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Buttenschoen M, Morris GM, Deane CM. PoseBusters: AI-based docking methods fail to generate physically valid poses or generalise to novel sequences. Chem Sci 2024; 15:3130-3139. [PMID: 38425520 PMCID: PMC10901501 DOI: 10.1039/d3sc04185a] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2023] [Accepted: 11/17/2023] [Indexed: 03/02/2024] Open
Abstract
The last few years have seen the development of numerous deep learning-based protein-ligand docking methods. They offer huge promise in terms of speed and accuracy. However, despite claims of state-of-the-art performance in terms of crystallographic root-mean-square deviation (RMSD), upon closer inspection, it has become apparent that they often produce physically implausible molecular structures. It is therefore not sufficient to evaluate these methods solely by RMSD to a native binding mode. It is vital, particularly for deep learning-based methods, that they are also evaluated on steric and energetic criteria. We present PoseBusters, a Python package that performs a series of standard quality checks using the well-established cheminformatics toolkit RDKit. The PoseBusters test suite validates chemical and geometric consistency of a ligand including its stereochemistry, and the physical plausibility of intra- and intermolecular measurements such as the planarity of aromatic rings, standard bond lengths, and protein-ligand clashes. Only methods that both pass these checks and predict native-like binding modes should be classed as having "state-of-the-art" performance. We use PoseBusters to compare five deep learning-based docking methods (DeepDock, DiffDock, EquiBind, TankBind, and Uni-Mol) and two well-established standard docking methods (AutoDock Vina and CCDC Gold) with and without an additional post-prediction energy minimisation step using a molecular mechanics force field. We show that both in terms of physical plausibility and the ability to generalise to examples that are distinct from the training data, no deep learning-based method yet outperforms classical docking tools. In addition, we find that molecular mechanics force fields contain docking-relevant physics missing from deep-learning methods. PoseBusters allows practitioners to assess docking and molecular generation methods and may inspire new inductive biases still required to improve deep learning-based methods, which will help drive the development of more accurate and more realistic predictions.
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Small-Molecule Inhibitor of Flaviviral NS3-NS5 Interaction with Broad-Spectrum Activity and Efficacy In Vivo. mBio 2023; 14:e0309722. [PMID: 36622141 PMCID: PMC9973282 DOI: 10.1128/mbio.03097-22] [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] [Indexed: 01/10/2023] Open
Abstract
Every year, dengue virus (DENV) causes one hundred million infections worldwide that can result in dengue disease and severe dengue. Two other mosquito-borne flaviviruses, i.e., Zika virus (ZIKV) and West Nile virus (WNV), are responsible of prolonged outbreaks and are associated with severe neurological diseases, congenital defects, and eventually death. These three viruses, despite their importance for global public health, still lack specific drug treatments. Here, we describe the structure-guided discovery of small molecules with pan-flavivirus antiviral potential by a virtual screening of ~1 million structures targeting the NS3-NS5 interaction surface of different flaviviruses. Two molecules inhibited the interaction between DENV NS3 and NS5 in vitro and the replication of all DENV serotypes as well as ZIKV and WNV and exhibited low propensity to select resistant viruses. Remarkably, one molecule demonstrated efficacy in a mouse model of dengue by reducing peak viremia, viral load in target organs, and associated tissue pathology. This study provides the proof of concept that targeting the flaviviral NS3-NS5 interaction is an effective therapeutic strategy able to reduce virus replication in vivo and discloses new chemical scaffolds that could be further developed, thus providing a significant milestone in the development of much awaited broad-spectrum antiflaviviral drugs. IMPORTANCE More than one-third of the human population is at risk of infection by different mosquito-borne flaviviruses. Despite this, no specific antiviral drug is currently available. In this work, using a computational approach based on molecular dynamics simulation and virtual screening of ~1 million small-molecule structures, we identified a compound that targets the interaction between the two sole flaviviral enzymes, i.e., NS3 and NS5. This compound demonstrated pan-serotype anti-DENV activity and pan-flavivirus potential in infected cells, low propensity to select viral resistant mutant viruses, and efficacy in a mouse model of dengue. Broad-spectrum antivirals are much awaited, and this work represents a significant advance toward the development of therapeutic molecules with extended antiflavivirus potential that act by an innovative mechanism and could be used alone or in combination with other antivirals.
