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Carpenter KA, Altman RB. Databases of ligand-binding pockets and protein-ligand interactions. Comput Struct Biotechnol J 2024; 23:1320-1338. [PMID: 38585646 PMCID: PMC10997877 DOI: 10.1016/j.csbj.2024.03.015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2024] [Revised: 03/16/2024] [Accepted: 03/17/2024] [Indexed: 04/09/2024] Open
Abstract
Many research groups and institutions have created a variety of databases curating experimental and predicted data related to protein-ligand binding. The landscape of available databases is dynamic, with new databases emerging and established databases becoming defunct. Here, we review the current state of databases that contain binding pockets and protein-ligand binding interactions. We have compiled a list of such databases, fifty-three of which are currently available for use. We discuss variation in how binding pockets are defined and summarize pocket-finding methods. We organize the fifty-three databases into subgroups based on goals and contents, and describe standard use cases. We also illustrate that pockets within the same protein are characterized differently across different databases. Finally, we assess critical issues of sustainability, accessibility and redundancy.
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Affiliation(s)
- Kristy A. Carpenter
- Department of Biomedical Data Science, Stanford University, Stanford, CA 94305, USA
| | - Russ B. Altman
- Department of Biomedical Data Science, Stanford University, Stanford, CA 94305, USA
- Department of Bioengineering, Stanford University, Stanford, CA 94305, USA
- Department of Genetics, Stanford University, Stanford, CA 94305, USA
- Department of Medicine, Stanford University, Stanford, CA 94305, USA
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2
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Wahl J. PheSA: An Open-Source Tool for Pharmacophore-Enhanced Shape Alignment. J Chem Inf Model 2024; 64:5944-5953. [PMID: 39092495 DOI: 10.1021/acs.jcim.4c00516] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/04/2024]
Abstract
PheSA is an open-source pharmacophore- and shape-based screening and molecular alignment tool that is fully open-source as part of OpenChemLib. Supporting standard ligand-based screening, flexible refinement of alignments, and receptor-guided shape docking, PheSA is a very flexible tool and can be used for different use cases in structure-based drug design. We present the algorithm and different benchmark studies that investigate the screening performance and also the quality of the generated alignments and the pose prediction performance of the receptor-guided PheSA algorithm. An important finding is the effect of the type of similarity metric used for measuring screening enrichment (symmetric Tanimoto versus asymmetric Tversky), whereby we could observe improved enrichment rates by using Tversky. PheSA exhibits enrichments on the DUD-E that are on par with commercial methods.
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Affiliation(s)
- Joel Wahl
- Scientific Computing Drug Discovery, Idorsia Pharmaceuticals Ltd, Hegenheimermattweg 91, CH-4123 Allschwil, Switzerland
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3
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Eguida M, Bret G, Sindt F, Li F, Chau I, Ackloo S, Arrowsmith C, Bolotokova A, Ghiabi P, Gibson E, Halabelian L, Houliston S, Harding RJ, Hutchinson A, Loppnau P, Perveen S, Seitova A, Zeng H, Schapira M, Rognan D. Subpocket Similarity-Based Hit Identification for Challenging Targets: Application to the WDR Domain of LRRK2. J Chem Inf Model 2024; 64:5344-5355. [PMID: 38916159 DOI: 10.1021/acs.jcim.4c00601] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/26/2024]
Abstract
We herewith applied a priori a generic hit identification method (POEM) for difficult targets of known three-dimensional structure, relying on the simple knowledge of physicochemical and topological properties of a user-selected cavity. Searching for local similarity to a set of fragment-bound protein microenvironments of known structure, a point cloud registration algorithm is first applied to align known subpockets to the target cavity. The resulting alignment then permits us to directly pose the corresponding seed fragments in a target cavity space not typically amenable to classical docking approaches. Last, linking potentially connectable atoms by a deep generative linker enables full ligand enumeration. When applied to the WD40 repeat (WDR) central cavity of leucine-rich repeat kinase 2 (LRRK2), an unprecedented binding site, POEM was able to quickly propose 94 potential hits, five of which were subsequently confirmed to bind in vitro to LRRK2-WDR.
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Affiliation(s)
- Merveille Eguida
- Laboratoire d'innovation thérapeutique, UMR7200 CNRS-Université de Strasbourg, F-67400 Illkirch, Strasbourg, France
| | - Guillaume Bret
- Laboratoire d'innovation thérapeutique, UMR7200 CNRS-Université de Strasbourg, F-67400 Illkirch, Strasbourg, France
| | - François Sindt
- Laboratoire d'innovation thérapeutique, UMR7200 CNRS-Université de Strasbourg, F-67400 Illkirch, Strasbourg, France
| | - Fengling Li
- Structural Genomics Consortium, University of Toronto, Toronto, ON M5G 1L7, Canada
| | - Irene Chau
- Structural Genomics Consortium, University of Toronto, Toronto, ON M5G 1L7, Canada
| | - Suzanne Ackloo
- Structural Genomics Consortium, University of Toronto, Toronto, ON M5G 1L7, Canada
| | - Cheryl Arrowsmith
- Structural Genomics Consortium, University of Toronto, Toronto, ON M5G 1L7, Canada
- Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario M5G 1L7, Canada
| | - Albina Bolotokova
- Structural Genomics Consortium, University of Toronto, Toronto, ON M5G 1L7, Canada
| | - Pegah Ghiabi
- Structural Genomics Consortium, University of Toronto, Toronto, ON M5G 1L7, Canada
| | - Elisa Gibson
- Structural Genomics Consortium, University of Toronto, Toronto, ON M5G 1L7, Canada
| | - Levon Halabelian
- Structural Genomics Consortium, University of Toronto, Toronto, ON M5G 1L7, Canada
- Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario M5G 1L7, Canada
| | - Scott Houliston
- Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario M5G 1L7, Canada
| | - Rachel J Harding
- Structural Genomics Consortium, University of Toronto, Toronto, ON M5G 1L7, Canada
- Department of Pharmacology & Toxicology, University of Toronto, Toronto, ON M5G 1L7, Canada
| | - Ashley Hutchinson
- Structural Genomics Consortium, University of Toronto, Toronto, ON M5G 1L7, Canada
| | - Peter Loppnau
- Structural Genomics Consortium, University of Toronto, Toronto, ON M5G 1L7, Canada
| | - Sumera Perveen
- Structural Genomics Consortium, University of Toronto, Toronto, ON M5G 1L7, Canada
| | - Almagul Seitova
- Structural Genomics Consortium, University of Toronto, Toronto, ON M5G 1L7, Canada
| | - Hong Zeng
- Structural Genomics Consortium, University of Toronto, Toronto, ON M5G 1L7, Canada
| | - Matthieu Schapira
- Structural Genomics Consortium, University of Toronto, Toronto, ON M5G 1L7, Canada
- Department of Pharmacology & Toxicology, University of Toronto, Toronto, ON M5G 1L7, Canada
| | - Didier Rognan
- Laboratoire d'innovation thérapeutique, UMR7200 CNRS-Université de Strasbourg, F-67400 Illkirch, Strasbourg, France
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4
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Zhou R, Fan J, Li S, Zeng W, Chen Y, Zheng X, Chen H, Liao J. LVPocket: integrated 3D global-local information to protein binding pockets prediction with transfer learning of protein structure classification. J Cheminform 2024; 16:79. [PMID: 38972994 PMCID: PMC11229186 DOI: 10.1186/s13321-024-00871-8] [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: 12/08/2023] [Accepted: 06/12/2024] [Indexed: 07/09/2024] Open
Abstract
BACKGROUND Previous deep learning methods for predicting protein binding pockets mainly employed 3D convolution, yet an abundance of convolution operations may lead the model to excessively prioritize local information, thus overlooking global information. Moreover, it is essential for us to account for the influence of diverse protein folding structural classes. Because proteins classified differently structurally exhibit varying biological functions, whereas those within the same structural class share similar functional attributes. RESULTS We proposed LVPocket, a novel method that synergistically captures both local and global information of protein structure through the integration of Transformer encoders, which help the model achieve better performance in binding pockets prediction. And then we tailored prediction models for data of four distinct structural classes of proteins using the transfer learning. The four fine-tuned models were trained on the baseline LVPocket model which was trained on the sc-PDB dataset. LVPocket exhibits superior performance on three independent datasets compared to current state-of-the-art methods. Additionally, the fine-tuned model outperforms the baseline model in terms of performance. SCIENTIFIC CONTRIBUTION We present a novel model structure for predicting protein binding pockets that provides a solution for relying on extensive convolutional computation while neglecting global information about protein structures. Furthermore, we tackle the impact of different protein folding structures on binding pocket prediction tasks through the application of transfer learning methods.
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Affiliation(s)
- Ruifeng Zhou
- School of Science, China Pharmaceutical University, Nanjing, 210009, Jiangsu, People's Republic of China
| | - Jing Fan
- School of Science, China Pharmaceutical University, Nanjing, 210009, Jiangsu, People's Republic of China
| | - Sishu Li
- School of Science, China Pharmaceutical University, Nanjing, 210009, Jiangsu, People's Republic of China
| | - Wenjie Zeng
- School of Science, China Pharmaceutical University, Nanjing, 210009, Jiangsu, People's Republic of China
| | - Yilun Chen
- School of Science, China Pharmaceutical University, Nanjing, 210009, Jiangsu, People's Republic of China
| | - Xiaoshan Zheng
- School of Science, China Pharmaceutical University, Nanjing, 210009, Jiangsu, People's Republic of China
| | - Hongyang Chen
- Research Center for Graph Computing, Zhejiang Lab, Hangzhou, 311121, Zhejiang, People's Republic of China.
| | - Jun Liao
- School of Science, China Pharmaceutical University, Nanjing, 210009, Jiangsu, People's Republic of China.
- Zhejiang Lab, Hangzhou, 311121, Zhejiang, People's Republic of China.
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5
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Mareuil F, Moine-Franel A, Kar A, Nilges M, Ciambur CB, Sperandio O. Protein interaction explorer (PIE): a comprehensive platform for navigating protein-protein interactions and ligand binding pockets. Bioinformatics 2024; 40:btae414. [PMID: 38917415 PMCID: PMC11223782 DOI: 10.1093/bioinformatics/btae414] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2024] [Revised: 06/12/2024] [Accepted: 06/24/2024] [Indexed: 06/27/2024] Open
Abstract
SUMMARY Protein Interaction Explorer (PIE) is a new web-based tool integrated to our database iPPI-DB, specifically crafted to support structure-based drug discovery initiatives focused on protein-protein interactions (PPIs). Drawing upon extensive structural data encompassing thousands of heterodimer complexes, including those with successful ligands, PIE provides a comprehensive suite of tools dedicated to aid decision-making in PPI drug discovery. PIE enables researchers/bioinformaticians to identify and characterize crucial factors such as the presence of binding pockets or functional binding sites at the interface, predicting hot spots, and foreseeing similar protein-embedded pockets for potential repurposing efforts. AVAILABILITY AND IMPLEMENTATION PIE is user-friendly and readily accessible at https://ippidb.pasteur.fr/targetcentric/. It relies on the NGL visualizer.
