De Novo Protein Design for Novel Folds using Guided Conditional Wasserstein Generative Adversarial Networks (gcWGAN)
By Mostafa Karimi, Shaowen Zhu, Yue Cao, Yang Shen. 2019
11 citations recorded by the source corpus.
Motivation Facing data quickly accumulating on protein sequence and structure, this study is addressing the following question: to what extent could current data alone reveal deep insights into the sequence-structure relationship, such that new sequences can be designed accordingly for novel structure folds? Results We have developed novel deep generative models, constructed low-dimensional and generalizable representation of fold space, exploited sequence data with and without paired structures, and developed ultra-fast fold predictor as an oracle providing feedback. The resulting semi-supervised gcWGAN is assessed with the oracle over 100 novel folds not in the training set and found to generate more yields and cover 3.6 times more target folds compared to a competing data-driven method (cVAE). Assessed with structure predictor over representative novel folds (including one not even part of basis folds), gcWGAN designs are found to have comparable or better fold accuracy yet much more sequence diversity and novelty than cVAE. gcWGAN explores uncharted sequence space to design proteins by learning from current sequence-structure data. The ultra fast data-driven model can be a powerful addition to principle-driven design methods through generating seed designs or tailoring sequence space. Availability Data and source codes will be available upon request. Contact yshen@tamu.edu Supplementary information Supplementary data are available at Bioinformatics online.
Subfields: Molecular Biology
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In Molecular Biology
- Namrata Anand
- Raphael R. Eguchi
- Bryan McCann
- Ali Madani
- Nikhil Vijay Naik
- Po-Ssu Huang
- Richard Socher
- Nitish Shirish Keskar
Citations in the corpus
- ProGen: Language Modeling for Protein Generation (2020, methodology): “Recent generative modeling work [34, 22] has shown promising directions such as Ingraham et al.”