Modeling protein conformational dynamics using kinetic network models

Abstract

This thesis mainly focuses on modeling protein conformational dynamics using kinetic network models. Protein conformational dynamics are crucial for a wide range of biological processes including protein folding and the operation of key cellular machinery. Kinetic network models, especially Markov State Models (MSMs), have become a popular approach for investigating the conformational dynamics of proteins and other biomolecules. MSMs are typically built from numerous molecular dynamics simulations by dividing the sampled configurations into a large number of microstates based on geometric criteria. The resulting microstate model can then be coarse-grained into a more understandable macrostate model by lumping together rapidly mixing microstates into larger, metastable aggregates. However, finite sampling often results in the creation of many poorly sampled microstates. We propose formalism based on an algebraic principle for matrix approximation, i.e., the Nyström method, to deal with such poorly sampled microstates. Our scheme builds a hierarchy of microstates from high to low populations and progressively applies spectral clustering on sets of microstates within each level of the hierarchy. It helps spectral clustering identify metastable aggregates with highly populated microstates rather than being distracted by lowly populated states. We demonstrate the ability of this algorithm to discover the major metastable states on several protein systems: the alanine dipeptide, trpzip2 peptide and sugar-free D-glucose/D-galactose binding protein (apo GGBP). Particularly for trpzip2 system, a computational protocol of simulating the T-jump peptide unfolding experiments and the related transient IR and two-dimensional IR (2DIR) spectra based on the Markov state model (MSM) and nonlinear exciton propagation (NEP) methods are proposed. We show that results from MSMs constructed from a large number of simulations have a much better agreement with the equilibrium experimental 2DIR spectra compared to that generated from straightforward MD simulations starting from the folded state. The agreement of the simulation using MSMs and NEP with the experiment not only provides a justification for our protocol, but also provides the physical insight of the underlying spectroscopic observables. Besides FTIR and 2DIR, vibrationally resolved fluorescence spectra of the β-hairpin trpzip2 peptide at two temperatures as well as during a T-jump unfolding process are also simulated on the basis of a combination of Markov state models and quantum chemistry schemes in this thesis. Through further theoretical study, it is found that both the environment's electric field and the chromophores’ geometry distortions are responsible for tryptophan. The conformational dynamics of apo GGBP is a vital part of the entire binding process, understanding of which is important for the design of inhibitors or mutations of the protein. Markov State Models (MSMs) constructed from many all-atom molecular dynamics simulations in the explicit solvent have identified multiple meta-stable conformational states of the apo GGBP. These results suggest that domain-domain repulsive interactions play a crucial role in the conformational dynamics of apo GGBP. Moreover, the metastable states mapped out by our MSM for apo GGBP agrees well with previous simulation work, and the rapid equilibrium has also been observed in NMR experiment. The result suggests the existing crystal structures may not represent the dominant conformations in solution.

Date of Award	2013
Original language	English
Awarding Institution	The Hong Kong University of Science and Technology