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Kairys V, Baranauskiene L, Kazlauskiene M, Zubrienė A, Petrauskas V, Matulis D, Kazlauskas E. Recent advances in computational and experimental protein-ligand affinity determination techniques. Expert Opin Drug Discov 2024; 19:649-670. [PMID: 38715415 DOI: 10.1080/17460441.2024.2349169] [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: 03/18/2024] [Accepted: 04/25/2024] [Indexed: 05/22/2024]
Abstract
INTRODUCTION Modern drug discovery revolves around designing ligands that target the chosen biomolecule, typically proteins. For this, the evaluation of affinities of putative ligands is crucial. This has given rise to a multitude of dedicated computational and experimental methods that are constantly being developed and improved. AREAS COVERED In this review, the authors reassess both the industry mainstays and the newest trends among the methods for protein - small-molecule affinity determination. They discuss both computational affinity predictions and experimental techniques, describing their basic principles, main limitations, and advantages. Together, this serves as initial guide to the currently most popular and cutting-edge ligand-binding assays employed in rational drug design. EXPERT OPINION The affinity determination methods continue to develop toward miniaturization, high-throughput, and in-cell application. Moreover, the availability of data analysis tools has been constantly increasing. Nevertheless, cross-verification of data using at least two different techniques and careful result interpretation remain of utmost importance.
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Affiliation(s)
- Visvaldas Kairys
- Department of Bioinformatics, Institute of Biotechnology, Life Sciences Center, Vilnius University, Vilnius, Lithuania
| | - Lina Baranauskiene
- Department of Biothermodynamics and Drug Design, Institute of Biotechnology, Life Sciences Center, Vilnius University, Vilnius, Lithuania
| | | | - Asta Zubrienė
- Department of Biothermodynamics and Drug Design, Institute of Biotechnology, Life Sciences Center, Vilnius University, Vilnius, Lithuania
| | - Vytautas Petrauskas
- Department of Biothermodynamics and Drug Design, Institute of Biotechnology, Life Sciences Center, Vilnius University, Vilnius, Lithuania
| | - Daumantas Matulis
- Department of Biothermodynamics and Drug Design, Institute of Biotechnology, Life Sciences Center, Vilnius University, Vilnius, Lithuania
| | - Egidijus Kazlauskas
- Department of Biothermodynamics and Drug Design, Institute of Biotechnology, Life Sciences Center, Vilnius University, Vilnius, Lithuania
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Hou Y, Bai Y, Lu C, Wang Q, Wang Z, Gao J, Xu H. Applying molecular docking to pesticides. PEST MANAGEMENT SCIENCE 2023; 79:4140-4152. [PMID: 37547967 DOI: 10.1002/ps.7700] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/30/2023] [Revised: 07/17/2023] [Accepted: 08/05/2023] [Indexed: 08/08/2023]
Abstract
Pesticide creation is related to the development of sustainable agricultural and ecological safety, and molecular docking technology can effectively help in pesticide innovation. This paper introduces the basic theory behind molecular docking, pesticide databases, and docking software. It also summarizes the application of molecular docking in the pesticide field, including the virtual screening of lead compounds, detection of pesticides and their metabolites in the environment, reverse screening of pesticide targets, and the study of resistance mechanisms. Finally, problems with the use of molecular docking technology in pesticide creation are discussed, and prospects for the future use of molecular docking technology in new pesticide development are discussed. © 2023 Society of Chemical Industry.
