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Vittorio S, Lunghini F, Morerio P, Gadioli D, Orlandini S, Silva P, Jan Martinovic, Pedretti A, Bonanni D, Del Bue A, Palermo G, Vistoli G, Beccari AR. Addressing docking pose selection with structure-based deep learning: Recent advances, challenges and opportunities. Comput Struct Biotechnol J 2024; 23:2141-2151. [PMID: 38827235 PMCID: PMC11141151 DOI: 10.1016/j.csbj.2024.05.024] [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: 01/23/2024] [Revised: 05/15/2024] [Accepted: 05/15/2024] [Indexed: 06/04/2024] Open
Abstract
Molecular docking is a widely used technique in drug discovery to predict the binding mode of a given ligand to its target. However, the identification of the near-native binding pose in docking experiments still represents a challenging task as the scoring functions currently employed by docking programs are parametrized to predict the binding affinity, and, therefore, they often fail to correctly identify the ligand native binding conformation. Selecting the correct binding mode is crucial to obtaining meaningful results and to conveniently optimizing new hit compounds. Deep learning (DL) algorithms have been an area of a growing interest in this sense for their capability to extract the relevant information directly from the protein-ligand structure. Our review aims to present the recent advances regarding the development of DL-based pose selection approaches, discussing limitations and possible future directions. Moreover, a comparison between the performances of some classical scoring functions and DL-based methods concerning their ability to select the correct binding mode is reported. In this regard, two novel DL-based pose selectors developed by us are presented.
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Affiliation(s)
- Serena Vittorio
- Dipartimento di Scienze Farmaceutiche, Università degli Studi di Milano, Via Luigi Mangiagalli 25, I-20133 Milano, Italy
| | - Filippo Lunghini
- EXSCALATE, Dompé Farmaceutici SpA, Via Tommaso de Amicis 95, 80123 Naples, Italy
| | - Pietro Morerio
- Pattern Analysis and Computer Vision, Fondazione Istituto Italiano di Tecnologia, Via Morego, 30, 16163 Genova, Italy
| | - Davide Gadioli
- Dipartimento di Elettronica Informazione e Bioingegneria, Politecnico di Milano, Via Ponzio 34/5, I-20133 Milano, Italy
| | - Sergio Orlandini
- SCAI, SuperComputing Applications and Innovation Department, CINECA, Via dei Tizii 6, Rome 00185, Italy
| | - Paulo Silva
- IT4Innovations, VSB – Technical University of Ostrava, 17. listopadu 2172/15, 70800 Ostrava-Poruba, Czech Republic
| | - Jan Martinovic
- IT4Innovations, VSB – Technical University of Ostrava, 17. listopadu 2172/15, 70800 Ostrava-Poruba, Czech Republic
| | - Alessandro Pedretti
- Dipartimento di Scienze Farmaceutiche, Università degli Studi di Milano, Via Luigi Mangiagalli 25, I-20133 Milano, Italy
| | - Domenico Bonanni
- Department of Physical and Chemical Sciences, University of L′Aquila, via Vetoio, L′Aquila 67010, Italy
| | - Alessio Del Bue
- Pattern Analysis and Computer Vision, Fondazione Istituto Italiano di Tecnologia, Via Morego, 30, 16163 Genova, Italy
| | - Gianluca Palermo
- Dipartimento di Elettronica Informazione e Bioingegneria, Politecnico di Milano, Via Ponzio 34/5, I-20133 Milano, Italy
| | - Giulio Vistoli
- Dipartimento di Scienze Farmaceutiche, Università degli Studi di Milano, Via Luigi Mangiagalli 25, I-20133 Milano, Italy
| | - Andrea R. Beccari
- EXSCALATE, Dompé Farmaceutici SpA, Via Tommaso de Amicis 95, 80123 Naples, Italy
