Community network analysis (CNA) of correlated protein motions allows modeling of signals propagation in allosteric proteic systems. From standard classical molecular dynamics (MD) simulations, protein motions can be analysed by means of mutual information between pairs of amino acid residues, providing dynamical weighted networks that contains fundamental information of the communication among amino acids. The CNA method has been successfully applied to a variety of allosteric systems including an enzyme, a nuclear receptor and a bacterial adaptive immune system, providing characterization of the allosteric pathways. This method is complementary to network analyses based on different metrics and it is particularly powerful for studying large proteic systems, as it provides a coarse-grained view of the communication flows within large and complex networks.In this chapter, we focus on topology measurements of the adjacent amino acid networks for a data set of oligomeric proteins and some of its subnetworks. The aim is to present many mathematical tools in order to understand the structures of proteins implicitly coded in such networks and subnetworks. We mainly investigate four important networks by computing the number of connected components, the degree distribution, and assortativity measures. We compare each result in order to prove that the four networks have quite independent topologies.The process of allostery is often guided by subtle changes in the non-covalent interactions between residues of a protein. These changes may be brought about by minor perturbations by natural processes like binding of a ligand or protein-protein interaction. The challenge lies in capturing minute changes at the residue interaction level and following their propagation at local as well as global distances. While macromolecular effects of the phenomenon of allostery are inferred from experiments, a computational microscope can elucidate atomistic-level details leading to such macromolecular effects. Network formalism has served as an attractive means to follow this path and has been pursued further for the past couple of decades. In this chapter some concepts and methods are summarized, and recent advances are discussed. Specifically, the changes in strength of interactions (edge weight) and their repercussion on the overall protein organization (residue clustering) are highlighted. In this review, we adopt a graph spectral method to probe these subtle changes in a quantitative manner. Further, the power of this method is demonstrated for capturing re-ordering of side-chain interactions in response to ligand binding, which culminates into formation of a protein-protein complex in β2-adrenergic receptors.In this paper we report a procedure to analyze protein homodimer interfaces.We approached the problem by means of a topological methodology. In particular, we analyzed the subunits interface of about 50 homodimers and we have defined a few parameters that allow to organize these proteins in six different classes. The main characteristics of each class of homodimers have been discussed also taking into account their stabilization energy, as reported in the literature from the experimental measurements. A paradigmatic example for each class has been reported and a graphical representation proposed in order to better explain the meaning of the parameters chosen.Allosteric transmission refers to regulation of protein function at a distance. "Allostery" involves regulation and/or signal transduction induced by a perturbation event. Allostery, which has been coined the "second secret of life," is a fundamental property of most dynamics proteins. Most of critical questions surrounding allostery are largely unresolved. One of the key puzzles is to describe the physical mechanism of distant coupled conformational change. Another hot research area surrounding allostery is detection of allosteric pathways or regions (residues) in the protein that are the most critical for transmission of allosteric information. Using techniques inspired by mathematical rigidity theory and mechanical linkages, we have previously proposed a mechanistic model and description of allosteric transmission and an accompanying computational method, the Rigidity Transmission Allostery (RTA) algorithm. The RTA algorithm and method are designed to predict if mechanical perturbation of rigidity, for example, due to ligand binding, at one site of the protein can transmit and propagate across a protein structure and in turn cause a change in available conformational degrees of freedom and a change in conformation at a second distant site, equivalently resulting in allosteric transmission. The RTA algorithm is computationally very fast and can rapidly scan many unknown sites for allosteric transmission, identifying potential novel allosteric sites and quantify their allosteric effect. In this chapter we will discuss the rigidity-based mechanistic model of allosteric communication. As a case illustrative study, we will demonstrate RTA analysis on a G protein coupled receptor (GPCR) human adenosine A2A receptor.  https://www.selleckchem.com/products/pu-h71.html  Our method gives important implications and a novel prospective for general mechanistic description of allosteric communication.We review computational methods to locate energy transport networks in proteins that are based on the calculation of local energy diffusion in nanoscale systems. As an illustrative example, we discuss energy transport networks computed for the homodimeric hemoglobin from Scapharca inaequivalvis, where channels for facile energy transport, which include the cluster of water molecules at the interface of the globules, have been found to lie along pathways that experiments reveal are important in allosteric processes. We also review recent work on master equation simulations to model energy transport dynamics, including efforts to relate rate constants in the master equation to protein structural dynamics. Results for apomyoglobin involving relations between fluctuations in the length of hydrogen bonds and the energy flux between them are presented.Allostery is a fundamental regulatory mechanism in the majority of biological processes of molecular machines. Allostery is well-known as a dynamic-driven process, and thus, the molecular mechanism of allosteric signal transmission needs to be established. Elastic network models (ENMs) provide efficient methods for investigating the intrinsic dynamics and allosteric communication pathways in proteins. In this chapter, two ENM methods including Gaussian network model (GNM) coupled with Markovian stochastic model, as well as the anisotropic network model (ANM), were introduced to identify allosteric effects in hemoglobins. Techniques on model parameters, scripting and calculation, analysis, and visualization are shown step by step.