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Poxviruses are dangerous pathogens, which can cause fatal infection in unvaccinated individuals. The causative agent of smallpox in humans, variola virus , is closely related to the bovine vaccinia virus , yet the molecular basis of their selectivity is currently incompletely understood. Here, we examine the role of the electrostatics in the selectivity of the smallpox protein SPICE and vaccinia protein VCP toward the human and bovine complement protein C3b, a key component of the complement immune response. Electrostatic calculations, in-silico alanine-scan and electrostatic hotspot analysis, as introduced by Kieslich and Morikis ( PLoS Comput. Biol . 2012), are used to assess the electrostatic complementarity and to identify sites resistant to local perturbation where the electrostatic potential is likely to be evolutionary conserved. The calculations suggest that the bovine C3b is electrostatically prone to selectively bind its VCP ligand. On the other hand, the human isoform of C3b exhibits a lower electrostatic complementarity toward its SPICE ligand. Yet, the human C3b displays a highly preserved electrostatic core, which suggests that this isoform could be less selective in binding different ligands like SPICE and the human Factor H. This is supported by experimental cofactor activity assays revealing that the human C3b is prone to bind both SPICE and Factor H, which exhibit diverse electrostatic properties. Additional investigations considering mutants of SPICE and VCP that revert their selectivity reveal an “electrostatic switch” into the central modules of the ligands, supporting the critical role of the electrostatics in the selectivity. Taken together, these evidences provide insights into the selectivity mechanism of the complement regulator proteins encoded by the variola and vaccinia viruses to circumvent the complement immunity and exert their pathogenic action. These fundamental aspects are valuable for the development of novel vaccines and therapeutic strategies. 

Enzymes are catalysts in biochemical reactions that, by definition, increase rates of reactions without being altered or destroyed. However, when that enzyme is a protease, a subclass of enzymes that hydrolyze other proteins, and that protease is in a multiprotease system, protease-as-substrate dynamics must be included, challenging assumptions of enzyme inertness, shifting kinetic predictions of that system. Protease-on-protease inactivating hydrolysis can alter predicted protease concentrations used to determine pharmaceutical dosing strategies. Cysteine cathepsins are proteases capable of cathepsin cannibalism, where one cathepsin hydrolyzes another with substrate present, and misunderstanding of these dynamics may cause miscalculations of multiple proteases working in one proteolytic network of interactions occurring in a defined compartment. Once rates for individual protease-on-protease binding and catalysis are determined, proteolytic network dynamics can be explored using computational models of cooperative/competitive degradation by multiple proteases in one system, while simultaneously incorporating substrate cleavage. During parameter optimization, it was revealed that additional distraction reactions, where inactivated proteases become competitive inhibitors to remaining, active proteases, occurred, introducing another network reaction node. Taken together, improved predictions of substrate degradation in a multiple protease network were achieved after including reaction terms of autodigestion, inactivation, cannibalism, and distraction, altering kinetic considerations from other enzymatic systems, since enzyme can be lost to proteolytic degradation. We compiled and encoded these dynamics into an online platform (https://plattlab.shinyapps.io/catKLS/) for individual users to test hypotheses of specific perturbations to multiple cathepsins, substrates, and inhibitors, and predict shifts in proteolytic network reactions and system dynamics.

 

In this article, we present (1) a feature selection algorithm based on nonlinear support vector machine (SVM) for fault detection and diagnosis in continuous processes and (2) results for the Tennessee Eastman benchmark process. The presented feature selection algorithm is derived from the sensitivity analysis of the dualC‐SVM objective function. This enables simultaneous modeling and feature selection paving the way for simultaneous fault detection and diagnosis, where feature ranking guides fault diagnosis. We train fault‐specific two‐class SVM models to detect faulty operations, while using the feature selection algorithm to improve the accuracy and perform the fault diagnosis. Our results show that the developed SVM models outperform the available ones in the literature both in terms of detection accuracy and latency. Moreover, it is shown that the loss of information is minimized with the use of feature selection techniques compared to feature extraction techniques such as principal component analysis (PCA). This further facilitates a more accurate interpretation of the results. © 2018 American Institute of Chemical EngineersAIChE J, 65: 992–1005, 2019