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Wang A, Durrant JD. Open-Source Browser-Based Tools for Structure-Based Computer-Aided Drug Discovery. Molecules 2022; 27:4623. [PMID: 35889494 PMCID: PMC9319651 DOI: 10.3390/molecules27144623] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2022] [Revised: 07/17/2022] [Accepted: 07/18/2022] [Indexed: 01/27/2023] Open
Abstract
We here outline the importance of open-source, accessible tools for computer-aided drug discovery (CADD). We begin with a discussion of drug discovery in general to provide context for a subsequent discussion of structure-based CADD applied to small-molecule ligand discovery. Next, we identify usability challenges common to many open-source CADD tools. To address these challenges, we propose a browser-based approach to CADD tool deployment in which CADD calculations run in modern web browsers on users' local computers. The browser app approach eliminates the need for user-initiated download and installation, ensures broad operating system compatibility, enables easy updates, and provides a user-friendly graphical user interface. Unlike server apps-which run calculations "in the cloud" rather than on users' local computers-browser apps do not require users to upload proprietary information to a third-party (remote) server. They also eliminate the need for the difficult-to-maintain computer infrastructure required to run user-initiated calculations remotely. We conclude by describing some CADD browser apps developed in our lab, which illustrate the utility of this approach. Aside from introducing readers to these specific tools, we are hopeful that this review highlights the need for additional browser-compatible, user-friendly CADD software.
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Affiliation(s)
| | - Jacob D. Durrant
- Department of Biological Sciences, University of Pittsburgh, Pittsburgh, PA 15260, USA;
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52
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Wilkinson IVL, Pfanzelt M, Sieber SA. Functionalised Cofactor Mimics for Interactome Discovery and Beyond. Angew Chem Int Ed Engl 2022; 61:e202201136. [PMID: 35286003 PMCID: PMC9401033 DOI: 10.1002/anie.202201136] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2022] [Indexed: 11/09/2022]
Abstract
Cofactors are required for almost half of all enzyme reactions, but their functions and binding partners are not fully understood even after decades of research. Functionalised cofactor mimics that bind in place of the unmodified cofactor can provide answers, as well as expand the scope of cofactor activity. Through chemical proteomics approaches such as activity-based protein profiling, the interactome and localisation of the native cofactor in its physiological environment can be deciphered and previously uncharacterised proteins annotated. Furthermore, cofactors that supply functional groups to substrate biomolecules can be hijacked by mimics to site-specifically label targets and unravel the complex biology of post-translational protein modification. The diverse activity of cofactors has inspired the design of mimics for use as inhibitors, antibiotic therapeutics, and chemo- and biosensors, and cofactor conjugates have enabled the generation of novel enzymes and artificial DNAzymes.
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Affiliation(s)
- Isabel V L Wilkinson
- Centre for Functional Protein Assemblies, Technical University of Munich, Ernst-Otto-Fischer-Straße 8, 85748, Garching, Germany
| | - Martin Pfanzelt
- Centre for Functional Protein Assemblies, Technical University of Munich, Ernst-Otto-Fischer-Straße 8, 85748, Garching, Germany
| | - Stephan A Sieber
- Centre for Functional Protein Assemblies, Technical University of Munich, Ernst-Otto-Fischer-Straße 8, 85748, Garching, Germany
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53
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Molecular mechanism of interaction between fatty acid delta 6 desaturase and acyl-CoA by computational prediction. AMB Express 2022; 12:69. [PMID: 35680699 PMCID: PMC9184693 DOI: 10.1186/s13568-022-01410-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2021] [Accepted: 05/27/2022] [Indexed: 11/17/2022] Open
Abstract
Enzyme catalyzed desaturation of intracellular fatty acids plays an important role in various physiological and pathological processes related to lipids. Limited to the multiple transmembrane domains, it is difficult to obtain their three-dimensional structure of fatty acid desaturases. So how they interact with their substrates is unclear. Here, we predicted the complex of Micromonas pusilla delta 6 desaturase (MpFADS6) with the substrate linoleinyl-CoA (ALA-CoA) by trRosetta software and docking poses by Dock 6 software. The potential enzyme–substrate binding sites were anchored by analysis of the complex. Then, site-directed mutagenesis and activity verification clarified that W290, W224, and F352 were critical residues of the substrate tunnel and directly bonded to ALA-CoA. H94 and H69 were indispensable for transporting electrons with heme. H452, N445, and H358 significantly influenced the recognition and attraction of MpFADS6 to the substrate. These findings provide new insights and methods to determine the structure, mechanisms and directed transformation of membrane-bound desaturases. The structure of the Δ6 fatty acid desaturase and substrate complex is modeled. The substrate tunnel and key residues of MpFADS6 catalytic activity are determined. The new insights to determine the mechanism of the membrane-bound desaturases.
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54
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From single-omics to interactomics: How can ligand-induced perturbations modulate single-cell phenotypes? ADVANCES IN PROTEIN CHEMISTRY AND STRUCTURAL BIOLOGY 2022; 131:45-83. [PMID: 35871896 DOI: 10.1016/bs.apcsb.2022.05.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/26/2023]
Abstract
Cells suffer from perturbations by different stimuli, which, consequently, rise to individual alterations in their profile and function that may end up affecting the tissue as a whole. This is no different if we consider the effect of a therapeutic agent on a biological system. As cells are exposed to external ligands their profile can change at different single-omics levels. Detecting how these changes take place through different sequencing technologies is key to a better understanding of the effects of therapeutic agents. Single-cell RNA-sequencing stands out as one of the most common approaches for cell profiling and perturbation analysis. As a result, single-cell transcriptomics data can be integrated with other omics data sources, such as proteomics and epigenomics data, to clarify the perturbation effects and mechanism at the cell level. Appropriate computational tools are key to process and integrate the available information. This chapter focuses on the recent advances on ligand-induced perturbation and single-cell omics computational tools and algorithms, their current limitations, and how the deluge of data can be used to improve the current process of drug research and development.
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55
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Barnsley KK, Ondrechen MJ. Enzyme active sites: Identification and prediction of function using computational chemistry. Curr Opin Struct Biol 2022; 74:102384. [DOI: 10.1016/j.sbi.2022.102384] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2022] [Revised: 03/20/2022] [Accepted: 03/28/2022] [Indexed: 11/03/2022]
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56
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Wilkinson IVL, Pfanzelt M, Sieber SA. Funktionalisierte Cofaktor‐Analoga für die Erforschung von Interaktomen und darüber hinaus. Angew Chem Int Ed Engl 2022. [DOI: 10.1002/ange.202201136] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Affiliation(s)
- Isabel V. L. Wilkinson
- Centre for Functional Protein Assemblies Technische Universität München Ernst-Otto-Fischer-Straße 8 85748 Garching Deutschland
| | - Martin Pfanzelt
- Centre for Functional Protein Assemblies Technische Universität München Ernst-Otto-Fischer-Straße 8 85748 Garching Deutschland
| | - Stephan A. Sieber
- Centre for Functional Protein Assemblies Technische Universität München Ernst-Otto-Fischer-Straße 8 85748 Garching Deutschland
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57
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You Y, Lai X, Pan Y, Zheng H, Vera J, Liu S, Deng S, Zhang L. Artificial intelligence in cancer target identification and drug discovery. Signal Transduct Target Ther 2022; 7:156. [PMID: 35538061 PMCID: PMC9090746 DOI: 10.1038/s41392-022-00994-0] [Citation(s) in RCA: 68] [Impact Index Per Article: 34.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2021] [Revised: 03/14/2022] [Accepted: 04/05/2022] [Indexed: 02/08/2023] Open
Abstract
Artificial intelligence is an advanced method to identify novel anticancer targets and discover novel drugs from biology networks because the networks can effectively preserve and quantify the interaction between components of cell systems underlying human diseases such as cancer. Here, we review and discuss how to employ artificial intelligence approaches to identify novel anticancer targets and discover drugs. First, we describe the scope of artificial intelligence biology analysis for novel anticancer target investigations. Second, we review and discuss the basic principles and theory of commonly used network-based and machine learning-based artificial intelligence algorithms. Finally, we showcase the applications of artificial intelligence approaches in cancer target identification and drug discovery. Taken together, the artificial intelligence models have provided us with a quantitative framework to study the relationship between network characteristics and cancer, thereby leading to the identification of potential anticancer targets and the discovery of novel drug candidates.
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Affiliation(s)
- Yujie You
- College of Computer Science, Sichuan University, Chengdu, 610065, China
| | - Xin Lai
- Laboratory of Systems Tumor Immunology, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) and Universitätsklinikum Erlangen, Erlangen, 91052, Germany
| | - Yi Pan
- Faculty of Computer Science and Control Engineering, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Room D513, 1068 Xueyuan Avenue, Shenzhen University Town, Shenzhen, 518055, China
| | - Huiru Zheng
- School of Computing, Ulster University, Belfast, BT15 1ED, UK
| | - Julio Vera
- Laboratory of Systems Tumor Immunology, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) and Universitätsklinikum Erlangen, Erlangen, 91052, Germany
| | - Suran Liu
- College of Computer Science, Sichuan University, Chengdu, 610065, China
| | - Senyi Deng
- Institute of Thoracic Oncology, Department of Thoracic Surgery, West China Hospital, Sichuan University, Chengdu, 610065, China.
| | - Le Zhang
- College of Computer Science, Sichuan University, Chengdu, 610065, China.
- Key Laboratory of Systems Biology, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Hangzhou, 310024, China.
- Key Laboratory of Systems Health Science of Zhejiang Province, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou, 310024, China.
