Hidden Markov Modeling with Detailed Balance and Its Application to Single Protein Folding.

ABSTRACT: Hidden Markov modeling (HMM) has revolutionized kinetic studies of macromolecules. However, results from HMM often violate detailed balance when applied to the transitions under thermodynamic equilibrium, and the consequence of such violation has not been well understood. Here, to our knowledge, we developed a new HMM method that satisfies detailed balance (HMM-DB) and optimizes model parameters by gradient search. We used free energy of stable and transition states as independent fitting parameters and considered both normal and skew normal distributions of the measurement noise. We validated our method by analyzing simulated extension trajectories that mimicked experimental data of single protein folding from optical tweezers. We then applied HMM-DB to elucidate kinetics of regulated SNA

SUBMITTER: Zhang Y 

PROVIDER: S-EPMC5112951 | biostudies-literature | 2016 Nov

REPOSITORIES: biostudies-literature