Cargando…

High precision protein functional site detection using 3D convolutional neural networks

MOTIVATION: Accurate annotation of protein functions is fundamental for understanding molecular and cellular physiology. Data-driven methods hold promise for systematically deriving rules underlying the relationship between protein structure and function. However, the choice of protein structural re...

Descripción completa

Detalles Bibliográficos
Autores principales: Torng, Wen, Altman, Russ B
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6499237/
https://www.ncbi.nlm.nih.gov/pubmed/31051039
http://dx.doi.org/10.1093/bioinformatics/bty813
_version_ 1783415766972891136
author Torng, Wen
Altman, Russ B
author_facet Torng, Wen
Altman, Russ B
author_sort Torng, Wen
collection PubMed
description MOTIVATION: Accurate annotation of protein functions is fundamental for understanding molecular and cellular physiology. Data-driven methods hold promise for systematically deriving rules underlying the relationship between protein structure and function. However, the choice of protein structural representation is critical. Pre-defined biochemical features emphasize certain aspects of protein properties while ignoring others, and therefore may fail to capture critical information in complex protein sites. RESULTS: In this paper, we present a general framework that applies 3D convolutional neural networks (3DCNNs) to structure-based protein functional site detection. The framework can extract task-dependent features automatically from the raw atom distributions. We benchmarked our method against other methods and demonstrate better or comparable performance for site detection. Our deep 3DCNNs achieved an average recall of 0.955 at a precision threshold of 0.99 on PROSITE families, detected 98.89 and 92.88% of nitric oxide synthase and TRYPSIN-like enzyme sites in Catalytic Site Atlas, and showed good performance on challenging cases where sequence motifs are absent but a function is known to exist. Finally, we inspected the individual contributions of each atom to the classification decisions and show that our models successfully recapitulate known 3D features within protein functional sites. AVAILABILITY AND IMPLEMENTATION: The 3DCNN models described in this paper are available at https://simtk.org/projects/fscnn. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
format Online
Article
Text
id pubmed-6499237
institution National Center for Biotechnology Information
language English
publishDate 2019
publisher Oxford University Press
record_format MEDLINE/PubMed
spelling pubmed-64992372019-05-07 High precision protein functional site detection using 3D convolutional neural networks Torng, Wen Altman, Russ B Bioinformatics Original Papers MOTIVATION: Accurate annotation of protein functions is fundamental for understanding molecular and cellular physiology. Data-driven methods hold promise for systematically deriving rules underlying the relationship between protein structure and function. However, the choice of protein structural representation is critical. Pre-defined biochemical features emphasize certain aspects of protein properties while ignoring others, and therefore may fail to capture critical information in complex protein sites. RESULTS: In this paper, we present a general framework that applies 3D convolutional neural networks (3DCNNs) to structure-based protein functional site detection. The framework can extract task-dependent features automatically from the raw atom distributions. We benchmarked our method against other methods and demonstrate better or comparable performance for site detection. Our deep 3DCNNs achieved an average recall of 0.955 at a precision threshold of 0.99 on PROSITE families, detected 98.89 and 92.88% of nitric oxide synthase and TRYPSIN-like enzyme sites in Catalytic Site Atlas, and showed good performance on challenging cases where sequence motifs are absent but a function is known to exist. Finally, we inspected the individual contributions of each atom to the classification decisions and show that our models successfully recapitulate known 3D features within protein functional sites. AVAILABILITY AND IMPLEMENTATION: The 3DCNN models described in this paper are available at https://simtk.org/projects/fscnn. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. Oxford University Press 2019-05-01 2018-09-20 /pmc/articles/PMC6499237/ /pubmed/31051039 http://dx.doi.org/10.1093/bioinformatics/bty813 Text en © The Author(s) 2018. Published by Oxford University Press. http://creativecommons.org/licenses/by-nc/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com
spellingShingle Original Papers
Torng, Wen
Altman, Russ B
High precision protein functional site detection using 3D convolutional neural networks
title High precision protein functional site detection using 3D convolutional neural networks
title_full High precision protein functional site detection using 3D convolutional neural networks
title_fullStr High precision protein functional site detection using 3D convolutional neural networks
title_full_unstemmed High precision protein functional site detection using 3D convolutional neural networks
title_short High precision protein functional site detection using 3D convolutional neural networks
title_sort high precision protein functional site detection using 3d convolutional neural networks
topic Original Papers
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6499237/
https://www.ncbi.nlm.nih.gov/pubmed/31051039
http://dx.doi.org/10.1093/bioinformatics/bty813
work_keys_str_mv AT torngwen highprecisionproteinfunctionalsitedetectionusing3dconvolutionalneuralnetworks
AT altmanrussb highprecisionproteinfunctionalsitedetectionusing3dconvolutionalneuralnetworks