Using Local States To Drive the Sampling of Global Conformations in Proteins

Conformational changes associated with protein function often occur beyond the time scale currently accessible to unbiased molecular dynamics (MD) simulations, so that different approaches have been developed to accelerate their sampling. Here we investigate how the knowledge of backbone conformations preferentially adopted by protein fragments, as contained in precalculated libraries known as structural alphabets (SA), can be used to explore the landscape of protein conformations in MD simulations. We find that (a) enhancing the sampling of native local states in both metadynamics and steered MD simulations allows the recovery of global folded states in small proteins; (b) folded states can still be recovered when the amount of information on the native local states is reduced by using a low-resolution version of the SA, where states are clustered into macrostates; and (c) sequences of SA states derived from collections of structural motifs can be used to sample alternative conformations of preselected protein regions. The present findings have potential impact on several applications, ranging from protein model refinement to protein folding and design.