1
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Eccleston RC, Manko E, Campino S, Clark TG, Furnham N. A computational method for predicting the most likely evolutionary trajectories in the stepwise accumulation of resistance mutations. eLife 2023; 12:e84756. [PMID: 38132182 PMCID: PMC10807863 DOI: 10.7554/elife.84756] [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: 11/07/2022] [Accepted: 12/21/2023] [Indexed: 12/23/2023] Open
Abstract
Pathogen evolution of drug resistance often occurs in a stepwise manner via the accumulation of multiple mutations that in combination have a non-additive impact on fitness, a phenomenon known as epistasis. The evolution of resistance via the accumulation of point mutations in the DHFR genes of Plasmodium falciparum (Pf) and Plasmodium vivax (Pv) has been studied extensively and multiple studies have shown epistatic interactions between these mutations determine the accessible evolutionary trajectories to highly resistant multiple mutations. Here, we simulated these evolutionary trajectories using a model of molecular evolution, parameterised using Rosetta Flex ddG predictions, where selection acts to reduce the target-drug binding affinity. We observe strong agreement with pathways determined using experimentally measured IC50 values of pyrimethamine binding, which suggests binding affinity is strongly predictive of resistance and epistasis in binding affinity strongly influences the order of fixation of resistance mutations. We also infer pathways directly from the frequency of mutations found in isolate data, and observe remarkable agreement with the most likely pathways predicted by our mechanistic model, as well as those determined experimentally. This suggests mutation frequency data can be used to intuitively infer evolutionary pathways, provided sufficient sampling of the population.
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Affiliation(s)
- Ruth Charlotte Eccleston
- Department of Infection Biology, London School of Hygiene and Tropical MedicineLondonUnited Kingdom
| | - Emilia Manko
- Department of Infection Biology, London School of Hygiene and Tropical MedicineLondonUnited Kingdom
| | - Susana Campino
- Department of Infection Biology, London School of Hygiene and Tropical MedicineLondonUnited Kingdom
| | - Taane G Clark
- Department of Infection Biology, London School of Hygiene and Tropical MedicineLondonUnited Kingdom
- Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical MedicineLondonUnited Kingdom
| | - Nicholas Furnham
- Department of Infection Biology, London School of Hygiene and Tropical MedicineLondonUnited Kingdom
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2
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Velasco-Saavedra MA, Mar-Antonio E, Aguayo-Ortiz R. Molecular Insights into the Covalent Binding of Zoxamide to the β-Tubulin of Botrytis cinerea. J Chem Inf Model 2023; 63:6386-6395. [PMID: 37802126 DOI: 10.1021/acs.jcim.3c00911] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/08/2023]
Abstract
Botrytis cinerea is a fungal plant pathogen that causes significant economic losses in the agricultural industry worldwide. Fungicides that target microtubules, such as carbendazim (CBZ), diethofencarb (DEF), and zoxamide (ZOX), are widely used in crop protection against this pathogen. These groups of compounds exert their fungicidal activity by disrupting the microtubule assembly by binding to the β-tubulin subunit, provoking cell-cycle arrest and cell death. However, with the appearance of isolates resistant to these compounds, it is necessary to search for new alternatives to control this pathogenic fungus. In this work, we gained insight into the binding and stability of these fungicides in the benzimidazole binding site of B. cinerea β-tubulin through different computational approaches. Our molecular dynamics simulation replicas showed that R enantiomers of ZOX and its analog RH-4032 had better interaction profiles at the site compared to S enantiomers. The simulations also revealed that while the R-isomer fungicides formed H-bonds with the main chain carbonyl of V236 or the side chain residue of S314, only CBZ interacted with E198. Previous experimental data have identified key mutations in B. cinerea's β-tubulin gene that lead to the development of resistance or, on the contrary, increased sensitivity for treatment with these fungicide compounds. In agreement with experimental findings, alchemical free energy calculations showed that E198A and E198V mutations in B. cinerea β-tubulin have high sensitivity to (R)-ZOX, whereas the E198K mutation decreased its affinity. Similarly, the results obtained explain the resistance to CBZ of B. cinerea isolates with E198A/V/K mutations and the insensitivity of the wild-type organism to DEF. Our work provides a deeper insight into the molecular mechanism of action of these fungicides, highlighting the importance of understanding the interaction profiles to develop more effective antifungal agents.
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Affiliation(s)
- M Andrés Velasco-Saavedra
- Departamento de Farmacia, Facultad de Química, Universidad Nacional Autónoma de México, Mexico City 04510, Mexico
| | - Efrén Mar-Antonio
- Departamento de Farmacia, Facultad de Química, Universidad Nacional Autónoma de México, Mexico City 04510, Mexico
| | - Rodrigo Aguayo-Ortiz
- Departamento de Farmacia, Facultad de Química, Universidad Nacional Autónoma de México, Mexico City 04510, Mexico
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3
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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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4
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Zhuravleva SI, Zadorozhny AD, Shilov BV, Lagunin AA. Prediction of Amino Acid Substitutions in ABL1 Protein Leading to Tumor Drug Resistance Based on "Structure-Property" Relationship Classification Models. Life (Basel) 2023; 13:1807. [PMID: 37763211 PMCID: PMC10532460 DOI: 10.3390/life13091807] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2023] [Revised: 08/15/2023] [Accepted: 08/21/2023] [Indexed: 09/29/2023] Open
Abstract
Drug resistance to anticancer drugs is a serious complication in patients with cancer. Typically, drug resistance occurs due to amino acid substitutions (AAS) in drug target proteins. The study aimed at developing and validating a new approach to the creation of structure-property relationships (SPR) classification models to predict AASs leading to drug resistance to inhibitors of tyrosine-protein kinase ABL1. The approach was based on the representation of AASs as peptides described in terms of structural formulas. The data on drug-resistant and non-resistant variants of AAS for two isoforms of ABL1 were extracted from the COSMIC database. The given training sets (approximately 700 missense variants) were used for the creation of SPR models in MultiPASS software based on substructural atom-centric multiple neighborhoods of atom (MNA) descriptors for the description of the structural formula of protein fragments and a Bayesian-like algorithm for revealing structure-property relationships. It was found that MNA descriptors of the 6th level and peptides from 11 amino acid residues were the best combination for ABL1 isoform 1 with the prediction accuracy (AUC) of resistance to imatinib (0.897) and dasatinib (0.996). For ABL1 isoform 2 (resistance to imatinib), the best combination was MNA descriptors of the 6th level, peptides form 15 amino acids (AUC value was 0.909). The prediction of possible drug-resistant AASs was made for dbSNP and gnomAD data. The six selected most probable imatinib-resistant AASs were additionally validated by molecular modeling and docking, which confirmed the possibility of resistance for the E334V and T392I variants.
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Affiliation(s)
- Svetlana I. Zhuravleva
- Department of Bioinformatics, Pirogov Russian National Research Medical University, 117997 Moscow, Russia; (S.I.Z.); (A.D.Z.); (B.V.S.)
| | - Anton D. Zadorozhny
- Department of Bioinformatics, Pirogov Russian National Research Medical University, 117997 Moscow, Russia; (S.I.Z.); (A.D.Z.); (B.V.S.)
| | - Boris V. Shilov
- Department of Bioinformatics, Pirogov Russian National Research Medical University, 117997 Moscow, Russia; (S.I.Z.); (A.D.Z.); (B.V.S.)
| | - Alexey A. Lagunin
- Department of Bioinformatics, Pirogov Russian National Research Medical University, 117997 Moscow, Russia; (S.I.Z.); (A.D.Z.); (B.V.S.)
- Department of Bioinformatics, Institute of Biomedical Chemistry, 119121 Moscow, Russia
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5
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Bello M, Bandala C. Evaluating the ability of end-point methods to predict the binding affinity tendency of protein kinase inhibitors. RSC Adv 2023; 13:25118-25128. [PMID: 37614784 PMCID: PMC10443623 DOI: 10.1039/d3ra04916g] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2023] [Accepted: 08/14/2023] [Indexed: 08/25/2023] Open
Abstract
Because of the high economic cost of exploring the experimental impact of mutations occurring in kinase proteins, computational approaches have been employed as alternative methods for evaluating the structural and energetic aspects of kinase mutations. Among the main computational methods used to explore the affinity linked to kinase mutations are docking procedures and molecular dynamics (MD) simulations combined with end-point methods or alchemical methods. Although it is known that end-point methods are not able to reproduce experimental binding free energy (ΔG) values, it is also true that they are able to discriminate between a better or a worse ligand through the estimation of ΔG. In this contribution, we selected ten wild-type and mutant cocrystallized EGFR-inhibitor complexes containing experimental binding affinities to evaluate whether MMGBSA or MMPBSA approaches can predict the differences in affinity between the wild type and mutants forming a complex with a similar inhibitor. Our results show that a long MD simulation (the last 50 ns of a 100 ns-long MD simulation) using the MMGBSA method without considering the entropic components reproduced the experimental affinity tendency with a Pearson correlation coefficient of 0.779 and an R2 value of 0.606. On the other hand, the correlation between theoretical and experimental ΔΔG values indicates that the MMGBSA and MMPBSA methods are helpful for obtaining a good correlation using a short rather than a long simulation period.
