A Generative Model for Creating Path Delineated Helical Proteins

Abstract

Engineered proteins with precisely defined shapes can scaffold functional protein domains in 3D space to fine-tune their functions, such as the regulation of cellular signaling by ligand positioning or the design of self-assembling protein materials with specific forms. Methods for simply and efficiently generating the protein backbones to initiate these design processes remain limited. In this work, we develop a lightweight neural network to guide helix fragment assembly along a guideline using a GAN architecture and show that this approach can rapidly generate viable samples while being computationally inexpensive. Key to our approach is the transformation of the input structural data used for training into a parametric representation of helices to reduce the generator network size, which in turn facilitates rapid backpropagation to find specific helical arrangements during generation. This approach provides a method to quickly generate helical protein scaffolds.