1
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Revillo Imbernon J, Weibel JM, Ennifar E, Prévost G, Kellenberger E. Structural analysis of neomycin B and kanamycin A binding Aminoglycosides Modifying Enzymes (AME) and bacterial ribosomal RNA. Mol Inform 2024; 43:e202300339. [PMID: 38853661 DOI: 10.1002/minf.202300339] [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: 12/09/2023] [Revised: 03/06/2024] [Accepted: 03/13/2024] [Indexed: 06/11/2024]
Abstract
Aminoglycosides are crucial antibiotics facing challenges from bacterial resistance. This study addresses the importance of aminoglycoside modifying enzymes in the context of escalating resistance. Drawing upon over two decades of structural data in the Protein Data Bank, we focused on two key antibiotics, neomycin B and kanamycin A, to explore how the aminoglycoside structure is exploited by this family of enzymes. A systematic comparison across diverse enzymes and the RNA A-site target identified common characteristics in the recognition mode, while assessing the adaptability of neomycin B and kanamycin A in various environments.
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Affiliation(s)
- Julia Revillo Imbernon
- Laboratoire d'Innovation Thérapeutique, UMR7200 CNRS Université de Strasbourg, Faculté de Pharmacie, 74 route du Rhin, 67400, Illkirch-Graffenstaden, France
| | - Jean-Marc Weibel
- Laboratoire de Synthèse, Réactivité Organiques et Catalyse, Institut de Chimie, UMR 7177 CNRS, Université de Strasbourg, 4 rue Blaise Pascal, 67070, Strasbourg, France
| | - Eric Ennifar
- Architecture et Réactivité de l'ARN, CNRS UPR 9002, Institut de Biologie Moléculaire et Cellulaire, Université de Strasbourg, 2 Allée Konrad Roengen, 67084, Strasbourg, France
| | - Gilles Prévost
- Virulence bactérienne précoce: fonctions cellulaires et contrôle de l'infection aiguë et subaiguë, UR 7290 Université de Strasbourg, FMTS, ITI InnoVec, 3 rue Koeberlé, 67085, Strasbourg, France
| | - Esther Kellenberger
- Laboratoire d'Innovation Thérapeutique, UMR7200 CNRS Université de Strasbourg, Faculté de Pharmacie, 74 route du Rhin, 67400, Illkirch-Graffenstaden, France
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2
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Reim T, Ehrt C, Graef J, Günther S, Meents A, Rarey M. SiteMine: Large-scale binding site similarity searching in protein structure databases. Arch Pharm (Weinheim) 2024; 357:e2300661. [PMID: 38335311 DOI: 10.1002/ardp.202300661] [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: 11/13/2023] [Revised: 01/10/2024] [Accepted: 01/16/2024] [Indexed: 02/12/2024]
Abstract
Drug discovery and design challenges, such as drug repurposing, analyzing protein-ligand and protein-protein complexes, ligand promiscuity studies, or function prediction, can be addressed by protein binding site similarity analysis. Although numerous tools exist, they all have individual strengths and drawbacks with regard to run time, provision of structure superpositions, and applicability to diverse application domains. Here, we introduce SiteMine, an all-in-one database-driven, alignment-providing binding site similarity search tool to tackle the most pressing challenges of binding site comparison. The performance of SiteMine is evaluated on the ProSPECCTs benchmark, showing a promising performance on most of the data sets. The method performs convincingly regarding all quality criteria for reliable binding site comparison, offering a novel state-of-the-art approach for structure-based molecular design based on binding site comparisons. In a SiteMine showcase, we discuss the high structural similarity between cathepsin L and calpain 1 binding sites and give an outlook on the impact of this finding on structure-based drug design. SiteMine is available at https://uhh.de/naomi.
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Affiliation(s)
- Thorben Reim
- ZBH - Center for Bioinformatics, Universität Hamburg, Hamburg, Germany
| | - Christiane Ehrt
- ZBH - Center for Bioinformatics, Universität Hamburg, Hamburg, Germany
| | - Joel Graef
- ZBH - Center for Bioinformatics, Universität Hamburg, Hamburg, Germany
| | - Sebastian Günther
- Center for Free-Electron Laser Science CFEL, Deutsches Elektronen-Synchrotron DESY, Hamburg, Germany
| | - Alke Meents
- Center for Free-Electron Laser Science CFEL, Deutsches Elektronen-Synchrotron DESY, Hamburg, Germany
| | - Matthias Rarey
- ZBH - Center for Bioinformatics, Universität Hamburg, Hamburg, Germany
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3
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Pallante L, Cannariato M, Androutsos L, Zizzi EA, Bompotas A, Hada X, Grasso G, Kalogeras A, Mavroudi S, Di Benedetto G, Theofilatos K, Deriu MA. VirtuousPocketome: a computational tool for screening protein-ligand complexes to identify similar binding sites. Sci Rep 2024; 14:6296. [PMID: 38491261 PMCID: PMC10943019 DOI: 10.1038/s41598-024-56893-7] [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: 01/09/2024] [Accepted: 03/12/2024] [Indexed: 03/18/2024] Open
Abstract
Protein residues within binding pockets play a critical role in determining the range of ligands that can interact with a protein, influencing its structure and function. Identifying structural similarities in proteins offers valuable insights into their function and activation mechanisms, aiding in predicting protein-ligand interactions, anticipating off-target effects, and facilitating the development of therapeutic agents. Numerous computational methods assessing global or local similarity in protein cavities have emerged, but their utilization is impeded by complexity, impractical automation for amino acid pattern searches, and an inability to evaluate the dynamics of scrutinized protein-ligand systems. Here, we present a general, automatic and unbiased computational pipeline, named VirtuousPocketome, aimed at screening huge databases of proteins for similar binding pockets starting from an interested protein-ligand complex. We demonstrate the pipeline's potential by exploring a recently-solved human bitter taste receptor, i.e. the TAS2R46, complexed with strychnine. We pinpointed 145 proteins sharing similar binding sites compared to the analysed bitter taste receptor and the enrichment analysis highlighted the related biological processes, molecular functions and cellular components. This work represents the foundation for future studies aimed at understanding the effective role of tastants outside the gustatory system: this could pave the way towards the rationalization of the diet as a supplement to standard pharmacological treatments and the design of novel tastants-inspired compounds to target other proteins involved in specific diseases or disorders. The proposed pipeline is publicly accessible, can be applied to any protein-ligand complex, and could be expanded to screen any database of protein structures.
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Affiliation(s)
- Lorenzo Pallante
- Department of Mechanical and Aerospace Engineering, Politecnico di Torino, PolitoBIOMedLab, 10129, Torino, Italy
| | - Marco Cannariato
- Department of Mechanical and Aerospace Engineering, Politecnico di Torino, PolitoBIOMedLab, 10129, Torino, Italy
| | | | - Eric A Zizzi
- Department of Mechanical and Aerospace Engineering, Politecnico di Torino, PolitoBIOMedLab, 10129, Torino, Italy
| | - Agorakis Bompotas
- Industrial Systems Institute, Athena Research Center, 265 04, Patras, Greece
| | - Xhesika Hada
- Department of Mechanical and Aerospace Engineering, Politecnico di Torino, PolitoBIOMedLab, 10129, Torino, Italy
| | - Gianvito Grasso
- Dalle Molle Institute for Artificial Intelligence IDSIA USI-SUPSI, 6962, Lugano-Viganello, Switzerland
| | | | - Seferina Mavroudi
- Department of Nursing, School of Health Rehabilitation Sciences, University of Patras, 265 04, Patras, Greece
| | | | | | - Marco A Deriu
- Department of Mechanical and Aerospace Engineering, Politecnico di Torino, PolitoBIOMedLab, 10129, Torino, Italy.
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4
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Jaeger-Honz S, Klein K, Schreiber F. Systematic analysis, aggregation and visualisation of interaction fingerprints for molecular dynamics simulation data. J Cheminform 2024; 16:28. [PMID: 38475907 DOI: 10.1186/s13321-024-00822-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2023] [Accepted: 03/02/2024] [Indexed: 03/14/2024] Open
Abstract
Computational methods such as molecular docking or molecular dynamics (MD) simulations have been developed to simulate and explore the interactions between biomolecules. However, the interactions obtained using these methods are difficult to analyse and evaluate. Interaction fingerprints (IFPs) have been proposed to derive interactions from static 3D coordinates and transform them into 1D bit vectors. More recently, the concept has been applied to derive IFPs from MD simulations, which adds a layer of complexity by adding the temporal motion and dynamics of a system. As a result, many IFPs are obtained from one MD simulation, resulting in a large number of individual IFPs that are difficult to analyse compared to IFPs derived from static 3D structures. Scientific contribution: We introduce a new method to systematically aggregate IFPs derived from MD simulation data. In addition, we propose visualisations to effectively analyse and compare IFPs derived from MD simulation data to account for the temporal evolution of interactions and to compare IFPs across different MD simulations. This has been implemented as a freely available Python library and can therefore be easily adopted by other researchers and to different MD simulation datasets.
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Affiliation(s)
- Sabrina Jaeger-Honz
- Department of Computer and Information Science, University of Konstanz, Universitätsstrasse 10, 78464, Constance, Germany.
| | - Karsten Klein
- Department of Computer and Information Science, University of Konstanz, Universitätsstrasse 10, 78464, Constance, Germany
| | - Falk Schreiber
- Department of Computer and Information Science, University of Konstanz, Universitätsstrasse 10, 78464, Constance, Germany
- Faculty of Information Technology, Monash University, Clayton, VIC, 3800, Australia
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5
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Li Z, Huang R, Xia M, Patterson TA, Hong H. Fingerprinting Interactions between Proteins and Ligands for Facilitating Machine Learning in Drug Discovery. Biomolecules 2024; 14:72. [PMID: 38254672 PMCID: PMC10813698 DOI: 10.3390/biom14010072] [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: 11/02/2023] [Revised: 12/26/2023] [Accepted: 12/28/2023] [Indexed: 01/24/2024] Open
Abstract
Molecular recognition is fundamental in biology, underpinning intricate processes through specific protein-ligand interactions. This understanding is pivotal in drug discovery, yet traditional experimental methods face limitations in exploring the vast chemical space. Computational approaches, notably quantitative structure-activity/property relationship analysis, have gained prominence. Molecular fingerprints encode molecular structures and serve as property profiles, which are essential in drug discovery. While two-dimensional (2D) fingerprints are commonly used, three-dimensional (3D) structural interaction fingerprints offer enhanced structural features specific to target proteins. Machine learning models trained on interaction fingerprints enable precise binding prediction. Recent focus has shifted to structure-based predictive modeling, with machine-learning scoring functions excelling due to feature engineering guided by key interactions. Notably, 3D interaction fingerprints are gaining ground due to their robustness. Various structural interaction fingerprints have been developed and used in drug discovery, each with unique capabilities. This review recapitulates the developed structural interaction fingerprints and provides two case studies to illustrate the power of interaction fingerprint-driven machine learning. The first elucidates structure-activity relationships in β2 adrenoceptor ligands, demonstrating the ability to differentiate agonists and antagonists. The second employs a retrosynthesis-based pre-trained molecular representation to predict protein-ligand dissociation rates, offering insights into binding kinetics. Despite remarkable progress, challenges persist in interpreting complex machine learning models built on 3D fingerprints, emphasizing the need for strategies to make predictions interpretable. Binding site plasticity and induced fit effects pose additional complexities. Interaction fingerprints are promising but require continued research to harness their full potential.
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Affiliation(s)
- Zoe Li
- National Center for Toxicological Research, US Food and Drug Administration, Jefferson, AR 72079, USA; (Z.L.); (T.A.P.)
| | - Ruili Huang
- National Center for Advancing Translational Sciences, National Institutes of Health, Bethesda, MD 20892, USA; (R.H.); (M.X.)
| | - Menghang Xia
- National Center for Advancing Translational Sciences, National Institutes of Health, Bethesda, MD 20892, USA; (R.H.); (M.X.)
| | - Tucker A. Patterson
- National Center for Toxicological Research, US Food and Drug Administration, Jefferson, AR 72079, USA; (Z.L.); (T.A.P.)
| | - Huixiao Hong
- National Center for Toxicological Research, US Food and Drug Administration, Jefferson, AR 72079, USA; (Z.L.); (T.A.P.)
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6
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Wang DD, Wu W, Wang R. Structure-based, deep-learning models for protein-ligand binding affinity prediction. J Cheminform 2024; 16:2. [PMID: 38173000 PMCID: PMC10765576 DOI: 10.1186/s13321-023-00795-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2023] [Accepted: 12/10/2023] [Indexed: 01/05/2024] Open
Abstract
The launch of AlphaFold series has brought deep-learning techniques into the molecular structural science. As another crucial problem, structure-based prediction of protein-ligand binding affinity urgently calls for advanced computational techniques. Is deep learning ready to decode this problem? Here we review mainstream structure-based, deep-learning approaches for this problem, focusing on molecular representations, learning architectures and model interpretability. A model taxonomy has been generated. To compensate for the lack of valid comparisons among those models, we realized and evaluated representatives from a uniform basis, with the advantages and shortcomings discussed. This review will potentially benefit structure-based drug discovery and related areas.
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Affiliation(s)
- Debby D Wang
- School of Science and Technology, Hong Kong Metropolitan University, 81 Chung Hau Sreet, Ho Man Tin, Hong Kong, China
| | - Wenhui Wu
- College of Electronics and Information Engineering, Shenzhen University, Shenzhen, 518060, China
- Guangdong Key Laboratory of Intelligent Information Processing, Shenzhen University, Shenzhen, 518060, China
| | - Ran Wang
- School of Mathematical Science, Shenzhen University, Shenzhen, 518060, China.
- Guangdong Key Laboratory of Intelligent Information Processing, Shenzhen University, Shenzhen, 518060, China.
- Shenzhen Key Laboratory of Advanced Machine Learning and Applications, Shenzhen University, Shenzhen , 518060, China.
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7
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Chiesa L, Sick E, Kellenberger E. Predicting the duration of action of β2-adrenergic receptor agonists: Ligand and structure-based approaches. Mol Inform 2023; 42:e202300141. [PMID: 37872120 DOI: 10.1002/minf.202300141] [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: 06/11/2023] [Revised: 10/17/2023] [Accepted: 10/23/2023] [Indexed: 10/25/2023]
Abstract
Agonists of the β2 adrenergic receptor (ADRB2) are an important class of medications used for the treatment of respiratory diseases. They can be classified as short acting (SABA) or long acting (LABA), with each class playing a different role in patient management. In this work we explored both ligand-based and structure-based high-throughput approaches to classify β2-agonists based on their duration of action. A completely in-silico prediction pipeline using an AlphaFold generated structure was used for structure-based modelling. Our analysis identified the ligands' 3D structure and lipophilicity as the most relevant features for the prediction of the duration of action. Interaction-based methods were also able to select ligands with the desired duration of action, incorporating the bias directly in the structure-based drug discovery pipeline without the need for further processing.
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Affiliation(s)
- Luca Chiesa
- Laboratoire d'Innovation Thérapeutique, Faculté de Pharmacie, UMR7200 CNRS Université de Strasbourg, 67400, Illkirch, France
| | - Emilie Sick
- Laboratoire de Conception et Application de Molécules Bioactives, Faculté de Pharmacie, UMR7199 CNRS Université de Strasbourg, 67400, Illkirch, France
| | - Esther Kellenberger
- Laboratoire d'Innovation Thérapeutique, Faculté de Pharmacie, UMR7200 CNRS Université de Strasbourg, 67400, Illkirch, France
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8
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Zhao Z, Bourne PE. Rigid Scaffolds Are Promising for Designing Macrocyclic Kinase Inhibitors. ACS Pharmacol Transl Sci 2023; 6:1182-1191. [PMID: 37588756 PMCID: PMC10425998 DOI: 10.1021/acsptsci.3c00078] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2023] [Indexed: 08/18/2023]
Abstract
Macrocyclic kinase inhibitors (MKIs) are gaining attention due to their favorable selectivity and potential to overcome drug resistance, yet they remain challenging to design because of their novel structures. To facilitate the design and discovery of MKIs, we investigate MKI rational design starting from initial acyclic compounds by performing microsecond-scale atomistic simulations for multiple MKIs, constructing an MKI database, and analyzing MKIs using hierarchical cluster analysis. Our studies demonstrate that the binding modes of MKIs are like those of their corresponding acyclic counterparts against the same kinase targets. Importantly, within the respective binding sites, the MKI scaffolds retain the same conformations as their corresponding acyclic counterparts, demonstrating the rigidity of scaffolds before and after molecular cyclization. The MKI database includes 641 nanomole-level MKIs from 56 human kinases elucidating the features of rigid scaffolds and the core structures of MKIs. Collectively these results and resources can facilitate MKI development.
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Affiliation(s)
- Zheng Zhao
- School of Data Science and Department
of Biomedical Engineering, University of
Virginia, Charlottesville, Virginia 22904, United States
| | - Philip E. Bourne
- School of Data Science and Department
of Biomedical Engineering, University of
Virginia, Charlottesville, Virginia 22904, United States
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9
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Shiota K, Suma A, Ogawa H, Yamaguchi T, Iida A, Hata T, Matsushita M, Akutsu T, Tateno M. AQDnet: Deep Neural Network for Protein-Ligand Docking Simulation. ACS OMEGA 2023; 8:23925-23935. [PMID: 37426216 PMCID: PMC10324054 DOI: 10.1021/acsomega.3c02411] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/10/2023] [Accepted: 05/31/2023] [Indexed: 07/11/2023]
Abstract
We have developed an innovative system, AI QM Docking Net (AQDnet), which utilizes the three-dimensional structure of protein-ligand complexes to predict binding affinity. This system is novel in two respects: first, it significantly expands the training dataset by generating thousands of diverse ligand configurations for each protein-ligand complex and subsequently determining the binding energy of each configuration through quantum computation. Second, we have devised a method that incorporates the atom-centered symmetry function (ACSF), highly effective in describing molecular energies, for the prediction of protein-ligand interactions. These advancements have enabled us to effectively train a neural network to learn the protein-ligand quantum energy landscape (P-L QEL). Consequently, we have achieved a 92.6% top 1 success rate in the CASF-2016 docking power, placing first among all models assessed in the CASF-2016, thus demonstrating the exceptional docking performance of our model.
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Affiliation(s)
- Koji Shiota
- Innovation
to Implementation Laboratories, Central
Pharmaceutical Research Institute, Japan Tobacco Inc., Takatsuki, Osaka 569-1125, Japan
| | - Akira Suma
- Innovation
to Implementation Laboratories, Central
Pharmaceutical Research Institute, Japan Tobacco Inc., Takatsuki, Osaka 569-1125, Japan
| | - Hiroyuki Ogawa
- Innovation
to Implementation Laboratories, Central
Pharmaceutical Research Institute, Japan Tobacco Inc., Takatsuki, Osaka 569-1125, Japan
| | - Takuya Yamaguchi
- Innovation
to Implementation Laboratories, Central
Pharmaceutical Research Institute, Japan Tobacco Inc., Takatsuki, Osaka 569-1125, Japan
| | - Akio Iida
- Innovation
to Implementation Laboratories, Central
Pharmaceutical Research Institute, Japan Tobacco Inc., Takatsuki, Osaka 569-1125, Japan
| | - Takahiro Hata
- Innovation
to Implementation Laboratories, Central
Pharmaceutical Research Institute, Japan Tobacco Inc., Takatsuki, Osaka 569-1125, Japan
| | - Mutsuyoshi Matsushita
- Innovation
to Implementation Laboratories, Central
Pharmaceutical Research Institute, Japan Tobacco Inc., Takatsuki, Osaka 569-1125, Japan
| | - Tatsuya Akutsu
- Bioinformatics
Center, Institute for Chemical Research,
Kyoto University, Uji, Kyoto 611-0011, Japan
| | - Masaru Tateno
- Innovation
to Implementation Laboratories, Central
Pharmaceutical Research Institute, Japan Tobacco Inc., Takatsuki, Osaka 569-1125, Japan
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10
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Duay SS, Yap RCY, Gaitano AL, Santos JAA, Macalino SJY. Roles of Virtual Screening and Molecular Dynamics Simulations in Discovering and Understanding Antimalarial Drugs. Int J Mol Sci 2023; 24:ijms24119289. [PMID: 37298256 DOI: 10.3390/ijms24119289] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2023] [Revised: 05/16/2023] [Accepted: 05/23/2023] [Indexed: 06/12/2023] Open
Abstract
Malaria continues to be a global health threat, with approximately 247 million cases worldwide. Despite therapeutic interventions being available, patient compliance is a problem due to the length of treatment. Moreover, drug-resistant strains have emerged over the years, necessitating urgent identification of novel and more potent treatments. Given that traditional drug discovery often requires a great deal of time and resources, most drug discovery efforts now use computational methods. In silico techniques such as quantitative structure-activity relationship (QSAR), docking, and molecular dynamics (MD) can be used to study protein-ligand interactions and determine the potency and safety profile of a set of candidate compounds to help prioritize those tested using assays and animal models. This paper provides an overview of antimalarial drug discovery and the application of computational methods in identifying candidate inhibitors and elucidating their potential mechanisms of action. We conclude with the continued challenges and future perspectives in the field of antimalarial drug discovery.
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Affiliation(s)
- Searle S Duay
- Department of Chemistry, De La Salle University, Manila 0922, Philippines
| | - Rianne Casey Y Yap
- Department of Chemistry, De La Salle University, Manila 0922, Philippines
| | - Arturo L Gaitano
- Chemistry Department, Adamson University, Manila 1000, Philippines
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11
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Revillo Imbernon J, Chiesa L, Kellenberger E. Mining the Protein Data Bank to inspire fragment library design. Front Chem 2023; 11:1089714. [PMID: 36846858 PMCID: PMC9950109 DOI: 10.3389/fchem.2023.1089714] [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: 11/04/2022] [Accepted: 01/27/2023] [Indexed: 02/12/2023] Open
Abstract
The fragment approach has emerged as a method of choice for drug design, as it allows difficult therapeutic targets to be addressed. Success lies in the choice of the screened chemical library and the biophysical screening method, and also in the quality of the selected fragment and structural information used to develop a drug-like ligand. It has recently been proposed that promiscuous compounds, i.e., those that bind to several proteins, present an advantage for the fragment approach because they are likely to give frequent hits in screening. In this study, we searched the Protein Data Bank for fragments with multiple binding modes and targeting different sites. We identified 203 fragments represented by 90 scaffolds, some of which are not or hardly present in commercial fragment libraries. By contrast to other available fragment libraries, the studied set is enriched in fragments with a marked three-dimensional character (download at 10.5281/zenodo.7554649).
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Affiliation(s)
- Julia Revillo Imbernon
- Laboratoire d’Innovation Thérapeutique, Faculté de Pharmacie, UMR7200 CNRS Université de Strasbourg, Illkirch-Graffenstaden, France
| | - Luca Chiesa
- Laboratoire d’Innovation Thérapeutique, Faculté de Pharmacie, UMR7200 CNRS Université de Strasbourg, Illkirch-Graffenstaden, France
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12
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Clyde A, Liu X, Brettin T, Yoo H, Partin A, Babuji Y, Blaiszik B, Mohd-Yusof J, Merzky A, Turilli M, Jha S, Ramanathan A, Stevens R. AI-accelerated protein-ligand docking for SARS-CoV-2 is 100-fold faster with no significant change in detection. Sci Rep 2023; 13:2105. [PMID: 36747041 PMCID: PMC9901402 DOI: 10.1038/s41598-023-28785-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2022] [Accepted: 01/24/2023] [Indexed: 02/08/2023] Open
Abstract
Protein-ligand docking is a computational method for identifying drug leads. The method is capable of narrowing a vast library of compounds down to a tractable size for downstream simulation or experimental testing and is widely used in drug discovery. While there has been progress in accelerating scoring of compounds with artificial intelligence, few works have bridged these successes back to the virtual screening community in terms of utility and forward-looking development. We demonstrate the power of high-speed ML models by scoring 1 billion molecules in under a day (50 k predictions per GPU seconds). We showcase a workflow for docking utilizing surrogate AI-based models as a pre-filter to a standard docking workflow. Our workflow is ten times faster at screening a library of compounds than the standard technique, with an error rate less than 0.01% of detecting the underlying best scoring 0.1% of compounds. Our analysis of the speedup explains that another order of magnitude speedup must come from model accuracy rather than computing speed. In order to drive another order of magnitude of acceleration, we share a benchmark dataset consisting of 200 million 3D complex structures and 2D structure scores across a consistent set of 13 million "in-stock" molecules over 15 receptors, or binding sites, across the SARS-CoV-2 proteome. We believe this is strong evidence for the community to begin focusing on improving the accuracy of surrogate models to improve the ability to screen massive compound libraries 100 × or even 1000 × faster than current techniques and reduce missing top hits. The technique outlined aims to be a fast drop-in replacement for docking for screening billion-scale molecular libraries.
