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Liu Z, Bao Y, Wang W, Pan L, Wang H, Lin GN. Emden: A novel method integrating graph and transformer representations for predicting the effect of mutations on clinical drug response. Comput Biol Med 2023; 167:107678. [PMID: 37976823 DOI: 10.1016/j.compbiomed.2023.107678] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2023] [Revised: 10/22/2023] [Accepted: 11/06/2023] [Indexed: 11/19/2023]
Abstract
Precision medicine based on personalized genomics provides promising strategies to enhance the efficacy of molecular-targeted therapies. However, the clinical effectiveness of drugs has been severely limited due to genetic variations that lead to drug resistance. Predicting the impact of missense mutations on clinical drug response is an essential way to reduce the cost of clinical trials and understand genetic diseases. Here, we present Emden, a novel method integrating graph and transformer representations that predicts the effect of missense mutations on drug response through binary classification with interpretability. Emden utilized protein sequences-based features and drug structures as inputs for rapid prediction, employing competitive representation learning and demonstrating strong generalization capabilities and robustness. Our study showed promising potential for clinical drug guidance and deep insight into computer-assisted precision medicine. Emden is freely available as a web server at https://www.psymukb.net/Emden.
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Affiliation(s)
- Zhe Liu
- Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Yihang Bao
- Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Weidi Wang
- Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Liangwei Pan
- Department of Thoracic Surgery, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Han Wang
- School of Information Science and Technology, Institute of Computational Biology, Northeast Normal University, Changchun, China.
| | - Guan Ning Lin
- Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China; Shanghai Key Laboratory of Psychotic Disorders, Shanghai, China.
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2
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Huang YQ, Wang S, Gong DH, Kumar V, Dong YW, Hao GF. In silico resources help combat cancer drug resistance mediated by target mutations. Drug Discov Today 2023; 28:103686. [PMID: 37379904 DOI: 10.1016/j.drudis.2023.103686] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2023] [Revised: 05/31/2023] [Accepted: 06/20/2023] [Indexed: 06/30/2023]
Abstract
Drug resistance causes catastrophic cancer treatment failures. Mutations in target proteins with altered drug binding indicate a main mechanism of cancer drug resistance (CDR). Global research has generated considerable CDR-related data and well-established knowledge bases and predictive tools. Unfortunately, these resources are fragmented and underutilized. Here, we examine computational resources for exploring CDR caused by target mutations, analyzing these tools based on their functional characteristics, data capacity, data sources, methodologies and performance. We also discuss their disadvantages and provide examples of how potential inhibitors of CDR have been discovered using these resources. This toolkit is designed to help specialists explore resistance occurrence effectively and to explain resistance prediction to non-specialists easily.
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Affiliation(s)
- Yuan-Qin Huang
- National Key Laboratory of Green Pesticide, Key Laboratory of Green Pesticide and Agricultural Bioengineering, Ministry of Education, Guizhou University, Guiyang 550025, China
| | - Shuang Wang
- National Key Laboratory of Green Pesticide, Key Laboratory of Green Pesticide and Agricultural Bioengineering, Ministry of Education, Guizhou University, Guiyang 550025, China
| | - Dao-Hong Gong
- National Key Laboratory of Green Pesticide, Key Laboratory of Green Pesticide and Agricultural Bioengineering, Ministry of Education, Guizhou University, Guiyang 550025, China
| | - Vinit Kumar
- National Key Laboratory of Green Pesticide, Key Laboratory of Green Pesticide and Agricultural Bioengineering, Ministry of Education, Guizhou University, Guiyang 550025, China
| | - Ya-Wen Dong
- School of Pharmaceutical Sciences, Guizhou University, Guiyang 550025, China
| | - Ge-Fei Hao
- National Key Laboratory of Green Pesticide, Key Laboratory of Green Pesticide and Agricultural Bioengineering, Ministry of Education, Guizhou University, Guiyang 550025, China.
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Hahn DF, Bayly CI, Boby ML, Macdonald HEB, Chodera JD, Gapsys V, Mey ASJS, Mobley DL, Benito LP, Schindler CEM, Tresadern G, Warren GL. Best practices for constructing, preparing, and evaluating protein-ligand binding affinity benchmarks [Article v0.1]. LIVING JOURNAL OF COMPUTATIONAL MOLECULAR SCIENCE 2022; 4:1497. [PMID: 36382113 PMCID: PMC9662604 DOI: 10.33011/livecoms.4.1.1497] [Citation(s) in RCA: 22] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/01/2023]
Abstract
Free energy calculations are rapidly becoming indispensable in structure-enabled drug discovery programs. As new methods, force fields, and implementations are developed, assessing their expected accuracy on real-world systems (benchmarking) becomes critical to provide users with an assessment of the accuracy expected when these methods are applied within their domain of applicability, and developers with a way to assess the expected impact of new methodologies. These assessments require construction of a benchmark-a set of well-prepared, high quality systems with corresponding experimental measurements designed to ensure the resulting calculations provide a realistic assessment of expected performance when these methods are deployed within their domains of applicability. To date, the community has not yet adopted a common standardized benchmark, and existing benchmark reports suffer from a myriad of issues, including poor data quality, limited statistical power, and statistically deficient analyses, all of which can conspire to produce benchmarks that are poorly predictive of real-world performance. Here, we address these issues by presenting guidelines for (1) curating experimental data to develop meaningful benchmark sets, (2) preparing benchmark inputs according to best practices to facilitate widespread adoption, and (3) analysis of the resulting predictions to enable statistically meaningful comparisons among methods and force fields. We highlight challenges and open questions that remain to be solved in these areas, as well as recommendations for the collection of new datasets that might optimally serve to measure progress as methods become systematically more reliable. Finally, we provide a curated, versioned, open, standardized benchmark set adherent to these standards (PLBenchmarks) and an open source toolkit for implementing standardized best practices assessments (arsenic) for the community to use as a standardized assessment tool. While our main focus is free energy methods based on molecular simulations, these guidelines should prove useful for assessment of the rapidly growing field of machine learning methods for affinity prediction as well.
