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Kyobe S, Mwesigwa S, Nkurunungi G, Retshabile G, Egesa M, Katagirya E, Amujal M, Mlotshwa BC, Williams L, Sendagire H, On Behalf Of The CAfGEN Consortium, Kiragga D, Mardon G, Matshaba M, Hanchard NA, Kyosiimire-Lugemwa J, Robinson D. Identification of a Clade-Specific HLA-C*03:02 CTL Epitope GY9 Derived from the HIV-1 p17 Matrix Protein. Int J Mol Sci 2024; 25:9683. [PMID: 39273630 PMCID: PMC11395705 DOI: 10.3390/ijms25179683] [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: 10/07/2023] [Revised: 11/20/2023] [Accepted: 11/24/2023] [Indexed: 09/15/2024] Open
Abstract
Efforts towards an effective HIV-1 vaccine have remained mainly unsuccessful. There is increasing evidence for a potential role of HLA-C-restricted CD8+ T cell responses in HIV-1 control, including our recent report of HLA-C*03:02 among African children. However, there are no documented optimal HIV-1 CD8+ T cell epitopes restricted by HLA-C*03:02; additionally, the structural influence of HLA-C*03:02 on epitope binding is undetermined. Immunoinformatics approaches provide a fast and inexpensive method to discover HLA-restricted epitopes. Here, we employed immunopeptidomics to identify HLA-C*03:02 CD8+ T cell epitopes. We identified a clade-specific Gag-derived GY9 (GTEELRSLY) HIV-1 p17 matrix epitope potentially restricted to HLA-C*03:02. Residues E62, T142, and E151 in the HLA-C*03:02 binding groove and positions p3, p6, and p9 on the GY9 epitope are crucial in shaping and stabilizing the epitope binding. Our findings support the growing evidence of the contribution of HLA-C molecules to HIV-1 control and provide a prospect for vaccine strategies.
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Affiliation(s)
- Samuel Kyobe
- Department of Medical Microbiology, College of Health Sciences, Makerere University, Kampala P.O. Box 7072, Uganda
| | - Savannah Mwesigwa
- Department of Medical Microbiology, College of Health Sciences, Makerere University, Kampala P.O. Box 7072, Uganda
- Department of Immunology and Molecular Biology, College of Health Sciences, Makerere University, Kampala P.O. Box 7072, Uganda
| | - Gyaviira Nkurunungi
- The Medical Research Council/Uganda Virus Research Institute & London School Hygine Tropical Medicine Uganda Research Unit, Entebbe P.O. Box 49, Uganda
- Department of Infection Biology, London School of Hygiene & Tropical Medicine, Keppel Street London, London WC1E 7HT, UK
| | - Gaone Retshabile
- Department of Biological Sciences, University of Botswana, Gaborone Private Bag UB 0022, Botswana
| | - Moses Egesa
- The Medical Research Council/Uganda Virus Research Institute & London School Hygine Tropical Medicine Uganda Research Unit, Entebbe P.O. Box 49, Uganda
- Department of Infection Biology, London School of Hygiene & Tropical Medicine, Keppel Street London, London WC1E 7HT, UK
| | - Eric Katagirya
- Department of Immunology and Molecular Biology, College of Health Sciences, Makerere University, Kampala P.O. Box 7072, Uganda
| | - Marion Amujal
- Department of Immunology and Molecular Biology, College of Health Sciences, Makerere University, Kampala P.O. Box 7072, Uganda
| | - Busisiwe C Mlotshwa
- Department of Biological Sciences, University of Botswana, Gaborone Private Bag UB 0022, Botswana
| | - Lesedi Williams
- Department of Biological Sciences, University of Botswana, Gaborone Private Bag UB 0022, Botswana
| | - Hakim Sendagire
- Department of Medical Microbiology, College of Health Sciences, Makerere University, Kampala P.O. Box 7072, Uganda
| | | | - Dithan Kiragga
- Baylor College of Medicine Children's Foundation, Kampala P.O. Box 72052, Uganda
| | - Graeme Mardon
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA
- Department of Pathology and Immunology, Baylor College of Medicine, Houston, TX 77030, USA
| | - Mogomotsi Matshaba
- Pediatric Retrovirology, Department of Pediatrics, Baylor College of Medicine, Houston, TX 77030, USA
- Botswana-Baylor Children's Clinical Centre of Excellence, Gaborone Private Bag BR 129, Botswana
| | - Neil A Hanchard
- National Human Genome Research Institute, National Institutes of Health, 50 South Drive, Bethesda, MD 20892, USA
| | - Jacqueline Kyosiimire-Lugemwa
- The Medical Research Council/Uganda Virus Research Institute & London School Hygine Tropical Medicine Uganda Research Unit, Entebbe P.O. Box 49, Uganda
| | - David Robinson
- Department of Chemistry and Forensics, School of Science and Technology, Nottingham Trent University Clifton Lane, Nottingham NG11 8NS, UK
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Smith MD, Darryl Quarles L, Demerdash O, Smith JC. Drugging the entire human proteome: Are we there yet? Drug Discov Today 2024; 29:103891. [PMID: 38246414 DOI: 10.1016/j.drudis.2024.103891] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2023] [Revised: 01/12/2024] [Accepted: 01/16/2024] [Indexed: 01/23/2024]
Abstract
Each of the ∼20,000 proteins in the human proteome is a potential target for compounds that bind to it and modify its function. The 3D structures of most of these proteins are now available. Here, we discuss the prospects for using these structures to perform proteome-wide virtual HTS (VHTS). We compare physics-based (docking) and AI VHTS approaches, some of which are now being applied with large databases of compounds to thousands of targets. Although preliminary proteome-wide screens are now within our grasp, further methodological developments are expected to improve the accuracy of the results.
