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Velloso JPL, de Sá AGC, Pires DEV, Ascher DB. Engineering G protein-coupled receptors for stabilization. Protein Sci 2024; 33:e5000. [PMID: 38747401 PMCID: PMC11094779 DOI: 10.1002/pro.5000] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2023] [Revised: 03/21/2024] [Accepted: 04/10/2024] [Indexed: 05/19/2024]
Abstract
G protein-coupled receptors (GPCRs) are one of the most important families of targets for drug discovery. One of the limiting steps in the study of GPCRs has been their stability, with significant and time-consuming protein engineering often used to stabilize GPCRs for structural characterization and drug screening. Unfortunately, computational methods developed using globular soluble proteins have translated poorly to the rational engineering of GPCRs. To fill this gap, we propose GPCR-tm, a novel and personalized structurally driven web-based machine learning tool to study the impacts of mutations on GPCR stability. We show that GPCR-tm performs as well as or better than alternative methods, and that it can accurately rank the stability changes of a wide range of mutations occurring in various types of class A GPCRs. GPCR-tm achieved Pearson's correlation coefficients of 0.74 and 0.46 on 10-fold cross-validation and blind test sets, respectively. We observed that the (structural) graph-based signatures were the most important set of features for predicting destabilizing mutations, which points out that these signatures properly describe the changes in the environment where the mutations occur. More specifically, GPCR-tm was able to accurately rank mutations based on their effect on protein stability, guiding their rational stabilization. GPCR-tm is available through a user-friendly web server at https://biosig.lab.uq.edu.au/gpcr_tm/.
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Affiliation(s)
- João Paulo L. Velloso
- School of Chemistry and Molecular Biosciences, The Australian Centre for EcogenomicsThe University of QueenslandBrisbaneQueenslandAustralia
- Computational Biology and Clinical InformaticsBaker Heart and Diabetes InstituteMelbourneVictoriaAustralia
- Baker Department of Cardiometabolic HealthThe University of MelbourneParkvilleVictoriaAustralia
| | - Alex G. C. de Sá
- School of Chemistry and Molecular Biosciences, The Australian Centre for EcogenomicsThe University of QueenslandBrisbaneQueenslandAustralia
- Computational Biology and Clinical InformaticsBaker Heart and Diabetes InstituteMelbourneVictoriaAustralia
- Baker Department of Cardiometabolic HealthThe University of MelbourneParkvilleVictoriaAustralia
| | - Douglas E. V. Pires
- School of Computing and Information SystemsThe University of MelbourneParkvilleVictoriaAustralia
| | - David B. Ascher
- School of Chemistry and Molecular Biosciences, The Australian Centre for EcogenomicsThe University of QueenslandBrisbaneQueenslandAustralia
- Computational Biology and Clinical InformaticsBaker Heart and Diabetes InstituteMelbourneVictoriaAustralia
- Baker Department of Cardiometabolic HealthThe University of MelbourneParkvilleVictoriaAustralia
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2
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Lin TE, Yen D, HuangFu W, Wu Y, Hsu J, Yen S, Sung T, Hsieh J, Pan S, Yang C, Huang W, Hsu K. An ensemble machine learning model generates a focused screening library for the identification of CDK8 inhibitors. Protein Sci 2024; 33:e5007. [PMID: 38723187 PMCID: PMC11081523 DOI: 10.1002/pro.5007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2024] [Revised: 03/26/2024] [Accepted: 04/13/2024] [Indexed: 05/13/2024]
Abstract
The identification of an effective inhibitor is an important starting step in drug development. Unfortunately, many issues such as the characterization of protein binding sites, the screening library, materials for assays, etc., make drug screening a difficult proposition. As the size of screening libraries increases, more resources will be inefficiently consumed. Thus, new strategies are needed to preprocess and focus a screening library towards a targeted protein. Herein, we report an ensemble machine learning (ML) model to generate a CDK8-focused screening library. The ensemble model consists of six different algorithms optimized for CDK8 inhibitor classification. The models were trained using a CDK8-specific fragment library along with molecules containing CDK8 activity. The optimized ensemble model processed a commercial library containing 1.6 million molecules. This resulted in a CDK8-focused screening library containing 1,672 molecules, a reduction of more than 99.90%. The CDK8-focused library was then subjected to molecular docking, and 25 candidate compounds were selected. Enzymatic assays confirmed six CDK8 inhibitors, with one compound producing an IC50 value of ≤100 nM. Analysis of the ensemble ML model reveals the role of the CDK8 fragment library during training. Structural analysis of molecules reveals the hit compounds to be structurally novel CDK8 inhibitors. Together, the results highlight a pipeline for curating a focused library for a specific protein target, such as CDK8.