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Wang L, He X, Ji B, Han F, Niu T, Cai L, Zhai J, Hao D, Wang J. Geometry Optimization Algorithms in Conjunction with the Machine Learning Potential ANI-2x Facilitate the Structure-Based Virtual Screening and Binding Mode Prediction. Biomolecules 2024; 14:648. [PMID: 38927052 PMCID: PMC11201553 DOI: 10.3390/biom14060648] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2024] [Revised: 05/27/2024] [Accepted: 05/29/2024] [Indexed: 06/28/2024] Open
Abstract
Structure-based virtual screening utilizes molecular docking to explore and analyze ligand-macromolecule interactions, crucial for identifying and developing potential drug candidates. Although there is availability of several widely used docking programs, the accurate prediction of binding affinity and binding mode still presents challenges. In this study, we introduced a novel protocol that combines our in-house geometry optimization algorithm, the conjugate gradient with backtracking line search (CG-BS), which is capable of restraining and constraining rotatable torsional angles and other geometric parameters with a highly accurate machine learning potential, ANI-2x, renowned for its precise molecular energy predictions reassembling the wB97X/6-31G(d) model. By integrating this protocol with binding pose prediction using the Glide, we conducted additional structural optimization and potential energy prediction on 11 small molecule-macromolecule and 12 peptide-macromolecule systems. We observed that ANI-2x/CG-BS greatly improved the docking power, not only optimizing binding poses more effectively, particularly when the RMSD of the predicted binding pose by Glide exceeded around 5 Å, but also achieving a 26% higher success rate in identifying those native-like binding poses at the top rank compared to Glide docking. As for the scoring and ranking powers, ANI-2x/CG-BS demonstrated an enhanced performance in predicting and ranking hundreds or thousands of ligands over Glide docking. For example, Pearson's and Spearman's correlation coefficients remarkedly increased from 0.24 and 0.14 with Glide docking to 0.85 and 0.69, respectively, with the addition of ANI-2x/CG-BS for optimizing and ranking small molecules binding to the bacterial ribosomal aminoacyl-tRNA receptor. These results suggest that ANI-2x/CG-BS holds considerable potential for being integrated into virtual screening pipelines due to its enhanced docking performance.
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Affiliation(s)
- Luxuan Wang
- Department of Pharmaceutical Sciences, Computational Chemical Genomics Screening Center, School of Pharmacy, University of Pittsburgh, Pittsburgh, PA 15261, USA; (L.W.); (X.H.); (B.J.); (F.H.); (T.N.); (L.C.); (J.Z.)
| | - Xibing He
- Department of Pharmaceutical Sciences, Computational Chemical Genomics Screening Center, School of Pharmacy, University of Pittsburgh, Pittsburgh, PA 15261, USA; (L.W.); (X.H.); (B.J.); (F.H.); (T.N.); (L.C.); (J.Z.)
| | - Beihong Ji
- Department of Pharmaceutical Sciences, Computational Chemical Genomics Screening Center, School of Pharmacy, University of Pittsburgh, Pittsburgh, PA 15261, USA; (L.W.); (X.H.); (B.J.); (F.H.); (T.N.); (L.C.); (J.Z.)
| | - Fengyang Han
- Department of Pharmaceutical Sciences, Computational Chemical Genomics Screening Center, School of Pharmacy, University of Pittsburgh, Pittsburgh, PA 15261, USA; (L.W.); (X.H.); (B.J.); (F.H.); (T.N.); (L.C.); (J.Z.)
| | - Taoyu Niu
- Department of Pharmaceutical Sciences, Computational Chemical Genomics Screening Center, School of Pharmacy, University of Pittsburgh, Pittsburgh, PA 15261, USA; (L.W.); (X.H.); (B.J.); (F.H.); (T.N.); (L.C.); (J.Z.)
| | - Lianjin Cai
- Department of Pharmaceutical Sciences, Computational Chemical Genomics Screening Center, School of Pharmacy, University of Pittsburgh, Pittsburgh, PA 15261, USA; (L.W.); (X.H.); (B.J.); (F.H.); (T.N.); (L.C.); (J.Z.)
