A parallel multi-objective optimization procedure for protein structure prediction

Resumen

Proteins are chains of amino acids whose sequence determines its 3D structure after a folding process. As the 3D structure of a protein exclusively determines its functionality (transport and transduction of biological signals, the possible enzymatic activity of some proteins, etc.), there is a high interest in the determination of the structure of any given proteins. Experimental methods such as X-ray crystallography and nuclear magnetic resonance (NMR) allow the determination of the 3D structure of a protein although they are complex and expensive. Thus, only about the 2% of the known proteins has known structures currently. The so called, protein structure prediction (PSP) problem is the approach to find the 3D structures of proteins by using computers. This work proposes an approach to the protein structure prediction (PSP) problem: PITAGORAS-PSP (Parallel Implemented procedure with Template information, Ab initio Global Optimization, and Rotamer Analysis and Statistics for Protein Structure Prediction). This way, taking into account its name, our procedure represents a hybrid approach that takes advantage of previous knowledge about the known protein structures to improve the effectiveness of an ab initio procedure for the PSP problem. Moreover, the procedure benefits from a parallel and distributed implementation of a multi-objective evolutionary approach that allows faster and wider exploration of the conformation space. The experimental results obtained from the present implementation of our procedure show improvements with respect to previously proposed procedures in the proteins selected as benchmarks from the CASP set (up to 28% of RMSD improvement with respect to one of the best procedures known at this moment in some proteins). We also present a new method to extract better torsion angles from protein structures, it can be used to build an improved data base for torsion angles that aids in the knowledge extraction from the known structures. Our hybrid approach can be used as the main method to predict protein structures, but it can be also used to refine predictions of other methods, due to its capabilities to take advantage of results from other approaches.