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Affiliation(s)
- Tony Eight Lin
- Graduate Institute of Cancer Biology and Drug Discovery, College of Medical Science and TechnologyTaipei Medical UniversityTaipeiTaiwan
- Ph.D. Program for Cancer Molecular Biology and Drug DiscoveryCollege of Medical Science and Technology, Taipei Medical UniversityTaipeiTaiwan
| | - Dyan Yen
- Graduate Institute of Cancer Biology and Drug Discovery, College of Medical Science and TechnologyTaipei Medical UniversityTaipeiTaiwan
| | - Wei‐Chun HuangFu
- Graduate Institute of Cancer Biology and Drug Discovery, College of Medical Science and TechnologyTaipei Medical UniversityTaipeiTaiwan
- Ph.D. Program for Cancer Molecular Biology and Drug DiscoveryCollege of Medical Science and Technology, Taipei Medical UniversityTaipeiTaiwan
- TMU Research Center of Cancer Translational MedicineTaipei Medical UniversityTaipeiTaiwan
| | - Yi‐Wen Wu
- Graduate Institute of Cancer Biology and Drug Discovery, College of Medical Science and TechnologyTaipei Medical UniversityTaipeiTaiwan
| | - Jui‐Yi Hsu
- Graduate Institute of Cancer Biology and Drug Discovery, College of Medical Science and TechnologyTaipei Medical UniversityTaipeiTaiwan
- Ph.D. Program for Cancer Molecular Biology and Drug DiscoveryCollege of Medical Science and Technology, Taipei Medical UniversityTaipeiTaiwan
| | - Shih‐Chung Yen
- Warshel Institute for Computational BiologyThe Chinese University of Hong Kong (Shenzhen)ShenzhenGuangdongPeople's Republic of China
| | - Tzu‐Ying Sung
- Biomedical Translation Research Center, Academia SinicaTaipeiTaiwan
| | - Jui‐Hua Hsieh
- Division of Translational ToxicologyNational Institute of Environmental Health Sciences, National Institutes of HealthDurhamNorth CarolinaUSA
| | - Shiow‐Lin Pan
- Graduate Institute of Cancer Biology and Drug Discovery, College of Medical Science and TechnologyTaipei Medical UniversityTaipeiTaiwan
- Ph.D. Program for Cancer Molecular Biology and Drug DiscoveryCollege of Medical Science and Technology, Taipei Medical UniversityTaipeiTaiwan
- TMU Research Center of Cancer Translational MedicineTaipei Medical UniversityTaipeiTaiwan
| | - Chia‐Ron Yang
- School of Pharmacy, College of MedicineNational Taiwan UniversityTaipeiTaiwan
| | - Wei‐Jan Huang
- Graduate Institute of Pharmacognosy, College of PharmacyTaipei Medical UniversityTaipeiTaiwan
| | - Kai‐Cheng Hsu
- Graduate Institute of Cancer Biology and Drug Discovery, College of Medical Science and TechnologyTaipei Medical UniversityTaipeiTaiwan
- Ph.D. Program for Cancer Molecular Biology and Drug DiscoveryCollege of Medical Science and Technology, Taipei Medical UniversityTaipeiTaiwan
- TMU Research Center of Cancer Translational MedicineTaipei Medical UniversityTaipeiTaiwan
- Cancer Center, Wan Fang HospitalTaipei Medical UniversityTaipeiTaiwan
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3
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Hiago Bellaver E, Eliza Redin E, Militão da Costa I, Schittler Moroni L, Pinto Kempka A. Food peptidomic analysis of bovine milk fermented by Lacticaseibacillus casei LBC 237: In silico prediction of bioactive peptides and anticancer potential. Food Res Int 2024; 180:114060. [PMID: 38395580 DOI: 10.1016/j.foodres.2024.114060] [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: 10/31/2023] [Revised: 01/16/2024] [Accepted: 01/24/2024] [Indexed: 02/25/2024]
Abstract
Bioactive peptides, which exhibited diverse biological activities such as anti-cancer, anti-inflammatory, bactericidal, antiviral, and quorum sensing properties, were considered promising alternative therapeutic agents. Sourced from various raw materials, particularly foods, these peptides garnered significant interest. In this context, the study focused on exploring bioactive peptides derived from bovine whole milk fermentation by Lacticaseibacillus casei LBC 237. Comprehensive peptidomic analysis and in silico predictions, with a specific emphasis on anti-cancer properties, were conducted. The study categorized peptides into BP-LBC, originating from the metabolism of L. casei LBC 237 and not matching any sequence in the Bos taurus database, and BP-MILK, matching a sequence in the Bos taurus database. Among the 143 identified peptides with potential biological activity, 33.56% were attributed to BP-LBC, while 66.43% originated from BP-MILK, demonstrating the important contribution of proteins in bovine milk in the generation of bioactive peptides. Hydrophobic peptides, enriched in Leucine, Lysine, and Proline, dominated both fractions, significantly influencing their functional properties. Pearson correlation analysis revealed inverse relationships between bioactive peptides, molecular weight, and anti-tumor activity in BP-MILK. The DGKVWEESLK peptide exhibited in silico activity against 10 different cancer cell lines. Studying the bioactive properties of peptides from familiar sources enhances the connection between food science and human health. In addition, in silico studies have been crucial in deepening our understanding of the bioactive potential of these peptides and their mode of action.