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Affiliation(s)
- Austin Clyde
- Argonne National Laboratory, Data Science and Learning Division, Chicago, Lemont, 60439, USA.
- Department of Computer Science, University of Chicago, Chicago, 60637, USA.
| | - Xuefeng Liu
- Department of Computer Science, University of Chicago, Chicago, 60637, USA
| | - Thomas Brettin
- Department of Computer Science, University of Chicago, Chicago, 60637, USA
- Argonne National Laboratory, Computing, Environment, and Life Sciences Directorate, Lemont, 60439, USA
| | - Hyunseung Yoo
- Argonne National Laboratory, Data Science and Learning Division, Chicago, Lemont, 60439, USA
| | - Alexander Partin
- Argonne National Laboratory, Data Science and Learning Division, Chicago, Lemont, 60439, USA
| | - Yadu Babuji
- Department of Computer Science, University of Chicago, Chicago, 60637, USA
| | - Ben Blaiszik
- Argonne National Laboratory, Data Science and Learning Division, Chicago, Lemont, 60439, USA
- University of Chicago, Globus, Chicago, 60637, USA
| | - Jamaludin Mohd-Yusof
- Los Alamos National Laboratory, Computer, Computational, and Statistical Sciences, Los Alamos, 87545, USA
| | - Andre Merzky
- Department of Electrical and Computer Engineering, Rutgers University, Piscataway, 08854, USA
- Brookhaven National Laboratory, Computational Sciences Initiative, Upton, 11973, USA
| | - Matteo Turilli
- Department of Electrical and Computer Engineering, Rutgers University, Piscataway, 08854, USA
- Brookhaven National Laboratory, Computational Sciences Initiative, Upton, 11973, USA
| | - Shantenu Jha
- Department of Electrical and Computer Engineering, Rutgers University, Piscataway, 08854, USA
- Brookhaven National Laboratory, Computational Sciences Initiative, Upton, 11973, USA
| | - Arvind Ramanathan
- Argonne National Laboratory, Data Science and Learning Division, Chicago, Lemont, 60439, USA
| | - Rick Stevens
- Department of Computer Science, University of Chicago, Chicago, 60637, USA
- Argonne National Laboratory, Computing, Environment, and Life Sciences Directorate, Lemont, 60439, USA
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13
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Gu L, Li B, Ming D. A multilayer dynamic perturbation analysis method for predicting ligand-protein interactions. BMC Bioinformatics 2022; 23:456. [PMID: 36324073 PMCID: PMC9628359 DOI: 10.1186/s12859-022-04995-2] [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: 05/09/2022] [Accepted: 10/19/2022] [Indexed: 01/24/2023] Open
Abstract
BACKGROUND Ligand-protein interactions play a key role in defining protein function, and detecting natural ligands for a given protein is thus a very important bioengineering task. In particular, with the rapid development of AI-based structure prediction algorithms, batch structural models with high reliability and accuracy can be obtained at low cost, giving rise to the urgent requirement for the prediction of natural ligands based on protein structures. In recent years, although several structure-based methods have been developed to predict ligand-binding pockets and ligand-binding sites, accurate and rapid methods are still lacking, especially for the prediction of ligand-binding regions and the spatial extension of ligands in the pockets. RESULTS In this paper, we proposed a multilayer dynamics perturbation analysis (MDPA) method for predicting ligand-binding regions based solely on protein structure, which is an extended version of our previously developed fast dynamic perturbation analysis (FDPA) method. In MDPA/FDPA, ligand binding tends to occur in regions that cause large changes in protein conformational dynamics. MDPA, examined using a standard validation dataset of ligand-protein complexes, yielded an averaged ligand-binding site prediction Matthews coefficient of 0.40, with a prediction precision of at least 50% for 71% of the cases. In particular, for 80% of the cases, the predicted ligand-binding region overlaps the natural ligand by at least 50%. The method was also compared with other state-of-the-art structure-based methods. CONCLUSIONS MDPA is a structure-based method to detect ligand-binding regions on protein surface. Our calculations suggested that a range of spaces inside the protein pockets has subtle interactions with the protein, which can significantly impact on the overall dynamics of the protein. This work provides a valuable tool as a starting point upon which further docking and analysis methods can be used for natural ligand detection in protein functional annotation. The source code of MDPA method is freely available at: https://github.com/mingdengming/mdpa .
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Affiliation(s)
- Lin Gu
- grid.412022.70000 0000 9389 5210College of Biotechnology and Pharmaceutical Engineering, Nanjing Tech University, Biotech Building Room B1-404, 30 South Puzhu Road, Jiangbei New District, Nanjing City, 211816 Jiangsu People’s Republic of China
| | - Bin Li
- grid.412022.70000 0000 9389 5210College of Biotechnology and Pharmaceutical Engineering, Nanjing Tech University, Biotech Building Room B1-404, 30 South Puzhu Road, Jiangbei New District, Nanjing City, 211816 Jiangsu People’s Republic of China
| | - Dengming Ming
- grid.412022.70000 0000 9389 5210College of Biotechnology and Pharmaceutical Engineering, Nanjing Tech University, Biotech Building Room B1-404, 30 South Puzhu Road, Jiangbei New District, Nanjing City, 211816 Jiangsu People’s Republic of China
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14
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Chiesa L, Kellenberger E. One class classification for the detection of β2 adrenergic receptor agonists using single-ligand dynamic interaction data. J Cheminform 2022; 14:74. [PMID: 36309734 PMCID: PMC9617447 DOI: 10.1186/s13321-022-00654-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2022] [Accepted: 10/17/2022] [Indexed: 11/22/2022] Open
Abstract
G protein-coupled receptors are involved in many biological processes, relaying the extracellular signal inside the cell. Signaling is regulated by the interactions between receptors and their ligands, it can be stimulated by agonists, or inhibited by antagonists or inverse agonists. The development of a new drug targeting a member of this family requires to take into account the pharmacological profile of the designed ligands in order to elicit the desired response. The structure-based virtual screening of chemical libraries may prioritize a specific class of ligands by combining docking results and ligand binding information provided by crystallographic structures. The performance of the method depends on the relevance of the structural data, in particular the conformation of the targeted site, the binding mode of the reference ligand, and the approach used to compare the interactions formed by the docked ligand with those formed by the reference ligand in the crystallographic structure. Here, we propose a new method based on the conformational dynamics of a single protein–ligand reference complex to improve the biased selection of ligands with specific pharmacological properties in a structure-based virtual screening exercise. Interactions patterns between a reference agonist and the receptor, here exemplified on the β2 adrenergic receptor, were extracted from molecular dynamics simulations of the agonist/receptor complex and encoded in graphs used to train a one-class machine learning classifier. Different conditions were tested: low to high affinity agonists, varying simulation duration, considering or ignoring hydrophobic contacts, and tuning of the classifier parametrization. The best models applied to post-process raw data from retrospective virtual screening obtained by docking of test libraries effectively filtered out irrelevant poses, discarding inactive and non-agonist ligands while identifying agonists. Taken together, our results suggest that consistency of the binding mode during the simulation is a key to the success of the method.
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Affiliation(s)
- Luca Chiesa
- Laboratoire d'innovation Thérapeutique, Faculté de Pharmacie, UMR7200 CNRS Université de Strasbourg, 67400, Illkirch, France
| | - Esther Kellenberger
- Laboratoire d'innovation Thérapeutique, Faculté de Pharmacie, UMR7200 CNRS Université de Strasbourg, 67400, Illkirch, France.
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15
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Eguida M, Rognan D. Estimating the Similarity between Protein Pockets. Int J Mol Sci 2022; 23:12462. [PMID: 36293316 PMCID: PMC9604425 DOI: 10.3390/ijms232012462] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2022] [Revised: 10/15/2022] [Accepted: 10/16/2022] [Indexed: 10/28/2023] Open
Abstract
With the exponential increase in publicly available protein structures, the comparison of protein binding sites naturally emerged as a scientific topic to explain observations or generate hypotheses for ligand design, notably to predict ligand selectivity for on- and off-targets, explain polypharmacology, and design target-focused libraries. The current review summarizes the state-of-the-art computational methods applied to pocket detection and comparison as well as structural druggability estimates. The major strengths and weaknesses of current pocket descriptors, alignment methods, and similarity search algorithms are presented. Lastly, an exhaustive survey of both retrospective and prospective applications in diverse medicinal chemistry scenarios illustrates the capability of the existing methods and the hurdle that still needs to be overcome for more accurate predictions.
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Affiliation(s)
| | - Didier Rognan
- Laboratoire d’Innovation Thérapeutique, UMR7200 CNRS-Université de Strasbourg, 67400 Illkirch, France
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16
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Zhao Z, Bourne PE. Harnessing systematic protein-ligand interaction fingerprints for drug discovery. Drug Discov Today 2022; 27:103319. [PMID: 35850431 DOI: 10.1016/j.drudis.2022.07.004] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2022] [Revised: 07/04/2022] [Accepted: 07/12/2022] [Indexed: 12/15/2022]
Abstract
Determining protein-ligand interaction characteristics and mechanisms is crucial to the drug discovery process. Here, we review recent progress and successful applications of a systematic protein-ligand interaction fingerprint (IFP) approach for investigating proteome-wide protein-ligand interactions for drug development. Specifically, we review the use of this IFP approach for revealing polypharmacology across the kinome, predicting promising targets from which to design allosteric inhibitors and covalent kinase inhibitors, uncovering the binding mechanisms of drugs of interest, and demonstrating resistant mechanisms of specific drugs. Together, we demonstrate that the IFP strategy is efficient and practical for drug design research for protein kinases as targets and is extensible to other protein families.
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Affiliation(s)
- Zheng Zhao
- School of Data Science and Department of Biomedical Engineering, University of Virginia, Charlottesville, VA 22904, USA.
| | - Philip E Bourne
- School of Data Science and Department of Biomedical Engineering, University of Virginia, Charlottesville, VA 22904, USA.
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17
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Sankar S, Chandran Sakthivel N, Chandra N. Fast Local Alignment of Protein Pockets (FLAPP): A System-Compiled Program for Large-Scale Binding Site Alignment. J Chem Inf Model 2022; 62:4810-4819. [PMID: 36122166 DOI: 10.1021/acs.jcim.2c00967] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Protein function is a direct consequence of its sequence, structure, and the arrangement at the binding site. Bioinformatics using sequence analysis is typically used to gain a first insight into protein function. Protein structures, on the other hand, provide a higher resolution platform into understanding functions. As the protein structural information is increasingly becoming available through experimental structure determination and through advances in computational methods for structure prediction, the opportunity to utilize these data is also increasing. Structural analysis of small molecule ligand binding sites in particular provides a direct and more accurate window to infer protein function. However, it remains a poorly utilized resource due to the huge computational cost of existing methods that make large-scale structural comparisons of binding sites prohibitive. Here, we present an algorithm called FLAPP that produces very rapid atomic level alignments. By combining clique matching in graphs and the power of modern CPU architectures, FLAPP aligns a typical pair of binding sites at ∼12.5 ms using a single CPU core, ∼1 ms using 12 cores on a standard desktop machine, and performs a PDB-wide scan in 1-2 min. We perform rigorous validation of the algorithm at multiple levels of complexity and show that FLAPP provides accurate alignments. We also present a case study involving vitamin B12 binding sites to showcase the usefulness of FLAPP for performing an exhaustive alignment-based PDB-wide scan. We expect that this tool will be invaluable to the scientific community to quickly align millions of site pairs on a normal desktop machine to gain insights into protein function and drug discovery for drug target and off-target identification and polypharmacology.