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Affiliation(s)
- Yang Hou
- Engineering Research Center of Pesticide of Heilongjiang Province, College of Advanced Agriculture and Ecological Environment, Heilongjiang University, Harbin, China
| | - Yuqian Bai
- Engineering Research Center of Pesticide of Heilongjiang Province, College of Advanced Agriculture and Ecological Environment, Heilongjiang University, Harbin, China
| | - Chang Lu
- Engineering Research Center of Pesticide of Heilongjiang Province, College of Advanced Agriculture and Ecological Environment, Heilongjiang University, Harbin, China
| | - Qiuchan Wang
- Engineering Research Center of Pesticide of Heilongjiang Province, College of Advanced Agriculture and Ecological Environment, Heilongjiang University, Harbin, China
| | - Zishi Wang
- Engineering Research Center of Pesticide of Heilongjiang Province, College of Advanced Agriculture and Ecological Environment, Heilongjiang University, Harbin, China
| | - Jinsheng Gao
- Engineering Research Center of Pesticide of Heilongjiang Province, College of Advanced Agriculture and Ecological Environment, Heilongjiang University, Harbin, China
| | - Hongliang Xu
- Engineering Research Center of Pesticide of Heilongjiang Province, College of Advanced Agriculture and Ecological Environment, Heilongjiang University, Harbin, China
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Masters MR, Mahmoud AH, Wei Y, Lill MA. Deep Learning Model for Efficient Protein-Ligand Docking with Implicit Side-Chain Flexibility. J Chem Inf Model 2023; 63:1695-1707. [PMID: 36916514 DOI: 10.1021/acs.jcim.2c01436] [Citation(s) in RCA: 15] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/16/2023]
Abstract
Protein-ligand docking is an essential tool in structure-based drug design with applications ranging from virtual high-throughput screening to pose prediction for lead optimization. Most docking programs for pose prediction are optimized for redocking to an existing cocrystallized protein structure, ignoring protein flexibility. In real-world drug design applications, however, protein flexibility is an essential feature of the ligand-binding process. Flexible protein-ligand docking still remains a significant challenge to computational drug design. To target this challenge, we present a deep learning (DL) model for flexible protein-ligand docking based on the prediction of an intermolecular Euclidean distance matrix (EDM), making the typical use of iterative search algorithms obsolete. The model was trained on a large-scale data set of protein-ligand complexes and evaluated on independent test sets. Our model generates high quality poses for a diverse set of protein and ligand structures and outperforms comparable docking methods.
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Affiliation(s)
- Matthew R Masters
- Department of Pharmaceutical Sciences, University of Basel, Klingelbergstrasse 50, 4056 Basel, Switzerland
| | - Amr H Mahmoud
- Department of Pharmaceutical Sciences, University of Basel, Klingelbergstrasse 50, 4056 Basel, Switzerland
| | - Yao Wei
- Department of Pharmaceutical Sciences, University of Basel, Klingelbergstrasse 50, 4056 Basel, Switzerland
| | - Markus A Lill
- Department of Pharmaceutical Sciences, University of Basel, Klingelbergstrasse 50, 4056 Basel, Switzerland
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Zhang H, Lin X, Wei Y, Zhang H, Liao L, Wu H, Pan Y, Wu X. Validation of Deep Learning-Based DFCNN in Extremely Large-Scale Virtual Screening and Application in Trypsin I Protease Inhibitor Discovery. Front Mol Biosci 2022; 9:872086. [PMID: 35720125 PMCID: PMC9200220 DOI: 10.3389/fmolb.2022.872086] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2022] [Accepted: 04/21/2022] [Indexed: 11/28/2022] Open
Abstract
Computational methods with affordable computational resources are highly desirable for identifying active drug leads from millions of compounds. This requires a model that is both highly efficient and relatively accurate, which cannot be achieved by most of the current methods. In real virtual screening (VS) application scenarios, the desired method should perform much better in selecting active compounds by prediction than by random chance. Here, we systematically evaluate the performance of our previously developed DFCNN model in large-scale virtual screening, and the results show our method has approximately 22 times the success rate compared to the random chance on average with a score cutoff of 0.99. Of the 102 test cases, 10 cases have more than 98 times the success rate of a random guess. Interestingly, in three cases, the prediction success rate is 99 times that of a random guess by a score cutoff of 0.99. This indicates that in most situations after our extremely large-scale VS, the dataset can be reduced 20 to 100 times for the next step of virtual screening based on docking or MD simulation. Furthermore, we have employed an experimental method to verify our computational method by finding several activity inhibitors for Trypsin I Protease. In addition, we also show its proof-of-concept application in de novo drug screening. The results indicate the massive potential of this method in the first step of the real drug development workflow. Moreover, DFCNN only takes about 0.0000225s for one protein–compound prediction on average with 80 Intel CPU cores (2.00 GHz) and 60 GB RAM, which is at least tens of thousands of times faster than AutoDock Vina or Schrödinger high-throughput virtual screening. Additionally, an online webserver based on DFCNN for large-scale screening is available at http://cbblab.siat.ac.cn/DFCNN/index.php for the convenience of the users.