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Affiliation(s)
- David F. Hahn
- Computational Chemistry,Janssen Research & Development, Turnhoutseweg 30, Beerse B-2340, Belgium
| | | | - Melissa L. Boby
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY 10065 USA
| | - Hannah E. Bruce Macdonald
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY 10065 USA
- MSD R&D Innovation Centre, 120 Moorgate, London EC2M 6UR, United Kingdom
| | - John D. Chodera
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY 10065 USA
| | - Vytautas Gapsys
- Computational Biomolecular Dynamics Group, Max Planck Institute for Biophysical Chemistry, Göttingen, Germany
| | - Antonia S. J. S. Mey
- EaStCHEM School of Chemistry, David Brewster Road, Joseph Black Building, The King’s Buildings, Edinburgh, EH9 3FJ, UK
| | - David L. Mobley
- Departments of Pharmaceutical Sciences and Chemistry, University of California, Irvine, CA USA
| | - Laura Perez Benito
- Computational Chemistry,Janssen Research & Development, Turnhoutseweg 30, Beerse B-2340, Belgium
| | | | - Gary Tresadern
- Computational Chemistry,Janssen Research & Development, Turnhoutseweg 30, Beerse B-2340, Belgium
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Sun T, Chen Y, Wen Y, Zhu Z, Li M. PremPLI: a machine learning model for predicting the effects of missense mutations on protein-ligand interactions. Commun Biol 2021; 4:1311. [PMID: 34799678 PMCID: PMC8604987 DOI: 10.1038/s42003-021-02826-3] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2021] [Accepted: 10/26/2021] [Indexed: 02/07/2023] Open
Abstract
Resistance to small-molecule drugs is the main cause of the failure of therapeutic drugs in clinical practice. Missense mutations altering the binding of ligands to proteins are one of the critical mechanisms that result in genetic disease and drug resistance. Computational methods have made a lot of progress for predicting binding affinity changes and identifying resistance mutations, but their prediction accuracy and speed are still not satisfied and need to be further improved. To address these issues, we introduce a structure-based machine learning method for quantitatively estimating the effects of single mutations on ligand binding affinity changes (named as PremPLI). A comprehensive comparison of the predictive performance of PremPLI with other available methods on two benchmark datasets confirms that our approach performs robustly and presents similar or even higher predictive accuracy than the approaches relying on first-principle statistical mechanics and mixed physics- and knowledge-based potentials while requires much less computational resources. PremPLI can be used for guiding the design of ligand-binding proteins, identifying and understanding disease driver mutations, and finding potential resistance mutations for different drugs. PremPLI is freely available at https://lilab.jysw.suda.edu.cn/research/PremPLI/ and allows to do large-scale mutational scanning. Sun et al. present PremPLI, a machine learning approach and web tool to predict how missense mutations in a drug’s target protein will affect the drug’s binding affinity. PremPLI can be applied to identify drug resistance mechanisms in cancer cells and microorganisms and develop drugs to combat drug resistance.
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Affiliation(s)
- Tingting Sun
- Center for Systems Biology, Department of Bioinformatics, School of Biology and Basic Medical Sciences, Soochow University, 215123, Suzhou, China
| | - Yuting Chen
- Center for Systems Biology, Department of Bioinformatics, School of Biology and Basic Medical Sciences, Soochow University, 215123, Suzhou, China
| | - Yuhao Wen
- Center for Systems Biology, Department of Bioinformatics, School of Biology and Basic Medical Sciences, Soochow University, 215123, Suzhou, China
| | - Zefeng Zhu
- Center for Systems Biology, Department of Bioinformatics, School of Biology and Basic Medical Sciences, Soochow University, 215123, Suzhou, China
| | - Minghui Li
- Center for Systems Biology, Department of Bioinformatics, School of Biology and Basic Medical Sciences, Soochow University, 215123, Suzhou, China.
