Abstract
BackgroundAlthough experimental methods for determining protein structure are providing high resolution structures, they cannot keep the pace at which amino acid sequences are resolved on the scale of entire genomes. For a considerable fraction of proteins whose structures will not be determined experimentally, computational methods can provide valuable information. The value of structural models in biological research depends critically on their quality. Development of high-accuracy computational methods that reliably generate near-experimental quality structural models is an important, unsolved problem in the protein structure modeling.ResultsLarge sets of structural decoys have been generated using reduced conformational space protein modeling tool CABS. Subsequently, the reduced models were subject to all-atom reconstruction. Then, the resulting detailed models were energy-minimized using state-of-the-art all-atom force field, assuming fixed positions of the alpha carbons. It has been shown that a very short minimization leads to the proper ranking of the quality of the models (distance from the native structure), when the all-atom energy is used as the ranking criterion. Additionally, we performed test on medium and low accuracy decoys built via classical methods of comparative modeling. The test placed our model evaluation procedure among the state-of-the-art protein model assessment methods.ConclusionThese test computations show that a large scale high resolution protein structure prediction is possible, not only for small but also for large protein domains, and that it should be based on a hierarchical approach to the modeling protocol. We employed Molecular Mechanics with fixed alpha carbons to rank-order the all-atom models built on the scaffolds of the reduced models. Our tests show that a physic-based approach, usually considered computationally too demanding for large-scale applications, can be effectively used in such studies.
Highlights
Experimental methods for determining protein structure are providing high resolution structures, they cannot keep the pace at which amino acid sequences are resolved on the scale of entire genomes
The high-resolution models could be built by means of various comparative modeling procedures, it is sometimes possible to obtain good models in a template-free modeling of small globular proteins [2,3,4,5]
Several different approaches to the optimal model selection have been proposed – such as the use of empirical or knowledge-based potentials [6,7] derived from the databases of experimental structures
Summary
Experimental methods for determining protein structure are providing high resolution structures, they cannot keep the pace at which amino acid sequences are resolved on the scale of entire genomes. Development of highaccuracy computational methods that reliably generate near-experimental quality structural models is an important, unsolved problem in the protein structure modeling. More expensive computationally, is the evaluation of conformational energy by means of Molecular Mechanics force fields [8,9,10]. Another approach to the model selection is the structural clustering, especially useful when large set of models must be assessed [11]. Learning-based scoring functions can be developed using machine learning methods e.g. support vector machines [12], neural networks [13,14], etc
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