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Flachsenberg F, Ehrt C, Gutermuth T, Rarey M. Redocking the PDB. J Chem Inf Model 2024; 64:219-237. [PMID: 38108627 DOI: 10.1021/acs.jcim.3c01573] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2023]
Abstract
Molecular docking is a standard technique in structure-based drug design (SBDD). It aims to predict the 3D structure of a small molecule in the binding site of a receptor (often a protein). Despite being a common technique, it often necessitates multiple tools and involves manual steps. Here, we present the JAMDA preprocessing and docking workflow that is easy to use and allows fully automated docking. We evaluate the JAMDA docking workflow on binding sites extracted from the complete PDB and derive key factors determining JAMDA's docking performance. With that, we try to remove most of the bias due to manual intervention and provide a realistic estimate of the redocking performance of our JAMDA preprocessing and docking workflow for any PDB structure. On this large PDBScan22 data set, our JAMDA workflow finds a pose with an RMSD of at most 2 Å to the crystal ligand on the top rank for 30.1% of the structures. When applying objective structure quality filters to the PDBScan22 data set, the success rate increases to 61.8%. Given the prepared structures from the JAMDA preprocessing pipeline, both JAMDA and the widely used AutoDock Vina perform comparably on this filtered data set (the PDBScan22-HQ data set).
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Affiliation(s)
- Florian Flachsenberg
- Universität Hamburg, ZBH - Center for Bioinformatics, Bundesstraße 43, 20146 Hamburg, Germany
| | - Christiane Ehrt
- Universität Hamburg, ZBH - Center for Bioinformatics, Bundesstraße 43, 20146 Hamburg, Germany
| | - Torben Gutermuth
- Universität Hamburg, ZBH - Center for Bioinformatics, Bundesstraße 43, 20146 Hamburg, Germany
| | - Matthias Rarey
- Universität Hamburg, ZBH - Center for Bioinformatics, Bundesstraße 43, 20146 Hamburg, Germany
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2
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Ramli AH, Mohd Faudzi SM. Diarylpentanoids, the privileged scaffolds in antimalarial and anti-infectives drug discovery: A review. Arch Pharm (Weinheim) 2023; 356:e2300391. [PMID: 37806761 DOI: 10.1002/ardp.202300391] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2023] [Revised: 09/17/2023] [Accepted: 09/18/2023] [Indexed: 10/10/2023]
Abstract
Asia is a hotspot for infectious diseases, including malaria, dengue fever, tuberculosis, and the pandemic COVID-19. Emerging infectious diseases have taken a heavy toll on public health and the economy and have been recognized as a major cause of morbidity and mortality, particularly in Southeast Asia. Infectious disease control is a major challenge, but many surveillance systems and control strategies have been developed and implemented. These include vector control, combination therapies, vaccine development, and the development of new anti-infectives. Numerous newly discovered agents with pharmacological anti-infective potential are being actively and extensively studied for their bioactivity, toxicity, selectivity, and mode of action, but many molecules lose their efficacy over time due to resistance developments. These facts justify the great importance of the search for new, effective, and safe anti-infectives. Diarylpentanoids, a curcumin derivative, have been developed as an alternative with better bioavailability and metabolism as a therapeutic agent. In this review, the mechanisms of action and potential targets of antimalarial drugs as well as the classes of antimalarial drugs are presented. The bioactivity of diarylpentanoids as a potential scaffold for a new class of anti-infectives and their structure-activity relationships are also discussed in detail.
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Affiliation(s)
- Amirah H Ramli
- Natural Medicines and Products Research Laboratory, Institute of Bioscience, Universiti Putra Malaysia, Serdang, Malaysia
| | - Siti M Mohd Faudzi
- Natural Medicines and Products Research Laboratory, Institute of Bioscience, Universiti Putra Malaysia, Serdang, Malaysia
- Department of Chemistry, Faculty of Science, Universiti Putra Malaysia, Serdang, Malaysia
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3
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Xia Y, Pan X, Shen HB. LigBind: identifying binding residues for over 1000 ligands with relation-aware graph neural networks. J Mol Biol 2023; 435:168091. [PMID: 37054909 DOI: 10.1016/j.jmb.2023.168091] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2022] [Revised: 03/22/2023] [Accepted: 04/05/2023] [Indexed: 04/15/2023]
Abstract
Identifying the interactions between proteins and ligands is significant for drug discovery and design. Considering the diverse binding patterns of ligands, the ligand-specific methods are trained per ligand to predict binding residues. However, most of the existing ligand-specific methods ignore shared binding preferences among various ligands and generally only cover a limited number of ligands with a sufficient number of known binding proteins. In this study, we propose a relation-aware framework LigBind with graph-level pre-training to enhance the ligand-specific binding residue predictions for 1159 ligands, which can effectively cover the ligands with a few known binding proteins. LigBind first pre-trains a graph neural network-based feature extractor for ligand-residue pairs and relation-aware classifiers for similar ligands. Then, LigBind is fine-tuned with ligand-specific binding data, where a domain adaptive neural network is designed to automatically leverage the diversity and similarity of various ligand-binding patterns for accurate binding residue prediction. We construct ligand-specific benchmark datasets of 1159 ligands and 16 unseen ligands, which are used to evaluate the effectiveness of LigBind. The results demonstrate the LigBind's efficacy on the large-scale ligand-specific benchmark datasets, and generalizes well to unseen ligands. LigBind also enables accurate identification of the ligand-binding residues in the main protease, papain-like protease and the RNA-dependent RNA polymerase of SARS-CoV-2. The webserver and source codes of LigBind are available at http://www.csbio.sjtu.edu.cn/bioinf/LigBind/ and https://github.com/YYingXia/LigBind/ for academic use.
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Affiliation(s)
- Ying Xia
- Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, and Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai 200240, China
| | - Xiaoyong Pan
- Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, and Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai 200240, China.
| | - Hong-Bin Shen
- Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, and Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai 200240, China.
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4
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Identifying HSV-1 Inhibitors from Natural Compounds via Virtual Screening Targeting Surface Glycoprotein D. Pharmaceuticals (Basel) 2022; 15:ph15030361. [PMID: 35337158 PMCID: PMC8955139 DOI: 10.3390/ph15030361] [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: 01/28/2022] [Revised: 03/07/2022] [Accepted: 03/11/2022] [Indexed: 02/05/2023] Open
Abstract
Herpes simplex virus (HSV) infections are a worldwide health problem in need of new effective treatments. Of particular interest is the identification of antiviral agents that act via different mechanisms compared to current drugs, as these could interact synergistically with first-line antiherpetic agents to accelerate the resolution of HSV-1-associated lesions. For this study, we applied a structure-based molecular docking approach targeting the nectin-1 and herpesvirus entry mediator (HVEM) binding interfaces of the viral glycoprotein D (gD). More than 527,000 natural compounds were virtually screened using Autodock Vina and then filtered for favorable ADMET profiles. Eight top hits were evaluated experimentally in African green monkey kidney cell line (VERO) cells, which yielded two compounds with potential antiherpetic activity. One active compound (1-(1-benzofuran-2-yl)-2-[(5Z)-2H,6H,7H,8H-[1,3] dioxolo[4,5-g]isoquinoline-5-ylidene]ethenone) showed weak but significant antiviral activity. Although less potent than antiherpetic agents, such as acyclovir, it acted at the viral inactivation stage in a dose-dependent manner, suggesting a novel mode of action. These results highlight the feasibility of in silico approaches for identifying new antiviral compounds, which may be further optimized by medicinal chemistry approaches.
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5
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Li M, Hu J, Wang Y, Li Y, Zhang L, Liu Z. Challenging Reverse Screening: A Benchmark Study for Comprehensive Evaluation. Mol Inform 2021; 41:e2100063. [PMID: 34787366 DOI: 10.1002/minf.202100063] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2021] [Accepted: 10/15/2021] [Indexed: 11/08/2022]
Abstract
As an efficient way of computational target prediction, reverse docking can find not only potential targets but also binding modes for a query ligand. Though the number of available docking tools keeps expanding, there is still not a comprehensive evaluation study which can uncover the advantages and limitations of these strategies in the research field of computational target-fishing. In this study, we propose a brand-new evaluation dataset tailor-made for reverse docking, which is composed of a true positive set (the core set) and two negative sets (the similar decoy set and the dissimilar decoy set). The proposed evaluation dataset can assess the prediction performance of docking tools as various values affected by varying degrees of inter-target ranking bias. The performance of four classical docking programs (AutoDock, AutoDock Vina, Glide and GOLD) was evaluated utilizing our dataset, and a biased prediction performance was observed regarding binding site properties. The results demonstrated that Glide (SP) and Glide(XP) had the best capacity to find true targets whether there was inter-target ranking bias or not.
