Conformational Ensembles from Experimental Data and Computer Simulations

Conformational Ensembles from Experimental Data and Computer Simulations

Poster Abstracts

75-POS Board 35 Bridging the Gap Between Markov Stability Theory and Protein Dynamics Experiments at All Timescales Robert Peach 1 , David Klug 1 , Sophia Yaliraki 1 , Keith Willison 1 , Mauricio Barahona 2 , Liming Ying 3 . 1 Imperial College, London, United Kingdom, 2 Imperial College, London, United Kingdom, 3 Imperial College, London, United Kingdom. Objective: The hierarchy of timescales over which protein dynamics occurs is difficult to probe both experimentally and computationally where motions cover approximately ten orders of magnitude in timescale. We have developed Markov Stability, an atomistic graph theoretical method that is able to explore protein dynamics across all of these temporal and spatial scales by finding communities of atoms that are biologically relevant. Markov Stability is able to provide information regarding the timescale of dynamics and has the ability to identify functionally and dynamically important residues. We compare the computational results with single-molecule FRET and Fluorescence correlation spectroscopy (FCS) to both test the computational predictions and to calibrate a relationship between measured physical parameters and Markov Stability. Methods: The main methods used were Markov Stability (an atomistic graph theoretical community detection method), single-molecule FRET and FCS on a confocal microscope. The experimental system is Aquifex Adenylate Kinase, a well-studied and understood system and an ideal proving ground for experimental tests of the Markov Stability method. Results: Computational mutagenesis identified a number of residues that altered protein dynamics and molecular stability. The key result was the straight line correlation between the computational score of a mutation and the shift in population equilibrium as measured by single- molecule FRET. Additionally we have found a correlation between Markov time and the dynamical rates of subdomain motion obtained using FCS. Furthermore, a correlation between melting temperatures and predicted scores were identified in the core domain. Conclusions: We have experimentally validated the predictions from Markov Stability analysis of Adenylate Kinase by showing a linear relationship between measured and calculated parameters. In order to do this we successfully identified key functional residues through computational mutagenesis providing a tool that can be used for protein engineering.

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