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Affiliation(s)
- Emyr Hiago Bellaver
- Santa Catarina State University. Department of Animal Production and Food Science, Multicentric Graduate Program in Biochemistry and Molecular Biology. Lages, SC, Brazil
| | - Eduarda Eliza Redin
- Santa Catarina State University. Department of Food Engineering and Chemical Engineering, Pinhalzinho, SC, Brazil.
| | - Ingrid Militão da Costa
- Santa Catarina State University. Department of Food Engineering and Chemical Engineering, Pinhalzinho, SC, Brazil.
| | - Liziane Schittler Moroni
- Santa Catarina State University. Department of Food Engineering and Chemical Engineering, Pinhalzinho, SC, Brazil.
| | - Aniela Pinto Kempka
- Santa Catarina State University. Department of Animal Production and Food Science, Multicentric Graduate Program in Biochemistry and Molecular Biology. Lages, SC, Brazil; Santa Catarina State University. Department of Food Engineering and Chemical Engineering, Pinhalzinho, SC, Brazil.
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4
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Yamada T, Sakurabayashi S, Sugiura N, Haneoka H, Nakatani K. NMR analysis of 15N-labeled naphthyridine carbamate dimer (NCD) to contiguous CGG/CGG units in DNA. Chem Commun (Camb) 2024. [PMID: 38415500 DOI: 10.1039/d4cc00544a] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/29/2024]
Abstract
The structure of the complex formed by naphthyridine carbamate dimer (NCD) binding to CGG repeat sequences in DNA, associated with fragile X syndrome, has been elucidated using 15N-labeled NCD and 1H-15N HSQC. In a fully saturated state, two NCD molecules consistently bind to each CGG/CGG unit, maintaining a 1 : 2 binding stoichiometry.
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Affiliation(s)
- Takeshi Yamada
- Department of Regulatory Bioorganic Chemistry, SANKEN, Osaka University, Mihogaoka 8-1, Ibaraki, Osaka 567-0047, Japan.
| | - Shuhei Sakurabayashi
- Department of Regulatory Bioorganic Chemistry, SANKEN, Osaka University, Mihogaoka 8-1, Ibaraki, Osaka 567-0047, Japan.
| | - Noriaki Sugiura
- Department of Regulatory Bioorganic Chemistry, SANKEN, Osaka University, Mihogaoka 8-1, Ibaraki, Osaka 567-0047, Japan.
| | - Hitoshi Haneoka
- Comprehensive Analysis Center, SANKEN, Osaka University, Mihogaoka 8-1, Ibaraki, Osaka 567-0047, Japan
| | - Kazuhiko Nakatani
- Department of Regulatory Bioorganic Chemistry, SANKEN, Osaka University, Mihogaoka 8-1, Ibaraki, Osaka 567-0047, Japan.
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5
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Velloso JPL, Kovacs AS, Pires DEV, Ascher DB. AI-driven GPCR analysis, engineering, and targeting. Curr Opin Pharmacol 2024; 74:102427. [PMID: 38219398 DOI: 10.1016/j.coph.2023.102427] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2023] [Revised: 12/12/2023] [Accepted: 12/13/2023] [Indexed: 01/16/2024]
Abstract
This article investigates the role of recent advances in Artificial Intelligence (AI) to revolutionise the study of G protein-coupled receptors (GPCRs). AI has been applied to many areas of GPCR research, including the application of machine learning (ML) in GPCR classification, prediction of GPCR activation levels, modelling GPCR 3D structures and interactions, understanding G-protein selectivity, aiding elucidation of GPCRs structures, and drug design. Despite progress, challenges in predicting GPCR structures and addressing the complex nature of GPCRs remain, providing avenues for future research and development.
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Affiliation(s)
- João P L Velloso
- Structural Biology and Bioinformatics, Department of Biochemistry and Pharmacology, University of Melbourne, Melbourne, Victoria, Australia; 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, University of Queensland, Brisbane, Queensland, Australia
| | - Aaron S Kovacs
- Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia; School of Chemistry and Molecular Biosciences, University of Queensland, Brisbane, Queensland, Australia
| | - Douglas E V Pires
- Structural Biology and Bioinformatics, Department of Biochemistry and Pharmacology, University of Melbourne, Melbourne, Victoria, Australia; 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 Computing and Information Systems, University of Melbourne, Melbourne, Victoria, Australia.
| | - David B Ascher
- Structural Biology and Bioinformatics, Department of Biochemistry and Pharmacology, University of Melbourne, Melbourne, Victoria, Australia; 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, University of Queensland, Brisbane, Queensland, Australia.
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6
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Abstract
The greatest challenge in drug discovery remains the high rate of attrition across the different phases of the process, which cost the industry billions of dollars every year. While all phases remain crucial to ensure pharmaceutical-level safety, quality, and efficacy of the end product, streamlining these efforts toward compounds with success potential is pivotal for a more efficient and cost-effective process. The use of artificial intelligence (AI) within the pharmaceutical industry aims at just this, and has applications in preclinical screening for biological activity, optimization of pharmacokinetic properties for improved drug formulation, early toxicity prediction which reduces attrition, and pre-emptively screening for genetic changes in the biological target to improve therapeutic longevity. Here, we present a series of in silico tools that address these applications in small molecule development and describe how they can be embedded within the current pharmaceutical development pipeline.
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Affiliation(s)
- Adam Serghini
- School of Chemistry and Molecular Biosciences, University of Queensland, St Lucia, QLD, Australia
- Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne, VIC, Australia
| | - Stephanie Portelli
- School of Chemistry and Molecular Biosciences, University of Queensland, St Lucia, QLD, Australia.
| | - David B Ascher
- School of Chemistry and Molecular Biosciences, University of Queensland, St Lucia, QLD, Australia.
- Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne, VIC, Australia.
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7
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Thirunavukkarasu MK, Veerappapillai S, Karuppasamy R. Computational biophysics approach towards the discovery of multi-kinase blockers for the management of MAPK pathway dysregulation. Mol Divers 2023; 27:2093-2110. [PMID: 36260173 DOI: 10.1007/s11030-022-10545-y] [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: 06/25/2022] [Accepted: 10/06/2022] [Indexed: 10/24/2022]
Abstract
The MAPK pathway is important in human lung cancer and is improperly activated in a substantial proportion through number of ways. Strategies on dual-targeting RAF and MEK are an alternative option to diminish the limitations in this pathway inhibition. Hence, we implemented parallel pharmacophore screening of 11,808 DrugBank compounds against RAF and MEK. ADHRR and DHHRR were modeled as a pharmacophore hypothesis for RAF and MEK respectively. Importantly, these hypotheses resulted an AUC value of > 0.90 with the external data set. As a result of phase screening, glide docking, and prime-MM/GBSA scoring, it is determined that DB08424 and DB08907 have the best chances of acting as multi-kinase inhibitors. The pi-cation interaction with key amino acid residues of both target receptors may responsible for the stronger binding with these kinases. Cumulative 600 ns MD simulation studies validate the binding ability of these compounds. Significantly, the hit compounds resulted higher number of stable conformational state with less atomic movements than the reference compound against both targets. The anti-cancer efficacy of the lead compounds was validated through machine learning-based approaches. These findings suggest that DB08424 and DB08907 might be novel molecules to be explored further experimentally to block the MAPK signaling in lung cancer patients.
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Affiliation(s)
- Muthu Kumar Thirunavukkarasu
- Department of Biotechnology, School of Bio Sciences and Technology, Vellore Institute of Technology, Vellore, Tamil Nadu, 632014, India
| | - Shanthi Veerappapillai
- Department of Biotechnology, School of Bio Sciences and Technology, Vellore Institute of Technology, Vellore, Tamil Nadu, 632014, India
| | - Ramanathan Karuppasamy
- Department of Biotechnology, School of Bio Sciences and Technology, Vellore Institute of Technology, Vellore, Tamil Nadu, 632014, India.
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8
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Halawani R, Buchert M, Chen YPP. Deep learning exploration of single-cell and spatially resolved cancer transcriptomics to unravel tumour heterogeneity. Comput Biol Med 2023; 164:107274. [PMID: 37506451 DOI: 10.1016/j.compbiomed.2023.107274] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2023] [Revised: 07/03/2023] [Accepted: 07/16/2023] [Indexed: 07/30/2023]
Abstract
Tumour heterogeneity is one of the critical confounding aspects in decoding tumour growth. Malignant cells display variations in their gene transcription profiles and mutation spectra even when originating from a single progenitor cell. Single-cell and spatial transcriptomics sequencing have recently emerged as key technologies for unravelling tumour heterogeneity. Single-cell sequencing promotes individual cell-type identification through transcriptome-wide gene expression measurements of each cell. Spatial transcriptomics facilitates identification of cell-cell interactions and the structural organization of heterogeneous cells within a tumour tissue through associating spatial RNA abundance of cells at distinct spots in the tissue section. However, extracting features and analyzing single-cell and spatial transcriptomics data poses challenges. Single-cell transcriptome data is extremely noisy and its sparse nature and dropouts can lead to misinterpretation of gene expression and the misclassification of cell types. Deep learning predictive power can overcome data challenges, provide high-resolution analysis and enhance precision oncology applications that involve early cancer prognosis, diagnosis, patient survival estimation and anti-cancer therapy planning. In this paper, we provide a background to and review of the recent progress of deep learning frameworks to investigate tumour heterogeneity using both single-cell and spatial transcriptomics data types.
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Affiliation(s)
- Raid Halawani
- Department of Computer Science and Information Technology, La Trobe University, Melbourne, Australia
| | - Michael Buchert
- School of Cancer Medicine, La Trobe University, Melbourne, Victoria, Australia; Olivia Newton-John Cancer Research Institute, Melbourne, Victoria, Australia
| | - Yi-Ping Phoebe Chen
- Department of Computer Science and Information Technology, La Trobe University, Melbourne, Australia.
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9
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Venkatraman V. FP-MAP: an extensive library of fingerprint-based molecular activity prediction tools. Front Chem 2023; 11:1239467. [PMID: 37649967 PMCID: PMC10462816 DOI: 10.3389/fchem.2023.1239467] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2023] [Accepted: 07/31/2023] [Indexed: 09/01/2023] Open
Abstract
Discovering new drugs for disease treatment is challenging, requiring a multidisciplinary effort as well as time, and resources. With a view to improving hit discovery and lead compound identification, machine learning (ML) approaches are being increasingly used in the decision-making process. Although a number of ML-based studies have been published, most studies only report fragments of the wider range of bioactivities wherein each model typically focuses on a particular disease. This study introduces FP-MAP, an extensive atlas of fingerprint-based prediction models that covers a diverse range of activities including neglected tropical diseases (caused by viral, bacterial and parasitic pathogens) as well as other targets implicated in diseases such as Alzheimer's. To arrive at the best predictive models, performance of ≈4,000 classification/regression models were evaluated on different bioactivity data sets using 12 different molecular fingerprints. The best performing models that achieved test set AUC values of 0.62-0.99 have been integrated into an easy-to-use graphical user interface that can be downloaded from https://gitlab.com/vishsoft/fpmap.