| | - Jingchen Zhai
- Department of Pharmaceutical Sciences, Computational Chemical Genomics Screening Center, School of Pharmacy, University of Pittsburgh, Pittsburgh, PA 15261, USA; (L.W.); (X.H.); (B.J.); (F.H.); (T.N.); (L.C.); (J.Z.)
| | - Dongxiao Hao
- School of Electronics and Information Engineering, Ankang University, Ankang 725000, China
| | - Junmei Wang
- Department of Pharmaceutical Sciences, Computational Chemical Genomics Screening Center, School of Pharmacy, University of Pittsburgh, Pittsburgh, PA 15261, USA; (L.W.); (X.H.); (B.J.); (F.H.); (T.N.); (L.C.); (J.Z.)
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Moshawih S, Bu ZH, Goh HP, Kifli N, Lee LH, Goh KW, Ming LC. Consensus holistic virtual screening for drug discovery: a novel machine learning model approach. J Cheminform 2024; 16:62. [PMID: 38807196 PMCID: PMC11134635 DOI: 10.1186/s13321-024-00855-8] [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: 12/03/2023] [Accepted: 05/10/2024] [Indexed: 05/30/2024] Open
Abstract
In drug discovery, virtual screening is crucial for identifying potential hit compounds. This study aims to present a novel pipeline that employs machine learning models that amalgamates various conventional screening methods. A diverse array of protein targets was selected, and their corresponding datasets were subjected to active/decoy distribution analysis prior to scoring using four distinct methods: QSAR, Pharmacophore, docking, and 2D shape similarity, which were ultimately integrated into a single consensus score. The fine-tuned machine learning models were ranked using the novel formula "w_new", consensus scores were calculated, and an enrichment study was performed for each target. Distinctively, consensus scoring outperformed other methods in specific protein targets such as PPARG and DPP4, achieving AUC values of 0.90 and 0.84, respectively. Remarkably, this approach consistently prioritized compounds with higher experimental PIC50 values compared to all other screening methodologies. Moreover, the models demonstrated a range of moderate to high performance in terms of R2 values during external validation. In conclusion, this novel workflow consistently delivered superior results, emphasizing the significance of a holistic approach in drug discovery, where both quantitative metrics and active enrichment play pivotal roles in identifying the best virtual screening methodology.Scientific contributionWe presented a novel consensus scoring workflow in virtual screening, merging diverse methods for enhanced compound selection. We also introduced 'w_new', a groundbreaking metric that intricately refines machine learning model rankings by weighing various model-specific parameters, revolutionizing their efficacy in drug discovery in addition to other domains.
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Affiliation(s)
- Said Moshawih
- PAPRSB Institute of Health Sciences, Universiti Brunei Darussalam, Gadong, Brunei Darussalam.
- Faculty of Data Science and Information Technology, INTI International University, Nilai, Malaysia.
| | - Zhen Hui Bu
- Faculty of Computing and Engineering, Quest International University, Ipoh, Malaysia
| | - Hui Poh Goh
- PAPRSB Institute of Health Sciences, Universiti Brunei Darussalam, Gadong, Brunei Darussalam
| | - Nurolaini Kifli
- PAPRSB Institute of Health Sciences, Universiti Brunei Darussalam, Gadong, Brunei Darussalam
| | - Lam Hong Lee
- Faculty of Computing and Engineering, Quest International University, Ipoh, Malaysia
| | - Khang Wen Goh
- Faculty of Data Science and Information Technology, INTI International University, Nilai, Malaysia
| | - Long Chiau Ming
- PAPRSB Institute of Health Sciences, Universiti Brunei Darussalam, Gadong, Brunei Darussalam
- School of Medical and Life Sciences, Sunway University, Sunway City, Malaysia
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Kotev M, Diaz Gonzalez C. Molecular Dynamics and Other HPC Simulations for Drug Discovery. Methods Mol Biol 2024; 2716:265-291. [PMID: 37702944 DOI: 10.1007/978-1-0716-3449-3_12] [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] [Indexed: 09/14/2023]
Abstract
High performance computing (HPC) is taking an increasingly important place in drug discovery. It makes possible the simulation of complex biochemical systems with high precision in a short time, thanks to the use of sophisticated algorithms. It promotes the advancement of knowledge in fields that are inaccessible or difficult to access through experimentation and it contributes to accelerating the discovery of drugs for unmet medical needs while reducing costs. Herein, we report how computational performance has evolved over the past years, and then we detail three domains where HPC is essential. Molecular dynamics (MD) is commonly used to explore the flexibility of proteins, thus generating a better understanding of different possible approaches to modulate their activity. Modeling and simulation of biopolymer complexes enables the study of protein-protein interactions (PPI) in healthy and disease states, thus helping the identification of targets of pharmacological interest. Virtual screening (VS) also benefits from HPC to predict in a short time, among millions or billions of virtual chemical compounds, the best potential ligands that will be tested in relevant assays to start a rational drug design process.
