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Rezaei MA, Li Y, Wu D, Li X, Li C. Deep Learning in Drug Design: Protein-Ligand Binding Affinity Prediction. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2022; 19:407-417. [PMID: 33360998 PMCID: PMC8942327 DOI: 10.1109/tcbb.2020.3046945] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
Computational drug design relies on the calculation of binding strength between two biological counterparts especially a chemical compound, i.e., a ligand, and a protein. Predicting the affinity of protein-ligand binding with reasonable accuracy is crucial for drug discovery, and enables the optimization of compounds to achieve better interaction with their target protein. In this paper, we propose a data-driven framework named DeepAtom to accurately predict the protein-ligand binding affinity. With 3D Convolutional Neural Network (3D-CNN) architecture, DeepAtom could automatically extract binding related atomic interaction patterns from the voxelized complex structure. Compared with the other CNN based approaches, our light-weight model design effectively improves the model representational capacity, even with the limited available training data. We carried out validation experiments on the PDBbind v.2016 benchmark and the independent Astex Diverse Set. We demonstrate that the less feature engineering dependent DeepAtom approach consistently outperforms the other baseline scoring methods. We also compile and propose a new benchmark dataset to further improve the model performances. With the new dataset as training input, DeepAtom achieves Pearson's R=0.83 and RMSE=1.23 pK units on the PDBbind v.2016 core set. The promising results demonstrate that DeepAtom models can be potentially adopted in computational drug development protocols such as molecular docking and virtual screening.
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Affiliation(s)
- Mohammad A. Rezaei
- Department of Medicinal Chemistry, Center for Natural Products, Drug Discovery and Development (CNPD3), University of Florida
| | - Yanjun Li
- Large-scale Intelligent Systems Laboratory, NSF Center for Big Learning, University of Florida Gainesville, FL, USA
| | - Dapeng Wu
- Large-scale Intelligent Systems Laboratory, NSF Center for Big Learning, University of Florida Gainesville, FL, USA
| | - Xiaolin Li
- Cognization Lab, Palo Alto, California, USA
| | - Chenglong Li
- Department of Medicinal Chemistry, Center for Natural Products, Drug Discovery and Development (CNPD3), University of Florida
- Large-scale Intelligent Systems Laboratory, NSF Center for Big Learning, University of Florida Gainesville, FL, USA
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Veit-Acosta M, de Azevedo Junior WF. Computational Prediction of Binding Affinity for CDK2-ligand Complexes. A Protein Target for Cancer Drug Discovery. Curr Med Chem 2021; 29:2438-2455. [PMID: 34365938 DOI: 10.2174/0929867328666210806105810] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2021] [Revised: 06/15/2021] [Accepted: 06/22/2021] [Indexed: 11/22/2022]
Abstract
BACKGROUND CDK2 participates in the control of eukaryotic cell-cycle progression. Due to the great interest in CDK2 for drug development and the relative easiness in crystallizing this enzyme, we have over 400 structural studies focused on this protein target. This structural data is the basis for the development of computational models to estimate CDK2-ligand binding affinity. OBJECTIVE This work focuses on the recent developments in the application of supervised machine learning modeling to develop scoring functions to predict the binding affinity of CDK2. METHOD We employed the structures available at the protein data bank and the ligand information accessed from the BindingDB, Binding MOAD, and PDBbind to evaluate the predictive performance of machine learning techniques combined with physical modeling used to calculate binding affinity. We compared this hybrid methodology with classical scoring functions available in docking programs. RESULTS Our comparative analysis of previously published models indicated that a model created using a combination of a mass-spring system and cross-validated Elastic Net to predict the binding affinity of CDK2-inhibitor complexes outperformed classical scoring functions available in AutoDock4 and AutoDock Vina. CONCLUSION All studies reviewed here suggest that targeted machine learning models are superior to classical scoring functions to calculate binding affinities. Specifically for CDK2, we see that the combination of physical modeling with supervised machine learning techniques exhibits improved predictive performance to calculate the protein-ligand binding affinity. These results find theoretical support in the application of the concept of scoring function space.