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Affiliation(s)
- Mingna Li
- State Key Laboratory of Natural and Biomimetic Drugs, School of Pharmaceutical Sciences, Peking University, Xueyuan Road 38, Haidian District, 100191, Beijing, P.R. China
| | - Jianxing Hu
- State Key Laboratory of Natural and Biomimetic Drugs, School of Pharmaceutical Sciences, Peking University, Xueyuan Road 38, Haidian District, 100191, Beijing, P.R. China
| | - Yanxing Wang
- State Key Laboratory of Natural and Biomimetic Drugs, School of Pharmaceutical Sciences, Peking University, Xueyuan Road 38, Haidian District, 100191, Beijing, P.R. China
| | - Yibo Li
- Academy for Advanced Interdisciplinary Studies, Peking University, Yiheyuan Road 5, Haidian District, Beijing, P.R. China
| | - Liangren Zhang
- State Key Laboratory of Natural and Biomimetic Drugs, School of Pharmaceutical Sciences, Peking University, Xueyuan Road 38, Haidian District, 100191, Beijing, P.R. China
| | - Zhenming Liu
- State Key Laboratory of Natural and Biomimetic Drugs, School of Pharmaceutical Sciences, Peking University, Xueyuan Road 38, Haidian District, 100191, Beijing, P.R. China
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6
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Deep learning in target prediction and drug repositioning: Recent advances and challenges. Drug Discov Today 2021; 27:1796-1814. [PMID: 34718208 DOI: 10.1016/j.drudis.2021.10.010] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2021] [Revised: 09/02/2021] [Accepted: 10/21/2021] [Indexed: 12/12/2022]
Abstract
Drug repositioning is an attractive strategy for discovering new therapeutic uses for approved or investigational drugs, with potentially shorter development timelines and lower development costs. Various computational methods have been used in drug repositioning, promoting the efficiency and success rates of this approach. Recently, deep learning (DL) has attracted wide attention for its potential in target prediction and drug repositioning. Here, we provide an overview of the basic principles of commonly used DL architectures and their applications in target prediction and drug repositioning, and discuss possible ways of dealing with current challenges to help achieve its expected potential for drug repositioning.
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7
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Drug target inference by mining transcriptional data using a novel graph convolutional network framework. Protein Cell 2021; 13:281-301. [PMID: 34677780 PMCID: PMC8532448 DOI: 10.1007/s13238-021-00885-0] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2021] [Accepted: 09/08/2021] [Indexed: 12/14/2022] Open
Abstract
A fundamental challenge that arises in biomedicine is the need to characterize compounds in a relevant cellular context in order to reveal potential on-target or off-target effects. Recently, the fast accumulation of gene transcriptional profiling data provides us an unprecedented opportunity to explore the protein targets of chemical compounds from the perspective of cell transcriptomics and RNA biology. Here, we propose a novel Siamese spectral-based graph convolutional network (SSGCN) model for inferring the protein targets of chemical compounds from gene transcriptional profiles. Although the gene signature of a compound perturbation only provides indirect clues of the interacting targets, and the biological networks under different experiment conditions further complicate the situation, the SSGCN model was successfully trained to learn from known compound-target pairs by uncovering the hidden correlations between compound perturbation profiles and gene knockdown profiles. On a benchmark set and a large time-split validation dataset, the model achieved higher target inference accuracy as compared to previous methods such as Connectivity Map. Further experimental validations of prediction results highlight the practical usefulness of SSGCN in either inferring the interacting targets of compound, or reversely, in finding novel inhibitors of a given target of interest.
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8
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Recent Advances in In Silico Target Fishing. Molecules 2021; 26:molecules26175124. [PMID: 34500568 PMCID: PMC8433825 DOI: 10.3390/molecules26175124] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2021] [Revised: 08/14/2021] [Accepted: 08/18/2021] [Indexed: 12/24/2022] Open
Abstract
In silico target fishing, whose aim is to identify possible protein targets for a query molecule, is an emerging approach used in drug discovery due its wide variety of applications. This strategy allows the clarification of mechanism of action and biological activities of compounds whose target is still unknown. Moreover, target fishing can be employed for the identification of off targets of drug candidates, thus recognizing and preventing their possible adverse effects. For these reasons, target fishing has increasingly become a key approach for polypharmacology, drug repurposing, and the identification of new drug targets. While experimental target fishing can be lengthy and difficult to implement, due to the plethora of interactions that may occur for a single small-molecule with different protein targets, an in silico approach can be quicker, less expensive, more efficient for specific protein structures, and thus easier to employ. Moreover, the possibility to use it in combination with docking and virtual screening studies, as well as the increasing number of web-based tools that have been recently developed, make target fishing a more appealing method for drug discovery. It is especially worth underlining the increasing implementation of machine learning in this field, both as a main target fishing approach and as a further development of already applied strategies. This review reports on the main in silico target fishing strategies, belonging to both ligand-based and receptor-based approaches, developed and applied in the last years, with a particular attention to the different web tools freely accessible by the scientific community for performing target fishing studies.
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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: 106] [Impact Index Per Article: 35.3] [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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10
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Synthesis and structural characterization of new benzylidene glycosides, cytotoxicity against cancer cell lines and molecular modeling studies. J Mol Struct 2021. [DOI: 10.1016/j.molstruc.2021.130186] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
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11
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Schaller E, Ma A, Gosch LC, Klefenz A, Schaller D, Goehringer N, Kaps L, Schuppan D, Volkamer A, Schobert R, Biersack B, Nitzsche B, Höpfner M. New 3-Aryl-2-(2-thienyl)acrylonitriles with High Activity Against Hepatoma Cells. Int J Mol Sci 2021; 22:2243. [PMID: 33668139 PMCID: PMC7956560 DOI: 10.3390/ijms22052243] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2021] [Revised: 02/19/2021] [Accepted: 02/20/2021] [Indexed: 12/12/2022] Open
Abstract
New 2-(thien-2-yl)-acrylonitriles with putative kinase inhibitory activity were prepared and tested for their antineoplastic efficacy in hepatoma models. Four out of the 14 derivatives were shown to inhibit hepatoma cell proliferation at (sub-)micromolar concentrations with IC50 values below that of the clinically relevant multikinase inhibitor sorafenib, which served as a reference. Colony formation assays as well as primary in vivo examinations of hepatoma tumors grown on the chorioallantoic membrane of fertilized chicken eggs (CAM assay) confirmed the excellent antineoplastic efficacy of the new derivatives. Their mode of action included an induction of apoptotic capsase-3 activity, while no contribution of unspecific cytotoxic effects was observed in LDH-release measurements. Kinase profiling of cancer relevant protein kinases identified the two 3-aryl-2-(thien-2-yl)acrylonitrile derivatives 1b and 1c as (multi-)kinase inhibitors with a preferential activity against the VEGFR-2 tyrosine kinase. Additional bioinformatic analysis of the VEGFR-2 binding modes by docking and molecular dynamics calculations supported the experimental findings and indicated that the hydroxy group of 1c might be crucial for its distinct inhibitory potency against VEGFR-2. Forthcoming studies will further unveil the underlying mode of action of the promising new derivatives as well as their suitability as an urgently needed novel approach in HCC treatment.
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Affiliation(s)
- Eva Schaller
- Organic Chemistry Laboratory, University of Bayreuth, Universitätsstrasse 30, 95440 Bayreuth, Germany; (E.S.); (R.S.)
| | - Andi Ma
- Institute of Physiology, Charité-Universitätsmedizin Berlin, Charitéplatz 1, 10117 Berlin, Germany; (A.M.); (L.C.G.); (N.G.); (B.N.); (M.H.)
| | - Lisa Chiara Gosch
- Institute of Physiology, Charité-Universitätsmedizin Berlin, Charitéplatz 1, 10117 Berlin, Germany; (A.M.); (L.C.G.); (N.G.); (B.N.); (M.H.)
- In Silico Toxicology and Structural Bioinformatics, Institute of Physiology, Charité-Universitätsmedizin Berlin, Charitéplatz 1, 10117 Berlin, Germany; (D.S.); (A.V.)
| | - Adrian Klefenz
- Institute of Translational Immunology, University Medical Center of the Johannes Gutenberg University, Langenbeckstrasse 1, 55131 Mainz, Germany; (A.K.); (L.K.); (D.S.)
| | - David Schaller
- In Silico Toxicology and Structural Bioinformatics, Institute of Physiology, Charité-Universitätsmedizin Berlin, Charitéplatz 1, 10117 Berlin, Germany; (D.S.); (A.V.)
| | - Nils Goehringer
- Institute of Physiology, Charité-Universitätsmedizin Berlin, Charitéplatz 1, 10117 Berlin, Germany; (A.M.); (L.C.G.); (N.G.); (B.N.); (M.H.)
| | - Leonard Kaps
- Institute of Translational Immunology, University Medical Center of the Johannes Gutenberg University, Langenbeckstrasse 1, 55131 Mainz, Germany; (A.K.); (L.K.); (D.S.)
| | - Detlef Schuppan
- Institute of Translational Immunology, University Medical Center of the Johannes Gutenberg University, Langenbeckstrasse 1, 55131 Mainz, Germany; (A.K.); (L.K.); (D.S.)