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Affiliation(s)
- Haiping Zhang
- Center for High Performance Computing, Joint Engineering Research Center for Health Big Data Intelligent Analysis Technology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
- *Correspondence: Haiping Zhang, ; Yi Pan, ; Xuli Wu,
| | - Xiao Lin
- School of Medicine, Shenzhen University, Shenzhen, China
| | - Yanjie Wei
- Center for High Performance Computing, Joint Engineering Research Center for Health Big Data Intelligent Analysis Technology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Huiling Zhang
- Center for High Performance Computing, Joint Engineering Research Center for Health Big Data Intelligent Analysis Technology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Linbu Liao
- College of Software Technology, Zhejiang University, Hangzhou, China
| | - Hao Wu
- Center for High Performance Computing, Joint Engineering Research Center for Health Big Data Intelligent Analysis Technology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Yi Pan
- Center for High Performance Computing, Joint Engineering Research Center for Health Big Data Intelligent Analysis Technology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
- *Correspondence: Haiping Zhang, ; Yi Pan, ; Xuli Wu,
| | - Xuli Wu
- School of Medicine, Shenzhen University, Shenzhen, China
- *Correspondence: Haiping Zhang, ; Yi Pan, ; Xuli Wu,
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Murugan NA, Podobas A, Gadioli D, Vitali E, Palermo G, Markidis S. A Review on Parallel Virtual Screening Softwares for High-Performance Computers. Pharmaceuticals (Basel) 2022; 15:63. [PMID: 35056120 PMCID: PMC8780228 DOI: 10.3390/ph15010063] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2021] [Revised: 12/19/2021] [Accepted: 12/28/2021] [Indexed: 02/01/2023] Open
Abstract
Drug discovery is the most expensive, time-demanding, and challenging project in biopharmaceutical companies which aims at the identification and optimization of lead compounds from large-sized chemical libraries. The lead compounds should have high-affinity binding and specificity for a target associated with a disease, and, in addition, they should have favorable pharmacodynamic and pharmacokinetic properties (grouped as ADMET properties). Overall, drug discovery is a multivariable optimization and can be carried out in supercomputers using a reliable scoring function which is a measure of binding affinity or inhibition potential of the drug-like compound. The major problem is that the number of compounds in the chemical spaces is huge, making the computational drug discovery very demanding. However, it is cheaper and less time-consuming when compared to experimental high-throughput screening. As the problem is to find the most stable (global) minima for numerous protein-ligand complexes (on the order of 106 to 1012), the parallel implementation of in silico virtual screening can be exploited to ensure drug discovery in affordable time. In this review, we discuss such implementations of parallelization algorithms in virtual screening programs. The nature of different scoring functions and search algorithms are discussed, together with a performance analysis of several docking softwares ported on high-performance computing architectures.
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Affiliation(s)
- Natarajan Arul Murugan
- Department of Computer Science, School of Electrical Engineering and Computer Science, KTH Royal Institute of Technology, SE-10044 Stockholm, Sweden;
| | - Artur Podobas
- Department of Computer Science, School of Electrical Engineering and Computer Science, KTH Royal Institute of Technology, SE-10044 Stockholm, Sweden;
| | - Davide Gadioli
- Dipartimento di Elettronica, Infomazione e Bioingegneria, Politecnico di Milano, 20133 Milano, Italy; (D.G.); (E.V.); (G.P.)
| | - Emanuele Vitali
- Dipartimento di Elettronica, Infomazione e Bioingegneria, Politecnico di Milano, 20133 Milano, Italy; (D.G.); (E.V.); (G.P.)
| | - Gianluca Palermo
- Dipartimento di Elettronica, Infomazione e Bioingegneria, Politecnico di Milano, 20133 Milano, Italy; (D.G.); (E.V.); (G.P.)