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Affiliation(s)
- Micholas Dean Smith
- University of Tennessee/Oak Ridge National Laboratory Center for Molecular Biophysics, Oak Ridge, TN 37830, USA; Department of Biochemistry and Cellular and Molecular Biology, University of Tennessee, Knoxville, TN 37996, USA
| | - L Darryl Quarles
- Departments of Medicine, University of Tennessee Health Science Center, Memphis, TN 38163, USA; ORRxD LLC, 3404 Olney Drive, Durham, NC 27705, USA
| | - Omar Demerdash
- Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN 37830, USA
| | - Jeremy C Smith
- University of Tennessee/Oak Ridge National Laboratory Center for Molecular Biophysics, Oak Ridge, TN 37830, USA; Department of Biochemistry and Cellular and Molecular Biology, University of Tennessee, Knoxville, TN 37996, USA.
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3
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Kazempour-Dizaji M, Mojtabavi S, Sadri A, Ghanbarpour A, Faramarzi MA, Navidpour L. Arylureidoaurones: Synthesis, in vitro α-glucosidase, and α-amylase inhibition activity. Bioorg Chem 2023; 139:106709. [PMID: 37442042 DOI: 10.1016/j.bioorg.2023.106709] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2023] [Revised: 06/14/2023] [Accepted: 06/28/2023] [Indexed: 07/15/2023]
Abstract
Because of the colossal global burden of diabetes, there is an urgent need for more effective and safer drugs. We designed and synthesized a new series of aurone derivatives possessing phenylureido or bis-phenylureido moieties as α-glucosidase and α-amylase inhibitors. Most of the synthesized phenylureidoaurones have demonstrated superior inhibition activities (IC50s of 9.6-339.9 μM) against α-glucosidase relative to acarbose (IC50 = 750.0 μM) as the reference drug. Substitution of aurone analogues with two phenylureido substituents at the 5-position of the benzofuranone moiety and the 3' or 4' positions of the 2-phenyl ring resulted in compounds with almost 120-180 times more potent inhibitory activities than acarbose. The aurone analogue possessing two phenylureido substitutions at 5 and 4' positions (13) showed the highest inhibition activity with an IC50 of 4.2 ± 0.1 μM. Kinetic studies suggested their inhibition mode to be competitive. We also investigated the binding mode of the most potent compounds using the consensually docked 4D-QSAR methodology. Furthermore, these analogues showed weak-to-moderate non-competitive inhibitory activity against α-amylase. 5-Methyl substituted aurone with 4'-phenylureido moiety (6e) demonstrated the highest inhibition activity on α-amylase with an IC50 of 142.0 ± 1.6 μM relative to acarbose (IC50 = 108 ± 1.2 μM). Our computational studies suggested that these analogues interact with a hydrophilic allosteric site in α-amylase, located far from the enzyme active site at the N-terminal.
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Affiliation(s)
- Mohammad Kazempour-Dizaji
- Department of Medicinal Chemistry, Faculty of Pharmacy, Tehran University of Medical Sciences, Tehran 14176, Iran
| | - Somayeh Mojtabavi
- Department of Pharmaceutical Biotechnology, Faculty of Pharmacy, Tehran University of Medical Sciences, P.O. Box 14155-6451, Tehran 14176, Iran
| | - Arash Sadri
- Department of Medicinal Chemistry, Faculty of Pharmacy, Tehran University of Medical Sciences, Tehran 14176, Iran; Interdisciplinary Neuroscience Research Program, Students' Scientific Research Center, Tehran University of Medical Sciences, Tehran, Iran; Lyceum Scientific Charity, Iran
| | - Araz Ghanbarpour
- Department of Medicinal Chemistry, Faculty of Pharmacy, Tehran University of Medical Sciences, Tehran 14176, Iran
| | - Mohammad Ali Faramarzi
- Department of Pharmaceutical Biotechnology, Faculty of Pharmacy, Tehran University of Medical Sciences, P.O. Box 14155-6451, Tehran 14176, Iran
| | - Latifeh Navidpour
- Department of Medicinal Chemistry, Faculty of Pharmacy, Tehran University of Medical Sciences, Tehran 14176, Iran.
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Corrêa EJA, Carvalho FC, de Castro Oliveira JA, Bertolucci SKV, Scotti MT, Silveira CH, Guedes FC, Melo JOF, de Melo-Minardi RC, de Lima LHF. Elucidating the molecular mechanisms of essential oils' insecticidal action using a novel cheminformatics protocol. Sci Rep 2023; 13:4598. [PMID: 36944648 PMCID: PMC10028760 DOI: 10.1038/s41598-023-29981-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Accepted: 02/14/2023] [Indexed: 03/23/2023] Open
Abstract
Essential oils (EOs) are a promising source for novel environmentally safe insecticides. However, the structural diversity of their compounds poses challenges to accurately elucidate their biological mechanisms of action. We present a new chemoinformatics methodology aimed at predicting the impact of essential oil (EO) compounds on the molecular targets of commercial insecticides. Our approach merges virtual screening, chemoinformatics, and machine learning to identify custom signatures and reference molecule clusters. By assigning a molecule to a cluster, we can determine its most likely interaction targets. Our findings reveal that the main targets of EOs are juvenile hormone-specific proteins (JHBP and MET) and octopamine receptor agonists (OctpRago). Three of the twenty clusters show strong similarities to the juvenile hormone, steroids, and biogenic amines. For instance, the methodology successfully identified E-Nerolidol, for which literature points indications of disrupting insect metamorphosis and neurochemistry, as a potential insecticide in these pathways. We validated the predictions through experimental bioassays, observing symptoms in blowflies that were consistent with the computational results. This new approach sheds a higher light on the ways of action of EO compounds in nature and biotechnology. It also opens new possibilities for understanding how molecules can interfere with biological systems and has broad implications for areas such as drug design.
