1
|
Gu L, Li B, Ming D. A multilayer dynamic perturbation analysis method for predicting ligand-protein interactions. BMC Bioinformatics 2022; 23:456. [PMID: 36324073 PMCID: PMC9628359 DOI: 10.1186/s12859-022-04995-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2022] [Accepted: 10/19/2022] [Indexed: 01/24/2023] Open
Abstract
BACKGROUND Ligand-protein interactions play a key role in defining protein function, and detecting natural ligands for a given protein is thus a very important bioengineering task. In particular, with the rapid development of AI-based structure prediction algorithms, batch structural models with high reliability and accuracy can be obtained at low cost, giving rise to the urgent requirement for the prediction of natural ligands based on protein structures. In recent years, although several structure-based methods have been developed to predict ligand-binding pockets and ligand-binding sites, accurate and rapid methods are still lacking, especially for the prediction of ligand-binding regions and the spatial extension of ligands in the pockets. RESULTS In this paper, we proposed a multilayer dynamics perturbation analysis (MDPA) method for predicting ligand-binding regions based solely on protein structure, which is an extended version of our previously developed fast dynamic perturbation analysis (FDPA) method. In MDPA/FDPA, ligand binding tends to occur in regions that cause large changes in protein conformational dynamics. MDPA, examined using a standard validation dataset of ligand-protein complexes, yielded an averaged ligand-binding site prediction Matthews coefficient of 0.40, with a prediction precision of at least 50% for 71% of the cases. In particular, for 80% of the cases, the predicted ligand-binding region overlaps the natural ligand by at least 50%. The method was also compared with other state-of-the-art structure-based methods. CONCLUSIONS MDPA is a structure-based method to detect ligand-binding regions on protein surface. Our calculations suggested that a range of spaces inside the protein pockets has subtle interactions with the protein, which can significantly impact on the overall dynamics of the protein. This work provides a valuable tool as a starting point upon which further docking and analysis methods can be used for natural ligand detection in protein functional annotation. The source code of MDPA method is freely available at: https://github.com/mingdengming/mdpa .
Collapse
Affiliation(s)
- Lin Gu
- grid.412022.70000 0000 9389 5210College of Biotechnology and Pharmaceutical Engineering, Nanjing Tech University, Biotech Building Room B1-404, 30 South Puzhu Road, Jiangbei New District, Nanjing City, 211816 Jiangsu People’s Republic of China
| | - Bin Li
- grid.412022.70000 0000 9389 5210College of Biotechnology and Pharmaceutical Engineering, Nanjing Tech University, Biotech Building Room B1-404, 30 South Puzhu Road, Jiangbei New District, Nanjing City, 211816 Jiangsu People’s Republic of China
| | - Dengming Ming
- grid.412022.70000 0000 9389 5210College of Biotechnology and Pharmaceutical Engineering, Nanjing Tech University, Biotech Building Room B1-404, 30 South Puzhu Road, Jiangbei New District, Nanjing City, 211816 Jiangsu People’s Republic of China
| |
Collapse
|
2
|
Alencar WLM, da Silva Arouche T, Neto AFG, de Castro Ramalho T, de Carvalho Júnior RN, de Jesus Chaves Neto AM. Interactions of Co, Cu, and non-metal phthalocyanines with external structures of SARS-CoV-2 using docking and molecular dynamics. Sci Rep 2022; 12:3316. [PMID: 35228662 PMCID: PMC8885651 DOI: 10.1038/s41598-022-07396-w] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2021] [Accepted: 02/10/2022] [Indexed: 02/06/2023] Open
Abstract
The new coronavirus, SARS-CoV-2, caused the COVID-19 pandemic, characterized by its high rate of contamination, propagation capacity, and lethality rate. In this work, we approach the use of phthalocyanines as an inhibitor of SARS-CoV-2, as they present several interactive properties of the phthalocyanines (Pc) of Cobalt (CoPc), Copper (CuPc) and without a metal group (NoPc) can interact with SARS-CoV-2, showing potential be used as filtering by adsorption on paints on walls, masks, clothes, and air conditioning filters. Molecular modeling techniques through Molecular Docking and Molecular Dynamics were used, where the target was the external structures of the virus, but specifically the envelope protein, main protease, and Spike glycoprotein proteases. Using the g_MM-GBSA module and with it, the molecular docking studies show that the ligands have interaction characteristics capable of adsorbing the structures. Molecular dynamics provided information on the root-mean-square deviation of the atomic positions provided values between 1 and 2.5. The generalized Born implicit solvation model, Gibbs free energy, and solvent accessible surface area approach were used. Among the results obtained through molecular dynamics, it was noticed that interactions occur since Pc could bind to residues of the active site of macromolecules, demonstrating good interactions; in particular with CoPc. Molecular couplings and free energy showed that S-gly active site residues interacted strongly with phthalocyanines with values of - 182.443 kJ/mol (CoPc), 158.954 kJ/mol (CuPc), and - 129.963 kJ/mol (NoPc). The interactions of Pc's with SARS-CoV-2 may predict some promising candidates for antagonists to the virus, which if confirmed through experimental approaches, may contribute to resolving the global crisis of the COVID-19 pandemic.
