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Abstract. l-Asparaginases have been essential anticancer biopharmaceuticals for nearly half a century, particularly in the treatment of acute lymphoblastic

Molecular simulations of proteins are well-known to be computationally expensive. Here, we present a new latent-space-based method for modeling protein conformational flexibility at a very affordable computational cost. The method is data-driven and employs an autoencoder-based machine learning model for reversible dimensionality reduction of diverse conformations of the protein studied. Next, samples are selected from the low-dimensional latent space via Monte Carlo sampling. The folding and unfolding of the miniproteins can be sampled in minutes of computational time. We validated the method on four model systems: Tryptophan Cage, nonfolding variant of Tryptophan Cage, Villin headpiece, and human β-2-syntrophin PDZ domain (miniproteins with 20, 20, 35, and 95 residues, respectively). All systems were modeled at an all-atom resolution. Tryptophan Cage and Villin miniproteins show very similar populations of folded/unfolded states sampled by Monte Carlo simulations as the reference MD trajectories calculated by D. E. Shaw Research.

The design of good collective variables for analysis and the enhancement of sampling of molecular simulations is not a trivial task. It often relies on the know

Early proteins likely arose from a chemically limited set of amino acids available through prebiotic chemistry, raising a central question in molecular evolution: could such primitive compositions yie...