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Comparative methods in molecular biology and evolution rely almost entirely upon the analysis of DNA sequence and protein structure, both static forms of information. However, it is widely accepted that protein function results from dynamic machine-like motions induced during molecular interactions, a type of data for which comparative methods of analysis are challenged by the large fraction of protein motion created by random thermal noise induced by the surrounding solvent. Here, we introduce ATOMDANCE, a suite of statistical and kernel-based machine learning tools designed for identifying and comparing functional states of proteins captured in noisy molecular dynamics simulations. ATOMDANCE employs interpretable Gaussian kernel functions to compute site-wise maximum mean discrepancy (MMD) between learned features representing functional protein states (e.g. bound vs. unbound, wild-type vs. mutant). ATOMDANCE derives empirical p-values identifying functional similarities/differences in dynamics at specific sites on the protein. Depending upon mode of protein function, the machine learner can be trained upon either local atom fluctuations or reduced atom correlation matrices. Heat maps identifying amino acid sites with coordinated states of dynamics can also be produced as well. ATOMDANCE also employs MMD to contextually analyze all possible random amino-acid replacements thus allowing for a site-wise test of neutral vs. non-neutral evolution in the divergence of dynamic function in protein homologs. ATOMDANCE offers a user-friendly interface and requires as input only the structure, topolopy and trajectory files for each of the two proteins being compared. A separate interface for generating molecular dynamics simulations via open-source tools is offered as well.

From the beginning of the different approaches for analyzing and assessing the information related risk affecting organization, the two factors deriving risk are the damages or losses incurred to the organization and the probability of occurring of those risk incidents. Many qualitative and quantitative models have been proposed to estimate the above two factors considering the asset centric and software centric approaches. This paper proposes an attack centric framework that considers approaches of an attacker and different characteristics of attack in computing the overall impact of attack which can then be used to effectively calculate the overall loss incurred to the organization in the event of successful attack. This framework cognate with the existing ones and steps forward with a new mathematical approach to estimate the cost of any type of loss incurred to the organization due to the information security breach. Also the framework considers the cost of implementing security as loss in the event of security measure failed in providing appropriate protection against the threats. General TermsRisk assessment framework.