1
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Zhang Y, Wu K, Li Y, Wu S, Warshel A, Bai C. Predicting Mutational Effects on Ca 2+-Activated Chloride Conduction of TMEM16A Based on a Simulation Study. J Am Chem Soc 2024; 146:4665-4679. [PMID: 38319142 DOI: 10.1021/jacs.3c11940] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2024]
Abstract
The dysfunction and defects of ion channels are associated with many human diseases, especially for loss-of-function mutations in ion channels such as cystic fibrosis transmembrane conductance regulator mutations in cystic fibrosis. Understanding ion channels is of great current importance for both medical and fundamental purposes. Such an understanding should include the ability to predict mutational effects and describe functional and mechanistic effects. In this work, we introduce an approach to predict mutational effects based on kinetic information (including reaction barriers and transition state locations) obtained by studying the working mechanism of target proteins. Specifically, we take the Ca2+-activated chloride channel TMEM16A as an example and utilize the computational biology model to predict the mutational effects of key residues. Encouragingly, we verified our predictions through electrophysiological experiments, demonstrating a 94% prediction accuracy regarding mutational directions. The mutational strength assessed by Pearson's correlation coefficient is -0.80 between our calculations and the experimental results. These findings suggest that the proposed methodology is reliable and can provide valuable guidance for revealing functional mechanisms and identifying key residues of the TMEM16A channel. The proposed approach can be extended to a broad scope of biophysical systems.
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Affiliation(s)
- Yue Zhang
- Warshel Institute for Computational Biology, School of Life and Health Sciences, School of Medicine, The Chinese University of Hong Kong (Shenzhen), Shenzhen 518172, China
- School of Chemistry and Materials Science, University of Science and Technology of China, Hefei 230026, China
| | - Kang Wu
- South China Hospital, Health Science Center, Shenzhen University, Shenzhen 518116, China
| | - Yuqing Li
- South China Hospital, Health Science Center, Shenzhen University, Shenzhen 518116, China
| | - Song Wu
- South China Hospital, Health Science Center, Shenzhen University, Shenzhen 518116, China
| | - Arieh Warshel
- Department of Chemistry, University of Southern California, Los Angeles, California 90089-1062, United States
| | - Chen Bai
- Warshel Institute for Computational Biology, School of Life and Health Sciences, School of Medicine, The Chinese University of Hong Kong (Shenzhen), Shenzhen 518172, China
- Chenzhu Biotechnology Co., Ltd., Hangzhou 310005, China
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2
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Wang H, Lin M, Chen G, Xiao Z, Shuai X. Nanodrug regulates ROS homeostasis via enhancing fatty acid oxidation and inhibiting autophagy to overcome tumor drug resistance. Biomater Sci 2023; 11:7179-7187. [PMID: 37740286 DOI: 10.1039/d3bm01139a] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/24/2023]
Abstract
The treatment of drug-resistant tumors poses a significant challenge in the field of tumor therapy. Disrupting the homeostasis of reactive oxygen species (ROS) within tumor cells may represent a pivotal strategy for overcoming the prevalent issue of drug resistance. However, the restricted sustainability of ROS generation and the increased autophagy capacity exhibited by tumor cells hinder the application of ROS-based therapies. In this study, we developed liposome nanoparticles (Ato/CQ@L) for co-encapsulation of atorvastatin (Ato), an activator of AMP-activated protein kinase (AMPK), and chloroquine (CQ), an autophagy inhibitor. Upon internalization by tumor cells, Ato upregulated carnitine palmitoyltransferase 1(CPT1) concentration and promoted fatty acid oxidation (FAO) within the tumor cells. The process of FAO coupled with an abundance of fatty acid substrates, facilitates a sustained generation of ROS production. Concurrently, a positive feedback loop is established between escalated concentration of ROS and AMPK protein levels, resulting in a persistent elevation in ROS levels. In addition, CQ disrupted lysosomes, leading to an increased lysosomal pH and reducing autophagy in tumor cells. In both in vivo and in vitro experiments, the Ato/CQ@L treatment group exhibited a considerable enhancement in tumor cell apoptosis, validating the efficacy of this combined therapy. In summary, the combined therapy involving Ato and CQ addresses the inherent limitations of conventional ROS therapy, which include insufficient ROS production and increased autophagy. This approach holds significant potential as a treatment strategy for drug-resistant triple-negative breast cancer.