Collapse
Affiliation(s)
- Wilson Luna Machado Alencar
- Laboratory of Preparation and Computation of Nanomaterials (LPCN), Federal University of Pará, C. P. 479, Belem, PA, 66075-110, Brazil
- Pos-Graduation Program in Engineering of Natural Resources of the Amazon, ITEC, Federal University of Pará, C. P. 2626, Belém, PA, 66050-540, Brazil
- Federal Institute of Pará (IFPA), C. P. BR 316, Km 61, Castanhal, PA, 68740-970, Brazil
| | - Tiago da Silva Arouche
- Laboratory of Preparation and Computation of Nanomaterials (LPCN), Federal University of Pará, C. P. 479, Belem, PA, 66075-110, Brazil
| | | | | | - Raul Nunes de Carvalho Júnior
- Pos-Graduation Program in Engineering of Natural Resources of the Amazon, ITEC, Federal University of Pará, C. P. 2626, Belém, PA, 66050-540, Brazil
- Pos-Graduation Program in Chemical Engineering, ITEC, Federal University of Pará, C. P. 479, Belém, PA, 66075-900, Brazil
| | - Antonio Maia de Jesus Chaves Neto
- Laboratory of Preparation and Computation of Nanomaterials (LPCN), Federal University of Pará, C. P. 479, Belem, PA, 66075-110, Brazil.
- Pos-Graduation Program in Engineering of Natural Resources of the Amazon, ITEC, Federal University of Pará, C. P. 2626, Belém, PA, 66050-540, Brazil.
- Pos-Graduation Program in Chemical Engineering, ITEC, Federal University of Pará, C. P. 479, Belém, PA, 66075-900, Brazil.
- National Professional Master's in Physics Teaching, Federal University of Pará, C. P. 479, Belém, PA, 66075-110, Brazil.
| |
Collapse
|
3
|
Kessler A, Kouznetsova VL, Tsigelny IF. Targeting Epigenetic Regulators Using Machine Learning: Potential Sirtuin 2 Inhibitors. JOURNAL OF COMPUTATIONAL BIOPHYSICS AND CHEMISTRY 2021. [DOI: 10.1142/s2737416521500526] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/21/2023]
Abstract
Sirtuin 2 (SIRT2) is a nicotinamide adenine dinucleotide (NAD+)-dependent deacetylase that has been identified as a target for many diseases, including Parkinson’s disease (PD) and leukemia. Using 234 SIRT2 inhibitors from the ZINC15 database, we generated molecular descriptors with PaDEL and constructed a machine-learning (ML) model for the binary classification of SIRT2 inhibitors. To predict compounds with novel inhibitory mechanisms, we then applied the model on the ZINC15/FDA subset, yielding 107 potential SIRT2 inhibitors. For validation of these substances, we employed the binding analysis software AutoDock Vina to perform virtual screening, with which 43 compounds were considered best inhibitors at the [Formula: see text][Formula: see text]kcal/mol binding affinity threshold. Our results demonstrate the potential of ligand-based (LB) ML techniques in conjunction with receptor-based virtual screening (RBVS) to facilitate the drug discovery or repurposing.