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Pavan M, Bassani D, Bolcato G, Bissaro M, Sturles M, Moro S. Computational strategies to identify new drug candidates against neuroinflammation. Curr Med Chem 2022; 29:4756-4775. [PMID: 35135446 DOI: 10.2174/0929867329666220208095122] [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: 08/11/2021] [Revised: 12/09/2021] [Accepted: 12/13/2021] [Indexed: 11/22/2022]
Abstract
The even more increasing application of computational approaches in these last decades has deeply modified the process of discovery and commercialization of new therapeutic entities. This is especially true in the field of neuroinflammation, in which both the peculiar anatomical localization and the presence of the blood-brain barrier makeit mandatory to finely tune the candidates' physicochemical properties from the early stages of the discovery pipeline. The aim of this review is therefore to provide a general overview to the readers about the topic of neuroinflammation, together with the most common computational strategies that can be exploited to discover and design small molecules controlling neuroinflammation, especially those based on the knowledge of the three-dimensional structure of the biological targets of therapeutic interest. The techniques used to describe the molecular recognition mechanisms, such as molecular docking and molecular dynamics, will therefore be eviscerated, highlighting their advantages and their limitations. Finally, we report several case studies in which computational methods have been applied in drug discovery on neuroinflammation, focusing on the last decade's research.
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Affiliation(s)
- Matteo Pavan
- Molecular Modeling Section (MMS), Department of Pharmaceutical and Pharmacological Sciences University of Padova, via Marzolo 5, 35131 Padova, Italy
| | - Davide Bassani
- Molecular Modeling Section (MMS), Department of Pharmaceutical and Pharmacological Sciences University of Padova, via Marzolo 5, 35131 Padova, Italy
- Molecular Modeling Section (MMS), Department of Pharmaceutical and Pharmacological Sciences University of Padova, via Marzolo 5, 35131 Padova, Italy
| | - Giovanni Bolcato
- Molecular Modeling Section (MMS), Department of Pharmaceutical and Pharmacological Sciences University of Padova, via Marzolo 5, 35131 Padova, Italy
| | - Maicol Bissaro
- Molecular Modeling Section (MMS), Department of Pharmaceutical and Pharmacological Sciences University of Padova, via Marzolo 5, 35131 Padova, Italy
| | - Mattia Sturles
- Molecular Modeling Section (MMS), Department of Pharmaceutical and Pharmacological Sciences University of Padova, via Marzolo 5, 35131 Padova, Italy
| | - Stefano Moro
- Molecular Modeling Section (MMS), Department of Pharmaceutical and Pharmacological Sciences University of Padova, via Marzolo 5, 35131 Padova, Italy
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Bassani D, Pavan M, Bolcato G, Sturlese M, Moro S. Re-Exploring the Ability of Common Docking Programs to Correctly Reproduce the Binding Modes of Non-Covalent Inhibitors of SARS-CoV-2 Protease Mpro. Pharmaceuticals (Basel) 2022; 15:ph15020180. [PMID: 35215293 PMCID: PMC8878732 DOI: 10.3390/ph15020180] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2022] [Revised: 01/26/2022] [Accepted: 01/28/2022] [Indexed: 01/27/2023] Open
Abstract
In the latest few decades, molecular docking has imposed itself as one of the most used approaches for computational drug discovery. Several docking benchmarks have been published, comparing the performance of different algorithms in respect to a molecular target of interest, usually evaluating their ability in reproducing the experimental data, which, in most cases, comes from X-ray structures. In this study, we elucidated the variation of the performance of three docking algorithms, namely GOLD, Glide, and PLANTS, in replicating the coordinates of the crystallographic ligands of SARS-CoV-2 main protease (Mpro). Through the comparison of the data coming from docking experiments and the values derived from the calculation of the solvent exposure of the crystallographic ligands, we highlighted the importance of this last variable for docking performance. Indeed, we underlined how an increase in the percentage of the ligand surface exposed to the solvent in a crystallographic complex makes it harder for the docking algorithms to reproduce its conformation. We further validated our hypothesis through molecular dynamics simulations, showing that the less stable protein–ligand complexes (in terms of root-mean-square deviation and root-mean-square fluctuation) tend to be derived from the cases in which the solvent exposure of the ligand in the starting system is higher.
