Benchmarking Inverse Statistical Approaches for Protein Structure and Design with Exactly Solvable Models.

ABSTRACT: Inverse statistical approaches to determine protein structure and function from Multiple Sequence Alignments (MSA) are emerging as powerful tools in computational biology. However the underlying assumptions of the relationship between the inferred effective Potts Hamiltonian and real protein structure and energetics remain untested so far. Here we use lattice protein model (LP) to benchmark those inverse statistical approaches. We build MSA of highly stable sequences in target LP structures, and infer the effective pairwise Potts Hamiltonians from those MSA. We find that inferred Potts Hamiltonians reproduce many important aspects of 'true' LP structures and energetics. Careful analysis reveals that effective pairwise couplings in inferred Potts Hamiltonians depend not only on the energeti

SUBMITTER: Jacquin H 

PROVIDER: S-EPMC4866778 | biostudies-literature | 2016 May

REPOSITORIES: biostudies-literature