Collapse
Affiliation(s)
- Andrew Kessler
- REHS program, San Diego Supercomputer Center, UC San Diego, California, USA
| | | | - Igor F. Tsigelny
- San Diego Supercomputer Center, UC San Diego, California, USA
- BiAna, San Diego, California, USA
- Department of Neurosciences, UC San Diego, California, USA
| |
Collapse
|
4
|
Li S, Cai C, Gong J, Liu X, Li H. A fast protein binding site comparison algorithm for proteome-wide protein function prediction and drug repurposing. Proteins 2021; 89:1541-1556. [PMID: 34245187 DOI: 10.1002/prot.26176] [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: 02/09/2021] [Revised: 06/26/2021] [Accepted: 06/30/2021] [Indexed: 01/18/2023]
Abstract
The expansion of three-dimensional protein structures and enhanced computing power have significantly facilitated our understanding of protein sequence/structure/function relationships. A challenge in structural genomics is to predict the function of uncharacterized proteins. Protein function deconvolution based on global sequence or structural homology is impracticable when a protein relates to no other proteins with known function, and in such cases, functional relationships can be established by detecting their local ligand binding site similarity. Here, we introduce a sequence order-independent comparison algorithm, PocketShape, for structural proteome-wide exploration of protein functional site by fully considering the geometry of the backbones, orientation of the sidechains, and physiochemical properties of the pocket-lining residues. PocketShape is efficient in distinguishing similar from dissimilar ligand binding site pairs by retrieving 99.3% of the similar pairs while rejecting 100% of the dissimilar pairs on a dataset containing 1538 binding site pairs. This method successfully classifies 83 enzyme structures with diverse functions into 12 clusters, which is highly in accordance with the actual structural classification of proteins classification. PocketShape also achieves superior performances than other methods in protein profiling based on experimental data. Potential new applications for representative SARS-CoV-2 drugs Remdesivir and 11a are predicted. The high accuracy and time-efficient characteristics of PocketShape will undoubtedly make it a promising complementary tool for proteome-wide protein function inference and drug repurposing study.
Collapse
Affiliation(s)
- Shiliang Li
- State Key Laboratory of Bioreactor Engineering, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai, China
| | - Chaoqian Cai
- State Key Laboratory of Bioreactor Engineering, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai, China.,School of Information Science and Engineering, East China University of Science and Technology, Shanghai, China
| | - Jiayu Gong
- State Key Laboratory of Bioreactor Engineering, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai, China.,School of Information Science and Engineering, East China University of Science and Technology, Shanghai, China
| | - Xiaofeng Liu
- State Key Laboratory of Bioreactor Engineering, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai, China
| | - Honglin Li
- State Key Laboratory of Bioreactor Engineering, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai, China.,School of Information Science and Engineering, East China University of Science and Technology, Shanghai, China.,Research and Development Department, Jiangzhong Pharmaceutical Co., Ltd., Nanchang, China
| |
Collapse
|
5
|
Simonovsky M, Meyers J. DeeplyTough: Learning Structural Comparison of Protein Binding Sites. J Chem Inf Model 2020; 60:2356-2366. [DOI: 10.1021/acs.jcim.9b00554] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Affiliation(s)
- Martin Simonovsky
- BenevolentAI, London W1T 5HD, U.K
- École des Ponts ParisTech, Champs sur Marne 77455, France
- Université Paris-Est, Champs sur Marne 77455, France
| | | |
Collapse
|
6
|
Meyers J, Chessum NEA, Ali S, Mok NY, Wilding B, Pasqua AE, Rowlands M, Tucker MJ, Evans LE, Rye CS, O’Fee L, Le Bihan YV, Burke R, Carter M, Workman P, Blagg J, Brown N, van Montfort RLM, Jones K, Cheeseman MD. Privileged Structures and Polypharmacology within and between Protein Families. ACS Med Chem Lett 2018; 9:1199-1204. [PMID: 30613326 PMCID: PMC6295861 DOI: 10.1021/acsmedchemlett.8b00364] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2018] [Accepted: 11/16/2018] [Indexed: 12/31/2022] Open
Abstract
Polypharmacology is often a key contributor to the efficacy of a drug, but is also a potential risk. We investigated two hits discovered via a cell-based phenotypic screen, the CDK9 inhibitor CCT250006 (1) and the pirin ligand CCT245232 (2), to establish methodology to elucidate their secondary protein targets. Using computational pocket-based analysis, we discovered intrafamily polypharmacology for our kinase inhibitor, despite little overall sequence identity. The interfamily polypharmacology of 2 with B-Raf was used to discover a novel pirin ligand from a very small but privileged compound library despite no apparent ligand or binding site similarity. Our data demonstrates that in areas of drug discovery where intrafamily polypharmacology is often an issue, ligand dissimilarity cannot necessarily be used to assume different off-target profiles and that understanding interfamily polypharmacology will be important in the future to reduce the risk of idiopathic toxicity and in the design of screening libraries.