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Affiliation(s)
- Eduardo José Azevedo Corrêa
- Multicenter Program in Postgraduate in Biochemistry and Molecular Biology, Federal University of São João del-Rei, Campus Divinópolis, Divinópolis, MG, Brazil
- Minas Gerais Agricultural Research Company (EPAMIG), Pitangui, MG, Brazil
| | - Frederico Chaves Carvalho
- Department of Computer Science, Institute of Exact Sciences-ICEx, Federal University of Minas Gerais, Campus Belo Horizonte, Belo Horizonte, MG, Brazil
| | | | - Suzan Kelly Vilela Bertolucci
- Laboratory of Phytochemistry and Medicinal Plants, Department of Agriculture, Federal University of Lavras, Lavras, MG, Brazil
| | - Marcus Tullius Scotti
- Chemistry Department, Exact and Nature Sciences Center, Federal University of Paraiba, Campus I, João Pessoa, PB, Brazil
| | | | - Fabiana Costa Guedes
- Technological Sciences Institute, Federal University of Itajubá, Itabira, MG, Brazil
| | - Júlio Onésio Ferreira Melo
- Department of Exact and Biological Sciences, Federal University of São João Del-Rei, Sete Lagoas Campus, Sete Lagoas, MG, Brazil
| | - Raquel Cardoso de Melo-Minardi
- Department of Computer Science, Institute of Exact Sciences-ICEx, Federal University of Minas Gerais, Campus Belo Horizonte, Belo Horizonte, MG, Brazil
| | - Leonardo Henrique França de Lima
- Multicenter Program in Postgraduate in Biochemistry and Molecular Biology, Federal University of São João del-Rei, Campus Divinópolis, Divinópolis, MG, Brazil.
- Department of Exact and Biological Sciences, Federal University of São João Del-Rei, Sete Lagoas Campus, Sete Lagoas, MG, Brazil.
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5
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Zhu H, Yang J, Huang N. Assessment of the Generalization Abilities of Machine-Learning Scoring Functions for Structure-Based Virtual Screening. J Chem Inf Model 2022; 62:5485-5502. [PMID: 36268980 DOI: 10.1021/acs.jcim.2c01149] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
In structure-based virtual screening (SBVS), it is critical that scoring functions capture protein-ligand atomic interactions. By focusing on the local domains of ligand binding pockets, a standardized pocket Pfam-based clustering (Pfam-cluster) approach was developed to assess the cross-target generalization ability of machine-learning scoring functions (MLSFs). Subsequently, 12 typical MLSFs were evaluated using random cross-validation (Random-CV), protein sequence similarity-based cross-validation (Seq-CV), and pocket Pfam-based cross-validation (Pfam-CV) methods. Surprisingly, all of the tested models showed decreased performances from Random-CV to Seq-CV to Pfam-CV experiments, not showing satisfactory generalization capacity. Our interpretable analysis suggested that the predictions on novel targets by MLSFs were dependent on buried solvent-accessible surface area (SASA)-related features of complex structures, with greater predicted binding affinities on complexes owning larger protein-ligand interfaces. By combining buried SASA-related features with target-specific patterns that were only shared among structurally similar compounds in the same cluster, the random forest (RF)-Score attained a good performance in the Random-CV test. Based on these findings, we strongly advise assessing the generalization ability of MLSFs with the Pfam-cluster approach and being cautious with the features learned by MLSFs.
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Affiliation(s)
- Hui Zhu
- Tsinghua Institute of Multidisciplinary Biomedical Research, Tsinghua University, Beijing, China102206, China.,National Institute of Biological Sciences, 7 Science Park Road, Zhongguancun Life Science Park, Beijing102206, China
| | - Jincai Yang
- National Institute of Biological Sciences, 7 Science Park Road, Zhongguancun Life Science Park, Beijing102206, China
| | - Niu Huang
- Tsinghua Institute of Multidisciplinary Biomedical Research, Tsinghua University, Beijing, China102206, China.,National Institute of Biological Sciences, 7 Science Park Road, Zhongguancun Life Science Park, Beijing102206, China
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6
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Meli R, Morris GM, Biggin PC. Scoring Functions for Protein-Ligand Binding Affinity Prediction using Structure-Based Deep Learning: A Review. FRONTIERS IN BIOINFORMATICS 2022; 2:885983. [PMID: 36187180 PMCID: PMC7613667 DOI: 10.3389/fbinf.2022.885983] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2022] [Accepted: 05/11/2022] [Indexed: 01/01/2023] Open
Abstract
The rapid and accurate in silico prediction of protein-ligand binding free energies or binding affinities has the potential to transform drug discovery. In recent years, there has been a rapid growth of interest in deep learning methods for the prediction of protein-ligand binding affinities based on the structural information of protein-ligand complexes. These structure-based scoring functions often obtain better results than classical scoring functions when applied within their applicability domain. Here we review structure-based scoring functions for binding affinity prediction based on deep learning, focussing on different types of architectures, featurization strategies, data sets, methods for training and evaluation, and the role of explainable artificial intelligence in building useful models for real drug-discovery applications.
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Affiliation(s)
- Rocco Meli
- Department of Biochemistry, University of Oxford, Oxford, United Kingdom
| | - Garrett M. Morris
- Department of Statistics, University of Oxford, Oxford, United Kingdom
| | - Philip C. Biggin
- Department of Biochemistry, University of Oxford, Oxford, United Kingdom
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7
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Using diverse potentials and scoring functions for the development of improved machine-learned models for protein-ligand affinity and docking pose prediction. J Comput Aided Mol Des 2021; 35:1095-1123. [PMID: 34708263 DOI: 10.1007/s10822-021-00423-4] [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: 05/03/2021] [Accepted: 10/11/2021] [Indexed: 10/20/2022]
Abstract
The advent of computational drug discovery holds the promise of significantly reducing the effort of experimentalists, along with monetary cost. More generally, predicting the binding of small organic molecules to biological macromolecules has far-reaching implications for a range of problems, including metabolomics. However, problems such as predicting the bound structure of a protein-ligand complex along with its affinity have proven to be an enormous challenge. In recent years, machine learning-based methods have proven to be more accurate than older methods, many based on simple linear regression. Nonetheless, there remains room for improvement, as these methods are often trained on a small set of features, with a single functional form for any given physical effect, and often with little mention of the rationale behind choosing one functional form over another. Moreover, it is not entirely clear why one machine learning method is favored over another. In this work, we endeavor to undertake a comprehensive effort towards developing high-accuracy, machine-learned scoring functions, systematically investigating the effects of machine learning method and choice of features, and, when possible, providing insights into the relevant physics using methods that assess feature importance. Here, we show synergism among disparate features, yielding adjusted R2 with experimental binding affinities of up to 0.871 on an independent test set and enrichment for native bound structures of up to 0.913. When purely physical terms that model enthalpic and entropic effects are used in the training, we use feature importance assessments to probe the relevant physics and hopefully guide future investigators working on this and other computational chemistry problems.
