Computational analysis of long-range allosteric communications in CFTR.

Malfunction of the CFTR protein results in cystic fibrosis, one of the most common hereditary diseases. CFTR functions as an anion channel, the gating of which is controlled by long-range allosteric communications. Allostery also has direct bearings on CF treatment: the most effective CFTR drugs modulate its activity allosterically. Herein, we integrated Gaussian network model, transfer entropy, and anisotropic normal mode-Langevin dynamics and investigated the allosteric communications network of CFTR. The results are in remarkable agreement with experimental observations and mutational analysis and provide extensive novel insight. We identified residues that serve as pivotal allosteric sources and transducers, many of which correspond to disease-causing mutations. We find that in the ATP-free form, dynamic fluctuations of the residues that comprise the ATP-binding sites facilitate the initial binding of the nucleotide. Subsequent binding of ATP then brings to the fore and focuses on dynamic fluctuations that were present in a latent and diffuse form in the absence of ATP. We demonstrate that drugs that potentiate CFTR's conductance do so not by directly acting on the gating residues, but rather by mimicking the allosteric signal sent by the ATP-binding sites. We have also uncovered a previously undiscovered allosteric 'hotspot' located proximal to the docking site of the phosphorylated regulatory (R) domain, thereby establishing a molecular foundation for its phosphorylation-dependent excitatory role. This study unveils the molecular underpinnings of allosteric connectivity within CFTR and highlights a novel allosteric 'hotspot' that could serve as a promising target for the development of novel therapeutic interventions.

Subsets of Slow Dynamic Modes Reveal Global Information Sources as Allosteric Sites.

Allostery is a key biological control mechanism, and dynamic information flow provides a perspective to describe allosteric interactions in causal relationships. Here, as a novel implementation of the Gaussian Network Model (GNM) based Transfer Entropy (TE) calculations, we show that the dissection of dynamic information into subsets of slow dynamic modes discloses different layers of multi-directional allosteric pathways inherent in a given protein structure. In these subsets of slow modes, the degree of collectivity (Col) in the information transfer of residues with their TE values (TECol score) identifies distinct residues as powerful effectors, global information sources; showing themselves with a high dynamic capacity to collectively disseminate information to others. As exemplified on aspartate transcarbamoylase (ATCase), Na+/K+-adenosine triphosphatase (Na+/K+-ATPase), and human transient receptor potential melastatin 2 (TRPM2) along with a dataset of 20 proteins, these specific residues are associated with known active and allosteric sites. These information source residues, which collectively control others and lead allosteric communication pathways, hint at plausible binding sites for structure-based rational drug design.