Collapse
Affiliation(s)
- Joshua Meyers
- Cancer
Research UK Cancer Therapeutics Unit and Division of Structural Biology, The Institute of Cancer Research, London SW7 3RP, United Kingdom
| | - Nicola E. A. Chessum
- Cancer
Research UK Cancer Therapeutics Unit and Division of Structural Biology, The Institute of Cancer Research, London SW7 3RP, United Kingdom
| | - Salyha Ali
- Cancer
Research UK Cancer Therapeutics Unit and Division of Structural Biology, The Institute of Cancer Research, London SW7 3RP, United Kingdom
| | - N. Yi Mok
- Cancer
Research UK Cancer Therapeutics Unit and Division of Structural Biology, The Institute of Cancer Research, London SW7 3RP, United Kingdom
| | - Birgit Wilding
- Cancer
Research UK Cancer Therapeutics Unit and Division of Structural Biology, The Institute of Cancer Research, London SW7 3RP, United Kingdom
| | - A. Elisa Pasqua
- Cancer
Research UK Cancer Therapeutics Unit and Division of Structural Biology, The Institute of Cancer Research, London SW7 3RP, United Kingdom
| | - Martin Rowlands
- Cancer
Research UK Cancer Therapeutics Unit and Division of Structural Biology, The Institute of Cancer Research, London SW7 3RP, United Kingdom
| | - Michael J. Tucker
- Cancer
Research UK Cancer Therapeutics Unit and Division of Structural Biology, The Institute of Cancer Research, London SW7 3RP, United Kingdom
| | - Lindsay E. Evans
- Cancer
Research UK Cancer Therapeutics Unit and Division of Structural Biology, The Institute of Cancer Research, London SW7 3RP, United Kingdom
| | - Carl S. Rye
- Cancer
Research UK Cancer Therapeutics Unit and Division of Structural Biology, The Institute of Cancer Research, London SW7 3RP, United Kingdom
| | - Lisa O’Fee
- Cancer
Research UK Cancer Therapeutics Unit and Division of Structural Biology, The Institute of Cancer Research, London SW7 3RP, United Kingdom
| | - Yann-Vaï Le Bihan
- Cancer
Research UK Cancer Therapeutics Unit and Division of Structural Biology, The Institute of Cancer Research, London SW7 3RP, United Kingdom
| | - Rosemary Burke
- Cancer
Research UK Cancer Therapeutics Unit and Division of Structural Biology, The Institute of Cancer Research, London SW7 3RP, United Kingdom
| | - Michael Carter
- Cancer
Research UK Cancer Therapeutics Unit and Division of Structural Biology, The Institute of Cancer Research, London SW7 3RP, United Kingdom
| | - Paul Workman
- Cancer
Research UK Cancer Therapeutics Unit and Division of Structural Biology, The Institute of Cancer Research, London SW7 3RP, United Kingdom
| | - Julian Blagg
- Cancer
Research UK Cancer Therapeutics Unit and Division of Structural Biology, The Institute of Cancer Research, London SW7 3RP, United Kingdom
| | - Nathan Brown
- Cancer
Research UK Cancer Therapeutics Unit and Division of Structural Biology, The Institute of Cancer Research, London SW7 3RP, United Kingdom
| | - Rob L. M. van Montfort
- Cancer
Research UK Cancer Therapeutics Unit and Division of Structural Biology, The Institute of Cancer Research, London SW7 3RP, United Kingdom
| | - Keith Jones
- Cancer
Research UK Cancer Therapeutics Unit and Division of Structural Biology, The Institute of Cancer Research, London SW7 3RP, United Kingdom
| | - Matthew D. Cheeseman
- Cancer
Research UK Cancer Therapeutics Unit and Division of Structural Biology, The Institute of Cancer Research, London SW7 3RP, United Kingdom
| |
Collapse
|
7
|
Ehrt C, Brinkjost T, Koch O. A benchmark driven guide to binding site comparison: An exhaustive evaluation using tailor-made data sets (ProSPECCTs). PLoS Comput Biol 2018; 14:e1006483. [PMID: 30408032 PMCID: PMC6224041 DOI: 10.1371/journal.pcbi.1006483] [Citation(s) in RCA: 41] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2018] [Accepted: 09/02/2018] [Indexed: 11/24/2022] Open
Abstract
