Global protein function prediction in protein-protein interaction networks

By A. Vázquez, Alessandro Flammini, Amos Maritan, Alessandro Vespignani. 2003

665 citations recorded by the source corpus.

The determination of protein functions is one of the most challenging problems of the post-genomic era. The sequencing of entire genomes and the possibility to access gene's co-expression patterns has moved the attention from the study of single proteins or small complexes to that of the entire proteome. In this context, the search for reliable methods for proteins' function assignment is of uttermost importance. Previous approaches to deduce the unknown function of a class of proteins have exploited sequence similarities or clustering of co-regulated genes, phylogenetic profiles, protein-protein interactions, and protein complexes. We propose to assign functional classes to proteins from their network of physical interactions, by minimizing the number of interacting proteins with different categories. The function assignment is made on a global scale and depends on the entire connectivity pattern of the protein network. Multiple functional assignments are made possible as a consequence of the existence of multiple equivalent solutions. The method is applied to the yeast Saccharomices Cerevisiae protein-protein interaction network. Robustness is tested in presence of a high percentage of unclassified proteins and under deletion/insertion of interactions.

Subfields: Molecular Biology, Graph Learning, Evaluation & Benchmarks

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Citations in the corpus

node2vec: Scalable Feature Learning for Networks (2016, background): “Link prediction is useful in a wide variety of domains; for instance, in genomics, it helps us discover novel interactions between genes, and in social networks, it can identify real-world friends [2, 34].”
Using PPI network autocorrelation in hierarchical multi-label classification trees for gene function prediction (2013, methodology): “However, the above approaches [29,30,45-47] do not consider the hierarchy of categories.”
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GeneMANIA: a real-time multiple association network integration algorithm for predicting gene function (2008, background): “While there are a large variety of label propagation algorithms (for example, those based on functional flow [24,25] or Markov random fields [26]), our interest in the Gaussian field algorithm stems from the fact that it has a welldefined solution, it is derived from a principled framework, and it h”
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Integrative approaches for finding modular structure in biological networks (2013, background): “Additionally, comparative analyses of conserved modules can supplement sequence-matching techniques for the prediction of function 113,126–129 , on the basis of the premise that interaction partners of orthologous genes or proteins are also likely to be functionally conserved.”
Network-based prediction of protein function (2007, background): “Vazquez et al (2003) aim at assigning a function s v to each unannotated protein v so as to maximize the number of edges that connect proteins (unannotated or previously annotated) assigned with the same function.”
Functional Knowledge Transfer for High-accuracy Prediction of Under-studied Biological Processes (2013, background): “For example, gene expression and protein interaction profiles can be used by machine learning methods to associate novel genes to pathways based on previously known pathway members [15,16].”
Graph theory and networks in Biology. (2006, background): “Two more sophisticated approaches to protein function prediction that avoid the above mentioned difficulty were described in [189, 190].”
Enhancing the Prioritization of Disease-Causing Genes through Tissue Specific Protein Interaction Networks (2012, methodology): “In recent years, PPI networks were shown to be a powerful tool in many fields of molecular biology, such as predicting protein annotation and more [19,49,50].”
Protein interaction networks from yeast to human. (2004, background): “Implicitly, modules are also the basis of approaches that functionally classify proteins according to their network neighbors [64–66].”
Uncovering Biological Network Function via Graphlet Degree Signatures (2008, background): “…to the whole set of unclassified proteins in a network is given a score, counting the number of interacting pairs of nodes with no common function; the functional assignment with the lowest score maxi izes the presence of the same function among interacting proteins (Vazquez et al., 2003).”