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Biophysical and computational methods to analyze amino acid interaction networks in proteins.

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TLDR
A number of different structural and computational methods have been developed to interrogate amino acid networks, including analyses of X-ray crystallographic data and structures, computer simulations, NMR data, and covariation among protein sequences, and the critical insights that such methods provide into protein function are described.
Abstract
Globular proteins are held together by interacting networks of amino acid residues. A number of different structural and computational methods have been developed to interrogate these amino acid networks. In this review, we describe some of these methods, including analyses of X-ray crystallographic data and structures, computer simulations, NMR data, and covariation among protein sequences, and indicate the critical insights that such methods provide into protein function. This information can be leveraged towards the design of new allosteric drugs, and the engineering of new protein function and protein regulation strategies.

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Interactome networks and human disease

TL;DR: Why interactome networks are important to consider in biology, how they can be mapped and integrated with each other, what global properties are starting to emerge from interactome network models, and how these properties may relate to human disease are detailed.
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An optimal distance cutoff for contact-based Protein Structure Networks using side-chain centers of mass

TL;DR: This study selected a dataset of proteins with different fold and size and assessed the two fundamental properties of the PSN, i.e. hubs and connected components, and identified an optimal cutoff of 5 Å that is robust to changes in the force field and the proteins.
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gRINN: a tool for calculation of residue interaction energies and protein energy network analysis of molecular dynamics simulations.

TL;DR: GRINN utilizes the features of NAMD or GROMACS MD simulation packages and automatizes the steps necessary to extract residue-residue interaction energies from user-supplied simulation trajectories, greatly simplifying the analysis for the end-user.
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Integration of network models and evolutionary analysis into high-throughput modeling of protein dynamics and allosteric regulation: theory, tools and applications.

TL;DR: Fundamental advances in protein dynamics, network theory and coevolutionary analysis that have provided foundation for rapidly growing computational tools for modeling of allosteric regulation are reviewed.
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Energetic redistribution in allostery to execute protein function.

TL;DR: Kumawat and Chakrabarty demonstrate that indeed even in dynamic allostery enthalpy plays a role by redistributing internal energies, especially electrostatic interaction energies, among residues upon perturbation in PDZ3 domain protein.
References
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Journal ArticleDOI

A Set of Measures of Centrality Based on Betweenness

TL;DR: A family of new measures of point and graph centrality based on early intuitions of Bavelas (1948) is introduced in this paper, which define centrality in terms of the degree to which a point falls on the shortest path between others and there fore has a potential for control of communication.
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Secondary-structure matching (SSM), a new tool for fast protein structure alignment in three dimensions

TL;DR: The present paper describes the SSM algorithm of protein structure comparison in three dimensions, which includes an original procedure of matching graphs built on the protein's secondary-structure elements, followed by an iterative three-dimensional alignment of protein backbone Calpha atoms.
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Random graphs with arbitrary degree distributions and their applications.

TL;DR: It is demonstrated that in some cases random graphs with appropriate distributions of vertex degree predict with surprising accuracy the behavior of the real world, while in others there is a measurable discrepancy between theory and reality, perhaps indicating the presence of additional social structure in the network that is not captured by the random graph.
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The energy landscapes and motions of proteins.

TL;DR: The concepts that emerge from studies of the conformational substates and the motions between them permit a quantitative discussion of one simple reaction, the binding of small ligands such as carbon monoxide to myoglobin.
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