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58
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Santana CA, Izidoro SC, de Melo-Minardi RC, Tyzack JD, Ribeiro AJM, Pires DEV, Thornton JM, de A Silveira S. GRaSP-web: a machine learning strategy to predict binding sites based on residue neighborhood graphs. Nucleic Acids Res 2022; 50:W392-W397. [PMID: 35524575 PMCID: PMC9252730 DOI: 10.1093/nar/gkac323] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2022] [Revised: 04/14/2022] [Accepted: 04/22/2022] [Indexed: 11/14/2022] Open
Abstract
Proteins are essential macromolecules for the maintenance of living systems. Many of them perform their function by interacting with other molecules in regions called binding sites. The identification and characterization of these regions are of fundamental importance to determine protein function, being a fundamental step in processes such as drug design and discovery. However, identifying such binding regions is not trivial due to the drawbacks of experimental methods, which are costly and time-consuming. Here we propose GRaSP-web, a web server that uses GRaSP (Graph-based Residue neighborhood Strategy to Predict binding sites), a residue-centric method based on graphs that uses machine learning to predict putative ligand binding site residues. The method outperformed 6 state-of-the-art residue-centric methods (MCC of 0.61). Also, GRaSP-web is scalable as it takes 10-20 seconds to predict binding sites for a protein complex (the state-of-the-art residue-centric method takes 2-5h on the average). It proved to be consistent in predicting binding sites for bound/unbound structures (MCC 0.61 for both) and for a large dataset of multi-chain proteins (4500 entries, MCC 0.61). GRaSPWeb is freely available at https://grasp.ufv.br.
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Affiliation(s)
- Charles A Santana
- Department of Biochemistry and Immunology, Universidade Federal de Minas Gerais, Belo Horizonte 31270-901, Brazil.,Department of Computer Science, Universidade Federal de Minas Gerais, Belo Horizonte 31270-901, Brazil
| | - Sandro C Izidoro
- Institute of Technological Sciences (ICT), Advanced Campus at Itabira, Universidade Federal de Itajubá, Itabira 35903-087, Brazil
| | - Raquel C de Melo-Minardi
- Department of Biochemistry and Immunology, Universidade Federal de Minas Gerais, Belo Horizonte 31270-901, Brazil.,Department of Computer Science, Universidade Federal de Minas Gerais, Belo Horizonte 31270-901, Brazil
| | - Jonathan D Tyzack
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - António J M Ribeiro
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Douglas E V Pires
- School of Computing and Information Systems, University of Melbourne, Parkville 3052, Australia
| | - Janet M Thornton
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Sabrina de A Silveira
- Department of Computer Science, Universidade Federal de Viçosa, Viçosa 36570-900, Brazil
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59
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Zhou Y, Jiang Y, Chen SJ. RNA-ligand molecular docking: advances and challenges. WILEY INTERDISCIPLINARY REVIEWS. COMPUTATIONAL MOLECULAR SCIENCE 2022; 12:e1571. [PMID: 37293430 PMCID: PMC10250017 DOI: 10.1002/wcms.1571] [Citation(s) in RCA: 23] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/26/2021] [Accepted: 07/20/2021] [Indexed: 12/16/2022]
Abstract
With rapid advances in computer algorithms and hardware, fast and accurate virtual screening has led to a drastic acceleration in selecting potent small molecules as drug candidates. Computational modeling of RNA-small molecule interactions has become an indispensable tool for RNA-targeted drug discovery. The current models for RNA-ligand binding have mainly focused on the docking-and-scoring method. Accurate docking and scoring should tackle four crucial problems: (1) conformational flexibility of ligand, (2) conformational flexibility of RNA, (3) efficient sampling of binding sites and binding poses, and (4) accurate scoring of different binding modes. Moreover, compared with the problem of protein-ligand docking, predicting ligand binding to RNA, a negatively charged polymer, is further complicated by additional effects such as metal ion effects. Thermodynamic models based on physics-based and knowledge-based scoring functions have shown highly encouraging success in predicting ligand binding poses and binding affinities. Recently, kinetic models for ligand binding have further suggested that including dissociation kinetics (residence time) in ligand docking would result in improved performance in estimating in vivo drug efficacy. More recently, the rise of deep-learning approaches has led to new tools for predicting RNA-small molecule binding. In this review, we present an overview of the recently developed computational methods for RNA-ligand docking and their advantages and disadvantages.
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Affiliation(s)
- Yuanzhe Zhou
- Department of Physics and Astronomy, Department of Biochemistry, Institute of Data Sciences and Informatics, University of Missouri, Columbia, MO 65211-7010, USA
| | - Yangwei Jiang
- Department of Physics and Astronomy, Department of Biochemistry, Institute of Data Sciences and Informatics, University of Missouri, Columbia, MO 65211-7010, USA
| | - Shi-Jie Chen
- Department of Physics and Astronomy, Department of Biochemistry, Institute of Data Sciences and Informatics, University of Missouri, Columbia, MO 65211-7010, USA
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60
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Venkatraman V, Colligan TH, Lesica GT, Olson DR, Gaiser J, Copeland CJ, Wheeler TJ, Roy A. Drugsniffer: An Open Source Workflow for Virtually Screening Billions of Molecules for Binding Affinity to Protein Targets. Front Pharmacol 2022; 13:874746. [PMID: 35559261 PMCID: PMC9086895 DOI: 10.3389/fphar.2022.874746] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2022] [Accepted: 04/04/2022] [Indexed: 11/13/2022] Open
Abstract
The SARS-CoV2 pandemic has highlighted the importance of efficient and effective methods for identification of therapeutic drugs, and in particular has laid bare the need for methods that allow exploration of the full diversity of synthesizable small molecules. While classical high-throughput screening methods may consider up to millions of molecules, virtual screening methods hold the promise of enabling appraisal of billions of candidate molecules, thus expanding the search space while concurrently reducing costs and speeding discovery. Here, we describe a new screening pipeline, called drugsniffer, that is capable of rapidly exploring drug candidates from a library of billions of molecules, and is designed to support distributed computation on cluster and cloud resources. As an example of performance, our pipeline required ∼40,000 total compute hours to screen for potential drugs targeting three SARS-CoV2 proteins among a library of ∼3.7 billion candidate molecules.
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Affiliation(s)
- Vishwesh Venkatraman
- Department of Chemistry, Norwegian University of Science and Technology, Trondheim, Norway
| | - Thomas H. Colligan
- Department of Computer Science, University of Montana, Missoula, MT, United States
| | - George T. Lesica
- Department of Computer Science, University of Montana, Missoula, MT, United States
| | - Daniel R. Olson
- Department of Computer Science, University of Montana, Missoula, MT, United States
| | - Jeremiah Gaiser
- Department of Computer Science, University of Montana, Missoula, MT, United States
| | - Conner J. Copeland
- Department of Computer Science, University of Montana, Missoula, MT, United States
| | - Travis J. Wheeler
- Department of Computer Science, University of Montana, Missoula, MT, United States
| | - Amitava Roy
- Department of Computer Science, University of Montana, Missoula, MT, United States
- Rocky Mountain Laboratories, Bioinformatics and Computational Biosciences Branch, Office of Cyber Infrastructure and Computational Biology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Hamilton, MT, United States
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61
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Chelur VR, Priyakumar UD. BiRDS - Binding Residue Detection from Protein Sequences Using Deep ResNets. J Chem Inf Model 2022; 62:1809-1818. [PMID: 35414182 DOI: 10.1021/acs.jcim.1c00972] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Protein-drug interactions play important roles in many biological processes and therapeutics. Predicting the binding sites of a protein helps to discover such interactions. New drugs can be designed to optimize these interactions, improving protein function. The tertiary structure of a protein decides the binding sites available to the drug molecule, but the determination of the 3D structure is slow and expensive. Conversely, the determination of the amino acid sequence is swift and economical. Although quick and accurate prediction of the binding site using just the sequence is challenging, the application of Deep Learning, which has been hugely successful in several biochemical tasks, makes it feasible. BiRDS is a Residual Neural Network that predicts the protein's most active binding site using sequence information. SC-PDB, an annotated database of druggable binding sites, is used for training the network. Multiple Sequence Alignments of the proteins in the database are generated using DeepMSA, and features such as Position-Specific Scoring Matrix, Secondary Structure, and Relative Solvent Accessibility are extracted. During training, a weighted binary cross-entropy loss function is used to counter the substantial imbalance in the two classes of binding and nonbinding residues. A novel test set SC6K is introduced to compare binding-site prediction methods. BiRDS achieves an AUROC score of 0.87, and the center of 25% of its predicted binding sites lie within 4 Å of the center of the actual binding site.
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Affiliation(s)
- Vineeth R Chelur
- Center for Computational Natural Sciences & Bioinformatics International Institute of Information Technology Hyderabad 500032, India
| | - U Deva Priyakumar
- Center for Computational Natural Sciences & Bioinformatics International Institute of Information Technology Hyderabad 500032, India
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62
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McGreig JE, Uri H, Antczak M, Sternberg MJE, Michaelis M, Wass MN. 3DLigandSite: structure-based prediction of protein-ligand binding sites. Nucleic Acids Res 2022; 50:W13-W20. [PMID: 35412635 PMCID: PMC9252821 DOI: 10.1093/nar/gkac250] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2022] [Revised: 03/13/2022] [Accepted: 04/03/2022] [Indexed: 01/13/2023] Open
Abstract
3DLigandSite is a web tool for the prediction of ligand-binding sites in proteins. Here, we report a significant update since the first release of 3DLigandSite in 2010. The overall methodology remains the same, with candidate binding sites in proteins inferred using known binding sites in related protein structures as templates. However, the initial structural modelling step now uses the newly available structures from the AlphaFold database or alternatively Phyre2 when AlphaFold structures are not available. Further, a sequence-based search using HHSearch has been introduced to identify template structures with bound ligands that are used to infer the ligand-binding residues in the query protein. Finally, we introduced a machine learning element as the final prediction step, which improves the accuracy of predictions and provides a confidence score for each residue predicted to be part of a binding site. Validation of 3DLigandSite on a set of 6416 binding sites obtained 92% recall at 75% precision for non-metal binding sites and 52% recall at 75% precision for metal binding sites. 3DLigandSite is available at https://www.wass-michaelislab.org/3dligandsite. Users submit either a protein sequence or structure. Results are displayed in multiple formats including an interactive Mol* molecular visualization of the protein and the predicted binding sites.