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Affiliation(s)
- Vishwesh Venkatraman
- Department of Chemistry, Norwegian University of Science and Technology, Trondheim, Norway
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10
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Aljarf R, Tang S, Pires DEV, Ascher DB. embryoTox: Using Graph-Based Signatures to Predict the Teratogenicity of Small Molecules. J Chem Inf Model 2023; 63:432-441. [PMID: 36595441 DOI: 10.1021/acs.jcim.2c00824] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
Abstract
Teratogenic drugs can lead to extreme fetal malformation and consequently critically influence the fetus's health, yet the teratogenic risks associated with most approved drugs are unknown. Here, we propose a novel predictive tool, embryoTox, which utilizes a graph-based signature representation of the chemical structure of a small molecule to predict and classify molecules likely to be safe during pregnancy. embryoTox was trained and validated using in vitro bioactivity data of over 700 small molecules with characterized teratogenicity effects. Our final model achieved an area under the receiver operating characteristic curve (AUC) of up to 0.96 on 10-fold cross-validation and 0.82 on nonredundant blind tests, outperforming alternative approaches. We believe that our predictive tool will provide a practical resource for optimizing screening libraries to determine effective and safe molecules to use during pregnancy. To provide a simple and integrated platform to rapidly screen for potential safe molecules and their risk factors, we made embryoTox freely available online at https://biosig.lab.uq.edu.au/embryotox/.
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Affiliation(s)
- Raghad Aljarf
- Structural Biology and Bioinformatics, Department of Biochemistry, University of Melbourne, Parkville 3052, Victoria, Australia
- Systems and Computational Biology, Bio21 Institute, University of Melbourne, Parkville 3052, Victoria, Australia
- Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne 3004, Victoria, Australia
| | - Simon Tang
- Structural Biology and Bioinformatics, Department of Biochemistry, University of Melbourne, Parkville 3052, Victoria, Australia
- Systems and Computational Biology, Bio21 Institute, University of Melbourne, Parkville 3052, Victoria, Australia
| | - Douglas E V Pires
- Systems and Computational Biology, Bio21 Institute, University of Melbourne, Parkville 3052, Victoria, Australia
- Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne 3004, Victoria, Australia
- School of Computing and Information Systems, University of Melbourne, Parkville 3052, Victoria, Australia
| | - David B Ascher
- Structural Biology and Bioinformatics, Department of Biochemistry, University of Melbourne, Parkville 3052, Victoria, Australia
- Systems and Computational Biology, Bio21 Institute, University of Melbourne, Parkville 3052, Victoria, Australia
- Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne 3004, Victoria, Australia
- School of Chemistry and Molecular Biosciences, University of Queensland, St Lucia 4072, Queensland, Australia
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11
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Exploration of natural product database for the identification of potent inhibitor against IDH2 mutational variants for glioma therapy. J Mol Model 2022; 29:6. [PMID: 36484830 DOI: 10.1007/s00894-022-05409-z] [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: 10/20/2022] [Accepted: 12/04/2022] [Indexed: 12/13/2022]
Abstract
Mutation in isocitrate dehydrogenase 2 (mIDH2) is an oncogenic driver prevalently reported in various cancer types including gliomas. To date, enasidenib is the only FDA-approved drug widely used as a mIDH2 (R140Q) inhibitor. However, dose-limiting toxicity and modest brain penetrating capability restrict its use as a plausible mIDH2 inhibitor. Furthermore, secondary site mutations (Q316E and I319M) were identified in patients with enasidenib treatments resulting in acquired therapeutic resistance. Hence, in the present investigation, we aimed to identify novel and potent drug-like compounds to overcome the existing drawbacks using an integrated in-silico strategy. A sum of 1574 natural compounds from the naturally occurring plant-based anti-cancerous compound activity target (NPACT) database was proclaimed and subjected to molecular docking. The binding affinities of the resultant natural compounds were rescored using MM-GBSA scoring functions. The resultant lead molecules were subjected to anticancer activity prediction using the machine-learning model. Furthermore, the toxicity and drug-likeliness of the lead compounds were investigated using ADMET properties. Eventually, the integrated in silico approach resulted in a lead molecule, namely squalene (NPACT00954) against mIDH2 protein. The screened compound was subjected to mutational analysis accomplishing second-site mutations. Interestingly, squalene exhibited appreciable binding affinity alongside good brain penetrating potential than enasidenib. Indeed, the reproducibility and significance of our results are examined by running 3 replicas of 100-ns simulations per system using the random initial velocities of the atoms generated by Maxwell distribution at a given temperature. Thus, we hypothesize from our results that further optimization of squalene could be beneficial for the treatment and management of glioma in the near future.