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Affiliation(s)
- Martina Veit-Acosta
- Western Michigan University, 1903 Western, Michigan Ave, Kalamazoo, MI 49008. United States
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Bitencourt-Ferreira G, Rizzotto C, de Azevedo Junior WF. Machine Learning-Based Scoring Functions, Development and Applications with SAnDReS. Curr Med Chem 2021; 28:1746-1756. [PMID: 32410551 DOI: 10.2174/0929867327666200515101820] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2019] [Revised: 04/06/2020] [Accepted: 04/07/2020] [Indexed: 11/22/2022]
Abstract
BACKGROUND Analysis of atomic coordinates of protein-ligand complexes can provide three-dimensional data to generate computational models to evaluate binding affinity and thermodynamic state functions. Application of machine learning techniques can create models to assess protein-ligand potential energy and binding affinity. These methods show superior predictive performance when compared with classical scoring functions available in docking programs. OBJECTIVE Our purpose here is to review the development and application of the program SAnDReS. We describe the creation of machine learning models to assess the binding affinity of protein-ligand complexes. METHODS SAnDReS implements machine learning methods available in the scikit-learn library. This program is available for download at https://github.com/azevedolab/sandres. SAnDReS uses crystallographic structures, binding and thermodynamic data to create targeted scoring functions. RESULTS Recent applications of the program SAnDReS to drug targets such as Coagulation factor Xa, cyclin-dependent kinases and HIV-1 protease were able to create targeted scoring functions to predict inhibition of these proteins. These targeted models outperform classical scoring functions. CONCLUSION Here, we reviewed the development of machine learning scoring functions to predict binding affinity through the application of the program SAnDReS. Our studies show the superior predictive performance of the SAnDReS-developed models when compared with classical scoring functions available in the programs such as AutoDock4, Molegro Virtual Docker and AutoDock Vina.
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Affiliation(s)
| | - Camila Rizzotto
- Pontifical Catholic University of Rio Grande do Sul - PUCRS, Porto Alegre-RS, Brazil
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Sulimov VB, Kutov DC, Taschilova AS, Ilin IS, Tyrtyshnikov EE, Sulimov AV. Docking Paradigm in Drug Design. Curr Top Med Chem 2021; 21:507-546. [PMID: 33292135 DOI: 10.2174/1568026620666201207095626] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2020] [Revised: 09/28/2020] [Accepted: 10/16/2020] [Indexed: 11/22/2022]
Abstract
Docking is in demand for the rational computer aided structure based drug design. A review of docking methods and programs is presented. Different types of docking programs are described. They include docking of non-covalent small ligands, protein-protein docking, supercomputer docking, quantum docking, the new generation of docking programs and the application of docking for covalent inhibitors discovery. Taking into account the threat of COVID-19, we present here a short review of docking applications to the discovery of inhibitors of SARS-CoV and SARS-CoV-2 target proteins, including our own result of the search for inhibitors of SARS-CoV-2 main protease using docking and quantum chemical post-processing. The conclusion is made that docking is extremely important in the fight against COVID-19 during the process of development of antivirus drugs having a direct action on SARS-CoV-2 target proteins.
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Affiliation(s)
- Vladimir B Sulimov
- Research Computer Center of Lomonosov Moscow State University, Moscow, Russian Federation
| | - Danil C Kutov
- Research Computer Center of Lomonosov Moscow State University, Moscow, Russian Federation
| | - Anna S Taschilova
- Research Computer Center of Lomonosov Moscow State University, Moscow, Russian Federation
| | - Ivan S Ilin
- Research Computer Center of Lomonosov Moscow State University, Moscow, Russian Federation
| | - Eugene E Tyrtyshnikov
- Institute of Numerical Mathematics of Russian Academy of Sciences, Moscow, Russian Federation
| | - Alexey V Sulimov
- Research Computer Center of Lomonosov Moscow State University, Moscow, Russian Federation
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Shahbaaz M, Qari SH, Abdellattif MH, Hussien MA. Structural analyses and classification of novel isoniazid resistance coupled mutational landscapes in Mycobacterium tuberculosis: a combined molecular docking and MD simulation study. J Biomol Struct Dyn 2020; 40:4791-4800. [PMID: 33345744 DOI: 10.1080/07391102.2020.1861986] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
Abstract