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8
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Kadukova M, Machado KDS, Chacón P, Grudinin S. KORP-PL: a coarse-grained knowledge-based scoring function for protein-ligand interactions. Bioinformatics 2021; 37:943-950. [PMID: 32840574 DOI: 10.1093/bioinformatics/btaa748] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2020] [Revised: 07/27/2020] [Accepted: 08/18/2020] [Indexed: 11/12/2022] Open
Abstract
MOTIVATION Despite the progress made in studying protein-ligand interactions and the widespread application of docking and affinity prediction tools, improving their precision and efficiency still remains a challenge. Computational approaches based on the scoring of docking conformations with statistical potentials constitute a popular alternative to more accurate but costly physics-based thermodynamic sampling methods. In this context, a minimalist and fast sidechain-free knowledge-based potential with a high docking and screening power can be very useful when screening a big number of putative docking conformations. RESULTS Here, we present a novel coarse-grained potential defined by a 3D joint probability distribution function that only depends on the pairwise orientation and position between protein backbone and ligand atoms. Despite its extreme simplicity, our approach yields very competitive results with the state-of-the-art scoring functions, especially in docking and screening tasks. For example, we observed a twofold improvement in the median 5% enrichment factor on the DUD-E benchmark compared to Autodock Vina results. Moreover, our results prove that a coarse sidechain-free potential is sufficient for a very successful docking pose prediction. AVAILABILITYAND IMPLEMENTATION The standalone version of KORP-PL with the corresponding tests and benchmarks are available at https://team.inria.fr/nano-d/korp-pl/ and https://chaconlab.org/modeling/korp-pl. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Maria Kadukova
- Univ. Grenoble Alpes, CNRS, Inria, Grenoble INP, LJK, 38000 Grenoble, France.,Research Center for Molecular Mechanisms of Aging and Age-Related Diseases, Moscow Institute of Physics and Technology, 141701 Dolgoprudniy, Russia
| | - Karina Dos Santos Machado
- Univ. Grenoble Alpes, CNRS, Inria, Grenoble INP, LJK, 38000 Grenoble, France.,Computational Biology Laboratory, Centro de Ciências Computacionais, Universidade Federal do Rio Grande - FURG, Rio Grande, RS 96201-090, Brazil
| | - Pablo Chacón
- Department of Biological Physical Chemistry, Rocasolano Institute of Physical Chemistry C.S.I.C, Madrid 28006, Spain
| | - Sergei Grudinin
- Univ. Grenoble Alpes, CNRS, Inria, Grenoble INP, LJK, 38000 Grenoble, France
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9
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Aït Amiri S, Deboux C, Soualmia F, Chaaya N, Louet M, Duplus E, Betuing S, Nait Oumesmar B, Masurier N, El Amri C. Identification of First-in-Class Inhibitors of Kallikrein-Related Peptidase 6 That Promote Oligodendrocyte Differentiation. J Med Chem 2021; 64:5667-5688. [PMID: 33949859 DOI: 10.1021/acs.jmedchem.0c02175] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Multiple sclerosis (MS) is an autoimmune demyelinating disease of the central nervous system (CNS) that causes severe motor, sensory, and cognitive impairments. Kallikrein-related peptidase (KLK)6 is the most abundant serine protease secreted in the CNS, mainly by oligodendrocytes, the myelin-producing cells of the CNS, and KLK6 is assumed to be a robust biomarker of MS, since it is highly increased in the cerebrospinal fluid (CSF) of MS patients. Here, we report the design and biological evaluation of KLK6's low-molecular-weight inhibitors, para-aminobenzyl derivatives. Interestingly, selected hit compounds were selective of the KLK6 proteolytic network encompassing KLK1 and plasmin that also participate in the development of MS physiopathology. Moreover, hits were found noncytotoxic on primary cultures of murine neurons and oligodendrocyte precursor cells (OPCs). Among them, two compounds (32 and 42) were shown to promote the differentiation of OPCs into mature oligodendrocytes in vitro constituting thus emerging leads for the development of regenerative therapies.