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Affiliation(s)
- Martiniano Bello
- Laboratorio de Diseño y Desarrollo de Nuevos Fármacos e Innovación Biotecnológica, Escuela Superior de Medicina, Instituto Politécnico Nacional, Plan de San Luis y Diaz Mirón s/n, Col. Casco de Santo Tomas Ciudad de México 11340 Mexico
| | - Cindy Bandala
- Escuela Superior de Medicina, Instituto Politécnico Nacional México City 11340 Mexico
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6
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Zhang I, Rufa DA, Pulido I, Henry MM, Rosen LE, Hauser K, Singh S, Chodera JD. Identifying and Overcoming the Sampling Challenges in Relative Binding Free Energy Calculations of a Model Protein:Protein Complex. J Chem Theory Comput 2023; 19:4863-4882. [PMID: 37450482 PMCID: PMC11219094 DOI: 10.1021/acs.jctc.3c00333] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/18/2023]
Abstract
Relative alchemical binding free energy calculations are routinely used in drug discovery projects to optimize the affinity of small molecules for their drug targets. Alchemical methods can also be used to estimate the impact of amino acid mutations on protein:protein binding affinities, but these calculations can involve sampling challenges due to the complex networks of protein and water interactions frequently present in protein:protein interfaces. We investigate these challenges by extending a graphics processing unit (GPU)-accelerated open-source relative free energy calculation package (Perses) to predict the impact of amino acid mutations on protein:protein binding. Using the well-characterized model system barnase:barstar, we describe analyses for identifying and characterizing sampling problems in protein:protein relative free energy calculations. We find that mutations with sampling problems often involve charge-changes, and inadequate sampling can be attributed to slow degrees of freedom that are mutation-specific. We also explore the accuracy and efficiency of current state-of-the-art approaches─alchemical replica exchange and alchemical replica exchange with solute tempering─for overcoming relevant sampling problems. By employing sufficiently long simulations, we achieve accurate predictions (RMSE 1.61, 95% CI: [1.12, 2.11] kcal/mol), with 86% of estimates within 1 kcal/mol of the experimentally determined relative binding free energies and 100% of predictions correctly classifying the sign of the changes in binding free energies. Ultimately, we provide a model workflow for applying protein mutation free energy calculations to protein:protein complexes, and importantly, catalog the sampling challenges associated with these types of alchemical transformations. Our free open-source package (Perses) is based on OpenMM and is available at https://github.com/choderalab/perses.
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Affiliation(s)
- Ivy Zhang
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY 10065
- Tri-Institutional PhD Program in Computational Biology and Medicine, Weill Cornell Medical College, Cornell University, New York, NY 10065
| | - Dominic A. Rufa
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY 10065
- Tri-Institutional PhD Program in Chemical Biology, Weill Cornell Medical College, Cornell University, New York, NY 10065
| | - Iván Pulido
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY 10065
| | - Michael M. Henry
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY 10065
| | | | | | - Sukrit Singh
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY 10065
| | - John D. Chodera
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY 10065
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7
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Narkhede YB, Bhardwaj A, Motsa BB, Saxena R, Sharma T, Chapagain PP, Stahelin RV, Wiest O. Elucidating Residue-Level Determinants Affecting Dimerization of Ebola Virus Matrix Protein Using High-Throughput Site Saturation Mutagenesis and Biophysical Approaches. J Phys Chem B 2023; 127:6449-6461. [PMID: 37458567 DOI: 10.1021/acs.jpcb.3c01759] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/28/2023]
Abstract
The Ebola virus (EBOV) is a filamentous virus that acquires its lipid envelope from the plasma membrane of the host cell it infects. EBOV assembly and budding from the host cell plasma membrane are mediated by a peripheral protein, known as the matrix protein VP40. VP40 is a 326 amino acid protein with two domains that are loosely linked. The VP40 N-terminal domain (NTD) contains a hydrophobic α-helix, which mediates VP40 dimerization. The VP40 C-terminal domain has a cationic patch, which mediates interactions with anionic lipids and a hydrophobic region that mediates VP40 dimer-dimer interactions. The VP40 dimer is necessary for trafficking to the plasma membrane inner leaflet and interactions with anionic lipids to mediate the VP40 assembly and oligomerization. Despite significant structural information available on the VP40 dimer structure, little is known on how the VP40 dimer is stabilized and how residues outside the NTD hydrophobic portion of the α-helical dimer interface contribute to dimer stability. To better understand how VP40 dimer stability is maintained, we performed computational studies using per-residue energy decomposition and site saturation mutagenesis. These studies revealed a number of novel keystone residues for VP40 dimer stability just adjacent to the α-helical dimer interface as well as distant residues in the VP40 CTD that can stabilize the VP40 dimer form. Experimental studies with representative VP40 mutants in vitro and in cells were performed to test computational predictions that reveal residues that alter VP40 dimer stability. Taken together, these studies provide important biophysical insights into VP40 dimerization and may be useful in strategies to weaken or alter the VP40 dimer structure as a means of inhibiting the EBOV assembly.
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Affiliation(s)
- Yogesh B Narkhede
- Department of Chemistry and Biochemistry, University of Notre Dame, Notre Dame, Indiana 46556, United States
| | - Atul Bhardwaj
- Department of Chemistry and Biochemistry, University of Notre Dame, Notre Dame, Indiana 46556, United States
| | - Balindile B Motsa
- Department of Medicinal Chemistry & Molecular Pharmacology, Purdue Institute of Inflammation, Immunology, and Infectious Disease, Purdue University, West Lafayette, Indiana 47907, United States
| | - Roopashi Saxena
- Department of Medicinal Chemistry & Molecular Pharmacology, Purdue Institute of Inflammation, Immunology, and Infectious Disease, Purdue University, West Lafayette, Indiana 47907, United States
| | | | | | - Robert V Stahelin
- Department of Medicinal Chemistry & Molecular Pharmacology, Purdue Institute of Inflammation, Immunology, and Infectious Disease, Purdue University, West Lafayette, Indiana 47907, United States
| | - Olaf Wiest
- Department of Chemistry and Biochemistry, University of Notre Dame, Notre Dame, Indiana 46556, United States
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8
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Yang Z, Ye Z, Qiu J, Feng R, Li D, Hsieh C, Allcock J, Zhang S. A mutation-induced drug resistance database (MdrDB). Commun Chem 2023; 6:123. [PMID: 37316673 DOI: 10.1038/s42004-023-00920-7] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2023] [Accepted: 06/02/2023] [Indexed: 06/16/2023] Open
Abstract
Mutation-induced drug resistance is a significant challenge to the clinical treatment of many diseases, as structural changes in proteins can diminish drug efficacy. Understanding how mutations affect protein-ligand binding affinities is crucial for developing new drugs and therapies. However, the lack of a large-scale and high-quality database has hindered the research progresses in this area. To address this issue, we have developed MdrDB, a database that integrates data from seven publicly available datasets, which is the largest database of its kind. By integrating information on drug sensitivity and cell line mutations from Genomics of Drug Sensitivity in Cancer and DepMap, MdrDB has substantially expanded the existing drug resistance data. MdrDB is comprised of 100,537 samples of 240 proteins (which encompass 5119 total PDB structures), 2503 mutations, and 440 drugs. Each sample brings together 3D structures of wild type and mutant protein-ligand complexes, binding affinity changes upon mutation (ΔΔG), and biochemical features. Experimental results with MdrDB demonstrate its effectiveness in significantly enhancing the performance of commonly used machine learning models when predicting ΔΔG in three standard benchmarking scenarios. In conclusion, MdrDB is a comprehensive database that can advance the understanding of mutation-induced drug resistance, and accelerate the discovery of novel chemicals.
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Affiliation(s)
- Ziyi Yang
- Tencent Quantum Laboratory, Shenzhen, 518057, Guangdong, China
| | - Zhaofeng Ye
- Tencent Quantum Laboratory, Shenzhen, 518057, Guangdong, China
| | - Jiezhong Qiu
- Tencent Quantum Laboratory, Shenzhen, 518057, Guangdong, China
| | - Rongjun Feng
- Tencent Quantum Laboratory, Shenzhen, 518057, Guangdong, China
| | - Danyu Li
- Tencent Quantum Laboratory, Shenzhen, 518057, Guangdong, China
| | - Changyu Hsieh
- Tencent Quantum Laboratory, Shenzhen, 518057, Guangdong, China
| | | | - Shengyu Zhang
- Tencent Quantum Laboratory, Shenzhen, 518057, Guangdong, China.