The automated comparison of protein-ligand binding sites provides useful insights into yet unexplored site similarities. Various stages of computational and chemical biology research can benefit from this knowledge. The search for putative off-targets and the establishment of polypharmacological effects by comparing binding sites led to promising results for numerous projects. Although many cavity comparison methods are available, a comprehensive analysis to guide the choice of a tool for a specific application is wanting. Moreover, the broad variety of binding site modeling approaches, comparison algorithms, and scoring metrics impedes this choice. Herein, we aim to elucidate strengths and weaknesses of binding site comparison methodologies. A detailed benchmark study is the only possibility to rationalize the selection of appropriate tools for different scenarios. Specific evaluation data sets were developed to shed light on multiple aspects of binding site comparison. An assembly of all applied benchmark sets (ProSPECCTs–Protein Site Pairs for the Evaluation of Cavity Comparison Tools) is made available for the evaluation and optimization of further and still emerging methods. The results indicate the importance of such analyses to facilitate the choice of a methodology that complies with the requirements of a specific scientific challenge. Binding site similarities are useful in the context of promiscuity prediction, drug repurposing, the analysis of protein-ligand and protein-protein complexes, function prediction, and further fields of general interest in chemical biology and biochemistry. Many years of research have led to the development of a multitude of methods for binding site analysis and comparison. On the one hand, their availability supports research. On the other hand, the huge number of methods hampers the efficient selection of a specific tool. Our research is dedicated to the analysis of different cavity comparison tools. We use several binding site data sets to establish guidelines which can be applied to ensure a successful application of comparison methods by circumventing potential pitfalls.
Collapse
Affiliation(s)
- Christiane Ehrt
- Faculty of Chemistry and Chemical Biology, TU Dortmund University, Dortmund, Germany
| | - Tobias Brinkjost
- Faculty of Chemistry and Chemical Biology, TU Dortmund University, Dortmund, Germany
- Department of Computer Science, TU Dortmund University, Dortmund, Germany
| | - Oliver Koch
- Faculty of Chemistry and Chemical Biology, TU Dortmund University, Dortmund, Germany
- * E-mail: ,
| |
Collapse
|
8
|
Krivák R, Hoksza D. P2Rank: machine learning based tool for rapid and accurate prediction of ligand binding sites from protein structure. J Cheminform 2018; 10:39. [PMID: 30109435 PMCID: PMC6091426 DOI: 10.1186/s13321-018-0285-8] [Citation(s) in RCA: 166] [Impact Index Per Article: 27.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2017] [Accepted: 06/29/2018] [Indexed: 01/29/2023] Open
Abstract
Background Ligand binding site prediction from protein structure has many applications related to elucidation of protein function and structure based drug discovery. It often represents only one step of many in complex computational drug design efforts. Although many methods have been published to date, only few of them are suitable for use in automated pipelines or for processing large datasets.
These use cases require stability and speed, which disqualifies many of the recently introduced tools that are either template based or available only as web servers. Results We present P2Rank, a stand-alone template-free tool for prediction of ligand binding sites based on machine learning. It is based on prediction of ligandability of local chemical neighbourhoods that are centered on points placed on the solvent accessible surface of a protein.
