Protein secondary structure optimization using an improved artificial bee colony algorithm based on AB off-lattice model

Abstract

Predicting the secondary structure of protein has been the focus of scientific research for decades, but it remains to be a challenge in bioinformatics due to the increasing computation complexity. In this paper, AB off-lattice model is introduced to transforms the prediction task into a numerical optimization problem. Artificial Bee Colony algorithm (ABC) is an effective swarm intelligence algorithm, which works well in exploration but poor at exploitation. To improve the convergence performance of ABC, a novel internal feedback strategy based ABC (IF-ABC) is proposed. In this strategy, internal states are fully used in each of the iterations to guide subsequent searching process, and to balance local exploration with global exploitation. We provide the mechanism together with the convergence proof of the modified algorithm. Simulations are conducted on artificial Fibonacci sequences and real sequences in the database of Protein Data Bank (PDB). The analysis implies that IF-ABC is more effective to improve convergence rate than ABC, and can be employed for this specific protein structure prediction issues.