MOTIVATION: Molecular Dynamics (MD) simulations are essential for investigating protein dynamics and function. Although significant advances have been made in integrating simulation techniques and machine learning, there are still challenges in selecting the most suitable data representation for learning. Graph embedding is a powerful computational method that automatically learns low-dimensional representations of nodes in a graph while preserving graph topology and node properties, thereby bridging graph structures and machine learning methods. Graph embeddings hold great potential for efficiently representing MD simulation data and studying protein dynamics. RESULTS: We present MDGraphEmb, a Python library built on MDAnalysis, specifically designed to convert protein MD simulation trajectories into graph-based representations and corresponding graph embeddings. This transformation enables the compression of high-dimensional, noisy trajectories from protein simulations into tabular formats suitable for machine learning. MDGraphEmb provides a framework that supports a range of graph embedding techniques and machine learning models, enabling the creation of workflows to analyse protein dynamics and identify important protein conformations. Graph embedding effectively captures and compresses structural information from protein MD simulation data, making it applicable to diverse downstream machine-learning classification tasks. We present an application for encoding and detecting important protein conformations from molecular dynamics simulations to classify functional states, using adenylate kinase (ADK) as the main case study. To assess the generalizability of the approach, two additional systems, Plantaricin E (PlnE) and HIV-1 protease are included as supplementary validation examples. A performance comparison of different graph embedding methods combined with machine learning models is also provided. AVAILABILITY AND IMPLEMENTATION: MDGraphEMB GitHub Repository: https://github.com/FerdoosHN/MDGraphEMB.

In this paper, we quantify the privacy gain of synthetic patient data drawn from two generative models, MST and PrivBayes, which is based on real anonymized primary care patient data. This evaluation is implemented for two types of inference attacks, namely membership and attribute inference attacks using a new toolbox, TAPAS. The aim is to quantitatively evaluate the privacy gain of each attack where these two differentially private generators and different threat models are used with a focus on black-box knowledge. The evaluation that was carried out in this paper demonstrates that vulnerabilities of synthetic patient data depend on the different attack scenarios, threat models, and algorithms used to generate the synthetic patient data. It was shown empirically that although the synthetic patient data achieved high privacy gain in most attack scenarios, it does not behave uniformly against adversarial attacks, and some records and outliers remain vulnerable depending on the attack scenario. Moreover, it was shown that the PrivBayes generator is the more robust generator in comparison to MST in terms of the privacy-preservation of synthetic data.