Generalized Biomolecular Modeling and Design with RoseTTAFold All-Atom

By Rohith Krishna, Jue Wang, Woody Ahern, Pascal Sturmfels, Preetham Venkatesh, Indrek Kalvet, G. Lee, Felix S. Morey-Burrows, Ivan V. Anishchenko, Ian R. Humphreys and 12 more. 2023

924 citations recorded by the source corpus.

Although AlphaFold2 (AF2) and RoseTTAFold (RF) have transformed structural biology by enabling high-accuracy protein structure modeling, they are unable to model covalent modifications or interactions with small molecules and other non-protein molecules that can play key roles in biological function. Here, we describe RoseTTAFold All-Atom (RFAA), a deep network capable of modeling full biological assemblies containing proteins, nucleic acids, small molecules, metals, and covalent modifications given the sequences of the polymers and the atomic bonded geometry of the small molecules and covalent modifications. Following training on structures of full biological assemblies in the Protein Data Bank (PDB), RFAA has comparable protein structure prediction accuracy to AF2, excellent performance in CAMEO for flexible backbone small molecule docking, and reasonable prediction accuracy for protein covalent modifications and assemblies of proteins with multiple nucleic acid chains and small molecules which, to our knowledge, no existing method can model simultaneously. By fine-tuning on diffusive denoising tasks, we develop RFdiffusion All-Atom (RFdiffusionAA), which generates binding pockets by directly building protein structures around small molecules and other non-protein molecules. Starting from random distributions of amino acid residues surrounding target small molecules, we design and experimentally validate proteins that bind the cardiac disease therapeutic digoxigenin, the enzymatic cofactor heme, and optically active bilin molecules with potential for expanding the range of wavelengths captured by photosynthesis. We anticipate that RFAA and RFdiffusionAA will be widely useful for modeling and designing complex biomolecular systems.

Subfields: Materials Chemistry, Molecular Biology, Computational Biology & Bioinformatics

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Citations in the corpus

Accurate structure prediction of biomolecular interactions with AlphaFold 3 (2024, methodology): “A wide range of predictors for various specific interaction types has been developed 16–28 , as well as one generalist method developed concurrently with the present work 29 , but the accuracy of such deep-learning attempts has been mixed and often below that of physics-inspired methods 30,31 .”
Scalable emulation of protein equilibrium ensembles with generative deep learning (2025, positive): “While structure prediction systems for predicting biomolecular complexes already exist [2, 3], a key obstacle to extending the emulator to other scopes different from proteins, as well as further improving it for the current scope, is the lack of training data.”