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12
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Novel indole-guanidine hybrids as potential anticancer agents: Design, synthesis and biological evaluation. Chem Biol Interact 2022; 368:110242. [DOI: 10.1016/j.cbi.2022.110242] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2022] [Revised: 09/24/2022] [Accepted: 10/24/2022] [Indexed: 11/17/2022]
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13
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Antiproliferative Activity Predictor: A New Reliable In Silico Tool for Drug Response Prediction against NCI60 Panel. Int J Mol Sci 2022; 23:ijms232214374. [PMID: 36430850 PMCID: PMC9694168 DOI: 10.3390/ijms232214374] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2022] [Revised: 11/13/2022] [Accepted: 11/16/2022] [Indexed: 11/22/2022] Open
Abstract
In vitro antiproliferative assays still represent one of the most important tools in the anticancer drug discovery field, especially to gain insights into the mechanisms of action of anticancer small molecules. The NCI-DTP (National Cancer Institute Developmental Therapeutics Program) undoubtedly represents the most famous project aimed at rapidly testing thousands of compounds against multiple tumor cell lines (NCI60). The large amount of biological data stored in the National Cancer Institute (NCI) database and many other databases has led researchers in the fields of computational biology and medicinal chemistry to develop tools to predict the anticancer properties of new agents in advance. In this work, based on the available antiproliferative data collected by the NCI and the manipulation of molecular descriptors, we propose the new in silico Antiproliferative Activity Predictor (AAP) tool to calculate the GI50 values of input structures against the NCI60 panel. This ligand-based protocol, validated by both internal and external sets of structures, has proven to be highly reliable and robust. The obtained GI50 values of a test set of 99 structures present an error of less than ±1 unit. The AAP is more powerful for GI50 calculation in the range of 4-6, showing that the results strictly correlate with the experimental data. The encouraging results were further supported by the examination of an in-house database of curcumin analogues that have already been studied as antiproliferative agents. The AAP tool identified several potentially active compounds, and a subsequent evaluation of a set of molecules selected by the NCI for the one-dose/five-dose antiproliferative assays confirmed the great potential of our protocol for the development of new anticancer small molecules. The integration of the AAP tool in the free web service DRUDIT provides an interesting device for the discovery and/or optimization of anticancer drugs to the medicinal chemistry community. The training set will be updated with new NCI-tested compounds to cover more chemical spaces, activities, and cell lines. Currently, the same protocol is being developed for predicting the TGI (total growth inhibition) and LC50 (median lethal concentration) parameters to estimate toxicity profiles of small molecules.
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14
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Iftkhar S, de Sá AGC, Velloso JPL, Aljarf R, Pires DEV, Ascher DB. cardioToxCSM: A Web Server for Predicting Cardiotoxicity of Small Molecules. J Chem Inf Model 2022; 62:4827-4836. [PMID: 36219164 DOI: 10.1021/acs.jcim.2c00822] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
The design of novel, safe, and effective drugs to treat human diseases is a challenging venture, with toxicity being one of the main sources of attrition at later stages of development. Failure due to toxicity incurs a significant increase in costs and time to market, with multiple drugs being withdrawn from the market due to their adverse effects. Cardiotoxicity, for instance, was responsible for the failure of drugs such as fenspiride, propoxyphene, and valdecoxib. While significant effort has been dedicated to mitigate this issue by developing computational approaches that aim to identify molecules likely to be toxic, including quantitative structure-activity relationship models and machine learning methods, current approaches present limited performance and interpretability. To overcome these, we propose a new web-based computational method, cardioToxCSM, which can predict six types of cardiac toxicity outcomes, including arrhythmia, cardiac failure, heart block, hERG toxicity, hypertension, and myocardial infarction, efficiently and accurately. cardioToxCSM was developed using the concept of graph-based signatures, molecular descriptors, toxicophore matchings, and molecular fingerprints, leveraging explainable machine learning, and was validated internally via different cross validation schemes and externally via low-redundancy blind sets. The models presented robust performances with areas under ROC curves of up to 0.898 on 5-fold cross-validation, consistent with metrics on blind tests. Additionally, our models provide interpretation of the predictions by identifying whether substructures that are commonly enriched in toxic compounds were present. We believe cardioToxCSM will provide valuable insight into the potential cardiotoxicity of small molecules early on drug screening efforts. The method is made freely available as a web server at https://biosig.lab.uq.edu.au/cardiotoxcsm.