| | - Stefano Markidis
- Department of Computer Science, School of Electrical Engineering and Computer Science, KTH Royal Institute of Technology, SE-10044 Stockholm, Sweden;
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Pihan E, Kotev M, Rabal O, Beato C, Diaz Gonzalez C. Fine tuning for success in structure-based virtual screening. J Comput Aided Mol Des 2021; 35:1195-1206. [PMID: 34799816 DOI: 10.1007/s10822-021-00431-4] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2021] [Accepted: 11/08/2021] [Indexed: 11/30/2022]
Abstract
Structure-based virtual screening plays a significant role in drug-discovery. The method virtually docks millions of compounds from corporate or public libraries into a binding site of a disease-related protein structure, allowing for the selection of a small list of potential ligands for experimental testing. Many algorithms are available for docking and assessing the affinity of compounds for a targeted protein site. The performance of affinity estimation calculations is highly dependent on the size and nature of the site, therefore a rationale for selecting the best protocol is required. To address this issue, we have developed an automated calibration process, implemented in a Knime workflow. It consists of four steps: preparation of a protein test set with structures and models of the target, preparation of a compound test set with target-related ligands and decoys, automatic test of 24 scoring/rescoring protocols for each target structure and model, and graphical display of results. The automation of the process combined with execution on high performance computing resources greatly reduces the duration of the calibration phase, and the test of many combinations of algorithms on various target conformations results in a rational and optimal choice of the best protocol. Here, we present this tool and exemplify its application in setting-up an optimal protocol for SBVS against Retinoid X Receptor alpha.
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Affiliation(s)
- Emilie Pihan
- Computational Drug Discovery, Evotec (France) SAS, Campus Curie, 195 Route d'Espagne, 31036, Toulouse, France.
| | - Martin Kotev
- Computational Drug Discovery, Evotec (France) SAS, Campus Curie, 195 Route d'Espagne, 31036, Toulouse, France
| | - Obdulia Rabal
- Computational Drug Discovery, Evotec (France) SAS, Campus Curie, 195 Route d'Espagne, 31036, Toulouse, France
| | - Claudia Beato
- Aptuit (Verona) Srl, an Evotec Company, Via Alessandro Fleming, 4, 37135, Verona, Italy
| | - Constantino Diaz Gonzalez
- Computational Drug Discovery, Evotec (France) SAS, Campus Curie, 195 Route d'Espagne, 31036, Toulouse, France
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Gupta R, Srivastava D, Sahu M, Tiwari S, Ambasta RK, Kumar P. Artificial intelligence to deep learning: machine intelligence approach for drug discovery. Mol Divers 2021; 25:1315-1360. [PMID: 33844136 PMCID: PMC8040371 DOI: 10.1007/s11030-021-10217-3] [Citation(s) in RCA: 264] [Impact Index Per Article: 88.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2021] [Accepted: 03/22/2021] [Indexed: 02/06/2023]
Abstract
Drug designing and development is an important area of research for pharmaceutical companies and chemical scientists. However, low efficacy, off-target delivery, time consumption, and high cost impose a hurdle and challenges that impact drug design and discovery. Further, complex and big data from genomics, proteomics, microarray data, and clinical trials also impose an obstacle in the drug discovery pipeline. Artificial intelligence and machine learning technology play a crucial role in drug discovery and development. In other words, artificial neural networks and deep learning algorithms have modernized the area. Machine learning and deep learning algorithms have been implemented in several drug discovery processes such as peptide synthesis, structure-based virtual screening, ligand-based virtual screening, toxicity prediction, drug monitoring and release, pharmacophore modeling, quantitative structure-activity relationship, drug repositioning, polypharmacology, and physiochemical activity. Evidence from the past strengthens the implementation of artificial intelligence and deep learning in this field. Moreover, novel data mining, curation, and management techniques provided critical support to recently developed modeling algorithms. In summary, artificial intelligence and deep learning advancements provide an excellent opportunity for rational drug design and discovery process, which will eventually impact mankind. The primary concern associated with drug design and development is time consumption and production cost. Further, inefficiency, inaccurate target delivery, and inappropriate dosage are other hurdles that inhibit the process of drug delivery and development. With advancements in technology, computer-aided drug design integrating artificial intelligence algorithms can eliminate the challenges and hurdles of traditional drug design and development. Artificial intelligence is referred to as superset comprising machine learning, whereas machine learning comprises supervised learning, unsupervised learning, and reinforcement learning. Further, deep learning, a subset of machine learning, has been extensively implemented in drug design and development. The artificial neural network, deep neural network, support vector machines, classification and regression, generative adversarial networks, symbolic learning, and meta-learning are examples of the algorithms applied to the drug design and discovery process. Artificial intelligence has been applied to different areas of drug design and development process, such as from peptide synthesis to molecule design, virtual screening to molecular docking, quantitative structure-activity relationship to drug repositioning, protein misfolding to protein-protein interactions, and molecular pathway identification to polypharmacology. Artificial intelligence principles have been applied to the classification of active and inactive, monitoring drug release, pre-clinical and clinical development, primary and secondary drug screening, biomarker development, pharmaceutical manufacturing, bioactivity identification and physiochemical properties, prediction of toxicity, and identification of mode of action.
