Learning a peptide-protein binding affinity predictor with kernel ridge regression

TL;DR

This paper introduces a specialized string kernel for peptides and pseudo-sequences, combined with kernel ridge regression, achieving state-of-the-art accuracy in predicting peptide-protein binding affinities across multiple benchmarks.

Contribution

A novel physico-chemical property-based string kernel and a dynamic programming algorithm for efficient computation, enabling accurate peptide-protein binding affinity prediction.

Findings

Outperforms current state-of-the-art methods on benchmark datasets

Accurately predicts binding affinities for any peptide-protein pair

Applicable to various biological activity prediction tasks

Abstract

We propose a specialized string kernel for small bio-molecules, peptides and pseudo-sequences of binding interfaces. The kernel incorporates physico-chemical properties of amino acids and elegantly generalize eight kernels, such as the Oligo, the Weighted Degree, the Blended Spectrum, and the Radial Basis Function. We provide a low complexity dynamic programming algorithm for the exact computation of the kernel and a linear time algorithm for it's approximation. Combined with kernel ridge regression and SupCK, a novel binding pocket kernel, the proposed kernel yields biologically relevant and good prediction accuracy on the PepX database. For the first time, a machine learning predictor is capable of accurately predicting the binding affinity of any peptide to any protein. The method was also applied to both single-target and pan-specific Major Histocompatibility Complex class II benchmark datasets and three Quantitative Structure Affinity Model benchmark datasets. On all benchmarks, our method significantly (p-value < 0.057) outperforms the current state-of-the-art methods at predicting peptide-protein binding affinities. The proposed approach is flexible and can be applied to predict any quantitative biological activity. The method should be of value to a large segment of the research community with the potential to accelerate peptide-based drug and vaccine development.