- Division of Gastroenterology, Beth Israel Deaconess Medical Center, Harvard Medical School, 330 Brookline Ave, Boston, MA 02215, USA
| | - Andrea Volkamer
- In Silico Toxicology and Structural Bioinformatics, Institute of Physiology, Charité-Universitätsmedizin Berlin, Charitéplatz 1, 10117 Berlin, Germany; (D.S.); (A.V.)
| | - Rainer Schobert
- Organic Chemistry Laboratory, University of Bayreuth, Universitätsstrasse 30, 95440 Bayreuth, Germany; (E.S.); (R.S.)
| | - Bernhard Biersack
- Organic Chemistry Laboratory, University of Bayreuth, Universitätsstrasse 30, 95440 Bayreuth, Germany; (E.S.); (R.S.)
| | - Bianca Nitzsche
- Institute of Physiology, Charité-Universitätsmedizin Berlin, Charitéplatz 1, 10117 Berlin, Germany; (A.M.); (L.C.G.); (N.G.); (B.N.); (M.H.)
| | - Michael Höpfner
- Institute of Physiology, Charité-Universitätsmedizin Berlin, Charitéplatz 1, 10117 Berlin, Germany; (A.M.); (L.C.G.); (N.G.); (B.N.); (M.H.)
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12
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Roy S, Dhaneshwar S, Bhasin B. Drug Repurposing: An Emerging Tool for Drug Reuse, Recycling and Discovery. Curr Drug Res Rev 2021; 13:101-119. [PMID: 33573567 DOI: 10.2174/2589977513666210211163711] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2020] [Revised: 09/07/2020] [Accepted: 10/26/2020] [Indexed: 11/22/2022]
Abstract
Drug repositioning or repurposing is a revolutionary breakthrough in drug development that focuses on rediscovering new uses for old therapeutic agents. Drug repositioning can be defined more precisely as the process of exploring new indications for an already approved drug while drug repurposing includes overall re-development approaches grounded in the identical chemical structure of the active drug moiety as in the original product. The repositioning approach accelerates the drug development process, curtails the cost and risk inherent to drug development. The strategy focuses on the polypharmacology of drugs to unlocks novel opportunities for logically designing more efficient therapeutic agents for unmet medical disorders. Drug repositioning also expresses certain regulatory challenges that hamper its further utilization. The review outlines the eminent role of drug repositioning in new drug discovery, methods to predict the molecular targets of a drug molecule, advantages that the strategy offers to the pharmaceutical industries, explaining how the industrial collaborations with academics can assist in the discovering more repositioning opportunities. The focus of the review is to highlight the latest applications of drug repositioning in various disorders. The review also includes a comparison of old and new therapeutic uses of repurposed drugs, assessing their novel mechanisms of action and pharmacological effects in the management of various disorders. Various restrictions and challenges that repurposed drugs come across during their development and regulatory phases are also highlighted.
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Affiliation(s)
- Supriya Roy
- Amity Institute of Pharmacy, Amity University Uttar Pradesh, Lucknow Campus, India
| | - Suneela Dhaneshwar
- Amity Institute of Pharmacy, Amity University Uttar Pradesh, Lucknow Campus, India
| | - Bhavya Bhasin
- Poona College of Pharmacy, Bharati Vidyapeeth University, Pune, India
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Schöning-Stierand K, Diedrich K, Fährrolfes R, Flachsenberg F, Meyder A, Nittinger E, Steinegger R, Rarey M. ProteinsPlus: interactive analysis of protein-ligand binding interfaces. Nucleic Acids Res 2020; 48:W48-W53. [PMID: 32297936 PMCID: PMC7319454 DOI: 10.1093/nar/gkaa235] [Citation(s) in RCA: 121] [Impact Index Per Article: 30.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2020] [Revised: 03/19/2020] [Accepted: 04/14/2020] [Indexed: 01/22/2023] Open
Abstract
Due to the increasing amount of publicly available protein structures searching, enriching and investigating these data still poses a challenging task. The ProteinsPlus web service (https://proteins.plus) offers a broad range of tools addressing these challenges. The web interface to the tool collection focusing on protein–ligand interactions has been geared towards easy and intuitive access to a large variety of functionality for life scientists. Since our last publication, the ProteinsPlus web service has been extended by additional services as well as it has undergone substantial infrastructural improvements. A keyword search functionality was added on the start page of ProteinsPlus enabling users to work on structures without knowing their PDB code. The tool collection has been augmented by three tools: StructureProfiler validates ligands and active sites using selection criteria of well-established protein–ligand benchmark data sets, WarPP places water molecules in the ligand binding sites of a protein, and METALizer calculates, predicts and scores coordination geometries of metal ions based on surrounding complex atoms. Additionally, all tools provided by ProteinsPlus are available through a REST service enabling the automated integration in structure processing and modeling pipelines.
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Affiliation(s)
| | - Konrad Diedrich
- Universität Hamburg, ZBH - Center for Bioinformatics (ZBH), 20146 Hamburg, Germany
| | - Rainer Fährrolfes
- Universität Hamburg, ZBH - Center for Bioinformatics (ZBH), 20146 Hamburg, Germany
| | - Florian Flachsenberg
- Universität Hamburg, ZBH - Center for Bioinformatics (ZBH), 20146 Hamburg, Germany
| | - Agnes Meyder
- Universität Hamburg, ZBH - Center for Bioinformatics (ZBH), 20146 Hamburg, Germany
| | - Eva Nittinger
- Universität Hamburg, ZBH - Center for Bioinformatics (ZBH), 20146 Hamburg, Germany
| | - Ruben Steinegger
- Universität Hamburg, ZBH - Center for Bioinformatics (ZBH), 20146 Hamburg, Germany
| | - Matthias Rarey
- Universität Hamburg, ZBH - Center for Bioinformatics (ZBH), 20146 Hamburg, Germany
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14
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Du J, Guo J, Kang D, Li Z, Wang G, Wu J, Zhang Z, Fang H, Hou X, Huang Z, Li G, Lu X, Liu X, Ouyang L, Rao L, Zhan P, Zhang X, Zhang Y. New techniques and strategies in drug discovery. CHINESE CHEM LETT 2020. [DOI: 10.1016/j.cclet.2020.03.028] [Citation(s) in RCA: 59] [Impact Index Per Article: 14.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
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15
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Ansar S, Vetrivel U. KinomeRun: An interactive utility for kinome target screening and interaction fingerprint analysis towards holistic visualization on kinome tree. Chem Biol Drug Des 2020; 96:1162-1175. [PMID: 32418310 DOI: 10.1111/cbdd.13705] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2020] [Revised: 04/15/2020] [Accepted: 05/06/2020] [Indexed: 12/27/2022]
Abstract
Kinases are key targets for many of the pathological conditions. Inverse screening of ligands serves as an essential mode to identify potential kinase targets in modern drug discovery research. Hence, we intend to develop KinomeRun, a robust pipeline for inverse screening and kinome tree visualization through the seamless integration of kinome structures, docking and kinome-drug interaction fingerprint analysis. In this pipeline, the hurdle of residue numbering in kinome is also resolved by creating a common index file with the conserved kinase pocket residues for comparative interaction analysis. KinomeRun can be used to screen the ligands of interest docked against multiple kinase structures in parallel around the kinase binding site and also to filter out the targets with unique interaction patterns. This automation is essential for prioritization of kinase targets that show specificity for a given drug and will also serve as a crucial tool kit for holistic approaches in kinase drug discovery. KinomeRun is developed using python and bash programming language and is distributed freely under the GNU GPL licence-3.0 and can be downloaded at https://github.com/inpacdb/KinomeRun. The tutorial videos for installation, target screening and customized filtration are available at https://www.youtube.com/playlist?list=PLuIaEFtMVgQ7v__WigQH9ilGVxrfI1LKs and also be downloaded for offline viewing from the github link.
