Abstract
A core task in computational structural biology is the search of conformational space for low energy configurations of a biological macromolecule. Because conformational space has a very high dimensionality, the most successful search methods integrate some form of prior knowledge into a general sampling algorithm to reduce the effective dimensionality. However, integrating multiple types of constraints can be challenging. To streamline the incorporation of diverse constraints, we developed the Broker: an extension of the Rosetta macromolecular modeling suite that can express a wide range of protocols using constraints by combining small, independent modules, each of which implements a different set of constraints. We demonstrate expressiveness of the Broker through several code vignettes. The framework enables rapid protocol development in both biomolecular design and structural modeling tasks and thus is an important step towards exposing the rich functionality of Rosetta’s core libraries to a growing community of users addressing a diverse set of tasks in computational biology.
Highlights
The core task in modeling structures of biological macromolecules—including structure prediction[1,2], structure determination from sparse experimental data[3,4], and biomolecular design[5,6,7]—is searching conformational space for low-energy configurations
The number of dimensions grows with the size of the atomic system, making the conformational space of typical systems impossible to sample exhaustively
The later stages of RASREC [18], for example, use β-strand pair constraints to intensify sampling of topologies deemed successful in earlier stages. This strategy has proven essential for extending structure determination with CS-Rosetta to larger protein structures[32,37]
Summary
The core task in modeling structures of biological macromolecules—including structure prediction[1,2], structure determination from sparse experimental data[3,4], and biomolecular design[5,6,7]—is searching conformational space for low-energy configurations. Successful sampling algorithms incorporate as much information as possible about the biomolecular system in question to focus the search on the most productive regions of conformational space. Prior knowledge is highly diverse, both in origin and in its implications for sampling, and numerous successful methods in macromolecular modeling are based on incorporating one or several different types of prior knowledge into the model. For instance, rely on atomic coordinates from experimental data (e.g. I-TASSER[8], RosettaCM[9], numerous design strategies [7,10,11]).
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