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9
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Huang YQ, Sun P, Chen Y, Liu HX, Hao GF, Song BA. Bioinformatics toolbox for exploring target mutation-induced drug resistance. Brief Bioinform 2023; 24:7026012. [PMID: 36738254 DOI: 10.1093/bib/bbad033] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2022] [Revised: 12/25/2022] [Accepted: 01/14/2023] [Indexed: 02/05/2023] Open
Abstract
Drug resistance is increasingly among the main issues affecting human health and threatening agriculture and food security. In particular, developing approaches to overcome target mutation-induced drug resistance has long been an essential part of biological research. During the past decade, many bioinformatics tools have been developed to explore this type of drug resistance, and they have become popular for elucidating drug resistance mechanisms in a low cost, fast and effective way. However, these resources are scattered and underutilized, and their strengths and limitations have not been systematically analyzed and compared. Here, we systematically surveyed 59 freely available bioinformatics tools for exploring target mutation-induced drug resistance. We analyzed and summarized these resources based on their functionality, data volume, data source, operating principle, performance, etc. And we concisely discussed the strengths, limitations and application examples of these tools. Specifically, we tested some predictive tools and offered some thoughts from the clinician's perspective. Hopefully, this work will provide a useful toolbox for researchers working in the biomedical, pesticide, bioinformatics and pharmaceutical engineering fields, and a good platform for non-specialists to quickly understand drug resistance prediction.
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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, P. R. China
| | - Ping Sun
- National Key Laboratory of Green Pesticide, Key Laboratory of Green Pesticide and Agricultural Bioengineering, Ministry of Education, Guizhou University, Guiyang 550025, P. R. China
| | - Yi Chen
- National Key Laboratory of Green Pesticide, Key Laboratory of Green Pesticide and Agricultural Bioengineering, Ministry of Education, Guizhou University, Guiyang 550025, P. R. China
| | - Huan-Xiang Liu
- Faculty of Applied Science, Macao Polytechnic University, Macao 999078, SAR, 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, P. R. China
| | - Bao-An Song
- National Key Laboratory of Green Pesticide, Key Laboratory of Green Pesticide and Agricultural Bioengineering, Ministry of Education, Guizhou University, Guiyang 550025, P. R. China
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10
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Suriñach A, Hospital A, Westermaier Y, Jordà L, Orozco-Ruiz S, Beltrán D, Colizzi F, Andrio P, Soliva R, Municoy M, Gelpí JL, Orozco M. High-Throughput Prediction of the Impact of Genetic Variability on Drug Sensitivity and Resistance Patterns for Clinically Relevant Epidermal Growth Factor Receptor Mutations from Atomistic Simulations. J Chem Inf Model 2023; 63:321-334. [PMID: 36576351 DOI: 10.1021/acs.jcim.2c01344] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Abstract
Mutations in the kinase domain of the epidermal growth factor receptor (EGFR) can be drivers of cancer and also trigger drug resistance in patients receiving chemotherapy treatment based on kinase inhibitors. A priori knowledge of the impact of EGFR variants on drug sensitivity would help to optimize chemotherapy and design new drugs that are effective against resistant variants before they emerge in clinical trials. To this end, we explored a variety of in silico methods, from sequence-based to "state-of-the-art" atomistic simulations. We did not find any sequence signal that can provide clues on when a drug-related mutation appears or the impact of such mutations on drug activity. Low-level simulation methods provide limited qualitative information on regions where mutations are likely to cause alterations in drug activity, and they can predict around 70% of the impact of mutations on drug efficiency. High-level simulations based on nonequilibrium alchemical free energy calculations show predictive power. The integration of these "state-of-the-art" methods into a workflow implementing an interface for parallel distribution of the calculations allows its automatic and high-throughput use, even for researchers with moderate experience in molecular simulations.
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Affiliation(s)
- Aristarc Suriñach
- Nostrum Biodiscovery, Av. Josep Tarradellas 8-10, 08029 Barcelona, Spain
| | - Adam Hospital
- Institute for Research in Biomedicine (IRB Barcelona), Barcelona Institute of Science and Technology, Barcelona 08028, Spain
| | - Yvonne Westermaier
- Nostrum Biodiscovery, Av. Josep Tarradellas 8-10, 08029 Barcelona, Spain
| | - Luis Jordà
- Barcelona Supercomputing Center (BSC), Plaça Eusebi Güell, 1-3, Barcelona 08034, Spain
| | - Sergi Orozco-Ruiz
- Barcelona Supercomputing Center (BSC), Plaça Eusebi Güell, 1-3, Barcelona 08034, Spain
| | - Daniel Beltrán
- Institute for Research in Biomedicine (IRB Barcelona), Barcelona Institute of Science and Technology, Barcelona 08028, Spain
| | - Francesco Colizzi
- Institute for Research in Biomedicine (IRB Barcelona), Barcelona Institute of Science and Technology, Barcelona 08028, Spain
| | - Pau Andrio
- Barcelona Supercomputing Center (BSC), Plaça Eusebi Güell, 1-3, Barcelona 08034, Spain
| | - Robert Soliva
- Nostrum Biodiscovery, Av. Josep Tarradellas 8-10, 08029 Barcelona, Spain
| | - Martí Municoy
- Nostrum Biodiscovery, Av. Josep Tarradellas 8-10, 08029 Barcelona, Spain
| | - Josep Lluís Gelpí
- Barcelona Supercomputing Center (BSC), Plaça Eusebi Güell, 1-3, Barcelona 08034, Spain.,Department Biochemistry and Molecular Biomedicine, University of Barcelona, Barcelona 08029, Spain
| | - Modesto Orozco
- Institute for Research in Biomedicine (IRB Barcelona), Barcelona Institute of Science and Technology, Barcelona 08028, Spain.,Department Biochemistry and Molecular Biomedicine, University of Barcelona, Barcelona 08029, Spain
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11
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Brankin AE, Fowler PW. Predicting antibiotic resistance in complex protein targets using alchemical free energy methods. J Comput Chem 2022; 43:1771-1782. [PMID: 36054249 PMCID: PMC9545121 DOI: 10.1002/jcc.26979] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2022] [Revised: 07/26/2022] [Accepted: 07/26/2022] [Indexed: 11/10/2022]
Abstract
Drug resistant Mycobacterium tuberculosis, which mostly results from single nucleotide polymorphisms in antibiotic target genes, poses a major threat to tuberculosis treatment outcomes. Relative binding free energy (RBFE) calculations can rapidly predict the effects of mutations, but this approach has not been tested on large, complex proteins. We use RBFE calculations to predict the effects of M. tuberculosis RNA polymerase and DNA gyrase mutations on rifampicin and moxifloxacin susceptibility respectively. These mutations encompass a range of amino acid substitutions with known effects and include large steric perturbations and charged moieties. We find that moderate numbers (n = 3-15) of short RBFE calculations can predict resistance in cases where the mutation results in a large change in the binding free energy. We show that the method lacks discrimination in cases with either a small change in energy or that involve charged amino acids, and we investigate how these calculation errors may be decreased.
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Affiliation(s)
- Alice E. Brankin
- Nuffield Department of Medicine, John Radcliffe HospitalUniversity of OxfordOxfordUK
| | - Philip W. Fowler
- Nuffield Department of Medicine, John Radcliffe HospitalUniversity of OxfordOxfordUK
- National Institute of Health Research Oxford Biomedical Research CentreJohn Radcliffe HospitalOxfordUK
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12
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Flores-León CD, Dominguez L, Aguayo-Ortiz R. Molecular basis of Toxoplasma gondii oryzalin resistance from a novel α-tubulin binding site model. Arch Biochem Biophys 2022; 730:109398. [PMID: 36116504 DOI: 10.1016/j.abb.2022.109398] [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: 08/18/2022] [Accepted: 09/12/2022] [Indexed: 11/18/2022]
Abstract
Oryzalin (ORY) is a dinitroaniline derivative that inhibits the microtubule polymerization in plants and parasitic protozoa by selectively binding to the α-tubulin subunit. This herbicidal agent exhibits good antiprotozoal activity against major human parasites, such as Toxoplasma gondii (toxoplasmosis), Leishmania mexicana (leishmaniasis), and Plasmodium falciparum (malaria). Previous chemical mutagenesis assays on T. gondii α-tubulin (TgAT) have identified key mutations that lead to ORY resistance. Herein, we employed alchemical free energy methods and molecular dynamics simulations to determine if the ORY resistance mutations either decrease the TgAT's affinity of the compound or increase the protein stability. Our results here suggest that L136F and V202F mutations significantly decrease the affinity of ORY to TgAT, while T239I and V252L mutations diminish TgAT's flexibility. On the other hand, protein stability predictors determined that R243S mutation reduces TgAT stability due to the loss of its salt bridge interaction with E27. Interestingly, molecular dynamics simulations confirm that the loss of this key interaction leads to ORY binding site closure. Our study provides a better insight into the TgAT-ORY interaction, further supporting our recently proposed ORY-binding site.
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Affiliation(s)
- Carlos D Flores-León
- Departamento de Farmacia, Facultad de Química, Universidad Nacional Autónoma de México, Mexico City, 04510, Mexico
| | - Laura Dominguez
- Departamento de Fisicoquímica, Facultad de Química, Universidad Nacional Autónoma de México, Mexico City, 04510, Mexico
| | - Rodrigo Aguayo-Ortiz
- Departamento de Farmacia, Facultad de Química, Universidad Nacional Autónoma de México, Mexico City, 04510, Mexico.