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Affiliation(s)
- Rohan Gupta
- Molecular Neuroscience and Functional Genomics Laboratory, Department of Biotechnology, Delhi Technological University (Formerly DCE), Shahbad Daulatpur, Bawana Road, Delhi, 110042, India
| | - Devesh Srivastava
- Molecular Neuroscience and Functional Genomics Laboratory, Department of Biotechnology, Delhi Technological University (Formerly DCE), Shahbad Daulatpur, Bawana Road, Delhi, 110042, India
| | - Mehar Sahu
- Molecular Neuroscience and Functional Genomics Laboratory, Department of Biotechnology, Delhi Technological University (Formerly DCE), Shahbad Daulatpur, Bawana Road, Delhi, 110042, India
| | - Swati Tiwari
- Molecular Neuroscience and Functional Genomics Laboratory, Department of Biotechnology, Delhi Technological University (Formerly DCE), Shahbad Daulatpur, Bawana Road, Delhi, 110042, India
| | - Rashmi K Ambasta
- Molecular Neuroscience and Functional Genomics Laboratory, Department of Biotechnology, Delhi Technological University (Formerly DCE), Shahbad Daulatpur, Bawana Road, Delhi, 110042, India
| | - Pravir Kumar
- Molecular Neuroscience and Functional Genomics Laboratory, Department of Biotechnology, Delhi Technological University (Formerly DCE), Shahbad Daulatpur, Bawana Road, Delhi, 110042, India.
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Maia EHB, Assis LC, de Oliveira TA, da Silva AM, Taranto AG. Structure-Based Virtual Screening: From Classical to Artificial Intelligence. Front Chem 2020; 8:343. [PMID: 32411671 PMCID: PMC7200080 DOI: 10.3389/fchem.2020.00343] [Citation(s) in RCA: 209] [Impact Index Per Article: 52.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2019] [Accepted: 04/01/2020] [Indexed: 12/15/2022] Open
Abstract
The drug development process is a major challenge in the pharmaceutical industry since it takes a substantial amount of time and money to move through all the phases of developing of a new drug. One extensively used method to minimize the cost and time for the drug development process is computer-aided drug design (CADD). CADD allows better focusing on experiments, which can reduce the time and cost involved in researching new drugs. In this context, structure-based virtual screening (SBVS) is robust and useful and is one of the most promising in silico techniques for drug design. SBVS attempts to predict the best interaction mode between two molecules to form a stable complex, and it uses scoring functions to estimate the force of non-covalent interactions between a ligand and molecular target. Thus, scoring functions are the main reason for the success or failure of SBVS software. Many software programs are used to perform SBVS, and since they use different algorithms, it is possible to obtain different results from different software using the same input. In the last decade, a new technique of SBVS called consensus virtual screening (CVS) has been used in some studies to increase the accuracy of SBVS and to reduce the false positives obtained in these experiments. An indispensable condition to be able to utilize SBVS is the availability of a 3D structure of the target protein. Some virtual databases, such as the Protein Data Bank, have been created to store the 3D structures of molecules. However, sometimes it is not possible to experimentally obtain the 3D structure. In this situation, the homology modeling methodology allows the prediction of the 3D structure of a protein from its amino acid sequence. This review presents an overview of the challenges involved in the use of CADD to perform SBVS, the areas where CADD tools support SBVS, a comparison between the most commonly used tools, and the techniques currently used in an attempt to reduce the time and cost in the drug development process. Finally, the final considerations demonstrate the importance of using SBVS in the drug development process.
