Sample-Based Models of Protein Energy Landscapes and Slow Structural Rearrangements.

Abstract

Proteins often undergo slow structural rearrangements that involve several angstroms and surpass the nanosecond timescale. These spatiotemporal scales challenge physics-based simulations and open the way to sample-based models of structural dynamics. This article improves an understanding of current capabilities and limitations of sample-based models of dynamics. Borrowing from widely used concepts in evolutionary computation, this article introduces two conflicting aspects of sampling capability and quantifies them via statistical (and graphical) analysis tools. This allows not only conducting a principled comparison of different sample-based algorithms but also understanding which algorithmic ingredients to use as knobs via which to control sampling and, in turn, the accuracy and detail of modeled structural rearrangements. We demonstrate the latter by proposing two powerful variants of a recently published sample-based algorithm. We believe that this work will advance the adoption of sample-based models as reliable tools for modeling slow protein structural rearrangements.