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Affiliation(s)
- Santhosh Sankar
- Department of Biochemistry, Indian Institute of Science, Bangalore 560012, Karnataka, India
| | | | - Nagasuma Chandra
- Department of Biochemistry, Indian Institute of Science, Bangalore 560012, Karnataka, India.,BioSystems Science and Engineering, Indian Institute of Science, Bangalore 560012, Karnataka, India
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18
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Fassio AV, Shub L, Ponzoni L, McKinley J, O’Meara MJ, Ferreira RS, Keiser MJ, de Melo Minardi RC. Prioritizing Virtual Screening with Interpretable Interaction Fingerprints. J Chem Inf Model 2022; 62:4300-4318. [DOI: 10.1021/acs.jcim.2c00695] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
- Alexandre V. Fassio
- São Carlos Institute of Physics, University of São Paulo, São Carlos, São Paulo 13563-120, Brazil
- Department of Biochemistry and Immunology, Federal University of Minas Gerais, Belo Horizonte, Minas Gerais 31270-901, Brazil
| | - Laura Shub
- Department of Pharmaceutical Chemistry, Department of Bioengineering & Therapeutic Sciences, Institute for Neurodegenerative Diseases, Kavli Institute for Fundamental Neuroscience, Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, California 94143, United States
| | - Luca Ponzoni
- Department of Pharmaceutical Chemistry, Department of Bioengineering & Therapeutic Sciences, Institute for Neurodegenerative Diseases, Kavli Institute for Fundamental Neuroscience, Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, California 94143, United States
| | - Jessica McKinley
- Gilead Sciences, Inc., Foster City, California 94404, United States
| | - Matthew J. O’Meara
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan 48109, United States
| | - Rafaela S. Ferreira
- Department of Biochemistry and Immunology, Federal University of Minas Gerais, Belo Horizonte, Minas Gerais 31270-901, Brazil
| | - Michael J. Keiser
- Department of Pharmaceutical Chemistry, Department of Bioengineering & Therapeutic Sciences, Institute for Neurodegenerative Diseases, Kavli Institute for Fundamental Neuroscience, Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, California 94143, United States
| | - Raquel C. de Melo Minardi
- Department of Computer Science, Federal University of Minas Gerais, Belo Horizonte, Minas Gerais 31270-901, Brazil
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19
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fingeRNAt—A novel tool for high-throughput analysis of nucleic acid-ligand interactions. PLoS Comput Biol 2022; 18:e1009783. [PMID: 35653385 PMCID: PMC9197077 DOI: 10.1371/journal.pcbi.1009783] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2021] [Revised: 06/14/2022] [Accepted: 05/06/2022] [Indexed: 11/19/2022] Open
Abstract
Computational methods play a pivotal role in drug discovery and are widely applied in virtual screening, structure optimization, and compound activity profiling. Over the last decades, almost all the attention in medicinal chemistry has been directed to protein-ligand binding, and computational tools have been created with this target in mind. With novel discoveries of functional RNAs and their possible applications, RNAs have gained considerable attention as potential drug targets. However, the availability of bioinformatics tools for nucleic acids is limited. Here, we introduce fingeRNAt—a software tool for detecting non-covalent interactions formed in complexes of nucleic acids with ligands. The program detects nine types of interactions: (i) hydrogen and (ii) halogen bonds, (iii) cation-anion, (iv) pi-cation, (v) pi-anion, (vi) pi-stacking, (vii) inorganic ion-mediated, (viii) water-mediated, and (ix) lipophilic interactions. However, the scope of detected interactions can be easily expanded using a simple plugin system. In addition, detected interactions can be visualized using the associated PyMOL plugin, which facilitates the analysis of medium-throughput molecular complexes. Interactions are also encoded and stored as a bioinformatics-friendly Structural Interaction Fingerprint (SIFt)—a binary string where the respective bit in the fingerprint is set to 1 if a particular interaction is present and to 0 otherwise. This output format, in turn, enables high-throughput analysis of interaction data using data analysis techniques. We present applications of fingeRNAt-generated interaction fingerprints for visual and computational analysis of RNA-ligand complexes, including analysis of interactions formed in experimentally determined RNA-small molecule ligand complexes deposited in the Protein Data Bank. We propose interaction fingerprint-based similarity as an alternative measure to RMSD to recapitulate complexes with similar interactions but different folding. We present an application of interaction fingerprints for the clustering of molecular complexes. This approach can be used to group ligands that form similar binding networks and thus have similar biological properties. The fingeRNAt software is freely available at https://github.com/n-szulc/fingeRNAt.
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20
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Yang C, Zhang Y. Delta Machine Learning to Improve Scoring-Ranking-Screening Performances of Protein-Ligand Scoring Functions. J Chem Inf Model 2022; 62:2696-2712. [PMID: 35579568 DOI: 10.1021/acs.jcim.2c00485] [Citation(s) in RCA: 22] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
Abstract
Protein-ligand scoring functions are widely used in structure-based drug design for fast evaluation of protein-ligand interactions, and it is of strong interest to develop scoring functions with machine-learning approaches. In this work, by expanding the training set, developing physically meaningful features, employing our recently developed linear empirical scoring function Lin_F9 (Yang, C. J. Chem. Inf. Model. 2021, 61, 4630-4644) as the baseline, and applying extreme gradient boosting (XGBoost) with Δ-machine learning, we have further improved the robustness and applicability of machine-learning scoring functions. Besides the top performances for scoring-ranking-screening power tests of the CASF-2016 benchmark, the new scoring function ΔLin_F9XGB also achieves superior scoring and ranking performances in different structure types that mimic real docking applications. The scoring powers of ΔLin_F9XGB for locally optimized poses, flexible redocked poses, and ensemble docked poses of the CASF-2016 core set achieve Pearson's correlation coefficient (R) values of 0.853, 0.839, and 0.813, respectively. In addition, the large-scale docking-based virtual screening test on the LIT-PCBA data set demonstrates the reliability and robustness of ΔLin_F9XGB in virtual screening application. The ΔLin_F9XGB scoring function and its code are freely available on the web at (https://yzhang.hpc.nyu.edu/Delta_LinF9_XGB).
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Affiliation(s)
- Chao Yang
- Department of Chemistry, New York University, New York, New York 10003, United States
| | - Yingkai Zhang
- Department of Chemistry, New York University, New York, New York 10003, United States.,NYU-ECNU Center for Computational Chemistry at NYU Shanghai, Shanghai 200062, China
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21
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Kumar SP, Dixit NY, Patel CN, Rawal RM, Pandya HA. PharmRF: A machine-learning scoring function to identify the best protein-ligand complexes for structure-based pharmacophore screening with high enrichments. J Comput Chem 2022; 43:847-863. [PMID: 35301752 DOI: 10.1002/jcc.26840] [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] [Received: 12/18/2021] [Revised: 02/14/2022] [Accepted: 02/26/2022] [Indexed: 11/09/2022]
Abstract
Structure-based pharmacophore models are often developed by selecting a single protein-ligand complex with good resolution and better binding affinity data which prevents the analysis of other structures having a similar potential to act as better templates. PharmRF is a pharmacophore-based scoring function for selecting the best crystal structures with the potential to attain high enrichment rates in pharmacophore-based virtual screening prospectively. The PharmRF scoring function is trained and tested on the PDBbind v2018 protein-ligand complex dataset and employs a random forest regressor to correlate protein pocket descriptors and ligand pharmacophoric elements with binding affinity. PharmRF score represents the calculated binding affinity which identifies high-affinity ligands by thorough pruning of all the PDB entries available for a particular protein of interest with a high PharmRF score. Ligands with high PharmRF scores can provide a better basis for structure-based pharmacophore enumerations with a better enrichment rate. Evaluated on 10 protein-ligand systems of the DUD-E dataset, PharmRF achieved superior performance (average success rate: 77.61%, median success rate: 87.16%) than Vina docking score (75.47%, 79.39%). PharmRF was further evaluated using the CASF-2016 benchmark set yielding a moderate correlation of 0.591 with experimental binding affinity, similar in performance to 25 scoring functions tested on this dataset. Independent assessment of PharmRF on 8 protein-ligand systems of LIT-PCBA dataset exhibited average and median success rates of 57.55% and 74.72% with 4 targets attaining success rate > 90%. The PharmRF scoring model, scripts, and related resources can be accessed at https://github.com/Prasanth-Kumar87/PharmRF.
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Affiliation(s)
- Sivakumar Prasanth Kumar
- Institute of Defence Studies and Research, Gujarat University, Ahmedabad, India.,Department of Life Sciences, University School of Sciences, Gujarat University, Ahmedabad, India.,Department of Botany, Bioinformatics, and Climate Change Impacts Management, University School of Sciences, Gujarat University, Ahmedabad, India
| | - Nandan Y Dixit
- Department of Botany, Bioinformatics, and Climate Change Impacts Management, University School of Sciences, Gujarat University, Ahmedabad, India
| | - Chirag N Patel
- Department of Botany, Bioinformatics, and Climate Change Impacts Management, University School of Sciences, Gujarat University, Ahmedabad, India
| | - Rakesh M Rawal
- Institute of Defence Studies and Research, Gujarat University, Ahmedabad, India.,Department of Life Sciences, University School of Sciences, Gujarat University, Ahmedabad, India
| | - Himanshu A Pandya
- Institute of Defence Studies and Research, Gujarat University, Ahmedabad, India.,Department of Life Sciences, University School of Sciences, Gujarat University, Ahmedabad, India.,Department of Botany, Bioinformatics, and Climate Change Impacts Management, University School of Sciences, Gujarat University, Ahmedabad, India
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22
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Polypharmacology: The science of multi-targeting molecules. Pharmacol Res 2022; 176:106055. [PMID: 34990865 DOI: 10.1016/j.phrs.2021.106055] [Citation(s) in RCA: 33] [Impact Index Per Article: 16.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/15/2021] [Revised: 12/23/2021] [Accepted: 12/31/2021] [Indexed: 12/28/2022]
Abstract
Polypharmacology is a concept where a molecule can interact with two or more targets simultaneously. It offers many advantages as compared to the conventional single-targeting molecules. A multi-targeting drug is much more efficacious due to its cumulative efficacy at all of its individual targets making it much more effective in complex and multifactorial diseases like cancer, where multiple proteins and pathways are involved in the onset and development of the disease. For a molecule to be polypharmacologic in nature, it needs to possess promiscuity which is the ability to interact with multiple targets; and at the same time avoid binding to antitargets which would otherwise result in off-target adverse effects. There are certain structural features and physicochemical properties which when present would help researchers to predict if the designed molecule would possess promiscuity or not. Promiscuity can also be identified via advanced state-of-the-art computational methods. In this review, we also elaborate on the methods by which one can intentionally incorporate promiscuity in their molecules and make them polypharmacologic. The polypharmacology paradigm of "one drug-multiple targets" has numerous applications especially in drug repurposing where an already established drug is redeveloped for a new indication. Though designing a polypharmacological drug is much more difficult than designing a single-targeting drug, with the current technologies and information regarding different diseases and chemical functional groups, it is plausible for researchers to intentionally design a polypharmacological drug and unlock its advantages.
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23
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Protein-ligand binding affinity prediction based on profiles of intermolecular contacts. Comput Struct Biotechnol J 2022; 20:1088-1096. [PMID: 35317230 PMCID: PMC8902473 DOI: 10.1016/j.csbj.2022.02.004] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2021] [Revised: 02/08/2022] [Accepted: 02/08/2022] [Indexed: 11/30/2022] Open
Abstract
As a key element in structure-based drug design, binding affinity prediction (BAP) for putative protein-ligand complexes can be efficiently achieved by the incorporation of structural descriptors and machine-learning models. However, developing concise descriptors that will lead to accurate and interpretable BAP remains a difficult problem in this field. Herein, we introduce the profiles of intermolecular contacts (IMCPs) as descriptors for machine-learning-based BAP. IMCPs describe each group of protein-ligand contacts by the count and average distance of the group members, and collaborate closely with classical machine-learning models. Performed on multiple validation sets, IMCP-based models often result in better BAP accuracy than those originating from other similar descriptors. Additionally, IMCPs are simple and concise, and easy to interpret in model training. These descriptors highly conclude the structural information of protein-ligand complexes and can be easily updated with personalized profile features. IMCPs have been implemented in the BAP Toolkit on github ( https://github.com/debbydanwang/BAP).
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24
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Wang DD, Chan MT, Yan H. Structure-based protein-ligand interaction fingerprints for binding affinity prediction. Comput Struct Biotechnol J 2021; 19:6291-6300. [PMID: 34900139 PMCID: PMC8637032 DOI: 10.1016/j.csbj.2021.11.018] [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: 08/30/2021] [Revised: 11/09/2021] [Accepted: 11/13/2021] [Indexed: 11/17/2022] Open
Abstract
Binding affinity prediction (BAP) using protein–ligand complex structures is crucial to computer-aided drug design, but remains a challenging problem. To achieve efficient and accurate BAP, machine-learning scoring functions (SFs) based on a wide range of descriptors have been developed. Among those descriptors, protein–ligand interaction fingerprints (IFPs) are competitive due to their simple representations, elaborate profiles of key interactions and easy collaborations with machine-learning algorithms. In this paper, we have adopted a building-block-based taxonomy to review a broad range of IFP models, and compared representative IFP-based SFs in target-specific and generic scoring tasks. Atom-pair-counts-based and substructure-based IFPs show great potential in these tasks.
