Validation of protein structure models using network similarity score

Abstract

Accurate structural validation of proteins is of extreme importance in studies like protein structure prediction, analysis of molecular dynamic simulation trajectories and finding subtle changes in very similar structures. The benchmarks for today's structure validation are scoring methods like global distance test-total structure (GDT-TS), TM-score and root mean square deviations (RMSD). However, there is a lack of methods that look at both the protein backbone and side-chain structures at the global connectivity level and provide information about the differences in connectivity. To address this gap, a graph spectral based method (NSS-network similarity score) which has been recently developed to rigorously compare networks in diverse fields, is adopted to compare protein structures both at the backbone and at the side-chain noncovalent connectivity levels. In this study, we validate the performance of NSS by investigating protein structures from X-ray structures, modeling (including CASP models), and molecular dynamics simulations. Further, we systematically identify the local and the global regions of the structures contributing to the difference in NSS, through the components of the score, a feature unique to this spectral based scoring scheme. It is demonstrated that the method can quantify subtle differences in connectivity compared to a reference protein structure and can form a robust basis for protein structure comparison. Additionally, we have also introduced a network-based method to analyze fluctuations in side chain interactions (edge-weights) in an ensemble of structures, which can be an useful tool for the analysis of MD trajectories.

Influence of Disease-Causing Mutations on Protein Structural Networks

“…The network dissimilarity score (NDS) iis used to compare two networks with identical number of nodes to generate a difference score that quantifies the dissimilarity in their spectra and the weight of edges (Gadiyaram et al, 2017;Ghosh et al, 2017). The adjacency matrix is a representation of a network which is generated as described in the All Atom -Protein Structure Network Model.…”

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

“…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]…”

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Gauging Functional Brain Activity: From Distinguishability to Accessibility

“…More generally, observed data may be classified up to a given property (e.g., homotopy, symmetry, etc) or by obstructions to one of them. Conversely, comparing experimental conditions involves comparing their associated (e.g., network) structure, each structure involving its own set of operations and restrictions, and sometimes adding further structure (Simas et al, 2015; Gadiyaram et al, 2016; Schieber et al, 2017).…”