title: Defining Low-Dimensional Projections to Guide Protein Conformational Sampling.
abstract: Exploring the conformational space of proteins is critical to characterize their functions. Numerous methods have been proposed to sample a protein's conformational space, including techniques developed in the field of robotics and known as sampling-based motion-planning algorithms (or sampling-based planners). However, these algorithms suffer from the curse of dimensionality when applied to large proteins. Many sampling-based planners attempt to mitigate this issue by keeping track of sampling density to guide conformational sampling toward unexplored regions of the conformational space. This is often done using low-dimensional projections as an indirect way to reduce the dimensionality of the exploration problem. However, how to choose an appropriate projection and how much it influences the planner's performance are still poorly understood issues. In this article, we introduce two methodologies defining low-dimensional projections that can be used by sampling-based planners for protein conformational sampling. The first method leverages information about a protein's flexibility to construct projections that can efficiently guide conformational sampling, when expert knowledge is available. The second method builds similar projections automatically, without expert intervention. We evaluate the projections produced by both methodologies on two conformational search problems involving three middle-size proteins. Our experiments demonstrate that (i) defining projections based on expert knowledge can benefit conformational sampling and (ii) automatically constructing such projections is a reasonable alternative.
authors.affiliation: Department of Computer Science, Rice University , Houston, Texas.
authors.forename: Anastasia
authors.initials: A
authors.lastname: Novinskaya
authors.affiliation: Department of Computer Science, Rice University , Houston, Texas.
authors.forename: Didier
authors.initials: D
authors.lastname: Devaurs
authors.affiliation: Department of Computer Science, Rice University , Houston, Texas.
authors.forename: Mark
authors.initials: M
authors.lastname: Moll
authors.affiliation: Department of Computer Science, Rice University , Houston, Texas.
authors.forename: Lydia E
authors.initials: LE
authors.lastname: Kavraki
mesh_terms: D000465:Algorithms; D001426:Bacterial Proteins / Q000737:chemistry*; D002147:Calmodulin / Q000737:chemistry*; D002352:Carrier Proteins / Q000737:chemistry*; D000458:Cyanobacteria / Q000737:chemistry; D004926:Escherichia coli / Q000737:chemistry; D029968:Escherichia coli Proteins / Q000737:chemistry*; D006801:Humans; D008958:Models, Molecular; D033902:Periplasmic Binding Proteins / Q000737:chemistry*; D000072756:Protein Conformation, alpha-Helical; D000072757:Protein Conformation, beta-Strand
chemical_list: D001426:Bacterial Proteins; D002147:Calmodulin; D002352:Carrier Proteins; D029968:Escherichia coli Proteins; D033902:Periplasmic Binding Proteins; C028003:RbsB protein, E coli; C107733:cyanovirin N
keywords: low-dimensional projection; protein conformational sampling; protein flexibility; sampling-based planning.