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Title: Complete fold annotation of the human proteome using a novel structural feature space

Journal Article · · Scientific Reports
DOI:https://doi.org/10.1038/srep46321· OSTI ID:1366516
 [1];  [2];  [3]
  1. Univ. of Pennsylvania, Philadelphia, PA (United States). Genomics and Computational Biology Program
  2. Univ. of Pennsylvania, Philadelphia, PA (United States). Dept. of Computer Science
  3. Univ. of Pennsylvania, Philadelphia, PA (United States). Genomics and Computational Biology Program; Univ. of Pennsylvania, Philadelphia, PA (United States). Dept. of Biology
+ Show Author Affiliations

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.

Research Organization:
Krell Institute, Ames, IA (United States)
Sponsoring Organization:
USDOE
Grant/Contract Number:
FG02-97ER25308
OSTI ID:
1366516
Journal Information:
Scientific Reports, Vol. 7; ISSN 2045-2322
Publisher:
Nature Publishing GroupCopyright Statement
Country of Publication:
United States
Language:
English
Citation Metrics:
Cited by: 3 works
Citation information provided by
Web of Science

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Cited By (2)

Comprehensive catalog of dendritically localized mRNA isoforms from sub-cellular sequencing of single mouse neurons journal January 2019
Comprehensive catalog of dendritically localized mRNA isoforms from sub-cellular sequencing of single mouse neurons posted_content March 2018

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