GreMuTRRR: A Novel Genetic Algorithm to Solve Distance Geometry Problem for Protein Structures

TL;DR

This paper introduces GreMuTRRR, a genetic algorithm designed to solve the Euclidean distance geometry problem for protein structure prediction using incomplete NMR data, outperforming standard genetic algorithms.

Contribution

The paper presents a novel genetic algorithm with greedy mutation, twin removal, and random restart techniques for improved protein structure prediction from sparse NMR data.

Findings

Significantly outperforms standard genetic algorithms on benchmark datasets.

Effective in reconstructing protein structures from incomplete distance data.

Enhances search efficiency with specialized mutation and diversification strategies.

Abstract

Nuclear Magnetic Resonance (NMR) Spectroscopy is a widely used technique to predict the native structure of proteins. However, NMR machines are only able to report approximate and partial distances between pair of atoms. To build the protein structure one has to solve the Euclidean distance geometry problem given the incomplete interval distance data produced by NMR machines. In this paper, we propose a new genetic algorithm for solving the Euclidean distance geometry problem for protein structure prediction given sparse NMR data. Our genetic algorithm uses a greedy mutation operator to intensify the search, a twin removal technique for diversification in the population and a random restart method to recover stagnation. On a standard set of benchmark dataset, our algorithm significantly outperforms standard genetic algorithms.