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Affiliation(s)
- Samdani Ansar
- Centre for Bioinformatics, Kamalnayan Bajaj Institute for Research in Vision and Ophthalmology, Vision Research Foundation, Sankara Nethralaya, Chennai, India.,School of Chemical and Biotechnology, SASTRA Deemed University, Thanjavur, India
| | - Umashankar Vetrivel
- Centre for Bioinformatics, Kamalnayan Bajaj Institute for Research in Vision and Ophthalmology, Vision Research Foundation, Sankara Nethralaya, Chennai, India.,National Institute of Traditional Medicine, Indian Council of Medical Research, Department of Health Research (Govt. of India), Belagavi, India
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16
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17
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De Vita S, Lauro G, Ruggiero D, Terracciano S, Riccio R, Bifulco G. Protein Preparation Automatic Protocol for High-Throughput Inverse Virtual Screening: Accelerating the Target Identification by Computational Methods. J Chem Inf Model 2019; 59:4678-4690. [DOI: 10.1021/acs.jcim.9b00428] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/28/2023]
Affiliation(s)
- Simona De Vita
- Department of Pharmacy, University of Salerno, via Giovanni Paolo II 132, 84084 Fisciano, Italy
| | - Gianluigi Lauro
- Department of Pharmacy, University of Salerno, via Giovanni Paolo II 132, 84084 Fisciano, Italy
| | - Dafne Ruggiero
- Department of Pharmacy, University of Salerno, via Giovanni Paolo II 132, 84084 Fisciano, Italy
| | - Stefania Terracciano
- Department of Pharmacy, University of Salerno, via Giovanni Paolo II 132, 84084 Fisciano, Italy
| | - Raffaele Riccio
- Department of Pharmacy, University of Salerno, via Giovanni Paolo II 132, 84084 Fisciano, Italy
| | - Giuseppe Bifulco
- Department of Pharmacy, University of Salerno, via Giovanni Paolo II 132, 84084 Fisciano, Italy
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Vucicevic J, Nikolic K, Mitchell JB. Rational Drug Design of Antineoplastic Agents Using 3D-QSAR, Cheminformatic, and Virtual Screening Approaches. Curr Med Chem 2019; 26:3874-3889. [DOI: 10.2174/0929867324666170712115411] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2017] [Revised: 06/06/2017] [Accepted: 06/13/2017] [Indexed: 01/07/2023]
Abstract
Background:Computer-Aided Drug Design has strongly accelerated the development of novel antineoplastic agents by helping in the hit identification, optimization, and evaluation.Results:Computational approaches such as cheminformatic search, virtual screening, pharmacophore modeling, molecular docking and dynamics have been developed and applied to explain the activity of bioactive molecules, design novel agents, increase the success rate of drug research, and decrease the total costs of drug discovery. Similarity, searches and virtual screening are used to identify molecules with an increased probability to interact with drug targets of interest, while the other computational approaches are applied for the design and evaluation of molecules with enhanced activity and improved safety profile.Conclusion:In this review are described the main in silico techniques used in rational drug design of antineoplastic agents and presented optimal combinations of computational methods for design of more efficient antineoplastic drugs.
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Affiliation(s)
- Jelica Vucicevic
- Department of Pharmaceutical Chemistry, Faculty of Pharmacy, University of Belgrade, Vojvode Stepe 450, 11000 Belgrade, Serbia
| | - Katarina Nikolic
- Department of Pharmaceutical Chemistry, Faculty of Pharmacy, University of Belgrade, Vojvode Stepe 450, 11000 Belgrade, Serbia
| | - John B.O. Mitchell
- EaStCHEM School of Chemistry and Biomedical Sciences Research Complex, University of St Andrews, St Andrews KY16 9ST, United Kingdom
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19
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Mei Y, Pan D, Jiang Y, Zhang W, Yao X, Dai Y, Yu Y, Yao X. Target discovery of chlorogenic acid derivatives from the flower buds of Lonicera macranthoides and their MAO B inhibitory mechanism. Fitoterapia 2019; 134:297-304. [DOI: 10.1016/j.fitote.2018.12.009] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2018] [Revised: 12/10/2018] [Accepted: 12/16/2018] [Indexed: 01/30/2023]
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20
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Sydow D, Burggraaff L, Szengel A, van Vlijmen HWT, IJzerman AP, van Westen GJP, Volkamer A. Advances and Challenges in Computational Target Prediction. J Chem Inf Model 2019; 59:1728-1742. [DOI: 10.1021/acs.jcim.8b00832] [Citation(s) in RCA: 58] [Impact Index Per Article: 11.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Affiliation(s)
- Dominique Sydow
- In silico Toxicology, Institute of Physiology, Charité − Universitätsmedizin Berlin, Charitéplatz 1, 10117 Berlin, Germany
| | - Lindsey Burggraaff
- Division of Drug Discovery and Safety, Leiden Academic Centre for Drug Research, Leiden University, P.O. Box 9502, 2300 RA, Leiden, The Netherlands
| | - Angelika Szengel
- In silico Toxicology, Institute of Physiology, Charité − Universitätsmedizin Berlin, Charitéplatz 1, 10117 Berlin, Germany
| | - Herman W. T. van Vlijmen
- Division of Drug Discovery and Safety, Leiden Academic Centre for Drug Research, Leiden University, P.O. Box 9502, 2300 RA, Leiden, The Netherlands
- Computational Chemistry, Janssen Research & Development, Turnhoutseweg 30, B-2340 Beerse, Belgium
| | - Adriaan P. IJzerman
- Division of Drug Discovery and Safety, Leiden Academic Centre for Drug Research, Leiden University, P.O. Box 9502, 2300 RA, Leiden, The Netherlands
| | - Gerard J. P. van Westen
- Division of Drug Discovery and Safety, Leiden Academic Centre for Drug Research, Leiden University, P.O. Box 9502, 2300 RA, Leiden, The Netherlands
| | - Andrea Volkamer
- In silico Toxicology, Institute of Physiology, Charité − Universitätsmedizin Berlin, Charitéplatz 1, 10117 Berlin, Germany
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21
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Nogueira MS, Koch O. The Development of Target-Specific Machine Learning Models as Scoring Functions for Docking-Based Target Prediction. J Chem Inf Model 2019; 59:1238-1252. [DOI: 10.1021/acs.jcim.8b00773] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Affiliation(s)
- Mauro S. Nogueira
- Faculty of Chemistry and Chemical Biology, TU Dortmund University, Otto-Hahn-Straße 6, 44227, Dortmund, Germany
| | - Oliver Koch
- Faculty of Chemistry and Chemical Biology, TU Dortmund University, Otto-Hahn-Straße 6, 44227, Dortmund, Germany
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22
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Kim SS, Aprahamian ML, Lindert S. Improving inverse docking target identification with Z-score selection. Chem Biol Drug Des 2019; 93:1105-1116. [PMID: 30604454 DOI: 10.1111/cbdd.13453] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2018] [Revised: 10/22/2018] [Accepted: 11/17/2018] [Indexed: 12/12/2022]
Abstract
The utilization of inverse docking methods for target identification has been driven by an increasing demand for efficient tools for detecting potential drug side-effects. Despite impressive achievements in the field of inverse docking, identifying true positives from a pool of potential targets still remains challenging. Notably, most of the developed techniques have low accuracies, limit the pool of possible targets that can be investigated or are not easy to use for non-experts due to a lack of available scripts or webserver. Guided by our finding that the absolute docking score was a poor indication of a ligand's protein target, we developed a novel "combined Z-score" method that used a weighted fraction of ligand and receptor-based Z-scores to identify the most likely binding target of a ligand. With our combined Z-score method, an additional 14%, 3.6%, and 6.3% of all ligand-protein pairs of the Astex, DUD, and DUD-E databases, respectively, were correctly predicted compared to a docking score-based selection. The combined Z-score had the highest area under the curve in a ROC curve analysis of all three datasets and the enrichment factor for the top 1% predictions using the combined Z-score analysis was the highest for the Astex and DUD-E datasets. Additionally, we developed a user-friendly python script (compatible with both Python2 and Python3) that enables users to employ the combined Z-score analysis for target identification using a user-defined list of ligands and targets. We are providing this python script and a user tutorial as part of the supplemental information.
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Affiliation(s)
- Stephanie S Kim
- Department of Chemistry and Biochemistry, Ohio State University, Columbus, Ohio
| | | | - Steffen Lindert
- Department of Chemistry and Biochemistry, Ohio State University, Columbus, Ohio
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23
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Choudhury C, Narahari Sastry G. Pharmacophore Modelling and Screening: Concepts, Recent Developments and Applications in Rational Drug Design. CHALLENGES AND ADVANCES IN COMPUTATIONAL CHEMISTRY AND PHYSICS 2019. [DOI: 10.1007/978-3-030-05282-9_2] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
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24
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Krivák R, Hoksza D. P2Rank: machine learning based tool for rapid and accurate prediction of ligand binding sites from protein structure. J Cheminform 2018; 10:39. [PMID: 30109435 PMCID: PMC6091426 DOI: 10.1186/s13321-018-0285-8] [Citation(s) in RCA: 157] [Impact Index Per Article: 26.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2017] [Accepted: 06/29/2018] [Indexed: 01/29/2023] Open
Abstract
Background Ligand binding site prediction from protein structure has many applications related to elucidation of protein function and structure based drug discovery. It often represents only one step of many in complex computational drug design efforts. Although many methods have been published to date, only few of them are suitable for use in automated pipelines or for processing large datasets.
These use cases require stability and speed, which disqualifies many of the recently introduced tools that are either template based or available only as web servers. Results We present P2Rank, a stand-alone template-free tool for prediction of ligand binding sites based on machine learning. It is based on prediction of ligandability of local chemical neighbourhoods that are centered on points placed on the solvent accessible surface of a protein.
