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The Little Known Universe of Short Proteins in Insects: A Machine Learning Approach

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Short Views on Insect Genomics and Proteomics

Part of the book series: Entomology in Focus ((ENFO,volume 3))

Abstract

Modern genomics and proteomics technologies are turning out immense quantities of sequenced proteins. The only feasible way to assign functions to this flood of sequences is by applying state-of-the-art computational methods for automated functional annotation. We illustrate the significance of machine learning tools in identifying and annotating short bioactive proteins and peptides from insect genomes. Over 500,000 full-length proteins from insects are currently archived in databases, of which ~15 % are short proteins. Currently, most short sequences remain uncharacterized. We developed a platform to systematically identify the functional class of short toxin-like peptides in metazoa. We present data from eight representative genomes (140,000 proteins) that cover the main phylogenetic branches of Hexapoda. The platform is a trained machine-predictor that successfully identified ~800 toxin-like candidates, 250 of them predicted with high confidence. These proteins’ functions include ion channel inhibition, protease inhibitors, antimicrobial peptides, and components of the innate immune system. Our systematic approach can be expanded to new genomes and other biological classes of proteins. Using similar methodologies, we illustrate the success of identifying overlooked neuropeptide precursors. The systematic discovery of insect neuropeptides and short toxin-like proteins allows developing new strategies for pest control and manipulating insects’ behavior. The overlooked secreted short peptides are discussed with respect to their evolution and potential applications in biotechnology.

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Abbreviations

ANN:

Artificial neural network

AUC:

Area under ROC curve

CAFA:

Critical automatic functional annotation

ClanTox:

Classifier of animal toxins

CNS:

Central nervous system

CV:

Cross validation

ETH:

Ecdysis-triggering hormone

HMM:

Hidden Markov model

ICI:

Ion channel inhibitor

MFS:

Major facilitator superfamily

ML:

Machine learning

MS:

Mass spectrometry

nAChR:

Nicotinic acetylcholine receptors

NGF:

Nerve growth factor

NP:

Neuropeptide

NPP:

Neuropeptide precursor

OCLP:

ω-Conotoxin-like protein

PSSM:

Position-specific scoring matrix

SP:

Signal peptide

SVM:

Support vector machine

TIL:

Trypsin inhibitor like

TOLIPs:

Toxin-like proteins

TLS:

Toxin-like stability

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Acknowledgments

We thank Noam Kaplan for his seminal contribution to the work. Many of the ideas and formulation of the problem resulted from many stimulating discussions. N.K.’s beautiful thesis was used as a starting point for this chapter. The development and maintenance of ClanTox, NeuroPID, and the protein database have been a long-term effort of many people over the last 10 years. We thank Solange Karsenty for her contribution to the development of ClanTox and NeuroPID platforms and the system group in the School of Computer Science and Engineering for long-term support of our web servers.

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Authors and Affiliations

  1. Department of Biological Chemistry, Life Sciences Institute, The Hebrew University of Jerusalem, Jerusalem, Israel

    Dan Ofer & Michal Linial

  2. School of Computer Science and Engineering, The Hebrew University of Jerusalem, Jerusalem, Israel

    Nadav Rappoport

Authors
  1. Dan Ofer
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  2. Nadav Rappoport
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  3. Michal Linial
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Correspondence to Michal Linial .

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Editors and Affiliations

  1. Department of Biochemistry and Molecular Biophysics, Kansas State University, Manhattan, Kansas, USA

    Chandrasekar Raman

  2. Biological Sciences Department Center for Biotech. and Life Sciences, University of Rhode Island, Kingston, Rhode Island, USA

    Marian R. Goldsmith

  3. Department of Internal Medicine, Yale University School of Medicine, New Haven, Connecticut, USA

    Tolulope A. Agunbiade

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Ofer, D., Rappoport, N., Linial, M. (2015). The Little Known Universe of Short Proteins in Insects: A Machine Learning Approach. In: Raman, C., Goldsmith, M., Agunbiade, T. (eds) Short Views on Insect Genomics and Proteomics. Entomology in Focus, vol 3. Springer, Cham. https://doi.org/10.1007/978-3-319-24235-4_8

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