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Affiliation(s)
- Jake E McGreig
- School of Biosciences, Division of Natural Sciences, University of Kent, Canterbury, Kent CT2 7NJ, UK
| | - Hannah Uri
- School of Biosciences, Division of Natural Sciences, University of Kent, Canterbury, Kent CT2 7NJ, UK
| | - Magdalena Antczak
- School of Biosciences, Division of Natural Sciences, University of Kent, Canterbury, Kent CT2 7NJ, UK
| | - Michael J E Sternberg
- Centre for Integrative Systems Biology and Bioinformatics, Department of Life Sciences, Imperial College London, London SW7 2AZ, UK
| | - Martin Michaelis
- School of Biosciences, Division of Natural Sciences, University of Kent, Canterbury, Kent CT2 7NJ, UK
| | - Mark N Wass
- School of Biosciences, Division of Natural Sciences, University of Kent, Canterbury, Kent CT2 7NJ, UK
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63
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Schweke H, Mucchielli MH, Chevrollier N, Gosset S, Lopes A. SURFMAP: A Software for Mapping in Two Dimensions Protein Surface Features. J Chem Inf Model 2022; 62:1595-1601. [DOI: 10.1021/acs.jcim.1c01269] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Hugo Schweke
- Université Paris-Saclay, CEA, CNRS, Institute for Integrative Biology of the Cell (I2BC), Gif-sur-Yvette 91198, France
- Department of Chemical and Structural Biology, Weizmann Institute of Science, Rehovot 7610001, Israel
| | - Marie-Hélène Mucchielli
- Université Paris-Saclay, CNRS, INRAE, Université Evry, Institute of Plant Sciences Paris-Saclay (IPS2), Gif-sur-Yvette 91190, France
- Université de Paris, Institute of Plant Sciences Paris-Saclay (IPS2), Gif-sur-Yvette 91190, France
| | | | - Simon Gosset
- Université Paris-Saclay, CNRS, INRAE, Université Evry, Institute of Plant Sciences Paris-Saclay (IPS2), Gif-sur-Yvette 91190, France
- Université de Paris, Institute of Plant Sciences Paris-Saclay (IPS2), Gif-sur-Yvette 91190, France
| | - Anne Lopes
- Université Paris-Saclay, CEA, CNRS, Institute for Integrative Biology of the Cell (I2BC), Gif-sur-Yvette 91198, France
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64
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Putative dual inhibitors of mTOR and RET kinase from natural products: Pharmacophore-based hierarchical virtual screening. J Mol Liq 2022. [DOI: 10.1016/j.molliq.2022.118562] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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65
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Cammarota RC, Liu W, Bacsa J, Davies HML, Sigman MS. Mechanistically Guided Workflow for Relating Complex Reactive Site Topologies to Catalyst Performance in C–H Functionalization Reactions. J Am Chem Soc 2022; 144:1881-1898. [DOI: 10.1021/jacs.1c12198] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Affiliation(s)
- Ryan C. Cammarota
- Department of Chemistry, University of Utah, 315 South 1400 East, Salt Lake City, Utah 84112, United States
| | - Wenbin Liu
- Department of Chemistry, Emory University, 1515 Dickey Drive, Atlanta, Georgia 30322, United States
| | - John Bacsa
- Department of Chemistry, Emory University, 1515 Dickey Drive, Atlanta, Georgia 30322, United States
| | - Huw M. L. Davies
- Department of Chemistry, Emory University, 1515 Dickey Drive, Atlanta, Georgia 30322, United States
| | - Matthew S. Sigman
- Department of Chemistry, University of Utah, 315 South 1400 East, Salt Lake City, Utah 84112, United States
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66
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Dhakal A, McKay C, Tanner JJ, Cheng J. Artificial intelligence in the prediction of protein-ligand interactions: recent advances and future directions. Brief Bioinform 2022; 23:bbab476. [PMID: 34849575 PMCID: PMC8690157 DOI: 10.1093/bib/bbab476] [Citation(s) in RCA: 78] [Impact Index Per Article: 39.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2021] [Revised: 09/28/2021] [Accepted: 10/15/2021] [Indexed: 12/13/2022] Open
Abstract
New drug production, from target identification to marketing approval, takes over 12 years and can cost around $2.6 billion. Furthermore, the COVID-19 pandemic has unveiled the urgent need for more powerful computational methods for drug discovery. Here, we review the computational approaches to predicting protein-ligand interactions in the context of drug discovery, focusing on methods using artificial intelligence (AI). We begin with a brief introduction to proteins (targets), ligands (e.g. drugs) and their interactions for nonexperts. Next, we review databases that are commonly used in the domain of protein-ligand interactions. Finally, we survey and analyze the machine learning (ML) approaches implemented to predict protein-ligand binding sites, ligand-binding affinity and binding pose (conformation) including both classical ML algorithms and recent deep learning methods. After exploring the correlation between these three aspects of protein-ligand interaction, it has been proposed that they should be studied in unison. We anticipate that our review will aid exploration and development of more accurate ML-based prediction strategies for studying protein-ligand interactions.
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Affiliation(s)
- Ashwin Dhakal
- Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO, 65211, USA
| | - Cole McKay
- Department of Biochemistry, University of Missouri, Columbia, MO, 65211, USA
| | - John J Tanner
- Department of Biochemistry, University of Missouri, Columbia, MO, 65211, USA
- Department of Chemistry, University of Missouri, Columbia, MO, 65211, USA
| | - Jianlin Cheng
- Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO, 65211, USA
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67
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Parate S, Kumar V, Chan Hong J, Lee KW. Investigating natural compounds against oncogenic RET tyrosine kinase using pharmacoinformatic approaches for cancer therapeutics. RSC Adv 2022; 12:1194-1207. [PMID: 35425116 PMCID: PMC8978841 DOI: 10.1039/d1ra07328a] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2021] [Accepted: 11/30/2021] [Indexed: 01/01/2023] Open
Abstract
Rearranged during transfection (RET) tyrosine kinase is a transmembrane receptor tyrosine kinase regulating vital aspects of cellular proliferation, differentiation, and survival. An outstanding challenge in designing protein kinase inhibitors is due to the development of drug resistance. The “gain of function” mutations in the RET gate-keeper residue, Val804, confers resistance to the majority of known RET inhibitors, including vandetanib. To curtail this resistance, researchers developed selpercatinib (LOXO-292) against the RET gate-keeper mutant forms – V804M and V804L. In the present in silico investigation, a receptor–ligand pharmacophore model was generated to identify small molecule inhibitors effective for wild-type (WT) as well as mutant RET kinase variants. The generated model was employed to screen 144 766 natural products (NPs) available in the ZINC database and the retrieved NPs were filtered for their drug-likeness. The resulting 2696 drug-like NPs were subjected to molecular docking with the RET WT kinase domain and a total of 27 molecules displayed better dock scores than the reference inhibitors – vandetanib and selpercatinib. From 27 NPs, an aggregate of 12 compounds demonstrated better binding free energy (BFE) scores than the reference inhibitors, towards RET. Thus, the 12 NPs were also subjected to docking, simulation, and BFE estimation towards the constructed gate-keeper RET mutant structures. The BFE calculations revealed 3 hits with better BFE scores than the reference inhibitors towards WT, V804M, and V804L RET variants. Thus, the scaffolds of hit compounds presented in this study could act as potent RET inhibitors and further provide insights for drug optimization targeting aberrant activation of RET signaling, specifically the mutation of gate-keeper residue – Val804. Identification of natural product inhibitors against rearranged during transfection (RET) tyrosine kinase as cancer therapeutics using combination of in silico techniques.![]()
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Affiliation(s)
- Shraddha Parate
- Division of Applied Life Science, Plant Molecular Biology and Biotechnology Research Center (PMBBRC), Gyeongsang National University (GNU), 501 Jinju-daero, Jinju 52828, Korea
| | - Vikas Kumar
- Division of Life Sciences, Department of Bio & Medical Big Data (BK21 Program), Research Institute of Natural Science (RINS), Gyeongsang National University (GNU), 501 Jinju-daero, Jinju 52828, Korea
| | - Jong Chan Hong
- Division of Applied Life Science, Plant Molecular Biology and Biotechnology Research Center (PMBBRC), Gyeongsang National University (GNU), 501 Jinju-daero, Jinju 52828, Korea
| | - Keun Woo Lee
- Division of Life Sciences, Department of Bio & Medical Big Data (BK21 Program), Research Institute of Natural Science (RINS), Gyeongsang National University (GNU), 501 Jinju-daero, Jinju 52828, Korea
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Wei X, Wu X, Cheng Z, Wu Q, Cao C, Xu X, Shang H. Botanical drugs: a new strategy for structure-based target prediction. Brief Bioinform 2021; 23:6409695. [PMID: 34698349 DOI: 10.1093/bib/bbab425] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2021] [Revised: 09/08/2021] [Accepted: 09/17/2021] [Indexed: 11/14/2022] Open
Abstract
Target identification of small molecules is an important and still changeling work in the area of drug discovery, especially for botanical drug development. Indistinct understanding of the relationships of ligand-protein interactions is one of the main obstacles for drug repurposing and identification of off-targets. In this study, we collected 9063 crystal structures of ligand-binding proteins released from January, 1995 to April, 2021 in PDB bank, and split the complexes into 5133 interaction pairs of ligand atoms and protein fragments (covalently linked three heavy atoms) with interatomic distance ≤5 Å. The interaction pairs were grouped into ligand atoms with the same SYBYL atom type surrounding each type of protein fragment, which were further clustered via Bayesian Gaussian Mixture Model (BGMM). Gaussian distributions with ligand atoms ≥20 were identified as significant interaction patterns. Reliability of the significant interaction patterns was validated by comparing the difference of number of significant interaction patterns between the docked poses with higher and lower similarity to the native crystal structures. Fifty-one candidate targets of brucine, strychnine and icajine involved in Semen Strychni (Mǎ Qián Zǐ) and eight candidate targets of astragaloside-IV, formononetin and calycosin-7-glucoside involved in Astragalus (Huáng Qí) were predicted by the significant interaction patterns, in combination with docking, which were consistent with the therapeutic effects of Semen Strychni and Astragalus for cancer and chronic pain. The new strategy in this study improves the accuracy of target identification for small molecules, which will facilitate discovery of botanical drugs.
