Protein Function Prediction by Clustering of Protein-Protein Interaction Network

2012 ◽  
pp. 39-49 ◽  
Author(s):  
Ivana Cingovska ◽  
Aleksandra Bogojeska ◽  
Kire Trivodaliev ◽  
Slobodan Kalajdziski
Keyword(s):  
Protein Interaction ◽  
Protein Interaction Network ◽  
Protein Function ◽  
Protein Function Prediction ◽  
Interaction Network ◽  
Function Prediction ◽  
Protein Protein Interaction ◽  
Protein Protein Interaction Network

CORRELATION BETWEEN FUNCTIONAL ANNOTATION AND TOPOLOGY OF PROTEIN-PROTEIN INTERACTION NETWORK

2008 ◽  
Vol 22 (06) ◽  
pp. 719-726 ◽  
Author(s):  
JIUN-YAN HUANG
Keyword(s):  
Protein Interaction ◽  
Protein Interaction Network ◽  
Protein Function ◽  
Functional Annotation ◽  
Interaction Network ◽  
Protein Interaction Data ◽  
Characteristic Distance ◽  
Protein Protein Interaction ◽  
Functional Correlation ◽  
Protein Protein Interaction Network

The functional annotation of proteins was believed to be related to the topology of the protein-protein interaction network. People utilized the protein-protein interaction network to infer the protein function by various methods. Here, we select the protein interaction data of Saccharomyces cerevisia and calculated the correlation between functional annotation of proteins and the topology of protein-protein interaction network. The result shows that the functional correlation decays exponentially with the distance between two proteins, and beyond the characteristic distance, it has no correlation.

COMPUTATIONAL METHOD FOR PROTEIN FUNCTION PREDICTION BY CONSTRUCTING PROTEIN INTERACTION NETWORK DICTIONARY

2006 ◽  
Vol 20 (02) ◽  
pp. 285-295 ◽  
Author(s):  
HEE-JEONG JIN ◽  
HWAN-GUE CHO
Keyword(s):  
Protein Interaction ◽  
Protein Interaction Network ◽  
Protein Function ◽  
Protein Function Prediction ◽  
Interaction Network ◽  
Computational Method ◽  
Chi Square ◽  
Protein Protein Interaction ◽  
Protein Functions ◽  
Novel Method

In the post-genomic era, predicting protein function is a challenging problem. It is difficult and burdensome work to unravel the functions of a protein by wet experiments only. In this paper, we propose a novel method to predict protein functions by building a "Protein Interaction Network Dictionary (PIND)". This method deduces the protein functions by searching the most similar "words"(an anagram of functions in neighbor proteins on a protein–protein interaction graph) using global alignments. An evaluation of sensitivity and specificity shows that this PIND approach outperforms previous approaches such as Majority Rule and Chi-Square measure, and that it competes with the recently introduced Random Markov Model approach.

scholarly journals FunPred 3.0: improved protein function prediction using protein interaction network

PeerJ ◽  
2019 ◽  
Vol 7 ◽  
pp. e6830 ◽  
Author(s):  
Sovan Saha ◽  
Piyali Chatterjee ◽  
Subhadip Basu ◽  
Mita Nasipuri ◽  
Dariusz Plewczynski
Keyword(s):  
Protein Interaction ◽  
Protein Interactions ◽  
Protein Interaction Network ◽  
Protein Function ◽  
Protein Function Prediction ◽  
Experimental Studies ◽  
Interaction Network ◽  
Function Prediction ◽  
The Self ◽  
Functional Annotations

Proteins are the most versatile macromolecules in living systems and perform crucial biological functions. In the advent of the post-genomic era, the next generation sequencing is done routinely at the population scale for a variety of species. The challenging problem is to massively determine the functions of proteins that are yet not characterized by detailed experimental studies. Identification of protein functions experimentally is a laborious and time-consuming task involving many resources. We therefore propose the automated protein function prediction methodology using in silico algorithms trained on carefully curated experimental datasets. We present the improved protein function prediction tool FunPred 3.0, an extended version of our previous methodology FunPred 2, which exploits neighborhood properties in protein–protein interaction network (PPIN) and physicochemical properties of amino acids. Our method is validated using the available functional annotations in the PPIN network of Saccharomyces cerevisiae in the latest Munich information center for protein (MIPS) dataset. The PPIN data of S. cerevisiae in MIPS dataset includes 4,554 unique proteins in 13,528 protein–protein interactions after the elimination of the self-replicating and the self-interacting protein pairs. Using the developed FunPred 3.0 tool, we are able to achieve the mean precision, the recall and the F-score values of 0.55, 0.82 and 0.66, respectively. FunPred 3.0 is then used to predict the functions of unpredicted protein pairs (incomplete and missing functional annotations) in MIPS dataset of S. cerevisiae. The method is also capable of predicting the subcellular localization of proteins along with its corresponding functions. The code and the complete prediction results are available freely at: https://github.com/SovanSaha/FunPred-3.0.git.

scholarly journals FunPred-1: Protein function prediction from a protein interaction network using neighborhood analysis

2014 ◽  
Vol 19 (4) ◽  
Author(s):  
Sovan Saha ◽  
Piyali Chatterjee ◽  
Subhadip Basu ◽  
Mahantapas Kundu ◽  
Mita Nasipuri
Keyword(s):  
Protein Interaction ◽  
Protein Interaction Network ◽  
Protein Function ◽  
Sequence Data ◽  
Biological Activities ◽  
Protein Function Prediction ◽  
Interaction Network ◽  
Search Space ◽  
Function Prediction ◽  
Clustering Coefficient

AbstractProteins are responsible for all biological activities in living organisms. Thanks to genome sequencing projects, large amounts of DNA and protein sequence data are now available, but the biological functions of many proteins are still not annotated in most cases. The unknown function of such non-annotated proteins may be inferred or deduced from their neighbors in a protein interaction network. In this paper, we propose two new methods to predict protein functions based on network neighborhood properties. FunPred 1.1 uses a combination of three simple-yet-effective scoring techniques: the neighborhood ratio, the protein path connectivity and the relative functional similarity. FunPred 1.2 applies a heuristic approach using the edge clustering coefficient to reduce the search space by identifying densely connected neighborhood regions. The overall accuracy achieved in FunPred 1.2 over 8 functional groups involving hetero-interactions in 650 yeast proteins is around 87%, which is higher than the accuracy with FunPred 1.1. It is also higher than the accuracy of many of the state-of-the-art protein function prediction methods described in the literature. The test datasets and the complete source code of the developed software are now freely available at http://code.google.com/p/cmaterbioinfo/.

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