Design of protein-binding proteins from the target structure alone

By Longxing Cao, B. Coventry, I. Goreshnik, Buwei Huang, W. Sheffler, J. Park, K. Jude, I. Marković, R. Kadam, K. Verschueren and 25 more. 2022

514 citations recorded by the source corpus.

The design of proteins that bind to a specific site on the surface of a target protein using no information other than the three-dimensional structure of the target remains a challenge1–5. Here we describe a general solution to this problem that starts with a broad exploration of the vast space of possible binding modes to a selected region of a protein surface, and then intensifies the search in the vicinity of the most promising binding modes. We demonstrate the broad applicability of this approach through the de novo design of binding proteins to 12 diverse protein targets with different shapes and surface properties. Biophysical characterization shows that the binders, which are all smaller than 65 amino acids, are hyperstable and, following experimental optimization, bind their targets with nanomolar to picomolar affinities. We succeeded in solving crystal structures of five of the binder–target complexes, and all five closely match the corresponding computational design models. Experimental data on nearly half a million computational designs and hundreds of thousands of point mutants provide detailed feedback on the strengths and limitations of the method and of our current understanding of protein–protein interactions, and should guide improvements of both. Our approach enables the targeted design of binders to sites of interest on a wide variety of proteins for therapeutic and diagnostic applications. A design pipeline is presented whereby binding proteins can be designed de novo without the need for prior information on binding hotspots or fragments from structures of complexes with binding partners.

Subfields: Materials Chemistry, Molecular Biology, Radiology, Nuclear Medicine and Imaging, Computational Biology & Bioinformatics, Computational Chemistry & Drug Discovery

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Citations in the corpus

Robust deep learning based protein sequence design using ProteinMPNN (2022, methodology): “Second panel: helical bundle scaffolds were docked to the exposed face of the peptide using RIFDOCK (19), and Rosetta remodel was used to build loops connecting the peptide to the scaffolds.”
Diffusion probabilistic modeling of protein backbones in 3D for the motif-scaffolding problem (2022, critical): “…native or prespecified protein fragments, but are limited to finding a suitable match in the Protein Data Bank (PDB) and cannot adapt the scaffold to compensate for slight structural mismatches (Cao et al., 2022; Silva et al., 2016; Yang et al., 2021; Sesterhenn et al., 2020; Linsky et al., 2020).”
AlphaFold2 can predict structural and phenotypic effects of single mutations (2022, background): “23 On a smaller scale, AF2 can be used to screen potential mutants in costly experiments where the number of mutations is limited.”
Identifying well-folded de novo proteins in the new era of accurate structure prediction (2022, background): “5%) following these approaches [especially without information about known interfaces with the target (Cao et al., 2022)], often requiring directed evolution of the initial hits.”
Broadly applicable and accurate protein design by integrating structure prediction networks and diffusion generative models (2022, unknown): “The ability to design binders de novo using the physically based Rosetta method was recently described 40 , and subsequently, the utility of ProteinMPNN and AF2 for sequence design and design filtering respectively has improved design success rates 21 .”
De novo design of luciferases using deep learning (2023, methodology): “Next, around each conformer, we used the RifGen method 29,30 to enumerate rotamer interaction fields (RIFs) on three-dimensional grids consisting of millions of placements of amino acid side chains making hydrogen-bonding and nonpolar interactions with DTZ (Fig.”
Improving de novo protein binder design with deep learning (2022, unknown): “Recently,ageneralRosetta-basedapproachtodesigning binding proteins using only the structure of the target was developed and used to design binding proteins to 13 different target sites 13 .”
Sampling of structure and sequence space of small protein folds (2021, background): “20 – 23) is the most predictive feature for the folding of a de novo designed protein, followed by the number of hydrophobic residues within the protein core.”