Book of Abstracts: Albany 2011
June 14-18 2011
©Adenine Press (2010)
Intrinsic Dynamics and Allostery: Learning from Theory, Computations and Experiments
The significance of protein dynamics in achieving molecular functions in the cell is widely recognized. The old view, ‘a unique structure for each protein’, is now replaced by ‘an ensemble of conformers’ accessible near native state conditions. Many studies suggest that fluctuations between the conformers, or transitions between their representative substates, underlie, if not enable, functional events. Elastic network models (ENMs) and spectral graph theoretical analysis methods are broadly used for exploring the collective dynamics intrinsically accessible to biomolecular systems to elucidate structure-encoded dynamics and function. In parallel with the multiplicity of conformers accessible under physiological conditions, the Protein Data Bank contains multiple structures of the same protein in different forms (e.g., orthologs, mutants, substates visited during an allosteric cycle, or various complexes, multimers or assemblies). These datasets usually convey valuable information on functional changes in structure. Furthermore, they can help benchmark and improve theoretical models, computational methods and software. Our recent work suggests that the information inferred from these experimental datasets and those predicted by theory and computations can be advantageously combined to gain insights into the allosteric mechanisms of activation or inhibition of target proteins. Recent applications will be presented, along with a discussion of the limits of applicability of ENM-based approaches and molecular dynamics simulations, and future directions to overcome these limitations (1).
Support from NIH 5R01GM086238-02 is gratefully acknowledged.
Department of Computational and Systems Biology, School of Medicine