MOTIVATION: Molecular dynamics (MD) simulations provide detailed atomistic insights into biomolecular processes, but comparing independent trajectories remains challenging due to stochastic divergence. Misaligned simulations can obscure shared mechanisms or exaggerate differences, limiting reproducibility and mechanistic interpretation. A generalizable, unsupervised method for synchronizing and comparing MD trajectories across systems and conditions is, therefore, needed. RESULTS: We introduce NetMD, a computational framework that synchronizes and analyzes MD trajectories by integrating graph-based representations with dynamic time warping. Trajectory frames are converted into residue-contact graphs, entropy-filtered to retain variable interactions, and embedded as low-dimensional vectors. NetMD aligns these vectorized trajectories through time-warping barycenter averaging, generating a consensus trajectory while pruning outlier simulations. Applied to transporters, demethylases, and large protein complexes relevant to neurological disease pathways and cancer, NetMD revealed shared multiphase dynamics and identified mutation- or ligand-specific deviations. This unsupervised, time-resolved approach enables direct comparison of MD ensembles across heterogeneous conditions. NetMD is robust and broadly applicable, providing a tool for uncovering conserved patterns and critical divergences in biomolecular dynamics. AVAILABILITY AND IMPLEMENTATION: NetMD is freely available at https://github.com/mazzalab/NetMD.

Hundreds of human proteins were found to establish transient interactions with rather degenerated consensus DNA sequences or motifs. Identifying these motifs and the genomic sites where interactions occur represent one of the most challenging research goals in modern molecular biology and bioinformatics. The last twenty years witnessed an explosion of computational tools designed to perform this task, whose performance has been last compared fifteen years ago. Here, we survey sixteen of them, benchmark their ability to identify known motifs nested in twenty-nine simulated sequence datasets, and finally report their strengths, weaknesses, and complementarity.