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ISSEC: inferring contacts among protein secondary structure elements using deep object detection

BACKGROUND: The formation of contacts among protein secondary structure elements (SSEs) is an important step in protein folding as it determines topology of protein tertiary structure; hence, inferring inter-SSE contacts is crucial to protein structure prediction. One of the existing strategies infe...

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Autores principales: Zhang, Qi, Zhu, Jianwei, Ju, Fusong, Kong, Lupeng, Sun, Shiwei, Zheng, Wei-Mou, Bu, Dongbo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7643357/
https://www.ncbi.nlm.nih.gov/pubmed/33153432
http://dx.doi.org/10.1186/s12859-020-03793-y
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author Zhang, Qi
Zhu, Jianwei
Ju, Fusong
Kong, Lupeng
Sun, Shiwei
Zheng, Wei-Mou
Bu, Dongbo
author_facet Zhang, Qi
Zhu, Jianwei
Ju, Fusong
Kong, Lupeng
Sun, Shiwei
Zheng, Wei-Mou
Bu, Dongbo
author_sort Zhang, Qi
collection PubMed
description BACKGROUND: The formation of contacts among protein secondary structure elements (SSEs) is an important step in protein folding as it determines topology of protein tertiary structure; hence, inferring inter-SSE contacts is crucial to protein structure prediction. One of the existing strategies infers inter-SSE contacts directly from the predicted possibilities of inter-residue contacts without any preprocessing, and thus suffers from the excessive noises existing in the predicted inter-residue contacts. Another strategy defines SSEs based on protein secondary structure prediction first, and then judges whether each candidate SSE pair could form contact or not. However, it is difficult to accurately determine boundary of SSEs due to the errors in secondary structure prediction. The incorrectly-deduced SSEs definitely hinder subsequent prediction of the contacts among them. RESULTS: We here report an accurate approach to infer the inter-SSE contacts (thus called as ISSEC) using the deep object detection technique. The design of ISSEC is based on the observation that, in the inter-residue contact map, the contacting SSEs usually form rectangle regions with characteristic patterns. Therefore, ISSEC infers inter-SSE contacts through detecting such rectangle regions. Unlike the existing approach directly using the predicted probabilities of inter-residue contact, ISSEC applies the deep convolution technique to extract high-level features from the inter-residue contacts. More importantly, ISSEC does not rely on the pre-defined SSEs. Instead, ISSEC enumerates multiple candidate rectangle regions in the predicted inter-residue contact map, and for each region, ISSEC calculates a confidence score to measure whether it has characteristic patterns or not. ISSEC employs greedy strategy to select non-overlapping regions with high confidence score, and finally infers inter-SSE contacts according to these regions. CONCLUSIONS: Comprehensive experimental results suggested that ISSEC outperformed the state-of-the-art approaches in predicting inter-SSE contacts. We further demonstrated the successful applications of ISSEC to improve prediction of both inter-residue contacts and tertiary structure as well.
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spelling pubmed-76433572020-11-06 ISSEC: inferring contacts among protein secondary structure elements using deep object detection Zhang, Qi Zhu, Jianwei Ju, Fusong Kong, Lupeng Sun, Shiwei Zheng, Wei-Mou Bu, Dongbo BMC Bioinformatics Methodology BACKGROUND: The formation of contacts among protein secondary structure elements (SSEs) is an important step in protein folding as it determines topology of protein tertiary structure; hence, inferring inter-SSE contacts is crucial to protein structure prediction. One of the existing strategies infers inter-SSE contacts directly from the predicted possibilities of inter-residue contacts without any preprocessing, and thus suffers from the excessive noises existing in the predicted inter-residue contacts. Another strategy defines SSEs based on protein secondary structure prediction first, and then judges whether each candidate SSE pair could form contact or not. However, it is difficult to accurately determine boundary of SSEs due to the errors in secondary structure prediction. The incorrectly-deduced SSEs definitely hinder subsequent prediction of the contacts among them. RESULTS: We here report an accurate approach to infer the inter-SSE contacts (thus called as ISSEC) using the deep object detection technique. The design of ISSEC is based on the observation that, in the inter-residue contact map, the contacting SSEs usually form rectangle regions with characteristic patterns. Therefore, ISSEC infers inter-SSE contacts through detecting such rectangle regions. Unlike the existing approach directly using the predicted probabilities of inter-residue contact, ISSEC applies the deep convolution technique to extract high-level features from the inter-residue contacts. More importantly, ISSEC does not rely on the pre-defined SSEs. Instead, ISSEC enumerates multiple candidate rectangle regions in the predicted inter-residue contact map, and for each region, ISSEC calculates a confidence score to measure whether it has characteristic patterns or not. ISSEC employs greedy strategy to select non-overlapping regions with high confidence score, and finally infers inter-SSE contacts according to these regions. CONCLUSIONS: Comprehensive experimental results suggested that ISSEC outperformed the state-of-the-art approaches in predicting inter-SSE contacts. We further demonstrated the successful applications of ISSEC to improve prediction of both inter-residue contacts and tertiary structure as well. BioMed Central 2020-11-05 /pmc/articles/PMC7643357/ /pubmed/33153432 http://dx.doi.org/10.1186/s12859-020-03793-y Text en © The Author(s) 2020 Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Methodology
Zhang, Qi
Zhu, Jianwei
Ju, Fusong
Kong, Lupeng
Sun, Shiwei
Zheng, Wei-Mou
Bu, Dongbo
ISSEC: inferring contacts among protein secondary structure elements using deep object detection
title ISSEC: inferring contacts among protein secondary structure elements using deep object detection
title_full ISSEC: inferring contacts among protein secondary structure elements using deep object detection
title_fullStr ISSEC: inferring contacts among protein secondary structure elements using deep object detection
title_full_unstemmed ISSEC: inferring contacts among protein secondary structure elements using deep object detection
title_short ISSEC: inferring contacts among protein secondary structure elements using deep object detection
title_sort issec: inferring contacts among protein secondary structure elements using deep object detection
topic Methodology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7643357/
https://www.ncbi.nlm.nih.gov/pubmed/33153432
http://dx.doi.org/10.1186/s12859-020-03793-y
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