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Efficient prediction of human protein-protein interactions at a global scale

Journal article
Authors A. Schoenrock
B. Samanfar
S. Pitre
M. Hooshyar
K. Jin
C. A. Phillips
Hui Wang
S. Phanse
K. Omidi
Y. Gui
M. Alamgir
A. Wong
Fredrik Barrenäs
M. Babu
Mikael Benson
M. A. Langston
J. R. Green
F. Dehne
A. Golshani
Published in Bmc Bioinformatics
Volume 15
Pages Article nr. 383
ISSN 1471-2105
Publication year 2014
Published at Institute of Clinical Sciences, Department of Pediatrics
Pages Article nr. 383
Language en
Links dx.doi.org/10.1186/s12859-014-0383-...
Keywords Protein-protein interactions, Computational prediction, Human proteome, Massively parallel, YEAST SACCHAROMYCES-CEREVISIAE, SHORT POLYPEPTIDE SEQUENCES, INTERACTION, NETWORKS, ALLERGIC RHINITIS, DNA-DAMAGE, COMPUTATIONAL METHODS, MESSENGER-RNA, BREAST-CANCER, AURORA B, DISEASE, Biochemical Research Methods, Biotechnology & Applied Microbiology, Mathematical & Computational Biology
Subject categories Molecular biology

Abstract

Background: Our knowledge of global protein-protein interaction (PPI) networks in complex organisms such as humans is hindered by technical limitations of current methods. Results: On the basis of short co-occurring polypeptide regions, we developed a tool called MP-PIPE capable of predicting a global human PPI network within 3 months. With a recall of 23% at a precision of 82.1%, we predicted 172,132 putative PPIs. We demonstrate the usefulness of these predictions through a range of experiments. Conclusions: The speed and accuracy associated with MP-PIPE can make this a potential tool to study individual human PPI networks (from genomic sequences alone) for personalized medicine.

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http://www.gu.se/english/research/publication/?publicationId=213126
Utskriftsdatum: 2020-07-03