G-VAE, a Geometric Convolutional VAE for ProteinStructure Generation
- URL: http://arxiv.org/abs/2106.11920v1
- Date: Tue, 22 Jun 2021 16:52:48 GMT
- Title: G-VAE, a Geometric Convolutional VAE for ProteinStructure Generation
- Authors: Hao Huang, Boulbaba Ben Amor, Xichan Lin, Fan Zhu, Yi Fang
- Abstract summary: We introduce a joint geometric-neural networks approach for comparing, deforming and generating 3D protein structures.
Our method is able to generate plausible structures, different from the structures in the training data.
- Score: 41.66010308405784
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: Analyzing the structure of proteins is a key part of understanding their
functions and thus their role in biology at the molecular level. In addition,
design new proteins in a methodical way is a major engineering challenge. In
this work, we introduce a joint geometric-neural networks approach for
comparing, deforming and generating 3D protein structures. Viewing protein
structures as 3D open curves, we adopt the Square Root Velocity Function (SRVF)
representation and leverage its suitable geometric properties along with Deep
Residual Networks (ResNets) for a joint registration and comparison. Our
ResNets handle better large protein deformations while being more
computationally efficient. On top of the mathematical framework, we further
design a Geometric Variational Auto-Encoder (G-VAE), that once trained, maps
original, previously unseen structures, into a low-dimensional (latent)
hyper-sphere. Motivated by the spherical structure of the pre-shape space, we
naturally adopt the von Mises-Fisher (vMF) distribution to model our hidden
variables. We test the effectiveness of our models by generating novel protein
structures and predicting completions of corrupted protein structures.
Experimental results show that our method is able to generate plausible
structures, different from the structures in the training data.
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