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Affiliation(s)
- Xuxu Wei
- Key laboratory of Chinese internal medicine of MOE, Dongzhimen Hospital, BUCM, Beijing, China.,School of Medicine, Wuhan University of Science and Technology, Wuhan, Hubei 430081, China
| | - Xiang Wu
- School of Computer Science and Technology, Wuhan University of Science and Technology, Wuhan, Hubei 430081, China
| | - Zeyu Cheng
- School of Computer Science and Technology, Wuhan University of Science and Technology, Wuhan, Hubei 430081, China
| | - Qingming Wu
- School of Medicine, Wuhan University of Science and Technology, Wuhan, Hubei 430081, China
| | - Chen Cao
- Department of Biochemistry & Molecular Biology, Alberta Children's Hospital Research Institute, University of Calgary, Calgary, AB, Canada
| | - Xue Xu
- School of Medicine, Wuhan University of Science and Technology, Wuhan, Hubei 430081, China
| | - Hongcai Shang
- Key laboratory of Chinese internal medicine of MOE, Dongzhimen Hospital, BUCM, Beijing, China.,Evidence-Based Medicine Research Centre, Jiangxi University of Chinese Medicine, Jiangxi, China
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69
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Maiti P, Manna J, Thammathong J, Evans B, Dubey KD, Banerjee S, Dunbar GL. Tetrahydrocurcumin Has Similar Anti-Amyloid Properties as Curcumin: In Vitro Comparative Structure-Activity Studies. Antioxidants (Basel) 2021; 10:antiox10101592. [PMID: 34679727 PMCID: PMC8533373 DOI: 10.3390/antiox10101592] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2021] [Revised: 09/30/2021] [Accepted: 10/04/2021] [Indexed: 11/16/2022] Open
Abstract
Despite its potent anti-amyloid properties, the utility of curcumin (Cur) for the treatment of Alzheimer's disease (AD) is limited due to its low bioavailability. Tetrahydrocurcumin (THC), a more stable metabolite has been found in Cur-treated tissues. We compared the anti-amyloid and neuroprotective properties of curcumin, bisdemethoxycurcumin (BDMC), demethoxycurcumin (DMC) and THC using molecular docking/dynamics, in-silico and in vitro studies. We measured the binding affinity, H-bonding capabilities of these compounds with amyloid beta protein (Aβ). Dot blot assays, photo-induced cross linking of unmodified protein (PICUP) and transmission electron microscopy (TEM) were performed to monitor the Aβ aggregation inhibition using these compounds. Neuroprotective effects of these derivatives were evaluated in N2a, CHO and SH-SY5Y cells using Aβ42 (10 µM) as a toxin. Finally, Aβ-binding capabilities were compared in the brain tissue derived from the 5× FAD mouse model of AD. We observed that THC had similar binding capability and Aβ aggregation inhibition such as keto/enol Cur and it was greater than BDMC and DMC. All these derivatives showed a similar degree of neuroprotection in vitro and labeled Aβ-plaques ex vivo. Overall, ECur and THC showed greater anti-amyloid properties than other derivatives. Therefore, THC, a more stable and bioavailable metabolite may provide greater therapeutic efficacy in AD than other turmeric derivatives.
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Affiliation(s)
- Panchanan Maiti
- Field Neurosciences Institute, Ascension St. Mary’s Hospital, Saginaw, MI 48604, USA;
- Field Neurosciences Institute Laboratory for Restorative Neurology, Central Michigan University, Mt. Pleasant, MI 48859, USA
- Program in Neuroscience, Central Michigan University, Mt. Pleasant, MI 48859, USA
- Department of Psychology, Central Michigan University, Mt. Pleasant, MI 48859, USA
- Brain Research Laboratory, College of Health and Human Services, Saginaw Valley State University, Saginaw, MI 48604, USA
- Correspondence: (P.M.); (G.L.D.)
| | - Jayeeta Manna
- Field Neurosciences Institute, Ascension St. Mary’s Hospital, Saginaw, MI 48604, USA;
| | - Joshua Thammathong
- Department of Physical Sciences, College of Science, Technology, Engineering, and Mathematics, University of Arkansas Fort Smith, Fort Smith, AR 72913, USA; (J.T.); (B.E.); (S.B.)
| | - Bobbi Evans
- Department of Physical Sciences, College of Science, Technology, Engineering, and Mathematics, University of Arkansas Fort Smith, Fort Smith, AR 72913, USA; (J.T.); (B.E.); (S.B.)
| | - Kshatresh Dutta Dubey
- Department of Chemistry and Center for Informatics, School of Natural Sciences, Shiv Nadar University, Delhi-NCR, Gautam Buddha Nagar 201314, India;
| | - Souvik Banerjee
- Department of Physical Sciences, College of Science, Technology, Engineering, and Mathematics, University of Arkansas Fort Smith, Fort Smith, AR 72913, USA; (J.T.); (B.E.); (S.B.)
| | - Gary L. Dunbar
- Field Neurosciences Institute, Ascension St. Mary’s Hospital, Saginaw, MI 48604, USA;
- Field Neurosciences Institute Laboratory for Restorative Neurology, Central Michigan University, Mt. Pleasant, MI 48859, USA
- Program in Neuroscience, Central Michigan University, Mt. Pleasant, MI 48859, USA
- Department of Psychology, Central Michigan University, Mt. Pleasant, MI 48859, USA
- Correspondence: (P.M.); (G.L.D.)
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Bhowmick S, Saha A, AlFaris NA, ALTamimi JZ, ALOthman ZA, Aldayel TS, Wabaidur SM, Islam MA. Structure-based identification of galectin-1 selective modulators in dietary food polyphenols: a pharmacoinformatics approach. Mol Divers 2021; 26:1697-1714. [PMID: 34482478 PMCID: PMC9209356 DOI: 10.1007/s11030-021-10297-1] [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: 06/20/2021] [Accepted: 08/12/2021] [Indexed: 11/29/2022]
Abstract
Abstract In this study, a set of dietary polyphenols was comprehensively studied for the selective identification of the potential inhibitors/modulators for galectin-1. Galectin-1 is a potent prognostic indicator of tumor progression and a highly regarded therapeutic target for various pathological conditions. This indicator is composed of a highly conserved carbohydrate recognition domain (CRD) that accounts for the binding affinity of β-galactosides. Although some small molecules have been identified as galectin-1 inhibitors/modulators, there are limited studies on the identification of novel compounds against this attractive therapeutic target. The extensive computational techniques include potential drug binding site recognition on galectin-1, binding affinity predictions of ~ 500 polyphenols, molecular docking, and dynamic simulations of galectin-1 with selective dietary polyphenol modulators, followed by the estimation of binding free energy for the identification of dietary polyphenol-based galectin-1 modulators. Initially, a deep neural network-based algorithm was utilized for the prediction of the druggable binding site and binding affinity. Thereafter, the intermolecular interactions of the polyphenol compounds with galectin-1 were critically explored through the extra-precision docking technique. Further, the stability of the interaction was evaluated through the conventional atomistic 100 ns dynamic simulation study. The docking analyses indicated the high interaction affinity of different amino acids at the CRD region of galectin-1 with the proposed five polyphenols. Strong and consistent interaction stability was suggested from the simulation trajectories of the selected dietary polyphenol under the dynamic conditions. Also, the conserved residue (His44, Asn46, Arg48, Val59, Asn61, Trp68, Glu71, and Arg73) associations suggest high affinity and selectivity of polyphenols toward galectin-1 protein. Graphic Abstract ![]()
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Affiliation(s)
- Shovonlal Bhowmick
- Department of Chemical Technology, University of Calcutta, 92, A.P.C. Road, Kolkata, 700009, India
| | - Achintya Saha
- Department of Chemical Technology, University of Calcutta, 92, A.P.C. Road, Kolkata, 700009, India.
| | - Nora Abdullah AlFaris
- Nutrition and Food Science, Department of Physical Sport Science, Princess Nourah Bint Abdulrahman University, P.O. Box 84428, Riyadh, 11671, Saudi Arabia
| | - Jozaa Zaidan ALTamimi
- Nutrition and Food Science, Department of Physical Sport Science, Princess Nourah Bint Abdulrahman University, P.O. Box 84428, Riyadh, 11671, Saudi Arabia
| | - Zeid A ALOthman
- Department of Chemistry, College of Science, King Saud University, P.O. Box 2455, Riyadh, 11451, Saudi Arabia
| | - Tahany Saleh Aldayel
- Nutrition and Food Science, Department of Physical Sport Science, Princess Nourah Bint Abdulrahman University, P.O. Box 84428, Riyadh, 11671, Saudi Arabia
| | - Saikh Mohammad Wabaidur
- Department of Chemistry, College of Science, King Saud University, P.O. Box 2455, Riyadh, 11451, Saudi Arabia
| | - Md Ataul Islam
- Division of Pharmacy and Optometry, School of Health Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Oxford Road, Manchester, M13 9PL, UK. .,Department of Chemical Pathology, Faculty of Health Sciences, University of Pretoria and National Health Laboratory Service Tshwane Academic Division, Pretoria, South Africa.
