Workshop: Machine Learning in Structural Biology

HelixGAN: A bidirectional Generative Adversarial Network with search in latent space for generation under constraints

Xuezhi Xie · Philip Kim


Protein engineering has become an important field in biomedicine with application in therapeutics, diagnostics and synthetic biology. Due to the complexity of protein structure de novo computational design remains a difficult problem. As helices are an abundant structural feature and play a vital role in determination of the protein structure, full atom de novo computational design for helices would be an important step. Here, we apply Wasserstein bi-directional Generative Adversarial Networks to generate full atom helical structures. To design for structure or function, we allow the design according to structural constraints and introduce a novel Markov Chain Monte Carlo search mechanism with the encoder such that the generated helices match target "hotspot" residues structures. Our model generates helices matching well to the target hotpots (within 3 Å RMSD) and with near-native geometries for a large fraction of the test cases. We demonstrate that our approach is able to quickly generate structurally plausible solutions, bringing us closer to the final goal of full atom computational protein design.

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