Complete fold annotation of the human proteome using a novel structural feature space
Abstract
Recognition of protein structural fold is the starting point for many structure prediction tools and protein function inference. Fold prediction is computationally demanding and recognizing novel folds is difficult such that the majority of proteins have not been annotated for fold classification. Here we describe a new machine learning approach using a novel feature space that can be used for accurate recognition of all 1,221 currently known folds and inference of unknown novel folds. We show that our method achieves better than 94% accuracy even when many folds have only one training example. We demonstrate the utility of this method by predicting the folds of 34,330 human protein domains and showing that these predictions can yield useful insights into potential biological function, such as prediction of RNA-binding ability. Finally, our method can be applied to de novo fold prediction of entire proteomes and identify candidate novel fold families.
- Authors:
-
- Univ. of Pennsylvania, Philadelphia, PA (United States). Genomics and Computational Biology Program
- Univ. of Pennsylvania, Philadelphia, PA (United States). Dept. of Computer Science
- Univ. of Pennsylvania, Philadelphia, PA (United States). Genomics and Computational Biology Program; Univ. of Pennsylvania, Philadelphia, PA (United States). Dept. of Biology
- Publication Date:
- Research Org.:
- Krell Institute, Ames, IA (United States)
- Sponsoring Org.:
- USDOE
- OSTI Identifier:
- 1366516
- Grant/Contract Number:
- FG02-97ER25308
- Resource Type:
- Accepted Manuscript
- Journal Name:
- Scientific Reports
- Additional Journal Information:
- Journal Volume: 7; Journal ID: ISSN 2045-2322
- Publisher:
- Nature Publishing Group
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 59 BASIC BIOLOGICAL SCIENCES
Citation Formats
Middleton, Sarah A., Illuminati, Joseph, and Kim, Junhyong. Complete fold annotation of the human proteome using a novel structural feature space. United States: N. p., 2017.
Web. doi:10.1038/srep46321.
Middleton, Sarah A., Illuminati, Joseph, & Kim, Junhyong. Complete fold annotation of the human proteome using a novel structural feature space. United States. https://doi.org/10.1038/srep46321
Middleton, Sarah A., Illuminati, Joseph, and Kim, Junhyong. Thu .
"Complete fold annotation of the human proteome using a novel structural feature space". United States. https://doi.org/10.1038/srep46321. https://www.osti.gov/servlets/purl/1366516.
@article{osti_1366516,
title = {Complete fold annotation of the human proteome using a novel structural feature space},
author = {Middleton, Sarah A. and Illuminati, Joseph and Kim, Junhyong},
abstractNote = {Recognition of protein structural fold is the starting point for many structure prediction tools and protein function inference. Fold prediction is computationally demanding and recognizing novel folds is difficult such that the majority of proteins have not been annotated for fold classification. Here we describe a new machine learning approach using a novel feature space that can be used for accurate recognition of all 1,221 currently known folds and inference of unknown novel folds. We show that our method achieves better than 94% accuracy even when many folds have only one training example. We demonstrate the utility of this method by predicting the folds of 34,330 human protein domains and showing that these predictions can yield useful insights into potential biological function, such as prediction of RNA-binding ability. Finally, our method can be applied to de novo fold prediction of entire proteomes and identify candidate novel fold families.},
doi = {10.1038/srep46321},
journal = {Scientific Reports},
number = ,
volume = 7,
place = {United States},
year = {Thu Apr 13 00:00:00 EDT 2017},
month = {Thu Apr 13 00:00:00 EDT 2017}
}
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