Information Flow and Allosteric Communication in Proteins

Dynamically driven correlations in elastic net models reveal sequence of events and causality in proteins

An explicit analytic solution is given for the Langevin equation applied to the Gaussian Network Model of a protein subjected to both a random and a deterministic periodic force. Synchronous and asynchronous components of time correlation functions are derived and an expression for phase differences in the time correlations of residue pairs is obtained. The synchronous component enables the determination of dynamic communities within the protein structure. The asynchronous component reveals causality, where the time correlation function between residues i and j differs depending on whether i is observed before j or vice versa, resulting in directional information flow. Driver and driven residues in the allosteric process of cyclophilin A and human NAD-dependent isocitrate dehydrogenase are determined by a perturbation-scanning technique. Factors affecting phase differences between fluctuations of residues, such as network topology, connectivity, and residue centrality, are identified. Within the constraints of the isotropic Gaussian Network Model, our results show that asynchronicity increases with viscosity and distance between residues, decreases with increasing connectivity, and decreases with increasing levels of eigenvector centrality.

Computational Insights into Prostaglandin E2 Ligand Binding and Activation of G-Protein-Coupled Receptors

G-protein coupled receptors (GPCRs) are eukaryotic integral membrane proteins that regulate signal transduction cascade pathways implicated in a variety of human diseases and are consequently of interest as drug targets. For this reason, it is of interest to investigate the way in which specific ligands bind and trigger conformational changes in the receptor during activation and how this in turn modulates intracellular signaling. In the present study, we investigate the way in which the ligand Prostaglandin E2 interacts with three GPCRs in the E-prostanoid family: EP1, EP2, and EP3. We examine information transfer pathways based on long-time scale molecular dynamics simulations using transfer entropy and betweenness centrality to measure the physical transfer of information among residues in the system. We monitor specific residues involved in binding to the ligand and investigate how the information transfer behavior of these residues changes upon ligand binding. Our results provide key insights that enable a deeper understanding of EP activation and signal transduction functioning pathways at the molecular level, as well as enabling us to make some predictions about the activation pathway for the EP1 receptor, for which little structural information is currently available. Our results should advance ongoing efforts in the development of potential therapeutics targeting these receptors.

Mutual information analysis of mutation, nonlinearity, and triple interactions in proteins

Mutations are the cause of several diseases as well as the underlying force of evolution. A thorough understanding of their biophysical consequences is essential. We present a computational framework for evaluating different levels of mutual information (MI) and its dependence on mutation. We used molecular dynamics trajectories of the third PDZ domain and its different mutations. Nonlinear MI between all residue pairs are calculated by tensor Hermite polynomials up to the fifth order and compared with results from multivariate Gaussian distribution of joint probabilities. We show that MI is written as the sum of a Gaussian and a nonlinear component. Results for the PDZ domain show that the Gaussian term gives a sufficiently accurate representation of MI when compared with nonlinear terms up to the fifth order. Changes in MI between residue pairs show the characteristic patterns resulting from specific mutations. Emergence of new peaks in the MI versus residue index plots of mutated PDZ shows how mutation may change allosteric pathways. Triple correlations are characterized by evaluating MI between triplets of residues. We observed that certain triplets are strongly affected by mutation. Susceptibility of residues to perturbation is obtained by MI and discussed in terms of linear response theory.