Projecting these new trajectories onto the same TICA space (Figure ) revealed that our simulation has explored all four basins. This demonstrates that diverse starting structures generated by af2rave effectively bypassed free energy barriers between conformational states, accelerating sampling compared to traditional long MD simulations. Despite using only approximately 1 µs of aggregate simulation time, the protocol achieved coverage comparable to the original 1.8 ms trajectory, capturing nearly all key regions in the TICA space. The workflow of AlphaFold2-RAVE AlphaFold2-RAVE (af2rave) takes a protein sequence as input and generates diverse structures using a modified AlphaFold2 protocol with varying reduced MSA depths. The resulting structures are first passed through a filter (often by RMSD to a reference structure) and then analyzed as an ensemble to identify the most variable pairwise distances within a user-defined selection, providing a coarse set of features to work with. Subsequently, regular-space clustering selects a few representative structures as cluster centers to initiate short MD simulations. The resulting trajectories provide additional sampling to further reduce the initially selected features to a smaller set with AMINO. Finally, SPIB is applied to these reduced degrees of freedom to define states and label the structures. A 2D latent space is simultaneously generated to visualize the relative relationships between the structures, which can also be used as collective variables (CVs) for potential further enhanced sampling.