We propose a new computational method for exploring chromatin structural organization based on Markov State Modelling of Hi-C data represented as an interaction network between genomic loci. A Markov process describes the random walk of a traveling probe in the corresponding energy landscape, mimicking the motion of a biomolecule involved in chromatin function.
AlloMAPS database provides data on the causality and energetics of allosteric communication obtained with the structure-based statistical mechanical model of allostery (SBSMMA). The database contains data on allosteric signaling in three sets of proteins and protein chains: (i) 46 proteins with comprehensively annotated functional and allosteric sites; (ii) 1908 protein chains from PDBselect set of chains with low (<25%) sequence identity; (iii) 33 proteins with more than 50 known pathological SNPs in each molecule.
Motivation: Allostery is an omnipresent mechanism of the function modulation in proteins via either effector binding or mutations in the exosites. Despite the growing number of on-line servers and databases devoted to prediction/classification of allosteric sites and their characteristics, there is a lack of resources for an efficient and quick estimation of the causality and energetics of allosteric communication.