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Affiliation(s)
- Sabrina Aït Amiri
- Faculty of Sciences and Engineering, IBPS, UMR 8256 CNRS-UPMC, ERL INSERM U1164, Biological Adaptation and Ageing, Sorbonne Université, F-75252 Paris, France
| | - Cyrille Deboux
- Institut du Cerveau, Inserm U 1127, CNRS UMR 7725, Sorbonne Université, F-75013 Paris, France
| | - Feryel Soualmia
- Faculty of Sciences and Engineering, IBPS, UMR 8256 CNRS-UPMC, ERL INSERM U1164, Biological Adaptation and Ageing, Sorbonne Université, F-75252 Paris, France
| | - Nancy Chaaya
- Faculty of Sciences and Engineering, IBPS, UMR 8256 CNRS-UPMC, ERL INSERM U1164, Biological Adaptation and Ageing, Sorbonne Université, F-75252 Paris, France
| | - Maxime Louet
- Institut des Biomolécules Max Mousseron, Université de Montpellier, CNRS, ENSCM, F-34093 Montpellier, France
| | - Eric Duplus
- Faculty of Sciences and Engineering, IBPS, UMR 8256 CNRS-UPMC, ERL INSERM U1164, Biological Adaptation and Ageing, Sorbonne Université, F-75252 Paris, France
| | - Sandrine Betuing
- Faculty of Sciences and Engineering, IBPS, UMR 8246-CNRS/INSERM U1130, Neurosciences Paris Seine, Sorbonne Université, F-75252 Paris, France
| | - Brahim Nait Oumesmar
- Institut du Cerveau, Inserm U 1127, CNRS UMR 7725, Sorbonne Université, F-75013 Paris, France
| | - Nicolas Masurier
- Institut des Biomolécules Max Mousseron, Université de Montpellier, CNRS, ENSCM, F-34093 Montpellier, France
| | - Chahrazade El Amri
- Faculty of Sciences and Engineering, IBPS, UMR 8256 CNRS-UPMC, ERL INSERM U1164, Biological Adaptation and Ageing, Sorbonne Université, F-75252 Paris, France
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Dudutienė V, Zubrienė A, Kairys V, Smirnov A, Smirnovienė J, Leitans J, Kazaks A, Tars K, Manakova L, Gražulis S, Matulis D. Isoform-Selective Enzyme Inhibitors by Exploring Pocket Size According to the Lock-and-Key Principle. Biophys J 2020; 119:1513-1524. [PMID: 32971003 PMCID: PMC7642266 DOI: 10.1016/j.bpj.2020.08.037] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2020] [Revised: 08/20/2020] [Accepted: 08/21/2020] [Indexed: 10/23/2022] Open
Abstract
In the design of high-affinity and enzyme isoform-selective inhibitors, we applied an approach of augmenting the substituents attached to the benzenesulfonamide scaffold in three ways, namely, substitutions at the 3,5- or 2,4,6-positions or expansion of the condensed ring system. The increased size of the substituents determined the spatial limitations of the active sites of the 12 catalytically active human carbonic anhydrase (CA) isoforms until no binding was observed because of the inability of the compounds to fit in the active site. This approach led to the discovery of high-affinity and high-selectivity compounds for the anticancer target CA IX and antiobesity target CA VB. The x-ray crystallographic structures of compounds bound to CA IX showed the positions of the bound compounds, whereas computational modeling confirmed that steric clashes prevent the binding of these compounds to other isoforms and thus avoid undesired side effects. Such an approach, based on the Lock-and-Key principle, could be used for the development of enzyme-specific drug candidate compounds.
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Affiliation(s)
- Virginija Dudutienė
- Department of Biothermodynamics and Drug Design, Vilnius University, Vilnius, Lithuania
| | - Asta Zubrienė
- Department of Biothermodynamics and Drug Design, Vilnius University, Vilnius, Lithuania
| | - Visvaldas Kairys
- Department of Bioinformatics, Institute of Biotechnology, Life Sciences Center, Vilnius University, Vilnius, Lithuania
| | - Alexey Smirnov
- Department of Biothermodynamics and Drug Design, Vilnius University, Vilnius, Lithuania
| | - Joana Smirnovienė
- Department of Biothermodynamics and Drug Design, Vilnius University, Vilnius, Lithuania
| | - Janis Leitans
- Latvian Biomedical Research and Study Centre, Riga, Latvia
| | - Andris Kazaks
- Latvian Biomedical Research and Study Centre, Riga, Latvia
| | - Kaspars Tars
- Latvian Biomedical Research and Study Centre, Riga, Latvia
| | - Lena Manakova
- Department of Protein-DNA Interactions, Institute of Biotechnology, Life Sciences Center, Vilnius University, Vilnius, Lithuania
| | - Saulius Gražulis
- Department of Protein-DNA Interactions, Institute of Biotechnology, Life Sciences Center, Vilnius University, Vilnius, Lithuania
| | - Daumantas Matulis
- Department of Biothermodynamics and Drug Design, Vilnius University, Vilnius, Lithuania.
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11
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Bukhdruker S, Varaksa T, Grabovec I, Marin E, Shabunya P, Kadukova M, Grudinin S, Kavaleuski A, Gusach A, Gilep A, Borshchevskiy V, Strushkevich N. Hydroxylation of Antitubercular Drug Candidate, SQ109, by Mycobacterial Cytochrome P450. Int J Mol Sci 2020; 21:E7683. [PMID: 33081390 PMCID: PMC7589583 DOI: 10.3390/ijms21207683] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2020] [Revised: 10/12/2020] [Accepted: 10/14/2020] [Indexed: 01/04/2023] Open
Abstract
Spreading of the multidrug-resistant (MDR) strains of the one of the most harmful pathogen Mycobacterium tuberculosis (Mtb) generates the need for new effective drugs. SQ109 showed activity against resistant Mtb and already advanced to Phase II/III clinical trials. Fast SQ109 degradation is attributed to the human liver Cytochrome P450s (CYPs). However, no information is available about interactions of the drug with Mtb CYPs. Here, we show that Mtb CYP124, previously assigned as a methyl-branched lipid monooxygenase, binds and hydroxylates SQ109 in vitro. A 1.25 Å-resolution crystal structure of the CYP124-SQ109 complex unambiguously shows two conformations of the drug, both positioned for hydroxylation of the ω-methyl group in the trans position. The hydroxylated SQ109 presumably forms stabilizing H-bonds with its target, Mycobacterial membrane protein Large 3 (MmpL3). We anticipate that Mtb CYPs could function as analogs of drug-metabolizing human CYPs affecting pharmacokinetics and pharmacodynamics of antitubercular (anti-TB) drugs.
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Affiliation(s)
- Sergey Bukhdruker
- Research Center for Molecular Mechanisms of Aging and Age-Related Diseases, Moscow Institute of Physics and Technology, 141701 Dolgoprudny, Russia; (S.B.); (E.M.); (M.K.); (A.G.)