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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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González-Paz L, Hurtado-León ML, Lossada C, Fernández-Materán FV, Vera-Villalobos J, Loroño M, Paz JL, Jeffreys L, Alvarado YJ. Comparative study of the interaction of ivermectin with proteins of interest associated with SARS-CoV-2: A computational and biophysical approach. Biophys Chem 2021; 278:106677. [PMID: 34428682 PMCID: PMC8373590 DOI: 10.1016/j.bpc.2021.106677] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2021] [Revised: 08/13/2021] [Accepted: 08/15/2021] [Indexed: 01/18/2023]
Abstract
The SARS-CoV-2 pandemic has accelerated the study of existing drugs. The mixture of homologs called ivermectin (avermectin-B1a [HB1a] + avermectin-B1b [HB1b]) has shown antiviral activity against SARS-CoV-2 in vitro. However, there are few reports on the behavior of each homolog. We investigated the interaction of each homolog with promising targets of interest associated with SARS-CoV-2 infection from a biophysical and computational-chemistry perspective using docking and molecular dynamics. We observed a differential behavior for each homolog, with an affinity of HB1b for viral structures, and of HB1a for host structures considered. The induced disturbances were differential and influenced by the hydrophobicity of each homolog and of the binding pockets. We present the first comparative analysis of the potential theoretical inhibitory effect of both avermectins on biomolecules associated with COVID-19, and suggest that ivermectin through its homologs, has a multiobjective behavior.
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Affiliation(s)
- Lenin González-Paz
- Universidad del Zulia (LUZ), Facultad Experimental de Ciencias (FEC), Departamento de Biología, Laboratorio de Genética y Biología Molecular (LGBM), 4001 Maracaibo, Venezuela; Instituto Venezolano de Investigaciones Científicas (IVIC), Centro de Estudios Botánicos y Agroforestales (CEBA), Laboratorio de Protección Vegetal (LPV), 4001 Maracaibo, Venezuela.
| | - María Laura Hurtado-León
- Universidad del Zulia (LUZ), Facultad Experimental de Ciencias (FEC), Departamento de Biología, Laboratorio de Genética y Biología Molecular (LGBM), 4001 Maracaibo, Venezuela
| | - Carla Lossada
- Instituto Venezolano de Investigaciones Científicas (IVIC), Centro de Investigación y Tecnología de Materiales (CITeMA), Laboratorio de Caracterización Molecular y Biomolecular, 4001 Maracaibo, Venezuela
| | - Francelys V Fernández-Materán
- Instituto Venezolano de Investigaciones Científicas (IVIC), Centro de Investigación y Tecnología de Materiales (CITeMA), Laboratorio de Caracterización Molecular y Biomolecular, 4001 Maracaibo, Venezuela
| | - Joan Vera-Villalobos
- Facultad de Ciencias Naturales y Matemáticas, Departamento de Química y Ciencias Ambientales, Laboratorio de Análisis Químico Instrumental (LAQUINS), Escuela Superior Politécnica del Litoral, Guayaquil, Ecuador
| | - Marcos Loroño
- Departamento Académico de Química Analítica e Instrumental, Facultad de Química e Ingeniería Química, Universidad Nacional Mayor de San Marcos, Lima, Peru
| | - J L Paz
- Departamento Académico de Química Inorgánica, Facultad de Química e Ingeniería Química, Universidad Nacional Mayor de San Marcos, Lima, Peru
| | - Laura Jeffreys
- Centre for Drugs and Diagnostics, Department of Tropical Disease Biology, Liverpool School of Tropical Medicine, Pembroke Place, Liverpool L3 5QA, UK
| | - Ysaias J Alvarado
- Instituto Venezolano de Investigaciones Científicas (IVIC), Centro de Investigación y Tecnología de Materiales (CITeMA), Laboratorio de Caracterización Molecular y Biomolecular, 4001 Maracaibo, Venezuela.