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Affiliation(s)
- Debby D Wang
- School of Health Science and Engineering, University of Shanghai for Science and Technology, 516 Jungong Rd, Shanghai 200093, China
| | - Moon-Tong Chan
- School of Science and Technology, Hong Kong Metropolitan University, 30 Good Shepherd St, Ho Man Tin, Hong Kong
| | - Hong Yan
- Department of Electrical Engineering, City University of Hong Kong, Tat Chee Avenue, Kowloon, Hong Kong
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25
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Xu Z, Wauchope OR, Frank AT. Navigating Chemical Space by Interfacing Generative Artificial Intelligence and Molecular Docking. J Chem Inf Model 2021; 61:5589-5600. [PMID: 34633194 DOI: 10.1021/acs.jcim.1c00746] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Here, we report the implementation and application of a simple, structure-aware framework to generate target-specific screening libraries. Our approach combines advances in generative artificial intelligence (AI) with conventional molecular docking to explore chemical space conditioned on the unique physicochemical properties of the active site of a biomolecular target. As a demonstration, we used our framework, which we refer to as sample-and-dock, to construct focused libraries for cyclin-dependent kinase type-2 (CDK2) and the active site of the main protease (Mpro) of the SARS-CoV-2 virus. We envision that the sample-and-dock framework could be used to generate theoretical maps of the chemical space specific to a given target and so provide information about its molecular recognition characteristics.
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Affiliation(s)
- Ziqiao Xu
- Chemistry Department, University of Michigan, 930 North University Avenue, Ann Arbor, Michigan 48109, United States
| | - Orrette R Wauchope
- Department of Natural Sciences, City University of New York, Baruch College, New York, New York 10010, United States
| | - Aaron T Frank
- Biophysics Program, University of Michigan, 930 North University Avenue, Ann Arbor, Michigan 48109, United States
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26
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The Structural Determinants for α 1-Adrenergic/Serotonin Receptors Activity among Phenylpiperazine-Hydantoin Derivatives. Molecules 2021; 26:molecules26227025. [PMID: 34834117 PMCID: PMC8623851 DOI: 10.3390/molecules26227025] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2021] [Revised: 11/15/2021] [Accepted: 11/17/2021] [Indexed: 12/26/2022] Open
Abstract
Several studies confirmed the reciprocal interactions between adrenergic and serotoninergic systems and the influence of these phenomena on the pathogenesis of anxiety. Hence, searching for chemical agents with a multifunctional pharmacodynamic profile may bring highly effective therapy for CNS disorders. This study presents a deep structural insight into the hydantoin-arylpiperazine group and their serotonin/α-adrenergic activity. The newly synthesized compounds were tested in the radioligand binding assay and the intrinsic activity was evaluated for the selected derivatives. The computer-aided SAR analysis enabled us to answer questions about the influence of particular structural fragments on selective vs. multifunctional activity. As a result of the performed investigations, there were two leading structures: (a) compound 12 with multifunctional adrenergic-serotonin activity, which is a promising candidate to be an effective anxiolytic agent; (b) compound 14 with high α1A/α1D affinity and selectivity towards α1B, which is recommended due to the elimination of probable cardiotoxic effect. The structural conclusions of this work provide significant support for future lead optimization in order to achieve the desired pharmacodynamic profile in searching for new CNS-modulating agents.
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27
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Mordalski S, Wojtuch A, Podolak I, Kurczab R, Bojarski AJ. 2D SIFt: a matrix of ligand-receptor interactions. J Cheminform 2021; 13:66. [PMID: 34496955 PMCID: PMC8424890 DOI: 10.1186/s13321-021-00545-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2021] [Accepted: 08/21/2021] [Indexed: 11/10/2022] Open
Abstract
Depicting a ligand-receptor complex via Interaction Fingerprints has been shown to be both a viable data visualization and an analysis tool. The spectrum of its applications ranges from simple visualization of the binding site through analysis of molecular dynamics runs, to the evaluation of the homology models and virtual screening. Here we present a novel tool derived from the Structural Interaction Fingerprints providing a detailed and unique insight into the interactions between receptor and specific regions of the ligand (grouped into pharmacophore features) in the form of a matrix, a 2D-SIFt descriptor. The provided implementation is easy to use and extends the python library, allowing the generation of interaction matrices and their manipulation (reading and writing as well as producing the average 2D-SIFt). The library for handling the interaction matrices is available via repository http://bitbucket.org/zchl/sift2d.
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Affiliation(s)
- Stefan Mordalski
- Department of Medicinal Chemistry, Maj Institute of Pharmacology Polish Academy of Sciences, Krakow, Poland.
| | - Agnieszka Wojtuch
- Faculty of Mathematics and Computer Science, Jagiellonian University, Krakow, Poland
| | - Igor Podolak
- Faculty of Mathematics and Computer Science, Jagiellonian University, Krakow, Poland
| | - Rafał Kurczab
- Department of Medicinal Chemistry, Maj Institute of Pharmacology Polish Academy of Sciences, Krakow, Poland
| | - Andrzej J Bojarski
- Department of Medicinal Chemistry, Maj Institute of Pharmacology Polish Academy of Sciences, Krakow, Poland
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28
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Subtype-selective mechanisms of negative allosteric modulators binding to group I metabotropic glutamate receptors. Acta Pharmacol Sin 2021; 42:1354-1367. [PMID: 33122823 PMCID: PMC8285414 DOI: 10.1038/s41401-020-00541-z] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2020] [Accepted: 09/15/2020] [Indexed: 02/06/2023] Open
Abstract
Group I metabotropic glutamate receptors (mGlu1 and mGlu5) are promising targets for multiple psychiatric and neurodegenerative disorders. Understanding the subtype selectivity of mGlu1 and mGlu5 allosteric sites is essential for the rational design of novel modulators with single- or dual-target mechanism of action. In this study, starting from the deposited mGlu1 and mGlu5 crystal structures, we utilized computational modeling approaches integrating docking, molecular dynamics simulation, and efficient post-trajectory analysis to reveal the subtype-selective mechanism of mGlu1 and mGlu5 to 10 diverse drug scaffolds representing known negative allosteric modulators (NAMs) in the literature. The results of modeling identified six pairs of non-conserved residues and four pairs of conserved ones as critical features to distinguish the selective NAMs binding to the corresponding receptors. In addition, nine pairs of residues are beneficial to the development of novel dual-target NAMs of group I metabotropic glutamate receptors. Furthermore, the binding modes of a reported dual-target NAM (VU0467558) in mGlu1 and mGlu5 were predicted to verify the identified residues that play key roles in the receptor selectivity and the dual-target binding. The results of this study can guide rational structure-based design of novel NAMs, and the approach can be generally applicable to characterize the features of selectivity for other G-protein-coupled receptors.
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29
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Bai B, Zou R, Chan HCS, Li H, Yuan S. MolADI: A Web Server for Automatic Analysis of Protein-Small Molecule Dynamic Interactions. Molecules 2021; 26:molecules26154625. [PMID: 34361778 PMCID: PMC8347168 DOI: 10.3390/molecules26154625] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2021] [Revised: 07/28/2021] [Accepted: 07/28/2021] [Indexed: 11/16/2022] Open
Abstract
Protein-ligand interaction analysis is important for drug discovery and rational protein design. The existing online tools adopt only a single conformation of the complex structure for calculating and displaying the interactions, whereas both protein residues and ligand molecules are flexible to some extent. The interactions evolved with time in the trajectories are of greater interest. MolADI is a user-friendly online tool which analyzes the protein-ligand interactions in detail for either a single structure or a trajectory. Interactions can be viewed easily with both 2D graphs and 3D representations. MolADI is available as a web application.
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Affiliation(s)
- Bing Bai
- Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, 1068 Xueyuan Avenue, Shenzhen 518055, China; (B.B.); (R.Z.); (H.C.S.C.)
- Shenzhen Institute of Advanced Technology, University of Chinese Academy of Sciences, 1068 Xueyuan Avenue, Shenzhen 518055, China
| | - Rongfeng Zou
- Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, 1068 Xueyuan Avenue, Shenzhen 518055, China; (B.B.); (R.Z.); (H.C.S.C.)
- Shenzhen Institute of Advanced Technology, University of Chinese Academy of Sciences, 1068 Xueyuan Avenue, Shenzhen 518055, China
- AlphaMol Science Ltd., 1068 Xueyuan Avenue, Shenzhen 518055, China
| | - H. C. Stephen Chan
- Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, 1068 Xueyuan Avenue, Shenzhen 518055, China; (B.B.); (R.Z.); (H.C.S.C.)
- Shenzhen Institute of Advanced Technology, University of Chinese Academy of Sciences, 1068 Xueyuan Avenue, Shenzhen 518055, China
| | - Hongchun Li
- Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, 1068 Xueyuan Avenue, Shenzhen 518055, China; (B.B.); (R.Z.); (H.C.S.C.)
- Shenzhen Institute of Advanced Technology, University of Chinese Academy of Sciences, 1068 Xueyuan Avenue, Shenzhen 518055, China
- Correspondence: (H.L.); (S.Y.)
| | - Shuguang Yuan
- Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, 1068 Xueyuan Avenue, Shenzhen 518055, China; (B.B.); (R.Z.); (H.C.S.C.)
- Shenzhen Institute of Advanced Technology, University of Chinese Academy of Sciences, 1068 Xueyuan Avenue, Shenzhen 518055, China
- AlphaMol Science Ltd., 1068 Xueyuan Avenue, Shenzhen 518055, China
- Correspondence: (H.L.); (S.Y.)
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30
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Hassan S, Töpel M, Aronsson H. Ligand Binding Site Comparison - LiBiSCo - a web-based tool for analyzing interactions between proteins and ligands to explore amino acid specificity within active sites. Proteins 2021; 89:1530-1540. [PMID: 34240464 DOI: 10.1002/prot.26175] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2020] [Revised: 06/18/2021] [Accepted: 06/23/2021] [Indexed: 11/12/2022]
Abstract
Interaction between protein and ligands are ubiquitous in a biological cell, and understanding these interactions at the atom level in protein-ligand complexes is crucial for structural bioinformatics and drug discovery. Here, we present a web-based protein-ligand interaction application named Ligand Binding Site Comparison (LiBiSCo) for comparing the amino acid residues interacting with atoms of a ligand molecule between different protein-ligand complexes available in the Protein Data Bank (PDB) database. The comparison is performed at the ligand atom level irrespectively of having binding site similarity or not between the protein structures of interest. The input used in LiBiSCo is one or several PDB IDs of protein-ligand complex(es) and the tool returns a list of identified interactions at ligand atom level including both bonded and non-bonded interactions. A sequence profile for the interaction for each ligand atoms is provided as a WebLogo. The LiBiSco is useful in understanding ligand binding specificity and structural promiscuity among families that are structurally unrelated. The LiBiSCo tool can be accessed through https://albiorix.bioenv.gu.se/LiBiSCo/HomePage.py.
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Affiliation(s)
- Sameer Hassan
- Department of Biological and Environmental Sciences, University of Gothenburg, Gothenburg, Sweden.,Karolinska Institutet, Division of Neurogeriatrics, Stockholm, Sweden
| | - Mats Töpel
- Department of Marine Science, University of Gothenburg, Gothenburg, Sweden
| | - Henrik Aronsson
- Department of Biological and Environmental Sciences, University of Gothenburg, Gothenburg, Sweden
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31
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Tomovic K, Ilic BS, Smelcerovic A. Structure-Activity Relationship Analysis of Cocrystallized Gliptin-like Pyrrolidine, Trifluorophenyl, and Pyrimidine-2,4-Dione Dipeptidyl Peptidase-4 Inhibitors. J Med Chem 2021; 64:9639-9648. [PMID: 34190540 DOI: 10.1021/acs.jmedchem.1c00293] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
Abstract
Approved and potent reported dipeptidyl peptidase-4 (DPP-4) inhibitors with gliptin-like structures are classified here according to their structures and mechanisms of the inhibition in three groups: (i) those with pyrrolidine or analogs as P1 fragment with α-aminoacyl linker, (ii) structures with trifluorophenyl moiety or analogs as P1 fragment with β-aminobutanoyl linker, and (iii) DPP-4 inhibitors with pyrimidine-2,4-dione or analogs as P1' fragment. The structure-activity relationship analysis was performed for those whose cocrystallized structures with the enzyme were published. While inhibitors with pyrrolidine and trifluorophenyl moiety or analogs as P1 fragment bind in a similar way in S1, S2 and S2 extensive domains of the enzyme, the binding mode of pyrimidine-2,4-dione derivatives/analogs differs with additional interactions in S1' and S2' pockets. Three general schemes of fragmented gliptins and gliptin-like structures with the enzyme and protein-ligand interaction fingerprints were made, which might be useful in the creation of DPP-4 inhibitor's design strategies.