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Affiliation(s)
- Fabien Mareuil
- Bioinformatics and Biostatistics Hub, Département Biologie Computationnelle, Institut Pasteur, USR 3756 CNRS, 75015 Paris, France
| | - Alexandra Moine-Franel
- Structural Bioinformatics Unit, Department of Structural Biology and Chemistry, Institut Pasteur, Université de Paris, CNRS UMR3528, 75015 Paris, France
- Collège Doctoral, Sorbonne Université, 75005 Paris, France
| | - Anuradha Kar
- Structural Bioinformatics Unit, Department of Structural Biology and Chemistry, Institut Pasteur, Université de Paris, CNRS UMR3528, 75015 Paris, France
| | - Michael Nilges
- Structural Bioinformatics Unit, Department of Structural Biology and Chemistry, Institut Pasteur, Université de Paris, CNRS UMR3528, 75015 Paris, France
| | - Constantin Bogdan Ciambur
- Structural Bioinformatics Unit, Department of Structural Biology and Chemistry, Institut Pasteur, Université de Paris, CNRS UMR3528, 75015 Paris, France
| | - Olivier Sperandio
- Structural Bioinformatics Unit, Department of Structural Biology and Chemistry, Institut Pasteur, Université de Paris, CNRS UMR3528, 75015 Paris, France
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6
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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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7
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Moine-Franel A, Mareuil F, Nilges M, Ciambur CB, Sperandio O. A comprehensive dataset of protein-protein interactions and ligand binding pockets for advancing drug discovery. Sci Data 2024; 11:402. [PMID: 38643260 PMCID: PMC11032347 DOI: 10.1038/s41597-024-03233-z] [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: 12/20/2023] [Accepted: 04/05/2024] [Indexed: 04/22/2024] Open
Abstract
This dataset represents a collection of pocket-centric structural data related to protein-protein interactions (PPIs) and PPI-related ligand binding sites. The dataset includes high-quality structural information on more than 23,000 pockets, 3,700 proteins on more than 500 organisms, and nearly 3500 ligands that can aid researchers in the fields of bioinformatics, structural biology, and drug discovery. It encompasses a diverse set of PPI complexes with more than 1,700 unique protein families including some with associated ligands, enabling detailed investigations into molecular interactions at the atomic level. This article introduces an indispensable resource designed to unlock the full potential of PPIs while pioneering a novel metric for pocket similarity for hypothesizing protein partners repurposing.
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Affiliation(s)
- Alexandra Moine-Franel
- Structural Bioinformatics Unit, Department of Structural Biology and Chemistry, Institut Pasteur, Université de Paris, CNRS UMR3528, Paris, France
- Collège Doctoral, Sorbonne Université, Paris, F-75005, France
| | - Fabien Mareuil
- Structural Bioinformatics Unit, Department of Structural Biology and Chemistry, Institut Pasteur, Université de Paris, CNRS UMR3528, Paris, France
| | - Michael Nilges
- Structural Bioinformatics Unit, Department of Structural Biology and Chemistry, Institut Pasteur, Université de Paris, CNRS UMR3528, Paris, France
| | - Constantin Bogdan Ciambur
- Structural Bioinformatics Unit, Department of Structural Biology and Chemistry, Institut Pasteur, Université de Paris, CNRS UMR3528, Paris, France
| | - Olivier Sperandio
- Structural Bioinformatics Unit, Department of Structural Biology and Chemistry, Institut Pasteur, Université de Paris, CNRS UMR3528, Paris, France.
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8
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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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9
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Carbery A, Buttenschoen M, Skyner R, von Delft F, Deane CM. Learnt representations of proteins can be used for accurate prediction of small molecule binding sites on experimentally determined and predicted protein structures. J Cheminform 2024; 16:32. [PMID: 38486231 PMCID: PMC10941399 DOI: 10.1186/s13321-024-00821-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2023] [Accepted: 03/01/2024] [Indexed: 03/17/2024] Open
Abstract
Protein-ligand binding site prediction is a useful tool for understanding the functional behaviour and potential drug-target interactions of a novel protein of interest. However, most binding site prediction methods are tested by providing crystallised ligand-bound (holo) structures as input. This testing regime is insufficient to understand the performance on novel protein targets where experimental structures are not available. An alternative option is to provide computationally predicted protein structures, but this is not commonly tested. However, due to the training data used, computationally-predicted protein structures tend to be extremely accurate, and are often biased toward a holo conformation. In this study we describe and benchmark IF-SitePred, a protein-ligand binding site prediction method which is based on the labelling of ESM-IF1 protein language model embeddings combined with point cloud annotation and clustering. We show that not only is IF-SitePred competitive with state-of-the-art methods when predicting binding sites on experimental structures, but it performs better on proxies for novel proteins where low accuracy has been simulated by molecular dynamics. Finally, IF-SitePred outperforms other methods if ensembles of predicted protein structures are generated.
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Affiliation(s)
- Anna Carbery
- Oxford Protein Informatics Group, Department of Statistics, University of Oxford, Oxford, OX1 3LB, UK
- Diamond Light Source, Harwell Science and Innovation Campus, Didcot, OX11 0DE, UK
| | - Martin Buttenschoen
- Oxford Protein Informatics Group, Department of Statistics, University of Oxford, Oxford, OX1 3LB, UK
| | - Rachael Skyner
- OMass Therapeutics, Building 4000, Chancellor Court, John Smith Drive, ARC Oxford, OX4 2GX, UK
| | - Frank von Delft
- Diamond Light Source, Harwell Science and Innovation Campus, Didcot, OX11 0DE, UK
- Centre for Medicines Discovery, University of Oxford, Oxford, OX3 7DQ, UK
- Research Complex at Harwell, Harwell Science and Innovation Campus, Didcot, OX11 0FA, United Kingdom
- Department of Biochemistry, University of Johannesburg, Johannesburg, 2006, South Africa
| | - Charlotte M Deane
- Oxford Protein Informatics Group, Department of Statistics, University of Oxford, Oxford, OX1 3LB, UK.
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10
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Liu Y, Li P, Tu S, Xu L. RefinePocket: An Attention-Enhanced and Mask-Guided Deep Learning Approach for Protein Binding Site Prediction. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2023; 20:3314-3321. [PMID: 37040253 DOI: 10.1109/tcbb.2023.3265640] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/19/2023]
Abstract
Protein binding site prediction is an important prerequisite task of drug discovery and design. While binding sites are very small, irregular and varied in shape, making the prediction very challenging. Standard 3D U-Net has been adopted to predict binding sites but got stuck with unsatisfactory prediction results, incomplete, out-of-bounds, or even failed. The reason is that this scheme is less capable of extracting the chemical interactions of the entire region and hardly takes into account the difficulty of segmenting complex shapes. In this paper, we propose a refined U-Net architecture, called RefinePocket, consisting of an attention-enhanced encoder and a mask-guided decoder. During encoding, taking binding site proposal as input, we employ Dual Attention Block (DAB) hierarchically to capture rich global information, exploring residue relationship and chemical correlations in spatial and channel dimensions respectively. Then, based on the enhanced representation extracted by the encoder, we devise Refine Block (RB) in the decoder to enable self-guided refinement of uncertain regions gradually, resulting in more precise segmentation. Experiments show that DAB and RB complement and promote each other, making RefinePocket has an average improvement of 10.02% on DCC and 4.26% on DVO compared with the state-of-the-art method on four test sets.
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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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Briand MA, Dreano L, Legehar A, Grazhdankin E, Ghemtio L, Xhaard H. Exploring cooperative molecular contacts using a PostgreSQL database system. Mol Inform 2023; 42:e2200235. [PMID: 36653303 DOI: 10.1002/minf.202200235] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2022] [Revised: 12/30/2022] [Accepted: 01/18/2023] [Indexed: 01/20/2023]
Abstract
Cooperative molecular contacts play an important role in protein structure and ligand binding. Here, we constructed a PostgreSQL database that stores structural information in the form of atomic environments and allows flexible mining of molecular contacts. Taking the Ser-His-Asp/Glu catalytic triad as a first test case, we demonstrate that the presence of a carboxylate oxygen atom in the vicinity of a His is associated with shorter Ser-OH..N-His bond in the PDB30 subset. We prospectively mine catalytic triads in unannotated proteins, suggesting catalytic functions for unannotated proteins. As a second test case, we demonstrate that this database system can include ligand atoms, represented by Sybyl atom types, by evaluating the proportion of counter-ions for ligand carboxylate oxygens.
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Affiliation(s)
- Mael A Briand
- Drug Research Program, Division of Pharmaceutical Chemistry and Technology, Faculty of Pharmacy, FI-00014 University of Helsinki, P.O. Box 56 (Viikinkaari 5 E), Helsinki, Finland
| | - Loïc Dreano
- Drug Research Program, Division of Pharmaceutical Chemistry and Technology, Faculty of Pharmacy, FI-00014 University of Helsinki, P.O. Box 56 (Viikinkaari 5 E), Helsinki, Finland
| | - Ashenafi Legehar
- Drug Research Program, Division of Pharmaceutical Chemistry and Technology, Faculty of Pharmacy, FI-00014 University of Helsinki, P.O. Box 56 (Viikinkaari 5 E), Helsinki, Finland
| | - Evgeni Grazhdankin
- Drug Research Program, Division of Pharmaceutical Chemistry and Technology, Faculty of Pharmacy, FI-00014 University of Helsinki, P.O. Box 56 (Viikinkaari 5 E), Helsinki, Finland
| | - Léo Ghemtio
- Drug Research Program, Division of Pharmaceutical Chemistry and Technology, Faculty of Pharmacy, FI-00014 University of Helsinki, P.O. Box 56 (Viikinkaari 5 E), Helsinki, Finland
| | - Henri Xhaard
- Drug Research Program, Division of Pharmaceutical Chemistry and Technology, Faculty of Pharmacy, FI-00014 University of Helsinki, P.O. Box 56 (Viikinkaari 5 E), Helsinki, Finland
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13
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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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14
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Eguida M, Schmitt-Valencia C, Hibert M, Villa P, Rognan D. Target-Focused Library Design by Pocket-Applied Computer Vision and Fragment Deep Generative Linking. J Med Chem 2022; 65:13771-13783. [PMID: 36256484 DOI: 10.1021/acs.jmedchem.2c00931] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
We here describe a computational approach (POEM: Pocket Oriented Elaboration of Molecules) to drive the generation of target-focused libraries while taking advantage of all publicly available structural information on protein-ligand complexes. A collection of 31 384 PDB-derived images with key shapes and pharmacophoric properties, describing fragment-bound microenvironments, is first aligned to the query target cavity by a computer vision method. The fragments of the most similar PDB subpockets are then directly positioned in the query cavity using the corresponding image transformation matrices. Lastly, suitable connectable atoms of oriented fragment pairs are linked by a deep generative model to yield fully connected molecules. POEM was applied to generate a library of 1.5 million potential cyclin-dependent kinase 8 inhibitors. By synthesizing and testing as few as 43 compounds, a few nanomolar inhibitors were quickly obtained with limited resources in just two iterative cycles.
