The Power of Universal Contextualized Protein Embeddings in Cross-species Protein Function Prediction

Computationally annotating proteins with a molecular function is a difficult problem that is made even harder due to the limited amount of available labeled protein training data. Unsupervised protein embeddings partly circumvent this limitation by learning a universal protein representation from ma...

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Autores principales:	Irene van den Bent, Stavros Makrodimitris, Marcel Reinders
Formato:	article
Lenguaje:	EN
Publicado:	SAGE Publishing 2021
Materias:	Evolution QH359-425
Acceso en línea:	https://doaj.org/article/6517605b2ea149948d0f7ac12058a55c
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spelling	oai:doaj.org-article:6517605b2ea149948d0f7ac12058a55c2021-12-03T23:03:40ZThe Power of Universal Contextualized Protein Embeddings in Cross-species Protein Function Prediction1176-934310.1177/11769343211062608https://doaj.org/article/6517605b2ea149948d0f7ac12058a55c2021-12-01T00:00:00Zhttps://doi.org/10.1177/11769343211062608https://doaj.org/toc/1176-9343Computationally annotating proteins with a molecular function is a difficult problem that is made even harder due to the limited amount of available labeled protein training data. Unsupervised protein embeddings partly circumvent this limitation by learning a universal protein representation from many unlabeled sequences. Such embeddings incorporate contextual information of amino acids, thereby modeling the underlying principles of protein sequences insensitive to the context of species. We used an existing pre-trained protein embedding method and subjected its molecular function prediction performance to detailed characterization, first to advance the understanding of protein language models, and second to determine areas of improvement. Then, we applied the model in a transfer learning task by training a function predictor based on the embeddings of annotated protein sequences of one training species and making predictions on the proteins of several test species with varying evolutionary distance. We show that this approach successfully generalizes knowledge about protein function from one eukaryotic species to various other species, outperforming both an alignment-based and a supervised-learning-based baseline. This implies that such a method could be effective for molecular function prediction in inadequately annotated species from understudied taxonomic kingdoms.Irene van den BentStavros MakrodimitrisMarcel ReindersSAGE PublishingarticleEvolutionQH359-425ENEvolutionary Bioinformatics, Vol 17 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Evolution QH359-425
spellingShingle	Evolution QH359-425 Irene van den Bent Stavros Makrodimitris Marcel Reinders The Power of Universal Contextualized Protein Embeddings in Cross-species Protein Function Prediction
description	Computationally annotating proteins with a molecular function is a difficult problem that is made even harder due to the limited amount of available labeled protein training data. Unsupervised protein embeddings partly circumvent this limitation by learning a universal protein representation from many unlabeled sequences. Such embeddings incorporate contextual information of amino acids, thereby modeling the underlying principles of protein sequences insensitive to the context of species. We used an existing pre-trained protein embedding method and subjected its molecular function prediction performance to detailed characterization, first to advance the understanding of protein language models, and second to determine areas of improvement. Then, we applied the model in a transfer learning task by training a function predictor based on the embeddings of annotated protein sequences of one training species and making predictions on the proteins of several test species with varying evolutionary distance. We show that this approach successfully generalizes knowledge about protein function from one eukaryotic species to various other species, outperforming both an alignment-based and a supervised-learning-based baseline. This implies that such a method could be effective for molecular function prediction in inadequately annotated species from understudied taxonomic kingdoms.
format	article
author	Irene van den Bent Stavros Makrodimitris Marcel Reinders
author_facet	Irene van den Bent Stavros Makrodimitris Marcel Reinders
author_sort	Irene van den Bent
title	The Power of Universal Contextualized Protein Embeddings in Cross-species Protein Function Prediction
title_short	The Power of Universal Contextualized Protein Embeddings in Cross-species Protein Function Prediction
title_full	The Power of Universal Contextualized Protein Embeddings in Cross-species Protein Function Prediction
title_fullStr	The Power of Universal Contextualized Protein Embeddings in Cross-species Protein Function Prediction
title_full_unstemmed	The Power of Universal Contextualized Protein Embeddings in Cross-species Protein Function Prediction
title_sort	power of universal contextualized protein embeddings in cross-species protein function prediction
publisher	SAGE Publishing
publishDate	2021
url	https://doaj.org/article/6517605b2ea149948d0f7ac12058a55c
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The Power of Universal Contextualized Protein Embeddings in Cross-species Protein Function Prediction

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