We show that P2Rank outperforms several existing tools, which include two widely used stand-alone tools (Fpocket, SiteHound), a comprehensive consensus based tool (MetaPocket 2.0), and a recent deep learning based method (DeepSite). P2Rank belongs to the fastest available tools (requires under 1 s for prediction on one protein), with additional advantage of multi-threaded implementation. Conclusions P2Rank is a new open source software package for ligand binding site prediction from protein structure. It is available as a user-friendly stand-alone command line program and a Java library. P2Rank has a lightweight installation and does not depend on other bioinformatics tools or large structural or sequence databases. Thanks to its speed and ability to make fully automated predictions, it is particularly well suited for processing large datasets or as a component of scalable structural bioinformatics pipelines. Electronic supplementary material The online version of this article (10.1186/s13321-018-0285-8) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Radoslav Krivák
- Department of Software Engineering, Charles University, Prague, Czech Republic.
| | - David Hoksza
- Department of Software Engineering, Charles University, Prague, Czech Republic.
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25
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Ban T, Ohue M, Akiyama Y. Multiple grid arrangement improves ligand docking with unknown binding sites: Application to the inverse docking problem. Comput Biol Chem 2018; 73:139-146. [DOI: 10.1016/j.compbiolchem.2018.02.008] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2017] [Revised: 02/05/2018] [Accepted: 02/10/2018] [Indexed: 01/19/2023]
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26
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Dutta D, Das R, Mandal C, Mandal C. Structure-Based Kinase Profiling To Understand the Polypharmacological Behavior of Therapeutic Molecules. J Chem Inf Model 2017; 58:68-89. [PMID: 29243930 DOI: 10.1021/acs.jcim.7b00227] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
Several drugs elicit their therapeutic efficacy by modulating multiple cellular targets and possess varied polypharmacological actions. The identification of the molecular targets of a potent bioactive molecule is essential in determining its overall polypharmacological profile. Experimental procedures are expensive and time-consuming. Therefore, computational approaches are actively implemented in rational drug discovery. Here, we demonstrate a computational pipeline, based on reverse virtual screening technique using several consensus scoring strategies, and perform structure-based kinase profiling of 12 FDA-approved drugs. This target prediction showed an overall good performance, with an average AU-ROC greater than 0.85 for most drugs, and identified the true targets even at the top 2% cutoff. In contrast, 10 non-kinase binder drugs exhibited lower binding efficiency and appeared in the bottom of ranking list. Subsequently, we validated this pipeline on a potent therapeutic molecule, mahanine, whose polypharmacological profile related to targeting kinases is unknown. Our target-prediction method identified different kinases. Furthermore, we have experimentally validated that mahanine is able to modulate multiple kinases that are involved in cross-talk with different signaling molecules, which thereby exhibits its polypharmacological action. More importantly, in vitro kinase assay exhibited the inhibitory effect of mahanine on two such predicted kinases' (mTOR and VEGFR2) activity, with IC50 values being ∼12 and ∼22 μM, respectively. Next, we generated a comprehensive drug-protein interaction fingerprint that explained the basis of their target selectivity. We observed that it is controlled by variations in kinase conformations followed by significant differences in crucial hydrogen-bond and van der Waals interactions. Such structure-based kinase profiling could provide useful information in revealing the unknown targets of therapeutic molecules from their polypharmacological behavior and would assist in drug discovery.
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Affiliation(s)
- Devawati Dutta
- Cancer Biology and Inflammatory Disorder Division, Council of Scientific and Industrial Research-Indian Institute of Chemical Biology , Kolkata 700032, India
| | - Ranjita Das
- Cancer Biology and Inflammatory Disorder Division, Council of Scientific and Industrial Research-Indian Institute of Chemical Biology , Kolkata 700032, India
| | - Chhabinath Mandal
- National Institute of Pharmaceutical Education and Research , Kolkata 700032, India
| | - Chitra Mandal
- Cancer Biology and Inflammatory Disorder Division, Council of Scientific and Industrial Research-Indian Institute of Chemical Biology , Kolkata 700032, India
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27
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Malik V, Dhanjal JK, Kumari A, Radhakrishnan N, Singh K, Sundar D. Function and structure-based screening of compounds, peptides and proteins to identify drug candidates. Methods 2017; 131:10-21. [DOI: 10.1016/j.ymeth.2017.08.010] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2017] [Revised: 08/21/2017] [Accepted: 08/21/2017] [Indexed: 01/01/2023] Open
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28
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From cheminformatics to structure-based design: Web services and desktop applications based on the NAOMI library. J Biotechnol 2017; 261:207-214. [DOI: 10.1016/j.jbiotec.2017.06.004] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2017] [Revised: 05/31/2017] [Accepted: 06/07/2017] [Indexed: 02/06/2023]
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29
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Meyder A, Nittinger E, Lange G, Klein R, Rarey M. Estimating Electron Density Support for Individual Atoms and Molecular Fragments in X-ray Structures. J Chem Inf Model 2017; 57:2437-2447. [PMID: 28981269 DOI: 10.1021/acs.jcim.7b00391] [Citation(s) in RCA: 51] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/12/2023]
Abstract
Macromolecular structures resolved by X-ray crystallography are essential for life science research. While some methods exist to automatically quantify the quality of the electron density fit, none of them is without flaws. Especially the question of how well individual parts like atoms, small fragments, or molecules are supported by electron density is difficult to quantify. While taking experimental uncertainties correctly into account, they do not offer an answer on how reliable an individual atom position is. A rapid quantification of this atomic position reliability would be highly valuable in structure-based molecular design. To overcome this limitation, we introduce the electron density score EDIA for individual atoms and molecular fragments. EDIA assesses rapidly, automatically, and intuitively the fit of individual as well as multiple atoms (EDIAm) into electron density accompanied by an integrated error analysis. The computation is based on the standard 2fo - fc electron density map in combination with the model of the molecular structure. For evaluating partial structures, EDIAm shows significant advantages compared to the real-space R correlation coefficient (RSCC) and the real-space difference density Z score (RSZD) from the molecular modeler's point of view. Thus, EDIA abolishes the time-consuming step of visually inspecting the electron density during structure selection and curation. It supports daily modeling tasks of medicinal and computational chemists and enables a fully automated assembly of large-scale, high-quality structure data sets. Furthermore, EDIA scores can be applied for model validation and method development in computer-aided molecular design. In contrast to measuring the deviation from the structure model by root-mean-squared deviation, EDIA scores allow comparison to the underlying experimental data taking its uncertainty into account.
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Affiliation(s)
- Agnes Meyder
- ZBH-Center for Bioinformatics, Universität Hamburg , Hamburg 20146, Germany
| | - Eva Nittinger
- ZBH-Center for Bioinformatics, Universität Hamburg , Hamburg 20146, Germany
| | | | | | - Matthias Rarey
- ZBH-Center for Bioinformatics, Universität Hamburg , Hamburg 20146, Germany
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30
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Inhester T, Nittinger E, Sommer K, Schmidt P, Bietz S, Rarey M. NAOMInova: Interactive Geometric Analysis of Noncovalent Interactions in Macromolecular Structures. J Chem Inf Model 2017; 57:2132-2142. [DOI: 10.1021/acs.jcim.7b00291] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Therese Inhester
- ZBH - Center for Bioinformatics, Universität Hamburg, Bundesstrasse 43, 20146 Hamburg, Germany
| | - Eva Nittinger
- ZBH - Center for Bioinformatics, Universität Hamburg, Bundesstrasse 43, 20146 Hamburg, Germany
| | - Kai Sommer
- ZBH - Center for Bioinformatics, Universität Hamburg, Bundesstrasse 43, 20146 Hamburg, Germany
| | - Pascal Schmidt
- ZBH - Center for Bioinformatics, Universität Hamburg, Bundesstrasse 43, 20146 Hamburg, Germany
| | - Stefan Bietz
- ZBH - Center for Bioinformatics, Universität Hamburg, Bundesstrasse 43, 20146 Hamburg, Germany
| | - Matthias Rarey
- ZBH - Center for Bioinformatics, Universität Hamburg, Bundesstrasse 43, 20146 Hamburg, Germany
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31
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Li GB, Yu ZJ, Liu S, Huang LY, Yang LL, Lohans CT, Yang SY. IFPTarget: A Customized Virtual Target Identification Method Based on Protein–Ligand Interaction Fingerprinting Analyses. J Chem Inf Model 2017; 57:1640-1651. [PMID: 28661143 DOI: 10.1021/acs.jcim.7b00225] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Affiliation(s)
- Guo-Bo Li
- Key
Laboratory of Drug Targeting and Drug Delivery System of Ministry
of Education, West China School of Pharmacy, Sichuan University, Sichuan 610041, China
| | - Zhu-Jun Yu
- Key
Laboratory of Drug Targeting and Drug Delivery System of Ministry
of Education, West China School of Pharmacy, Sichuan University, Sichuan 610041, China
| | - Sha Liu
- Key
Laboratory of Drug Targeting and Drug Delivery System of Ministry
of Education, West China School of Pharmacy, Sichuan University, Sichuan 610041, China
| | - Lu-Yi Huang
- Laboratory
of Biotherapy and Cancer Center, West China Hospital, West China Medical
School, Sichuan University, Sichuan 610041, China
| | - Ling-Ling Yang
- College
of Food and Bioengineering, Xihua University, Sichuan 610039, China
| | - Christopher T. Lohans
- Department
of Chemistry, University of Oxford, 12 Mansfield Road, Oxford, OX1 3TA, United Kingdom
| | - Sheng-Yong Yang
- Laboratory
of Biotherapy and Cancer Center, West China Hospital, West China Medical
School, Sichuan University, Sichuan 610041, China
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32
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Rasolohery I, Moroy G, Guyon F. PatchSearch: A Fast Computational Method for Off-Target Detection. J Chem Inf Model 2017; 57:769-777. [PMID: 28282119 DOI: 10.1021/acs.jcim.6b00529] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Many therapeutic molecules are known to bind several proteins, which can be different from the initially targeted one. Such unexpected interactions with proteins called off-targets can lead to adverse effects. Potential off-target identification is important to predict to avoid drug side effects or to discover new targets for existing drugs. We propose a new program named PatchSearch that implements local nonsequential searching for similar binding sites on protein surfaces with a controlled amount of flexibility. It is based on detection of quasi-cliques in product graphs representing all the possible matchings between two compared structures. This method has been benchmarked on a large diversity of ligands and on five data sets ranging from 12 to more than 7000 protein structures. The experiments conducted in this study show that the PatchSearch method could be useful in the early identification of off-targets. The program and the benchmarks presented in this paper are available as an R package at https://github.com/MTiPatchSearch .