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71
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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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72
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Veit-Acosta M, de Azevedo Junior WF. Computational Prediction of Binding Affinity for CDK2-ligand Complexes. A Protein Target for Cancer Drug Discovery. Curr Med Chem 2021; 29:2438-2455. [PMID: 34365938 DOI: 10.2174/0929867328666210806105810] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2021] [Revised: 06/15/2021] [Accepted: 06/22/2021] [Indexed: 11/22/2022]
Abstract
BACKGROUND CDK2 participates in the control of eukaryotic cell-cycle progression. Due to the great interest in CDK2 for drug development and the relative easiness in crystallizing this enzyme, we have over 400 structural studies focused on this protein target. This structural data is the basis for the development of computational models to estimate CDK2-ligand binding affinity. OBJECTIVE This work focuses on the recent developments in the application of supervised machine learning modeling to develop scoring functions to predict the binding affinity of CDK2. METHOD We employed the structures available at the protein data bank and the ligand information accessed from the BindingDB, Binding MOAD, and PDBbind to evaluate the predictive performance of machine learning techniques combined with physical modeling used to calculate binding affinity. We compared this hybrid methodology with classical scoring functions available in docking programs. RESULTS Our comparative analysis of previously published models indicated that a model created using a combination of a mass-spring system and cross-validated Elastic Net to predict the binding affinity of CDK2-inhibitor complexes outperformed classical scoring functions available in AutoDock4 and AutoDock Vina. CONCLUSION All studies reviewed here suggest that targeted machine learning models are superior to classical scoring functions to calculate binding affinities. Specifically for CDK2, we see that the combination of physical modeling with supervised machine learning techniques exhibits improved predictive performance to calculate the protein-ligand binding affinity. These results find theoretical support in the application of the concept of scoring function space.
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Affiliation(s)
- Martina Veit-Acosta
- Western Michigan University, 1903 Western, Michigan Ave, Kalamazoo, MI 49008. United States
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Radaeva M, Ton AT, Hsing M, Ban F, Cherkasov A. Drugging the 'undruggable'. Therapeutic targeting of protein-DNA interactions with the use of computer-aided drug discovery methods. Drug Discov Today 2021; 26:2660-2679. [PMID: 34332092 DOI: 10.1016/j.drudis.2021.07.018] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2021] [Revised: 06/22/2021] [Accepted: 07/17/2021] [Indexed: 02/09/2023]
Abstract
Transcription factors (TFs) act as major oncodrivers in many cancers and are frequently regarded as high-value therapeutic targets. The functionality of TFs relies on direct protein-DNA interactions, which are notoriously difficult to target with small molecules. However, this prior view of the 'undruggability' of protein-DNA interfaces has shifted substantially in recent years, in part because of significant advances in computer-aided drug discovery (CADD). In this review, we highlight recent examples of successful CADD campaigns resulting in drug candidates that directly interfere with protein-DNA interactions of several key cancer TFs, including androgen receptor (AR), ETS-related gene (ERG), MYC, thymocyte selection-associated high mobility group box protein (TOX), topoisomerase II (TOP2), and signal transducer and activator of transcription 3 (STAT3). Importantly, these findings open novel and compelling avenues for therapeutic targeting of over 1600 human TFs implicated in many conditions including and beyond cancer.
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Affiliation(s)
- Mariia Radaeva
- Vancouver Prostate Centre and the Department of Urologic Sciences, University of British Columbia, 2660 Oak Street, Vancouver, BC V6H 3Z6, Canada
| | - Anh-Tien Ton
- Vancouver Prostate Centre and the Department of Urologic Sciences, University of British Columbia, 2660 Oak Street, Vancouver, BC V6H 3Z6, Canada
| | - Michael Hsing
- Vancouver Prostate Centre and the Department of Urologic Sciences, University of British Columbia, 2660 Oak Street, Vancouver, BC V6H 3Z6, Canada
| | - Fuqiang Ban
- Vancouver Prostate Centre and the Department of Urologic Sciences, University of British Columbia, 2660 Oak Street, Vancouver, BC V6H 3Z6, Canada
| | - Artem Cherkasov
- Vancouver Prostate Centre and the Department of Urologic Sciences, University of British Columbia, 2660 Oak Street, Vancouver, BC V6H 3Z6, Canada.
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74
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Xu J, Li Q, Zhang J, Li X, Sun T. In Silico Structural and Functional Analysis of Cold Shock Proteins in Pseudomonas fluorescens PF08 from Marine Fish. J Food Prot 2021; 84:1446-1454. [PMID: 33852731 DOI: 10.4315/jfp-21-044] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2021] [Accepted: 04/08/2021] [Indexed: 12/18/2022]
Abstract
ABSTRACT Pseudomonas fluorescens is a specific spoilage microorganism of refrigerated marine fish, and is highly adapted to low temperature. Cold shock proteins (CSPs) play an important role in cold adaptation of bacteria. In this study, CSP genes were identified from the genome of P. fluorescens PF08 by search of the conserved domain of CSPs with HMMER software, and the CSP physicochemical properties, structures, and functions were analyzed through bioinformatics. Five typical CSPs were identified in the P. fluorescens PF08 genome (PfCSPs). All five PfCSPs are small hydrophilic acidic proteins with a molecular mass of ca. 7.4 kDa. They are located in the cytoplasm and are nonsecretory and nontransmembrane proteins. Multiple sequence alignment analysis indicated that the CSPs are highly conserved between species, especially in DNA-binding sites and RNA-binding motifs that can bind to single-stranded DNA and RNA. The five PfCSPs clustered with CspD from Escherichia coli and Salmonella Typhimurium, which suggests a close homology and high functional similarity among the five PfCSPs and CspD. The secondary and tertiary structures of the PfCSPs are in accordance with the characteristics of the CSP family, and ligand binding sites with higher likelihood were found in PfCSPs. The five PfCSPs were predicted to interact with some of the same proteins that are involved in virulence, stress responses (including to low temperature), cell growth, ribosome assembly, and RNA degradation. The results provide further elucidation of the function of CSPs in adaptation to low temperatures by P. fluorescens. HIGHLIGHTS
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Affiliation(s)
- Jinxiu Xu
- College of Food Science and Engineering, Bohai University, National & Local Joint Engineering Research Center of Storage, Processing and Safety Control Technology for Fresh Agricultural and Aquatic Products, Jinzhou, Liaoning 121013, People's Republic of China
| | - Qiuying Li
- College of Food Science and Engineering, Bohai University, National & Local Joint Engineering Research Center of Storage, Processing and Safety Control Technology for Fresh Agricultural and Aquatic Products, Jinzhou, Liaoning 121013, People's Republic of China
| | - Jingyang Zhang
- College of Food Science and Engineering, Bohai University, National & Local Joint Engineering Research Center of Storage, Processing and Safety Control Technology for Fresh Agricultural and Aquatic Products, Jinzhou, Liaoning 121013, People's Republic of China
| | - Xuepeng Li
- College of Food Science and Engineering, Bohai University, National & Local Joint Engineering Research Center of Storage, Processing and Safety Control Technology for Fresh Agricultural and Aquatic Products, Jinzhou, Liaoning 121013, People's Republic of China
| | - Tong Sun
- College of Food Science and Engineering, Bohai University, National & Local Joint Engineering Research Center of Storage, Processing and Safety Control Technology for Fresh Agricultural and Aquatic Products, Jinzhou, Liaoning 121013, People's Republic of China
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75
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Guterres H, Park SJ, Zhang H, Im W. CHARMM-GUI LBS Finder & Refiner for Ligand Binding Site Prediction and Refinement. J Chem Inf Model 2021; 61:3744-3751. [PMID: 34296608 DOI: 10.1021/acs.jcim.1c00561] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Abstract
A protein performs its task by binding a variety of ligands in its local region that is also known as the ligand-binding-site (LBS). Therefore, accurate prediction, characterization, and refinement of LBS can facilitate protein functional annotations and structure-based drug design. In this work, we present CHARMM-GUI LBS Finder & Refiner (https://www.charmm-gui.org/input/lbsfinder) that predicts potential LBS, offers interactive features for local LBS structure analysis, and prepares various molecular dynamics (MD) systems and inputs by setting up distance restraint potentials for LBS structure refinement. LBS Finder & Refiner supports 5 different commonly used simulation programs, such as NAMD, AMBER, GROMACS, GENESIS, and OpenMM, for LBS structure refinement together with hydrogen mass repartitioning. The capability of LBS Finder & Refiner is illustrated through LBS structure predictions and refinements of 48 modeled and 20 apo benchmark target proteins. Overall, successful LBS structure predictions and refinements are seen in our benchmark tests. We hope that LBS Finder & Refiner is useful to predict, characterize, and refine potential LBS on any given protein of interest.
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Affiliation(s)
- Hugo Guterres
- Departments of Biological Sciences, Chemistry, Bioengineering, and Computer Science and Engineering, Lehigh University, Bethlehem, Pennsylvania 18015, United States
| | - Sang-Jun Park
- Departments of Biological Sciences, Chemistry, Bioengineering, and Computer Science and Engineering, Lehigh University, Bethlehem, Pennsylvania 18015, United States
| | - Han Zhang
- Departments of Biological Sciences, Chemistry, Bioengineering, and Computer Science and Engineering, Lehigh University, Bethlehem, Pennsylvania 18015, United States
| | - Wonpil Im
- Departments of Biological Sciences, Chemistry, Bioengineering, and Computer Science and Engineering, Lehigh University, Bethlehem, Pennsylvania 18015, United States
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76
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Melse O, Hecht S, Antes I. DynaBiS: A hierarchical sampling algorithm to identify flexible binding sites for large ligands and peptides. Proteins 2021; 90:18-32. [PMID: 34288078 DOI: 10.1002/prot.26182] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2020] [Revised: 06/24/2021] [Accepted: 07/11/2021] [Indexed: 11/11/2022]
Abstract
Knowing the ligand or peptide binding site in proteins is highly important to guide drug discovery, but experimental elucidation of the binding site is difficult. Therefore, various computational approaches have been developed to identify potential binding sites in protein structures. However, protein and ligand flexibility are often neglected in these methods due to efficiency considerations despite the recognition that protein-ligand interactions can be strongly affected by mutual structural adaptations. This is particularly true if the binding site is unknown, as the screening will typically be performed based on an unbound protein structure. Herein we present DynaBiS, a hierarchical sampling algorithm to identify flexible binding sites for a target ligand with explicit consideration of protein and ligand flexibility, inspired by our previously presented flexible docking algorithm DynaDock. DynaBiS applies soft-core potentials between the ligand and the protein, thereby allowing a certain protein-ligand overlap resulting in efficient sampling of conformational adaptation effects. We evaluated DynaBiS and other commonly used binding site identification algorithms against a diverse evaluation set consisting of 26 proteins featuring peptide as well as small ligand binding sites. We show that DynaBiS outperforms the other evaluated methods for the identification of protein binding sites for large and highly flexible ligands such as peptides, both with a holo or apo structure used as input.
