ISLAND: in-silico proteins binding affinity prediction using sequence information
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RESEARCH
Open Access
ISLAND: in-silico proteins binding affinity prediction using sequence information Wajid Arshad Abbasi1,2*, Adiba Yaseen2, Fahad Ul Hassan2, Saiqa Andleeb3 and Fayyaz Ul Amir Afsar Minhas4* * Correspondence: wajidarshad@ gmail.com; fayyaz.minhas@warwick. ac.uk; [email protected] 1 Computational Biology and Data Analysis Laboratory, Department of Computer Science and Information Technology, King Abdullah Campus, University of Azad Jammu & Kashmir, Muzaffarabad, Pakistan 4 Department of Computer Science and the PathLAKE Consortium, University of Warwick, Coventry, UK Full list of author information is available at the end of the article
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. Keywords: Protein sequence analysis, Protein-protein interaction, Support vector machines, Web services, Binding affinity
Background Protein binding affinity is a key factor in enabling protein interactions and defining structure-function relationships that drive biological processes [1]. Accurate measurement of binding affinity is crucial in understanding complex biochemical pathways and to uncover protein interaction networks. It is also measured as part of drug discovery © The Author(s). 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and repro
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