Scaffolding protein functional sites using deep learning

By Jue Wang, S. Lisanza, David Juergens, D. Tischer, Joseph L. Watson, Karla M. Castro, R. Ragotte, A. Saragovi, L. Milles, Minkyung Baek and 14 more. 2022

414 citations recorded by the source corpus.

The binding and catalytic functions of proteins are generally mediated by a small number of functional residues held in place by the overall protein structure. Here, we describe deep learning approaches for scaffolding such functional sites without needing to prespecify the fold or secondary structure of the scaffold. The first approach, “constrained hallucination,” optimizes sequences such that their predicted structures contain the desired functional site. The second approach, “inpainting,” starts from the functional site and fills in additional sequence and structure to create a viable protein scaffold in a single forward pass through a specifically trained RoseTTAFold network. We use these two methods to design candidate immunogens, receptor traps, metalloproteins, enzymes, and protein-binding proteins and validate the designs using a combination of in silico and experimental tests. Description Designing around function Protein design has had success in finding sequences that fold into a desired conformation, but designing functional proteins remains challenging. Wang et al. describe two deep-learning methods to design proteins that contain prespecified functional sites. In the first, they found sequences predicted to fold into stable structures that contain the functional site. In the second, they retrained a structure prediction network to recover the sequence and full structure of a protein given only the functional site. The authors demonstrate their methods by designing proteins containing a variety of functional motifs. —VV Deep-learning methods enable the scaffolding of desired functional residues within a well-folded designed protein.

Subfields: Computational Theory and Mathematics, Molecular Biology, Computational Biology & Bioinformatics

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Citations in the corpus

AlphaFold2 and its applications in the fields of biology and medicine (2023, neutral): “120 Target prediction .”
Improving de novo protein binder design with deep learning (2022, positive): “…multiple sequence alignments (which contain rich co-evolutionary information on residues likely to be in contact, etc), but for de novo designedsequences,whicharegenerallymorestableandmoreregular than naturally occurring proteins, accurate predictions can be obtained from single sequences 18, 19 .”
Practical and Asymptotically Exact Conditional Sampling in Diffusion Models (2023, positive): “The biochemical functions of proteins are typically imparted by a small number of atoms (a motif ), that are stabilized by the overall protein structure, known as the scaffold (Wang et al., 2022).”
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 gradient-based design of novel protein structures and even scaffolding binding loops 56,57 .”
Structure-based drug design with equivariant diffusion models (2022, positive): “Generating compounds, or parts thereof, conditioned on a given molecular context is reminiscent of inpainting, a technique originally introduced for completing missing parts of images 32,33 but also adopted in other domains, including biomolecular structures 34 .”
Efficient and accurate prediction of protein structure using RoseTTAFold2 (2023, background): “We have found that RF can be readily extended to protein design by fine-tuning for inpainting 15 and diffusion tasks 16 .”
De novo design of luciferases using deep learning (2023, methodology): “The family-wide hallucination approach used the de novo sequence and structure discovery capability of unconstrained protein hallucination 17,18 for loop and variable regions, and structure-guided sequence optimization for core regions.”
De novo design of protein structure and function with RFdiffusion (2023, neutral): “From left to right: (1) symmetric design model, (2) AF2 prediction of design following sequence design with ProteinMPNN, (3) 2D class averages showing both top and side views (scale bar, 60 Å for all class averages) and (4) 3D reconstructions from class averages with the design model fit into the de”
De novo protein design by inversion of the AlphaFold structure prediction network (2022, methodology): “This method successfully designed de novo proteins with active sites, epitopes, and protein binding sites (Wang et al., 2022).”
Broadly applicable and accurate protein design by integrating structure prediction networks and diffusion generative models (2022, positive): “generative modeling of images when predicting the noise added in the forward process (rather than ), we reasoned that by predicting we could better leverage the inductive biases of x 0 x 0 RoseTTAFold pre-trained for structure prediction to produce realistic structures (as in [4]).”