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Affiliation(s)
- Saba Iftkhar
- School of Chemistry and Molecular Biosciences, University of Queensland, St Lucia 4072, Queensland, Australia.,Systems and Computational Biology, Bio21 Institute, University of Melbourne, Parkville 3052, Victoria, Australia.,Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne 3004, Victoria, Australia
| | - Alex G C de Sá
- School of Chemistry and Molecular Biosciences, University of Queensland, St Lucia 4072, Queensland, Australia.,Systems and Computational Biology, Bio21 Institute, University of Melbourne, Parkville 3052, Victoria, Australia.,Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne 3004, Victoria, Australia.,Baker Department of Cardiometabolic Health, Melbourne Medical School, University of Melbourne, Parkville 3010, Victoria, Australia
| | - João P L Velloso
- School of Chemistry and Molecular Biosciences, University of Queensland, St Lucia 4072, Queensland, Australia.,Systems and Computational Biology, Bio21 Institute, University of Melbourne, Parkville 3052, Victoria, Australia.,Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne 3004, Victoria, Australia
| | - Raghad Aljarf
- Systems and Computational Biology, Bio21 Institute, University of Melbourne, Parkville 3052, Victoria, Australia.,Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne 3004, Victoria, Australia.,Baker Department of Cardiometabolic Health, Melbourne Medical School, University of Melbourne, Parkville 3010, Victoria, Australia
| | - Douglas E V Pires
- Systems and Computational Biology, Bio21 Institute, University of Melbourne, Parkville 3052, Victoria, Australia.,Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne 3004, Victoria, Australia.,School of Computing and Information Systems, University of Melbourne, Parkville 3052, Victoria, Australia
| | - David B Ascher
- School of Chemistry and Molecular Biosciences, University of Queensland, St Lucia 4072, Queensland, Australia.,Systems and Computational Biology, Bio21 Institute, University of Melbourne, Parkville 3052, Victoria, Australia.,Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne 3004, Victoria, Australia.,Baker Department of Cardiometabolic Health, Melbourne Medical School, University of Melbourne, Parkville 3010, Victoria, Australia
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15
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de Sá AGC, Long Y, Portelli S, Pires DEV, Ascher DB. toxCSM: comprehensive prediction of small molecule toxicity profiles. Brief Bioinform 2022; 23:6673851. [PMID: 35998885 DOI: 10.1093/bib/bbac337] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2022] [Revised: 07/17/2022] [Accepted: 07/23/2022] [Indexed: 01/29/2023] Open
Abstract
Drug discovery is a lengthy, costly and high-risk endeavour that is further convoluted by high attrition rates in later development stages. Toxicity has been one of the main causes of failure during clinical trials, increasing drug development time and costs. To facilitate early identification and optimisation of toxicity profiles, several computational tools emerged aiming at improving success rates by timely pre-screening drug candidates. Despite these efforts, there is an increasing demand for platforms capable of assessing both environmental as well as human-based toxicity properties at large scale. Here, we present toxCSM, a comprehensive computational platform for the study and optimisation of toxicity profiles of small molecules. toxCSM leverages on the well-established concepts of graph-based signatures, molecular descriptors and similarity scores to develop 36 models for predicting a range of toxicity properties, which can assist in developing safer drugs and agrochemicals. toxCSM achieved an Area Under the Receiver Operating Characteristic (ROC) Curve (AUC) of up to 0.99 and Pearson's correlation coefficients of up to 0.94 on 10-fold cross-validation, with comparable performance on blind test sets, outperforming all alternative methods. toxCSM is freely available as a user-friendly web server and API at http://biosig.lab.uq.edu.au/toxcsm.
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Affiliation(s)
- Alex G C de Sá
- School of Chemistry and Molecular Biosciences, University of Queensland, Brisbane City, Queensland, 4072, Australia.,Systems and Computational Biology, Bio21 Institute, University of Melbourne, Parkville, Victoria, 3052, Australia.,Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne, Victoria, 3004, Australia.,Baker Department of Cardiometabolic Health, University of Melbourne, Parkville, Victoria, 3010, Australia
| | - Yangyang Long
- Systems and Computational Biology, Bio21 Institute, University of Melbourne, Parkville, Victoria, 3052, Australia.,Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne, Victoria, 3004, Australia.,School of Computing and Information Systems, University of Melbourne, Parkville, Victoria, 3052, Australia
| | - Stephanie Portelli
- School of Chemistry and Molecular Biosciences, University of Queensland, Brisbane City, Queensland, 4072, Australia.,Systems and Computational Biology, Bio21 Institute, University of Melbourne, Parkville, Victoria, 3052, Australia.,Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne, Victoria, 3004, Australia
| | - Douglas E V Pires
- Systems and Computational Biology, Bio21 Institute, University of Melbourne, Parkville, Victoria, 3052, Australia.,Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne, Victoria, 3004, Australia.,School of Computing and Information Systems, University of Melbourne, Parkville, Victoria, 3052, Australia
| | - David B Ascher
- School of Chemistry and Molecular Biosciences, University of Queensland, Brisbane City, Queensland, 4072, Australia.,Systems and Computational Biology, Bio21 Institute, University of Melbourne, Parkville, Victoria, 3052, Australia.,Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne, Victoria, 3004, Australia.,Baker Department of Cardiometabolic Health, University of Melbourne, Parkville, Victoria, 3010, Australia
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16
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Pires DEV, Stubbs KA, Mylne JS, Ascher DB. cropCSM: designing safe and potent herbicides with graph-based signatures. Brief Bioinform 2022; 23:6535680. [PMID: 35211724 PMCID: PMC9155605 DOI: 10.1093/bib/bbac042] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2021] [Revised: 01/26/2022] [Accepted: 01/27/2022] [Indexed: 12/11/2022] Open
Abstract
Herbicides have revolutionised weed management, increased crop yields and improved profitability allowing for an increase in worldwide food security. Their widespread use, however, has also led to a rise in resistance and concerns about their environmental impact. Despite the need for potent and safe herbicidal molecules, no herbicide with a new mode of action has reached the market in 30 years. Although development of computational approaches has proven invaluable to guide rational drug discovery pipelines, leading to higher hit rates and lower attrition due to poor toxicity, little has been done in contrast for herbicide design. To fill this gap, we have developed cropCSM, a computational platform to help identify new, potent, nontoxic and environmentally safe herbicides. By using a knowledge-based approach, we identified physicochemical properties and substructures enriched in safe herbicides. By representing the small molecules as a graph, we leveraged these insights to guide the development of predictive models trained and tested on the largest collected data set of molecules with experimentally characterised herbicidal profiles to date (over 4500 compounds). In addition, we developed six new environmental and human toxicity predictors, spanning five different species to assist in molecule prioritisation. cropCSM was able to correctly identify 97% of herbicides currently available commercially, while predicting toxicity profiles with accuracies of up to 92%. We believe cropCSM will be an essential tool for the enrichment of screening libraries and to guide the development of potent and safe herbicides. We have made the method freely available through a user-friendly webserver at http://biosig.unimelb.edu.au/crop_csm.