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Affiliation(s)
- Eduardo Habib Bechelane Maia
- Laboratory of Pharmaceutical Medicinal Chemistry, Federal University of São João Del Rei, Divinópolis, Brazil.,Federal Center for Technological Education of Minas Gerais-CEFET-MG, Belo Horizonte, Brazil
| | - Letícia Cristina Assis
- Laboratory of Pharmaceutical Medicinal Chemistry, Federal University of São João Del Rei, Divinópolis, Brazil
| | | | | | - Alex Gutterres Taranto
- Laboratory of Pharmaceutical Medicinal Chemistry, Federal University of São João Del Rei, Divinópolis, Brazil
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Maia EHB, Medaglia LR, da Silva AM, Taranto AG. Molecular Architect: A User-Friendly Workflow for Virtual Screening. ACS OMEGA 2020; 5:6628-6640. [PMID: 32258898 PMCID: PMC7114615 DOI: 10.1021/acsomega.9b04403] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/21/2019] [Accepted: 03/06/2020] [Indexed: 05/02/2023]
Abstract
Computer-assisted drug design (CADD) methods have greatly contributed to the development of new drugs. Among CADD methodologies, virtual screening (VS) can enrich the compound collection with molecules that have the desired physicochemical and pharmacophoric characteristics that are needed to become drugs. Many free tools are available for this purpose, but they are difficult to use and do not have a graphical user interface. Furthermore, several free tools must be used to carry out the entire VS process, requiring the user to process the results of one software program so that they can be used in another program, adding a potential source of human error. Moreover, some software programs require knowledge of advanced computational skills, such as programming languages. This context has motivated us to develop Molecular Architect (MolAr). MolAr is a workflow with a simple and intuitive interface that acts in an integrated and automated form to perform the entire VS process, from protein preparation (homology modeling and protonation state) to virtual screening. MolAr carries out VS through AutoDock Vina, DOCK 6, or a consensus of the two. Two case studies were conducted to demonstrate the performance of MolAr. In the first study, the feasibility of using MolAr for DNA-ligand systems was assessed. Both AutoDock Vina and DOCK 6 showed good results in performing VS in DNA-ligand systems. However, the use of consensus virtual screening was able to enrich the results. According to the area under the ROC curve and the enrichment factors, consensus VS was better able to predict the positions of the active ligands. The second case study was performed on 8 targets from the DUD-E database and 10 active ligands for each target. The results demonstrated that using the final ligand conformation provided by AutoDock Vina as an input for DOCK 6 improved the DOCK 6 ROC curves by up to 42% in VS. These case studies demonstrated that MolAr is capable conducting the VS process and is an easy-to-use and effective tool. MolAr is available for download free of charge at http: //www.drugdiscovery.com.br/software/.
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Affiliation(s)
- Eduardo H. B. Maia
- Laboratório
de Quêmica Farmaĉutica Medicinal, Universidade Federal de São João Del-Rei, Divinópolis 35501-296, Minas Gerais, Brazil
- Centro
Federal de Educação Tecnológica de Minas Gerais,
CEFET-MG, Campus Divinópolis, Divinópolis 35503-822, MG, Brazil
| | | | - Alisson Marques da Silva
- Centro
Federal de Educação Tecnológica de Minas Gerais,
CEFET-MG, Campus Divinópolis, Divinópolis 35503-822, MG, Brazil
| | - Alex G. Taranto
- Laboratório
de Quêmica Farmaĉutica Medicinal, Universidade Federal de São João Del-Rei, Divinópolis 35501-296, Minas Gerais, Brazil
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10
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Morrone JA, Weber JK, Huynh T, Luo H, Cornell WD. Combining Docking Pose Rank and Structure with Deep Learning Improves Protein-Ligand Binding Mode Prediction over a Baseline Docking Approach. J Chem Inf Model 2020; 60:4170-4179. [PMID: 32077698 DOI: 10.1021/acs.jcim.9b00927] [Citation(s) in RCA: 52] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
We present a simple, modular graph-based convolutional neural network that takes structural information from protein-ligand complexes as input to generate models for activity and binding mode prediction. Complex structures are generated by a standard docking procedure and fed into a dual-graph architecture that includes separate subnetworks for the ligand bonded topology and the ligand-protein contact map. Recent work has indicated that data set bias drives many past promising results derived from combining deep learning and docking. Our dual-graph network allows contributions from ligand identity that give rise to such biases to be distinguished from effects of protein-ligand interactions on classification. We show that our neural network is capable of learning from protein structural information when, as in the case of binding mode prediction, an unbiased data set is constructed. We next develop a deep learning model for binding mode prediction that uses docking ranking as input in combination with docking structures. This strategy mirrors past consensus models and outperforms a baseline docking program (AutoDock Vina) in a variety of tests, including on cross-docking data sets that mimic real-world docking use cases. Furthermore, the magnitudes of network predictions serve as reliable measures of model confidence.