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Affiliation(s)
- Merveille Eguida
- Laboratoire d'Innovation Thérapeutique, UMR7200 CNRS-Université de Strasbourg, F-67400Illkirch, France
| | - Christel Schmitt-Valencia
- Plateforme de Chimie Biologique Intégrative de Strasbourg, UAR3286 CNRS-Université de Strasbourg, Institut du Médicament de Strasbourg, F-67400Illkirch, France
| | - Marcel Hibert
- Laboratoire d'Innovation Thérapeutique, UMR7200 CNRS-Université de Strasbourg, F-67400Illkirch, France
| | - Pascal Villa
- Plateforme de Chimie Biologique Intégrative de Strasbourg, UAR3286 CNRS-Université de Strasbourg, Institut du Médicament de Strasbourg, F-67400Illkirch, France
| | - Didier Rognan
- Laboratoire d'Innovation Thérapeutique, UMR7200 CNRS-Université de Strasbourg, F-67400Illkirch, France
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15
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Radoux CJ, Vianello F, McGreig J, Desai N, Bradley AR. The druggable genome: Twenty years later. FRONTIERS IN BIOINFORMATICS 2022; 2:958378. [PMID: 36304325 PMCID: PMC9580872 DOI: 10.3389/fbinf.2022.958378] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2022] [Accepted: 08/15/2022] [Indexed: 11/13/2022] Open
Abstract
The concept of the druggable genome has been with us for 20 years. During this time, researchers have developed several methods and resources to help assess a target’s druggability. In parallel, evidence for target-disease associations has been collated at scale by Open Targets. More recently, the Protein Data Bank in Europe (PDBe) have built a knowledge base matching per-residue annotations with available protein structure. While each resource is useful in isolation, we believe there is enormous potential in bringing all relevant data into a single knowledge graph, from gene-level to protein residue. Automation is vital for the processing and assessment of all available structures. We have developed scalable, automated workflows that provide hotspot-based druggability assessments for all available structures across large numbers of targets. Ultimately, we will run our method at a proteome scale, an ambition made more realistic by the arrival of AlphaFold 2. Bringing together annotations from the residue up to the gene level and building connections within the graph to represent pathways or protein-protein interactions will create complexity that mirrors the biological systems they represent. Such complexity is difficult for the human mind to utilise effectively, particularly at scale. We believe that graph-based AI methods will be able to expertly navigate such a knowledge graph, selecting the targets of the future.
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16
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Aguti R, Gardini E, Bertazzo M, Decherchi S, Cavalli A. Probabilistic Pocket Druggability Prediction via One-Class Learning. Front Pharmacol 2022; 13:870479. [PMID: 35847005 PMCID: PMC9278401 DOI: 10.3389/fphar.2022.870479] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2022] [Accepted: 03/24/2022] [Indexed: 12/31/2022] Open
Abstract
The choice of target pocket is a key step in a drug discovery campaign. This step can be supported by in silico druggability prediction. In the literature, druggability prediction is often approached as a two-class classification task that distinguishes between druggable and non-druggable (or less druggable) pockets (or voxels). Apart from obvious cases, however, the non-druggable class is conceptually ambiguous. This is because any pocket (or target) is only non-druggable until a drug is found for it. It is therefore more appropriate to adopt a one-class approach, which uses only unambiguous information, namely, druggable pockets. Here, we propose using the import vector domain description (IVDD) algorithm to support this task. IVDD is a one-class probabilistic kernel machine that we previously introduced. To feed the algorithm, we use customized DrugPred descriptors computed via NanoShaper. Our results demonstrate the feasibility and effectiveness of the approach. In particular, we can remove or mitigate biases chiefly due to the labeling.
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Affiliation(s)
- Riccardo Aguti
- Computational and Chemical Biology, Fondazione Istituto Italiano di Tecnologia, Genoa, Italy.,Department of Pharmacy and Biotechnology, University of Bologna, Bologna, Italy
| | - Erika Gardini
- Computational and Chemical Biology, Fondazione Istituto Italiano di Tecnologia, Genoa, Italy.,Department of Pharmacy and Biotechnology, University of Bologna, Bologna, Italy
| | - Martina Bertazzo
- Computational and Chemical Biology, Fondazione Istituto Italiano di Tecnologia, Genoa, Italy
| | - Sergio Decherchi
- Computational and Chemical Biology, Fondazione Istituto Italiano di Tecnologia, Genoa, Italy
| | - Andrea Cavalli
- Computational and Chemical Biology, Fondazione Istituto Italiano di Tecnologia, Genoa, Italy.,Department of Pharmacy and Biotechnology, University of Bologna, Bologna, Italy
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17
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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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18
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Eguida M, Rognan D. Unexpected similarity between HIV-1 reverse transcriptase and tumor necrosis factor binding sites revealed by computer vision. J Cheminform 2021; 13:90. [PMID: 34814950 PMCID: PMC8609734 DOI: 10.1186/s13321-021-00567-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2021] [Accepted: 11/06/2021] [Indexed: 11/10/2022] Open
Abstract
Rationalizing the identification of hidden similarities across the repertoire of druggable protein cavities remains a major hurdle to a true proteome-wide structure-based discovery of novel drug candidates. We recently described a new computational approach (ProCare), inspired by numerical image processing, to identify local similarities in fragment-based subpockets. During the validation of the method, we unexpectedly identified a possible similarity in the binding pockets of two unrelated targets, human tumor necrosis factor alpha (TNF-α) and HIV-1 reverse transcriptase (HIV-1 RT). Microscale thermophoresis experiments confirmed the ProCare prediction as two of the three tested and FDA-approved HIV-1 RT inhibitors indeed bind to soluble human TNF-α trimer. Interestingly, the herein disclosed similarity could be revealed neither by state-of-the-art binding sites comparison methods nor by ligand-based pairwise similarity searches, suggesting that the point cloud registration approach implemented in ProCare, is uniquely suited to identify local and unobvious similarities among totally unrelated targets.
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Affiliation(s)
- Merveille Eguida
- 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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19
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Siebs E, Shanina E, Kuhaudomlarp S, da Silva Figueiredo Celestino Gomes P, Fortin C, Seeberger PH, Rognan D, Rademacher C, Imberty A, Titz A. Targeting the Central Pocket of the Pseudomonas aeruginosa Lectin LecA. Chembiochem 2021; 23:e202100563. [PMID: 34788491 PMCID: PMC9300185 DOI: 10.1002/cbic.202100563] [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: 10/28/2021] [Revised: 11/16/2021] [Indexed: 12/19/2022]
Abstract
Pseudomonas aeruginosa is an opportunistic ESKAPE pathogen that produces two lectins, LecA and LecB, as part of its large arsenal of virulence factors. Both carbohydrate‐binding proteins are central to the initial and later persistent infection processes, i. e. bacterial adhesion and biofilm formation. The biofilm matrix is a major resistance determinant and protects the bacteria against external threats such as the host immune system or antibiotic treatment. Therefore, the development of drugs against the P. aeruginosa biofilm is of particular interest to restore efficacy of antimicrobials. Carbohydrate‐based inhibitors for LecA and LecB were previously shown to efficiently reduce biofilm formations. Here, we report a new approach for inhibiting LecA with synthetic molecules bridging the established carbohydrate‐binding site and a central cavity located between two LecA protomers of the lectin tetramer. Inspired by in silico design, we synthesized various galactosidic LecA inhibitors with aromatic moieties targeting this central pocket. These compounds reached low micromolar affinities, validated in different biophysical assays. Finally, X‐ray diffraction analysis revealed the interactions of this compound class with LecA. This new mode of action paves the way to a novel route towards inhibition of P. aeruginosa biofilms.
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Affiliation(s)
- Eike Siebs
- Chemical Biology of Carbohydrates (CBCH), Helmholtz-Institute for Pharmaceutical Research Saarland (HIPS), Helmholtz Centre for Infection Research, 66123, Saarbrücken, Germany.,Department of Chemistry, Saarland University, 66123, Saarbrücken, Germany.,Deutsches Zentrum für Infektionsforschung (DZIF) Standort Hannover-, Braunschweig, Germany
| | - Elena Shanina
- Department of Biomolecular Systems, Max Planck Institute of Colloids and Interfaces, 14424, Potsdam, Germany.,Institute of Chemistry and Biochemistry, Department of Biology, Chemistry and Pharmacy, Freie Universität Berlin, 14195, Berlin, Germany
| | - Sakonwan Kuhaudomlarp
- Université Grenoble Alpes, CNRS, CERMAV, 38000, Grenoble, France.,Department of Biochemistry and Centre for Excellence in Protein and Enzyme Technology, Faculty of Science, Mahidol University, Bangkok, Thailand
| | | | - Cloé Fortin
- Chemical Biology of Carbohydrates (CBCH), Helmholtz-Institute for Pharmaceutical Research Saarland (HIPS), Helmholtz Centre for Infection Research, 66123, Saarbrücken, Germany
| | - Peter H Seeberger
- Department of Biomolecular Systems, Max Planck Institute of Colloids and Interfaces, 14424, Potsdam, Germany.,Institute of Chemistry and Biochemistry, Department of Biology, Chemistry and Pharmacy, Freie Universität Berlin, 14195, Berlin, Germany
| | - Didier Rognan
- Laboratoire d'Innovation Thérapeutique, UMR 7200 CNRS-Université de Strasbourg, Strasbourg, 67400, Illkirch, France
| | - Christoph Rademacher
- Department of Biomolecular Systems, Max Planck Institute of Colloids and Interfaces, 14424, Potsdam, Germany.,Institute of Chemistry and Biochemistry, Department of Biology, Chemistry and Pharmacy, Freie Universität Berlin, 14195, Berlin, Germany.,Department of Pharmaceutical Sciences, University of Vienna, Althanstrasse 14, 1090, Vienna, Austria.,Department of Microbiology, Immunology and Genetics, University of Vienna, Max F. Perutz Labs, Biocenter 5, 1030, Vienna, Austria
| | - Anne Imberty
- Université Grenoble Alpes, CNRS, CERMAV, 38000, Grenoble, France
| | - Alexander Titz
- Chemical Biology of Carbohydrates (CBCH), Helmholtz-Institute for Pharmaceutical Research Saarland (HIPS), Helmholtz Centre for Infection Research, 66123, Saarbrücken, Germany.,Department of Chemistry, Saarland University, 66123, Saarbrücken, Germany.,Deutsches Zentrum für Infektionsforschung (DZIF) Standort Hannover-, Braunschweig, Germany
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20
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Mattox DE, Bailey-Kellogg C. Comprehensive analysis of lectin-glycan interactions reveals determinants of lectin specificity. PLoS Comput Biol 2021; 17:e1009470. [PMID: 34613971 PMCID: PMC8523061 DOI: 10.1371/journal.pcbi.1009470] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2021] [Revised: 10/18/2021] [Accepted: 09/22/2021] [Indexed: 12/23/2022] Open
Abstract
Lectin-glycan interactions facilitate inter- and intracellular communication in many processes including protein trafficking, host-pathogen recognition, and tumorigenesis promotion. Specific recognition of glycans by lectins is also the basis for a wide range of applications in areas including glycobiology research, cancer screening, and antiviral therapeutics. To provide a better understanding of the determinants of lectin-glycan interaction specificity and support such applications, this study comprehensively investigates specificity-conferring features of all available lectin-glycan complex structures. Systematic characterization, comparison, and predictive modeling of a set of 221 complementary physicochemical and geometric features representing these interactions highlighted specificity-conferring features with potential mechanistic insight. Univariable comparative analyses with weighted Wilcoxon-Mann-Whitney tests revealed strong statistical associations between binding site features and specificity that are conserved across unrelated lectin binding sites. Multivariable modeling with random forests demonstrated the utility of these features for predicting the identity of bound glycans based on generalized patterns learned from non-homologous lectins. These analyses revealed global determinants of lectin specificity, such as sialic acid glycan recognition in deep, concave binding sites enriched for positively charged residues, in contrast to high mannose glycan recognition in fairly shallow but well-defined pockets enriched for non-polar residues. Focused fine specificity analysis of hemagglutinin interactions with human-like and avian-like glycans uncovered features representing both known and novel mutations related to shifts in influenza tropism from avian to human tissues. As the approach presented here relies on co-crystallized lectin-glycan pairs for studying specificity, it is limited in its inferences by the quantity, quality, and diversity of the structural data available. Regardless, the systematic characterization of lectin binding sites presented here provides a novel approach to studying lectin specificity and is a step towards confidently predicting new lectin-glycan interactions.