- Institute of Biological Information Processing (IBI-7: Structural Biochemistry), Research Center Jülich, 52428 Jülich, Germany
- JuStruct: Jülich Center for Structural Biology, Research Center Jülich, 52428 Jülich, Germany
| | - Tatsiana Varaksa
- Institute of Bioorganic Chemistry, National Academy of Sciences of Belarus, 220141 Minsk, Belarus; (T.V.); (I.G.); (P.S.); (A.K.); (A.G.)
| | - Irina Grabovec
- Institute of Bioorganic Chemistry, National Academy of Sciences of Belarus, 220141 Minsk, Belarus; (T.V.); (I.G.); (P.S.); (A.K.); (A.G.)
| | - Egor Marin
- Research Center for Molecular Mechanisms of Aging and Age-Related Diseases, Moscow Institute of Physics and Technology, 141701 Dolgoprudny, Russia; (S.B.); (E.M.); (M.K.); (A.G.)
| | - Polina Shabunya
- Institute of Bioorganic Chemistry, National Academy of Sciences of Belarus, 220141 Minsk, Belarus; (T.V.); (I.G.); (P.S.); (A.K.); (A.G.)
| | - Maria Kadukova
- Research Center for Molecular Mechanisms of Aging and Age-Related Diseases, Moscow Institute of Physics and Technology, 141701 Dolgoprudny, Russia; (S.B.); (E.M.); (M.K.); (A.G.)
- Grenoble Alpes University, CNRS, Inria, Grenoble INP, LJK, 38000 Grenoble, France;
| | - Sergei Grudinin
- Grenoble Alpes University, CNRS, Inria, Grenoble INP, LJK, 38000 Grenoble, France;
| | - Anton Kavaleuski
- Institute of Bioorganic Chemistry, National Academy of Sciences of Belarus, 220141 Minsk, Belarus; (T.V.); (I.G.); (P.S.); (A.K.); (A.G.)
| | - Anastasiia Gusach
- Research Center for Molecular Mechanisms of Aging and Age-Related Diseases, Moscow Institute of Physics and Technology, 141701 Dolgoprudny, Russia; (S.B.); (E.M.); (M.K.); (A.G.)
| | - Andrei Gilep
- Institute of Bioorganic Chemistry, National Academy of Sciences of Belarus, 220141 Minsk, Belarus; (T.V.); (I.G.); (P.S.); (A.K.); (A.G.)
- Department of Proteomic Research and Mass Spectrometry, Institute of Biomedical Chemistry, 119435 Moscow, Russia
- R&D Department, MT-Medicals LLC, 121205 Moscow, Russia
| | - Valentin Borshchevskiy
- Research Center for Molecular Mechanisms of Aging and Age-Related Diseases, Moscow Institute of Physics and Technology, 141701 Dolgoprudny, Russia; (S.B.); (E.M.); (M.K.); (A.G.)
- Institute of Biological Information Processing (IBI-7: Structural Biochemistry), Research Center Jülich, 52428 Jülich, Germany
- JuStruct: Jülich Center for Structural Biology, Research Center Jülich, 52428 Jülich, Germany
| | - Natallia Strushkevich
- Institute of Bioorganic Chemistry, National Academy of Sciences of Belarus, 220141 Minsk, Belarus; (T.V.); (I.G.); (P.S.); (A.K.); (A.G.)
- Center for Computational and Data-Intensive Science and Engineering (CDISE), Skolkovo Institute of Science and Technology, 121205 Moscow, Russia
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12
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Bao J, He X, Zhang JZ. Development of a New Scoring Function for Virtual Screening: APBScore. J Chem Inf Model 2020; 60:6355-6365. [DOI: 10.1021/acs.jcim.0c00474] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Affiliation(s)
- Jingxiao Bao
- Shanghai Engineering Research Center of Molecular Therapeutics and New Drug Development, School of Chemistry and Molecular Engineering, East China Normal University, Shanghai 200062, China
| | - Xiao He
- Shanghai Engineering Research Center of Molecular Therapeutics and New Drug Development, School of Chemistry and Molecular Engineering, East China Normal University, Shanghai 200062, China
- NYU-ECNU Center for Computational Chemistry, NYU Shanghai, Shanghai 200062, China
| | - John Z.H. Zhang
- Shanghai Engineering Research Center of Molecular Therapeutics and New Drug Development, School of Chemistry and Molecular Engineering, East China Normal University, Shanghai 200062, China
- NYU-ECNU Center for Computational Chemistry, NYU Shanghai, Shanghai 200062, China
- Department of Chemistry, New York University, New York, New York 10003, United States
- Collaborative Innovation Center of Extreme Optics, Shanxi University, Taiyuan, Shanxi 030006, China
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13
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Karlov D, Sosnin S, Fedorov MV, Popov P. graphDelta: MPNN Scoring Function for the Affinity Prediction of Protein-Ligand Complexes. ACS OMEGA 2020; 5:5150-5159. [PMID: 32201802 PMCID: PMC7081425 DOI: 10.1021/acsomega.9b04162] [Citation(s) in RCA: 50] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/06/2019] [Accepted: 02/21/2020] [Indexed: 06/04/2023]
Abstract
In this work, we present graph-convolutional neural networks for the prediction of binding constants of protein-ligand complexes. We derived the model using multi task learning, where the target variables are the dissociation constant (K d), inhibition constant (K i), and half maximal inhibitory concentration (IC50). Being rigorously trained on the PDBbind dataset, the model achieves the Pearson correlation coefficient of 0.87 and the RMSE value of 1.05 in pK units, outperforming recently developed 3D convolutional neural network model K deep.