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13
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Yu Y, Wang Z, Wang L, Tian S, Hou T, Sun H. Predicting the mutation effects of protein–ligand interactions via end-point binding free energy calculations: strategies and analyses. J Cheminform 2022; 14:56. [PMID: 35987841 PMCID: PMC9392442 DOI: 10.1186/s13321-022-00639-y] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2022] [Accepted: 08/08/2022] [Indexed: 12/04/2022] Open
Abstract
Protein mutations occur frequently in biological systems, which may impact, for example, the binding of drugs to their targets through impairing the critical H-bonds, changing the hydrophobic interactions, etc. Thus, accurately predicting the effects of mutations on biological systems is of great interests to various fields. Unfortunately, it is still unavailable to conduct large-scale wet-lab mutation experiments because of the unaffordable experimental time and financial costs. Alternatively, in silico computation can serve as a pioneer to guide the experiments. In fact, numerous pioneering works have been conducted from computationally cheaper machine-learning (ML) methods to the more expensive alchemical methods with the purpose to accurately predict the mutation effects. However, these methods usually either cannot result in a physically understandable model (ML-based methods) or work with huge computational resources (alchemical methods). Thus, compromised methods with good physical characteristics and high computational efficiency are expected. Therefore, here, we conducted a comprehensive investigation on the mutation issues of biological systems with the famous end-point binding free energy calculation methods represented by MM/GBSA and MM/PBSA. Different computational strategies considering different length of MD simulations, different value of dielectric constants and whether to incorporate entropy effects to the predicted total binding affinities were investigated to provide a more accurate way for predicting the energetic change upon protein mutations. Overall, our result shows that a relatively long MD simulation (e.g. 100 ns) benefits the prediction accuracy for both MM/GBSA and MM/PBSA (with the best Pearson correlation coefficient between the predicted ∆∆G and the experimental data of ~ 0.44 for a challenging dataset). Further analyses shows that systems involving large perturbations (e.g. multiple mutations and large number of atoms change in the mutation site) are much easier to be accurately predicted since the algorithm works more sensitively to the large change of the systems. Besides, system-specific investigation reveals that conformational adjustment is needed to refine the micro-environment of the manually mutated systems and thus lead one to understand why longer MD simulation is necessary to improve the predicting result. The proposed strategy is expected to be applied in large-scale mutation effects investigation with interpretation.
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14
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da Silva GM, Yang J, Leang B, Huang J, Weinreich DM, Rubenstein BM. Covalent docking and molecular dynamics simulations reveal the specificity-shifting mutations Ala237Arg and Ala237Lys in TEM beta-lactamase. PLoS Comput Biol 2022; 18:e1009944. [PMID: 35759512 PMCID: PMC9269908 DOI: 10.1371/journal.pcbi.1009944] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2022] [Revised: 07/08/2022] [Accepted: 06/01/2022] [Indexed: 11/18/2022] Open
Abstract
The rate of modern drug discovery using experimental screening methods still lags behind the rate at which pathogens mutate, underscoring the need for fast and accurate predictive simulations of protein evolution. Multidrug-resistant bacteria evade our defenses by expressing a series of proteins, the most famous of which is the 29-kilodalton enzyme, TEM β-lactamase. Considering these challenges, we applied a covalent docking heuristic to measure the effects of all possible alanine 237 substitutions in TEM due to this codon’s importance for catalysis and effects on the binding affinities of commercially-available β-lactam compounds. In addition to the usual mutations that reduce substrate binding due to steric hindrance, we identified two distinctive specificity-shifting TEM mutations, Ala237Arg and Ala237Lys, and their respective modes of action. Notably, we discovered and verified through minimum inhibitory concentration assays that, while these mutations and their bulkier side chains lead to steric clashes that curtail ampicillin binding, these same groups foster salt bridges with the negatively-charged side-chain of the cephalosporin cefixime, widely used in the clinic to treat multi-resistant bacterial infections. To measure the stability of these unexpected interactions, we used molecular dynamics simulations and found the binding modes to be stable despite the application of biasing forces. Finally, we found that both TEM mutants also bind strongly to other drugs containing negatively-charged R-groups, such as carumonam and ceftibuten. As with cefixime, this increased binding affinity stems from a salt bridge between the compounds’ negative moieties and the positively-charged side chain of the arginine or lysine, suggesting a shared mechanism. In addition to reaffirming the power of using simulations as molecular microscopes, our results can guide the rational design of next-generation β-lactam antibiotics and bring the community closer to retaking the lead against the recurrent threat of multidrug-resistant pathogens.
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Affiliation(s)
- Gabriel Monteiro da Silva
- Department of Molecular and Cell Biology, Brown University, Providence, Rhode Island, United States of America
- * E-mail:
| | - Jordan Yang
- Department of Chemistry, Brown University, Providence, Rhode Island, United States of America
| | - Bunlong Leang
- Department of Health and Human Biology, Brown University, Providence, Rhode Island, United States of America
| | - Jessie Huang
- Department of Chemistry, Wellesley College, Wellesley, Massachusetts, United States of America
| | - Daniel M. Weinreich
- Department of Ecology and Evolutionary Biology, Brown University, Providence, Rhode Island, United States of America
| | - Brenda M. Rubenstein
- Department of Chemistry, Brown University, Providence, Rhode Island, United States of America
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15
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Gorostiola González M, Janssen APA, IJzerman AP, Heitman LH, van Westen GJP. Oncological drug discovery: AI meets structure-based computational research. Drug Discov Today 2022; 27:1661-1670. [PMID: 35301149 DOI: 10.1016/j.drudis.2022.03.005] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2021] [Revised: 01/22/2022] [Accepted: 03/09/2022] [Indexed: 02/08/2023]
Abstract
The integration of machine learning and structure-based methods has proven valuable in the past as a way to prioritize targets and compounds in early drug discovery. In oncological research, these methods can be highly beneficial in addressing the diversity of neoplastic diseases portrayed by the different hallmarks of cancer. Here, we review six use case scenarios for integrated computational methods, namely driver prediction, computational mutagenesis, (off)-target prediction, binding site prediction, virtual screening, and allosteric modulation analysis. We address the heterogeneity of integration approaches and individual methods, while acknowledging their current limitations and highlighting their potential to bring drugs for personalized oncological therapies to the market faster.
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Affiliation(s)
- Marina Gorostiola González
- Division of Drug Discovery and Safety, Leiden Academic Centre for Drug Research, Leiden University, The Netherlands; Oncode Institute, Utrecht, The Netherlands
| | - Antonius P A Janssen
- Oncode Institute, Utrecht, The Netherlands; Molecular Physiology, Leiden Institute of Chemistry, Leiden University, The Netherlands
| | - Adriaan P IJzerman
- Division of Drug Discovery and Safety, Leiden Academic Centre for Drug Research, Leiden University, The Netherlands
| | - Laura H Heitman
- Division of Drug Discovery and Safety, Leiden Academic Centre for Drug Research, Leiden University, The Netherlands; Oncode Institute, Utrecht, The Netherlands
| | - Gerard J P van Westen
- Division of Drug Discovery and Safety, Leiden Academic Centre for Drug Research, Leiden University, The Netherlands.
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16
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Yang ZY, Ye ZF, Xiao YJ, Hsieh CY, Zhang SY. SPLDExtraTrees: robust machine learning approach for predicting kinase inhibitor resistance. Brief Bioinform 2022; 23:6543900. [PMID: 35262669 DOI: 10.1093/bib/bbac050] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2021] [Revised: 01/17/2022] [Accepted: 01/31/2022] [Indexed: 12/25/2022] Open
Abstract
Drug resistance is a major threat to the global health and a significant concern throughout the clinical treatment of diseases and drug development. The mutation in proteins that is related to drug binding is a common cause for adaptive drug resistance. Therefore, quantitative estimations of how mutations would affect the interaction between a drug and the target protein would be of vital significance for the drug development and the clinical practice. Computational methods that rely on molecular dynamics simulations, Rosetta protocols, as well as machine learning methods have been proven to be capable of predicting ligand affinity changes upon protein mutation. However, the severely limited sample size and heavy noise induced overfitting and generalization issues have impeded wide adoption of machine learning for studying drug resistance. In this paper, we propose a robust machine learning method, termed SPLDExtraTrees, which can accurately predict ligand binding affinity changes upon protein mutation and identify resistance-causing mutations. Especially, the proposed method ranks training data following a specific scheme that starts with easy-to-learn samples and gradually incorporates harder and diverse samples into the training, and then iterates between sample weight recalculations and model updates. In addition, we calculate additional physics-based structural features to provide the machine learning model with the valuable domain knowledge on proteins for these data-limited predictive tasks. The experiments substantiate the capability of the proposed method for predicting kinase inhibitor resistance under three scenarios and achieve predictive accuracy comparable with that of molecular dynamics and Rosetta methods with much less computational costs.
