Near-native protein loop sampling using nonparametric density estimation accommodating sparcity.

ABSTRACT: Unlike the core structural elements of a protein like regular secondary structure, template based modeling (TBM) has difficulty with loop regions due to their variability in sequence and structure as well as the sparse sampling from a limited number of homologous templates. We present a novel, knowledge-based method for loop sampling that leverages homologous torsion angle information to estimate a continuous joint backbone dihedral angle density at each loop position. The φ,ψ distributions are estimated via a Dirichlet process mixture of hidden Markov models (DPM-HMM). Models are quickly generated based on samples from these distributions and were enriched using an end-to-end distance filter. The performance of the DPM-HMM method was evaluated against a diverse test set in a leave-one-out

SUBMITTER: Joo H 

PROVIDER: S-EPMC3197639 | biostudies-literature | 2011 Oct

REPOSITORIES: biostudies-literature