De Novo Protein Design for Novel Folds Using Guided Conditional Wasserstein Generative Adversarial Networks
By Mostafa Karimi, Shaowen Zhu, Yue Cao, Yang Shen. 2020
80 citations recorded by the source corpus.
Although massive data is quickly accumulating on protein sequence and structure, there is a small and limited number of protein architectural types (or structural folds). This study is addressing the following question: how well could one reveal underlying sequence–structure relationships and design protein sequences for an arbitrary, potentially novel, structural fold? In response to the question, we have developed novel deep generative models, namely, semisupervised gcWGAN (guided, conditional, Wasserstein Generative Adversarial Networks). To overcome training difficulties and improve design qualities, we build our models on conditional Wasserstein GAN (WGAN) that uses Wasserstein distance in the loss function. Our major contributions include (1) constructing a low-dimensional and generalizable representation of the fold space for the conditional input, (2) developing an ultrafast sequence-to-fold predictor (or oracle) and incorporating its feedback into WGAN as a loss to guide model training, and (3) exploiting sequence data with and without paired structures to enable a semisupervised training strategy. Assessed by the oracle over 100 novel folds not in the training set, gcWGAN generates more successful designs and covers 3.5 times more target folds compared to a competing data-driven method (cVAE). Assessed by sequence- and structure-based predictors, gcWGAN designs are physically and biologically sound. Assessed by a structure predictor over representative novel folds, including one not even part of basis folds, gcWGAN designs have comparable or better fold accuracy yet much more sequence diversity and novelty than cVAE. The ultrafast data-driven model is further shown to boost the success of a principle-driven de novo method (RosettaDesign), through generating design seeds and tailoring design space. In conclusion, gcWGAN explores uncharted sequence space to design proteins by learning generalizable principles from current sequence–structure data.
Subfields: Molecular Biology
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In Molecular Biology
- David Baker
- Jeffrey J. Gray
- Xingjie Pan
- Tanja Kortemme
- Sergey Ovchinnikov
- Frank DiMaio
- Ivan V. Anishchenko
- Jens Meiler
Citations in the corpus
- De novo protein design by deep network hallucination (2020, background): “…of sequence 6 , and to identify amino acid sequences that either fit well onto specified backbone structures 7–10 or are conditioned on low-dimensional fold representations 11 ; models tailored to generate sequences and/or structures for specific protein families have also been developed 12–16 .”
- Deep Learning in Protein Structural Modeling and Design (2020, background): “Karimi et al.(210) developed gcWGAN that combined a CGAN and a guidance strategy to bias the generated sequences toward a desired structure.”
- Recent advances in de novo protein design: Principles, methods, and applications (2021, background): “A number of generative models were developed for sequence design, including generative adversarial networks (104), variational autoencoders (105, 106), and graph-based (107, 108) models.”