De Novo Protein Design for Novel Folds Using Guided Conditional Wasserstein Generative Adversarial Networks.

ABSTRACT: 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 genera

SUBMITTER: Karimi M 

PROVIDER: S-EPMC7775287 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature