Equivariant Encoding based GVAE (EqEn-GVAE) for Protein Tertiary Structure Generation

Extensive research on deep neural networks shows that complex deep learning models have considerably improved our ability to predict the native structure of a protein amino acid sequence. With the release of AlphaFold2, an entirely data-driven approach to machine learning, we can now predict the native tertiary structure of a given protein sequence with great precision. On the other hand, research into deep learning frameworks that can take into account protein structural plasticity is in its early stages. Obtaining a multi-structure view of a protein molecule remains an outstanding challenge in computational structural biology. In this paper, we make two key contributions. We first propose a novel end-to-end generative model framework, a new formulation under Equivariant Graph Neural Networks (EGNN) based encoding and Graph Variational Autoencoder (GVAE), advancing our ability to generate realistic tertiary structures. Most existing models rely on 2D convolution, with protein structures represented by contact maps or distance matrices. In contrast, our presented model learns over both 3D coordinates of protein structure and sequence directly. The second contribution of this paper is control of tertiary structure realism. We show that through the loss function, we can control properties of generated tertiary structures. We suggest different terms in the loss function and analyze how those terms allow us to recover realistic patterns, such as backbone, short-range, and long-range contacts that we find in tertiary structures. Additionally, we conduct a careful analysis along several metrics that measure the physical realism of generated tertiary structures and show that EGNN encoding-based GVAE are effective models for generating physically-realistic structures.