GraProStr – Graphs of Protein Structures: A Tool for Constructing the Graphs and Generating Graph Parameters for Protein Structures

Vijayabaskar, M.S.

doi:10.2174/1875036201105010053

Cited by 15 publications

(10 citation statements)

References 15 publications

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“…Protein structure residue network was created from the simulation trajectories, using each residue as node and non-covalent interactions within residues as edges, on the basis of C α -C α distances of neighboring residues being less than 6.5 Å distance cutoff. Network analysis was performed by GraProStr [ 39 ] for final snapshots (100 ns) of both isoforms at 311 K. The dimensions of channel/pore formed by active site cavity was measured using PoreWalker [ 40 ].…”

Section: Methodsmentioning

confidence: 99%

Molecular Dynamics Simulations and Structural Analysis to Decipher Functional Impact of a Twenty Residue Insert in the Ternary Complex of Mus musculus TdT Isoform

Mutt

Sowdhamini

2016

PLoS ONE

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Insertions/deletions are common evolutionary tools employed to alter the structural and functional repertoire of protein domains. An insert situated proximal to the active site or ligand binding site frequently impacts protein function; however, the effect of distal indels on protein activity and/or stability are often not studied. In this paper, we have investigated a distal insert, which influences the function and stability of a unique DNA polymerase, called terminal deoxynucleotidyl transferase (TdT). TdT (EC:2.7.7.31) is a monomeric 58 kDa protein belonging to family X of eukaryotic DNA polymerases and known for its role in V(D)J recombination as well as in non-homologous end-joining (NHEJ) pathways. Two murine isoforms of TdT, with a length difference of twenty residues and having different biochemical properties, have been studied. All-atom molecular dynamics simulations at different temperatures and interaction network analyses were performed on the short and long-length isoforms. We observed conformational changes in the regions distal to the insert position (thumb subdomain) in the longer isoform, which indirectly affects the activity and stability of the enzyme through a mediating loop (Loop1). A structural rationale could be provided to explain the reduced polymerization rate as well as increased thermosensitivity of the longer isoform caused by peripherally located length variations within a DNA polymerase. These observations increase our understanding of the roles of length variants in introducing functional diversity in protein families in general.

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Section: Methodsmentioning

confidence: 99%

Molecular Dynamics Simulations and Structural Analysis to Decipher Functional Impact of a Twenty Residue Insert in the Ternary Complex of Mus musculus TdT Isoform

Mutt

Sowdhamini

2016

PLoS ONE

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“…Thus, it is evident that a larger data set containing structures from a single protein (cationic trypsin) in different ligand-bound states also yields similar outcomes in terms of global permeation of local perturbation upon ligand binding. The computation of the matrices and the cliques can now be evaluated through our webserver GraProStr available at http://vishgraph.mbu.iisc.ernet.in/GraProStr/index.html (Vijayabaskar et al, 2011).…”

Section: Ligand/environment-induced Conformational Variations At Proxmentioning

confidence: 99%

Network approach for capturing ligand-induced subtle global changes in protein structures

Sukhwal

Bhattacharyya

Vishveshwara

2011

Acta Crystallogr D Biol Cryst

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Ligand-induced conformational changes in proteins are of immense functional relevance. It is a major challenge to elucidate the network of amino acids that are responsible for the percolation of ligand-induced conformational changes to distal regions in the protein from a global perspective. Functionally important subtle conformational changes (at the level of side-chain noncovalent interactions) upon ligand binding or as a result of environmental variations are also elusive in conventional studies such as those using root-mean-square deviations (r.m.s.d.s). In this article, the network representation of protein structures and their analyses provides an efficient tool to capture these variations (both drastic and subtle) in atomistic detail in a global milieu. A generalized graph theoretical metric, using network parameters such as cliques and/or communities, is used to determine similarities or differences between structures in a rigorous manner. The ligand-induced global rewiring in the protein structures is also quantified in terms of network parameters. Thus, a judicious use of graph theory in the context of protein structures can provide meaningful insights into global structural reorganizations upon perturbation and can also be helpful for rigorous structural comparison. Data sets for the present study include high-resolution crystal structures of serine proteases from the S1A family and are probed to quantify the ligand-induced subtle structural variations.

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“…Graph representations of proteins have been successfully used in machine learning and structural analysis projects in structural biology (Pires et al, 2011;2013;Cheng et al, 2008). Web-servers for computing protein structure graphs exist, however the lack of public APIs for programmatic access, limited featurisation schemes and incompatibility with deep learning libraries motivated the development of Graphein (Chakrabarty et al, 2019;Vijayabaskar et al, 2011).…”

Section: Introductionmentioning

confidence: 99%

Graphein - a Python Library for Geometric Deep Learning and Network Analysis on Protein Structures and Interaction Networks

Jamasb

Lió

Blundell

2020

Preprint

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Graphein is a python library for constructing graph and surface-mesh representations of protein structures for computational analysis. The library interfaces with popular geometric deep learning libraries: DGL, PyTorch Geometric and PyTorch3D. Geometric deep learning is emerging as a popular methodology in computational structural biology. As feature engineering is a vital step in a machine learning project, the library is designed to be highly flexible, allowing the user to parameterise the graph construction, scaleable to facilitate working with large protein complexes, and containing useful pre-processing tools for preparing experimental structure files. Graphein is also designed to facilitate network-based and graph-theoretic analyses of protein structures in a high-throughput manner. As example workflows, we make available two new protein structure-related datasets, previously unused by the geometric deep learning community.Availability and implementationGraphein is written in python. Source code, example usage and datasets, and documentation are made freely available under a MIT License at the following URL: https://github.com/a-r-j/graphein

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GraProStr – Graphs of Protein Structures: A Tool for Constructing the Graphs and Generating Graph Parameters for Protein Structures

Cited by 15 publications

References 15 publications

Molecular Dynamics Simulations and Structural Analysis to Decipher Functional Impact of a Twenty Residue Insert in the Ternary Complex of Mus musculus TdT Isoform

Molecular Dynamics Simulations and Structural Analysis to Decipher Functional Impact of a Twenty Residue Insert in the Ternary Complex of Mus musculus TdT Isoform

Network approach for capturing ligand-induced subtle global changes in protein structures

Graphein - a Python Library for Geometric Deep Learning and Network Analysis on Protein Structures and Interaction Networks

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