Abstract

BackgroundAccording to structure-dependent function of proteins, two main challenging problems called Protein Structure Prediction (PSP) and Inverse Protein Folding (IPF) are investigated. In spite of IPF essential applications, it has not been investigated as much as PSP problem.In fact, the ultimate goal of IPF problem or protein design is to create proteins with enhanced properties or even novel functions. One of the major computational challenges in protein design is its large sequence space, namely searching through all plausible sequences is impossible. Inasmuch as, protein secondary structure represents an appropriate primary scaffold of the protein conformation, undoubtedly studying the Protein Secondary Structure Inverse Folding (PSSIF) problem is a quantum leap forward in protein design, as it can reduce the search space.In this paper, a novel genetic algorithm which uses native secondary sub-structures is proposed to solve PSSIF problem. In essence, evolutionary information can lead the algorithm to design appropriate amino acid sequences respective to the target secondary structures. Furthermore, they can be folded to tertiary structures almost similar to their reference 3D structures.ResultsThe proposed algorithm called GAPSSIF benefits from evolutionary information obtained by solved proteins in the PDB. Therefore, we construct a repository of protein secondary sub-structures to accelerate convergence of the algorithm.The secondary structure of designed sequences by GAPSSIF is comparable with those obtained by Evolver and EvoDesign. Although we do not explicitly consider tertiary structure features through the algorithm, the structural similarity of native and designed sequences declares acceptable values.ConclusionsUsing the evolutionary information of native structures can significantly improve the quality of designed sequences. In fact, the combination of this information and effective features such as solvent accessibility and torsion angles leads IPF problem to an efficient solution. GAPSSIF can be downloaded at http://bioinformatics.aut.ac.ir/GAPSSIF/.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-016-1199-y) contains supplementary material, which is available to authorized users.

Highlights

  • According to structure-dependent function of proteins, two main challenging problems called Protein Structure Prediction (PSP) and Inverse Protein Folding (IPF) are investigated

  • Since the function of a protein is dependent on its structure, some experimental methods are applied for tertiary structure determination

  • Computational methods have been known as favorable approaches for protein structure prediction (PSP) within the last two decades

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Summary

Introduction

According to structure-dependent function of proteins, two main challenging problems called Protein Structure Prediction (PSP) and Inverse Protein Folding (IPF) are investigated. Evolutionary information can lead the algorithm to design appropriate amino acid sequences respective to the target secondary structures. They can be folded to tertiary structures almost similar to their reference 3D structures. In PSP problem, an amino acid sequence is given as an input and the goal is to predict the best-adapted structure respective to its function In this regard, another essential problem called protein design or inverse protein folding (IPF) [1,2,3] is defined to identify a sequence of amino acids whose tertiary structure corresponds to a given target structure. Because of IPF NP-Hardness [4], it is impossible to give an exact algorithm to solve this problem

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