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Affiliation(s)
- Daniel E. Mattox
- Program in Quantitative Biomedical Sciences, Geisel School of Medicine at Dartmouth College, Hanover, New Hampshire, United States of America
| | - Chris Bailey-Kellogg
- Program in Quantitative Biomedical Sciences, Geisel School of Medicine at Dartmouth College, Hanover, New Hampshire, United States of America
- Department of Computer Science, Dartmouth College, Hanover, New Hampshire, United States of America
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21
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Tucker AN, Carlson TJ, Sarkar A. Challenges in Drug Discovery for Intracellular Bacteria. Pathogens 2021; 10:pathogens10091172. [PMID: 34578204 PMCID: PMC8468363 DOI: 10.3390/pathogens10091172] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2021] [Revised: 08/26/2021] [Accepted: 09/04/2021] [Indexed: 01/04/2023] Open
Abstract
Novel drugs are needed to treat a variety of persistent diseases caused by intracellular bacterial pathogens. Virulence pathways enable many functions required for the survival of these pathogens, including invasion, nutrient acquisition, and immune evasion. Inhibition of virulence pathways is an established route for drug discovery; however, many challenges remain. Here, we propose the biggest problems that must be solved to advance the field meaningfully. While it is established that we do not yet understand the nature of chemicals capable of permeating into the bacterial cell, this problem is compounded when targeting intracellular bacteria because we are limited to only those chemicals that can permeate through both human and bacterial outer envelopes. Unfortunately, many chemicals that permeate through the outer layers of mammalian cells fail to penetrate the bacterial cytoplasm. Another challenge is the lack of publicly available information on virulence factors. It is virtually impossible to know which virulence factors are clinically relevant and have broad cross-species and cross-strain distribution. In other words, we have yet to identify the best drug targets. Yes, standard genomics databases have much of the information necessary for short-term studies, but the connections with patient outcomes are yet to be established. Without comprehensive data on matters such as these, it is difficult to devise broad-spectrum, effective anti-virulence agents. Furthermore, anti-virulence drug discovery is hindered by the current state of technologies available for experimental investigation. Antimicrobial drug discovery was greatly advanced by the establishment and standardization of broth microdilution assays to measure the effectiveness of antimicrobials. However, the currently available models used for anti-virulence drug discovery are too broad, as they must address varied phenotypes, and too expensive to be generally adopted by many research groups. Therefore, we believe drug discovery against intracellular bacterial pathogens can be advanced significantly by overcoming the above hurdles.
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22
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Aggarwal R, Gupta A, Chelur V, Jawahar CV, Priyakumar UD. DeepPocket: Ligand Binding Site Detection and Segmentation using 3D Convolutional Neural Networks. J Chem Inf Model 2021; 62:5069-5079. [PMID: 34374539 DOI: 10.1021/acs.jcim.1c00799] [Citation(s) in RCA: 35] [Impact Index Per Article: 11.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
A structure-based drug design pipeline involves the development of potential drug molecules or ligands that form stable complexes with a given receptor at its binding site. A prerequisite to this is finding druggable and functionally relevant binding sites on the 3D structure of the protein. Although several methods for detecting binding sites have been developed beforehand, a majority of them surprisingly fail in the identification and ranking of binding sites accurately. The rapid adoption and success of deep learning algorithms in various sections of structural biology beckons the usage of such algorithms for accurate binding site detection. As a combination of geometry based software and deep learning, we report a novel framework, DeepPocket that utilizes 3D convolutional neural networks for the rescoring of pockets identified by Fpocket and further segments these identified cavities on the protein surface. Apart from this, we also propose another data set SC6K containing protein structures submitted in the Protein Data Bank (PDB) from January 1st, 2018, until February 28th, 2020, for ligand binding site (LBS) detection. DeepPocket's results on various binding site data sets and SC6K highlight its better performance over current state-of-the-art methods and good generalization ability over novel structures.
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Affiliation(s)
- Rishal Aggarwal
- International Institute of Information Technology, Hyderabad 500 032, India
| | - Akash Gupta
- International Institute of Information Technology, Hyderabad 500 032, India
| | - Vineeth Chelur
- International Institute of Information Technology, Hyderabad 500 032, India
| | - C V Jawahar
- International Institute of Information Technology, Hyderabad 500 032, India
| | - U Deva Priyakumar
- International Institute of Information Technology, Hyderabad 500 032, India
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23
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Rekand IH, Brenk R. DrugPred_RNA-A Tool for Structure-Based Druggability Predictions for RNA Binding Sites. J Chem Inf Model 2021; 61:4068-4081. [PMID: 34286972 PMCID: PMC8389535 DOI: 10.1021/acs.jcim.1c00155] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/14/2023]
Abstract
![]()
RNA is an emerging
target for drug discovery. However, like for
proteins, not all RNA binding sites are equally suited to be addressed
with conventional drug-like ligands. To this end, we have developed
the structure-based druggability predictor DrugPred_RNA to identify
druggable RNA binding sites. Due to the paucity of annotated RNA binding
sites, the predictor was trained on protein pockets, albeit using
only descriptors that can be calculated for both RNA and protein binding
sites. DrugPred_RNA performed well in discriminating druggable from
less druggable binding sites for the protein set and delivered predictions
for selected RNA binding sites that agreed with manual assignment.
In addition, most drug-like ligands contained in an RNA test set were
found in pockets predicted to be druggable, further adding confidence
to the performance of DrugPred_RNA. The method is robust against conformational
and sequence changes in the binding sites and can contribute to direct
drug discovery efforts for RNA targets.
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Affiliation(s)
- Illimar Hugo Rekand
- Department of Biomedicine, University of Bergen, Jonas Lies Vei, 5020 Bergen, Norway
| | - Ruth Brenk
- Department of Biomedicine, University of Bergen, Jonas Lies Vei, 5020 Bergen, Norway
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24
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CAVIAR: a method for automatic cavity detection, description and decomposition into subcavities. J Comput Aided Mol Des 2021; 35:737-750. [PMID: 34050420 DOI: 10.1007/s10822-021-00390-w] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2021] [Accepted: 05/11/2021] [Indexed: 10/21/2022]
Abstract
The accurate description of protein binding sites is essential to the determination of similarity and the application of machine learning methods to relate the binding sites to observed functions. This work describes CAVIAR, a new open source tool for generating descriptors for binding sites, using protein structures in PDB and mmCIF format as well as trajectory frames from molecular dynamics simulations as input. The applicability of CAVIAR descriptors is showcased by computing machine learning predictions of binding site ligandability. The method can also automatically assign subcavities, even in the absence of a bound ligand. The defined subpockets mimic the empirical definitions used in medicinal chemistry projects. It is shown that the experimental binding affinity scales relatively well with the number of subcavities filled by the ligand, with compounds binding to more than three subcavities having nanomolar or better affinities to the target. The CAVIAR descriptors and methods can be used in any machine learning-based investigations of problems involving binding sites, from protein engineering to hit identification. The full software code is available on GitHub and a conda package is hosted on Anaconda cloud.
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25
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dos Santos Vasconcelos CR, Rezende AM. Systematic in silico Evaluation of Leishmania spp. Proteomes for Drug Discovery. Front Chem 2021; 9:607139. [PMID: 33987166 PMCID: PMC8111926 DOI: 10.3389/fchem.2021.607139] [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: 09/16/2020] [Accepted: 03/24/2021] [Indexed: 11/18/2022] Open
Abstract
Leishmaniasis is a group of neglected infectious diseases, with approximately 1. 3 million new cases each year, for which the available therapies have serious limitations. Therefore, it is extremely important to apply efficient and low-cost methods capable of selecting the best therapeutic targets to speed up the development of new therapies against those diseases. Thus, we propose the use of integrated computational methods capable of evaluating the druggability of the predicted proteomes of Leishmania braziliensis and Leishmania infantum, species responsible for the different clinical manifestations of leishmaniasis in Brazil. The protein members of those proteomes were assessed based on their structural, chemical, and functional contexts applying methods that integrate data on molecular function, biological processes, subcellular localization, drug binding sites, druggability, and gene expression. These data were compared to those extracted from already known drug targets (BindingDB targets), which made it possible to evaluate Leishmania proteomes for their biological relevance and treatability. Through this methodology, we identified more than 100 proteins of each Leishmania species with druggability characteristics, and potential interaction with available drugs. Among those, 31 and 37 proteins of L. braziliensis and L. infantum, respectively, have never been tested as drug targets, and they have shown evidence of gene expression in the evolutionary stage of pharmacological interest. Also, some of those Leishmania targets showed an alignment similarity of <50% when compared to the human proteome, making these proteins pharmacologically attractive, as they present a reduced risk of side effects. The methodology used in this study also allowed the evaluation of opportunities for the repurposing of compounds as anti-leishmaniasis drugs, inferring potential interaction between Leishmania proteins and ~1,000 compounds, of which only 15 have already been tested as a treatment for leishmaniasis. Besides, a list of potential Leishmania targets to be tested using drugs described at BindingDB, such as the potential interaction of the DEAD box RNA helicase, TRYR, and PEPCK proteins with the Staurosporine compound, was made available to the public.
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Affiliation(s)
- Crhisllane Rafaele dos Santos Vasconcelos
- Bioinformatics Plataform, Microbiology Department, Instituto Aggeu Magalhães, Recife, Brazil
- Posgraduate Program in Genetics, Genetics Department, Universidade Federal de Pernambuco, Recife, Brazil
| | - Antonio Mauro Rezende
- Bioinformatics Plataform, Microbiology Department, Instituto Aggeu Magalhães, Recife, Brazil
- Posgraduate Program in Genetics, Genetics Department, Universidade Federal de Pernambuco, Recife, Brazil
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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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Abstract
INTRODUCTION Molecular docking has been consolidated as one of the most important methods in the molecular modeling field. It has been recognized as a prominent tool in the study of protein-ligand complexes, to describe intermolecular interactions, to accurately predict poses of multiple ligands, to discover novel promising bioactive compounds. Molecular docking methods have evolved in terms of their accuracy and reliability; but there are pending issues to solve for improving the connection between the docking results and the experimental evidence. AREAS COVERED In this article, the author reviews very recent innovative molecular docking applications with special emphasis on reverse docking, treatment of protein flexibility, the use of experimental data to guide the selection of docking poses, the application of Quantum mechanics(QM) in docking, and covalent docking. EXPERT OPINION There are several issues being worked on in recent years that will lead to important breakthroughs in molecular docking methods in the near future These developments are related to more efficient exploration of large datasets and receptor conformations, advances in electronic description, and the use of structural information for guiding the selection of results.
