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.
For
decades, studies have been conducted to understand and improve
how humans and machines interact; yet, the process systems engineering
field often focuses on algorithms that do not explicitly account for
human actions in the decision-making process. The article presents
a broad view of Human-in-The-Loop (HiTL) technology: how HiTL is viewed
by different disciplines, the role of cognitive science in understanding
fundamental aspects of human factors and human-machine interaction,
which are questions that remain open-ended, and how manufacturing
should include a more technologically updated and ethical use of human
factors to realize the true potential of Industry 4.0. A Smart Control
Room framework is presented with preliminary results and future directions
to explicitly consider the HiTL in process operations.
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