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Affiliation(s)
- Dmitry
S. Karlov
- Skolkovo
Institute of Science and Technology, Moscow 143026, Russia
| | - Sergey Sosnin
- Skolkovo
Institute of Science and Technology, Moscow 143026, Russia
- Skolkovo
Innovation Center,Syntelly LLC, 42 Bolshoy Boulevard, Moscow 143026, Russia
| | - Maxim V. Fedorov
- Skolkovo
Institute of Science and Technology, Moscow 143026, Russia
- Skolkovo
Innovation Center,Syntelly LLC, 42 Bolshoy Boulevard, Moscow 143026, Russia
- University
of Strathclyde, Physics
John Anderson Building, 107 Rottenrow East, Glasgow UK G4 0NG, U.K.
| | - Petr Popov
- Skolkovo
Institute of Science and Technology, Moscow 143026, Russia
- Moscow
Institute of Physics and Technology, Dolgoprudny 141701, Russia
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14
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Ribeiro VS, Santana CA, Fassio AV, Cerqueira FR, da Silveira CH, Romanelli JPR, Patarroyo-Vargas A, Oliveira MGA, Gonçalves-Almeida V, Izidoro SC, de Melo-Minardi RC, Silveira SDA. visGReMLIN: graph mining-based detection and visualization of conserved motifs at 3D protein-ligand interface at the atomic level. BMC Bioinformatics 2020; 21:80. [PMID: 32164574 PMCID: PMC7068867 DOI: 10.1186/s12859-020-3347-7] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022] Open
Abstract
Background Interactions between proteins and non-proteic small molecule ligands play important roles in the biological processes of living systems. Thus, the development of computational methods to support our understanding of the ligand-receptor recognition process is of fundamental importance since these methods are a major step towards ligand prediction, target identification, lead discovery, and more. This article presents visGReMLIN, a web server that couples a graph mining-based strategy to detect motifs at the protein-ligand interface with an interactive platform to visually explore and interpret these motifs in the context of protein-ligand interfaces. Results To illustrate the potential of visGReMLIN, we conducted two cases in which our strategy was compared with previous experimentally and computationally determined results. visGReMLIN allowed us to detect patterns previously documented in the literature in a totally visual manner. In addition, we found some motifs that we believe are relevant to protein-ligand interactions in the analyzed datasets. Conclusions We aimed to build a visual analytics-oriented web server to detect and visualize common motifs at the protein-ligand interface. visGReMLIN motifs can support users in gaining insights on the key atoms/residues responsible for protein-ligand interactions in a dataset of complexes.
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Affiliation(s)
- Vagner S Ribeiro
- Department of Computer Science, Universidade Federal de Viçosa, Viçosa, 36570-900, Brazil
| | - Charles A Santana
- Department of Biochemistry and Immunology, Universidade Federal de Minas Gerais, Belo Horizonte, 31270-901, Brazil
| | - Alexandre V Fassio
- Department of Biochemistry and Immunology, Universidade Federal de Minas Gerais, Belo Horizonte, 31270-901, Brazil
| | - Fabio R Cerqueira
- Department of Production Engineering, Universidade Federal Fluminense, Petrópolis, 25650-050, Brazil
| | - Carlos H da Silveira
- Department of Computer Engineering, Advanced Campus at Itabira, Universidade Federal de Itajubá, Itabira, 35903-087, Brazil
| | - João P R Romanelli
- Department of Computer Engineering, Advanced Campus at Itabira, Universidade Federal de Itajubá, Itabira, 35903-087, Brazil
| | - Adriana Patarroyo-Vargas
- Department of Biochemistry and Molecular Biology, Universidade Federal de Viçosa, Viçosa, 36570-900, Brazil
| | - Maria G A Oliveira
- Department of Biochemistry and Molecular Biology, Universidade Federal de Viçosa, Viçosa, 36570-900, Brazil.,Instituto de Biotecnologia aplicada à Agropecuária (BIOAGRO), Universidade Federal de Viçosa, Viçosa, 36570-900, Brazil
| | - Valdete Gonçalves-Almeida
- Department of Biochemistry and Immunology, Universidade Federal de Minas Gerais, Belo Horizonte, 31270-901, Brazil
| | - Sandro C Izidoro
- Department of Computer Engineering, Advanced Campus at Itabira, Universidade Federal de Itajubá, Itabira, 35903-087, Brazil
| | - Raquel C de Melo-Minardi
- Department of Computer Science, Universidade Federal de Minas Gerais, Belo Horizonte, 31270-901, Brazil
| | - Sabrina de A Silveira
- Department of Computer Science, Universidade Federal de Viçosa, Viçosa, 36570-900, Brazil. .,European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Hinxton, CB10 1SD, UK.
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15
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Kotelnikov S, Alekseenko A, Liu C, Ignatov M, Padhorny D, Brini E, Lukin M, Coutsias E, Dill KA, Kozakov D. Sampling and refinement protocols for template-based macrocycle docking: 2018 D3R Grand Challenge 4. J Comput Aided Mol Des 2019; 34:179-189. [PMID: 31879831 DOI: 10.1007/s10822-019-00257-1] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2019] [Accepted: 11/19/2019] [Indexed: 12/25/2022]
Abstract
We describe a new template-based method for docking flexible ligands such as macrocycles to proteins. It combines Monte-Carlo energy minimization on the manifold, a fast manifold search method, with BRIKARD for complex flexible ligand searching, and with the MELD accelerator of Replica-Exchange Molecular Dynamics simulations for atomistic degrees of freedom. Here we test the method in the Drug Design Data Resource blind Grand Challenge competition. This method was among the best performers in the competition, giving sub-angstrom prediction quality for the majority of the targets.
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Affiliation(s)
- Sergei Kotelnikov
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY, USA.,Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, NY, USA.,Innopolis University, Innopolis, Russia
| | - Andrey Alekseenko
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY, USA.,Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, NY, USA
| | - Cong Liu
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY, USA.,Department of Chemistry, Stony Brook University, Stony Brook, NY, USA
| | - Mikhail Ignatov
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY, USA.,Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, NY, USA.,Institute for Advanced Computational Sciences, Stony Brook University, Stony Brook, NY, USA
| | - Dzmitry Padhorny
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY, USA.,Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, NY, USA
| | - Emiliano Brini
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY, USA
| | - Mark Lukin
- Department of Pharmacological Sciences, Stony Brook University, Stony Brook, NY, USA
| | - Evangelos Coutsias
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY, USA.,Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, NY, USA
| | - Ken A Dill
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY, USA.,Department of Chemistry, Stony Brook University, Stony Brook, NY, USA.,Department of Physics and Astronomy, Stony Brook University, Stony Brook, NY, USA
| | - Dima Kozakov
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY, USA. .,Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, NY, USA. .,Institute for Advanced Computational Sciences, Stony Brook University, Stony Brook, NY, USA.