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Affiliation(s)
- Katarina Tomovic
- Department of Pharmacy, Faculty of Medicine, University of Nis, Bulevar Dr Zorana Djindjica 81, 18000 Nis, Serbia
| | - Budimir S Ilic
- Department of Chemistry, Faculty of Medicine, University of Nis, Bulevar Dr Zorana Djindjica 81, 18000 Nis, Serbia
| | - Andrija Smelcerovic
- Department of Chemistry, Faculty of Medicine, University of Nis, Bulevar Dr Zorana Djindjica 81, 18000 Nis, Serbia
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32
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Shi M, Zhao M, Wang L, Liu K, Li P, Liu J, Cai X, Chen L, Xu D. Exploring the stability of inhibitor binding to SIK2 using molecular dynamics simulation and binding free energy calculation. Phys Chem Chem Phys 2021; 23:13216-13227. [PMID: 34086021 DOI: 10.1039/d1cp00717c] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
Salt inducible kinase 2 (SIK2) is a calcium/calmodulin-dependent protein kinase-like kinase that is implicated in a variety of biological phenomena, including cellular metabolism, growth, and apoptosis. SIK2 is the key target for various cancers, including ovarian, breast, prostate, and lung cancers. Although potent inhibitors of SIK2 are being developed, their binding stability and functional role are not presently known. In this work, we studied the detailed interactions between SIK2 and four of its inhibitors, HG-9-91-01, KIN112, MRT67307, and MRT199665, using molecular docking, molecular dynamics simulation, binding free energy calculation, and interaction fingerprint analysis. Intermolecular interactions revealed that HG-9-91-01 and KIN112 have stronger interactions with SIK2 than those of MRT199665 and MRT67307. The key residues involved in binding with SIK2 are conserved among all four inhibitors. Our results explain the detailed interaction of SIK2 with its inhibitors at the molecular level, thus paving the way for the development of targeted efficient anti-cancer drugs.
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Affiliation(s)
- Mingsong Shi
- State Key Laboratory of Biotherapy/Collaborative Innovation Center of Biotherapy and Cancer Center, West China Hospital of Sichuan University, Chengdu, Sichuan 610041, China.
| | - Min Zhao
- State Key Laboratory of Biotherapy/Collaborative Innovation Center of Biotherapy and Cancer Center, West China Hospital of Sichuan University, Chengdu, Sichuan 610041, China.
| | - Lun Wang
- State Key Laboratory of Biotherapy/Collaborative Innovation Center of Biotherapy and Cancer Center, West China Hospital of Sichuan University, Chengdu, Sichuan 610041, China.
| | - Kongjun Liu
- State Key Laboratory of Biotherapy/Collaborative Innovation Center of Biotherapy and Cancer Center, West China Hospital of Sichuan University, Chengdu, Sichuan 610041, China.
| | - Penghui Li
- College of Chemistry, MOE Key Laboratory of Green Chemistry and Technology, Sichuan University, Chengdu, Sichuan 610064, China.
| | - Jiang Liu
- State Key Laboratory of Biotherapy/Collaborative Innovation Center of Biotherapy and Cancer Center, West China Hospital of Sichuan University, Chengdu, Sichuan 610041, China.
| | - Xiaoying Cai
- State Key Laboratory of Biotherapy/Collaborative Innovation Center of Biotherapy and Cancer Center, West China Hospital of Sichuan University, Chengdu, Sichuan 610041, China.
| | - Lijuan Chen
- State Key Laboratory of Biotherapy/Collaborative Innovation Center of Biotherapy and Cancer Center, West China Hospital of Sichuan University, Chengdu, Sichuan 610041, China.
| | - Dingguo Xu
- College of Chemistry, MOE Key Laboratory of Green Chemistry and Technology, Sichuan University, Chengdu, Sichuan 610064, China. and Research Center for Material Genome Engineering, Sichuan University, Chengdu, Sichuan 610065, China
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33
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Tran-Nguyen VK, Bret G, Rognan D. True Accuracy of Fast Scoring Functions to Predict High-Throughput Screening Data from Docking Poses: The Simpler the Better. J Chem Inf Model 2021; 61:2788-2797. [PMID: 34109796 DOI: 10.1021/acs.jcim.1c00292] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Hundreds of fast scoring functions have been developed over the last 20 years to predict binding free energies from three-dimensional structures of protein-ligand complexes. Despite numerous statistical promises, we believe that none of them has been properly validated for daily prospective high-throughput virtual screening studies, mostly because in silico screening challenges usually employ artificially built and biased datasets. We here carry out a fully unbiased evaluation of four scoring functions (Pafnucy, ΔvinaRF20, IFP, and GRIM) on an in-house developed data collection of experimental high-confidence screening data (LIT-PCBA) covering about 3 million data points on 15 diverse pharmaceutical targets. All four scoring functions were applied to rescore the docking poses of LIT-PCBA compounds in conditions mimicking exactly standard drug discovery scenarios and were compared in terms of propensity to enrich true binders in the top 1%-ranked hit lists. Interestingly, rescoring based on simple interaction fingerprints or interaction graphs outperforms state-of-the-art machine learning and deep learning scoring functions in most of the cases. The current study notably highlights the strong tendency of deep learning methods to predict affinity values within a very narrow range centered on the mean value of samples used for training. Moreover, it suggests that knowledge of pre-existing binding modes is the key to detecting the most potent binders.
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Affiliation(s)
- Viet-Khoa Tran-Nguyen
- Laboratoire d'Innovation Thérapeutique, UMR 7200 CNRS-Université de Strasbourg, 67400 Illkirch, France
| | - Guillaume Bret
- Laboratoire d'Innovation Thérapeutique, UMR 7200 CNRS-Université de Strasbourg, 67400 Illkirch, France
| | - Didier Rognan
- Laboratoire d'Innovation Thérapeutique, UMR 7200 CNRS-Université de Strasbourg, 67400 Illkirch, France
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34
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Qin T, Zhu Z, Wang XS, Xia J, Wu S. Computational representations of protein-ligand interfaces for structure-based virtual screening. Expert Opin Drug Discov 2021; 16:1175-1192. [PMID: 34011222 DOI: 10.1080/17460441.2021.1929921] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Abstract
Introduction: Structure-based virtual screening (SBVS) is an essential strategy for hit identification. SBVS primarily uses molecular docking, which exploits the protein-ligand binding mode and associated affinity score for compound ranking. Previous studies have shown that computational representation of protein-ligand interfaces and the later establishment of machine learning models are efficacious in improving the accuracy of SBVS.Areas covered: The authors review the computational methods for representing protein-ligand interfaces, which include the traditional ones that use deliberately designed fingerprints and descriptors and the more recent methods that automatically extract features with deep learning. The effects of these methods on the performance of machine learning models are briefly discussed. Additionally, case studies that applied various computational representations to machine learning are cited with remarks.Expert opinion: It has become a trend to extract binding features automatically by deep learning, which uses a completely end-to-end representation. However, there is still plenty of scope for improvement . The interpretability of deep-learning models, the organization of data management, the quantity and quality of available data, and the optimization of hyperparameters could impact the accuracy of feature extraction. In addition, other important structural factors such as water molecules and protein flexibility should be considered.
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Affiliation(s)
- Tong Qin
- State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Department of New Drug Research and Development, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Zihao Zhu
- State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Department of New Drug Research and Development, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Xiang Simon Wang
- Artificial Intelligence and Drug Discovery Core Laboratory for District of Columbia Center for AIDS Research (DC CFAR), Department of Pharmaceutical Sciences, College of Pharmacy, Howard University, U.S.A
| | - Jie Xia
- State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Department of New Drug Research and Development, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Song Wu
- State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Department of New Drug Research and Development, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
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35
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Santana K, do Nascimento LD, Lima e Lima A, Damasceno V, Nahum C, Braga RC, Lameira J. Applications of Virtual Screening in Bioprospecting: Facts, Shifts, and Perspectives to Explore the Chemo-Structural Diversity of Natural Products. Front Chem 2021; 9:662688. [PMID: 33996755 PMCID: PMC8117418 DOI: 10.3389/fchem.2021.662688] [Citation(s) in RCA: 31] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2021] [Accepted: 02/25/2021] [Indexed: 12/22/2022] Open
Abstract
Natural products are continually explored in the development of new bioactive compounds with industrial applications, attracting the attention of scientific research efforts due to their pharmacophore-like structures, pharmacokinetic properties, and unique chemical space. The systematic search for natural sources to obtain valuable molecules to develop products with commercial value and industrial purposes remains the most challenging task in bioprospecting. Virtual screening strategies have innovated the discovery of novel bioactive molecules assessing in silico large compound libraries, favoring the analysis of their chemical space, pharmacodynamics, and their pharmacokinetic properties, thus leading to the reduction of financial efforts, infrastructure, and time involved in the process of discovering new chemical entities. Herein, we discuss the computational approaches and methods developed to explore the chemo-structural diversity of natural products, focusing on the main paradigms involved in the discovery and screening of bioactive compounds from natural sources, placing particular emphasis on artificial intelligence, cheminformatics methods, and big data analyses.
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Affiliation(s)
- Kauê Santana
- Instituto de Biodiversidade, Universidade Federal do Oeste do Pará, Santarém, Brazil
| | | | - Anderson Lima e Lima
- Instituto de Ciências Exatas e Naturais, Universidade Federal do Pará, Belém, Brazil
| | - Vinícius Damasceno
- Instituto de Ciências Exatas e Naturais, Universidade Federal do Pará, Belém, Brazil
| | - Claudio Nahum
- Instituto de Ciências Exatas e Naturais, Universidade Federal do Pará, Belém, Brazil
| | | | - Jerônimo Lameira
- Instituto de Ciências Biológicas, Universidade Federal do Pará, Belém, Brazil
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36
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Kimber TB, Chen Y, Volkamer A. Deep Learning in Virtual Screening: Recent Applications and Developments. Int J Mol Sci 2021; 22:4435. [PMID: 33922714 PMCID: PMC8123040 DOI: 10.3390/ijms22094435] [Citation(s) in RCA: 65] [Impact Index Per Article: 21.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2021] [Revised: 04/13/2021] [Accepted: 04/14/2021] [Indexed: 01/03/2023] Open
Abstract
Drug discovery is a cost and time-intensive process that is often assisted by computational methods, such as virtual screening, to speed up and guide the design of new compounds. For many years, machine learning methods have been successfully applied in the context of computer-aided drug discovery. Recently, thanks to the rise of novel technologies as well as the increasing amount of available chemical and bioactivity data, deep learning has gained a tremendous impact in rational active compound discovery. Herein, recent applications and developments of machine learning, with a focus on deep learning, in virtual screening for active compound design are reviewed. This includes introducing different compound and protein encodings, deep learning techniques as well as frequently used bioactivity and benchmark data sets for model training and testing. Finally, the present state-of-the-art, including the current challenges and emerging problems, are examined and discussed.
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Affiliation(s)
| | | | - Andrea Volkamer
- In Silico Toxicology and Structural Bioinformatics, Institute of Physiology, Charité-Universitätsmedizin Berlin, Charitéplatz 1, 10117 Berlin, Germany; (T.B.K.); (Y.C.)
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37
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Bhadra A, Yeturu K. Site2Vec: a reference frame invariant algorithm for vector embedding of protein–ligand binding sites. MACHINE LEARNING: SCIENCE AND TECHNOLOGY 2021. [DOI: 10.1088/2632-2153/abad88] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
Abstract
Protein–ligand interactions are one of the fundamental types of molecular interactions in living systems. Ligands are small molecules that interact with protein molecules at specific regions on their surfaces called binding sites. Binding sites would also determine ADMET properties of a drug molecule. Tasks such as assessment of protein functional similarity and detection of side effects of drugs need identification of similar binding sites of disparate proteins across diverse pathways. To this end, methods for computing similarities between binding sites are still evolving and is an active area of research even today. Machine learning methods for similarity assessment require feature descriptors of binding sites. Traditional methods based on hand engineered motifs and atomic configurations are not scalable across several thousands of sites. In this regard, deep neural network algorithms are now deployed which can capture very complex input feature space. However, one fundamental challenge in applying deep learning to structures of binding sites is the input representation and the reference frame. We report here a novel algorithm, Site2Vec, that derives reference frame invariant vector embedding of a protein–ligand binding site. The method is based on pairwise distances between representative points and chemical compositions in terms of constituent amino acids of a site. The vector embedding serves as a locality sensitive hash function for proximity queries and determining similar sites. The method has been the top performer with more than 95% quality scores in extensive benchmarking studies carried over 10 data sets and against 23 other site comparison methods in the field. The algorithm serves for high throughput processing and has been evaluated for stability with respect to reference frame shifts, coordinate perturbations and residue mutations. We also provide the method as a standalone executable and a web service hosted at (http://services.iittp.ac.in/bioinfo/home).