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King E, Aitchison E, Li H, Luo R. Recent Developments in Free Energy Calculations for Drug Discovery. Front Mol Biosci 2021; 8:712085. [PMID: 34458321 PMCID: PMC8387144 DOI: 10.3389/fmolb.2021.712085] [Citation(s) in RCA: 49] [Impact Index Per Article: 16.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2021] [Accepted: 07/27/2021] [Indexed: 01/11/2023] Open
Abstract
The grand challenge in structure-based drug design is achieving accurate prediction of binding free energies. Molecular dynamics (MD) simulations enable modeling of conformational changes critical to the binding process, leading to calculation of thermodynamic quantities involved in estimation of binding affinities. With recent advancements in computing capability and predictive accuracy, MD based virtual screening has progressed from the domain of theoretical attempts to real application in drug development. Approaches including the Molecular Mechanics Poisson Boltzmann Surface Area (MM-PBSA), Linear Interaction Energy (LIE), and alchemical methods have been broadly applied to model molecular recognition for drug discovery and lead optimization. Here we review the varied methodology of these approaches, developments enhancing simulation efficiency and reliability, remaining challenges hindering predictive performance, and applications to problems in the fields of medicine and biochemistry.
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Affiliation(s)
- Edward King
- Department of Molecular Biology and Biochemistry, University of California, Irvine, CA, United States
| | - Erick Aitchison
- Department of Molecular Biology and Biochemistry, University of California, Irvine, CA, United States
| | - Han Li
- Department of Chemical and Biomolecular Engineering, University of California, Irvine, CA, United States
| | - Ray Luo
- Department of Molecular Biology and Biochemistry, University of California, Irvine, CA, United States
- Department of Chemical and Biomolecular Engineering, University of California, Irvine, CA, United States
- Department of Materials Science and Engineering, University of California, Irvine, CA, United States
- Department of Biomedical Engineering, University of California, Irvine, CA, United States
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Vetrivel P, Kim SM, Ha SE, Kim HH, Bhosale PB, Senthil K, Kim GS. Compound Prunetin Induces Cell Death in Gastric Cancer Cell with Potent Anti-Proliferative Properties: In Vitro Assay, Molecular Docking, Dynamics, and ADMET Studies. Biomolecules 2020; 10:biom10071086. [PMID: 32708333 PMCID: PMC7408406 DOI: 10.3390/biom10071086] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2020] [Revised: 07/17/2020] [Accepted: 07/17/2020] [Indexed: 12/12/2022] Open
Abstract
Gastric cancer is the common type of malignancy positioned at second in mortality rate causing burden worldwide with increasing treatment options. Prunetin (PRU) is an O-methylated flavonoid that belongs to the group of isoflavone executing beneficial activities. In the present study, we investigated the anti-proliferative and cell death effect of the compound PRU in AGS gastric cancer cell line. The in vitro cytotoxic potential of PRU was evaluated and significant proliferation was observed. We identified that the mechanism of cell death was due to necroptosis through double staining and was confirmed by co-treatment with inhibitor necrostatin (Nec-1). We further elucidated the mechanism of action of necroptosis via receptor interacting protein kinase 3 (RIPK3) protein expression and it has been attributed by ROS generation through JNK activation. Furthermore, through computational analysis by molecular docking and dynamics simulation, the efficiency of compound prunetin against RIPK3 binding was validated. In addition, we also briefed the pharmacokinetic properties of the compound by in silico ADMET analysis.
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Affiliation(s)
- Preethi Vetrivel
- Research Institute of Life science and College of Veterinary Medicine, Gyeongsang National University, Gazwa, Jinju 52828, Korea; (P.V.); (S.M.K.); (S.E.H.); (H.H.K.); (P.B.B.)
| | - Seong Min Kim
- Research Institute of Life science and College of Veterinary Medicine, Gyeongsang National University, Gazwa, Jinju 52828, Korea; (P.V.); (S.M.K.); (S.E.H.); (H.H.K.); (P.B.B.)
| | - Sang Eun Ha
- Research Institute of Life science and College of Veterinary Medicine, Gyeongsang National University, Gazwa, Jinju 52828, Korea; (P.V.); (S.M.K.); (S.E.H.); (H.H.K.); (P.B.B.)
| | - Hun Hwan Kim
- Research Institute of Life science and College of Veterinary Medicine, Gyeongsang National University, Gazwa, Jinju 52828, Korea; (P.V.); (S.M.K.); (S.E.H.); (H.H.K.); (P.B.B.)
| | - Pritam Bhagwan Bhosale
- Research Institute of Life science and College of Veterinary Medicine, Gyeongsang National University, Gazwa, Jinju 52828, Korea; (P.V.); (S.M.K.); (S.E.H.); (H.H.K.); (P.B.B.)
| | - Kalaiselvi Senthil
- Department of Biochemistry, Biotechnology and Bioinformatics, Avinashilingam Institute for Home Science and Higher Education for Women, Coimbatore 641043, India;
| | - Gon Sup Kim
- Research Institute of Life science and College of Veterinary Medicine, Gyeongsang National University, Gazwa, Jinju 52828, Korea; (P.V.); (S.M.K.); (S.E.H.); (H.H.K.); (P.B.B.)
- Correspondence: ; Tel.: +82-010-3834-5823
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