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Affiliation(s)
- Joseph A Morrone
- Healthcare & Life Sciences Research, IBM TJ Watson Research Center, 1101 Kitchawan Road, Yorktown Heights, New York 10598, United States
| | - Jeffrey K Weber
- Healthcare & Life Sciences Research, IBM TJ Watson Research Center, 1101 Kitchawan Road, Yorktown Heights, New York 10598, United States
| | - Tien Huynh
- Healthcare & Life Sciences Research, IBM TJ Watson Research Center, 1101 Kitchawan Road, Yorktown Heights, New York 10598, United States
| | - Heng Luo
- Healthcare & Life Sciences Research, IBM TJ Watson Research Center, 1101 Kitchawan Road, Yorktown Heights, New York 10598, United States
| | - Wendy D Cornell
- Healthcare & Life Sciences Research, IBM TJ Watson Research Center, 1101 Kitchawan Road, Yorktown Heights, New York 10598, United States
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11
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Banegas-Luna AJ, Imbernón B, Llanes Castro A, Pérez-Garrido A, Cerón-Carrasco JP, Gesing S, Merelli I, D'Agostino D, Pérez-Sánchez H. Advances in distributed computing with modern drug discovery. Expert Opin Drug Discov 2018; 14:9-22. [PMID: 30484337 DOI: 10.1080/17460441.2019.1552936] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
INTRODUCTION Computational chemistry dramatically accelerates the drug discovery process and high-performance computing (HPC) can be used to speed up the most expensive calculations. Supporting a local HPC infrastructure is both costly and time-consuming, and, therefore, many research groups are moving from in-house solutions to remote-distributed computing platforms. Areas covered: The authors focus on the use of distributed technologies, solutions, and infrastructures to gain access to HPC capabilities, software tools, and datasets to run the complex simulations required in computational drug discovery (CDD). Expert opinion: The use of computational tools can decrease the time to market of new drugs. HPC has a crucial role in handling the complex algorithms and large volumes of data required to achieve specificity and avoid undesirable side-effects. Distributed computing environments have clear advantages over in-house solutions in terms of cost and sustainability. The use of infrastructures relying on virtualization reduces set-up costs. Distributed computing resources can be difficult to access, although web-based solutions are becoming increasingly available. There is a trade-off between cost-effectiveness and accessibility in using on-demand computing resources rather than free/academic resources. Graphics processing unit computing, with its outstanding parallel computing power, is becoming increasingly important.
