Protein folding prediction in the HP model using ions motion optimization with a greedy algorithm

Kuo-Chuan Wu,Li-Yeh Chuang,Cheng-Hong Yang,Yu-Shiun Lin,Hsueh-Wei Chang

doi:10.1186/s13040-018-0176-6

Abstract

BackgroundThe function of a protein is determined by its native protein structure. Among many protein prediction methods, the Hydrophobic-Polar (HP) model, an ab initio method, simplifies the protein folding prediction process in order to reduce the prediction complexity.ResultsIn this study, the ions motion optimization (IMO) algorithm was combined with the greedy algorithm (namely IMOG) and implemented to the HP model for the protein folding prediction based on the 2D-triangular-lattice model. Prediction results showed that the integration method IMOG provided a better prediction efficiency in a HP model. Compared to others, our proposed method turned out as superior in its prediction ability and resilience for most of the test sequences. The efficiency of the proposed method was verified by the prediction results. The global search capability and the ability to escape from the local best solution of IMO combined with a local search (greedy algorithm) to the new algorithm IMOG greatly improve the search for the best solution with reliable protein folding prediction.ConclusionOverall, the HP model integrated with IMO and a greedy algorithm as IMOG provides an improved way of protein structure prediction of high stability, high efficiency, and outstanding performance.

Highlights

The function of a protein is determined by its native protein structure
IMOG for 2D-HP-model We implemented the ions motion optimization (IMO) algorithm with a greedy algorithm as a local search strategy for the 2D-HP-model protein folding problem as follows: IMOG procedure This study presents an improved IMO with a greedy algorithm to be implemented in a 2D-HP-model process
The results show that the IMOG method proposed here predicts protein folding structure better than other available methods

Summary

Introduction

The function of a protein is determined by its native protein structure. The function of a given protein is determined by the native structure or its polymer structure, which correlates with particular protein functions [1]. The native three-dimensional structure of a protein primarily depends on its amino acid sequence [2]. The development of a highly efficient method for protein folding prediction is in high demand, for protein studies in biotechnology. Several methods have been proposed for protein structure prediction. Comparative modeling and fold recognition approaches commonly use a known protein structure database to train a model in order to classify an unknown protein structure [2]. The ab initio method provides a direct prediction using the primary structure or amino acid sequence of a given protein

Methods

Results

Discussion

Conclusion