De novo protein design by deep network hallucination

Anishchenko, Ivan; Chidyausiku, Tamuka M.; Овчинников, С. Г.; Pellock, S.J.; Baker, David

doi:10.1101/2020.07.22.211482

Cited by 49 publications

(49 citation statements)

References 21 publications

(27 reference statements)

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“…The Fleishman laboratory has developed methods achieving rich structure diversity through combining native protein fragments (Lapidoth et al., 2015), and has successfully designed enzymes with comparable characteristics to native enzymes (Netzer et al., 2018). Other recently developed methods use machine learning to directly generate protein sequences for desired or novel protein folds (Anand, Eguchi, Derry, Altman, & Huang, 2020; Anishchenko, Chidyausiku, Ovchinnikov, Pellock, & Baker, 2020), which at the same time can provide proteins with great structural diversity. Other methods include SEWING (Jacobs et al., 2016), junction fusion protein creation (Brunette et al., 2020), and loop‐helix‐loop unit combinatorial sampling (Pan et al., 2020).…”

Section: Commentarymentioning

confidence: 99%

De Novo Protein Design Using the Blueprint Builder in Rosetta

Lee

2020

CP Protein Science

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While native proteins cover diverse structural spaces and achieve various biological events, not many of them can directly serve human needs. One reason is that the native proteins usually contain idiosyncrasies evolved for their native functions but disfavoring engineering requirements. To overcome this issue, one strategy is to create de novo proteins which are designed to possess improved stability, high environmental tolerance, and enhanced engineering potential. Compared to other protein engineering strategies, in silico design of de novo proteins has significantly expanded the protein structural and sequence spaces, reduced wet lab workload, and incorporated engineered features in a guided and efficient manner. In the Baker laboratory we have been applying a design pipeline that uses the blueprint builder to design different folds of de novo proteins, and have successfully obtained libraries of de novo proteins with improved stability and engineering potential. In this article, we will use the design of de novo β‐barrel proteins as an example to describe the principles and basic procedures of the blueprint builder−based design pipeline. © 2020 Wiley Periodicals LLC. Basic Protocol 1: The construction of blueprints Alternate Protocol: Build blueprints based on existing protein .pdb files Basic Protocol 2: De novo protein design pipeline using the blueprint builder

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Section: Commentarymentioning

confidence: 99%

De Novo Protein Design Using the Blueprint Builder in Rosetta

Lee

2020

CP Protein Science

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“… 192 – CNN (input design) same as trRosetta – – 27 out of 129 sequence-structure pairs experimentally validated Anishchenko et al. 193 CNN, convolutional neural network; DCGAN, deep convolutional generative adversarial network; GAN, generative adversarial network; VAE, variational autoencoder. …”

Section: Protein Designmentioning

confidence: 89%

“…Anishchenko et al. 193 iterated sequences through the DL network, trRosetta, 103 to “hallucinate” 215 mutually compatible sequence-structure pairs in a manner similar to “input design”. 183 By maximizing the contrast between the distance distributions predicted by trRosetta and a background network trained on noise, they obtained new sequences with geometric maps with sharp geometric features.…”

Section: Protein Designmentioning

confidence: 99%

“…An important gap in current DL work in protein modeling, especially protein design (with few notable exceptions), 205 , 190 , 198 , 193 is the lack of experimental validation. Past blind challenges, e.g., CASP and CAPRI, and design claims have shown that experimental validation in this field is of paramount importance, where computational models are still prone to error.…”

Section: Outlook and Conclusionmentioning

confidence: 99%

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Deep Learning in Protein Structural Modeling and Design

et al. 2020

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Summary Deep learning is catalyzing a scientific revolution fueled by big data, accessible toolkits, and powerful computational resources, impacting many fields, including protein structural modeling. Protein structural modeling, such as predicting structure from amino acid sequence and evolutionary information, designing proteins toward desirable functionality, or predicting properties or behavior of a protein, is critical to understand and engineer biological systems at the molecular level. In this review, we summarize the recent advances in applying deep learning techniques to tackle problems in protein structural modeling and design. We dissect the emerging approaches using deep learning techniques for protein structural modeling and discuss advances and challenges that must be addressed. We argue for the central importance of structure, following the “sequence structure function” paradigm. This review is directed to help both computational biologists to gain familiarity with the deep learning methods applied in protein modeling, and computer scientists to gain perspective on the biologically meaningful problems that may benefit from deep learning techniques.

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“…The free hallucination loss is the KL divergence between the network predictions ( y ) and a background distribution ( b ) ( 7 ) .…”

Section: Oss Loss Lossmentioning

confidence: 99%

Design of proteins presenting discontinuous functional sites using deep learning

Tischer

Lisanza

Wang

et al. 2020

Preprint

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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.

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De novo protein design by deep network hallucination

Cited by 49 publications

References 21 publications

De Novo Protein Design Using the Blueprint Builder in Rosetta

De Novo Protein Design Using the Blueprint Builder in Rosetta

Deep Learning in Protein Structural Modeling and Design

Design of proteins presenting discontinuous functional sites using deep learning

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