{'fraction_non_alphanumeric': 0.01694915254237288, 'fraction_numerical': 0.007704160246533128, 'mean_word_length': 6.065217391304348, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
AlphaFold2-RAVE: From sequence to Boltzmann ensemble

While AlphaFold2 is rapidly being adopted as a new standard in protein structure predictions, it is limited to single structure prediction. This can be insufficient for the inherently dynamic world of biomolecules. Even with recent modifications towards conformational diversity, AlphaFold2 is devoid of providing thermodynamically ranked conformations. AlphaFold2-RAVE is an efficient protocol using the structural outputs from AlphaFold2 as initializations for AI augmented molecular dynamics. These simulations result in Boltzmann ranked ensembles, which we demonstrate on different proteins.