1
|
Raybould MIJ, Rees AR, Deane CM. Current strategies for detecting functional convergence across B-cell receptor repertoires. MAbs 2021; 13:1996732. [PMID: 34781829 PMCID: PMC8604390 DOI: 10.1080/19420862.2021.1996732] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2021] [Revised: 10/10/2021] [Accepted: 10/12/2021] [Indexed: 12/11/2022] Open
Abstract
Convergence across B-cell receptor (BCR) and antibody repertoires has become instrumental in prioritizing candidates in recent rapid therapeutic antibody discovery campaigns. It has also increased our understanding of the immune system, providing evidence for the preferential selection of BCRs to particular (immunodominant) epitopes post vaccination/infection. These important implications for both drug discovery and immunology mean that it is essential to consider the optimal way to combine experimental and computational technology when probing BCR repertoires for convergence signatures. Here, we discuss the theoretical basis for observing BCR repertoire functional convergence and explore factors of study design that can impact functional signal. We also review the computational arsenal available to detect antibodies with similar functional properties, highlighting opportunities enabled by recent clustering algorithms that exploit structural similarities between BCRs. Finally, we suggest future areas of development that should increase the power of BCR repertoire functional clustering.
Collapse
Affiliation(s)
- Matthew I. J. Raybould
- Oxford Protein Informatics Group, Department of Statistics, University of Oxford, Oxford, UK
| | | | - Charlotte M. Deane
- Oxford Protein Informatics Group, Department of Statistics, University of Oxford, Oxford, UK
| |
Collapse
|
2
|
Kumar AP, Nguyen MN, Verma C, Lukman S. Structural analysis of protein tyrosine phosphatase 1B reveals potentially druggable allosteric binding sites. Proteins 2018; 86:301-321. [PMID: 29235148 DOI: 10.1002/prot.25440] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2017] [Revised: 11/16/2017] [Accepted: 12/10/2017] [Indexed: 12/11/2022]
Abstract
Catalytic proteins such as human protein tyrosine phosphatase 1B (PTP1B), with conserved and highly polar active sites, warrant the discovery of druggable nonactive sites, such as allosteric sites, and potentially, therapeutic small molecules that can bind to these sites. Catalyzing the dephosphorylation of numerous substrates, PTP1B is physiologically important in intracellular signal transduction pathways in diverse cell types and tissues. Aberrant PTP1B is associated with obesity, diabetes, cancers, and neurodegenerative disorders. Utilizing clustering methods (based on root mean square deviation, principal component analysis, nonnegative matrix factorization, and independent component analysis), we have examined multiple PTP1B structures. Using the resulting representative structures in different conformational states, we determined consensus clustroids and used them to identify both known and novel binding sites, some of which are potentially allosteric. We report several lead compounds that could potentially bind to the novel PTP1B binding sites and can be further optimized. Considering the possibility for drug repurposing, we discovered homologous binding sites in other proteins, with ligands that could potentially bind to the novel PTP1B binding sites.
Collapse
Affiliation(s)
- Ammu Prasanna Kumar
- Department of Chemistry, College of Arts and Sciences, Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates
| | - Minh N Nguyen
- Bioinformatics Institute, Agency for Science, Technology and Research, Singapore
| | - Chandra Verma
- Bioinformatics Institute, Agency for Science, Technology and Research, Singapore.,Department of Biological Sciences, National University of Singapore, Singapore.,School of Biological Sciences, Nanyang Technological University, Singapore
| | - Suryani Lukman
- Department of Chemistry, College of Arts and Sciences, Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates
| |
Collapse
|
3
|
Lukman S, Nguyen MN, Sim K, Teo JCM. Discovery of Rab1 binding sites using an ensemble of clustering methods. Proteins 2017; 85:859-871. [PMID: 28120477 DOI: 10.1002/prot.25254] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2016] [Revised: 12/28/2016] [Accepted: 01/19/2017] [Indexed: 12/29/2022]
Abstract
Targeting non-native-ligand binding sites for potential investigative and therapeutic applications is an attractive strategy in proteins that share common native ligands, as in Rab1 protein. Rab1 is a subfamily member of Rab proteins, which are members of Ras GTPase superfamily. All Ras GTPase superfamily members bind to native ligands GTP and GDP, that switch on and off the proteins, respectively. Rab1 is physiologically essential for autophagy and transport between endoplasmic reticulum and Golgi apparatus. Pathologically, Rab1 is implicated in human cancers, a neurodegenerative disease, cardiomyopathy, and bacteria-caused infectious diseases. We have performed structural analyses on Rab1 protein using a unique ensemble of clustering methods, including multi-step principal component analysis, non-negative matrix factorization, and independent component analysis, to better identify representative Rab1 proteins than the application of a single clustering method alone does. We then used the identified representative Rab1 structures, resolved in multiple ligand states, to map their known and novel binding sites. We report here at least a novel binding site on Rab1, involving Rab1-specific residues that could be further explored for the rational design and development of investigative probes and/or therapeutic small molecules against the Rab1 protein. Proteins 2017; 85:859-871. © 2016 Wiley Periodicals, Inc.
