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Toward a detailed understanding of search trajectories in fragment assembly approaches to protein structure prediction.

ABSTRACT: 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.

SUBMITTER: Kandathil SM

PROVIDER: S-EPMC4982100 | biostudies-literature | 2016 Apr

REPOSITORIES: biostudies-literature

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Toward a detailed understanding of search trajectories in fragment assembly approaches to protein structure prediction.

Kandathil Shaun M SM Handl Julia J Lovell Simon C SC

Proteins 20160223 4

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 current ...[more]

PMID: 26799916

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Project description:BACKGROUND:Whenever suitable template structures are not available, usage of fragment-based protein structure prediction becomes the only practical alternative as pure ab initio techniques require massive computational resources even for very small proteins. However, inaccuracy of their energy functions and their stochastic nature imposes generation of a large number of decoys to explore adequately the solution space, limiting their usage to small proteins. Taking advantage of the uneven complexity of the sequence-structure relationship of short fragments, we adjusted the fragment insertion process by customising the number of available fragment templates according to the expected complexity of the predicted local secondary structure. Whereas the number of fragments is kept to its default value for coil regions, important and dramatic reductions are proposed for beta sheet and alpha helical regions, respectively. RESULTS:The evaluation of our fragment selection approach was conducted using an enhanced version of the popular Rosetta fragment-based protein structure prediction tool. It was modified so that the number of fragment candidates used in Rosetta could be adjusted based on the local secondary structure. Compared to Rosetta's standard predictions, our strategy delivered improved first models, +?24% and?+?6% in terms of GDT, when using 2000 and 20,000 decoys, respectively, while reducing significantly the number of fragment candidates. Furthermore, our enhanced version of Rosetta is able to deliver with 2000 decoys a performance equivalent to that produced by standard Rosetta while using 20,000 decoys. We hypothesise that, as the fragment insertion process focuses on the most challenging regions, such as coils, fewer decoys are needed to explore satisfactorily conformation spaces. CONCLUSIONS:Taking advantage of the high accuracy of sequence-based secondary structure predictions, we showed the value of that information to customise the number of candidates used during the fragment insertion process of fragment-based protein structure prediction. Experimentations conducted using standard Rosetta showed that, when using the recommended number of decoys, i.e. 20,000, our strategy produces better results. Alternatively, similar results can be achieved using only 2000 decoys. Consequently, we recommend the adoption of this strategy to either improve significantly model quality or reduce processing times by a factor 10.

Dataset Information

Toward a detailed understanding of search trajectories in fragment assembly approaches to protein structure prediction.

Publications

Toward a detailed understanding of search trajectories in fragment assembly approaches to protein structure prediction.

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