| The structural refinement of protein models is a challenging problem in protein structure prediction. Most attempts to refine comparative models lead to degradation rather than improvement in model quality, so most current comparative modelling procedures omit the refinement step. However, it has been shown that even in the absence of alignment errors and using optimal templates, methods based on a single template have intrinsic limitations, and that refinement is needed in order to improve model accuracy. It is thought that failure of current methods originates on one hand from the inaccuracy of the effective free energy functions adopted, and on the other hand from the difficulty to sample the high dimensional and rugged free energy in the search for the global minimum. Here we address this second issue. We define EVA (the Evolutionary and Vibrational Armonics subspace), a reduced sampling subspace that consists of a combination of evolutionarily favoured directions, defined by the principal components of the structural variation within a homologous family, plus topologically favoured directions, derived from the low frequency normal modes of the vibrational dynamics, up to 50 dimensions. This subspace is accurate enough so that the cores of most proteins can be represented within 1 A accuracy, and reduced enough so that we Replica Exchange Monte Carlo (REMC) can be applied. We show that the combination of the EVA subspace and REMC can essentially solve the optimization problem for backbone atoms in the reduced sampling subspace, even for rather rugged landscapes. |
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