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Affiliation(s)
- Antonio Jesús Banegas-Luna
- a Bioinformatics and High Performance Computing Research Group (BIO-HPC) , Universidad Católica de Murcia (UCAM) , Murcia , Spain
| | - Baldomero Imbernón
- a Bioinformatics and High Performance Computing Research Group (BIO-HPC) , Universidad Católica de Murcia (UCAM) , Murcia , Spain
| | - Antonio Llanes Castro
- a Bioinformatics and High Performance Computing Research Group (BIO-HPC) , Universidad Católica de Murcia (UCAM) , Murcia , Spain
| | - Alfonso Pérez-Garrido
- a Bioinformatics and High Performance Computing Research Group (BIO-HPC) , Universidad Católica de Murcia (UCAM) , Murcia , Spain
| | - José Pedro Cerón-Carrasco
- a Bioinformatics and High Performance Computing Research Group (BIO-HPC) , Universidad Católica de Murcia (UCAM) , Murcia , Spain
| | - Sandra Gesing
- b Center for Research Computing , University of Notre Dame , Notre Dame , IN , USA
| | - Ivan Merelli
- c Institute for Biomedical Technologies , National Research Council of Italy , Segrate (Milan) , Italy
| | - Daniele D'Agostino
- d Institute for Applied Mathematics and Information Technologies "E. Magenes" , National Research Council of Italy , Genoa , Italy
| | - Horacio Pérez-Sánchez
- a Bioinformatics and High Performance Computing Research Group (BIO-HPC) , Universidad Católica de Murcia (UCAM) , Murcia , Spain
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Bellera CL, Di Ianni ME, Talevi A. The application of molecular topology for ulcerative colitis drug discovery. Expert Opin Drug Discov 2017; 13:89-101. [PMID: 29088918 DOI: 10.1080/17460441.2018.1396314] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
INTRODUCTION Although the therapeutic arsenal against ulcerative colitis has greatly expanded (including the revolutionary advent of biologics), there remain patients who are refractory to current medications while the safety of the available therapeutics could also be improved. Molecular topology provides a theoretic framework for the discovery of new therapeutic agents in a very efficient manner, and its applications in the field of ulcerative colitis have slowly begun to flourish. Areas covered: After discussing the basics of molecular topology, the authors review QSAR models focusing on validated targets for the treatment of ulcerative colitis, entirely or partially based on topological descriptors. Expert opinion: The application of molecular topology to ulcerative colitis drug discovery is still very limited, and many of the existing reports seem to be strictly theoretic, with no experimental validation or practical applications. Interestingly, mechanism-independent models based on phenotypic responses have recently been reported. Such models are in agreement with the recent interest raised by network pharmacology as a potential solution for complex disorders. These and other similar studies applying molecular topology suggest that some therapeutic categories may present a 'topological pattern' that goes beyond a specific mechanism of action.
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Affiliation(s)
- Carolina L Bellera
- a Medicinal Chemistry/Laboratory of Bioactive Research and Development, Department of Biological Sciences, Faculty of Exact Sciences , University of La Plata (UNLP) , La Plata , Buenos Aires , Argentina
| | - Mauricio E Di Ianni
- a Medicinal Chemistry/Laboratory of Bioactive Research and Development, Department of Biological Sciences, Faculty of Exact Sciences , University of La Plata (UNLP) , La Plata , Buenos Aires , Argentina
| | - Alan Talevi
- a Medicinal Chemistry/Laboratory of Bioactive Research and Development, Department of Biological Sciences, Faculty of Exact Sciences , University of La Plata (UNLP) , La Plata , Buenos Aires , Argentina
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Brylinski M. Aromatic interactions at the ligand-protein interface: Implications for the development of docking scoring functions. Chem Biol Drug Des 2017; 91:380-390. [PMID: 28816025 DOI: 10.1111/cbdd.13084] [Citation(s) in RCA: 58] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2017] [Revised: 06/29/2017] [Accepted: 08/11/2017] [Indexed: 12/22/2022]
Abstract
The ability to design and fine-tune non-covalent interactions between organic ligands and proteins is indispensable to rational drug development. Aromatic stacking has long been recognized as one of the key constituents of ligand-protein interfaces. In this communication, we employ a two-parameter geometric model to conduct a large-scale statistical analysis of aromatic contacts in the experimental and computer-generated structures of ligand-protein complexes, considering various combinations of aromatic amino acid residues and ligand rings. The geometry of interfacial π-π stacking in crystal structures accords with experimental and theoretical data collected for simple systems, such as the benzene dimer. Many contemporary ligand docking programs implicitly treat aromatic stacking with van der Waals and Coulombic potentials. Although this approach generally provides a sufficient specificity to model aromatic interactions, the geometry of π-π contacts in high-scoring docking conformations could still be improved. The comprehensive analysis of aromatic geometries at ligand-protein interfaces lies the foundation for the development of type-specific statistical potentials to more accurately describe aromatic interactions in molecular docking. A Perl script to detect and calculate the geometric parameters of aromatic interactions in ligand-protein complexes is available at https://github.com/michal-brylinski/earomatic. The dataset comprising experimental complex structures and computer-generated models is available at https://osf.io/rztha/.
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Affiliation(s)
- Michal Brylinski
- Department of Biological Sciences, Louisiana State University, Baton Rouge, LA, USA.,Center for Computation & Technology, Louisiana State University, Baton Rouge, LA, USA
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