Structural Learning of Proteins Using Graph Convolutional Neural Networks

Zamora-Resendiz, Rafael; Crivelli, Silvia

doi:10.1101/610444

PreprintCited by 37Protein Structure and DynamicsMachine Learning in BioinformaticsComputational Drug Discovery Methods

Structural Learning of Proteins Using Graph Convolutional Neural Networks

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Zamora-Resendiz, Rafael, Crivelli, Silvia·2019·bioRxiv (Cold Spring Harbor Laboratory)

DOI 10.1101/610444

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Abstract

The exponential growth of protein structure databases has motivated the development of efficient deep learning methods that perform structural analysis tasks at large scale, ranging from the classification of experimentally determined proteins to the quality assessment and ranking of computationally generated protein models in the context of protein structure prediction. Yet, the literature discussing these methods does not usually interpret what the models learned from the training or identify specific data attributes that contribute to the classification or regression task. While 3D and 2D CNNs have been widely used to deal with structural data, they have several limitations when applied to structural proteomics data. We pose that graph-based convolutional neural networks (GCNNs) are an efficient alternative while producing results that are interpretable. In this work, we demonstrate the applicability of GCNNs to protein structure classification problems. We define a novel spatial graph convolution network architecture which employs graph reduction methods to reduce the total number of trainable parameters and promote abstraction in interme-diate representations. We show that GCNNs are able to learn effectively from simplistic graph representations of protein structures while providing the ability to interpret what the network learns during the training and how it applies it to perform its task. GCNNs perform comparably to their 2D CNN counterparts in predictive performance and they are outperformed by them in training speeds. The graph-based data representation allows GCNNs to be a more efficient option over 3D CNNs when working with large-scale datasets as preprocessing costs and data storage requirements are negligible in comparison.

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Computational decoys in DUD-E are chosen to be physically similar but topologically dissimilar to the actives.
“Computational decoys are chosen such that they are physically similar but topologically dissimilar to the actives.”
From Graph Convolutional Neural Networks for Predicting Drug-Target Interactions (2019) · cites this paper as [21]
The DUD-E dataset consists of 102 targets across different protein families, with on average 224 actives and over 10,000 decoys per target.
“The DUD-E dataset consists of 102 targets across different protein families.On average, each target has 224 actives and over 10,000 decoys.”
From Graph Convolutional Neural Networks for Predicting Drug-Target Interactions (2019) · cites this paper as [21]

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Protein sequence sampling and prediction from structural dataPreprint
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Authors: Zamora-Resendiz, R., & Crivelli, S.
Affiliations: Lawrence Berkeley National Laboratory (US)
Corresponding author: Crivelli, Silvia
ORCID: Crivelli, Silvia
Venue: bioRxiv (Cold Spring Harbor Laboratory)
Publisher: openRxiv
Published: 2019-04-16
DOI: 10.1101/610444
OpenAlex: W2939957600
Cited by: 37
References: 70
Citations by year: 2025: 1 · 2024: 2 · 2023: 4 · 2022: 5 · 2021: 15 · 2020: 9 · 2019: 1
Type: posted content
Language: en
Open access: green
doi.org
Locations: bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion)
Funders: U.S. Department of Energy (-AC02-05CH11231, Contract No. DE-AC02-05-CH11231, 05CH11231, No. DE-AC02-05-CH11231, DE AC02 05CH11231, DE-AC02-)
National Energy Research Scientific Computing Center (DE–AC02–05CH11231, 05CH11231, AC02-05CH11231, Contract No. DE-AC02-05CH11231)
Office of Science (No. DE-AC02- 05-CH11231, DEAC02–05CH11231, AC02-05CH11231, DE-AC02, 05CH11231, Contract No. DE-AC02-05CH11231)
University of California, Davis
Basic Energy Sciences (DE-AC02, Contract No. DE-AC02-05-CH11231, 05CH11231, DE-AC02-05CH11231., No. DE-AC02-05-CH11231, AC02–05CH11231)
Lawrence Berkeley National Laboratory (AC02‐05CH11231, DE-AC02-05CH11231, No. DE-AC02-05CH11231, Contract No. DE-AC02-05CH11231, 05CH11231)
Topics: Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Machine Learning in Bioinformatics
Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
Keywords: graph convolutional networks, protein structure classification, protein structure prediction, model interpretability, graph reduction, spatial graph convolution, protein 3D structure, protein structure database

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Structural Learning of Proteins Using Graph Convolutional Neural Networks

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