Causality, Transfer Entropy and Allosteric Communication Landscapes in Proteins with Harmonic Interactions

Abstract A fast and approximate method of generating allosteric communication landscapes is presented by using Schreiber's entropy transfer concept in combination with the Gaussian Network Model of proteins. Predictions of the model and the allosteric communication landscapes generated show that information transfer in proteins does not necessarily take place along a single path, but through an ensemble of pathways. The model emphasizes that knowledge of entropy only is not sufficient for determining allosteric communication and additional information based on time delayed correlations has to be introduced, which leads to the presence of causality in proteins. The model provides a simple tool for mapping entropy sink-source relations into pairs of residues. Residues that should be manipulated to control protein activity may be determined with this approach. This should be of great importance for allosteric drug design and for understanding the effects of mutations on protein function. The model is applied to determine allosteric communication in two proteins, Ubiquitin and Pyruvate Kinase. Predictions are in agreement with detailed molecular dynamics simulations and experimental evidence. Significance Proteins perform their function by an exchange of information within themselves and with their environments through correlated fluctuations of their atoms. Fluctuations of one atom may drive the fluctuations of another. Information transmitted in this way leads to allosteric communication which is described as the process in which action at one site of the protein is transmitted to another site at which the protein performs its activity. Disruption of allosteric communication by mutation for example leads to disease. The present paper incorporates information theoretic concepts into the well known Gaussian Network Model of proteins and allows for rapid characterization of allosteric communication landscapes for normal functioning as well as malfunctioning proteins.