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Affiliation(s)
- Inès Rasolohery
- Molécules Thérapeutiques in Silico, UMRS 973, Université Paris Diderot, INSERM , F-75013 Paris, France
| | - Gautier Moroy
- Molécules Thérapeutiques in Silico, UMRS 973, Université Paris Diderot, INSERM , F-75013 Paris, France
| | - Frédéric Guyon
- Molécules Thérapeutiques in Silico, UMRS 973, Université Paris Diderot, INSERM , F-75013 Paris, France
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33
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von Behren MM, Rarey M. Ligand-based virtual screening under partial shape constraints. J Comput Aided Mol Des 2017; 31:335-347. [PMID: 28315995 DOI: 10.1007/s10822-017-0011-z] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2016] [Accepted: 02/16/2017] [Indexed: 01/26/2023]
Abstract
Ligand-based virtual screening has proven to be a viable technology during the search for new lead structures in drug discovery. Despite the rapidly increasing number of published methods, meaningful shape matching as well as ligand and target flexibility still remain open challenges. In this work, we analyze the influence of knowledge-based sterical constraints on the performance of the recently published ligand-based virtual screening method mRAISE. We introduce the concept of partial shape matching enabling a more differentiated view on chemical structure. The new method is integrated into the LBVS tool mRAISE providing multiple options for such constraints. The applied constraints can either be derived automatically from a protein-ligand complex structure or by manual selection of ligand atoms. In this way, the descriptor directly encodes the fit of a ligand into the binding site. Furthermore, the conservation of close contacts between the binding site surface and the query ligand can be enforced. We validated our new method on the DUD and DUD-E datasets. Although the statistical performance remains on the same level, detailed analysis reveal that for certain and especially very flexible targets a significant improvement can be achieved. This is further highlighted looking at the quality of calculated molecular alignments using the recently introduced mRAISE dataset. The new partial shape constraints improved the overall quality of molecular alignments especially for difficult targets with highly flexible or different sized molecules. The software tool mRAISE is freely available on Linux operating systems for evaluation purposes and academic use (see http://www.zbh.uni-hamburg.de/raise ).
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Affiliation(s)
- Mathias M von Behren
- Center for Bioinformatics, University of Hamburg, Bundesstrasse 43, 20146, Hamburg, Germany
| | - Matthias Rarey
- Center for Bioinformatics, University of Hamburg, Bundesstrasse 43, 20146, Hamburg, Germany.
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34
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Chaudhari R, Tan Z, Huang B, Zhang S. Computational polypharmacology: a new paradigm for drug discovery. Expert Opin Drug Discov 2017; 12:279-291. [PMID: 28067061 PMCID: PMC7241838 DOI: 10.1080/17460441.2017.1280024] [Citation(s) in RCA: 61] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
Abstract
INTRODUCTION Over the past couple of years, the cost of drug development has sharply increased along with the high rate of clinical trial failures. Such increase in expenses is partially due to the inability of the "one drug - one target" approach to predict drug side effects and toxicities. To tackle this issue, an alternative approach, known as polypharmacology, is being adopted to study small molecule interactions with multiple targets. Apart from developing more potent and effective drugs, this approach allows for studies of off-target activities and the facilitation of drug repositioning. Although exhaustive polypharmacology studies in-vitro or in-vivo are not practical, computational methods of predicting unknown targets or side effects are being developed. Areas covered: This article describes various computational approaches that have been developed to study polypharmacology profiles of small molecules. It also provides a brief description of the algorithms used in these state-of-the-art methods. Expert opinion: Recent success in computational prediction of multi-targeting drugs has established polypharmacology as a promising alternative approach to tackle some of the daunting complications in drug discovery. This will not only help discover more effective agents, but also present tremendous opportunities to study novel target pharmacology and facilitate drug repositioning efforts in the pharmaceutical industry.
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Affiliation(s)
- Rajan Chaudhari
- Integrated Molecular Discovery Laboratory, Department of Experimental Therapeutics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030
| | - Zhi Tan
- Integrated Molecular Discovery Laboratory, Department of Experimental Therapeutics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030
- The University of Texas Graduate School of Biomedical Sciences, Houston, TX 77030
| | - Beibei Huang
- Integrated Molecular Discovery Laboratory, Department of Experimental Therapeutics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030
| | - Shuxing Zhang
- Integrated Molecular Discovery Laboratory, Department of Experimental Therapeutics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030
- The University of Texas Graduate School of Biomedical Sciences, Houston, TX 77030
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35
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Abstract
Designing drugs that can simultaneously interact with multiple targets is a promising approach for treating complicated diseases. Compared to using combinations of single target drugs, multitarget drugs have advantages of higher efficacy, improved safety profile, and simpler administration. Many in silico methods have been developed to approach different aspects of this polypharmacology-guided drug design, particularly for drug repurposing and multitarget drug design. In this review, we summarize recent progress in computational multitarget drug design and discuss their advantages and limitations. Perspectives for future drug development will also be discussed.
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Affiliation(s)
- Weilin Zhang
- Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies (AAIS), Peking University , Beijing 100871, People's Republic of China
| | - Jianfeng Pei
- Center for Quantitative Biology, Academy for Advanced Interdisciplinary Studies (AAIS), Peking University , Beijing 100871, People's Republic of China
| | - Luhua Lai
- Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies (AAIS), Peking University , Beijing 100871, People's Republic of China.,Center for Quantitative Biology, Academy for Advanced Interdisciplinary Studies (AAIS), Peking University , Beijing 100871, People's Republic of China.,BNLMS, State Key Laboratory for Structural Chemistry of Unstable and Stable Species, College of Chemistry and Molecular Engineering, Peking University , Beijing 100871, People's Republic of China
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36
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Luo Q, Zhao L, Hu J, Jin H, Liu Z, Zhang L. The scoring bias in reverse docking and the score normalization strategy to improve success rate of target fishing. PLoS One 2017; 12:e0171433. [PMID: 28196116 PMCID: PMC5308821 DOI: 10.1371/journal.pone.0171433] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2016] [Accepted: 01/20/2017] [Indexed: 12/28/2022] Open
Abstract
Target fishing often relies on the use of reverse docking to identify potential target proteins of ligands from protein database. The limitation of reverse docking is the accuracy of current scoring funtions used to distinguish true target from non-target proteins. Many contemporary scoring functions are designed for the virtual screening of small molecules without special optimization for reverse docking, which would be easily influenced by the properties of protein pockets, resulting in scoring bias to the proteins with certain properties. This bias would cause lots of false positives in reverse docking, interferring the identification of true targets. In this paper, we have conducted a large-scale reverse docking (5000 molecules to 100 proteins) to study the scoring bias in reverse docking by DOCK, Glide, and AutoDock Vina. And we found that there were actually some frequency hits, namely interference proteins in all three docking procedures. After analyzing the differences of pocket properties between these interference proteins and the others, we speculated that the interference proteins have larger contact area (related to the size and shape of protein pockets) with ligands (for all three docking programs) or higher hydrophobicity (for Glide), which could be the causes of scoring bias. Then we applied the score normalization method to eliminate this scoring bias, which was effective to make docking score more balanced between different proteins in the reverse docking of benchmark dataset. Later, the Astex Diver Set was utilized to validate the effect of score normalization on actual cases of reverse docking, showing that the accuracy of target prediction significantly increased by 21.5% in the reverse docking by Glide after score normalization, though there was no obvious change in the reverse docking by DOCK and AutoDock Vina. Our results demonstrate the effectiveness of score normalization to eliminate the scoring bias and improve the accuracy of target prediction in reverse docking. Moreover, the properties of protein pockets causing scoring bias to certain proteins we found here can provide the theory basis to further optimize the scoring functions of docking programs for future research.