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38
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Diallo BN, Swart T, Hoppe HC, Tastan Bishop Ö, Lobb K. Potential repurposing of four FDA approved compounds with antiplasmodial activity identified through proteome scale computational drug discovery and in vitro assay. Sci Rep 2021; 11:1413. [PMID: 33446838 PMCID: PMC7809352 DOI: 10.1038/s41598-020-80722-2] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2020] [Accepted: 12/01/2020] [Indexed: 12/14/2022] Open
Abstract
Malaria elimination can benefit from time and cost-efficient approaches for antimalarials such as drug repurposing. In this work, 796 DrugBank compounds were screened against 36 Plasmodium falciparum targets using QuickVina-W. Hits were selected after rescoring using GRaph Interaction Matching (GRIM) and ligand efficiency metrics: surface efficiency index (SEI), binding efficiency index (BEI) and lipophilic efficiency (LipE). They were further evaluated in Molecular dynamics (MD). Twenty-five protein-ligand complexes were finally retained from the 28,656 (36 × 796) dockings. Hit GRIM scores (0.58 to 0.78) showed their molecular interaction similarity to co-crystallized ligands. Minimum LipE (3), SEI (23) and BEI (7) were in at least acceptable thresholds for hits. Binding energies ranged from -6 to -11 kcal/mol. Ligands showed stability in MD simulation with good hydrogen bonding and favorable protein-ligand interactions energy (the poorest being -140.12 kcal/mol). In vitro testing showed 4 active compounds with two having IC50 values in the single-digit μM range.
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Affiliation(s)
- Bakary N'tji Diallo
- Research Unit in Bioinformatics (RUBi), Department of Biochemistry and Microbiology, Rhodes University, Grahamstown, 6140, South Africa
| | - Tarryn Swart
- Department of Biochemistry and Microbiology, Rhodes University, Grahamstown, 6140, South Africa
| | - Heinrich C Hoppe
- Department of Biochemistry and Microbiology, Rhodes University, Grahamstown, 6140, South Africa
| | - Özlem Tastan Bishop
- Research Unit in Bioinformatics (RUBi), Department of Biochemistry and Microbiology, Rhodes University, Grahamstown, 6140, South Africa
| | - Kevin Lobb
- Research Unit in Bioinformatics (RUBi), Department of Biochemistry and Microbiology, Rhodes University, Grahamstown, 6140, South Africa.
- Department of Chemistry, Rhodes University, Grahamstown, 6140, South Africa.
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Vázquez J, López M, Gibert E, Herrero E, Luque FJ. Merging Ligand-Based and Structure-Based Methods in Drug Discovery: An Overview of Combined Virtual Screening Approaches. Molecules 2020; 25:E4723. [PMID: 33076254 PMCID: PMC7587536 DOI: 10.3390/molecules25204723] [Citation(s) in RCA: 77] [Impact Index Per Article: 19.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2020] [Revised: 10/06/2020] [Accepted: 10/11/2020] [Indexed: 12/20/2022] Open
Abstract
Virtual screening (VS) is an outstanding cornerstone in the drug discovery pipeline. A variety of computational approaches, which are generally classified as ligand-based (LB) and structure-based (SB) techniques, exploit key structural and physicochemical properties of ligands and targets to enable the screening of virtual libraries in the search of active compounds. Though LB and SB methods have found widespread application in the discovery of novel drug-like candidates, their complementary natures have stimulated continued efforts toward the development of hybrid strategies that combine LB and SB techniques, integrating them in a holistic computational framework that exploits the available information of both ligand and target to enhance the success of drug discovery projects. In this review, we analyze the main strategies and concepts that have emerged in the last years for defining hybrid LB + SB computational schemes in VS studies. Particularly, attention is focused on the combination of molecular similarity and docking, illustrating them with selected applications taken from the literature.
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Affiliation(s)
- Javier Vázquez
- Pharmacelera, Plaça Pau Vila, 1, Sector C 2a, Edificio Palau de Mar, 08039 Barcelona, Spain;
- Department of Nutrition, Food Science and Gastronomy, Faculty of Pharmacy and Food Sciences, Institute of Biomedicine (IBUB), and Institute of Theoretical and Computational Chemistry (IQTC-UB), University of Barcelona, Av. Prat de la Riba 171, E-08921 Santa Coloma de Gramanet, Spain
| | - Manel López
- AB Science, Parc Scientifique de Luminy, Zone Luminy Enterprise, Case 922, 163 Av. de Luminy, 13288 Marseille, France;
| | - Enric Gibert
- Pharmacelera, Plaça Pau Vila, 1, Sector C 2a, Edificio Palau de Mar, 08039 Barcelona, Spain;
| | - Enric Herrero
- Pharmacelera, Plaça Pau Vila, 1, Sector C 2a, Edificio Palau de Mar, 08039 Barcelona, Spain;
| | - F. Javier Luque
- Department of Nutrition, Food Science and Gastronomy, Faculty of Pharmacy and Food Sciences, Institute of Biomedicine (IBUB), and Institute of Theoretical and Computational Chemistry (IQTC-UB), University of Barcelona, Av. Prat de la Riba 171, E-08921 Santa Coloma de Gramanet, Spain
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40
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Benchmarking Data Sets from PubChem BioAssay Data: Current Scenario and Room for Improvement. Int J Mol Sci 2020; 21:ijms21124380. [PMID: 32575564 PMCID: PMC7352161 DOI: 10.3390/ijms21124380] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2020] [Revised: 06/15/2020] [Accepted: 06/18/2020] [Indexed: 11/17/2022] Open
Abstract
Developing realistic data sets for evaluating virtual screening methods is a task that has been tackled by the cheminformatics community for many years. Numerous artificially constructed data collections were developed, such as DUD, DUD-E, or DEKOIS. However, they all suffer from multiple drawbacks, one of which is the absence of experimental results confirming the impotence of presumably inactive molecules, leading to possible false negatives in the ligand sets. In light of this problem, the PubChem BioAssay database, an open-access repository providing the bioactivity information of compounds that were already tested on a biological target, is now a recommended source for data set construction. Nevertheless, there exist several issues with the use of such data that need to be properly addressed. In this article, an overview of benchmarking data collections built upon experimental PubChem BioAssay input is provided, along with a thorough discussion of noteworthy issues that one must consider during the design of new ligand sets from this database. The points raised in this review are expected to guide future developments in this regard, in hopes of offering better evaluation tools for novel in silico screening procedures.
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Adasme MF, Parisi D, Van Belle K, Salentin S, Haupt VJ, Jennings GS, Heinrich JC, Herman J, Sprangers B, Louat T, Moreau Y, Schroeder M. Structure-based drug repositioning explains ibrutinib as VEGFR2 inhibitor. PLoS One 2020; 15:e0233089. [PMID: 32459810 PMCID: PMC7252619 DOI: 10.1371/journal.pone.0233089] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2020] [Accepted: 04/28/2020] [Indexed: 11/18/2022] Open
Abstract
Many drugs are promiscuous and bind to multiple targets. On the one hand, these targets may be linked to unwanted side effects, but on the other, they may achieve a combined desired effect (polypharmacology) or represent multiple diseases (drug repositioning). With the growth of 3D structures of drug-target complexes, it is today possible to study drug promiscuity at the structural level and to screen vast amounts of drug-target interactions to predict side effects, polypharmacological potential, and repositioning opportunities. Here, we pursue such an approach to identify drugs inactivating B-cells, whose dysregulation can function as a driver of autoimmune diseases. Screening over 500 kinases, we identified 22 candidate targets, whose knock out impeded the activation of B-cells. Among these 22 is the gene KDR, whose gene product VEGFR2 is a prominent cancer target with anti-VEGFR2 drugs on the market for over a decade. The main result of this paper is that structure-based drug repositioning for the identified kinase targets identified the cancer drug ibrutinib as micromolar VEGFR2 inhibitor with a very high therapeutic index in B-cell inactivation. These findings prove that ibrutinib is not only acting on the Bruton’s tyrosine kinase BTK, against which it was designed. Instead, it may be a polypharmacological drug, which additionally targets angiogenesis via inhibition of VEGFR2. Therefore ibrutinib carries potential to treat other VEGFR2 associated disease. Structure-based drug repositioning explains ibrutinib’s anti VEGFR2 action through the conservation of a specific pattern of interactions of the drug with BTK and VEGFR2. Overall, structure-based drug repositioning was able to predict these findings at a fraction of the time and cost of a conventional screen.
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Affiliation(s)
- Melissa F. Adasme
- Biotechnology Center (BIOTEC), Technische Universität Dresden, Dresden, Germany
| | | | | | - Sebastian Salentin
- Biotechnology Center (BIOTEC), Technische Universität Dresden, Dresden, Germany
| | - V. Joachim Haupt
- Biotechnology Center (BIOTEC), Technische Universität Dresden, Dresden, Germany
- PharmAI GmbH, Dresden, Germany
| | - Gary S. Jennings
- Biotechnology Center (BIOTEC), Technische Universität Dresden, Dresden, Germany
| | | | - Jean Herman
- Interface Valorisation Platform (IVAP), KU Leuven, Leuven, Belgium
- Laboratory of Molecular Immunology (Rega institute), KU Leuven, Leuven, Belgium
- Department of Pediatric Nephrology and Solid Organ Transplantation, University Hospitals Leuven, Leuven, Belgium
| | - Ben Sprangers
- Interface Valorisation Platform (IVAP), KU Leuven, Leuven, Belgium
- Laboratory of Molecular Immunology (Rega institute), KU Leuven, Leuven, Belgium
- Department of Nephrology, University Hospitals Leuven, Leuven, Belgium
| | - Thierry Louat
- Interface Valorisation Platform (IVAP), KU Leuven, Leuven, Belgium
| | | | - Michael Schroeder
- Biotechnology Center (BIOTEC), Technische Universität Dresden, Dresden, Germany
- * E-mail:
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Zhao Z, Bourne PE. Revealing Acquired Resistance Mechanisms of Kinase-Targeted Drugs Using an on-the-Fly, Function-Site Interaction Fingerprint Approach. J Chem Theory Comput 2020; 16:3152-3161. [PMID: 32283024 DOI: 10.1021/acs.jctc.9b01134] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
Although kinase-targeted drugs have achieved significant clinical success, they are frequently subject to the limitations of drug resistance, which has become a primary vulnerability to targeted drug therapy. Therefore, deciphering resistance mechanisms is an important step in designing more efficacious, antiresistant drugs. Here we studied two FDA-approved kinase drugs: Crizotinib and Ceritinib, which are first- and second-generation anaplastic lymphoma kinase (ALK) targeted inhibitors, to unravel drug-resistance mechanisms. We used an on-the-fly, function-site interaction fingerprint (on-the-fly Fs-IFP) approach, combining binding free-energy surface calculations with the Fs-IFPs. Establishing the potentials of mean force and monitoring the atomic-scale protein-ligand interactions, before and after L1196M-induced drug resistance, revealed insights into drug-resistance/antiresistant mechanisms. Crizotinib prefers to bind the wild-type ALK kinase domain, whereas Ceritinib binds more favorably to the mutated ALK kinase domain, in agreement with experimental results. We determined that ALK kinase-drug interactions in the region of the front pocket are associated with drug resistance. Additionally, we find that the L1196M mutation does not simply alter the binding modes of inhibitors but also affects the flexibility of the entire ALK kinase domain. Our work provides an understanding of the mechanisms of ALK drug resistance, confirms the usefulness of the on-the-fly Fs-IFP approach, and provides a practical paradigm to study drug-resistance mechanisms in prospective drug discovery.