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Affiliation(s)
- Zi-Yi Yang
- Tencent Quantum Laboratory, Shenzhen, 518057, Guangdong, China
| | - Zhao-Feng Ye
- Tencent Quantum Laboratory, Shenzhen, 518057, Guangdong, China
| | - Yi-Jia Xiao
- Tencent Quantum Laboratory, Shenzhen, 518057, Guangdong, China.,Department of Computer Science and Technology, Tsinghua University, 100084, Beijing, China
| | - Chang-Yu Hsieh
- Tencent Quantum Laboratory, Shenzhen, 518057, Guangdong, China
| | - Sheng-Yu Zhang
- Tencent Quantum Laboratory, Shenzhen, 518057, Guangdong, China
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17
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Qu X, Dong L, Si Y, Zhao Y, Wang Q, Su P, Wang B. Reliable Prediction of the Protein-Ligand Binding Affinity Using a Charge Penetration Corrected AMOEBA Force Field: A Case Study of Drug Resistance Mutations in Abl Kinase. J Chem Theory Comput 2022; 18:1692-1700. [PMID: 35107298 DOI: 10.1021/acs.jctc.1c01005] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Protein mutations that directly impair drug binding are related to therapeutic resistance, and accurate prediction of their impact on drug binding would benefit drug design and clinical practice. Here, we have developed a scoring strategy that predicts the effect of the mutations on the protein-ligand binding affinity. In view of the critical importance of electrostatics in protein-ligand interactions, the charge penetration corrected AMOEBA force field (AMOEBA_CP model) was employed to improve the accuracy of the calculated electrostatic energy. We calculated the electrostatic energy using an energy decomposition analysis scheme based on the generalized Kohn-Sham (GKS-EDA). The AMOEBA_CP model was validated by a protein-fragment-ligand complex data set (Abl236) constructed from the co-crystal structures of the cancer target Abl kinase with six inhibitors. To predict ligand binding affinity changes upon protein mutation of Abl kinase, we used sampling protocol with multistep simulated annealing to search conformations of mutant proteins. The scoring strategy based on AMOEBA_CP model has achieved considerable performance in predicting resistance for 8 kinase inhibitors across 144 clinically identified point mutations. Overall, this study illustrates that the AMOEBA_CP model, which accurately treats electrostatics through penetration correction, enables the accurate prediction of the mutation-induced variation of protein-ligand binding affinity.
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Affiliation(s)
- Xiaoyang Qu
- State Key Laboratory of Physical Chemistry of Solid Surfaces and Fujian Provincial Key Laboratory of Theoretical and Computational Chemistry, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, P. R. China
| | - Lina Dong
- State Key Laboratory of Physical Chemistry of Solid Surfaces and Fujian Provincial Key Laboratory of Theoretical and Computational Chemistry, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, P. R. China
| | - Yubing Si
- College of Chemistry, Zhengzhou University, Zhengzhou 450001, P. R. China
| | - Yuan Zhao
- The Key Laboratory of Natural Medicine and Immuno-Engineering, Henan University, Kaifeng 475004, P. R. China
| | - Qiantao Wang
- Key Laboratory of Drug-Targeting and Drug Delivery System of the Education Ministry and Sichuan Province, Sichuan Engineering Laboratory for Plant-Sourced Drug and Sichuan Research Center for Drug Precision Industrial Technology, West China School of Pharmacy, Sichuan University, Chengdu 610041, P. R. China
| | - Peifeng Su
- State Key Laboratory of Physical Chemistry of Solid Surfaces and Fujian Provincial Key Laboratory of Theoretical and Computational Chemistry, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, P. R. China
| | - Binju Wang
- State Key Laboratory of Physical Chemistry of Solid Surfaces and Fujian Provincial Key Laboratory of Theoretical and Computational Chemistry, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, P. R. China
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18
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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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19
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Zhou Y, Portelli S, Pat M, Rodrigues CH, Nguyen TB, Pires DE, Ascher DB. Structure-guided machine learning prediction of drug resistance mutations in Abelson 1 kinase. Comput Struct Biotechnol J 2021; 19:5381-5391. [PMID: 34667533 PMCID: PMC8495037 DOI: 10.1016/j.csbj.2021.09.016] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2021] [Revised: 09/15/2021] [Accepted: 09/15/2021] [Indexed: 02/02/2023] Open
Abstract
Kinases play crucial roles in cellular signalling and biological processes with their dysregulation associated with diseases, including cancers. Kinase inhibitors, most notably those targeting ABeLson 1 (ABL1) kinase in chronic myeloid leukemia, have had a significant impact on cancer survival, yet emergence of resistance mutations can reduce their effectiveness, leading to therapeutic failure. Limited effort, however, has been devoted to developing tools to accurately identify ABL1 resistance mutations, as well as providing insights into their molecular mechanisms. Here we investigated the structural basis of ABL1 mutations modulating binding affinity of eight FDA-approved drugs. We found mutations impair affinity of type I and type II inhibitors differently and used this insight to developed a novel web-based diagnostic tool, SUSPECT-ABL, to pre-emptively predict resistance profiles and binding free-energy changes (ΔΔG) of all possible ABL1 mutations against inhibitors with different binding modes. Resistance mutations in ABL1 were successfully identified, achieving a Matthew's Correlation Coefficient of up to 0.73 and the resulting change in ligand binding affinity with a Pearson's correlation of up to 0.77, with performances consistent across non-redundant blind tests. Through an in silico saturation mutagenesis, our tool has identified possibly emerging resistance mutations, which offers opportunities for in vivo experimental validation. We believe SUSPECT-ABL will be an important tool not just for improving precision medicine efforts, but for facilitating the development of next-generation inhibitors that are less prone to resistance. We have made our tool freely available at http://biosig.unimelb.edu.au/suspect_abl/.
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Affiliation(s)
- Yunzhuo Zhou
- Systems and Computational Biology, Bio21 Institute, University of Melbourne, Melbourne, Victoria, Australia
- Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia
| | - Stephanie Portelli
- Systems and Computational Biology, Bio21 Institute, University of Melbourne, Melbourne, Victoria, Australia
- Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia
| | - Megan Pat
- Systems and Computational Biology, Bio21 Institute, University of Melbourne, Melbourne, Victoria, Australia
- Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia
| | - Carlos H.M. Rodrigues
- Systems and Computational Biology, Bio21 Institute, University of Melbourne, Melbourne, Victoria, Australia
- Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia
- School of Chemistry and Molecular Biosciences, The University of Queensland, Brisbane, Australia
| | - Thanh-Binh Nguyen
- Systems and Computational Biology, Bio21 Institute, University of Melbourne, Melbourne, Victoria, Australia
- Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia
- School of Chemistry and Molecular Biosciences, The University of Queensland, Brisbane, Australia
- Baker Department of Cardiometabolic Health, Melbourne Medical School, University of Melbourne, Melbourne, Victoria, Australia
| | - Douglas E.V. Pires
- Systems and Computational Biology, Bio21 Institute, University of Melbourne, Melbourne, Victoria, Australia
- Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia
- School of Chemistry and Molecular Biosciences, The University of Queensland, Brisbane, Australia
- School of Computing and Information Systems, University of Melbourne, Melbourne, Victoria, Australia
| | - David B. Ascher
- Systems and Computational Biology, Bio21 Institute, University of Melbourne, Melbourne, Victoria, Australia
- Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia
- School of Chemistry and Molecular Biosciences, The University of Queensland, Brisbane, Australia
- Baker Department of Cardiometabolic Health, Melbourne Medical School, University of Melbourne, Melbourne, Victoria, Australia
- Department of Biochemistry, University of Cambridge, 80 Tennis Ct Rd, Cambridge CB2 1GA, UK
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20
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Accurate absolute free energies for ligand-protein binding based on non-equilibrium approaches. Commun Chem 2021; 4:61. [PMID: 36697634 PMCID: PMC9814727 DOI: 10.1038/s42004-021-00498-y] [Citation(s) in RCA: 48] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2020] [Accepted: 03/24/2021] [Indexed: 01/28/2023] Open
Abstract
The accurate calculation of the binding free energy for arbitrary ligand-protein pairs is a considerable challenge in computer-aided drug discovery. Recently, it has been demonstrated that current state-of-the-art molecular dynamics (MD) based methods are capable of making highly accurate predictions. Conventional MD-based approaches rely on the first principles of statistical mechanics and assume equilibrium sampling of the phase space. In the current work we demonstrate that accurate absolute binding free energies (ABFE) can also be obtained via theoretically rigorous non-equilibrium approaches. Our investigation of ligands binding to bromodomains and T4 lysozyme reveals that both equilibrium and non-equilibrium approaches converge to the same results. The non-equilibrium approach achieves the same level of accuracy and convergence as an equilibrium free energy perturbation (FEP) method enhanced by Hamiltonian replica exchange. We also compare uni- and bi-directional non-equilibrium approaches and demonstrate that considering the work distributions from both forward and reverse directions provides substantial accuracy gains. In summary, non-equilibrium ABFE calculations are shown to yield reliable and well-converged estimates of protein-ligand binding affinity.