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Affiliation(s)
- Julio Caballero
- Departamento De Bioinformática, Centro De Bioinformática, Simulación Y Modelado (CBSM), Facultad De Ingeniería, Universidad De Talca, Talca, Chile
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28
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Eguida M, Rognan D. A Computer Vision Approach to Align and Compare Protein Cavities: Application to Fragment-Based Drug Design. J Med Chem 2020; 63:7127-7142. [DOI: 10.1021/acs.jmedchem.0c00422] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Affiliation(s)
- Merveille Eguida
- UMR 7200 CNRS-Université de Strasbourg, Laboratoire d’Innovation Thérapeutique, 67400 Illkirch, France
| | - Didier Rognan
- UMR 7200 CNRS-Université de Strasbourg, Laboratoire d’Innovation Thérapeutique, 67400 Illkirch, France
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29
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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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30
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Improving detection of protein-ligand binding sites with 3D segmentation. Sci Rep 2020; 10:5035. [PMID: 32193447 PMCID: PMC7081267 DOI: 10.1038/s41598-020-61860-z] [Citation(s) in RCA: 42] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2019] [Accepted: 10/03/2019] [Indexed: 11/08/2022] Open
Abstract
In recent years machine learning (ML) took bio- and cheminformatics fields by storm, providing new solutions for a vast repertoire of problems related to protein sequence, structure, and interactions analysis. ML techniques, deep neural networks especially, were proven more effective than classical models for tasks like predicting binding affinity for molecular complex. In this work we investigated the earlier stage of drug discovery process - finding druggable pockets on protein surface, that can be later used to design active molecules. For this purpose we developed a 3D fully convolutional neural network capable of binding site segmentation. Our solution has high prediction accuracy and provides intuitive representations of the results, which makes it easy to incorporate into drug discovery projects. The model's source code, together with scripts for most common use-cases is freely available at http://gitlab.com/cheminfIBB/kalasanty.
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31
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Yuan JH, Han SB, Richter S, Wade RC, Kokh DB. Druggability Assessment in TRAPP Using Machine Learning Approaches. J Chem Inf Model 2020; 60:1685-1699. [DOI: 10.1021/acs.jcim.9b01185] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Affiliation(s)
- Jui-Hung Yuan
- Molecular and Cellular Modeling Group, Heidelberg Institute of Theoretical Studies (HITS), 69118 Heidelberg, Germany
- Interdisciplinary Center for Scientific Computing (IWR), Heidelberg University, 69120 Heidelberg, Germany
| | - Sungho Bosco Han
- Molecular and Cellular Modeling Group, Heidelberg Institute of Theoretical Studies (HITS), 69118 Heidelberg, Germany
- Faculty of Biosciences, Heidelberg University, 69120 Heidelberg, Germany
| | - Stefan Richter
- Molecular and Cellular Modeling Group, Heidelberg Institute of Theoretical Studies (HITS), 69118 Heidelberg, Germany
| | - Rebecca C. Wade
- Molecular and Cellular Modeling Group, Heidelberg Institute of Theoretical Studies (HITS), 69118 Heidelberg, Germany
- Interdisciplinary Center for Scientific Computing (IWR), Heidelberg University, 69120 Heidelberg, Germany
- Faculty of Biosciences, Heidelberg University, 69120 Heidelberg, Germany
- Zentrum für Molekulare Biologie (ZMBH), DKFZ-ZMBH Alliance, Heidelberg University, 69120 Heidelberg, Germany
| | - Daria B. Kokh
- Molecular and Cellular Modeling Group, Heidelberg Institute of Theoretical Studies (HITS), 69118 Heidelberg, Germany
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32
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Froes TQ, Baldini RL, Vajda S, Castilho MS. Structure-based Druggability Assessment of Anti-virulence Targets from Pseudomonas aeruginosa. Curr Protein Pept Sci 2020; 20:1189-1203. [PMID: 31038064 DOI: 10.2174/1389203720666190417120758] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2018] [Revised: 02/12/2019] [Accepted: 02/28/2019] [Indexed: 11/22/2022]
Abstract
Antimicrobial Resistance (AMR) represents a serious threat to health and the global economy. However, interest in antibacterial drug development has decreased substantially in recent decades. Meanwhile, anti-virulence drug development has emerged as an attractive alternative to fight AMR. Although several macromolecular targets have been explored for this goal, their druggability is a vital piece of information that has been overlooked. This review explores this subject by showing how structure- based freely available in silico tools, such as PockDrug and FTMap, might be useful for designing novel inhibitors of the pyocyanin biosynthesis pathway and improving the potency/selectivity of compounds that target the Pseudomonas aeruginosa quorum sensing mechanism. The information provided by hotspot analysis, along with binding site features, reveals novel druggable targets (PhzA and PhzS) that remain largely unexplored. However, it also highlights that in silico druggability prediction tools have several limitations that might be overcome in the near future. Meanwhile, anti-virulence drug targets should be assessed by complementary methods, such as the combined use of FTMap/PockDrug, once the consensus druggability classification reduces the risk of wasting resources on undruggable proteins.
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Affiliation(s)
- Thamires Q Froes
- Programa de Pos-Graduacao em Biotecnologia da Universidade Estadual de Feira de Santana, Feira de Santana, BA, Brazil.,aculdade de Farmácia da Universidade Federal da Bahia, Bahia, Salvador, BA, Brazil
| | - Regina L Baldini
- Departamento de Bioquimica, Instituto de Quimica, Universidade de Sao Paulo. Sao Paulo, SP, Brazil
| | - Sandor Vajda
- College of Engineering, Boston University, Boston, MA, United States
| | - Marcelo S Castilho
- Programa de Pos-Graduacao em Biotecnologia da Universidade Estadual de Feira de Santana, Feira de Santana, BA, Brazil.,aculdade de Farmácia da Universidade Federal da Bahia, Bahia, Salvador, BA, Brazil.,College of Engineering, Boston University, Boston, MA, United States
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33
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Bryer A, Hadden-Perilla JA, Stone JE, Perilla JR. High-Performance Analysis of Biomolecular Containers to Measure Small-Molecule Transport, Transbilayer Lipid Diffusion, and Protein Cavities. J Chem Inf Model 2019; 59:4328-4338. [PMID: 31525965 PMCID: PMC6817393 DOI: 10.1021/acs.jcim.9b00324] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2019] [Indexed: 01/23/2023]
Abstract
Compartmentalization is a central theme in biology. Cells are composed of numerous membrane-enclosed structures, evolved to facilitate specific biochemical processes; viruses act as containers of genetic material, optimized to drive infection. Molecular dynamics simulations provide a mechanism to study biomolecular containers and the influence they exert on their environments; however, trajectory analysis software generally lacks knowledge of container interior versus exterior. Further, many relevant container analyses involve large-scale particle tracking endeavors, which may become computationally prohibitive with increasing system size. Here, a novel method based on 3-D ray casting is presented, which rapidly classifies the space surrounding biomolecular containers of arbitrary shape, enabling fast determination of the identities and counts of particles (e.g., solvent molecules) found inside and outside. The method is broadly applicable to the study of containers and enables high-performance characterization of properties such as solvent density, small-molecule transport, transbilayer lipid diffusion, and topology of protein cavities. The method is implemented in VMD, a widely used simulation analysis tool that supports personal computers, clouds, and parallel supercomputers, including ORNL's Summit and Titan and NCSA's Blue Waters, where the method can be employed to efficiently analyze trajectories encompassing millions of particles. The ability to rapidly characterize the spatial relationships of particles relative to a biomolecular container over many trajectory frames, irrespective of large particle counts, enables analysis of containers on a scale that was previously unfeasible, at a level of accuracy that was previously unattainable.
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Affiliation(s)
- Alexander
J. Bryer
- Department
of Chemistry and Biochemistry, University
of Delaware, Newark, Delaware 19716, United States
| | - Jodi A. Hadden-Perilla
- Department
of Chemistry and Biochemistry, University
of Delaware, Newark, Delaware 19716, United States
| | - John E. Stone
- Beckman
Institute for Advanced Science and Technology, University of Illinois at Urbana−Champaign, Urbana, Illinois 61801, United States
| | - Juan R. Perilla
- Department
of Chemistry and Biochemistry, University
of Delaware, Newark, Delaware 19716, United States
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34
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Da Silva F, Bret G, Teixeira L, Gonzalez CF, Rognan D. Exhaustive Repertoire of Druggable Cavities at Protein-Protein Interfaces of Known Three-Dimensional Structure. J Med Chem 2019; 62:9732-9742. [PMID: 31603323 DOI: 10.1021/acs.jmedchem.9b01184] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
Protein-protein interactions (PPIs) offer the unique opportunity to tailor ligands aimed at specifically stabilizing or disrupting the corresponding interfaces and providing a safer alternative to conventional ligands targeting monomeric macromolecules. Selecting biologically relevant protein-protein interfaces for either stabilization or disruption by small molecules is usually biology-driven on a case-by-case basis and does not follow a structural rationale that could be applied to an entire interactome. We herewith provide a first step to the latter goal by using a fully automated and structure-based workflow, applicable to any PPI of known three-dimensional (3D) structure, to identify and prioritize druggable cavities at and nearby PPIs of pharmacological interest. When applied to the entire Protein Data Bank, 164 514 druggable cavities were identified and classified in four groups (interfacial, rim, allosteric, orthosteric) according to their properties and spatial locations. Systematic comparison of PPI cavities with pockets deduced from druggable protein-ligand complexes shows almost no overlap in property space, suggesting that even the most druggable PPI cavities are unlikely to be addressed with conventional drug-like compound libraries. The archive is freely accessible at http://drugdesign.unistra.fr/ppiome .
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Affiliation(s)
- Franck Da Silva
- 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
| | - Leandro Teixeira
- Department of Microbiology and Cell Science, Genetics Institute, Institute of Food and Agricultural Sciences , University of Florida , Gainesville , Florida 32610-3610 , United States
| | - Claudio F Gonzalez
- Department of Microbiology and Cell Science, Genetics Institute, Institute of Food and Agricultural Sciences , University of Florida , Gainesville , Florida 32610-3610 , United States
| | - Didier Rognan
- Laboratoire d'Innovation Thérapeutique , UMR 7200 CNRS-Université de Strasbourg , 67400 Illkirch , France
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35
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Jacquemard C, Tran-Nguyen VK, Drwal MN, Rognan D, Kellenberger E. Local Interaction Density (LID), a Fast and Efficient Tool to Prioritize Docking Poses. Molecules 2019; 24:molecules24142610. [PMID: 31323745 PMCID: PMC6681060 DOI: 10.3390/molecules24142610] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2019] [Revised: 07/11/2019] [Accepted: 07/16/2019] [Indexed: 12/18/2022] Open
Abstract
Ligand docking at a protein site can be improved by prioritizing poses by similarity to validated binding modes found in the crystal structures of ligand/protein complexes. The interactions formed in the predicted model are searched in each of the reference 3D structures, taken individually. We propose to merge the information provided by all references, creating a single representation of all known binding modes. The method is called LID, an acronym for Local Interaction Density. LID was benchmarked in a pose prediction exercise on 19 proteins and 1382 ligands using PLANTS as docking software. It was also tested in a virtual screening challenge on eight proteins, with a dataset of 140,000 compounds from DUD-E and PubChem. LID significantly improved the performance of the docking program in both pose prediction and virtual screening. The gain is comparable to that obtained with a rescoring approach based on the individual comparison of reference binding modes (the GRIM method). Importantly, LID is effective with a small number of references. LID calculation time is negligible compared to the docking time.
