Identifying structural differences among proteins can be a non-trivial task. When contrasting ensembles of protein structures obtained from molecular dynamics simulations, biologically-relevant features can be easily overshadowed by spurious fluctuations. Here, we present SINATRA Pro, a computational pipeline designed to robustly identify topological differences between two sets of protein structures. Algorithmically, SINATRA Pro works by first taking in the 3D atomic coordinates for each protein snapshot and summarizing them according to their underlying topology. Statistically significant topological features are then projected back onto an user-selected representative protein structure, thus facilitating the visual identification of biophysical signatures of different protein ensembles. We assess the ability of SINATRA Pro to detect minute conformational changes in five independent protein systems of varying complexities. In all test cases, SINATRA Pro identifies known structural features that have been validated by previous experimental and computational studies, as well as novel features that are also likely to be biologically-relevant according to the literature. These results highlight SINATRA Pro as a promising method for facilitating the non-trivial task of pattern recognition in trajectories resulting from molecular dynamics simulations, with substantially increased resolution.

This paper addresses the secrecy performance of the downlink of a non-orthogonal multiple access network in the presence of multiple randomly located eavesdroppers. The network consists of a base station and a near receiver that are located inside a protected zone, free of eavesdroppers, while a far user is located outside. Herein, it is considered that the source transmits a superposed jamming signal to enhance the secrecy performance. In this sense, imperfections on the removal of the jamming signal by the legitimate receivers are also investigated. Integral-form exact and closed-form approximate expressions for the secrecy outage probability are derived by employing stochastic geometry tools. The expressions are corroborated via Monte Carlo simulations.