Idpconformergenerator: A flexible software suite for sampling the conformational space of disordered protein states.

Abstract

In the efforts to identify structure-function relationships of protein machinery, computational methods have developed rapidly as tools to assist the scientific community in both structural prediction as well as functional relationships in context of biological pathways. Although solving high resolution structural information has been clear for stable folded macromolecules, similar structure-function insight is more difficult to obtain for highly dynamic systems such as intrinsically disordered proteins (IDPs) which must be described as structural ensembles. Here, we present IDPConformerGenerator, a flexible, modular open-source software platform for generating and analyzing large diverse ensembles of disordered protein states. IDPConformerGenerator builds conformers that obey geometric, steric, and other physical restraints on the input sequence by sampling backbone phi (φ), psi (ψ), and omega (ω) torsion angles of relevant sequence fragments from loops and secondary structure elements extracted from folded protein structures in the RCSB Protein Data Bank. All-atom sidechains can be added using robust Monte Carlo algorithms with expanded rotamer libraries. IDPConformerGenerator has many user-configurable options enabling variable fractional sampling of secondary structures, supports Bayesian models for assessing the agreement of IDP ensembles for consistency with experimental data, and introduces a machine learning approach to transform between internal and Cartesian coordinates with reduced error. IDPConformerGenerator will facilitate the characterization of disordered proteins to provide structural insights into these states that have key biological functions. Ultimately, a better structural understanding of disordered protein ensembles can open up avenues for future research on pharmaceuticals targeting diseases mediated by IDPs.