Abstract
BackgroundMany structural bioinformatics approaches employ sequence profile-based threading techniques. To improve fold recognition rates, homology searching may include artificially evolved amino acid sequences, which were demonstrated to enhance the sensitivity of protein threading in targeting midnight zone templates.FindingsWe describe implementation details of eVolver, an optimization algorithm that evolves protein sequences to stabilize the respective structures by a variety of potentials, which are compatible with those commonly used in protein threading. In a case study focusing on LARG PDZ domain, we show that artificially evolved sequences have quite high capabilities to recognize the correct protein structures using standard sequence profile-based fold recognition.ConclusionsComputationally design protein sequences can be incorporated in existing sequence profile-based threading approaches to increase their sensitivity. They also provide a desired linkage between protein structure and function in in silico experiments that relate to e.g. the completeness of protein structure space, the origin of folds and protein universe. eVolver is freely available as a user-friendly webserver and a well-documented stand-alone software distribution at http://www.brylinski.org/evolver.
Highlights
Many structural bioinformatics approaches employ sequence profile-based threading techniques
In template-based protein structure modeling, sequence profile-based threading and fold recognition approaches [1] frequently fail to detect in the Protein Data Bank (PDB) [2] structurally similar templates whose sequence similarity to the target falls into the midnight zone [3]
The weight factors were optimized on a large dataset of native-like and decoy sequences constructed for the CATH library [11]
Summary
We developed eVolver, a method for the optimization of generic protein-like amino acid sequences to stabilize the respective structures. EVolver is available as a user-friendly webserver as well as a stand-alone software distribution, which can be installed locally in a high-performance computing environment to optimize amino acid sequences for large datasets, e.g. template libraries or synthetic structures. The former can be used to develop more sensitive threading approaches; the latter are widely used in studies on the completeness of protein structure space [29] as well as in research focusing on the origin of folds and protein universe [30,31]. Competing interests The author declares that he has no competing interests
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