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Affiliation(s)
- Célien Jacquemard
- Laboratoire D'innovation Thérapeutique, UMR7200, CNRS, Université de Strasbourg, 67400 Illkirch, France
| | - Viet-Khoa Tran-Nguyen
- Laboratoire D'innovation Thérapeutique, UMR7200, CNRS, Université de Strasbourg, 67400 Illkirch, France
| | - Malgorzata N Drwal
- Laboratoire D'innovation Thérapeutique, UMR7200, CNRS, Université de Strasbourg, 67400 Illkirch, France
| | - Didier Rognan
- Laboratoire D'innovation Thérapeutique, UMR7200, CNRS, Université de Strasbourg, 67400 Illkirch, France
| | - Esther Kellenberger
- Laboratoire D'innovation Thérapeutique, UMR7200, CNRS, Université de Strasbourg, 67400 Illkirch, France.
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36
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Chen Z, Zhang X, Peng C, Wang J, Xu Z, Chen K, Shi J, Zhu W. D3Pockets: A Method and Web Server for Systematic Analysis of Protein Pocket Dynamics. J Chem Inf Model 2019; 59:3353-3358. [DOI: 10.1021/acs.jcim.9b00332] [Citation(s) in RCA: 32] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Affiliation(s)
- Zhaoqiang Chen
- CAS Key Laboratory of Receptor Research, Drug Discovery and Design Center, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China
- University of Chinese Academy of Sciences, No. 19A Yuquan Road, Beijing 100049, China
| | - Xinben Zhang
- CAS Key Laboratory of Receptor Research, Drug Discovery and Design Center, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China
| | - Cheng Peng
- CAS Key Laboratory of Receptor Research, Drug Discovery and Design Center, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China
- University of Chinese Academy of Sciences, No. 19A Yuquan Road, Beijing 100049, China
| | - Jinan Wang
- CAS Key Laboratory of Receptor Research, Drug Discovery and Design Center, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China
| | - Zhijian Xu
- CAS Key Laboratory of Receptor Research, Drug Discovery and Design Center, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China
- University of Chinese Academy of Sciences, No. 19A Yuquan Road, Beijing 100049, China
| | - Kaixian Chen
- CAS Key Laboratory of Receptor Research, Drug Discovery and Design Center, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China
- University of Chinese Academy of Sciences, No. 19A Yuquan Road, Beijing 100049, China
- Open Studio for Druggability Research of Marine Natural Products, Pilot National Laboratory for Marine Science and Technology (Qingdao), 1 Wenhai Road, Aoshanwei, Jimo, Qingdao 266237, China
| | - Jiye Shi
- UCB Biopharma SPRL, Chemin du Foriest, Braine-l’ Alleud B-1420, Belgium
| | - Weiliang Zhu
- CAS Key Laboratory of Receptor Research, Drug Discovery and Design Center, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China
- University of Chinese Academy of Sciences, No. 19A Yuquan Road, Beijing 100049, China
- Open Studio for Druggability Research of Marine Natural Products, Pilot National Laboratory for Marine Science and Technology (Qingdao), 1 Wenhai Road, Aoshanwei, Jimo, Qingdao 266237, China
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37
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Ehrt C, Brinkjost T, Koch O. Binding site characterization - similarity, promiscuity, and druggability. MEDCHEMCOMM 2019; 10:1145-1159. [PMID: 31391887 DOI: 10.1039/c9md00102f] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/19/2019] [Accepted: 05/31/2019] [Indexed: 12/19/2022]
Abstract
The elucidation of non-obvious binding site similarities has provided useful indications for the establishment of polypharmacology, the identification of potential off-targets, or the repurposing of known drugs. The concept underlying all of these approaches is promiscuous binding which can be analyzed from a ligand-based or a binding site-based perspective. Herein, we applied methods for the automated analysis and comparison of protein binding sites to study promiscuous binding on a novel dataset of sites in complex with ligands sharing common shape and physicochemical properties. We show the suitability of this dataset for the benchmarking of novel binding site comparison methods. Our investigations also reveal promising directions for further in-depth analyses of promiscuity and druggability in a pocket-centered manner. Drawbacks concerning binding site similarity assessment and druggability prediction are outlined, enabling researchers to avoid the typical pitfalls of binding site analyses.
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Affiliation(s)
- Christiane Ehrt
- Faculty of Chemistry and Chemical Biology , TU Dortmund University , Dortmund , Germany
| | - Tobias Brinkjost
- Faculty of Chemistry and Chemical Biology , TU Dortmund University , Dortmund , Germany.,Department of Computer Science , TU Dortmund University , Dortmund , Germany
| | - Oliver Koch
- Faculty of Chemistry and Chemical Biology , TU Dortmund University , Dortmund , Germany
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38
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Sydow D, Burggraaff L, Szengel A, van Vlijmen HWT, IJzerman AP, van Westen GJP, Volkamer A. Advances and Challenges in Computational Target Prediction. J Chem Inf Model 2019; 59:1728-1742. [DOI: 10.1021/acs.jcim.8b00832] [Citation(s) in RCA: 58] [Impact Index Per Article: 11.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Affiliation(s)
- Dominique Sydow
- In silico Toxicology, Institute of Physiology, Charité − Universitätsmedizin Berlin, Charitéplatz 1, 10117 Berlin, Germany
| | - Lindsey Burggraaff
- Division of Drug Discovery and Safety, Leiden Academic Centre for Drug Research, Leiden University, P.O. Box 9502, 2300 RA, Leiden, The Netherlands
| | - Angelika Szengel
- In silico Toxicology, Institute of Physiology, Charité − Universitätsmedizin Berlin, Charitéplatz 1, 10117 Berlin, Germany
| | - Herman W. T. van Vlijmen
- Division of Drug Discovery and Safety, Leiden Academic Centre for Drug Research, Leiden University, P.O. Box 9502, 2300 RA, Leiden, The Netherlands
- Computational Chemistry, Janssen Research & Development, Turnhoutseweg 30, B-2340 Beerse, Belgium
| | - Adriaan P. IJzerman
- Division of Drug Discovery and Safety, Leiden Academic Centre for Drug Research, Leiden University, P.O. Box 9502, 2300 RA, Leiden, The Netherlands
| | - Gerard J. P. van Westen
- Division of Drug Discovery and Safety, Leiden Academic Centre for Drug Research, Leiden University, P.O. Box 9502, 2300 RA, Leiden, The Netherlands
| | - Andrea Volkamer
- In silico Toxicology, Institute of Physiology, Charité − Universitätsmedizin Berlin, Charitéplatz 1, 10117 Berlin, Germany
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39
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Tran-Nguyen VK, Da Silva F, Bret G, Rognan D. All in One: Cavity Detection, Druggability Estimate, Cavity-Based Pharmacophore Perception, and Virtual Screening. J Chem Inf Model 2019; 59:573-585. [PMID: 30563339 DOI: 10.1021/acs.jcim.8b00684] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
Abstract
Discovering the very first ligands of pharmacologically important targets in a fast and cost-efficient manner is an important issue in drug discovery. In the absence of structural information on either endogenous or synthetic ligands, computational chemists classically identify the very first hits by docking compound libraries to a binding site of interest, with well-known biases arising from the usage of scoring functions. We herewith propose a novel computational method tailored to ligand-free protein structures and consisting in the generation of simple cavity-based pharmacophores to which potential ligands could be aligned by the use of a smooth Gaussian function. The method, embedded in the IChem toolkit, automatically detects ligand-binding cavities, then predicts their structural druggability, and last creates a structure-based pharmacophore for predicted druggable binding sites. A companion tool (Shaper2) was designed to align ligands to cavity-derived pharmacophoric features. The proposed method is as efficient as state-of-the-art virtual screening methods (ROCS, Surflex-Dock) in both posing and virtual screening challenges. Interestingly, IChem-Shaper2 is clearly orthogonal to these latter methods in retrieving unique chemotypes from high-throughput virtual screening data.
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Affiliation(s)
- Viet-Khoa Tran-Nguyen
- Laboratoire d'Innovation Thérapeutique , UMR 7200 CNRS-Université de Strasbourg , 67400 Illkirch , France
| | - Franck Da Silva
- 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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40
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Ehrt C, Brinkjost T, Koch O. A benchmark driven guide to binding site comparison: An exhaustive evaluation using tailor-made data sets (ProSPECCTs). PLoS Comput Biol 2018; 14:e1006483. [PMID: 30408032 PMCID: PMC6224041 DOI: 10.1371/journal.pcbi.1006483] [Citation(s) in RCA: 41] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2018] [Accepted: 09/02/2018] [Indexed: 11/24/2022] Open
Abstract
The automated comparison of protein-ligand binding sites provides useful insights into yet unexplored site similarities. Various stages of computational and chemical biology research can benefit from this knowledge. The search for putative off-targets and the establishment of polypharmacological effects by comparing binding sites led to promising results for numerous projects. Although many cavity comparison methods are available, a comprehensive analysis to guide the choice of a tool for a specific application is wanting. Moreover, the broad variety of binding site modeling approaches, comparison algorithms, and scoring metrics impedes this choice. Herein, we aim to elucidate strengths and weaknesses of binding site comparison methodologies. A detailed benchmark study is the only possibility to rationalize the selection of appropriate tools for different scenarios. Specific evaluation data sets were developed to shed light on multiple aspects of binding site comparison. An assembly of all applied benchmark sets (ProSPECCTs–Protein Site Pairs for the Evaluation of Cavity Comparison Tools) is made available for the evaluation and optimization of further and still emerging methods. The results indicate the importance of such analyses to facilitate the choice of a methodology that complies with the requirements of a specific scientific challenge. Binding site similarities are useful in the context of promiscuity prediction, drug repurposing, the analysis of protein-ligand and protein-protein complexes, function prediction, and further fields of general interest in chemical biology and biochemistry. Many years of research have led to the development of a multitude of methods for binding site analysis and comparison. On the one hand, their availability supports research. On the other hand, the huge number of methods hampers the efficient selection of a specific tool. Our research is dedicated to the analysis of different cavity comparison tools. We use several binding site data sets to establish guidelines which can be applied to ensure a successful application of comparison methods by circumventing potential pitfalls.
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Affiliation(s)
- Christiane Ehrt
- Faculty of Chemistry and Chemical Biology, TU Dortmund University, Dortmund, Germany
| | - Tobias Brinkjost
- Faculty of Chemistry and Chemical Biology, TU Dortmund University, Dortmund, Germany
- Department of Computer Science, TU Dortmund University, Dortmund, Germany
| | - Oliver Koch
- Faculty of Chemistry and Chemical Biology, TU Dortmund University, Dortmund, Germany
- * E-mail: ,
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41
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Structure-Based Detection of Orthosteric and Allosteric Pockets at Protein-Protein Interfaces. Methods Mol Biol 2018. [PMID: 30334209 DOI: 10.1007/978-1-4939-8639-2_8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register]
Abstract
Protein-protein interfaces represent challenging but very promising targets to discover novel drugs with exquisite specificity profiles. We herewith chart for the first time all biologically relevant protein-protein interfaces of known X-ray structure and detect potentially druggable cavities at and nearby the interface. These cavities are then converted in simple 3D pharmacophore queries for identifying potential modulators (inhibitors, stabilizers) of druggable interfaces.
