Surrogate Modeling of the Relative Entropy for Inverse Design Using Smolyak Sparse Grids.

Relative entropy minimization, a statistical-mechanics approach for finding potential energy functions that produce target structural ensembles, has proven to be a powerful strategy for the inverse design of nanoparticle self-assembly. For a given target structure, the gradient of the relative entropy with respect to the adjustable parameters of the potential energy function is computed by performing a simulation, and then these parameters are updated using iterative gradient-based optimization. Small parameter updates per iteration and many iterations can be required for numerical stability, but this incurs considerable computational expense because a new simulation must be performed to reevaluate the gradient at each iteration.

Role of Electrostatic Hotspots in the Selectivity of Complement Control Proteins Toward Human and Bovine Complement Inhibition.

Poxviruses are dangerous pathogens, which can cause fatal infection in unvaccinated individuals. The causative agent of smallpox in humans, , is closely related to the bovine , 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.

Reassessing enzyme kinetics: Considering protease-as-substrate interactions in proteolytic networks.

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.

Dynamic Model of Protease State and Inhibitor Trafficking to Predict Protease Activity in Breast Cancer Cells.

Introduction: Cysteine cathepsins are implicated in breast cancer progression, produced by both transformed epithelial cells and infiltrated stromal cells in tumors, but to date, no cathepsin inhibitor has been approved for clinical use due to unexpected side effects. This study explores cellular feedback to cathepsin inhibitors that might yield non-intuitive responses, and uses computational models to determine underlying cathepsin-inhibitor dynamics.

Methods: MDA-MB-231 cells treated with E64 were tested by multiplex cathepsin zymography and immunoblotting to quantify total, active, and inactive cathepsins S and L.

A Nonlinear Support Vector Machine-Based Feature Selection Approach for Fault Detection and Diagnosis: Application to the Tennessee Eastman Process.

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 dual C-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.