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Affiliation(s)
- Martin Kotev
- Evotec SE, Integrated Drug Discovery, Molecular Architects, Campus Curie, Toulouse, France
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Escamilla-Gutiérrez A, Córdova-Espinoza MG, Sánchez-Monciváis A, Tecuatzi-Cadena B, Regalado-García AG, Medina-Quero K. In silico selection of aptamers for bacterial toxins detection. J Biomol Struct Dyn 2023; 41:10909-10918. [PMID: 36546716 DOI: 10.1080/07391102.2022.2159529] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2022] [Accepted: 12/10/2022] [Indexed: 12/24/2022]
Abstract
The most commonly used toxins in biological warfare are staphylococcal enterotoxin B (3SEB), cholera toxin (1XTC), and botulinum toxin (3BTA). Uncovering novel strategies for identifying these toxins is paramount; therefore, aptamers are used for this purpose. Aptamers are single-stranded DNA or RNA oligonucleotides selected via Systematic Evolution of Ligands by Exponential Enrichment (SELEX) with high binding affinity and specificity against target molecules. However, SELEX in vitro is tedious; hence, adopting alternative in silico molecular docking approaches is necessary. We aimed to conduct molecular docking with accessible tools and obtain RNA aptamers. First, 4,820,095 sequences obtained from an initial library of 9.5 × 109 Python script sequences were used. The GraphClust program was used to create representative groups or clusters, and the DoGSiteScorer (https://proteins.plus/) was used to conduct binding site detection of the proteins: 5DO4 (thrombin), 3SEB, 1XTC, and 3BTA. rDock, HDock, and PatchDock were adopted, combining different docking program results (consensus scoring), to improve receptor-ligand prediction. An analysis of the poses and root mean square deviation (RMSD) was performed, and 468 structurally different aptamers were obtained. The DoGSiteScorer program predicted the binding site of each protein to direct the interaction with the aptamer. Candidate aptamers for 3SEB, 1XTC, and 3BTA were selected according to the pose value considering the closeness of the interaction with a lower mean of 45.923 Å, 45.854 Å, and 72.490 Å, respectively.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Alejandro Escamilla-Gutiérrez
- Laboratorio de Bacteriología Médica, Departamento de Microbiología, Escuela Nacional de Ciencias Biológicas, Instituto Politécnico Nacional, Ciudad de México, México
- Hospital General, Instituto Mexicano del Seguro Social IMSS, Ciudad de México, México
| | - María Guadalupe Córdova-Espinoza
- Laboratorio de Bacteriología Médica, Departamento de Microbiología, Escuela Nacional de Ciencias Biológicas, Instituto Politécnico Nacional, Ciudad de México, México
- Laboratorio de Inmunología, Escuela Militar de Graduados de Sanidad, Secretaría de la Defensa Nacional, Ciudad de México, México
| | - Anahí Sánchez-Monciváis
- Laboratorio de Inmunología, Escuela Militar de Graduados de Sanidad, Secretaría de la Defensa Nacional, Ciudad de México, México
| | - Brenda Tecuatzi-Cadena
- Laboratorio de Inmunología, Escuela Militar de Graduados de Sanidad, Secretaría de la Defensa Nacional, Ciudad de México, México
| | - Ana Gabriela Regalado-García
- Laboratorio de Inmunología, Escuela Militar de Graduados de Sanidad, Secretaría de la Defensa Nacional, Ciudad de México, México
| | - Karen Medina-Quero
- Laboratorio de Inmunología, Escuela Militar de Graduados de Sanidad, Secretaría de la Defensa Nacional, Ciudad de México, México
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Cavasotto CN, Di Filippo JI. The Impact of Supervised Learning Methods in Ultralarge High-Throughput Docking. J Chem Inf Model 2023; 63:2267-2280. [PMID: 37036491 DOI: 10.1021/acs.jcim.2c01471] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/11/2023]
