# Two-phase protein folding optimization on a three-dimensional AB off-lattice model

## TL;DR

This paper introduces a two-phase differential evolution approach for protein folding on a 3D AB off-lattice model, improving search efficiency by focusing on hydrophobic core formation and energy minimization.

## Contribution

It proposes a novel two-phase optimization method with an auxiliary fitness function to enhance protein folding search efficiency and accuracy.

## Key findings

- Two-phase optimization significantly improves algorithm performance.
- The method outperforms existing state-of-the-art algorithms.
- Effective in locating low-energy conformations with good hydrophobic cores.

## Abstract

This paper presents a two-phase protein folding optimization on a three-dimensional AB off-lattice model. The first phase is responsible for forming conformations with a good hydrophobic core or a set of compact hydrophobic amino acid positions. These conformations are forwarded to the second phase, where an accurate search is performed with the aim of locating conformations with the best energy value. The optimization process switches between these two phases until the stopping condition is satisfied. An auxiliary fitness function was designed for the first phase, while the original fitness function is used in the second phase. The auxiliary fitness function includes an expression about the quality of the hydrophobic core. This expression is crucial for leading the search process to the promising solutions that have a good hydrophobic core and, consequently, improves the efficiency of the whole optimization process. Our differential evolution algorithm was used for demonstrating the efficiency of two-phase optimization. It was analyzed on well-known amino acid sequences that are used frequently in the literature. The obtained experimental results show that the employed two-phase optimization improves the efficiency of our algorithm significantly and that the proposed algorithm is superior to other state-of-the-art algorithms.