Spectral approaches for identifying kinetic features in molecular dynamics simulations of globular proteins

Proteins live in an environment of random thermal vibrations yet they convert this constant disorder into selective biological function. As data acquisition methods for resolving protein motions improve more of the randomness is also captured; there is thus a parallel need for analysis methods that filter out the disorder and clarify functionally-relevant protein behavior. Few behaviors are more relevant than folding in the first place, and this thesis opens by addressing which conformational states are kinetically relevant for promoting or inhibiting attainment of the folded native state. Our modeling approach discretizes simulation data into a network of nodes and edges representing, respectively, different protein conformations and observed conformational transitions. A perturbative strategy is then invoked to quantify the importance of each node, i.e. conformational substate, with regard to theoretical folding rates. On a test of 10 proteins this framework identifies unique ‘kinetic traps’ and ‘facilitator substates’ that sometimes evade detection with traditional RMSD-based analysis. We then apply spectral approaches and auto-regressive models to (1) address efficiency concerns for more general networks and (2) mimic protein flexibility with compact linear models

Discovering Conformational Sub-States Relevant to Protein Function

Background: Internal motions enable proteins to explore a range of conformations, even in the vicinity of native state. The role of conformational fluctuations in the designated function of a protein is widely debated. Emerging evidence suggests that sub-groups within the range of conformations (or sub-states) contain properties that may be functionally relevant. However, low populations in these sub-states and the transient nature of conformational transitions between these substates present significant challenges for their identification and characterization. Methods and Findings: To overcome these challenges we have developed a new computational technique, quasianharmonic analysis (QAA). QAA utilizes higher-order statistics of protein motions to identify sub-states in the conformational landscape. Further, the focus on anharmonicity allows identification of conformational fluctuations that enable transitions between sub-states. QAA applied to equilibrium simulations of human ubiquitin and T4 lysozyme reveals functionally relevant sub-states and protein motions involved in molecular recognition. In combination with a reaction pathway sampling method, QAA characterizes conformational sub-states associated with cis/trans peptidyl-prolyl isomerization catalyzed by the enzyme cyclophilin A. In these three proteins, QAA allows identification of conformational sub-states, with critical structural and dynamical features relevant to protein function. Conclusions: Overall, QAA provides a novel framework to intuitively understand the biophysical basis of conformational diversity and its relevance to protein function. © 2011 Ramanathan et al

Quantifying the Sources of Kinetic Frustration in Folding Simulations of Small Proteins

Experiments and atomistic simulations of polypeptides have revealed structural intermediates that promote or inhibit conformational transitions to the native state during folding. We invoke a concept of “kinetic frustration” to quantify the prevalence and impact of these behaviors on folding rates within a large set of atomistic simulation data for 10 fast-folding proteins, where each protein’s conformational space is represented as a Markov state model of conformational transitions. Our graph theoretic approach addresses what conformational features correlate with folding inhibition and therefore permits comparison among features within a single protein network and also more generally between proteins. Nonnative contacts and nonnative secondary structure formation can thus be quantitatively implicated in inhibiting folding for several of the tested peptides