PersGNN: Applying Topological Data Analysis and Geometric Deep Learning to Structure-Based Protein Function Prediction

10/30/2020
by   Nicolas Swenson, et al.
13

Understanding protein structure-function relationships is a key challenge in computational biology, with applications across the biotechnology and pharmaceutical industries. While it is known that protein structure directly impacts protein function, many functional prediction tasks use only protein sequence. In this work, we isolate protein structure to make functional annotations for proteins in the Protein Data Bank in order to study the expressiveness of different structure-based prediction schemes. We present PersGNN - an end-to-end trainable deep learning model that combines graph representation learning with topological data analysis to capture a complex set of both local and global structural features. While variations of these techniques have been successfully applied to proteins before, we demonstrate that our hybridized approach, PersGNN, outperforms either method on its own as well as a baseline neural network that learns from the same information. PersGNN achieves a 9.3 compared to the best individual model, as well as high F1 scores across different gene ontology categories, indicating the transferability of this approach.

READ FULL TEXT
POST COMMENT

Comments

There are no comments yet.

Authors

page 1

page 2

page 3

page 4

03/11/2022

Protein Representation Learning by Geometric Structure Pretraining

Learning effective protein representations is critical in a variety of t...
07/03/2018

Generalizable Protein Interface Prediction with End-to-End Learning

Predicting how proteins interact with one another - that is, which surfa...
09/29/2020

Incorporating network based protein complex discovery into automated model construction

We propose a method for gene expression based analysis of cancer phenoty...
10/07/2019

Combining docking pose rank and structure with deep learning improves protein-ligand binding mode prediction

We present a simple, modular graph-based convolutional neural network th...
03/02/2022

FastFold: Reducing AlphaFold Training Time from 11 Days to 67 Hours

Protein structure prediction is an important method for understanding ge...
12/03/2018

FoldingZero: Protein Folding from Scratch in Hydrophobic-Polar Model

De novo protein structure prediction from amino acid sequence is one of ...
11/12/2017

A Sequence-Based Mesh Classifier for the Prediction of Protein-Protein Interactions

The worldwide surge of multiresistant microbial strains has propelled th...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.