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16
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Kadukova M, Chupin V, Grudinin S. Docking rigid macrocycles using Convex-PL, AutoDock Vina, and RDKit in the D3R Grand Challenge 4. J Comput Aided Mol Des 2019; 34:191-200. [PMID: 31784861 DOI: 10.1007/s10822-019-00263-3] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2019] [Accepted: 11/22/2019] [Indexed: 12/15/2022]
Abstract
The D3R Grand Challenge 4 provided a brilliant opportunity to test macrocyclic docking protocols on a diverse high-quality experimental data. We participated in both pose and affinity prediction exercises. Overall, we aimed to use an automated structure-based docking pipeline built around a set of tools developed in our team. This exercise again demonstrated a crucial importance of the correct local ligand geometry for the overall success of docking. Starting from the second part of the pose prediction stage, we developed a stable pipeline for sampling macrocycle conformers. This resulted in the subangstrom average precision of our pose predictions. In the affinity prediction exercise we obtained average results. However, we could improve these when using docking poses submitted by the best predictors. Our docking tools including the Convex-PL scoring function are available at https://team.inria.fr/nano-d/software/.
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Affiliation(s)
- Maria Kadukova
- Univ. Grenoble Alpes, CNRS, Inria, Grenoble INP, LJK, 38000, Grenoble, France
- Moscow Institute of Physics and Technology, Dolgoprudny, Russia, 141700
| | - Vladimir Chupin
- Moscow Institute of Physics and Technology, Dolgoprudny, Russia, 141700
| | - Sergei Grudinin
- Univ. Grenoble Alpes, CNRS, Inria, Grenoble INP, LJK, 38000, Grenoble, France.
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17
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Moman E, Grishina MA, Potemkin VA. Nonparametric chemical descriptors for the calculation of ligand-biopolymer affinities with machine-learning scoring functions. J Comput Aided Mol Des 2019; 33:943-953. [PMID: 31728812 DOI: 10.1007/s10822-019-00248-2] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2019] [Accepted: 11/04/2019] [Indexed: 12/20/2022]
Abstract
The computational prediction of ligand-biopolymer affinities is a crucial endeavor in modern drug discovery and one that still poses major challenges. The choice of the appropriate computational method often reveals itself as a trade-off between accuracy and speed, with mathematical devices referred to as scoring functions being the fastest. Among the many shortcomings of scoring functions there is the lack of universal applicability to every molecular system. This is so largely due to their reliance on atom type perception and/or parametrization. This article proposes the use of nonparametric Model of Effective Radii of Atoms descriptors that can be readily computed for the entire Periodic Table and demonstrate that, in combination with machine learning algorithms, they can yield competitive performances and chemically meaningful insights.
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Affiliation(s)
- Edelmiro Moman
- South Ural State University, 20A Tchaikovsky Street, Chelyabinsk, Russian Federation, 454080.
| | - Maria A Grishina
- South Ural State University, 20A Tchaikovsky Street, Chelyabinsk, Russian Federation, 454080
| | - Vladimir A Potemkin
- South Ural State University, 20A Tchaikovsky Street, Chelyabinsk, Russian Federation, 454080
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18
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Kairys V, Baranauskiene L, Kazlauskiene M, Matulis D, Kazlauskas E. Binding affinity in drug design: experimental and computational techniques. Expert Opin Drug Discov 2019; 14:755-768. [DOI: 10.1080/17460441.2019.1623202] [Citation(s) in RCA: 29] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
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
| | | | - 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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19
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Li J, Fu A, Zhang L. An Overview of Scoring Functions Used for Protein-Ligand Interactions in Molecular Docking. Interdiscip Sci 2019; 11:320-328. [PMID: 30877639 DOI: 10.1007/s12539-019-00327-w] [Citation(s) in RCA: 185] [Impact Index Per Article: 37.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2018] [Revised: 02/06/2019] [Accepted: 03/06/2019] [Indexed: 12/17/2022]
Abstract
Currently, molecular docking is becoming a key tool in drug discovery and molecular modeling applications. The reliability of molecular docking depends on the accuracy of the adopted scoring function, which can guide and determine the ligand poses when thousands of possible poses of ligand are generated. The scoring function can be used to determine the binding mode and site of a ligand, predict binding affinity and identify the potential drug leads for a given protein target. Despite intensive research over the years, accurate and rapid prediction of protein-ligand interactions is still a challenge in molecular docking. For this reason, this study reviews four basic types of scoring functions, physics-based, empirical, knowledge-based, and machine learning-based scoring functions, based on an up-to-date classification scheme. We not only discuss the foundations of the four types scoring functions, suitable application areas and shortcomings, but also discuss challenges and potential future study directions.
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Affiliation(s)
- Jin Li
- College of Computer and Information Science, Southwest University, Chongqing, 400715, China.,School of Medical Information and Engineering, Southwest Medical University, Luzhou, 646000, China
| | - Ailing Fu
- College of Pharmaceutical Sciences, Southwest University, Chongqing, 400715, China
| | - Le Zhang
- College of Computer and Information Science, Southwest University, Chongqing, 400715, China. .,College of Computer Science, Sichuan University, Chengdu, 610065, China. .,Medical Big Data Center, Sichuan University, Chengdu, 610065, China. .,Zdmedical, Information Polytron Technologies Inc Chongqing, Chongqing, 401320, China.
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20
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Kadukova M, Grudinin S. Docking of small molecules to farnesoid X receptors using AutoDock Vina with the Convex-PL potential: lessons learned from D3R Grand Challenge 2. J Comput Aided Mol Des 2017; 32:151-162. [DOI: 10.1007/s10822-017-0062-1] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2017] [Accepted: 09/08/2017] [Indexed: 10/18/2022]
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