Conformational Ensembles from Experimental Data and Computer Simulations

Conformational Ensembles from Experimental Data

and Computer Simulations

Poster Abstracts

110

75-POS

Board 35

Bridging the Gap Between Markov Stability Theory and Protein Dynamics Experiments at

All Timescales

Robert Peach

, David Klug

, Sophia Yaliraki

, Keith Willison

, Mauricio Barahona

, Liming

Ying

Imperial College, London, United Kingdom,

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.