Investigation of dynamic allostery in motor proteins

Abstract

Allostery is a key concept in regulation of protein function. The communication pathways which are equivalent of the allosteric signaling or energy propagation pathways are the most significant aspect of allosteric mechanism. A Brownian dynamics simulation approach with force distribution analysis is proposed to determine key residues that have a role in allosteric signal transmission. Along, another stochastic approach -Monte Carlo path generation- is also implemented to define likely allosteric pathways through generating an ensemble of maximum probability paths. The force distribution analysis is tested on methionine repressor and purine repressor. The Monte Carlo path generation method is applied on various proteins such as PDZ domain, bovine rhodopsin and three different structures of myosin. The predicted residues that have a significant effect on the allosteric signaling are in agreement with the previous studies. As a case study, the main focus is Kinesin motor protein, which is a key element of cargo transport in intracellular environment. The ATP binding and the motility are directly related and communicated based on allosteric signaling in this molecule. The analyses based on both methodologies are aimed to define allosteric pathways and key sites in signaling. The predicted regions by both methods are successful in determination of the residues that plays a key role in signaling previously determined by other studies. Based on the graph centrality parameters and force distribution analysis, a novel allosteric site proposed (Cys 176). For the validation of algorithms as well as the specific allosteric sites therein, pulling experiments with atomic force microscopy are considered and performed experiments are simulated with some design plans proposed. The change in the rupture force by application of external force from different sites may provide information about the validity of the computational approaches and validate importance of the predicted sites. As a further study, three predicted sites will be tested via different functionalization methods. Overall, the proposed methodologies have potential to identify residues of importance in signaling event.