Prediction of allosteric communication pathways in proteins

Allostery in proteins is an essential phenomenon in biological processes. In this article, we present a computational model to predict paths of maximum information transfer between active and allosteric sites. In this information theoretic study, we use mutual information as the measure of information transfer, where transition probability of information from one residue to its contacting neighbors is proportional to the magnitude of mutual information between the two residues. Starting from a given residue and using a Hidden Markov Model, we successively determine the neighboring residues that eventually lead to a path of optimum information transfer. The Gaussian approximation of mutual information between residue pairs is adopted. The limits of validity of this approximation are discussed in terms of a nonlinear theory of mutual information and its reduction to the Gaussian form.

Predictions of the model are tested on six widely studied cases, CheY Bacterial Chemotaxis, B-cell Lymphoma extra-large (Bcl-xL), Human proline isomerase cyclophilin A (CypA), Dihydrofolate reductase (DHFR), HRas GTPase and Caspase-1. The communication transmission rendering the propagation of local fluctuations from the active sites throughout the structure in multiple paths correlate well with the known experimental data. Distinct paths originating from the active site may likely represent a multi functionality such as involving more than one allosteric site and/or pre-existence of some other functional states. Our model is computationally fast and simple and can give allosteric communication pathways, which are crucial for the understanding and control of protein functionality.

Supplementary data are available at Bioinformatics online.