Do Plausible Structures Guarantee Coherent Dynamics? Contextual Anomaly Localization in Protein Trajectories

Understanding how protein conformations evolve over time is essential for revealing the molecular mechanisms underlying biological function. Yet a trajectory can pass framewise structural checks and match a reference conformational distribution while containing temporally misplaced transitions, potentially misleading interpretations of conformational transition pathways. The challenge is to distinguish such contextual inconsistencies from legitimate fluctuations and conformational changes, which cannot be resolved by the magnitude of structural change alone. We introduce ProTAL, an offline framework that localizes trajectory anomalies by learning the compatibility between conformational transitions and their temporal context. ProTAL combines bidirectional latent predictions with residue-pair geometry across multiple temporal scales, integrating expectations of molecular evolution with direct evidence of structural change. Learning from unperturbed trajectories and controlled temporal corruptions, it localizes suspicious transitions without requiring paired reference trajectories at inference. On our ATLAS-derived protein-trajectory benchmark, ProTAL achieves substantially higher anomaly detection success rates than the conventional baselines evaluated. ProTAL adds transition-level diagnostics to structural and ensemble evaluation, helping researchers identify potentially misleading trajectory segments before drawing mechanistic conclusions. The code is available at the link: https://anonymous.4open.science/r/ProTAL-6EED.