Abstract
Structure-based virtual screening methods are, nowadays, one of the key pillars of computational drug discovery. In recent years, a series of studies have reported docking-based virtual screening campaigns of large databases ranging from hundreds to thousands of millions compounds, further identifying novel hits after experimental validation. As these larg-scale efforts are not generally accessible, machine learning-based protocols have emerged to accelerate the identification of virtual hits within an ultralarge chemical space, reaching impressive reductions in computational time. Herein, we illustrate the motivation and the problem behind the screening of large databases, providing an overview of key concepts and essential applications of machine learning-accelerated protocols, specifically concerning supervised learning methods. We also discuss where the field stands with these novel developments, highlighting possible insights for future studies.
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Affiliation(s)
- Claudio N Cavasotto
- Computational Drug Design and Biomedical Informatics Laboratory, Instituto de Investigaciones en Medicina Traslacional (IIMT), CONICET-Universidad Austral, Av. Juan Domingo Perón 1500, B1629AHJ Pilar, Argentina
- Facultad de Ciencias Biomédicas, and Facultad de Ingeniería, Universidad Austral, Av. Juan Domingo Perón 1500, B1629AHJ Pilar, Argentina
- Austral Institute for Applied Artificial Intelligence, Universidad Austral, Av. Juan Domingo Perón 1500, B1629AHJ Pilar, Argentina
| | - Juan I Di Filippo
- Computational Drug Design and Biomedical Informatics Laboratory, Instituto de Investigaciones en Medicina Traslacional (IIMT), CONICET-Universidad Austral, Av. Juan Domingo Perón 1500, B1629AHJ Pilar, Argentina
- Facultad de Ciencias Biomédicas, and Facultad de Ingeniería, Universidad Austral, Av. Juan Domingo Perón 1500, B1629AHJ Pilar, Argentina
- Austral Institute for Applied Artificial Intelligence, Universidad Austral, Av. Juan Domingo Perón 1500, B1629AHJ Pilar, Argentina
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Potlitz F, Link A, Schulig L. Advances in the discovery of new chemotypes through ultra-large library docking. Expert Opin Drug Discov 2023; 18:303-313. [PMID: 36714919 DOI: 10.1080/17460441.2023.2171984] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/31/2023]
Abstract
INTRODUCTION The size and complexity of virtual screening libraries in drug discovery have skyrocketed in recent years, reaching up to multiple billions of accessible compounds. However, virtual screening of such ultra-large libraries poses several challenges associated with preparing the libraries, sampling, and pre-selection of suitable compounds. The utilization of artificial intelligence (AI)-assisted screening approaches, such as deep learning, poses a promising countermeasure to deal with this rapidly expanding chemical space. For example, various AI-driven methods were recently successfully used to identify novel small molecule inhibitors of the SARS-CoV-2 main protease (Mpro). AREAS COVERED This review focuses on presenting various kinds of virtual screening methods suitable for dealing with ultra-large libraries. Challenges associated with these computational methodologies are discussed, and recent advances are highlighted in the example of the discovery of novel Mpro inhibitors targeting the SARS-CoV-2 virus. EXPERT OPINION With the rapid expansion of the virtual chemical space, the methodologies for docking and screening such quantities of molecules need to keep pace. Employment of AI-driven screening compounds has already been shown to be effective in a range from a few thousand to multiple billion compounds, furthered by de novo generation of drug-like molecules without human interference.
