Prediction of Functional Sites in SCOP Domains using Dynamics Perturbation Analysis

Dynamics perturbation analysis (DPA) finds regions in a protein structure where proteins are "ticklish", i.e., where interactions cause a large change in protein dynamics. Previously, such regions were shown to predict the location of native binding sites in a docking test set, but the more general applicability of DPA to the prediction of functional sites in proteins was not shown. Here we describe the results of applying an accelerated algorithm, called Fast DPA, to predict functional sites in over 50,000 SCOP domains

A Pedagogical Method Of Dealing With Students Risk Anxiety Of Investing In The Stock Market

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Evidence of Children at Revolutionary War Sites

No abstract available at this time

Adjoints and Low-rank Covariance Representation

Quantitative measures of the uncertainty of Earth System estimates can be as important as the estimates themselves. Second moments of estimation errors are described by the covariance matrix, whose direct calculation is impractical when the number of degrees of freedom of the system state is large. Ensemble and reduced-state approaches to prediction and data assimilation replace full estimation error covariance matrices by low-rank approximations. The appropriateness of such approximations depends on the spectrum of the full error covariance matrix, whose calculation is also often impractical. Here we examine the situation where the error covariance is a linear transformation of a forcing error covariance. We use operator norms and adjoints to relate the appropriateness of low-rank representations to the conditioning of this transformation. The analysis is used to investigate low-rank representations of the steady-state response to random forcing of an idealized discrete-time dynamical system