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Affiliation(s)
- Qiyao Luo
- State Key Laboratory of Natural and Biomimetic Drugs, School of Pharmaceutical Sciences, Peking University, Beijing, P. R. China
| | - Liang Zhao
- State Key Laboratory of Natural and Biomimetic Drugs, School of Pharmaceutical Sciences, Peking University, Beijing, P. R. China
| | - Jianxing Hu
- State Key Laboratory of Natural and Biomimetic Drugs, School of Pharmaceutical Sciences, Peking University, Beijing, P. R. China
| | - Hongwei Jin
- State Key Laboratory of Natural and Biomimetic Drugs, School of Pharmaceutical Sciences, Peking University, Beijing, P. R. China
| | - Zhenming Liu
- State Key Laboratory of Natural and Biomimetic Drugs, School of Pharmaceutical Sciences, Peking University, Beijing, P. R. China
- * E-mail: (ZL); (LZ)
| | - Liangren Zhang
- State Key Laboratory of Natural and Biomimetic Drugs, School of Pharmaceutical Sciences, Peking University, Beijing, P. R. China
- * E-mail: (ZL); (LZ)
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Inhester T, Bietz S, Hilbig M, Schmidt R, Rarey M. Index-Based Searching of Interaction Patterns in Large Collections of Protein–Ligand Interfaces. J Chem Inf Model 2017; 57:148-158. [DOI: 10.1021/acs.jcim.6b00561] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
- Therese Inhester
- Center for Bioinformatics, University of Hamburg, Bundesstrasse 43, 20146 Hamburg, Germany
| | - Stefan Bietz
- Center for Bioinformatics, University of Hamburg, Bundesstrasse 43, 20146 Hamburg, Germany
| | - Matthias Hilbig
- Center for Bioinformatics, University of Hamburg, Bundesstrasse 43, 20146 Hamburg, Germany
| | - Robert Schmidt
- Center for Bioinformatics, University of Hamburg, Bundesstrasse 43, 20146 Hamburg, Germany
| | - Matthias Rarey
- Center for Bioinformatics, University of Hamburg, Bundesstrasse 43, 20146 Hamburg, Germany
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Wong VKW, Law BYK, Yao XJ, Chen X, Xu SW, Liu L, Leung ELH. Advanced research technology for discovery of new effective compounds from Chinese herbal medicine and their molecular targets. Pharmacol Res 2016; 111:546-555. [DOI: 10.1016/j.phrs.2016.07.022] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/03/2016] [Revised: 07/19/2016] [Accepted: 07/19/2016] [Indexed: 02/07/2023]
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von Behren MM, Bietz S, Nittinger E, Rarey M. mRAISE: an alternative algorithmic approach to ligand-based virtual screening. J Comput Aided Mol Des 2016; 30:583-94. [PMID: 27565795 DOI: 10.1007/s10822-016-9940-1] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2016] [Accepted: 08/17/2016] [Indexed: 01/31/2023]
Abstract
Ligand-based virtual screening is a well established method to find new lead molecules in todays drug discovery process. In order to be applicable in day to day practice, such methods have to face multiple challenges. The most important part is the reliability of the results, which can be shown and compared in retrospective studies. Furthermore, in the case of 3D methods, they need to provide biologically relevant molecular alignments of the ligands, that can be further investigated by a medicinal chemist. Last but not least, they have to be able to screen large databases in reasonable time. Many algorithms for ligand-based virtual screening have been proposed in the past, most of them based on pairwise comparisons. Here, a new method is introduced called mRAISE. Based on structural alignments, it uses a descriptor-based bitmap search engine (RAISE) to achieve efficiency. Alignments created on the fly by the search engine get evaluated with an independent shape-based scoring function also used for ranking of compounds. The correct ranking as well as the alignment quality of the method are evaluated and compared to other state of the art methods. On the commonly used Directory of Useful Decoys dataset mRAISE achieves an average area under the ROC curve of 0.76, an average enrichment factor at 1 % of 20.2 and an average hit rate at 1 % of 55.5. With these results, mRAISE is always among the top performing methods with available data for comparison. To access the quality of the alignments calculated by ligand-based virtual screening methods, we introduce a new dataset containing 180 prealigned ligands for 11 diverse targets. Within the top ten ranked conformations, the alignment closest to X-ray structure calculated with mRAISE has a root-mean-square deviation of less than 2.0 Å for 80.8 % of alignment pairs and achieves a median of less than 2.0 Å for eight of the 11 cases. The dataset used to rate the quality of the calculated alignments is freely available at http://www.zbh.uni-hamburg.de/mraise-dataset.html . The table of all PDB codes contained in the ensembles can be found in the supplementary material. The software tool mRAISE is freely available for evaluation purposes and academic use (see http://www.zbh.uni-hamburg.de/raise ).
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Affiliation(s)
- Mathias M von Behren
- ZBH - Center for Bioinformatics, University of Hamburg, Bundesstraße 43, 20146, Hamburg, Germany
| | - Stefan Bietz
- ZBH - Center for Bioinformatics, University of Hamburg, Bundesstraße 43, 20146, Hamburg, Germany
| | - Eva Nittinger
- ZBH - Center for Bioinformatics, University of Hamburg, Bundesstraße 43, 20146, Hamburg, Germany
| | - Matthias Rarey
- ZBH - Center for Bioinformatics, University of Hamburg, Bundesstraße 43, 20146, Hamburg, Germany.
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Lee A, Lee K, Kim D. Using reverse docking for target identification and its applications for drug discovery. Expert Opin Drug Discov 2016; 11:707-15. [PMID: 27186904 DOI: 10.1080/17460441.2016.1190706] [Citation(s) in RCA: 69] [Impact Index Per Article: 8.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
Abstract
INTRODUCTION In contrast to traditional molecular docking, inverse or reverse docking is used for identifying receptors for a given ligand among a large number of receptors. Reverse docking can be used to discover new targets for existing drugs and natural compounds, explain polypharmacology and the molecular mechanism of a substance, find alternative indications of drugs through drug repositioning, and detecting adverse drug reactions and drug toxicity. AREAS COVERED In this review, the authors examine how reverse docking methods have evolved over the past fifteen years and how they have been used for target identification and related applications for drug discovery. They discuss various aspects of target databases, reverse docking tools and servers. EXPERT OPINION There are several issues related to reverse docking methods such as target structure dataset construction, computational efficiency, how to include receptor flexibility, and most importantly, how to properly normalize the docking scores. In order for reverse docking to become a truly useful tool for the drug discovery, these issues need to be adequately resolved.
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Affiliation(s)
- Aeri Lee
- a Department of Bio and Brain Engineering , KAIST , Daejeon , South Korea
| | - Kyoungyeul Lee
- a Department of Bio and Brain Engineering , KAIST , Daejeon , South Korea
| | - Dongsup Kim
- a Department of Bio and Brain Engineering , KAIST , Daejeon , South Korea
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Lavecchia A, Cerchia C. In silico methods to address polypharmacology: current status, applications and future perspectives. Drug Discov Today 2015; 21:288-98. [PMID: 26743596 DOI: 10.1016/j.drudis.2015.12.007] [Citation(s) in RCA: 130] [Impact Index Per Article: 14.4] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2015] [Revised: 11/20/2015] [Accepted: 12/21/2015] [Indexed: 12/15/2022]
Abstract
Polypharmacology, a new paradigm in drug discovery that focuses on multi-target drugs (MTDs), has potential application for drug repurposing, the process of finding new uses for existing approved drugs, prediction of off-target toxicities and rational design of MTDs. In this scenario, computational strategies have demonstrated great potential in predicting polypharmacology and in facilitating drug repurposing. Here, we provide a comprehensive overview of various computational approaches that enable the prediction and analysis of in vitro and in vivo drug-response phenotypes and outline their potential for drug discovery. We give an outlook on the latest advances in rational design of MTDs and discuss possible future directions of algorithm development in this field.
