Soliton concepts and the protein structure

TL;DR

This paper models protein loops as dark solitons of a generalized discrete nonlinear Schrödinger equation, revealing a modular approach that covers over 90% of proteins with high accuracy.

Contribution

It introduces a novel soliton-based framework for identifying protein structural modules, linking nonlinear physics to protein folding.

Findings

Over 90% of proteins can be modeled using a soliton library.

A small set of parameters describes most protein loops.

The approach enables both modeling and analysis of protein structures.

Abstract

Structural classification shows that the number of different protein folds is surprisingly small. It also appears that proteins are built in a modular fashion, from a relatively small number of components. Here we propose to identify the modular building blocks of proteins with the dark soliton solution of a generalized discrete nonlinear Schrodinger equation. For this we show that practically all protein loops can be obtained simply by scaling the size and by joining together a number of copies of the soliton, one after another. The soliton has only two loop specific parameters and we identify their possible values in Protein Data Bank. We show that with a collection of 200 sets of parameters, each determining a soliton profile that describes a different short loop, we cover over 90% of all proteins with experimental accuracy. We also present two examples that describe how the loop library can be employed both to model and to analyze the structure of folded proteins.