Drug resistance in Mycobacterium tuberculosis has become a major challenge to the current regime of treatment as well as to the containment of the disease globally. The molecular and genetic studies identified frequently occurring point mutations in the virulent protein such as KatG of M. tuberculosis resulted in the development of isoniazid tolerance in the pathogen. This study aims to analyze the structural basis of the disease mutations available in the literature as well as to predict novel alteration in the KatG which may cause similar deleterious effects. Around 15 experimentally derived mutations were included in this study and pathogenic mutational landscapes containing 60 site-specific alterations were predicted using the available in silico techniques. The effects of these mutations on the stability of the protein were studied and an exhaustive docking study was conducted for each classified perturbations, which identify the highest changes in the binding energies in p.Meth255Ile among experimental and p.Ala222Arg in computationally predicted mutations. Furthermore, the structural effects on these substitutions were analyzed using the principles of molecular dynamic simulations each for a 100 ns time scale, which validated the interaction studies. The outcome of this study may enable the identification of the novel drug resistance-associated point mutations which were not previously reported and may contribute significantly in a variety of experimental studies as well as facilitate the process of drug design and discovery.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Mohd Shahbaaz
- South African Medical Research Council Bioinformatics Unit, South African National Bioinformatics Institute, University of the Western Cape, Bellville, Cape Town, South Africa.,Laboratory of Computational Modeling of Drugs, South Ural State University, Chelyabinsk, Russia
| | - Sameer H Qari
- Biology Department, Aljumum University College, Umm Al-Qura University, Makkah, Saudi Arabia
| | - Magda H Abdellattif
- Department of Chemistry, College of Science, Deanship of Scientific Research, Taif University, Taif, Saudi Arabia
| | - Mostafa A Hussien
- Department of Chemistry, Faculty of Science, King Abdulaziz University, Jeddah, Saudi Arabia.,Department of Chemistry, Faculty of Science, Port Said University, Port Said, Egypt
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Yousuf M, Khan P, Shamsi A, Shahbaaz M, Hasan GM, Haque QMR, Christoffels A, Islam A, Hassan MI. Inhibiting CDK6 Activity by Quercetin Is an Attractive Strategy for Cancer Therapy. ACS OMEGA 2020; 5:27480-27491. [PMID: 33134711 PMCID: PMC7594119 DOI: 10.1021/acsomega.0c03975] [Citation(s) in RCA: 36] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/18/2020] [Accepted: 09/30/2020] [Indexed: 06/11/2023]
Abstract
Cyclin-dependent kinase 6 (CDK6) is a potential drug target that plays an important role in the progression of different types of cancers. We performed in silico and in vitro screening of different natural compounds and found that quercetin has a high binding affinity for the CDK6 and inhibits its activity with an IC50 = 5.89 μM. Molecular docking and a 200 ns whole atom simulation of the CDK6-quercetin complex provide insights into the binding mechanism and stability of the complex. Binding parameters ascertained by fluorescence and isothermal titration calorimetry studies revealed a binding constant in the range of 107 M-1 of quercetin to the CDK6. Thermodynamic parameters associated with the formation of the CDK6-quercetin complex suggested an electrostatic interaction-driven process. The cell-based protein expression studies in the breast (MCF-7) and lung (A549) cancer cells revealed that the treatment of quercetin decreases the expression of CDK6. Quercetin also decreases the viability and colony formation potential of selected cancer cells. Moreover, quercetin induces apoptosis, by decreasing the production of reactive oxygen species and CDK6 expression. Both in silico and in vitro studies highlight the significance of quercetin for the development of anticancer leads in terms of CDK6 inhibitors.
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Affiliation(s)
- Mohd Yousuf
- Department
of Biosciences, Jamia Millia Islamia, Jamia Nagar, New Delhi 110025, India
| | - Parvez Khan
- Centre
for Interdisciplinary Research in Basic Sciences, Jamia Millia Islamia, Jamia Nagar, New Delhi 110025, India
| | - Anas Shamsi
- Centre
for Interdisciplinary Research in Basic Sciences, Jamia Millia Islamia, Jamia Nagar, New Delhi 110025, India
| | - Mohd Shahbaaz
- South
African Medical Research Council Bioinformatics Unit, South African
National Bioinformatics Institute, University
of the Western Cape, Private Bag X17, Bellville, Cape Town 7535, South Africa
- Laboratory
of Computational Modeling of Drugs, South
Ural State University, 76 Lenin Prospekt, Chelyabinsk 454080, Russia
| | - Gulam Mustafa Hasan
- Department
of Biochemistry, College of Medicine, Prince
Sattam Bin Abdulaziz University, Al-Kharj 11942, Saudi Arabia
| | | | - Alan Christoffels
- South
African Medical Research Council Bioinformatics Unit, South African
National Bioinformatics Institute, University
of the Western Cape, Private Bag X17, Bellville, Cape Town 7535, South Africa
| | - Asimul Islam
- Centre
for Interdisciplinary Research in Basic Sciences, Jamia Millia Islamia, Jamia Nagar, New Delhi 110025, India
| | - Md. Imtaiyaz Hassan
- Centre
for Interdisciplinary Research in Basic Sciences, Jamia Millia Islamia, Jamia Nagar, New Delhi 110025, India
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