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Using PFP and ESG Protein Function Prediction Web Servers

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Protein Function Prediction

Part of the book series: Methods in Molecular Biology ((MIMB,volume 1611))

Abstract

Elucidating biological function of proteins is a fundamental problem in molecular biology and bioinformatics. Conventionally, protein function is annotated based on homology using sequence similarity search tools such as BLAST and FASTA. These methods perform well when obvious homologs exist for a query sequence; however, they will not provide any functional information otherwise. As a result, the functions of many genes in newly sequenced genomes are left unknown, which await functional interpretation. Here, we introduce two webservers for function prediction methods, which effectively use distantly related sequences to improve function annotation coverage and accuracy: Protein Function Prediction (PFP) and Extended Similarity Group (ESG). These two methods have been tested extensively in various benchmark studies and ranked among the top in community-based assessments for computational function annotation, including Critical Assessment of Function Annotation (CAFA) in 2010–2011 (CAFA1) and 2013–2014 (CAFA2). Both servers are equipped with user-friendly visualizations of predicted GO terms, which provide intuitive illustrations of relationships of predicted GO terms. In addition to PFP and ESG, we also introduce NaviGO, a server for the interactive analysis of GO annotations of proteins. All the servers are available at http://kiharalab.org/software.php.

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Acknowledgments

This work was supported partly by the National Institutes of Health (R01GM097528), the National Science Foundation (IIS1319551, DBI1262189, IOS1127027).

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Authors and Affiliations

  1. Department of Computer Science, Purdue University, West Lafayette, IN, 47907, USA

    Qing Wei, Ishita Khan & Daisuke Kihara

  2. Department of Biological Sciences, Purdue University, West Lafayette, IN, 47907, USA

    Joshua McGraw & Daisuke Kihara

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  1. Qing Wei
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  2. Joshua McGraw
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  3. Ishita Khan
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  4. Daisuke Kihara
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Corresponding author

Correspondence to Daisuke Kihara .

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Editors and Affiliations

  1. Department of Biological Sciences and Computer Science, Purdue University, West Lafayette, Indiana, USA

    Daisuke Kihara

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Wei, Q., McGraw, J., Khan, I., Kihara, D. (2017). Using PFP and ESG Protein Function Prediction Web Servers. In: Kihara, D. (eds) Protein Function Prediction. Methods in Molecular Biology, vol 1611. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-7015-5_1

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  • DOI: https://doi.org/10.1007/978-1-4939-7015-5_1

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  • Publisher Name: Humana Press, New York, NY

  • Print ISBN: 978-1-4939-7013-1

  • Online ISBN: 978-1-4939-7015-5

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