Identification of crucial elements for network integrity: a perturbation approach through graph spectral method

Gadiyaram, Vasundhara; Dighe, Anasuya; Vishveshwara, Saraswathi

doi:10.1007/s12572-018-0236-7

Cited by 6 publications

(13 citation statements)

References 21 publications

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“…Many such concepts are integrated together in computational programs to obtain critical biological insights (Greener and Sternberg, 2018;Verkhivker et al, 2020;Zhang et al, 2020). Network theory is a widely used approach which provides explicit information on the role of constituent amino acids on the stability of structure networks at a global level (Atilgan et al, 2010;Brown et al, 2017;Gadiyaram et al, 2018). A vast range of experimental and computational studies have taken up the challenge of correlating biological cellular functions to the molecular level changes.…”

Section: Introductionmentioning

confidence: 99%

Surveying the Side-Chain Network Approach to Protein Structure and Dynamics: The SARS-CoV-2 Spike Protein as an Illustrative Case

Halder

Anto

Subramanyan

et al. 2020

Front. Mol. Biosci.

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Network theory-based approaches provide valuable insights into the variations in global structural connectivity between different dynamical states of proteins. Our objective is to review network-based analyses to elucidate such variations, especially in the context of subtle conformational changes. We present technical details of the construction and analyses of protein structure networks, encompassing both the non-covalent connectivity and dynamics. We examine the selection of optimal criteria for connectivity based on the physical concept of percolation. We highlight the advantages of using side-chain-based network metrics in contrast to backbone measurements. As an illustrative example, we apply the described network approach to investigate the global conformational changes between the closed and partially open states of the SARS-CoV-2 spike protein. These conformational changes in the spike protein is crucial for coronavirus entry and fusion into human cells. Our analysis reveals global structural reorientations between the two states of the spike protein despite small changes between the two states at the backbone level. We also observe some differences at strategic locations in the structures, correlating with their functions, asserting the advantages of the side-chain network analysis. Finally, we present a view of allostery as a subtle synergistic-global change between the ligand and the receptor, the incorporation of which would enhance drug design strategies.

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Section: Introductionmentioning

confidence: 99%

Surveying the Side-Chain Network Approach to Protein Structure and Dynamics: The SARS-CoV-2 Spike Protein as an Illustrative Case

Halder

Anto

Subramanyan

et al. 2020

Front. Mol. Biosci.

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“…Several applications of analysis of side chain networks (from both conventional and spectral methods) relevant to protein structure-dynamics-function are presented in earlier review articles [21][22][23] A distinct advantage of graph spectral analysis is that the spectra of networks capture maximum information with minimal loss. Recently we have introduced methodological developments in graph spectral methods to analyse protein structure networks in a systematic manner [24][25][26]…”

Section: Network In the Context Of Protein Structure And Functionmentioning

confidence: 99%

“…The change due to the perturbation of a node or an edge in a network is estimated by quantifying the dissimilarity of the perturbed networks in comparison with the original one, using NSS, where the perturbations are created by systematic deletion of nodes/edges. The NSS thus obtained are used to derive perturbation scores 26 .…”

Section: By Combining the Above Components Nss Is Written As Thementioning

confidence: 99%

“…The graph spectral method (GSM) of network comparison (NSS) and the perturbation schemes (NPS,EPS) outlined above for the protein structure network (PSN) have great potential for a) large scale investigations related to characterizing the basic underlying principles in structural biology, b) employing as validation models, and c) generating extensive GSM data, which can further be used for structure prediction. In this section, we present a few examples to elucidate these points taken from recent works [24][25][26] . The first one demonstrates how new components to protein structure comparison can be incorporated.…”

Section: Illustrative Examples Highlighting the Capabilities Of The G...mentioning

confidence: 99%

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From Quantum Chemistry to Networks in Biology: A Graph Spectral Approach to Protein Structure Analyses

Gadiyaram

Vishveshwara

2019

J. Chem. Inf. Model.

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In this perspective article, we present a multidisciplinary approach for characterizing protein structure networks. We first place our approach in its historical context and describe the manner in which it synthesizes concepts from quantum chemistry, biology of polymer conformations, matrix mathematics, and percolation theory. We then explicitly provide the method for constructing the protein structure network in terms of non-covalently interacting amino acid side chains and show how a mine of information can be obtained from the graph spectra of these networks. Employing suitable mathematical approaches, such as the use of a weighted, Laplacian matrix to generate the spectra, enables us to develop rigorous methods for network comparison and to identify crucial nodes responsible for the network integrity through a perturbation approach. Our scoring methods have several applications in structural biology that are elusive to conventional methods of analyses. Here, we discuss the instances of: (a) Protein structure comparison that include the details of side chain connectivity, (b) The contribution to node clustering as a function of bound ligand, explaining the global effect of local changes in phenomena such as allostery and (c) The identification of crucial amino acids for structural integrity, derived purely from the spectra of the graph. We demonstrate how our method enables us to obtain valuable information on key proteins involved in cellular functions and diseases such as GPCR and HIV protease, and discuss the biological implications. We then briefly describe how concepts from percolation theory further augment our analyses. In our concluding perspective for future developments, we suggest a further unifying approach to protein structure analyses and a judicious choice of questions to employ our methods for larger, more complex networks, such as metabolic and disease networks.

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“…Thus, NSS is sensitive to minute changes in networks, making it a robust method to perform quantitative comparison between near-similar protein structures. The protocol has been earlier employed in the validation of protein structure models ( Ghosh et al., 2017 ) and for protein structure comparison ( Gadiyaram et al., 2019 ). We have extensively made use of this method in the current work for studying dissimilarity between structural networks of proteins and have termed the measure as the network dissimilarity score (NDS).…”

Section: Introductionmentioning

confidence: 99%

Understanding structural variability in proteins using protein structural networks

Prabantu

Gadiyaram

Vishveshwara

et al. 2022

Current Research in Structural Biology

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Identification of crucial elements for network integrity: a perturbation approach through graph spectral method

Cited by 6 publications

References 21 publications

Surveying the Side-Chain Network Approach to Protein Structure and Dynamics: The SARS-CoV-2 Spike Protein as an Illustrative Case

Surveying the Side-Chain Network Approach to Protein Structure and Dynamics: The SARS-CoV-2 Spike Protein as an Illustrative Case

From Quantum Chemistry to Networks in Biology: A Graph Spectral Approach to Protein Structure Analyses

Understanding structural variability in proteins using protein structural networks

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