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21
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Yang P, Cao R, Bao H, Wu X, Yang L, Zhu D, Zhang L, Peng L, Cai Y, Zhang W, Shao Y. Identification of Novel Alectinib-Resistant ALK Mutation G1202K with Sensitization to Lorlatinib: A Case Report and in silico Structural Modelling. Onco Targets Ther 2021; 14:2131-2138. [PMID: 33790576 PMCID: PMC8007639 DOI: 10.2147/ott.s293901] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2020] [Accepted: 02/25/2021] [Indexed: 11/23/2022] Open
Abstract
Background Drug resistance caused by G1202R/G1202del mutation in anaplastic lymphoma kinase (ALK) represents a great challenge in the clinic. The effect of other mutation(s) at G1202 on the available tyrosine kinase inhibitors (TKIs) in the clinic remains unknown. Case Presentation A 50-year-old Chinese male non-smoker with lung adenocarcinoma progressed with spinal metastasis after receiving chest radiation together with Pemetrexed and Cisplatin as adjuvant chemotherapy. Targeted next generation sequencing (NGS) identified EML4-ALK gene fusion in the resected left lung tissue. Local radiation followed by Crizotinib were used in the following treatment and the spinal metastasis was found to shrink, but the progression free survival (PFS) only lasted for 2 months with the appearance of brain metastasis. Afterwards, the patient benefited from the therapy of Alectinib with a PFS of 8 months. Then he progressed with metastases in right lung and pleural, and did not show response to the chemotherapy with Docetaxel plus Bevacizumab. The targeted sequencing consistently identified EML4-ALK gene fusion in both plasma and pleural effusion (PE), as well as a novel ALK G1202K mutation (c.3604_3605delGGinsAA). Given the lack of established or known drug treatment for this novel mutation, we implemented molecular dynamics (MD) simulation-guided drug sensitivity prediction, which results suggested Lorlatinib remains potent against G1202K mutant ALK. Therefore, Lorlatinib was used as the fourth-line therapy, which lead to the considerable efficacy with improved performance status (PS) score and reduced lung metastases. The structural mechanism underlying G1202K-induced drug resistance to different ALK-TKIs was also discussed. Conclusion Our case suggested the ALK-G1202K mutation may serve as a novel mechanism underlying the resistance to Alectinib, and provide direct evidence to support its sensitization to Lorlatinib. Our work represented an example of integrating in silico predictions into clinical practice.
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Affiliation(s)
- Ping Yang
- Department of Radiation Oncology, Tungwah Hospital of Sun Yat-Sen University, Dongguan, Guangdong, People's Republic of China
| | - Ran Cao
- Nanjing Geneseeq Technology Inc., Nanjing, Jiangsu, People's Republic of China
| | - Hua Bao
- Nanjing Geneseeq Technology Inc., Nanjing, Jiangsu, People's Republic of China
| | - Xue Wu
- Nanjing Geneseeq Technology Inc., Nanjing, Jiangsu, People's Republic of China
| | - Lingling Yang
- Nanjing Geneseeq Technology Inc., Nanjing, Jiangsu, People's Republic of China
| | - Dongqin Zhu
- Nanjing Geneseeq Technology Inc., Nanjing, Jiangsu, People's Republic of China
| | - Lu Zhang
- Department of Medical Oncology, Tungwah Hospital of Sun Yat-Sen University, Dongguan, Guangdong, People's Republic of China
| | - Liming Peng
- Department of Respiratory Medicine, Tungwah Hospital of Sun Yat-Sen University, Dongguan, Guangdong, People's Republic of China
| | - Yuefei Cai
- Department of Intervention, Tungwah Hospital of Sun Yat-Sen University, Dongguan, Guangdong, People's Republic of China
| | - Weijun Zhang
- Departments of Radiation Oncology, Cancer Center of Guangzhou Medical University, Guangzhou, Guangdong, People's Republic of China
| | - Yang Shao
- Nanjing Geneseeq Technology Inc., Nanjing, Jiangsu, People's Republic of China.,School of Public Health, Nanjing Medical University, Nanjing, Jiangsu, People's Republic of China
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22
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Yue S, Li Y, Chen X, Wang J, Li M, Chen Y, Wu D. FGFR-TKI resistance in cancer: current status and perspectives. J Hematol Oncol 2021; 14:23. [PMID: 33568192 PMCID: PMC7876795 DOI: 10.1186/s13045-021-01040-2] [Citation(s) in RCA: 70] [Impact Index Per Article: 23.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2020] [Accepted: 02/01/2021] [Indexed: 02/07/2023] Open
Abstract
Fibroblast growth factor receptors (FGFRs) play key roles in promoting the proliferation, differentiation, and migration of cancer cell. Inactivation of FGFRs by tyrosine kinase inhibitors (TKI) has achieved great success in tumor-targeted therapy. However, resistance to FGFR-TKI has become a concern. Here, we review the mechanisms of FGFR-TKI resistance in cancer, including gatekeeper mutations, alternative signaling pathway activation, lysosome-mediated TKI sequestration, and gene fusion. In addition, we summarize strategies to overcome resistance, including developing covalent inhibitors, developing dual-target inhibitors, adopting combination therapy, and targeting lysosomes, which will facilitate the transition to precision medicine and individualized treatment.
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Affiliation(s)
- Sitong Yue
- Department of Oncology, Laboratory of Structural Biology, NHC Key Laboratory of Cancer Proteomics, State Local Joint Engineering Laboratory for Anticancer Drugs, Xiangya Hospital, Central South University, Changsha, 410008, Hunan, China
| | - Yukun Li
- Clinical Anatomy and Reproductive Medicine Application Institute, Department of Histology and Embryology, Hunan Province Key Laboratory of Cancer Cellular and Molecular Pathology, University of South China, Hengyang, 421001, China
| | - Xiaojuan Chen
- Department of Oncology, Laboratory of Structural Biology, NHC Key Laboratory of Cancer Proteomics, State Local Joint Engineering Laboratory for Anticancer Drugs, Xiangya Hospital, Central South University, Changsha, 410008, Hunan, China
| | - Juan Wang
- Clinical Anatomy and Reproductive Medicine Application Institute, Department of Histology and Embryology, Hunan Province Key Laboratory of Cancer Cellular and Molecular Pathology, University of South China, Hengyang, 421001, China
| | - Meixiang Li
- Clinical Anatomy and Reproductive Medicine Application Institute, Department of Histology and Embryology, Hunan Province Key Laboratory of Cancer Cellular and Molecular Pathology, University of South China, Hengyang, 421001, China
| | - Yongheng Chen
- Department of Oncology, Laboratory of Structural Biology, NHC Key Laboratory of Cancer Proteomics, State Local Joint Engineering Laboratory for Anticancer Drugs, Xiangya Hospital, Central South University, Changsha, 410008, Hunan, China. .,National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, Hunan, 410008, China.
| | - Daichao Wu
- Department of Oncology, Laboratory of Structural Biology, NHC Key Laboratory of Cancer Proteomics, State Local Joint Engineering Laboratory for Anticancer Drugs, Xiangya Hospital, Central South University, Changsha, 410008, Hunan, China. .,Clinical Anatomy and Reproductive Medicine Application Institute, Department of Histology and Embryology, Hunan Province Key Laboratory of Cancer Cellular and Molecular Pathology, University of South China, Hengyang, 421001, China. .,National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, Hunan, 410008, China. .,W.M. Keck Laboratory for Structural Biology, University of Maryland Institute for Bioscience and Biotechnology Research, Rockville, MD, 20850, USA.
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Erguven M, Karakulak T, Diril MK, Karaca E. How Far Are We from the Rapid Prediction of Drug Resistance Arising Due to Kinase Mutations? ACS OMEGA 2021; 6:1254-1265. [PMID: 33490784 PMCID: PMC7818309 DOI: 10.1021/acsomega.0c04672] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/23/2020] [Accepted: 12/11/2020] [Indexed: 06/12/2023]
Abstract
In all living organisms, protein kinases regulate various cell signaling events through phosphorylation. The phosphorylation occurs upon transferring an ATP's terminal phosphate to a target residue. Because of the central role of protein kinases in several proliferative pathways, point mutations occurring within the kinase's ATP-binding site can lead to a constitutively active enzyme, and ultimately, to cancer. A select set of these point mutations can also make the enzyme drug resistant toward the available kinase inhibitors. Because of technical and economical limitations, rapid experimental exploration of the impact of these mutations remains to be a challenge. This underscores the importance of kinase-ligand binding affinity prediction tools that are poised to measure the efficacy of inhibitors in the presence of kinase mutations. To this end, here, we compare the performances of six web-based scoring tools (DSX-ONLINE, KDEEP, HADDOCK2.2, PDBePISA, Pose&Rank, and PRODIGY-LIG) in assessing the impact of kinase mutations on their interactions with their inhibitors. This assessment is carried out on a new structure-based BINDKIN benchmark we compiled. BINDKIN contains wild-type and mutant structure pairs of kinase-inhibitor complexes, together with their corresponding experimental binding affinities (in the form of IC50, K d, and K i). The performance of various web servers over BINDKIN shows that they cannot predict the binding affinities (ΔGs) of wild-type and mutant cases directly. Still, they could catch whether a mutation improves or worsens the ligand binding (ΔΔGs) where the highest Pearson's R correlation coefficient is reached by DSX-ONLINE over the K i dataset. When homology models are used instead of K i-associated crystal structures, DSX-ONLINE loses its predictive capacity. These results highlight that there is room to improve the available scoring functions to estimate the impact of protein kinase point mutations on inhibitor binding. The BINDKIN benchmark with all related results is freely accessible online (https://github.com/CSB-KaracaLab/BINDKIN).
