New Methods to Improve Protein Structure Prediction and Refinement

Abstract

We have developed and combined several novel methods to improve protein structure prediction from the amino acid sequence, and the structural refinement of protein models. One of the most promising developments in protein structure prediction are many-body potentials that take into account dense packing, and cooperativity of interactions in protein cores. We developed a method that uses whole protein information filtered through machine learners to score protein models based on their likeness to native structures. Testing on CASP 9 targets showed that our method is superior to the common DFIRE and its derivatives as well as to the current version of RWPlus, both of which are considered a standard in the field. By combing statistical contact potentials with entropies from the elastic network models of proteins we can compute free energy and improve coarse-grained modeling of protein structure and dynamics. The consideration of protein flexibility and its fluctuational dynamics improves protein structure prediction, and leads to a better refinement of computational models of proteins. We proposed a novel protein structural refinement procedure based on Anisotropic Network Model (ANM) of protein fluctuational dynamics and Go-like model of energy score. The starting structures were models from past CASP experiments. We changed positions of C-alpha atoms using ANM, creating a new set of 250 structures from the initial model, and computed energies of these structures using Go-like energy score. The top 5 coarse-grained structures were fully rebuilt with BBQ and Scrwl4. To remove bond stretches and the excluded volume clashes, short Molecular Mechanics simulations (up to 10,000 steps) were performed with OPLS-AA force field and implicit solvent GBSA-OBC. The whole structural refinement process was performed iteratively leading to the improvement of average RMSD from 3.8A to 2.6A in 50 iterations.