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Affiliation(s)
- Felix Potlitz
- Department of Pharmaceutical and Medicinal Chemistry, Institute of Pharmacy, University of Greifswald, Germany
| | - Andreas Link
- Department of Pharmaceutical and Medicinal Chemistry, Institute of Pharmacy, University of Greifswald, Germany
| | - Lukas Schulig
- Department of Pharmaceutical and Medicinal Chemistry, Institute of Pharmacy, University of Greifswald, Germany
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Agarwal R, Smith JC. Speed vs Accuracy: Effect on Ligand Pose Accuracy of Varying Box Size and Exhaustiveness in AutoDock Vina. Mol Inform 2023; 42:e2200188. [PMID: 36262028 DOI: 10.1002/minf.202200188] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2022] [Accepted: 10/19/2022] [Indexed: 11/11/2022]
Abstract
Structure-based virtual high-throughput screening involves docking chemical libraries to targets of interest. A parameter pertinent to the accuracy of the resulting pose is the root mean square deviation (RMSD) from a known crystallographic structure, i. e., the 'docking power'. Here, using a popular algorithm, Autodock Vina, as a model program, we evaluate the effects of varying two common docking parameters: the box size (the size of docking search space) and the exhaustiveness of the global search (the number of independent runs starting from random ligand conformations) on the RMSD from the PDBbind v2017 refined dataset of experimental protein-ligand complexes. Although it is clear that exhaustiveness is an important parameter, there is wide variation in the values used, with variation between 1 and >100. We, therefore, evaluated a combination of cubic boxes of different sizes and five exhaustiveness values (1, 8, 25, 50, 75, 100) within the range of those commonly adopted. The results show that the default exhaustiveness value of 8 performs well overall for most box sizes. In contrast, for all box sizes, but particularly for large boxes, an exhaustiveness value of 1 led to significantly higher median RMSD (mRMSD) values. The docking power was slightly improved with an exhaustiveness of 25, but the mRMSD changes little with values higher than 25. Therefore, although low exhaustiveness is computationally faster, the results are more likely to be far from reality, and, conversely, values >25 led to little improvement at the expense of computational resources. Overall, we recommend users to use at least the default exhaustiveness value of 8 for virtual screening calculations.
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Affiliation(s)
- Rupesh Agarwal
- UT/ORNL Center for Molecular Biophysics, Oak Ridge National Laboratory, Oak Ridge, TN 37831-6309, USA.,Department of Biochemistry and Cellular and Molecular Biology, University of Tennessee, Knoxville, 14311 Cumberland Avenue, Knoxville, TN 37996-1939, USA
| | - Jeremy C Smith
- UT/ORNL Center for Molecular Biophysics, Oak Ridge National Laboratory, Oak Ridge, TN 37831-6309, USA.,Department of Biochemistry and Cellular and Molecular Biology, University of Tennessee, Knoxville, 14311 Cumberland Avenue, Knoxville, TN 37996-1939, USA
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Scardino V, Di Filippo JI, Cavasotto CN. How good are AlphaFold models for docking-based virtual screening? iScience 2023; 26:105920. [PMID: 36686396 PMCID: PMC9852548 DOI: 10.1016/j.isci.2022.105920] [Citation(s) in RCA: 43] [Impact Index Per Article: 43.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2022] [Revised: 11/12/2022] [Accepted: 12/28/2022] [Indexed: 12/31/2022] Open
Abstract
A crucial component in structure-based drug discovery is the availability of high-quality three-dimensional structures of the protein target. Whenever experimental structures were not available, homology modeling has been, so far, the method of choice. Recently, AlphaFold (AF), an artificial-intelligence-based protein structure prediction method, has shown impressive results in terms of model accuracy. This outstanding success prompted us to evaluate how accurate AF models are from the perspective of docking-based drug discovery. We compared the high-throughput docking (HTD) performance of AF models with their corresponding experimental PDB structures using a benchmark set of 22 targets. The AF models showed consistently worse performance using four docking programs and two consensus techniques. Although AlphaFold shows a remarkable ability to predict protein architecture, this might not be enough to guarantee that AF models can be reliably used for HTD, and post-modeling refinement strategies might be key to increase the chances of success.