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Affiliation(s)
- Okke Melse
- TUM Center for Functional Protein Assemblies and TUM School of Life Sciences, Technische Universität München, Freising, Germany
| | - Sabrina Hecht
- TUM Center for Functional Protein Assemblies and TUM School of Life Sciences, Technische Universität München, Freising, Germany.,Quattro Research, Planegg, Germany
| | - Iris Antes
- TUM Center for Functional Protein Assemblies and TUM School of Life Sciences, Technische Universität München, Freising, Germany
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77
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Sharma K, Singh P, Amjad Beg M, Dohare R, Athar F, Ali Syed M. Revealing new therapeutic opportunities in hypertension through network-driven integrative genetic analysis and drug target prediction approach. Gene 2021; 801:145856. [PMID: 34293449 DOI: 10.1016/j.gene.2021.145856] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2021] [Revised: 06/17/2021] [Accepted: 07/15/2021] [Indexed: 02/06/2023]
Abstract
Epidemiological studies have established that untreated hypertension (HTN) is a major independent risk factor for developing cardiovascular diseases (CVD), stroke, renal failure, and other conditions. Several important studies have been published to prevent and manage HTN; however, antihypertensive agents' optimal choice remains controversial. Therefore, the present study is undertaken to update our knowledge in the primary treatment of HTN, specifically in the setting of other three important diseases. MicroRNAs (miRNAs) are remarkably stable short endogenous conserved non-coding RNAs that bind to the mRNA at its (3' UTR) to regulate its gene expression by causing translational repression or mRNA degradation. Through their coordinated activities on different pathways and networks, individual miRNAs control normal and pathological cellular processes. Therefore, to identify the critical miRNA-mRNA-TF interactions, we performed systematic bioinformatics analysis. We have also employed the molecular modelling and docking approach to identify the therapeutic target that delivers novel empathies into Food and Drug Administration approved and herbal drug response physiology. Gene Expression Omnibus (GEO) was employed to identify the differentially expressed genes (DEGs) and hub genes- KNG1, HLA-DPB1, CXCL8, IL1B, and BCL2. The HTN associated feed-forward loop (FFL) network included miR-9-5p, KNG1 and AR. We employed high throughput screening to get the best interacting compounds, telmisartan and limonin, that provided a significant docking score (-13.3 and -12.0 kcal/mol) and a potential protective effect that may help to combat the impact of HTN. The present study provides novel insight into HTN etiology through the identification of mRNAs and miRNAs and associated pathways.
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Affiliation(s)
- Kavita Sharma
- Translational Research Lab, Department of Biotechnology, Faculty of Natural Sciences, Jamia Millia Islamia, New Delhi 110025, India
| | - Prithvi Singh
- Centre for Interdisciplinary Research in Basic Sciences, Jamia Millia Islamia, New Delhi 110025, India
| | - Md Amjad Beg
- Centre for Interdisciplinary Research in Basic Sciences, Jamia Millia Islamia, New Delhi 110025, India
| | - Ravins Dohare
- Centre for Interdisciplinary Research in Basic Sciences, Jamia Millia Islamia, New Delhi 110025, India.
| | - Fareeda Athar
- Centre for Interdisciplinary Research in Basic Sciences, Jamia Millia Islamia, New Delhi 110025, India
| | - Mansoor Ali Syed
- Translational Research Lab, Department of Biotechnology, Faculty of Natural Sciences, Jamia Millia Islamia, New Delhi 110025, India.
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Tripathi MK, Nath A, Singh TP, Ethayathulla AS, Kaur P. Evolving scenario of big data and Artificial Intelligence (AI) in drug discovery. Mol Divers 2021; 25:1439-1460. [PMID: 34159484 PMCID: PMC8219515 DOI: 10.1007/s11030-021-10256-w] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2021] [Accepted: 06/14/2021] [Indexed: 12/24/2022]
Abstract
The accumulation of massive data in the plethora of Cheminformatics databases has made the role of big data and artificial intelligence (AI) indispensable in drug design. This has necessitated the development of newer algorithms and architectures to mine these databases and fulfil the specific needs of various drug discovery processes such as virtual drug screening, de novo molecule design and discovery in this big data era. The development of deep learning neural networks and their variants with the corresponding increase in chemical data has resulted in a paradigm shift in information mining pertaining to the chemical space. The present review summarizes the role of big data and AI techniques currently being implemented to satisfy the ever-increasing research demands in drug discovery pipelines.
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Affiliation(s)
- Manish Kumar Tripathi
- Department of Biophysics, All India Institute of Medical Sciences, New Delhi, 110029, India
| | - Abhigyan Nath
- Department of Biochemistry, Pt. Jawahar Lal Nehru Memorial Medical College, Raipur, 492001, India
| | - Tej P Singh
- Department of Biophysics, All India Institute of Medical Sciences, New Delhi, 110029, India
| | - A S Ethayathulla
- Department of Biophysics, All India Institute of Medical Sciences, New Delhi, 110029, India
| | - Punit Kaur
- Department of Biophysics, All India Institute of Medical Sciences, New Delhi, 110029, India.
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Stanzione F, Giangreco I, Cole JC. Use of molecular docking computational tools in drug discovery. PROGRESS IN MEDICINAL CHEMISTRY 2021; 60:273-343. [PMID: 34147204 DOI: 10.1016/bs.pmch.2021.01.004] [Citation(s) in RCA: 125] [Impact Index Per Article: 41.7] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
Abstract
Molecular docking has become an important component of the drug discovery process. Since first being developed in the 1980s, advancements in the power of computer hardware and the increasing number of and ease of access to small molecule and protein structures have contributed to the development of improved methods, making docking more popular in both industrial and academic settings. Over the years, the modalities by which docking is used to assist the different tasks of drug discovery have changed. Although initially developed and used as a standalone method, docking is now mostly employed in combination with other computational approaches within integrated workflows. Despite its invaluable contribution to the drug discovery process, molecular docking is still far from perfect. In this chapter we will provide an introduction to molecular docking and to the different docking procedures with a focus on several considerations and protocols, including protonation states, active site waters and consensus, that can greatly improve the docking results.
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Affiliation(s)
| | - Ilenia Giangreco
- Cambridge Crystallographic Data Centre, Cambridge, United Kingdom
| | - Jason C Cole
- Cambridge Crystallographic Data Centre, Cambridge, United Kingdom
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80
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Nance ML, Labonte JW, Adolf-Bryfogle J, Gray JJ. Development and Evaluation of GlycanDock: A Protein-Glycoligand Docking Refinement Algorithm in Rosetta. J Phys Chem B 2021; 125:10.1021/acs.jpcb.1c00910. [PMID: 34133179 PMCID: PMC8742512 DOI: 10.1021/acs.jpcb.1c00910] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
Carbohydrate chains are ubiquitous in the complex molecular processes of life. These highly diverse chains are recognized by a variety of protein receptors, enabling glycans to regulate many biological functions. High-resolution structures of protein-glycoligand complexes reveal the atomic details necessary to understand this level of molecular recognition and inform application-focused scientific and engineering pursuits. When experimental challenges hinder high-throughput determination of quality structures, computational tools can, in principle, fill the gap. In this work, we introduce GlycanDock, a residue-centric protein-glycoligand docking refinement algorithm developed within the Rosetta macromolecular modeling and design software suite. We performed a benchmark docking assessment using a set of 109 experimentally determined protein-glycoligand complexes as well as 62 unbound protein structures. The GlycanDock algorithm can sample and discriminate among protein-glycoligand models of native-like structural accuracy with statistical reliability from starting structures of up to 7 Å root-mean-square deviation in the glycoligand ring atoms. We show that GlycanDock-refined models qualitatively replicated the known binding specificity of a bacterial carbohydrate-binding module. Finally, we present a protein-glycoligand docking pipeline for generating putative protein-glycoligand complexes when only the glycoligand sequence and unbound protein structure are known. In combination with other carbohydrate modeling tools, the GlycanDock docking refinement algorithm will accelerate research in the glycosciences.
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Affiliation(s)
- Morgan L. Nance
- Program in Molecular Biophysics, Johns Hopkins University, Baltimore, Maryland 21218, United States
| | - Jason W. Labonte
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, Maryland 21218, United States
- Department of Chemistry, Franklin & Marshall College, Lancaster, Pennsylvania 17603, United States
- Department of Chemistry, Gettysburg College, Gettysburg, Pennsylvania 17325, United States
| | - Jared Adolf-Bryfogle
- Protein Design Lab, Institute for Protein Innovation, Boston, Massachusetts 02115, United States
- Division of Hematology/Oncology, Boston Children’s Hospital, Boston, Massachusetts 02115, United States
- Department of Pediatrics, Harvard Medical School, Boston, Massachusetts 02115, United States
| | - Jeffrey J. Gray
- Program in Molecular Biophysics, Johns Hopkins University, Baltimore, Maryland 21218, United States
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, Maryland 21218, United States
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81
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Koehl P, Delarue M, Orland H. Simultaneous Identification of Multiple Binding Sites in Proteins: A Statistical Mechanics Approach. J Phys Chem B 2021; 125:5052-5067. [PMID: 33973782 DOI: 10.1021/acs.jpcb.1c02658] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
We present an extension of the Poisson-Boltzmann model in which the solute of interest is immersed in an assembly of self-orienting Langevin water dipoles, anions, cations, and hydrophobic molecules, all of variable densities. Interactions between charges are controlled by electrostatics, while hydrophobic interactions are modeled with a Yukawa potential. We impose steric constraints by assuming that the system is represented on a cubic lattice. We also assume incompressibility; i.e., all sites of the lattice are occupied. This model, which we refer to as the Hydrophobic Dipolar Poisson-Boltzmann Langevin (HDPBL) model, leads to a system of two equations whose solutions give the water dipole, salt, and hydrophobic molecule densities, all of them in the presence of the others in a self-consistent way. We use those to study the organization of the ions, cosolvent, and solvent molecules around proteins. In particular, peaks of densities are expected to reveal, simultaneously, the presence of compatible binding sites of different kinds on a protein. We have tested and validated the ability of HDPBL to detect pockets in proteins that bind to hydrophobic ligands, polar ligands, and charged small probes as well as to characterize the binding sites of lipids for membrane proteins.