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42
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Regenass P, Abboud D, Daubeuf F, Lehalle C, Gizzi P, Riché S, Hachet-Haas M, Rohmer F, Gasparik V, Boeglin D, Haiech J, Knehans T, Rognan D, Heissler D, Marsol C, Villa P, Galzi JL, Hibert M, Frossard N, Bonnet D. Discovery of a Locally and Orally Active CXCL12 Neutraligand (LIT-927) with Anti-inflammatory Effect in a Murine Model of Allergic Airway Hypereosinophilia. J Med Chem 2018; 61:7671-7686. [PMID: 30106292 DOI: 10.1021/acs.jmedchem.8b00657] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Abstract
We previously reported Chalcone-4 (1) that binds the chemokine CXCL12, not its cognate receptors CXCR4 or CXCR7, and neutralizes its biological activity. However, this neutraligand suffers from limitations such as poor chemical stability, solubility, and oral activity. Herein, we report on the discovery of pyrimidinone 57 (LIT-927), a novel neutraligand of CXCL12 which displays a higher solubility than 1 and is no longer a Michael acceptor. While both 1 and 57 reduce eosinophil recruitment in a murine model of allergic airway hypereosinophilia, 57 is the only one to display inhibitory activity following oral administration. Thereby, we here describe 57 as the first orally active CXCL12 neutraligand with anti-inflammatory properties. Combined with a high binding selectivity for CXCL12 over other chemokines, 57 represents a powerful pharmacological tool to investigate CXCL12 physiology in vivo and to explore the activity of chemokine neutralization in inflammatory and related diseases.
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Affiliation(s)
- Pierre Regenass
- Laboratoire d'Innovation Thérapeutique , Faculté de Pharmacie, UMR7200 CNRS/Université de Strasbourg , 74 route du Rhin , 67401 Illkirch , France.,Labex MEDALIS , Université de Strasbourg , 67000 Strasbourg , France
| | - Dayana Abboud
- Biotechnologie et Signalisation Cellulaire , Ecole Supérieure de Biotechnologie de Strasbourg, UMR 7242 CNRS/Université de Strasbourg , Bld Sébastien Brant , 67412 Illkirch , France.,Labex MEDALIS , Université de Strasbourg , 67000 Strasbourg , France
| | - François Daubeuf
- Laboratoire d'Innovation Thérapeutique , Faculté de Pharmacie, UMR7200 CNRS/Université de Strasbourg , 74 route du Rhin , 67401 Illkirch , France.,Plate-forme de chimie biologique intégrative de Strasbourg , UMS 3286 CNRS/Université de Strasbourg , 67412 Illkirch , France.,Labex MEDALIS , Université de Strasbourg , 67000 Strasbourg , France
| | - Christine Lehalle
- Laboratoire d'Innovation Thérapeutique , Faculté de Pharmacie, UMR7200 CNRS/Université de Strasbourg , 74 route du Rhin , 67401 Illkirch , France.,Plate-forme de chimie biologique intégrative de Strasbourg , UMS 3286 CNRS/Université de Strasbourg , 67412 Illkirch , France.,Labex MEDALIS , Université de Strasbourg , 67000 Strasbourg , France
| | - Patrick Gizzi
- Plate-forme de chimie biologique intégrative de Strasbourg , UMS 3286 CNRS/Université de Strasbourg , 67412 Illkirch , France.,Labex MEDALIS , Université de Strasbourg , 67000 Strasbourg , France
| | - Stéphanie Riché
- Laboratoire d'Innovation Thérapeutique , Faculté de Pharmacie, UMR7200 CNRS/Université de Strasbourg , 74 route du Rhin , 67401 Illkirch , France.,Labex MEDALIS , Université de Strasbourg , 67000 Strasbourg , France
| | - Muriel Hachet-Haas
- Biotechnologie et Signalisation Cellulaire , Ecole Supérieure de Biotechnologie de Strasbourg, UMR 7242 CNRS/Université de Strasbourg , Bld Sébastien Brant , 67412 Illkirch , France.,Labex MEDALIS , Université de Strasbourg , 67000 Strasbourg , France
| | - François Rohmer
- Laboratoire d'Innovation Thérapeutique , Faculté de Pharmacie, UMR7200 CNRS/Université de Strasbourg , 74 route du Rhin , 67401 Illkirch , France.,Labex MEDALIS , Université de Strasbourg , 67000 Strasbourg , France
| | - Vincent Gasparik
- Laboratoire d'Innovation Thérapeutique , Faculté de Pharmacie, UMR7200 CNRS/Université de Strasbourg , 74 route du Rhin , 67401 Illkirch , France.,Labex MEDALIS , Université de Strasbourg , 67000 Strasbourg , France
| | - Damien Boeglin
- Laboratoire d'Innovation Thérapeutique , Faculté de Pharmacie, UMR7200 CNRS/Université de Strasbourg , 74 route du Rhin , 67401 Illkirch , France.,Labex MEDALIS , Université de Strasbourg , 67000 Strasbourg , France
| | - Jacques Haiech
- Laboratoire d'Innovation Thérapeutique , Faculté de Pharmacie, UMR7200 CNRS/Université de Strasbourg , 74 route du Rhin , 67401 Illkirch , France.,Labex MEDALIS , Université de Strasbourg , 67000 Strasbourg , France
| | - Tim Knehans
- Laboratoire d'Innovation Thérapeutique , Faculté de Pharmacie, UMR7200 CNRS/Université de Strasbourg , 74 route du Rhin , 67401 Illkirch , France.,Labex MEDALIS , Université de Strasbourg , 67000 Strasbourg , France
| | - Didier Rognan
- Laboratoire d'Innovation Thérapeutique , Faculté de Pharmacie, UMR7200 CNRS/Université de Strasbourg , 74 route du Rhin , 67401 Illkirch , France.,Labex MEDALIS , Université de Strasbourg , 67000 Strasbourg , France
| | - Denis Heissler
- Laboratoire d'Innovation Thérapeutique , Faculté de Pharmacie, UMR7200 CNRS/Université de Strasbourg , 74 route du Rhin , 67401 Illkirch , France.,Labex MEDALIS , Université de Strasbourg , 67000 Strasbourg , France
| | - Claire Marsol
- Laboratoire d'Innovation Thérapeutique , Faculté de Pharmacie, UMR7200 CNRS/Université de Strasbourg , 74 route du Rhin , 67401 Illkirch , France.,Plate-forme de chimie biologique intégrative de Strasbourg , UMS 3286 CNRS/Université de Strasbourg , 67412 Illkirch , France.,Labex MEDALIS , Université de Strasbourg , 67000 Strasbourg , France
| | - Pascal Villa
- Plate-forme de chimie biologique intégrative de Strasbourg , UMS 3286 CNRS/Université de Strasbourg , 67412 Illkirch , France.,Labex MEDALIS , Université de Strasbourg , 67000 Strasbourg , France
| | - Jean-Luc Galzi
- Biotechnologie et Signalisation Cellulaire , Ecole Supérieure de Biotechnologie de Strasbourg, UMR 7242 CNRS/Université de Strasbourg , Bld Sébastien Brant , 67412 Illkirch , France.,Labex MEDALIS , Université de Strasbourg , 67000 Strasbourg , France
| | - Marcel Hibert
- Laboratoire d'Innovation Thérapeutique , Faculté de Pharmacie, UMR7200 CNRS/Université de Strasbourg , 74 route du Rhin , 67401 Illkirch , France.,Labex MEDALIS , Université de Strasbourg , 67000 Strasbourg , France
| | - Nelly Frossard
- Laboratoire d'Innovation Thérapeutique , Faculté de Pharmacie, UMR7200 CNRS/Université de Strasbourg , 74 route du Rhin , 67401 Illkirch , France.,Labex MEDALIS , Université de Strasbourg , 67000 Strasbourg , France
| | - Dominique Bonnet
- Laboratoire d'Innovation Thérapeutique , Faculté de Pharmacie, UMR7200 CNRS/Université de Strasbourg , 74 route du Rhin , 67401 Illkirch , France.,Labex MEDALIS , Université de Strasbourg , 67000 Strasbourg , France
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43
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Krivák R, Hoksza D. P2Rank: machine learning based tool for rapid and accurate prediction of ligand binding sites from protein structure. J Cheminform 2018; 10:39. [PMID: 30109435 PMCID: PMC6091426 DOI: 10.1186/s13321-018-0285-8] [Citation(s) in RCA: 166] [Impact Index Per Article: 27.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2017] [Accepted: 06/29/2018] [Indexed: 01/29/2023] Open
Abstract
Background Ligand binding site prediction from protein structure has many applications related to elucidation of protein function and structure based drug discovery. It often represents only one step of many in complex computational drug design efforts. Although many methods have been published to date, only few of them are suitable for use in automated pipelines or for processing large datasets.
These use cases require stability and speed, which disqualifies many of the recently introduced tools that are either template based or available only as web servers. Results We present P2Rank, a stand-alone template-free tool for prediction of ligand binding sites based on machine learning. It is based on prediction of ligandability of local chemical neighbourhoods that are centered on points placed on the solvent accessible surface of a protein.
We show that P2Rank outperforms several existing tools, which include two widely used stand-alone tools (Fpocket, SiteHound), a comprehensive consensus based tool (MetaPocket 2.0), and a recent deep learning based method (DeepSite). P2Rank belongs to the fastest available tools (requires under 1 s for prediction on one protein), with additional advantage of multi-threaded implementation. Conclusions P2Rank is a new open source software package for ligand binding site prediction from protein structure. It is available as a user-friendly stand-alone command line program and a Java library. P2Rank has a lightweight installation and does not depend on other bioinformatics tools or large structural or sequence databases. Thanks to its speed and ability to make fully automated predictions, it is particularly well suited for processing large datasets or as a component of scalable structural bioinformatics pipelines. Electronic supplementary material The online version of this article (10.1186/s13321-018-0285-8) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Radoslav Krivák
- Department of Software Engineering, Charles University, Prague, Czech Republic.
| | - David Hoksza
- Department of Software Engineering, Charles University, Prague, Czech Republic.
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44
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Kumar A, Zhang KYJ. Advances in the Development of Shape Similarity Methods and Their Application in Drug Discovery. Front Chem 2018; 6:315. [PMID: 30090808 PMCID: PMC6068280 DOI: 10.3389/fchem.2018.00315] [Citation(s) in RCA: 81] [Impact Index Per Article: 13.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2018] [Accepted: 07/09/2018] [Indexed: 12/21/2022] Open
Abstract
Molecular similarity is a key concept in drug discovery. It is based on the assumption that structurally similar molecules frequently have similar properties. Assessment of similarity between small molecules has been highly effective in the discovery and development of various drugs. Especially, two-dimensional (2D) similarity approaches have been quite popular due to their simplicity, accuracy and efficiency. Recently, the focus has been shifted toward the development of methods involving the representation and comparison of three-dimensional (3D) conformation of small molecules. Among the 3D similarity methods, evaluation of shape similarity is now gaining attention for its application not only in virtual screening but also in molecular target prediction, drug repurposing and scaffold hopping. A wide range of methods have been developed to describe molecular shape and to determine the shape similarity between small molecules. The most widely used methods include atom distance-based methods, surface-based approaches such as spherical harmonics and 3D Zernike descriptors, atom-centered Gaussian overlay based representations. Several of these methods demonstrated excellent virtual screening performance not only retrospectively but also prospectively. In addition to methods assessing the similarity between small molecules, shape similarity approaches have been developed to compare shapes of protein structures and binding pockets. Additionally, shape comparisons between atomic models and 3D density maps allowed the fitting of atomic models into cryo-electron microscopy maps. This review aims to summarize the methodological advances in shape similarity assessment highlighting advantages, disadvantages and their application in drug discovery.