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Affiliation(s)
- Mehmet Erguven
- Izmir
Biomedicine and Genome Center, 35330 Izmir, Turkey
- Izmir
International Biomedicine and Genome Institute, Dokuz Eylul University, 35340 Izmir, Turkey
| | - Tülay Karakulak
- Izmir
Biomedicine and Genome Center, 35330 Izmir, Turkey
- Izmir
International Biomedicine and Genome Institute, Dokuz Eylul University, 35340 Izmir, Turkey
| | - M. Kasim Diril
- Izmir
Biomedicine and Genome Center, 35330 Izmir, Turkey
- Izmir
International Biomedicine and Genome Institute, Dokuz Eylul University, 35340 Izmir, Turkey
| | - Ezgi Karaca
- Izmir
Biomedicine and Genome Center, 35330 Izmir, Turkey
- Izmir
International Biomedicine and Genome Institute, Dokuz Eylul University, 35340 Izmir, Turkey
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24
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Wan S, Bhati AP, Zasada SJ, Coveney PV. Rapid, accurate, precise and reproducible ligand-protein binding free energy prediction. Interface Focus 2020; 10:20200007. [PMID: 33178418 PMCID: PMC7653346 DOI: 10.1098/rsfs.2020.0007] [Citation(s) in RCA: 62] [Impact Index Per Article: 15.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/11/2020] [Indexed: 02/06/2023] Open
Abstract
A central quantity of interest in molecular biology and medicine is the free energy of binding of a molecule to a target biomacromolecule. Until recently, the accurate prediction of binding affinity had been widely regarded as out of reach of theoretical methods owing to the lack of reproducibility of the available methods, not to mention their complexity, computational cost and time-consuming procedures. The lack of reproducibility stems primarily from the chaotic nature of classical molecular dynamics (MD) and the associated extreme sensitivity of trajectories to their initial conditions. Here, we review computational approaches for both relative and absolute binding free energy calculations, and illustrate their application to a diverse set of ligands bound to a range of proteins with immediate relevance in a number of medical domains. We focus on ensemble-based methods which are essential in order to compute statistically robust results, including two we have recently developed, namely thermodynamic integration with enhanced sampling and enhanced sampling of MD with an approximation of continuum solvent. Together, these form a set of rapid, accurate, precise and reproducible free energy methods. They can be used in real-world problems such as hit-to-lead and lead optimization stages in drug discovery, and in personalized medicine. These applications show that individual binding affinities equipped with uncertainty quantification may be computed in a few hours on a massive scale given access to suitable high-end computing resources and workflow automation. A high level of accuracy can be achieved using these approaches.
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Affiliation(s)
- Shunzhou Wan
- Centre for Computational Science, Department of Chemistry, University College London, London WC1H 0AJ, UK
| | - Agastya P. Bhati
- Centre for Computational Science, Department of Chemistry, University College London, London WC1H 0AJ, UK
| | - Stefan J. Zasada
- Centre for Computational Science, Department of Chemistry, University College London, London WC1H 0AJ, UK
| | - Peter V. Coveney
- Centre for Computational Science, Department of Chemistry, University College London, London WC1H 0AJ, UK
- Computational Science Laboratory, Institute for Informatics, Faculty of Science, University of Amsterdam, 1098XH Amsterdam, The Netherlands
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25
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Fowler PW. How quickly can we predict trimethoprim resistance using alchemical free energy methods? Interface Focus 2020; 10:20190141. [PMID: 33178416 PMCID: PMC7653339 DOI: 10.1098/rsfs.2019.0141] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/11/2020] [Indexed: 12/15/2022] Open
Abstract
The emergence of antimicrobial resistance threatens modern medicine and necessitates more personalized treatment of bacterial infections. Sequencing the whole genome of the pathogen(s) in a clinical sample offers one way to improve clinical microbiology diagnostic services, and has already been adopted for tuberculosis in some countries. A key weakness of a genetics clinical microbiology is it cannot return a result for rare or novel genetic variants and therefore predictive methods are required. Non-synonymous mutations in the S. aureus dfrB gene can be successfully classified as either conferring resistance (or not) by calculating their effect on the binding free energy of the antibiotic, trimethoprim. The underlying approach, alchemical free energy methods, requires large numbers of molecular dynamics simulations to be run. We show that a large number (N = 15) of binding free energies calculated from a series of very short (50 ps) molecular dynamics simulations are able to satisfactorily classify all seven mutations in our clinically derived testset. A result for a single mutation could therefore be returned in less than an hour, thereby demonstrating that this or similar methods are now sufficiently fast and reproducible for clinical use.
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Affiliation(s)
- Philip W. Fowler
- Nuffield Department of Medicine, John Radcliffe Hospital, University of Oxford, Oxford OX3 9DU, UK
- National Institute of Health Research Oxford Biomedical Research Centre, John Radcliffe Hospital, Headley Way, Oxford OX3 9DU, UK
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26
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Lin L, Lu Q, Cao R, Ou Q, Ma Y, Bao H, Wu X, Shao Y, Wang Z, Shen B. Acquired rare recurrent EGFR mutations as mechanisms of resistance to Osimertinib in lung cancer and in silico structural modelling. Am J Cancer Res 2020; 10:4005-4015. [PMID: 33294282 PMCID: PMC7716156] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2020] [Accepted: 10/01/2020] [Indexed: 06/12/2023] Open
Abstract
A growing number of progression on Osimertinib among EGFR-mutated lung cancers represents a great challenge clinically. Our study aims to gain insights into novel mechanisms of acquired resistance to Osimertinib. We performed genomic studies on 2 large independent cohorts of lung cancer patients with progressed diseases on different tyrosine kinase inhibitors (TKIs). In silico modeling was used to study the structural mechanism of selected EGFR mutations. Compared with the 1st-TKIs-resistant group, EGFR mutations C797S/G, L718Q/V, L792F/H were significantly more enriched in the Osimertinib-resistant cohort, whose sensitivities to Osimertinib were successfully predicted. Importantly, a total of 14 low-frequency EGFR mutations were exclusively or significantly observed in the Osimertinib-resistant group, 7 were predicted to dramatically reduce the binding affinity of EGFR to Osimertinib (G796S, V802F, T725M, Q791L/H, P794S/R). Analysis of pre-Osimertinib treatment samples of two patients supported that EGFR V802F and G796S were acquired during the treatment. In addition, EGFR G796S was predicted to be susceptible to gefitinib. This study represented the largest real-world data so far investigating Osimertinib resistance in EGFR-mutated lung cancer. We identified a collection of coexistent EGFR rare mutations and provided possible guidance for those patients who progressed on the first-line treatment of Osimertinib.