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Affiliation(s)
- Valeria Scardino
- Meton AI, Inc, Wilmington, DE 19801, USA
- Austral Institute for Applied Artificial Intelligence, Universidad Austral, Pilar, Buenos Aires, Argentina
| | - Juan I. Di Filippo
- Austral Institute for Applied Artificial Intelligence, Universidad Austral, Pilar, Buenos Aires, Argentina
- Computational Drug Design and Biomedical Informatics Laboratory, Instituto de Investigaciones en Medicina Traslacional (IIMT), Universidad Austral-CONICET, Pilar, Buenos Aires, Argentina
| | - Claudio N. Cavasotto
- Austral Institute for Applied Artificial Intelligence, Universidad Austral, Pilar, Buenos Aires, Argentina
- Computational Drug Design and Biomedical Informatics Laboratory, Instituto de Investigaciones en Medicina Traslacional (IIMT), Universidad Austral-CONICET, Pilar, Buenos Aires, Argentina
- Facultad de Ciencias Biomédicas, and Facultad de Ingeniería, Universidad Austral, Pilar, Buenos Aires, Argentina
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10
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Karthick K, Swarnalatha K. Pyridine Derivatives as Potential Inhibitors for Coronavirus SARS-CoV-2: A Molecular Docking Study. Bioinform Biol Insights 2023; 17:11779322221146651. [PMID: 37038549 PMCID: PMC10076986 DOI: 10.1177/11779322221146651] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2022] [Accepted: 11/21/2022] [Indexed: 01/09/2023] Open
Abstract
Coronavirus SARS-CoV-2, a causative agent for the global epidemic disease COVID-19, which has a highest modality rate. Several initiatives have been undertaken to repurpose current antiviral medications and tested the classic pyridine derivatives (PyDev), which have showed substantial therapeutic potential against a variety of illnesses and also have several biological functions such as, antibacterial, antiviral, and anti-inflammatory. However, limited reports are available for the treatment of Coronavirus SARS-CoV-2 using PyDev. Hence, the possibilities of the best-described PyDev molecules of powerful Coronavirus SARS-CoV-2 inhibitors have been attempted in this investigation. This study primarily focused on blocking four key targets of Coronavirus SARS-CoV-2 proteins. Terpyridine has shown the greatest inhibitory potential (with a binding energy of −8.8 kcal/mol) against all four coronavirus targets. This study results would pave the potential lead medication for Coronavirus SARS-CoV-2 therapeutic strategies.
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Affiliation(s)
- Kamaraj Karthick
- Photochemistry Research Laboratory, Department of Chemistry, Manonmaniam Sundaranar University, Tirunelveli, Tamil Nadu, India
| | - Kalaiyar Swarnalatha
- Photochemistry Research Laboratory, Department of Chemistry, Manonmaniam Sundaranar University, Tirunelveli, Tamil Nadu, India
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Nhat Phuong D, Flower DR, Chattopadhyay S, Chattopadhyay AK. Towards Effective Consensus Scoring in Structure-Based Virtual Screening. Interdiscip Sci 2023; 15:131-145. [PMID: 36550341 PMCID: PMC9941253 DOI: 10.1007/s12539-022-00546-8] [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: 05/21/2022] [Revised: 12/11/2022] [Accepted: 12/12/2022] [Indexed: 12/24/2022]
Abstract
Virtual screening (VS) is a computational strategy that uses in silico automated protein docking inter alia to rank potential ligands, or by extension rank protein-ligand pairs, identifying potential drug candidates. Most docking methods use preferred sets of physicochemical descriptors (PCDs) to model the interactions between host and guest molecules. Thus, conventional VS is often data-specific, method-dependent and with demonstrably differing utility in identifying candidate drugs. This study proposes four universality classes of novel consensus scoring (CS) algorithms that combine docking scores, derived from ten docking programs (ADFR, DOCK, Gemdock, Ledock, PLANTS, PSOVina, QuickVina2, Smina, Autodock Vina and VinaXB), using decoys from the DUD-E repository ( http://dude.docking.org/ ) against 29 MRSA-oriented targets to create a general VS formulation that can identify active ligands for any suitable protein target. Our results demonstrate that CS provides improved ligand-protein docking fidelity when compared to individual docking platforms. This approach requires only a small number of docking combinations and can serve as a viable and parsimonious alternative to more computationally expensive docking approaches. Predictions from our CS algorithm are compared against independent machine learning evaluations using the same docking data, complementing the CS outcomes. Our method is a reliable approach for identifying protein targets and high-affinity ligands that can be tested as high-probability candidates for drug repositioning.
