Abstract
Protein structure prediction (PSP) is concerned with the prediction of protein tertiary structure from primary structure and is a challenging calculation problem. After decades of research effort, numerous solutions have been proposed for optimisation methods based on energy models. However, further investigation and improvement is still needed to increase the accuracy and similarity of structures. This study presents a novel backbone angle preference factor, which is one of the factors inducing protein folding. The proposed multiobjective optimisation approach simultaneously considers energy models and backbone angle preferences to solve the ab initio PSP. To prove the effectiveness of the multiobjective optimisation approach based on the energy models and backbone angle preferences, 75 amino acid sequences with lengths ranging from 22 to 88 amino acids were selected from the CB513 data set to be the benchmarks. The data sets were highly dissimilar, therefore indicating that they are meaningful. The experimental results showed that the root-mean-square deviation (RMSD) of the multiobjective optimization approach based on energy model and backbone angle preferences was superior to those of typical energy models, indicating that the proposed approach can facilitate the ab initio PSP.
Highlights
Protein structure prediction (PSP) is among the most challenging and unsolved research areas in biology
The present study reports a series of experiments to evaluate the proposed methods on the multi-objective on the protein structure prediction problem
Seventy-five amino acid sequences with lengths ranging from 22 to 88 amino acids were selected from the CB513 data set to be the benchmarks
Summary
Protein structure prediction (PSP) is among the most challenging and unsolved research areas in biology. Most of the successful prediction methods have been designed to search for similar sequences in the Protein Data Bank (PDB) [1] for prediction, which is an approach named homology modelling [2]. Researchers have proposed to develop simplified lattice models to reduce the computational complexity in modelling protein tertiary structure, such as a 2-D square [3,4,5], 2-D triangular [6], 3-D cubic [7,8,9], and 3-D face-centered cubic (FCC) [10,11,12,13,14] lattice models. Studies on these simplified models have typically used Cα atoms, which are centers of amino acids, as the backbone of the protein structure [15], and the research results have elucidated the relationship between protein sequences and structures
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