ISLAND: In-Silico Prediction of Proteins Binding Affinity Using Sequence Descriptors

TL;DR

This paper introduces ISLAND, a novel sequence-based machine learning method for predicting protein binding affinity, outperforming existing methods and providing a practical tool accessible via a webserver and Python code.

Contribution

The paper presents ISLAND, a new sequence-only predictor for protein binding affinity that achieves better accuracy than existing methods and is publicly available.

Findings

ISLAND outperforms existing sequence-based predictors.

The method is validated on multiple datasets.

A webserver and Python implementation are provided.

Abstract

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 which limit their applicability to protein complexes with known structures. In this work, we explore sequence based protein binding affinity prediction using machine learning. Our paper highlights the fact that the 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. We also propose a novel sequence-only predictor of binding affinity 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 the URL: http://faculty.pieas.edu.pk/fayyaz/software.html#island.