Collapse
Affiliation(s)
- Suryani Lukman
- Khalifa University, Abu Dhabi Campus, PO Box, 127788, Abu Dhabi, United Arab Emirates
| | - Minh N Nguyen
- Bioinformatics Institute, Agency for Science, Technology and Research, 30 Biopolis Street, #07-01 Matrix, Singapore, 138671, Singapore
| | - Kelvin Sim
- OneAnalytix Pte Ltd, Onn Wah Building #04-01, 11 Changi South Lane Singapore, 486154, Singapore
| | - Jeremy C M Teo
- Khalifa University, Abu Dhabi Campus, PO Box, 127788, Abu Dhabi, United Arab Emirates
| |
Collapse
|
4
|
Ieong PU, Sørensen J, Vemu PL, Wong CW, Demir Ö, Williams NP, Wang J, Crawl D, Swift RV, Malmstrom RD, Altintas I, Amaro RE. Progress towards automated Kepler scientific workflows for computer-aided drug discovery and molecular simulations. ACTA ACUST UNITED AC 2014; 29:1745-1755. [PMID: 29399238 DOI: 10.1016/j.procs.2014.05.159] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
Abstract
We describe the development of automated workflows that support computed-aided drug discovery (CADD) and molecular dynamics (MD) simulations and are included as part of the National Biomedical Computational Resource (NBCR). The main workflow components include: file-management tasks, ligand force field parameterization, receptor-ligand molecular dynamics (MD) simulations, job submission and monitoring on relevant high-performance computing (HPC) resources, receptor structural clustering, virtual screening (VS), and statistical analyses of the VS results. The workflows aim to standardize simulation and analysis and promote best practices within the molecular simulation and CADD communities. Each component is developed as a stand-alone workflow, which allows easy integration into larger frameworks built to suit user needs, while remaining intuitive and easy to extend.
Collapse
Affiliation(s)
- Pek U Ieong
- Department of Chemistry and Biochemistry, University of California San Diego, 9500 Gilman Drive, MC 0340, La Jolla, CA 92093, USA.,National Biomedical Computation Resource, University of California San Diego, 9500 Gilman Drive, MC 0340, La Jolla, CA 92093, USA
| | - Jesper Sørensen
- Department of Chemistry and Biochemistry, University of California San Diego, 9500 Gilman Drive, MC 0340, La Jolla, CA 92093, USA
| | - Prasantha L Vemu
- Department of Chemistry and Biochemistry, University of California San Diego, 9500 Gilman Drive, MC 0340, La Jolla, CA 92093, USA
| | - Celia W Wong
- Department of Chemistry and Biochemistry, University of California San Diego, 9500 Gilman Drive, MC 0340, La Jolla, CA 92093, USA
| | - Özlem Demir
- Department of Chemistry and Biochemistry, University of California San Diego, 9500 Gilman Drive, MC 0340, La Jolla, CA 92093, USA
| | - Nadya P Williams
- San Diego Supercomputer Center, University of California San Diego, 9500 Gilman Drive, MC 0340, La Jolla, CA 92093, USA.,National Biomedical Computation Resource, University of California San Diego, 9500 Gilman Drive, MC 0340, La Jolla, CA 92093, USA
| | - Jianwu Wang
- San Diego Supercomputer Center, University of California San Diego, 9500 Gilman Drive, MC 0340, La Jolla, CA 92093, USA
| | - Daniel Crawl
- San Diego Supercomputer Center, University of California San Diego, 9500 Gilman Drive, MC 0340, La Jolla, CA 92093, USA.,National Biomedical Computation Resource, University of California San Diego, 9500 Gilman Drive, MC 0340, La Jolla, CA 92093, USA
| | - Robert V Swift
- Department of Chemistry and Biochemistry, University of California San Diego, 9500 Gilman Drive, MC 0340, La Jolla, CA 92093, USA
| | - Robert D Malmstrom
- Department of Chemistry and Biochemistry, University of California San Diego, 9500 Gilman Drive, MC 0340, La Jolla, CA 92093, USA.,National Biomedical Computation Resource, University of California San Diego, 9500 Gilman Drive, MC 0340, La Jolla, CA 92093, USA
| | - Ilkay Altintas
- San Diego Supercomputer Center, University of California San Diego, 9500 Gilman Drive, MC 0340, La Jolla, CA 92093, USA.,National Biomedical Computation Resource, University of California San Diego, 9500 Gilman Drive, MC 0340, La Jolla, CA 92093, USA
| | - Rommie E Amaro
- Department of Chemistry and Biochemistry, University of California San Diego, 9500 Gilman Drive, MC 0340, La Jolla, CA 92093, USA.,National Biomedical Computation Resource, University of California San Diego, 9500 Gilman Drive, MC 0340, La Jolla, CA 92093, USA
| |
Collapse
|