Conformational selection and induced fit are well-known contributors to ligand binding and allosteric effects in proteins. Molecular dynamics (MD) simulations now enable the theoretical study of protein-ligand binding in terms of ensembles of interconverting microstates and the population shifts characteristic of "dynamical allostery." Here we investigate protein-ligand binding and allostery based on a Markov state model (MSM) with states and rates obtained from all-atom MD simulations. As an exemplary case, we consider the single domain protein par-6 PDZ with and without ligand and allosteric effector. This is one of the smallest proteins in which allostery has been experimentally observed. In spite of the increased complexity intrinsic to a statistical ensemble perspective, we find that conformational selection and induced fit mechanisms can be readily identified in the analysis. In the nonallosteric pathway, MD-MSM shows that PDZ binds ligand via conformational selection. However, the allosteric pathway requires an activation step that involves a conformational change induced by the allosteric effector Cdc42. Once in the allosterically activated state, we find that ligand binding can proceed by conformational selection. Our MD-MSM model predicts that allostery in this and possibly other systems involves both induced fit and conformational selection, not just one or the other.

The idea of protein "sectors" posits that sparse subsets of amino acid residues form cooperative networks that are key elements of protein stability, ligand binding, and allosterism. To date, protein sectors have been calculated by the statistical coupling analysis (SCA) method of Ranganathan and coworkers via the spectral analysis of conservation-weighted evolutionary covariance matrices obtained from a multiple sequence alignments of homologous families of proteins. SCA sectors, a knowledgebased protocol, have been indentified with functional properties and allosterism for a number of systems. In this study, we investigate the utility of the sector idea for the analysis of physics-based molecular dynamics (MD) trajectories of proteins. Our test case for this procedure is PSD95-PDZ3, one of the smallest proteins for which allosterism has been observed. It has served previously as a model system for a number of prediction algorithms, and is well characterized by X-ray crystallography, NMR spectroscopy and site specific mutagenisis. All-atom MD simulations were performed for a total of 500 nanoseconds using AMBER, and MD-calculated covariance matrices for the fluctuations of residue displacements and non-bonded interaction energies were subjected to spectral analysis in a manner analogous to that of SCA. The composition of MD sectors was compared with results from SCA, site specific mutagenesis, and allosterism. The concordance indicates that MD sectors are a viable protocol for analyzing MD trajectories and provide insight into the physical origin of the phenomenon.

Mismatch repair (MMR) is an essential, evolutionarily conserved pathway that maintains genome stability by correcting base-pairing errors in DNA. Here we examine the sequence and structure of MutS MMR protein to decipher the amino acid framework underlying its two key activities—recognizing mismatches in DNA and using ATP to initiate repair. Statistical coupling analysis (SCA) identified a network (sector) of coevolved amino acids in the MutS protein family. The potential functional significance of this SCA sector was assessed by performing molecular dynamics (MD) simulations for alanine mutants of the top 5% of 160 residues in the distribution, and control nonsector residues. The effects on three independent metrics were monitored: (i) MutS domain conformational dynamics, (ii) hydrogen bonding between MutS and DNA/ATP, and (iii) relative ATP binding free energy. Each measure revealed that sector residues contribute more substantively to MutS structure–function than nonsector residues. Notably, sector mutations disrupted MutS contacts with DNA and/or ATP from a distance via contiguous pathways and correlated motions, supporting the idea that SCA can identify amino acid networks underlying allosteric communication. The combined SCA/MD approach yielded novel, experimentally testable hypotheses for unknown roles of many residues distributed across MutS, including some implicated in Lynch cancer syndrome.

Protein folding, considered to be the holy grail of molecular biology, remains intractable even after six decades since the report of the first crystal structure. Over 70,000 X-ray and NMR structures are now available in protein structural repositories and no physico-chemical solution is in sight. Molecular simulation methodologies have evolved to a stage to provide a computational solution to the tertiary structures of small proteins. Knowledge base driven methodologies are maturing in predicting the tertiary structures of query sequences which share high similarities with sequences of known structures in the databases. The void region thus seems to be medium (>100 amino acid residues) to large proteins with no sequence homologs in the databases and hence which has become a fertile ground for the genesis of hybrid models which exploit local similarities together with ab initio models to arrive at reasonable predictions. We describe here the development of Bhageerath an ab initio model and Bhageerath-H a hybrid model and present a critique on the current status of prediction of protein tertiary structures.

First and foremost, I gratefully acknowledge, respect, and appreciation to my mentor Prof. David L. Beveridge for his continuous support and guidance throughout my Ph.D. studies. I would also like to extend my sincerest gratitude, appreciation, and respect to my committee members Prof. Ishita Mukerji, Prof. Rich Olson, and Prof. Francis Starr for their continuous support with my research, thesis writing and funding. My mentor and committee members along with Prof. Manju M. Hingorani played a huge role in my scientific and professional development. I am also sincerely thankful to Dr. Kelly M. Thayer for her tremendous help on thesis research, writing and publications. The opportunity to work on MutS and MSH4-MSH5 collaborative projects would not have been possible without Prof. Manju M. Hingorani and Prof. Ishita Mukerji support and guidance. I was very lucky to work closely with the great experientialists and friends Bo Song and Sudipta Lahiri on these projects. I would also like to thank Chemistry and MB&B Wesleyan University for the funding and support and providing me the opportunity to do a TA with Dr. John Mantzaris. I was very fortunate to work with him. I am also grateful to Dr. David Langley or giving me a glimpse of industrial exposure and research discussion during his visits to the lab. I would also like to thank my former and current lab members Dr.