Deep learning methods for designing proteins scaffolding functional sites

By Jue Wang, S. Lisanza, David Juergens, D. Tischer, Ivan V. Anishchenko, Minkyung Baek, Joseph L. Watson, Jung-Ho Chun, L. Milles, J. Dauparas and 5 more. 2021

38 citations recorded by the source corpus.

Current approaches to de novo design of proteins harboring a desired binding or catalytic motif require pre-specification of an overall fold or secondary structure composition, and hence considerable trial and error can be required to identify protein structures capable of scaffolding an arbitrary functional site. Here we describe two complementary approaches to the general functional site design problem that employ the RosettaFold and AlphaFold neural networks which map input sequences to predicted structures. In the first “constrained hallucination” approach, we carry out gradient descent in sequence space to optimize a loss function which simultaneously rewards recapitulation of the desired functional site and the ideality of the surrounding scaffold, supplemented with problem-specific interaction terms, to design candidate immunogens presenting epitopes recognized by neutralizing antibodies, receptor traps for escape-resistant viral inhibition, metalloproteins and enzymes, and target binding proteins with designed interfaces expanding around known binding motifs. In the second “missing information recovery” approach, we start from the desired functional site and jointly fill in the missing sequence and structure information needed to complete the protein in a single forward pass through an updated RoseTTAFold trained to recover sequence from structure in addition to structure from sequence. We show that the two approaches have considerable synergy, and AlphaFold2 structure prediction calculations suggest that the approaches can accurately generate proteins containing a very wide array of functional sites.

Subfields: Molecular Biology

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Citations in the corpus

De novo protein design by deep network hallucination (2020, methodology): “Our hallucination approach can be readily extended to design new proteins using the recently developed RoseTTAFold 26 and AlphaFold2 27 networks 28,29 .”
Learning inverse folding from millions of predicted structures (2022, background): “…generate backbone structures (Anand & Huang, 2018; Eguchi et al., 2020), jointly generate structures and sequences (An-ishchenko et al., 2021; Wang et al., 2021), or model sequences directly (Rives et al., 2021; Madani et al., 2021; Shin et al., 2021; Gligorijevic et al., 2021; Bryant et…”
Diffusion probabilistic modeling of protein backbones in 3D for the motif-scaffolding problem (2022, methodology): “While experimental validation via X-ray crystallography remains the gold standard for evaluating computationally designed proteins, recent work [38, 25] has proposed to leverage highly accurate protein structure prediction neural networks as an in silico proxy for true structure.”
Fast, accurate antibody structure prediction from deep learning on massive set of natural antibodies (2022, methodology): “Meanwhile, methods like AlphaFold and RoseTTAFold have been adapted for 343 gradient-based design of novel protein structures and even sca olding binding loops (52, 53).”
Masked Inverse Folding with Sequence Transfer for Protein Representation Learning (2023, methodology): “More recent methods attempt to invert deep-learning protein structure prediction models (Jendrusch et al., 2021; Moffat et al., 2021; 2022; Anishchenko et al., 2021; Norn et al., 2021; Wang et al., 2021).”