We show that P2Rank outperforms several existing tools, which include two widely used stand-alone tools (Fpocket, SiteHound), a comprehensive consensus based tool (MetaPocket 2.0), and a recent deep learning based method (DeepSite). P2Rank belongs to the fastest available tools (requires under 1 s for prediction on one protein), with additional advantage of multi-threaded implementation. Conclusions P2Rank is a new open source software package for ligand binding site prediction from protein structure. It is available as a user-friendly stand-alone command line program and a Java library. P2Rank has a lightweight installation and does not depend on other bioinformatics tools or large structural or sequence databases. Thanks to its speed and ability to make fully automated predictions, it is particularly well suited for processing large datasets or as a component of scalable structural bioinformatics pipelines. Electronic supplementary material The online version of this article (10.1186/s13321-018-0285-8) contains supplementary material, which is available to authorized users.
Collapse
Affiliation(s)
- Radoslav Krivák
- Department of Software Engineering, Charles University, Prague, Czech Republic.
| | - David Hoksza
- Department of Software Engineering, Charles University, Prague, Czech Republic.
| |
Collapse
|
9
|
Brown N, Cambruzzi J, Cox PJ, Davies M, Dunbar J, Plumbley D, Sellwood MA, Sim A, Williams-Jones BI, Zwierzyna M, Sheppard DW. Big Data in Drug Discovery. PROGRESS IN MEDICINAL CHEMISTRY 2018; 57:277-356. [PMID: 29680150 DOI: 10.1016/bs.pmch.2017.12.003] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/02/2022]
Abstract
Interpretation of Big Data in the drug discovery community should enhance project timelines and reduce clinical attrition through improved early decision making. The issues we encounter start with the sheer volume of data and how we first ingest it before building an infrastructure to house it to make use of the data in an efficient and productive way. There are many problems associated with the data itself including general reproducibility, but often, it is the context surrounding an experiment that is critical to success. Help, in the form of artificial intelligence (AI), is required to understand and translate the context. On the back of natural language processing pipelines, AI is also used to prospectively generate new hypotheses by linking data together. We explain Big Data from the context of biology, chemistry and clinical trials, showcasing some of the impressive public domain sources and initiatives now available for interrogation.
Collapse
Affiliation(s)
| | | | | | | | | | | | | | - Aaron Sim
- BenevolentAI, London, United Kingdom
| | | | - Magdalena Zwierzyna
- BenevolentAI, London, United Kingdom; Institute of Cardiovascular Science, University College London, London, United Kingdom
| | | |
Collapse
|
10
|
Benstead-Hume G, Wooller SK, Pearl FMG. 'Big data' approaches for novel anti-cancer drug discovery. Expert Opin Drug Discov 2017; 12:599-609. [PMID: 28462602 DOI: 10.1080/17460441.2017.1319356] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
INTRODUCTION The development of improved cancer therapies is frequently cited as an urgent unmet medical need. Recent advances in platform technologies and the increasing availability of biological 'big data' are providing an unparalleled opportunity to systematically identify the key genes and pathways involved in tumorigenesis. The discoveries made using these new technologies may lead to novel therapeutic interventions. Areas covered: The authors discuss the current approaches that use 'big data' to identify cancer drivers. These approaches include the analysis of genomic sequencing data, pathway data, multi-platform data, identifying genetic interactions such as synthetic lethality and using cell line data. They review how big data is being used to identify novel drug targets. The authors then provide an overview of the available data repositories and tools being used at the forefront of cancer drug discovery. Expert opinion: Targeted therapies based on the genomic events driving the tumour will eventually inform treatment protocols. However, using a tailored approach to treat all tumour patients may require developing a large repertoire of targeted drugs.
Collapse
Affiliation(s)
- Graeme Benstead-Hume
- a Bioinformatics Group, School of Life Sciences , University of Sussex , Brighton , United Kingdom
| | - Sarah K Wooller
- a Bioinformatics Group, School of Life Sciences , University of Sussex , Brighton , United Kingdom
| | - Frances M G Pearl
- a Bioinformatics Group, School of Life Sciences , University of Sussex , Brighton , United Kingdom
| |
Collapse
|