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Affiliation(s)
- Do Nhat Phuong
- grid.7273.10000 0004 0376 4727Department of Mathematics, College of Engineering and Physical Sciences, Aston University, Birmingham, B4 7ET UK
| | - Darren R. Flower
- grid.7273.10000 0004 0376 4727Life and Health Sciences, Aston University, Birmingham, B4 7ET UK
| | | | - Amit K. Chattopadhyay
- grid.7273.10000 0004 0376 4727Department of Mathematics, College of Engineering and Physical Sciences, Aston University, Birmingham, B4 7ET UK
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Veríssimo GC, Serafim MSM, Kronenberger T, Ferreira RS, Honorio KM, Maltarollo VG. Designing drugs when there is low data availability: one-shot learning and other approaches to face the issues of a long-term concern. Expert Opin Drug Discov 2022; 17:929-947. [PMID: 35983695 DOI: 10.1080/17460441.2022.2114451] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
Abstract
INTRODUCTION Modern drug discovery generally is accessed by useful information from previous large databases or uncovering novel data. The lack of biological and/or chemical data tends to slow the development of scientific research and innovation. Here, approaches that may help provide solutions to generate or obtain enough relevant data or improve/accelerate existing methods within the last five years were reviewed. AREAS COVERED One-shot learning (OSL) approaches, structural modeling, molecular docking, scoring function space (SFS), molecular dynamics (MD), and quantum mechanics (QM) may be used to amplify the amount of available data to drug design and discovery campaigns, presenting methods, their perspectives, and discussions to be employed in the near future. EXPERT OPINION Recent works have successfully used these techniques to solve a range of issues in the face of data scarcity, including complex problems such as the challenging scenario of drug design aimed at intrinsically disordered proteins and the evaluation of potential adverse effects in a clinical scenario. These examples show that it is possible to improve and kickstart research from scarce available data to design and discover new potential drugs.
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Affiliation(s)
- Gabriel C Veríssimo
- Departamento de Produtos Farmacêuticos, Faculdade de Farmácia, Universidade Federal de Minas Gerais (UFMG), Belo Horizonte, Brazil
| | - Mateus Sá M Serafim
- Departamento de Microbiologia, Instituto de Ciências Biológicas, Universidade Federal de Minas Gerais (UFMG), Belo Horizonte, Brazil
| | - Thales Kronenberger
- Department of Medical Oncology and Pneumology, Internal Medicine VIII, University Hospital of Tübingen, Tübingen, Germany.,School of Pharmacy, Faculty of Health Sciences, University of Eastern Finland, Kuopio, Finland
| | - Rafaela S Ferreira
- Departamento de Bioquímica e Imunologia, Instituto de Ciências Biológicas, Universidade Federal de Minas Gerais (UFMG), Belo Horizonte, Brazil
| | - Kathia M Honorio
- Escola de Artes, Ciências e Humanidades, Universidade de São Paulo (USP), São Paulo, Brazil.,Centro de Ciências Naturais e Humanas, Universidade Federal do ABC (UFABC), Santo André, Brazil
| | - Vinícius G Maltarollo
- Departamento de Produtos Farmacêuticos, Faculdade de Farmácia, Universidade Federal de Minas Gerais (UFMG), Belo Horizonte, Brazil
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