Abstract
Coarse-grained (CG) methods for sampling protein conformational space have the potential to increase computational efficiency by reducing the degrees of freedom. The gain in computational efficiency of CG methods often comes at the expense of non-protein like local conformational features. This could cause problems when transitioning to full atom models in a hierarchical framework. Here, a CG potential energy function was validated by applying it to the problem of loop prediction. A novel method to sample the conformational space of backbone atoms was benchmarked using a standard test set consisting of 351 distinct loops. This method used a sequence-independent CG potential energy function representing the protein using -carbon positions only and sampling conformations with a Monte Carlo simulated annealing based protocol. Backbone atoms were added using a method previously described and then gradient minimised in the Rosetta force field. Despite the CG potential energy function being sequence-independent, the method performed similarly to methods that explicitly use either fragments of known protein backbones with similar sequences or residue-specific /-maps to restrict the search space. The method was also able to predict with sub-Angstrom accuracy two out of seven loops from recently solved crystal structures of proteins with low sequence and structure similarity to previously deposited structures in the PDB. The ability to sample realistic loop conformations directly from a potential energy function enables the incorporation of additional geometric restraints and the use of more advanced sampling methods in a way that is not possible to do easily with fragment replacement methods and also enable multi-scale simulations for protein design and protein structure prediction. These restraints could be derived from experimental data or could be design restraints in the case of computational protein design. C++ source code is available for download from http://www.sbg.bio.ic.ac.uk/phyre2/PD2/.
Highlights
The prediction of protein structure to atomic level resolution and the design of de novo proteins with large scale backbone sampling are largely unsolved problems there has been a great deal of progress in recent years
Coarse-grained models have been increasingly used for modelling large biomolecules over long time scales due to the computational efficiency provided by these methods [1,2,3]
These models vary in the degree of coarse-graining with some models representing multiple amino acid residues with one interaction centre [4], some representing each amino acid residue with a small number of interaction centres [5,6,7,8,9,10,11,12,13], and others that are intermediate between minimal and full atom models [14,15,16]
Summary
The prediction of protein structure to atomic level resolution and the design of de novo proteins with large scale backbone sampling are largely unsolved problems there has been a great deal of progress in recent years. Sampling protein conformational space using full atom models can be prohibitively computationally expensive so a variety of different approaches have been developed to reduce the search space. This can be achieved by using coarse-grained (CG) protein models, by assembling backbone models from short fragments taken from known protein structures or by a combination of both of these methods. It is possible to derive CG potential energy functions from physical principles [17]
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