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      Comparative Study of Elastic Network Model and Protein Contact Network for Protein Complexes: The Hemoglobin Case

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          Abstract

          The overall topology and interfacial interactions play key roles in understanding structural and functional principles of protein complexes. Elastic Network Model (ENM) and Protein Contact Network (PCN) are two widely used methods for high throughput investigation of structures and interactions within protein complexes. In this work, the comparative analysis of ENM and PCN relative to hemoglobin (Hb) was taken as case study. We examine four types of structural and dynamical paradigms, namely, conformational change between different states of Hbs, modular analysis, allosteric mechanisms studies, and interface characterization of an Hb. The comparative study shows that ENM has an advantage in studying dynamical properties and protein-protein interfaces, while PCN is better for describing protein structures quantitatively both from local and from global levels. We suggest that the integration of ENM and PCN would give a potential but powerful tool in structural systems biology.

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          Most cited references70

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          Global dynamics of proteins: bridging between structure and function.

          Biomolecular systems possess unique, structure-encoded dynamic properties that underlie their biological functions. Recent studies indicate that these dynamic properties are determined to a large extent by the topology of native contacts. In recent years, elastic network models used in conjunction with normal mode analyses have proven to be useful for elucidating the collective dynamics intrinsically accessible under native state conditions, including in particular the global modes of motions that are robustly defined by the overall architecture. With increasing availability of structural data for well-studied proteins in different forms (liganded, complexed, or free), there is increasing evidence in support of the correspondence between functional changes in structures observed in experiments and the global motions predicted by these coarse-grained analyses. These observed correlations suggest that computational methods may be advantageously employed for assessing functional changes in structure and allosteric mechanisms intrinsically favored by the native fold.
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            Network analysis of protein structures identifies functional residues.

            Identifying active site residues strictly from protein three-dimensional structure is a difficult task, especially for proteins that have few or no homologues. We transformed protein structures into residue interaction graphs (RIGs), where amino acid residues are graph nodes and their interactions with each other are the graph edges. We found that active site, ligand-binding and evolutionary conserved residues, typically have high closeness values. Residues with high closeness values interact directly or by a few intermediates with all other residues of the protein. Combining closeness and surface accessibility identified active site residues in 70% of 178 representative structures. Detailed structural analysis of specific enzymes also located other types of functional residues. These include the substrate binding sites of acetylcholinesterases and subtilisin, and the regions whose structural changes activate MAP kinase and glycogen phosphorylase. Our approach uses single protein structures, and does not rely on sequence conservation, comparison to other similar structures or any prior knowledge. Residue closeness is distinct from various sequence and structure measures and can thus complement them in identifying key protein residues. Closeness integrates the effect of the entire protein on single residues. Such natural structural design may be evolutionary maintained to preserve interaction redundancy and contribute to optimal setting of functional sites.
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              A network representation of protein structures: implications for protein stability.

              This study views each protein structure as a network of noncovalent connections between amino acid side chains. Each amino acid in a protein structure is a node, and the strength of the noncovalent interactions between two amino acids is evaluated for edge determination. The protein structure graphs (PSGs) for 232 proteins have been constructed as a function of the cutoff of the amino acid interaction strength at a few carefully chosen values. Analysis of such PSGs constructed on the basis of edge weights has shown the following: 1), The PSGs exhibit a complex topological network behavior, which is dependent on the interaction cutoff chosen for PSG construction. 2), A transition is observed at a critical interaction cutoff, in all the proteins, as monitored by the size of the largest cluster (giant component) in the graph. Amazingly, this transition occurs within a narrow range of interaction cutoff for all the proteins, irrespective of the size or the fold topology. And 3), the amino acid preferences to be highly connected (hub frequency) have been evaluated as a function of the interaction cutoff. We observe that the aromatic residues along with arginine, histidine, and methionine act as strong hubs at high interaction cutoffs, whereas the hydrophobic leucine and isoleucine residues get added to these hubs at low interaction cutoffs, forming weak hubs. The hubs identified are found to play a role in bringing together different secondary structural elements in the tertiary structure of the proteins. They are also found to contribute to the additional stability of the thermophilic proteins when compared to their mesophilic counterparts and hence could be crucial for the folding and stability of the unique three-dimensional structure of proteins. Based on these results, we also predict a few residues in the thermophilic and mesophilic proteins that can be mutated to alter their thermal stability.
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                Author and article information

                Journal
                Biomed Res Int
                Biomed Res Int
                BMRI
                BioMed Research International
                Hindawi Publishing Corporation
                2314-6133
                2314-6141
                2017
                22 January 2017
                : 2017
                : 2483264
                Affiliations
                1Center for Systems Biology, School of Electronic and Information Engineering, Soochow University, Suzhou 215006, China
                2Unit of Chemical-Physics Fundamentals in Chemical Engineering, Department of Engineering, Università Campus Bio-Medico di Roma, Rome, Italy
                3Environment and Health Department, Istituto Superiore di Sanità, Viale Regina Elena 299, 00161 Roma, Italy
                Author notes

                Academic Editor: Hesham H. Ali

                Author information
                http://orcid.org/0000-0002-8754-1541
                Article
                10.1155/2017/2483264
                5294226
                28243596
                22901e9a-f86c-4388-8f6b-e694c7085abf
                Copyright © 2017 Guang Hu et al.

                This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 22 June 2016
                : 17 November 2016
                : 20 December 2016
                Funding
                Funded by: National Natural Science Foundation of China
                Award ID: 21203131
                Award ID: 81302700
                Funded by: Natural Science Foundation of the Jiangsu Higher Education Institutions
                Award ID: 12KJB180014
                Award ID: 13KJB520022
                Funded by: China Postdoctoral Science Foundation
                Award ID: 2013M531406
                Award ID: 2016M590495
                Categories
                Review Article

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