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Affiliation(s)
- Patrice Koehl
- Department of Computer Science and Genome Center, University of California, Davis, California 95616, United States
| | - Marc Delarue
- Architecture et Dynamique des Macromolécules Biologiques, Département de Biologie Structurale et Chimie, UMR 3528 du CNRS, Institut Pasteur, 75015 Paris, France
| | - Henri Orland
- Institut de Physique Théorique, Université Paris-Saclay, CEA, 91191 Gif/Yvette Cedex, France
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82
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Revolutionizing enzyme engineering through artificial intelligence and machine learning. Emerg Top Life Sci 2021; 5:113-125. [PMID: 33835131 DOI: 10.1042/etls20200257] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2020] [Revised: 03/17/2021] [Accepted: 03/22/2021] [Indexed: 12/20/2022]
Abstract
The combinatorial space of an enzyme sequence has astronomical possibilities and exploring it with contemporary experimental techniques is arduous and often ineffective. Multi-target objectives such as concomitantly achieving improved selectivity, solubility and activity of an enzyme have narrow plausibility under approaches of restricted mutagenesis and combinatorial search. Traditional enzyme engineering approaches have a limited scope for complex optimization due to the requirement of a priori knowledge or experimental burden of screening huge protein libraries. The recent surge in high-throughput experimental methods including Next Generation Sequencing and automated screening has flooded the field of molecular biology with big-data, which requires us to re-think our concurrent approaches towards enzyme engineering. Artificial Intelligence (AI) and Machine Learning (ML) have great potential to revolutionize smart enzyme engineering without the explicit need for a complete understanding of the underlying molecular system. Here, we portray the role and position of AI techniques in the field of enzyme engineering along with their scope and limitations. In addition, we explain how the traditional approaches of directed evolution and rational design can be extended through AI tools. Recent successful examples of AI-assisted enzyme engineering projects and their deviation from traditional approaches are highlighted. A comprehensive picture of current challenges and future avenues for AI in enzyme engineering are also discussed.
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Pindelska E, Marczewska-Rak A, Jaśkowska J, Madura ID. Solvates of New Arylpiperazine Salicylamide Derivative-a Multi-Technique Approach to the Description of 5 HTR Ligand Structure and Interactions. Int J Mol Sci 2021; 22:ijms22094992. [PMID: 34066719 PMCID: PMC8125853 DOI: 10.3390/ijms22094992] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2021] [Revised: 04/22/2021] [Accepted: 04/26/2021] [Indexed: 11/21/2022] Open
Abstract
A new ligand for 5-HT1A and 5-HT7 receptors, an arylpiperazine salicylamide derivative with an inflexible spacer, is investigated to identify preferred fragments capable of creating essential intermolecular interactions in different solvates. To fully identify and characterize the obtained crystalline materials, various methods including powder and single-crystal X-ray diffraction, solid-state NMR, and thermal analysis were employed, supplemented by periodic ab initio calculations. The molecular conformation in different solvates, types, and hierarchy of intermolecular interactions as well as the crystal packing were investigated to provide data for future research focused on studying protein–ligand interactions. Based on various methods of crystal structure analysis, including the interaction energy calculation and programs using an artificial neural network, a salicylamide fragment was found to be crucial for intermolecular contacts, mostly of dispersion and electrostatic character. A supramolecular 2D kite-type layer of {4,4} topology was found to form in crystals. The closed voids between layers contain disordered solvents, very weakly interacting with the molecule and the layer. It has been postulated that the separation of the layers might be influenced by an increase in temperature or the size of the solvent; hence, only methanol and ethanol hemi-solvates could be obtained from a series of various alcohols.
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Affiliation(s)
- Edyta Pindelska
- Department of Analytical Chemistry and Biomaterials, Faculty of Pharmacy, Medical University of Warsaw, Banacha 1, 02-093 Warsaw, Poland
- Correspondence: (E.P.); (I.D.M.)
| | - Anna Marczewska-Rak
- Scientific Circle “Spektrum” at Department of Analytical Chemistry and Biomaterials, Faculty of Pharmacy, Medical University of Warsaw, Banacha 1, 02-093 Warsaw, Poland;
| | - Jolanta Jaśkowska
- Department of Organic Chemistry and Technology, Faculty of Chemical and Engineering and Technology, Cracow University of Technology, 24 Warszawska Street, 31-155 Cracow, Poland;
| | - Izabela D. Madura
- Faculty of Chemistry, Warsaw University of Technology, Noakowskiego 3, 00-664 Warsaw, Poland
- Correspondence: (E.P.); (I.D.M.)
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84
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Smirnovienė J, Smirnov A, Zakšauskas A, Zubrienė A, Petrauskas V, Mickevičiūtė A, Michailovienė V, Čapkauskaitė E, Manakova E, Gražulis S, Baranauskienė L, Chen W, Ladbury JE, Matulis D. Switching the Inhibitor-Enzyme Recognition Profile via Chimeric Carbonic Anhydrase XII. ChemistryOpen 2021; 10:567-580. [PMID: 33945229 PMCID: PMC8095314 DOI: 10.1002/open.202100042] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2021] [Revised: 03/08/2021] [Indexed: 01/02/2023] Open
Abstract
A key part of the optimization of small molecules in pharmaceutical inhibitor development is to vary the molecular design to enhance complementarity of chemical features of the compound with the positioning of amino acids in the active site of a target enzyme. Typically this involves iterations of synthesis, to modify the compound, and biophysical assay, to assess the outcomes. Selective targeting of the anti-cancer carbonic anhydrase isoform XII (CA XII), this process is challenging because the overall fold is very similar across the twelve CA isoforms. To enhance drug development for CA XII we used a reverse engineering approach where mutation of the key six amino acids in the active site of human CA XII into the CA II isoform was performed to provide a protein chimera (chCA XII) which is amenable to structure-based compound optimization. Through determination of structural detail and affinity measurement of the interaction with over 60 compounds we observed that the compounds that bound CA XII more strongly than CA II, switched their preference and bound more strongly to the engineered chimera, chCA XII, based on CA II, but containing the 6 key amino acids from CA XII, behaved as CA XII in its compound recognition profile. The structures of the compounds in the chimeric active site also resembled those determined for complexes with CA XII, hence validating this protein engineering approach in the development of new inhibitors.
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Affiliation(s)
- Joana Smirnovienė
- Department of Biothermodynamics and Drug DesignInstitute of BiotechnologyLife Sciences CenterVilnius UniversitySaulėtekio 7Vilnius10257Lithuania
| | - Alexey Smirnov
- Department of Biothermodynamics and Drug DesignInstitute of BiotechnologyLife Sciences CenterVilnius UniversitySaulėtekio 7Vilnius10257Lithuania
| | - Audrius Zakšauskas
- Department of Biothermodynamics and Drug DesignInstitute of BiotechnologyLife Sciences CenterVilnius UniversitySaulėtekio 7Vilnius10257Lithuania
| | - Asta Zubrienė
- Department of Biothermodynamics and Drug DesignInstitute of BiotechnologyLife Sciences CenterVilnius UniversitySaulėtekio 7Vilnius10257Lithuania
| | - Vytautas Petrauskas
- Department of Biothermodynamics and Drug DesignInstitute of BiotechnologyLife Sciences CenterVilnius UniversitySaulėtekio 7Vilnius10257Lithuania
| | - Aurelija Mickevičiūtė
- Department of Biothermodynamics and Drug DesignInstitute of BiotechnologyLife Sciences CenterVilnius UniversitySaulėtekio 7Vilnius10257Lithuania
| | - Vilma Michailovienė
- Department of Biothermodynamics and Drug DesignInstitute of BiotechnologyLife Sciences CenterVilnius UniversitySaulėtekio 7Vilnius10257Lithuania
| | - Edita Čapkauskaitė
- Department of Biothermodynamics and Drug DesignInstitute of BiotechnologyLife Sciences CenterVilnius UniversitySaulėtekio 7Vilnius10257Lithuania
| | - Elena Manakova
- Department of Protein-DNA InteractionsInstitute of BiotechnologyLife Sciences CenterVilnius UniversitySaulėtekio 7Vilnius10257Lithuania
| | - Saulius Gražulis
- Department of Protein-DNA InteractionsInstitute of BiotechnologyLife Sciences CenterVilnius UniversitySaulėtekio 7Vilnius10257Lithuania
| | - Lina Baranauskienė
- Department of Biothermodynamics and Drug DesignInstitute of BiotechnologyLife Sciences CenterVilnius UniversitySaulėtekio 7Vilnius10257Lithuania
| | - Wen‐Yih Chen
- Department of Chemical and Materials EngineeringInstitute of Systems Biology and BioinformaticsNational Central UniversityTaiwan
| | - John E. Ladbury
- School of Molecular and Cellular BiologyUniversity of LeedsLC Miall BuildingLeedsLS2 9JTUK
| | - Daumantas Matulis
- Department of Biothermodynamics and Drug DesignInstitute of BiotechnologyLife Sciences CenterVilnius UniversitySaulėtekio 7Vilnius10257Lithuania
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85
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Nazem F, Ghasemi F, Fassihi A, Dehnavi AM. 3D U-Net: A voxel-based method in binding site prediction of protein structure. J Bioinform Comput Biol 2021; 19:2150006. [PMID: 33866960 DOI: 10.1142/s0219720021500062] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Binding site prediction for new proteins is important in structure-based drug design. The identified binding sites may be helpful in the development of treatments for new viral outbreaks in the world when there is no information available about their pockets with COVID-19 being a case in point. Identification of the pockets using computational methods, as an alternative method, has recently attracted much interest. In this study, the binding site prediction is viewed as a semantic segmentation problem. An improved 3D version of the U-Net model based on the dice loss function is utilized to predict the binding sites accurately. The performance of the proposed model on the independent test datasets and SARS-COV-2 shows the segmentation model could predict the binding sites with a more accurate shape than the recently published deep learning model, i.e. DeepSite. Therefore, the model may help predict the binding sites of proteins and could be used in drug design for novel proteins.