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Tran-Nguyen VK, Jacquemard C, Rognan D. LIT-PCBA: An Unbiased Data Set for Machine Learning and Virtual Screening. J Chem Inf Model 2020; 60:4263-4273. [DOI: 10.1021/acs.jcim.0c00155] [Citation(s) in RCA: 42] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Affiliation(s)
- Viet-Khoa Tran-Nguyen
- Laboratoire d’Innovation Thérapeutique, UMR 7200 CNRS-Université de Strasbourg, 67400 Illkirch, France
| | - Célien Jacquemard
- Laboratoire d’Innovation Thérapeutique, UMR 7200 CNRS-Université de Strasbourg, 67400 Illkirch, France
| | - Didier Rognan
- Laboratoire d’Innovation Thérapeutique, UMR 7200 CNRS-Université de Strasbourg, 67400 Illkirch, France
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Parisi D, Adasme MF, Sveshnikova A, Bolz SN, Moreau Y, Schroeder M. Drug repositioning or target repositioning: A structural perspective of drug-target-indication relationship for available repurposed drugs. Comput Struct Biotechnol J 2020; 18:1043-1055. [PMID: 32419905 PMCID: PMC7215100 DOI: 10.1016/j.csbj.2020.04.004] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2019] [Revised: 03/31/2020] [Accepted: 04/04/2020] [Indexed: 12/18/2022] Open
Abstract
Drug repositioning aims to find new indications for existing drugs in order to reduce drug development cost and time. Currently,there are numerous stories of successful drug repositioning that have been reported and many repurposed drugs are already available on the market. Although drug repositioning is often a product of serendipity, repositioning opportunities can be uncovered systematically. There are three systematic approaches to drug repositioning: disease-centric approach, target-centric and drug-centric. Disease-centric approaches identify close relationships between an old and a new indication. A target-centric approach links a known target and its established drug to a new indication. Lastly, a drug-centric approach connects a known drug to a new target and its associated indication. These three approaches differ in their potential and their limitations, but above all else, in the required start information and computing power. This raises the question of which approach prevails in current drug discovery and what that implies for future developments. To address this question, we systematically evaluated over 100 drugs, 200 target structures and over 300 indications from the Drug Repositioning Database. Each analyzed case was classified as one of the three repositioning approaches. For the majority of cases (more than 60%) the disease-centric definition was assigned. Almost 30% of the cases were classified as target-centric and less than 10% as drug-centric approaches. We concluded that, despite the use of umbrella term “drug” repositioning, disease- and target-centric approaches have dominated the field until now. We propose the use of drug-centric approaches while discussing reasons, such as structure-based repositioning techniques, to exploit the full potential of drug-target-disease connections.
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Affiliation(s)
| | - Melissa F Adasme
- Biotechnology Center (BIOTEC), Technische Universität Dresden, 01307 Dresden, Germany
| | - Anastasia Sveshnikova
- Biotechnology Center (BIOTEC), Technische Universität Dresden, 01307 Dresden, Germany
| | | | - Yves Moreau
- ESAT-STADIUS, KU Leuven, B-3001 Heverlee, Belgium
| | - Michael Schroeder
- Biotechnology Center (BIOTEC), Technische Universität Dresden, 01307 Dresden, Germany
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45
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Katigbak J, Li H, Rooklin D, Zhang Y. AlphaSpace 2.0: Representing Concave Biomolecular Surfaces Using β-Clusters. J Chem Inf Model 2020; 60:1494-1508. [PMID: 31995373 PMCID: PMC7093224 DOI: 10.1021/acs.jcim.9b00652] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
Abstract
Modern rational modulator design and structure-function characterization often concentrate on concave regions of biomolecular surfaces, ranging from well-defined small-molecule binding sites to large protein-protein interaction interfaces. Here, we introduce a β-cluster as a pseudomolecular representation of fragment-centric pockets detected by AlphaSpace [J. Chem. Inf. Model. 2015, 55, 1585], a recently developed computational analysis tool for topographical mapping of biomolecular concavities. By mimicking the shape as well as atomic details of potential molecular binders, this new β-cluster representation allows direct pocket-to-ligand shape comparison and can be used to guide ligand optimization. Furthermore, we defined the β-score, the optimal Vina score of the β-cluster, as an indicator of pocket ligandability and developed an ensemble β-cluster approach, which allows one-to-one pocket mapping and comparison among aligned protein structures. We demonstrated the utility of β-cluster representation by applying the approach to a wide variety of problems including binding site detection and comparison, characterization of protein-protein interactions, and fragment-based ligand optimization. These new β-cluster functionalities have been implemented in AlphaSpace 2.0, which is freely available on the web at http://www.nyu.edu/projects/yzhang/AlphaSpace2.
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Affiliation(s)
- Joseph Katigbak
- Department of Chemistry, New York University, New York, New York 10003, United States
| | - Haotian Li
- Department of Chemistry, New York University, New York, New York 10003, United States
| | - David Rooklin
- Department of Chemistry, New York University, New York, New York 10003, United States
| | - Yingkai Zhang
- Department of Chemistry, New York University, New York, New York 10003, United States
- NYU-ECNU Center for Computational Chemistry at NYU Shanghai, Shanghai 200062, China
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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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Zhang Y, Zheng G, Fu T, Hong J, Li F, Yao X, Xue W, Zhu F. The binding mode of vilazodone in the human serotonin transporter elucidated by ligand docking and molecular dynamics simulations. Phys Chem Chem Phys 2020; 22:5132-5144. [PMID: 32073004 DOI: 10.1039/c9cp05764a] [Citation(s) in RCA: 31] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
Vilazodone is a novel antidepressant used for the treatment of major depressive disorder (MDD) with a primary action mechanism of inhibiting the human serotonin reuptake transporter (hSERT) and acting as a 5-HT1A receptor partial agonist. The interaction between vilazodone and the 5-HT1A receptor has been reported, however, the binding mode of vilazodone in the hSERT remains elusive. In the current study, to elucidate the molecular mechanism of vilazodone binding in the hSERT, the drug and its five analogs were docked into the hSERT crystal structure as initial conformations and were sampled by 400 ns molecular dynamics (MD) simulations. Through the analysis of the profiles of protein-ligand binding free energies, interaction fingerprints, and conformational rearrangements, the binding mode of vilazodone in the hSERT was revealed. As a result, unlike the classical antidepressants located in the S1 site of the hSERT, vilazodone adopted a linear pose in the binding pocket. Its arylpiperazine fragment occupies the central site (S1) and interacts with Y95, D98, I172, Y176, F335, F341, S438, and T439, while the indole fragment extends to the allosteric site (S2) via interacting with the ionic switch (R104/E403) between the two sites. The new insights obtained are not only helpful in understanding the binding mode of vilazodone in the hSERT, but also provide valuable guidance to the discovery of novel antidepressant drugs.
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Affiliation(s)
- Yang Zhang
- School of Pharmaceutical Sciences, Chongqing University, Chongqing 401331, China.
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Adasme MF, Parisi D, Sveshnikova A, Schroeder M. Structure-based drug repositioning: Potential and limits. Semin Cancer Biol 2020; 68:192-198. [PMID: 32032699 DOI: 10.1016/j.semcancer.2020.01.010] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2019] [Revised: 01/08/2020] [Accepted: 01/16/2020] [Indexed: 12/28/2022]
Abstract
Drug repositioning, the assignment of new therapeutic purposes to known drugs, is an established strategy with many repurposed drugs on the market and many more at experimental stage. We review three use cases, a herpes drug with benefits in cancer, a cancer drug with potential in autoimmune disease, and a selective and an unspecific drug binding the same target (GPCR). We explore these use cases from a structural point of view focusing on a deep understanding of the underlying drug-target interactions. We review tools and data needed for such a drug-centric structural repositioning approach. Finally, we show that the availability of data on targets is an important limiting factor to realize the full potential of structural drug-repositioning.
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Affiliation(s)
- Melissa F Adasme
- Biotechnology Center (BIOTEC), Technische Universität Dresden, 01307 Dresden, Germany
| | - Daniele Parisi
- Biotechnology Center (BIOTEC), Technische Universität Dresden, 01307 Dresden, Germany; ESAT-STADIUS, KU Leuven, B-3001 Heverlee, Belgium
| | - Anastasia Sveshnikova
- Biotechnology Center (BIOTEC), Technische Universität Dresden, 01307 Dresden, Germany
| | - Michael Schroeder
- Biotechnology Center (BIOTEC), Technische Universität Dresden, 01307 Dresden, Germany.
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Naderi M, Lemoine JM, Govindaraj RG, Kana OZ, Feinstein WP, Brylinski M. Binding site matching in rational drug design: algorithms and applications. Brief Bioinform 2019; 20:2167-2184. [PMID: 30169563 PMCID: PMC6954434 DOI: 10.1093/bib/bby078] [Citation(s) in RCA: 25] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2018] [Revised: 07/18/2018] [Accepted: 07/29/2018] [Indexed: 01/06/2023] Open
Abstract
Interactions between proteins and small molecules are critical for biological functions. These interactions often occur in small cavities within protein structures, known as ligand-binding pockets. Understanding the physicochemical qualities of binding pockets is essential to improve not only our basic knowledge of biological systems, but also drug development procedures. In order to quantify similarities among pockets in terms of their geometries and chemical properties, either bound ligands can be compared to one another or binding sites can be matched directly. Both perspectives routinely take advantage of computational methods including various techniques to represent and compare small molecules as well as local protein structures. In this review, we survey 12 tools widely used to match pockets. These methods are divided into five categories based on the algorithm implemented to construct binding-site alignments. In addition to the comprehensive analysis of their algorithms, test sets and the performance of each method are described. We also discuss general pharmacological applications of computational pocket matching in drug repurposing, polypharmacology and side effects. Reflecting on the importance of these techniques in drug discovery, in the end, we elaborate on the development of more accurate meta-predictors, the incorporation of protein flexibility and the integration of powerful artificial intelligence technologies such as deep learning.
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Affiliation(s)
- Misagh Naderi
- Department of Biological Sciences, Louisiana State University, Baton Rouge, LA 70803, USA
| | - Jeffrey Mitchell Lemoine
- Department of Biological Sciences, Louisiana State University, Baton Rouge, LA 70803, USA
- Division of Computer Science and Engineering, Louisiana State University, Baton Rouge, LA 70803, USA
| | | | - Omar Zade Kana
- Department of Biological Sciences, Louisiana State University, Baton Rouge, LA 70803, USA
| | - Wei Pan Feinstein
- High-Performance Computing, Louisiana State University, Baton Rouge, LA 70803, USA
| | - Michal Brylinski
- Department of Biological Sciences, Louisiana State University, Baton Rouge, LA 70803, USA
- Center for Computation & Technology, Louisiana State University, Baton Rouge, LA 70803, USA
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50
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Wang Z, Ji H. Targeting the Side-Chain Convergence of Hydrophobic α-Helical Hot Spots To Design Small-Molecule Mimetics: Key Binding Features for i, i + 3, and i + 7. J Med Chem 2019; 62:9906-9917. [PMID: 31593458 DOI: 10.1021/acs.jmedchem.9b01324] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
The conformational convergence of hydrophobic α-helical hot spots was revealed by analyzing α-helix-mediated protein-protein interaction (PPI) complex structures. The pharmacophore models were derived for hydrophobic α-helical hot spots at positions i, i + 3, and i + 7. These provide the foundation for designing generalizable scaffolds that can directly mimic the binding mode of the side chains of α-helical hot spots, offering a new class of small-molecule α-helix mimetics. For the first time, the protocol was developed to identify the PPI targets that have similar binding pockets, allowing evaluation of inhibitor selectivities between α-helix-mediated PPIs. The mimicry efficiency of the previously designed scaffold 1 was disclosed. The close positioning of this small molecule to the additional α-helical hot spots suggests that the decoration of this series of generalizable scaffolds can conveniently reach the binding pockets of additional α-helical hot spots to produce potent small-molecule inhibitors for α-helix-mediated PPIs.
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Affiliation(s)
- Zhen Wang
- Drug Discovery Department , H. Lee Moffitt Cancer Center & Research Institute , 12902 Magnolia Drive , Tampa , Florida 33612-9497 , United States.,Departments of Chemistry and Oncologic Sciences , University of South Florida , Tampa , Florida 33620-9497 , United States
| | - Haitao Ji
- Drug Discovery Department , H. Lee Moffitt Cancer Center & Research Institute , 12902 Magnolia Drive , Tampa , Florida 33612-9497 , United States.,Departments of Chemistry and Oncologic Sciences , University of South Florida , Tampa , Florida 33620-9497 , United States
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