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ISLAND: in-silico proteins binding affinity prediction using sequence information
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Abbasi, Wajid Arshad, Yaseen, Adiba, Hassan, Fahad Ul, Andleeb, Saiqa and Minhas, Fayyaz ul Amir Afsar (2020) ISLAND: in-silico proteins binding affinity prediction using sequence information. BioData Mining, 13 (1). 20. doi:10.1186/s13040-020-00231-w
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WRAP-ISLAND-in-silico-proteins-binding-affinity-prediction-using-sequence-information-Minhas-2020.pdf - Published Version - Requires a PDF viewer. Available under License Creative Commons Attribution 4.0. Download (938Kb) | Preview |
Official URL: https://doi.org/10.1186/s13040-020-00231-w
Abstract
Background:
Determining binding affinity in protein-protein interactions is important in the discovery and design of novel therapeutics and mutagenesis studies. Determination of binding affinity of proteins in the formation of protein complexes requires sophisticated, expensive and time-consuming experimentation which can be replaced with computational methods. Most computational prediction techniques require protein structures that limit their applicability to protein complexes with known structures. In this work, we explore sequence-based protein binding affinity prediction using machine learning.
Method:
We have used protein sequence information instead of protein structures along with machine learning techniques to accurately predict the protein binding affinity.
Results:
We present our findings that the true generalization performance of even the state-of-the-art sequence-only predictor is far from satisfactory and that the development of machine learning methods for binding affinity prediction with improved generalization performance is still an open problem. We have also proposed a sequence-based novel protein binding affinity predictor called ISLAND which gives better accuracy than existing methods over the same validation set as well as on external independent test dataset. A cloud-based webserver implementation of ISLAND and its python code are available at https://sites.google.com/view/wajidarshad/software.
Conclusion:
This paper highlights the fact that the true generalization performance of even the state-of-the-art sequence-only predictor of binding affinity is far from satisfactory and that the development of effective and practical methods in this domain is still an open problem.
| Item Type: | Journal Article | |||||||||
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| Subjects: | Q Science > QP Physiology | |||||||||
| Divisions: | Faculty of Science > Computer Science | |||||||||
| Library of Congress Subject Headings (LCSH): | Amino acid sequence, Protein-protein interactions, Protein binding | |||||||||
| Journal or Publication Title: | BioData Mining | |||||||||
| Publisher: | BioMed Central Ltd. | |||||||||
| ISSN: | 1756-0381 | |||||||||
| Official Date: | 25 November 2020 | |||||||||
| Dates: |
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| Volume: | 13 | |||||||||
| Number: | 1 | |||||||||
| Article Number: | 20 | |||||||||
| DOI: | 10.1186/s13040-020-00231-w | |||||||||
| Status: | Peer Reviewed | |||||||||
| Publication Status: | Published | |||||||||
| Access rights to Published version: | Open Access | |||||||||
| RIOXX Funder/Project Grant: |
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