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Affiliation(s)
- Antonio Lavecchia
- Department of Pharmacy, Drug Discovery Laboratory, University of Napoli Federico II, via D. Montesano 49, I-80131 Napoli, Italy.
| | - Carmen Cerchia
- Department of Pharmacy, Drug Discovery Laboratory, University of Napoli Federico II, via D. Montesano 49, I-80131 Napoli, Italy
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Mori M, Cau Y, Vignaroli G, Laurenzana I, Caivano A, Vullo D, Supuran CT, Botta M. Hit Recycling: Discovery of a Potent Carbonic Anhydrase Inhibitor by in Silico Target Fishing. ACS Chem Biol 2015; 10:1964-9. [PMID: 26121309 DOI: 10.1021/acschembio.5b00337] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
Abstract
In silico target fishing is an emerging tool in drug discovery, which is mostly used for primary target or off-target prediction and drug repositioning. In this work, we developed an in silico target fishing protocol to identify the primary target of GV2-20, a false-positive hit highlighted in a cell-based screen for 14-3-3 modulators. Although GV2-20 does not bind to 14-3-3 proteins, it showed remarkable antiproliferative effects in CML cells, thus raising interest toward the identification of its primary target. Six potential targets of GV2-20 were prioritized in silico and tested in vitro. Our results show that the molecule is a potent inhibitor of carbonic anhydrase 2 (CA2), thus confirming the predictive capability of our protocol. Most notably, GV2-20 experienced a remarkable selectivity for CA2, CA7, CA9, and CA12, and its scaffold was never explored before as a chemotype for CA inhibition, thus becoming an interesting lead candidate for further development.
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Affiliation(s)
- Mattia Mori
- Dipartimento
di Biotecnologie, Chimica e Farmacia, Università degli Studi di Siena, via Aldo Moro 2, I-53100 Siena, Italy
- Center
for Life Nano Science@Sapienza, Istituto Italiano di Tecnologia, viale Regina Elena 291, I-00161 Roma, Italy
| | - Ylenia Cau
- Dipartimento
di Biotecnologie, Chimica e Farmacia, Università degli Studi di Siena, via Aldo Moro 2, I-53100 Siena, Italy
| | - Giulia Vignaroli
- Dipartimento
di Biotecnologie, Chimica e Farmacia, Università degli Studi di Siena, via Aldo Moro 2, I-53100 Siena, Italy
| | - Ilaria Laurenzana
- IRCCS-Centro di Riferimento Oncologico Basilicata (CROB), Laboratory of Preclinical and Translational Research, Via Padre Pio 1, Rionero in Vulture 85028 Potenza, Italy
| | - Antonella Caivano
- IRCCS-Centro di Riferimento Oncologico Basilicata (CROB), Laboratory of Preclinical and Translational Research, Via Padre Pio 1, Rionero in Vulture 85028 Potenza, Italy
| | - Daniela Vullo
- Dipartimento
di Chimica, Laboratorio di Chimica Bioinorganica, Università degli Studi di Firenze, Polo Scientifico, Via della Lastruccia 3, 50019 Sesto Fiorentino (Firenze), Italy
| | - Claudiu T. Supuran
- Dipartimento
di Chimica, Laboratorio di Chimica Bioinorganica, Università degli Studi di Firenze, Polo Scientifico, Via della Lastruccia 3, 50019 Sesto Fiorentino (Firenze), Italy
- Dipartimento
NEUROFARBA, Sezione di Scienze Farmaceutiche, Università degli Studi di Firenze, Via Ugo Schiff 6, 50019 Sesto Fiorentino (Firenze), Italy
| | - Maurizio Botta
- Dipartimento
di Biotecnologie, Chimica e Farmacia, Università degli Studi di Siena, via Aldo Moro 2, I-53100 Siena, Italy
- Sbarro Institute
for Cancer Research and Molecular Medicine, Center for Biotechnology,
College of Science and Technology, Temple University, BioLife Science
Building, Suite 333, 1900 N 12th Street, Philadelphia, Pennsylvania 19122, United States
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Gfeller D, Zoete V. Protein homology reveals new targets for bioactive small molecules. Bioinformatics 2015; 31:2721-7. [PMID: 25900917 DOI: 10.1093/bioinformatics/btv214] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2015] [Accepted: 04/14/2015] [Indexed: 12/12/2022] Open
Abstract
MOTIVATION The functional impact of small molecules is increasingly being assessed in different eukaryotic species through large-scale phenotypic screening initiatives. Identifying the targets of these molecules is crucial to mechanistically understand their function and uncover new therapeutically relevant modes of action. However, despite extensive work carried out in model organisms and human, it is still unclear to what extent one can use information obtained in one species to make predictions in other species. RESULTS Here, for the first time, we explore and validate at a large scale the use of protein homology relationships to predict the targets of small molecules across different species. Our results show that exploiting target homology can significantly improve the predictions, especially for molecules experimentally tested in other species. Interestingly, when considering separately orthology and paralogy relationships, we observe that mapping small molecule interactions among orthologs improves prediction accuracy, while including paralogs does not improve and even sometimes worsens the prediction accuracy. Overall, our results provide a novel approach to integrate chemical screening results across multiple species and highlight the promises and remaining challenges of using protein homology for small molecule target identification. AVAILABILITY AND IMPLEMENTATION Homology-based predictions can be tested on our website http://www.swisstargetprediction.ch. CONTACT david.gfeller@unil.ch or vincent.zoete@isb-sib.ch. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- David Gfeller
- Department of Fundamental Oncology, Ludwig Center for Cancer Research, University of Lausanne, 1066 Epalinges, Switzerland and Swiss Institute of Bioinformatics (SIB), Quartier Sorge, Bâtiment Génopode, 1015 Lausanne, Switzerland
| | - Vincent Zoete
- Swiss Institute of Bioinformatics (SIB), Quartier Sorge, Bâtiment Génopode, 1015 Lausanne, Switzerland
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Neves BJ, Andrade CH, Cravo PVL. Natural products as leads in schistosome drug discovery. Molecules 2015; 20:1872-903. [PMID: 25625682 PMCID: PMC6272663 DOI: 10.3390/molecules20021872] [Citation(s) in RCA: 54] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2014] [Revised: 12/31/2014] [Accepted: 01/14/2015] [Indexed: 11/16/2022] Open
Abstract
Schistosomiasis is a neglected parasitic tropical disease that claims around 200,000 human lives every year. Praziquantel (PZQ), the only drug recommended by the World Health Organization for the treatment and control of human schistosomiasis, is now facing the threat of drug resistance, indicating the urgent need for new effective compounds to treat this disease. Therefore, globally, there is renewed interest in natural products (NPs) as a starting point for drug discovery and development for schistosomiasis. Recent advances in genomics, proteomics, bioinformatics, and cheminformatics have brought about unprecedented opportunities for the rapid and more cost-effective discovery of new bioactive compounds against neglected tropical diseases. This review highlights the main contributions that NP drug discovery and development have made in the treatment of schistosomiasis and it discusses how integration with virtual screening (VS) strategies may contribute to accelerating the development of new schistosomidal leads, especially through the identification of unexplored, biologically active chemical scaffolds and structural optimization of NPs with previously established activity.
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Affiliation(s)
- Bruno J Neves
- LabMol-Laboratory for Drug Design and Molecular Modeling, Faculdade de Farmácia, Universidade Federal de Goiás, Goiânia 74605-170, Brazil.
| | - Carolina H Andrade
- LabMol-Laboratory for Drug Design and Molecular Modeling, Faculdade de Farmácia, Universidade Federal de Goiás, Goiânia 74605-170, Brazil.
| | - Pedro V L Cravo
- GenoBio-Laboratory of Genomics and Biotechnology, Instituto de Patologia Tropical e Saúde Pública, Universidade Federal de Goiás, Goiânia 74605-050, Brazil.
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Cereto-Massagué A, Ojeda MJ, Valls C, Mulero M, Pujadas G, Garcia-Vallve S. Tools for in silico target fishing. Methods 2015; 71:98-103. [DOI: 10.1016/j.ymeth.2014.09.006] [Citation(s) in RCA: 92] [Impact Index Per Article: 10.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2014] [Revised: 09/18/2014] [Accepted: 09/19/2014] [Indexed: 12/17/2022] Open
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P2RANK: Knowledge-Based Ligand Binding Site Prediction Using Aggregated Local Features. ALGORITHMS FOR COMPUTATIONAL BIOLOGY 2015. [DOI: 10.1007/978-3-319-21233-3_4] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
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Schomburg KT, Rarey M. Benchmark Data Sets for Structure-Based Computational Target Prediction. J Chem Inf Model 2014; 54:2261-74. [DOI: 10.1021/ci500131x] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Affiliation(s)
- Karen T. Schomburg
- Center
for Bioinformatics, University of Hamburg, Bundesstrasse 43, 20146 Hamburg, Germany
| | - Matthias Rarey
- Center
for Bioinformatics, University of Hamburg, Bundesstrasse 43, 20146 Hamburg, Germany
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