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Combination of consensus and ensemble docking strategies for the discovery of human dihydroorotate dehydrogenase inhibitors. Sci Rep 2021; 11:11417. [PMID: 34075175 PMCID: PMC8169699 DOI: 10.1038/s41598-021-91069-7] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2021] [Accepted: 05/21/2021] [Indexed: 02/06/2023] Open
Abstract
The inconsistencies in the performance of the virtual screening (VS) process, depending on the used software and structural conformation of the protein, is a challenging issue in the drug design and discovery field. Varying performance, especially in terms of early recognition of the potential hit compounds, negatively affects the whole process and leads to unnecessary waste of the time and resources. Appropriate application of the ensemble docking and consensus-scoring approaches can significantly increase reliability of the VS results. Dihydroorotate dehydrogenase (DHODH) is a key enzyme in the pyrimidine biosynthesis pathway. It is considered as a valuable therapeutic target in cancer, autoimmune and viral diseases. Based on the conducted benchmark study and analysis of the effect of different combinations of the applied methods and approaches, here we suggested a structure-based virtual screening (SBVS) workflow that can be used to increase the reliability of VS.
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A Deep-Learning Approach toward Rational Molecular Docking Protocol Selection. Molecules 2020; 25:molecules25112487. [PMID: 32471211 PMCID: PMC7321124 DOI: 10.3390/molecules25112487] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2020] [Revised: 05/23/2020] [Accepted: 05/26/2020] [Indexed: 01/09/2023] Open
Abstract
While a plethora of different protein–ligand docking protocols have been developed over the past twenty years, their performances greatly depend on the provided input protein–ligand pair. In this study, we developed a machine-learning model that uses a combination of convolutional and fully connected neural networks for the task of predicting the performance of several popular docking protocols given a protein structure and a small compound. We also rigorously evaluated the performance of our model using a widely available database of protein–ligand complexes and different types of data splits. We further open-source all code related to this study so that potential users can make informed selections on which protocol is best suited for their particular protein–ligand pair.
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Majewski M, Barril X. Structural Stability Predicts the Binding Mode of Protein–Ligand Complexes. J Chem Inf Model 2020; 60:1644-1651. [DOI: 10.1021/acs.jcim.9b01062] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Affiliation(s)
- Maciej Majewski
- Institut de Biomedicina de la Universitat de Barcelona (IBUB) and Facultat de Farmacia, Universitat de Barcelona, Av. Joan XXIII 27-31, Barcelona 08028, Spain
| | - Xavier Barril
- Institut de Biomedicina de la Universitat de Barcelona (IBUB) and Facultat de Farmacia, Universitat de Barcelona, Av. Joan XXIII 27-31, Barcelona 08028, Spain
- Catalan Institution for Research and Advanced Studies (ICREA), Passeig Lluis Companys 23, Barcelona 08010, Spain
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Ropón-Palacios G, Chenet-Zuta ME, Olivos-Ramirez GE, Otazu K, Acurio-Saavedra J, Camps I. Potential novel inhibitors against emerging zoonotic pathogen Nipah virus: a virtual screening and molecular dynamics approach. J Biomol Struct Dyn 2019; 38:3225-3234. [DOI: 10.1080/07391102.2019.1655480] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
Affiliation(s)
- Georcki Ropón-Palacios
- Laboratório de Modelagem Computacional – LaModel, Instituto de Ciências Exatas – ICEx, Universidade Federal de Alfenas – UNIFAL-MG, Alfenas, Minas Gerais, Brazil
| | - Manuel E. Chenet-Zuta
- Facultad de Psicología, Universidad Nacional Autónoma de México, Distrito Federal, México
| | - Gustavo E. Olivos-Ramirez
- Laboratorio de Evaluación de Los Recursos Acuáticos y Cultivo de Especies Auxiliares, Departamento Académico de Biología, Microbiología y Biotecnología, Facultad de Ciencias, Universidad Nacional Del Santa, Nuevo Chimbote, Perú
| | - Kewin Otazu
- Facultad de Ciencias Biológicas, Universidad Nacional Del Altiplano, Puno, Perú
| | - Jorge Acurio-Saavedra
- Laboratorio de Genética Molecular, Departamento de Biología, Facultad de Ciencias, Universidad Nacional de San Antonio Abad del Cusco, Cusco, Perú
| | - Ihosvany Camps
- Laboratório de Modelagem Computacional – LaModel, Instituto de Ciências Exatas – ICEx, Universidade Federal de Alfenas – UNIFAL-MG, Alfenas, Minas Gerais, Brazil
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