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Affiliation(s)
- HaiYang Wang
- PCFM Lab of Ministry of Education, School of Materials Science and Engineering, Sun Yat-sen University, Guangzhou 510275, China.
| | - Minzhao Lin
- PCFM Lab of Ministry of Education, School of Materials Science and Engineering, Sun Yat-sen University, Guangzhou 510275, China.
| | - Gengjia Chen
- PCFM Lab of Ministry of Education, School of Materials Science and Engineering, Sun Yat-sen University, Guangzhou 510275, China.
| | - Zecong Xiao
- Nanomedicine Research Center, the Third Affiliated Hospital of Sun Yat-sen University, Guangzhou 510630, China.
| | - Xintao Shuai
- PCFM Lab of Ministry of Education, School of Materials Science and Engineering, Sun Yat-sen University, Guangzhou 510275, China.
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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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Jahnke W, Paladini J, Habazettl JM, Wiget A, Loo A, Cowan Jacob SW, Grzesiek S, Manley PW. Correspondence on “Synergy and Antagonism between Allosteric and Active‐Site Inhibitors of Abl Tyrosine Kinase”**. Angew Chem Int Ed Engl 2022. [DOI: 10.1002/ange.202117276] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Affiliation(s)
- Wolfgang Jahnke
- Novartis Institutes for Biomedical Research 4002 Basel Switzerland
| | | | | | - Andrea Wiget
- Research Institute of Organic Agriculture (FiBL) 5070 Frick Switzerland
| | - Alice Loo
- Novartis Institutes for BioMedical Research Cambridge MA 02139 USA
| | | | | | - Paul W. Manley
- Novartis Institutes for Biomedical Research 4002 Basel Switzerland
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6
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Jahnke W, Paladini J, Habazettl JM, Wiget A, Loo A, Cowan Jacob SW, Grzesiek S, Manley PW. Correspondence on “Synergy and Antagonism between Allosteric and Active‐Site Inhibitors of Abl Tyrosine Kinase”**. Angew Chem Int Ed Engl 2022; 61:e202117276. [DOI: 10.1002/anie.202117276] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2021] [Indexed: 11/06/2022]
Affiliation(s)
- Wolfgang Jahnke
- Novartis Institutes for Biomedical Research 4002 Basel Switzerland
| | | | | | - Andrea Wiget
- Research Institute of Organic Agriculture (FiBL) 5070 Frick Switzerland
| | - Alice Loo
- Novartis Institutes for BioMedical Research Cambridge MA 02139 USA
| | | | | | - Paul W. Manley
- Novartis Institutes for Biomedical Research 4002 Basel Switzerland
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7
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Padhi AK, Tripathi T. A comprehensive protein design protocol to identify resistance mutations and signatures of adaptation in pathogens. Brief Funct Genomics 2022; 22:195-203. [PMID: 35851634 DOI: 10.1093/bfgp/elac020] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2022] [Revised: 06/01/2022] [Accepted: 06/20/2022] [Indexed: 12/17/2022] Open
Abstract
Abstract
Most pathogens mutate and evolve over time to escape immune and drug pressure. To achieve this, they alter specific hotspot residues in their intracellular proteins to render the targeted drug(s) ineffective and develop resistance. Such hotspot residues may be located as a cluster or uniformly as a signature of adaptation in a protein. Identifying the hotspots and signatures is extremely important to comprehensively understand the disease pathogenesis and rapidly develop next-generation therapeutics. As experimental methods are time-consuming and often cumbersome, there is a need to develop efficient computational protocols and adequately utilize them. To address this issue, we present a unique computational protein design protocol that identifies hotspot residues, resistance mutations and signatures of adaptation in a pathogen’s protein against a bound drug. Using the protocol, the binding affinity between the designed mutants and drug is computed quickly, which offers predictions for comparison with biophysical experiments. The applicability and accuracy of the protocol are shown using case studies of a few protein–drug complexes. As a validation, resistance mutations in severe acute respiratory syndrome coronavirus 2 main protease (Mpro) against narlaprevir (an inhibitor of hepatitis C NS3/4A serine protease) are identified. Notably, a detailed methodology and description of the working principles of the protocol are presented. In conclusion, our protocol will assist in providing a first-hand explanation of adaptation, hotspot-residue variations and surveillance of evolving resistance mutations in a pathogenic protein.
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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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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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