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Affiliation(s)
- Lin Lin
- Department of Medical Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical CollegeBeijing, China
| | - Qiang Lu
- Department of Thoracic Surgery, Tangdu Hospital, The Air Force Military Medical UniversityXi’an, Shaanxi, China
| | - Ran Cao
- Translational Medicine Research Institute, Geneseeq Technology Inc.Toronto, ON, Canada
| | - Qiuxiang Ou
- Translational Medicine Research Institute, Geneseeq Technology Inc.Toronto, ON, Canada
| | - Yutong Ma
- Translational Medicine Research Institute, Geneseeq Technology Inc.Toronto, ON, Canada
| | - Hua Bao
- Translational Medicine Research Institute, Geneseeq Technology Inc.Toronto, ON, Canada
| | - Xue Wu
- Translational Medicine Research Institute, Geneseeq Technology Inc.Toronto, ON, Canada
| | - Yang Shao
- Nanjing Geneseeq Technology Inc.Nanjing, Jiangsu, China
- School of Public Health, Nanjing Medical UniversityNanjing, Jiangsu, China
| | - Zhaoxia Wang
- Department of Oncology, The Second Affiliated Hospital of Nanjing Medical UniversityNanjing, Jiangsu, China
| | - Bo Shen
- Department of Oncology, Jiangsu Cancer Hospital, Jiangsu Institute of Cancer Research, Nanjing Medical University Affiliated Cancer HospitalNanjing, Jiangsu, China
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27
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Oshima H, Re S, Sugita Y. Prediction of Protein–Ligand Binding Pose and Affinity Using the gREST+FEP Method. J Chem Inf Model 2020; 60:5382-5394. [DOI: 10.1021/acs.jcim.0c00338] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Affiliation(s)
- Hiraku Oshima
- Laboratory for Biomolecular Function Simulation, RIKEN Center for Biosystems Dynamics Research, Integrated Innovation Building 7F, 6-7-1 minatojima-minamimachi, Chuo-ku, Kobe, Hyogo 650-0047, Japan
| | - Suyong Re
- Laboratory for Biomolecular Function Simulation, RIKEN Center for Biosystems Dynamics Research, Integrated Innovation Building 7F, 6-7-1 minatojima-minamimachi, Chuo-ku, Kobe, Hyogo 650-0047, Japan
| | - Yuji Sugita
- Laboratory for Biomolecular Function Simulation, RIKEN Center for Biosystems Dynamics Research, Integrated Innovation Building 7F, 6-7-1 minatojima-minamimachi, Chuo-ku, Kobe, Hyogo 650-0047, Japan
- Theoretical Molecular Science Laboratory, RIKEN Cluster for Pioneering Research, 2-1 Hirosawa, Wako-shi, Saitama 351-0198, Japan
- Computational Biophysics Research Team, RIKEN Center for Computational Science, Integrated Innovation Building 7F, 6-7-1 minatojima-minamimachi, Chuo-ku, Kobe, Hyogo 650-0047, Japan
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28
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Surpeta B, Sequeiros-Borja CE, Brezovsky J. Dynamics, a Powerful Component of Current and Future in Silico Approaches for Protein Design and Engineering. Int J Mol Sci 2020; 21:E2713. [PMID: 32295283 PMCID: PMC7215530 DOI: 10.3390/ijms21082713] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2020] [Revised: 04/10/2020] [Accepted: 04/12/2020] [Indexed: 12/13/2022] Open
Abstract
Computational prediction has become an indispensable aid in the processes of engineering and designing proteins for various biotechnological applications. With the tremendous progress in more powerful computer hardware and more efficient algorithms, some of in silico tools and methods have started to apply the more realistic description of proteins as their conformational ensembles, making protein dynamics an integral part of their prediction workflows. To help protein engineers to harness benefits of considering dynamics in their designs, we surveyed new tools developed for analyses of conformational ensembles in order to select engineering hotspots and design mutations. Next, we discussed the collective evolution towards more flexible protein design methods, including ensemble-based approaches, knowledge-assisted methods, and provable algorithms. Finally, we highlighted apparent challenges that current approaches are facing and provided our perspectives on their further development.
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Affiliation(s)
- Bartłomiej Surpeta
- Laboratory of Biomolecular Interactions and Transport, Department of Gene Expression, Institute of Molecular Biology and Biotechnology, Faculty of Biology, Adam Mickiewicz University, Uniwersytetu Poznanskiego 6, 61-614 Poznan, Poland; (B.S.); (C.E.S.-B.)
- International Institute of Molecular and Cell Biology in Warsaw, Ks Trojdena 4, 02-109 Warsaw, Poland
| | - Carlos Eduardo Sequeiros-Borja
- Laboratory of Biomolecular Interactions and Transport, Department of Gene Expression, Institute of Molecular Biology and Biotechnology, Faculty of Biology, Adam Mickiewicz University, Uniwersytetu Poznanskiego 6, 61-614 Poznan, Poland; (B.S.); (C.E.S.-B.)
- International Institute of Molecular and Cell Biology in Warsaw, Ks Trojdena 4, 02-109 Warsaw, Poland
| | - Jan Brezovsky
- Laboratory of Biomolecular Interactions and Transport, Department of Gene Expression, Institute of Molecular Biology and Biotechnology, Faculty of Biology, Adam Mickiewicz University, Uniwersytetu Poznanskiego 6, 61-614 Poznan, Poland; (B.S.); (C.E.S.-B.)
- International Institute of Molecular and Cell Biology in Warsaw, Ks Trojdena 4, 02-109 Warsaw, Poland
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29
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Subramanian G, Vairagoundar R, Bowen SJ, Roush N, Zachary T, Javens C, Williams T, Janssen A, Gonzales A. Synthetic inhibitor leads of human tropomyosin receptor kinase A ( hTrkA). RSC Med Chem 2020; 11:370-377. [PMID: 33479642 DOI: 10.1039/c9md00554d] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2019] [Accepted: 12/22/2019] [Indexed: 11/21/2022] Open
Abstract
In silico virtual screening followed by in vitro biochemical, biophysical, and cellular screening resulted in the identification of distinctly different hTrkA kinase domain inhibitor scaffolds. X-ray structural analysis of representative inhibitors bound to hTrkA kinase domain defined the binding mode and rationalized the mechanism of action. Preliminary assessment of the sub-type selectivity against the closest hTrkB isoform, and early ADME guided the progression of select inhibitor leads in the screening cascade. The possibility of the actives sustaining to known hTrkA resistance mutations assessed in silico offers initial guidance into the required multiparametric lead optimization to arrive at a clinical candidate.
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Affiliation(s)
- Govindan Subramanian
- Veterinary Medicine Research & Development , Zoetis , 333 Portage Street , Kalamazoo , MI 49007 , USA .
| | - Rajendran Vairagoundar
- Veterinary Medicine Research & Development , Zoetis , 333 Portage Street , Kalamazoo , MI 49007 , USA .
| | - Scott J Bowen
- Veterinary Medicine Research & Development , Zoetis , 333 Portage Street , Kalamazoo , MI 49007 , USA .
| | - Nicole Roush
- Veterinary Medicine Research & Development , Zoetis , 333 Portage Street , Kalamazoo , MI 49007 , USA .
| | - Theresa Zachary
- Veterinary Medicine Research & Development , Zoetis , 333 Portage Street , Kalamazoo , MI 49007 , USA .
| | - Christopher Javens
- Veterinary Medicine Research & Development , Zoetis , 333 Portage Street , Kalamazoo , MI 49007 , USA .
| | - Tracey Williams
- Veterinary Medicine Research & Development , Zoetis , 333 Portage Street , Kalamazoo , MI 49007 , USA .
| | - Ann Janssen
- Veterinary Medicine Research & Development , Zoetis , 333 Portage Street , Kalamazoo , MI 49007 , USA .
| | - Andrea Gonzales
- Veterinary Medicine Research & Development , Zoetis , 333 Portage Street , Kalamazoo , MI 49007 , USA .
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30
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Gapsys V, Pérez-Benito L, Aldeghi M, Seeliger D, van Vlijmen H, Tresadern G, de Groot BL. Large scale relative protein ligand binding affinities using non-equilibrium alchemy. Chem Sci 2019; 11:1140-1152. [PMID: 34084371 PMCID: PMC8145179 DOI: 10.1039/c9sc03754c] [Citation(s) in RCA: 134] [Impact Index Per Article: 26.8] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2019] [Accepted: 12/01/2019] [Indexed: 12/14/2022] Open
Abstract
Ligand binding affinity calculations based on molecular dynamics (MD) simulations and non-physical (alchemical) thermodynamic cycles have shown great promise for structure-based drug design. However, their broad uptake and impact is held back by the notoriously complex setup of the calculations. Only a few tools other than the free energy perturbation approach by Schrödinger Inc. (referred to as FEP+) currently enable end-to-end application. Here, we present for the first time an approach based on the open-source software pmx that allows to easily set up and run alchemical calculations for diverse sets of small molecules using the GROMACS MD engine. The method relies on theoretically rigorous non-equilibrium thermodynamic integration (TI) foundations, and its flexibility allows calculations with multiple force fields. In this study, results from the Amber and Charmm force fields were combined to yield a consensus outcome performing on par with the commercial FEP+ approach. A large dataset of 482 perturbations from 13 different protein-ligand datasets led to an average unsigned error (AUE) of 3.64 ± 0.14 kJ mol-1, equivalent to Schrödinger's FEP+ AUE of 3.66 ± 0.14 kJ mol-1. For the first time, a setup is presented for overall high precision and high accuracy relative protein-ligand alchemical free energy calculations based on open-source software.
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Affiliation(s)
- Vytautas Gapsys
- Computational Biomolecular Dynamics Group, Department of Theoretical and Computational Biophysics, Max Planck Institute for Biophysical Chemistry D-37077 Göttingen Germany
| | - Laura Pérez-Benito
- Computational Chemistry, Janssen Research & Development, Janssen Pharmaceutica N. V. Turnhoutseweg 30 B-2340 Beerse Belgium
| | - Matteo Aldeghi
- Computational Biomolecular Dynamics Group, Department of Theoretical and Computational Biophysics, Max Planck Institute for Biophysical Chemistry D-37077 Göttingen Germany
| | - Daniel Seeliger
- Medicinal Chemistry, Boehringer Ingelheim Pharma GmbH & Co. KG Birkendorfer Strasse 65 D-88397 Biberach a.d. Riss Germany
| | - Herman van Vlijmen
- Computational Chemistry, Janssen Research & Development, Janssen Pharmaceutica N. V. Turnhoutseweg 30 B-2340 Beerse Belgium
| | - Gary Tresadern
- Computational Chemistry, Janssen Research & Development, Janssen Pharmaceutica N. V. Turnhoutseweg 30 B-2340 Beerse Belgium
| | - Bert L de Groot
- Computational Biomolecular Dynamics Group, Department of Theoretical and Computational Biophysics, Max Planck Institute for Biophysical Chemistry D-37077 Göttingen Germany
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31
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