragments generated from previous SARS-CoV protease inhibitors important for the lead optimization of SARS-CoV-2 protease inhibitors. This study surely offers different strategic options of lead optimization to the medicinal chemists to discover effective anti-viral agent against the devastating disease, COVID-19.
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Affiliation(s)
- Sk Abdul Amin
- Natural Science Laboratory, Division of Medicinal and Pharmaceutical Chemistry, Department of Pharmaceutical Technology, P. O. Box 17020, Jadavpur University, Kolkata, 700032, India
| | - Suvankar Banerjee
- Natural Science Laboratory, Division of Medicinal and Pharmaceutical Chemistry, Department of Pharmaceutical Technology, P. O. Box 17020, Jadavpur University, Kolkata, 700032, India
| | - Shovanlal Gayen
- Laboratory of Drug Design and Discovery, Department of Pharmaceutical Sciences, Dr. Harisingh Gour University, Sagar, MP, India.
| | - Tarun Jha
- Natural Science Laboratory, Division of Medicinal and Pharmaceutical Chemistry, Department of Pharmaceutical Technology, P. O. Box 17020, Jadavpur University, Kolkata, 700032, India.
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Kruger F, Fechner N, Stiefl N. Automated Identification of Chemical Series: Classifying like a Medicinal Chemist. J Chem Inf Model 2020; 60:2888-2902. [PMID: 32374165 DOI: 10.1021/acs.jcim.0c00204] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
We investigate different automated approaches for the classification of chemical series in early drug discovery, with the aim of closely mimicking human chemical series conception. Chemical series, which are commonly defined by hand-drawn scaffolds, organize datasets in drug discovery projects. Often, they form the basis for further project decisions. To trace and evaluate these decisions in historic and ongoing projects, it is important to know or reconstruct chemical series. There is not a unique correct definition of chemical series, and the human definition certainly involves a subjective bias. Hence, we first develop quality metrics for the chemical series definitions, evaluating the size and specificity of chemical series. These metrics are applied to categorize human series definitions and implemented in automated classification approaches. For the automated classification of chemical series, we test different fragmentation and similarity-based clustering algorithms and apply different approaches to infer series definitions from these clusters or sets of fragments. We benchmark the classification results against human-defined series from 30 internal projects. The best results in reproducing the composition of human-defined series are achieved when applying UPGMA (unweighted pair group method with arithmetic mean) clustering to the project dataset and calculating maximum common substructures of the clusters as series definitions. We evaluate this approach in more detail on a public dataset and assess its robustness by 10-fold cross-validation, each time sampling 40% of the dataset. Through these benchmarking and validation experiments, we show that the proposed automated approach is able to accurately and robustly identify human-defined series, which comply with a certain, predefined level of specificity and size. Suggesting a thoroughly tested algorithm for series classification, as well as quality metrics for series and several benchmarking approaches, this work lays the foundation for further analysis of project decisions, and it offers an enhanced understanding of the properties of human-defined chemical series.
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Affiliation(s)
- Franziska Kruger
- Novartis Institutes for BioMedical Research, Novartis Pharma AG, Novartis Campus, 4002 Basel, Switzerland
| | - Nikolas Fechner
- Novartis Institutes for BioMedical Research, Novartis Pharma AG, Novartis Campus, 4002 Basel, Switzerland
| | - Nikolaus Stiefl
- Novartis Institutes for BioMedical Research, Novartis Pharma AG, Novartis Campus, 4002 Basel, Switzerland
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Amin SA, Ghosh K, Gayen S, Jha T. Chemical-informatics approach to COVID-19 drug discovery: Monte Carlo based QSAR, virtual screening and molecular docking study of some in-house molecules as papain-like protease (PLpro) inhibitors. J Biomol Struct Dyn 2020; 39:4764-4773. [PMID: 32568618 PMCID: PMC7332872 DOI: 10.1080/07391102.2020.1780946] [Citation(s) in RCA: 62] [Impact Index Per Article: 15.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
World Health Organization characterized novel coronavirus disease (COVID-19), caused by severe acute respiratory syndrome (SARS) coronavirus-2 (SARS-CoV-2) as world pandemic. This infection has been spreading alarmingly by causing huge social and economic disruption. In order to response quickly, the inhibitors already designed against different targets of previous human coronavirus infections will be a great starting point for anti-SARS-CoV-2 inhibitors. In this study, our approach integrates different ligand based drug design strategies of some in-house chemicals. The study design was composed of some major aspects: (a) classification QSAR based data mining of diverse SARS-CoV papain-like protease (PLpro) inhibitors, (b) QSAR based virtual screening (VS) to identify in-house molecules that could be effective against putative target SARS-CoV PLpro and (c) finally validation of hits through receptor-ligand interaction analysis. This approach could be used to aid in the process of COVID-19 drug discovery. It will introduce key concepts, set the stage for QSAR based screening of active molecules against putative SARS-CoV-2 PLpro enzyme. Moreover, the QSAR models reported here would be of further use to screen large database. This study will assume that the reader is approaching the field of QSAR and molecular docking based drug discovery against SARS-CoV-2 PLpro with little prior knowledge. Communicated by Ramaswamy H. Sarma
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Affiliation(s)
- Sk Abdul Amin
- Natural Science Laboratory, Division of Medicinal and Pharmaceutical Chemistry, Department of Pharmaceutical Technology, Jadavpur University, Kolkata, India
| | - Kalyan Ghosh
- Laboratory of Drug Design and Discovery, Department of Pharmaceutical Sciences, Dr. Harisingh Gour University, Sagar, India
| | - Shovanlal Gayen
- Laboratory of Drug Design and Discovery, Department of Pharmaceutical Sciences, Dr. Harisingh Gour University, Sagar, India
| | - Tarun Jha
- Natural Science Laboratory, Division of Medicinal and Pharmaceutical Chemistry, Department of Pharmaceutical Technology, Jadavpur University, Kolkata, India
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