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Methods Mol Biol. 2017;1611:45-57. doi: 10.1007/978-1-4939-7015-5_5.

MPFit: Computational Tool for Predicting Moonlighting Proteins.

Author information

1
Department of Computer Science, Purdue University, West Lafayette, IN, 47907, USA.
2
Department of Biological Sciences, Purdue University, West Lafayette, IN, 47907, USA.
3
Department of Computer Science, Purdue University, West Lafayette, IN, 47907, USA. dkihara@purdue.edu.
4
Department of Biological Sciences, Purdue University, West Lafayette, IN, 47907, USA. dkihara@purdue.edu.

Abstract

An increasing number of proteins have been found which are capable of performing two or more distinct functions. These proteins, known as moonlighting proteins, have drawn much attention recently as they may play critical roles in disease pathways and development. However, because moonlighting proteins are often found serendipitously, our understanding of moonlighting proteins is still quite limited. In order to lay the foundation for systematic moonlighting proteins studies, we developed MPFit, a software package for predicting moonlighting proteins from their omics features including protein-protein and gene interaction networks. Here, we describe and demonstrate the algorithm of MPFit, the idea behind it, and provide instruction for using the software.

KEYWORDS:

Dual function; Feature imputation; Function annotation; Genome; Moonlighting proteins; Omics-data; Protein association; Protein function prediction

PMID:
28451971
DOI:
10.1007/978-1-4939-7015-5_5
[Indexed for MEDLINE]

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