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Affiliation(s)
| | - Kam Y. J. Zhang
- Laboratory for Structural Bioinformatics, Center for Biosystems Dynamics Research, RIKEN, Yokohama, Japan
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45
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Skalic M, Varela-Rial A, Jiménez J, Martínez-Rosell G, De Fabritiis G. LigVoxel: inpainting binding pockets using 3D-convolutional neural networks. Bioinformatics 2018; 35:243-250. [DOI: 10.1093/bioinformatics/bty583] [Citation(s) in RCA: 33] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2018] [Accepted: 07/04/2018] [Indexed: 11/13/2022] Open
Affiliation(s)
- Miha Skalic
- Computational Science Laboratory, Universitat Pompeu Fabra, Barcelona Biomedical Research Park (PRBB)
| | - Alejandro Varela-Rial
- Acellera, Barcelona Biomedical Research Park (PRBB), Doctor Aiguader 88, Barcelona, Spain
| | - José Jiménez
- Computational Science Laboratory, Universitat Pompeu Fabra, Barcelona Biomedical Research Park (PRBB)
| | - Gerard Martínez-Rosell
- Computational Science Laboratory, Universitat Pompeu Fabra, Barcelona Biomedical Research Park (PRBB)
| | - Gianni De Fabritiis
- Computational Science Laboratory, Universitat Pompeu Fabra, Barcelona Biomedical Research Park (PRBB)
- Institució Catalana de Recerca i Estudis Avançats (ICREA), Passeig Lluis Companys 23, Barcelona, Spain
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46
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Drwal MN, Bret G, Perez C, Jacquemard C, Desaphy J, Kellenberger E. Structural Insights on Fragment Binding Mode Conservation. J Med Chem 2018; 61:5963-5973. [PMID: 29906118 DOI: 10.1021/acs.jmedchem.8b00256] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023]
Abstract
Aiming at a deep understanding of fragment binding to ligandable targets, we performed a large scale analysis of the Protein Data Bank. Binding modes of 1832 drug-like ligands and 1079 fragments to 235 proteins were compared. We observed that the binding modes of fragments and their drug-like superstructures binding to the same protein are mostly conserved, thereby providing experimental evidence for the preservation of fragment binding modes during molecular growing. Furthermore, small chemical changes in the fragment are tolerated without alteration of the fragment binding mode. The exceptions to this observation generally involve conformational variability of the molecules. Our data analysis also suggests that, provided enough fragments have been crystallized within a protein, good interaction coverage of the binding pocket is achieved. Last, we extended our study to 126 crystallization additives and discuss in which cases they provide information relevant to structure-based drug design.
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Affiliation(s)
- Malgorzata N Drwal
- Laboratoire d'Innovation Thérapeutique , UMR7200, Université de Strasbourg , 74 Route du Rhin , 67401 Illkirch , France
| | - Guillaume Bret
- Laboratoire d'Innovation Thérapeutique , UMR7200, Université de Strasbourg , 74 Route du Rhin , 67401 Illkirch , France
| | - Carlos Perez
- Eli Lilly Research Laboratories , Avenida de la Industria, 30 , 28108 Alcobendas , Madrid , Spain
| | - Célien Jacquemard
- Laboratoire d'Innovation Thérapeutique , UMR7200, Université de Strasbourg , 74 Route du Rhin , 67401 Illkirch , France
| | - Jérémy Desaphy
- Lilly Research Laboratories, Eli Lilly and Company , Lilly Corporate Center , Indianapolis , Indiana 46285 , United States
| | - Esther Kellenberger
- Laboratoire d'Innovation Thérapeutique , UMR7200, Université de Strasbourg , 74 Route du Rhin , 67401 Illkirch , France
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47
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Jiménez J, Doerr S, Martínez-Rosell G, Rose AS, De Fabritiis G. DeepSite: protein-binding site predictor using 3D-convolutional neural networks. Bioinformatics 2018; 33:3036-3042. [PMID: 28575181 DOI: 10.1093/bioinformatics/btx350] [Citation(s) in RCA: 304] [Impact Index Per Article: 50.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2017] [Accepted: 05/30/2017] [Indexed: 11/13/2022] Open
Abstract
Motivation An important step in structure-based drug design consists in the prediction of druggable binding sites. Several algorithms for detecting binding cavities, those likely to bind to a small drug compound, have been developed over the years by clever exploitation of geometric, chemical and evolutionary features of the protein. Results Here we present a novel knowledge-based approach that uses state-of-the-art convolutional neural networks, where the algorithm is learned by examples. In total, 7622 proteins from the scPDB database of binding sites have been evaluated using both a distance and a volumetric overlap approach. Our machine-learning based method demonstrates superior performance to two other competitive algorithmic strategies. Availability and implementation DeepSite is freely available at www.playmolecule.org. Users can submit either a PDB ID or PDB file for pocket detection to our NVIDIA GPU-equipped servers through a WebGL graphical interface. Contact gianni.defabritiis@upf.edu. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- J Jiménez
- Computational Biophysics Laboratory (GRIB-IMIM), Universitat Pompeu Fabra, Barcelona Biomedical Research Park (PRBB), 08003 Barcelona, Spain
| | - S Doerr
- Computational Biophysics Laboratory (GRIB-IMIM), Universitat Pompeu Fabra, Barcelona Biomedical Research Park (PRBB), 08003 Barcelona, Spain
| | - G Martínez-Rosell
- Computational Biophysics Laboratory (GRIB-IMIM), Universitat Pompeu Fabra, Barcelona Biomedical Research Park (PRBB), 08003 Barcelona, Spain
| | - A S Rose
- San Diego Supercomputer Center, UC San Diego, MC 0505, 9500 Gilman Drive, La Jolla, CA 92093-0505. USA
| | - G De Fabritiis
- Computational Biophysics Laboratory (GRIB-IMIM), Universitat Pompeu Fabra, Barcelona Biomedical Research Park (PRBB), 08003 Barcelona, Spain.,ICREA, 08010 Barcelona, Spain
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48
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Sturm N, Tinivella A, Rastelli G. Exploration and Comparison of the Geometrical and Physicochemical Properties of an αC Allosteric Pocket in the Structural Kinome. J Chem Inf Model 2018; 58:1094-1103. [DOI: 10.1021/acs.jcim.7b00735] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/12/2023]
Affiliation(s)
- Noé Sturm
- Department of Life Sciences, University of Modena and Reggio Emilia, Via Campi 103, 41125 Modena, Italy
| | - Annachiara Tinivella
- Department of Life Sciences, University of Modena and Reggio Emilia, Via Campi 103, 41125 Modena, Italy
| | - Giulio Rastelli
- Department of Life Sciences, University of Modena and Reggio Emilia, Via Campi 103, 41125 Modena, Italy
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49
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Ding Y, Tang J, Guo F. Identification of Protein-Ligand Binding Sites by Sequence Information and Ensemble Classifier. J Chem Inf Model 2017; 57:3149-3161. [PMID: 29125297 DOI: 10.1021/acs.jcim.7b00307] [Citation(s) in RCA: 53] [Impact Index Per Article: 7.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Identifying protein-ligand binding sites is an important process in drug discovery and structure-based drug design. Detecting protein-ligand binding sites is expensive and time-consuming by traditional experimental methods. Hence, computational approaches provide many effective strategies to deal with this issue. Recently, lots of computational methods are based on structure information on proteins. However, these methods are limited in the common scenario, where both the sequence of protein target is known and sufficient 3D structure information is available. Studies indicate that sequence-based computational approaches for predicting protein-ligand binding sites are more practical. In this paper, we employ a novel computational model of protein-ligand binding sites prediction, using protein sequence. We apply the Discrete Cosine Transform (DCT) to extract feature from Position-Specific Score Matrix (PSSM). In order to improve the accuracy, Predicted Relative Solvent Accessibility (PRSA) information is also utilized. The predictor of protein-ligand binding sites is built by employing the ensemble weighted sparse representation model with random under-sampling. To evaluate our method, we conduct several comprehensive tests (12 types of ligands testing sets) for predicting protein-ligand binding sites. Results show that our method achieves better Matthew's correlation coefficient (MCC) than other outstanding methods on independent test sets of ATP (0.506), ADP (0.511), AMP (0.393), GDP (0.579), GTP (0.641), Mg2+ (0.317), Fe3+ (0.490) and HEME (0.640). Our proposed method outperforms earlier predictors (the performance of MCC) in 8 of the 12 ligands types.
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Affiliation(s)
- Yijie Ding
- School of Computer Science and Technology, Tianjin University , No. 135, Yaguan Road, Tianjin Haihe Education Park, Tianjin 300350, China
| | - Jijun Tang
- School of Computer Science and Technology, Tianjin University , No. 135, Yaguan Road, Tianjin Haihe Education Park, Tianjin 300350, China.,Department of Computer Science and Engineering, University of South Carolina , Columbia, South Carolina 29208, United States
| | - Fei Guo
- School of Computer Science and Technology, Tianjin University , No. 135, Yaguan Road, Tianjin Haihe Education Park, Tianjin 300350, China
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50
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Da Silva F, Desaphy J, Rognan D. IChem: A Versatile Toolkit for Detecting, Comparing, and Predicting Protein-Ligand Interactions. ChemMedChem 2017; 13:507-510. [PMID: 29024463 PMCID: PMC5901026 DOI: 10.1002/cmdc.201700505] [Citation(s) in RCA: 53] [Impact Index Per Article: 7.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2017] [Revised: 10/06/2017] [Indexed: 12/21/2022]
Abstract
Structure-based ligand design requires an exact description of the topology of molecular entities under scrutiny. IChem is a software package that reflects the many contributions of our research group in this area over the last decade. It facilitates and automates many tasks (e.g., ligand/cofactor atom typing, identification of key water molecules) usually left to the modeler's choice. It therefore permits the detection of molecular interactions between two molecules in a very precise and flexible manner. Moreover, IChem enables the conversion of intricate three-dimensional (3D) molecular objects into simple representations (fingerprints, graphs) that facilitate knowledge acquisition at very high throughput. The toolkit is an ideal companion for setting up and performing many structure-based design computations.
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Affiliation(s)
- Franck Da Silva
- Laboratoire d'Innovation Thérapeutique, UMR 7200 CNRS-Université de Strasbourg, 74 route du Rhin, 67400, Illkirch, France
| | - Jeremy Desaphy
- Laboratoire d'Innovation Thérapeutique, UMR 7200 CNRS-Université de Strasbourg, 74 route du Rhin, 67400, Illkirch, France.,Discovery Chemistry Research and Technologies, Eli Lilly and Company, Lilly Corporate Center, Indianapolis, IN, 46285, USA
| | - Didier Rognan
- Laboratoire d'Innovation Thérapeutique, UMR 7200 CNRS-Université de Strasbourg, 74 route du Rhin, 67400, Illkirch, France
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