Intra-protein binding peptide fragments have specific and intrinsic sequence patterns

TL;DR

This paper reveals that intra-protein binding peptide fragments exhibit specific, intrinsic sequence patterns that can be identified using deep learning, achieving high classification accuracy and aiding in understanding protein folding and interactions.

Contribution

It introduces a novel deep learning approach to uncover intrinsic sequence patterns in intra-protein binding peptide fragments, a previously uncharacterized phenomenon.

Findings

Achieved up to 93% accuracy in classifying binding vs. non-binding peptide fragments.

Discovered specific sequence patterns associated with intra-protein binding.

Potential to advance understanding of protein folding and interactions.

Abstract

The key finding in the DNA double helix model is the specific pairing or binding between nucleotides A-T and C-G, and the pairing rules are the molecule basis of genetic code. Unfortunately, no such rules have been discovered for proteins. Here we show that similar rules and intrinsic sequence patterns between intra-protein binding peptide fragments do exist, and they can be extracted using a deep learning algorithm. Multi-millions of binding and non-binding peptide fragments from currently available protein X-ray structures are classified with an accuracy of up to 93%. This discovery has the potential in helping solve protein folding and protein-protein interaction problems, two open and fundamental problems in molecular biology.