Design of proteins presenting discontinuous functional sites using deep learning

By D. Tischer, S. Lisanza, Jue Wang, Runze Dong, Ivan V. Anishchenko, L. Milles, Sergey Ovchinnikov, David Baker. 2020

40 citations recorded by the source corpus.

An outstanding challenge in protein design is the design of binders against therapeutically relevant target proteins via scaffolding the discontinuous binding interfaces present in their often large and complex binding partners. There is currently no method for sampling through the almost unlimited number of possible protein structures for those capable of scaffolding a specified discontinuous functional site; instead, current approaches make the sampling problem tractable by restricting search to structures composed of pre-defined secondary structural elements. Such restriction of search has the disadvantage that considerable trial and error can be required to identify architectures capable of scaffolding an arbitrary discontinuous functional site, and only a tiny fraction of possible architectures can be explored. Here we build on recent advances in de novo protein design by deep network hallucination to develop a solution to this problem which eliminates the need to pre-specify the structure of the scaffolding in any way. We use the trRosetta residual neural network, which maps input sequences to predicted inter-residue distances and orientations, to compute a loss function which simultaneously rewards recapitulation of a desired structural motif and the ideality of the surrounding scaffold, and generate diverse structures harboring the desired binding interface by optimizing this loss function by gradient descent. We illustrate the power and versatility of the method by scaffolding binding sites from proteins involved in key signaling pathways with a wide range of secondary structure compositions and geometries. The method should be broadly useful for designing small stable proteins containing complex functional sites.

Subfields: Computational Theory and Mathematics, Molecular Biology

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Citations in the corpus

Diffusion probabilistic modeling of protein backbones in 3D for the motif-scaffolding problem (2022, neutral): “[36] Doug Tischer, Sidney Lisanza, Jue Wang, Runze Dong, Ivan Anishchenko, Lukas F Milles, Sergey Ovchinnikov, and David Baker.”
AlphaDesign: A de novo protein design framework based on AlphaFold (2021, methodology): “[71, 72] use both Markov-Chain Monte Carlo and gradient descent as their means for optimisation.”
De novo protein design by inversion of the AlphaFold structure prediction network (2022, background): “Additionally, it was shown that a scaffold could be hallucinated to stabilize a functional site, supporting the desired structure (Tischer et al., 2020).”
Deep learning techniques have significantly impacted protein structure prediction and protein design. (2021, methodology): “The developed method was then extended in two additional studies, where the procedure was either completely constrained to design sequences for a ﬁxed fold [66] or to design sequences that recapitulated native interfaces [67], while allowing the remainder of the protein to be hallucinated freely.”
Antigen-Specific Antibody Design and Optimization with Diffusion-Based Generative Models for Protein Structures (2022, background): “Fix-backbone design is a common setting in the area of protein design [21, 20, 6, 46, 47].”
Using AlphaFold for Rapid and Accurate Fixed Backbone Protein Design (2021, background): “For example, functional design could be performed using inﬁlling (Tischer et al., 2020) around some active site, where the sequence around some pre-speciﬁed active site is designed.”
Iterative Refinement Graph Neural Network for Antibody Sequence-Structure Co-design (2021, methodology): “Similar approaches are used in protein design (Leaver-Fay et al., 2011; Tischer et al., 2020).”