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Affiliation(s)
- Fatemeh Nazem
- Department of Bioelectric and Biomedical Engineering, School of Advanced Technologies in Medicine, Isfahan University of Medical Sciences Hezar-Jerib Ave, Isfahan 81746 73461, Iran
| | - Fahimeh Ghasemi
- Department of Bioinformatics and Systems Biology, School of Advanced Technologies in Medicine, Isfahan University of Medical Sciences, Hezar-Jerib Ave, Isfahan 81746 73461, Iran
| | - Afshin Fassihi
- Department of Medicinal Chemistry, School of Pharmacology and Pharmaceutical Sciences, Isfahan University of Medical Sciences, Hezar-Jerib Ave, Isfahan 81746 73461, Iran
| | - Alireza Mehri Dehnavi
- Department of Bioelectric and Biomedical Engineering, School of Advanced Technologies in Medicine, Isfahan University of Medical Sciences Hezar-Jerib Ave, Isfahan 81746 73461, Iran
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86
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Chen PF, Ma XF, Sun LF, Tian Y, Fan YZ, Li P, Xiao Z, Zhu MX, Guo CR, Li C, Yu Y, Wang J. A conserved residue in the P2X4 receptor has a nonconserved function in ATP recognition. J Biol Chem 2021; 296:100655. [PMID: 33901491 PMCID: PMC8166750 DOI: 10.1016/j.jbc.2021.100655] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2020] [Revised: 04/05/2021] [Accepted: 04/08/2021] [Indexed: 12/11/2022] Open
Abstract
Highly conserved amino acids are generally anticipated to have similar functions across a protein superfamily, including that of the P2X ion channels, which are gated by extracellular ATP. However, whether and how these functions are conserved becomes less clear when neighboring amino acids are not conserved. Here, we investigate one such case, focused on the highly conserved residue from P2X4, E118 (rat P2X4 numbering, rP2X4), a P2X subtype associated with human neuropathic pain. When we compared the crystal structures of P2X4 with those of other P2X subtypes, including P2X3, P2X7, and AmP2X, we observed a slightly altered side-chain orientation of E118. We used protein chimeras, double-mutant cycle analysis, and molecular modeling to reveal that E118 forms specific contacts with amino acids in the "beak" region, which facilitates ATP binding to rP2X4. These contacts are not present in other subtypes because of sequence variance in the beak region, resulting in decoupling of this conserved residue from ATP recognition and/or channel gating of P2X receptors. Our study provides an example of a conserved residue with a specific role in functional proteins enabled by adjacent nonconserved residues. The unique role established by the E118-beak region contact provides a blueprint for the development of subtype-specific inhibitors of P2X4.
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Affiliation(s)
- Ping-Fang Chen
- Department of Pharmacology and Chemical Biology, Institute of Medical Sciences, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Xue-Fei Ma
- School of Basic Medicine and Clinical Pharmacy, China Pharmaceutical University, Nanjing, China; College of Bioscience and Biotechnology, Hunan Agricultural University, Changsha, China
| | - Liang-Fei Sun
- Department of Pharmacology and Chemical Biology, Institute of Medical Sciences, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yun Tian
- College of Bioscience and Biotechnology, Hunan Agricultural University, Changsha, China
| | - Ying-Zhe Fan
- Putuo Hospital, Shanghai University of Chinese Traditional Medicine, Shanghai, China
| | - Peiwang Li
- State Key Laboratory of Utilization of Woody Oil Resource, Hunan Academy of Forestry, Changsha, China
| | - Zhihong Xiao
- State Key Laboratory of Utilization of Woody Oil Resource, Hunan Academy of Forestry, Changsha, China
| | - Michael X Zhu
- Department of Integrative Biology and Pharmacology, McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, Texas, USA
| | - Chang-Run Guo
- School of Basic Medicine and Clinical Pharmacy, China Pharmaceutical University, Nanjing, China
| | - Changzhu Li
- State Key Laboratory of Utilization of Woody Oil Resource, Hunan Academy of Forestry, Changsha, China.
| | - Ye Yu
- Department of Pharmacology and Chemical Biology, Institute of Medical Sciences, Shanghai Jiao Tong University School of Medicine, Shanghai, China; School of Basic Medicine and Clinical Pharmacy, China Pharmaceutical University, Nanjing, China; State Key Laboratory for Chemistry and Molecular Engineering of Medicinal Resources, Collaborative Innovation Center for Guangxi Ethnic Medicine, School of Chemistry and Pharmaceutical Sciences, Guangxi Normal University, Guilin, China.
| | - Jin Wang
- School of Basic Medicine and Clinical Pharmacy, China Pharmaceutical University, Nanjing, China.
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87
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Khan N, Husain Q, Qayyum N. Enhanced dye decolorization efficiency of gellan gum complexed Ziziphus mauritiana peroxidases in a stirred batch process. Int J Biol Macromol 2020; 165:2000-2009. [DOI: 10.1016/j.ijbiomac.2020.09.250] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2020] [Revised: 08/31/2020] [Accepted: 09/29/2020] [Indexed: 12/17/2022]
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88
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Fernández-Ballester G, Fernández-Carvajal A, Ferrer-Montiel A. Targeting thermoTRP ion channels: in silico preclinical approaches and opportunities. Expert Opin Ther Targets 2020; 24:1079-1097. [PMID: 32972264 DOI: 10.1080/14728222.2020.1820987] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
INTRODUCTION A myriad of cellular pathophysiological responses are mediated by polymodal ion channels that respond to chemical and physical stimuli such as thermoTRP channels. Intriguingly, these channels are pivotal therapeutic targets with limited clinical pharmacology. In silico methods offer an unprecedented opportunity for discovering new lead compounds targeting thermoTRP channels with improved pharmacological activity and therapeutic index. AREAS COVERED This article reviews the progress on thermoTRP channel pharmacology because of (i) advances in solving their atomic structure using cryo-electron microscopy and, (ii) progress on computational techniques including homology modeling, molecular docking, virtual screening, molecular dynamics, ADME/Tox and artificial intelligence. Together, they have increased the number of lead compounds with clinical potential to treat a variety of pathologies. We used original and review articles from Pubmed (1997-2020), as well as the clinicaltrials.gov database, containing the terms thermoTRP, artificial intelligence, docking, and molecular dynamics. EXPERT OPINION The atomic structure of thermoTRP channels along with computational methods constitute a realistic first line strategy for designing drug candidates with improved pharmacology and clinical translation. In silico approaches can also help predict potential side-effects that can limit clinical development of drug candidates. Together, they should provide drug candidates with upgraded therapeutic properties.
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Affiliation(s)
- Gregorio Fernández-Ballester
- Professor Gregorio Fernández-Ballester. Instituto de Investigación, Desarrollo e Innovación en Biotecnología Sanitaria de Elche (IDiBE), Universitas Miguel Hernández , Alicante, Spain
| | - Asia Fernández-Carvajal
- Professor Gregorio Fernández-Ballester. Instituto de Investigación, Desarrollo e Innovación en Biotecnología Sanitaria de Elche (IDiBE), Universitas Miguel Hernández , Alicante, Spain
| | - Antonio Ferrer-Montiel
- Professor Gregorio Fernández-Ballester. Instituto de Investigación, Desarrollo e Innovación en Biotecnología Sanitaria de Elche (IDiBE), Universitas Miguel Hernández , Alicante, Spain
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89
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Sharma N, Kumar P, Giri R. Polysaccharides like pentagalloylglucose, parishin a and stevioside inhibits the viral entry by binding the Zika virus envelope protein. J Biomol Struct Dyn 2020; 39:6008-6020. [PMID: 32705969 DOI: 10.1080/07391102.2020.1797538] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
ZIKV belongs to a flavivirus family in which class II fusion proteins involve a low pH-dependent membrane fusion leading to infection of host cells. Envelope (E) protein is primarily responsible for the viral host membrane fusion and is the major target for inhibiting viral entry. Our findings reveal that compounds like PGG, Parishin A, and Stevioside have shown a high affinity for E protein and found to be active against various other viral infections. The binding of these molecules to E protein was found to decrease the RMSD and RMSF values of the ligand protein complex and restricted the Radius of Gyration in molecular dynamics simulation analysis. Further, the binding free energy calculations suggested the stability of complexes throughout simulations trajectory that could reduce the flexibility of the linker so as to block the folding back event of membrane fusion. A recent study has shown that PGG inhibits the early stages of viral entry in HCV and ZIKV. Therefore, we propose that PGG inhibits the entry of virion via binding the E protein and restricting the conformational rearrangement during membrane fusion.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Nitin Sharma
- Indian Institute of Technology Mandi, VPO Kamand, Himachal Pradesh, India
| | - Prateek Kumar
- Indian Institute of Technology Mandi, VPO Kamand, Himachal Pradesh, India
| | - Rajanish Giri
- Indian Institute of Technology Mandi, VPO Kamand, Himachal Pradesh, India
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