Knowledge-transfer learning for prediction of matrix metalloprotease substrate-cleavage sites

Abstract Matrix Metalloproteases (MMPs) are an important family of proteases that play crucial roles in key cellular and disease processes. Therefore, MMPs constitute important targets for drug design, development and delivery. Advanced proteomic technologies have identified type-specific target sub...

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Autores principales:	Yanan Wang, Jiangning Song, Tatiana T. Marquez-Lago, André Leier, Chen Li, Trevor Lithgow, Geoffrey I. Webb, Hong-Bin Shen
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2017
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/002cf20c31204fe68fab2fc87b963dab
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spelling	oai:doaj.org-article:002cf20c31204fe68fab2fc87b963dab2021-12-02T11:40:31ZKnowledge-transfer learning for prediction of matrix metalloprotease substrate-cleavage sites10.1038/s41598-017-06219-72045-2322https://doaj.org/article/002cf20c31204fe68fab2fc87b963dab2017-07-01T00:00:00Zhttps://doi.org/10.1038/s41598-017-06219-7https://doaj.org/toc/2045-2322Abstract Matrix Metalloproteases (MMPs) are an important family of proteases that play crucial roles in key cellular and disease processes. Therefore, MMPs constitute important targets for drug design, development and delivery. Advanced proteomic technologies have identified type-specific target substrates; however, the complete repertoire of MMP substrates remains uncharacterized. Indeed, computational prediction of substrate-cleavage sites associated with MMPs is a challenging problem. This holds especially true when considering MMPs with few experimentally verified cleavage sites, such as for MMP-2, -3, -7, and -8. To fill this gap, we propose a new knowledge-transfer computational framework which effectively utilizes the hidden shared knowledge from some MMP types to enhance predictions of other, distinct target substrate-cleavage sites. Our computational framework uses support vector machines combined with transfer machine learning and feature selection. To demonstrate the value of the model, we extracted a variety of substrate sequence-derived features and compared the performance of our method using both 5-fold cross-validation and independent tests. The results show that our transfer-learning-based method provides a robust performance, which is at least comparable to traditional feature-selection methods for prediction of MMP-2, -3, -7, -8, -9 and -12 substrate-cleavage sites on independent tests. The results also demonstrate that our proposed computational framework provides a useful alternative for the characterization of sequence-level determinants of MMP-substrate specificity.Yanan WangJiangning SongTatiana T. Marquez-LagoAndré LeierChen LiTrevor LithgowGeoffrey I. WebbHong-Bin ShenNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 7, Iss 1, Pp 1-15 (2017)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Yanan Wang Jiangning Song Tatiana T. Marquez-Lago André Leier Chen Li Trevor Lithgow Geoffrey I. Webb Hong-Bin Shen Knowledge-transfer learning for prediction of matrix metalloprotease substrate-cleavage sites
description	Abstract Matrix Metalloproteases (MMPs) are an important family of proteases that play crucial roles in key cellular and disease processes. Therefore, MMPs constitute important targets for drug design, development and delivery. Advanced proteomic technologies have identified type-specific target substrates; however, the complete repertoire of MMP substrates remains uncharacterized. Indeed, computational prediction of substrate-cleavage sites associated with MMPs is a challenging problem. This holds especially true when considering MMPs with few experimentally verified cleavage sites, such as for MMP-2, -3, -7, and -8. To fill this gap, we propose a new knowledge-transfer computational framework which effectively utilizes the hidden shared knowledge from some MMP types to enhance predictions of other, distinct target substrate-cleavage sites. Our computational framework uses support vector machines combined with transfer machine learning and feature selection. To demonstrate the value of the model, we extracted a variety of substrate sequence-derived features and compared the performance of our method using both 5-fold cross-validation and independent tests. The results show that our transfer-learning-based method provides a robust performance, which is at least comparable to traditional feature-selection methods for prediction of MMP-2, -3, -7, -8, -9 and -12 substrate-cleavage sites on independent tests. The results also demonstrate that our proposed computational framework provides a useful alternative for the characterization of sequence-level determinants of MMP-substrate specificity.
format	article
author	Yanan Wang Jiangning Song Tatiana T. Marquez-Lago André Leier Chen Li Trevor Lithgow Geoffrey I. Webb Hong-Bin Shen
author_facet	Yanan Wang Jiangning Song Tatiana T. Marquez-Lago André Leier Chen Li Trevor Lithgow Geoffrey I. Webb Hong-Bin Shen
author_sort	Yanan Wang
title	Knowledge-transfer learning for prediction of matrix metalloprotease substrate-cleavage sites
title_short	Knowledge-transfer learning for prediction of matrix metalloprotease substrate-cleavage sites
title_full	Knowledge-transfer learning for prediction of matrix metalloprotease substrate-cleavage sites
title_fullStr	Knowledge-transfer learning for prediction of matrix metalloprotease substrate-cleavage sites
title_full_unstemmed	Knowledge-transfer learning for prediction of matrix metalloprotease substrate-cleavage sites
title_sort	knowledge-transfer learning for prediction of matrix metalloprotease substrate-cleavage sites
publisher	Nature Portfolio
publishDate	2017
url	https://doaj.org/article/002cf20c31204fe68fab2fc87b963dab
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Knowledge-transfer learning for prediction of matrix metalloprotease substrate-cleavage sites

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