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Affiliation(s)
- Douglas E V Pires
- School of Computing and Information Systems at the University of Melbourne
| | - Keith A Stubbs
- School of Molecular Sciences at the University of Western Australia
| | - Joshua S Mylne
- Curtin University and Deputy Director of the Centre for Crop and Disease Management
| | - David B Ascher
- University of Queensland, and head of Computational Biology and Clinical Informatics at the Baker Institute and Systems
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17
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Alhadrami HA, Alkhatabi H, Abduljabbar FH, Abdelmohsen UR, Sayed AM. Anticancer Potential of Green Synthesized Silver Nanoparticles of the Soft Coral Cladiella pachyclados Supported by Network Pharmacology and In Silico Analyses. Pharmaceutics 2021; 13:1846. [PMID: 34834261 PMCID: PMC8621232 DOI: 10.3390/pharmaceutics13111846] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2021] [Revised: 10/24/2021] [Accepted: 10/28/2021] [Indexed: 12/15/2022] Open
Abstract
Cladiella-derived natural products have shown promising anticancer properties against many human cancer cell lines. In the present investigation, we found that an ethyl acetate extract of Cladiella pachyclados (CE) collected from the Red Sea could inhibit the human breast cancer (BC) cells (MCF and MDA-MB-231) in vitro (IC50 24.32 ± 1.1 and 9.55 ± 0.19 µg/mL, respectively). The subsequent incorporation of the Cladiella extract into the green synthesis of silver nanoparticles (AgNPs) resulted in significantly more activity against both cancer cell lines (IC50 5.62 ± 0.89 and 1.72 ± 0.36, respectively); the efficacy was comparable to that of doxorubicin with much-enhanced selectivity. To explore the mode of action of this extract, various in silico and network-pharmacology-based analyses were performed in the light of the LC-HRESIMS-identified compounds in the CE extract. Firstly, using two independent machine-learning-based prediction software platforms, most of the identified compounds in CE were predicted to inhibit both MCF7 and MDA-MB-231. Moreover, they were predicted to have low toxicity towards normal cell lines. Secondly, approximately 242 BC-related molecular targets were collected from various databases and used to construct a protein-protein interaction (PPI) network, which revealed the most important molecular targets and signaling pathways in the pathogenesis of BC. All the identified compounds in the extract were then subjected to inverse docking against all proteins hosted in the Protein Data bank (PDB) to discover the BC-related proteins that these compounds can target. Approximately, 10.74% of the collected BC-related proteins were potential targets for 70% of the compounds identified in CE. Further validation of the docking results using molecular dynamic simulations (MDS) and binding free energy calculations revealed that only 2.47% of the collected BC-related proteins could be targeted by 30% of the CE-derived compounds. According to docking and MDS experiments, protein-pathway and compound-protein interaction networks were constructed to determine the signaling pathways that the CE compounds could influence. This paper highlights the potential of marine natural products as effective anticancer agents and reports the discovery of novel anti-breast cancer AgNPs.
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Affiliation(s)
- Hani A. Alhadrami
- Department of Medical Laboratory Technology, Faculty of Applied Medical Sciences, King Abdulaziz University, Jeddah 21589, Saudi Arabia; (H.A.A.); (H.A.)
- Molecular Diagnostic Lab., King Abdulaziz University Hospital, King Abdulaziz University, Jeddah 21589, Saudi Arabia
- Special Infectious Agent Unit, King Fahad Medical Research Center, King Abdulaziz University, Jeddah 21589, Saudi Arabia
| | - Heba Alkhatabi
- Department of Medical Laboratory Technology, Faculty of Applied Medical Sciences, King Abdulaziz University, Jeddah 21589, Saudi Arabia; (H.A.A.); (H.A.)
- Center of Excellence in Genomic Medicine Research, King Abdulaziz University, Jeddah 21589, Saudi Arabia
| | - Fahad H. Abduljabbar
- Department of Orthopedic Surgery, Faculty of Medicine, King Abdulaziz University, Jeddah 21589, Saudi Arabia;
| | - Usama Ramadan Abdelmohsen
- Department of Pharmacognosy, Faculty of Pharmacy, Minia University, Minia 61519, Egypt
- Department of Pharmacognosy, Faculty of Pharmacy, Deraya University, New Minia 61111, Egypt
| | - Ahmed M. Sayed
- Department of Pharmacognosy, Faculty of Pharmacy, Nahda University, Beni-Suef 62513, Egypt
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18
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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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