Energy functions, fragment libraries, and search methods constitute three key components of fragment-assembly methods for protein structure prediction, which are all crucial for their ability to generate high-accuracy predictions. All of these components are tightly coupled; efficient searching becomes more important as the quality of fragment libraries decreases. Given these relationships, there is currently a poor understanding of the strengths and weaknesses of the sampling approaches currently used in fragment-assembly techniques. Here, we determine how the performance of search techniques can be assessed in a meaningful manner, given the above problems. We describe a set of techniques that aim to reduce the impact of the energy function, and assess exploration in view of the search space defined by a given fragment library. We illustrate our approach using Rosetta and EdaFold, and show how certain features of these methods encourage or limit conformational exploration. We demonstrate that individual trajectories of Rosetta are susceptible to local minima in the energy landscape, and that this can be linked to non-uniform sampling across the protein chain. We show that EdaFold's novel approach can help balance broad exploration with locating good low-energy conformations. This occurs through two mechanisms which cannot be readily differentiated using standard performance measures: exclusion of false minima, followed by an increasingly focused search in low-energy regions of conformational space. Measures such as ours can be helpful in characterizing new fragment-based methods in terms of the quality of conformational exploration realized.
Keywords: EdaFold; Rosetta; conformational sampling; entropy; exploration; multidimensional scaling; search heuristic.
© 2016 Wiley Periodicals, Inc.