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Reverse Nearest Neighbor Search on a Protein-Protein Interaction Network to Infer Protein-Disease Associations.

ABSTRACT: The associations between proteins and diseases are crucial information for investigating pathological mechanisms. However, the number of known and reliable protein-disease associations is quite small. In this study, an analysis framework to infer associations between proteins and diseases was developed based on a large data set of a human protein-protein interaction network integrating an effective network search, namely, the reverse k-nearest neighbor (RkNN) search. The RkNN search was used to identify an impact of a protein on other proteins. Then, associations between proteins and diseases were inferred statistically. The method using the RkNN search yielded a much higher precision than a random selection, standard nearest neighbor search, or when applying the method to a random protein-protein interaction network. All protein-disease pair candidates were verified by a literature search. Supporting evidence for 596 pairs was identified. In addition, cluster analysis of these candidates revealed 10 promising groups of diseases to be further investigated experimentally. This method can be used to identify novel associations to better understand complex relationships between proteins and diseases.

SUBMITTER: Suratanee A

PROVIDER: S-EPMC5513527 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature

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Reverse Nearest Neighbor Search on a Protein-Protein Interaction Network to Infer Protein-Disease Associations.

Suratanee Apichat A Plaimas Kitiporn K

Bioinformatics and biology insights 20170713

The associations between proteins and diseases are crucial information for investigating pathological mechanisms. However, the number of known and reliable protein-disease associations is quite small. In this study, an analysis framework to infer associations between proteins and diseases was developed based on a large data set of a human protein-protein interaction network integrating an effective network search, namely, the reverse k-nearest neighbor (RkNN) search. The RkN ...[more]

PMID: 28757797

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Project description:In many protein-protein interaction (PPI) networks, densely connected hub proteins are more likely to be essential proteins. This is referred to as the "centrality-lethality rule", which indicates that the topological placement of a protein in PPI network is connected with its biological essentiality. Though such connections are observed in many PPI networks, the underlying topological properties for these connections are not yet clearly understood. Some suggested putative connections are the involvement of essential proteins in the maintenance of overall network connections, or that they play a role in essential protein clusters. In this work, we have attempted to examine the placement of essential proteins and the network topology from a different perspective by determining the correlation of protein essentiality and reverse nearest neighbor topology (RNN).The RNN topology is a weighted directed graph derived from PPI network, and it is a natural representation of the topological dependences between proteins within the PPI network. Similar to the original PPI network, we have observed that essential proteins tend to be hub proteins in RNN topology. Additionally, essential genes are enriched in clusters containing many hub proteins in RNN topology (RNN protein clusters). Based on these two properties of essential genes in RNN topology, we have proposed a new measure; the RNN cluster centrality. Results from a variety of PPI networks demonstrate that RNN cluster centrality outperforms other centrality measures with regard to the proportion of selected proteins that are essential proteins. We also investigated the biological importance of RNN clusters.This study reveals that RNN cluster centrality provides the best correlation of protein essentiality and placement of proteins in PPI network. Additionally, merged RNN clusters were found to be topologically important in that essential proteins are significantly enriched in RNN clusters, and biologically important because they play an important role in many Gene Ontology (GO) processes.

Project description:Escherichia coli single-strand (ss) DNA-binding protein (SSB) is an essential protein that binds ssDNA intermediates formed during genome maintenance. SSB homotetramers bind ssDNA in several modes differing in occluded site size and cooperativity. The 35-site-size ((SSB)35) mode favored at low [NaCl] and high SSB/DNA ratios displays high "unlimited" nearest-neighbor cooperativity (?35), forming long protein clusters, whereas the 65-site-size ((SSB)65) mode in which ssDNA wraps completely around the tetramer is favored at higher [NaCl] (>200 mM) and displays "limited" cooperativity (?65), forming only dimers of tetramers. In addition, a non-nearest-neighbor high cooperativity can also occur in the (SSB)65 mode on long ssDNA even at physiological salt concentrations in the presence of glutamate and requires its intrinsically disordered C-terminal linker (IDL) region. However, whether cooperativity exists between the different modes and the role of the IDL in nearest-neighbor cooperativity has not been probed. Here, we combine sedimentation velocity and fluorescence titration studies to examine nearest-neighbor cooperativity in each binding mode and between binding modes using (dT)70 and (dT)140. We find that the (SSB)35 mode always shows extremely high "unlimited" cooperativity that requires the IDL. At high salt, wild-type SSB and a variant without the IDL, SSB-?L, bind in the (SSB)65 mode but show little cooperativity, although cooperativity increases at lower [NaCl] for wild-type SSB. We also find significant intermode nearest-neighbor cooperativity (?65/35), with ?65 ? ?65/35 <?35. The intrinsically disordered region of SSB is required for all cooperative interactions; however, in contrast to the non-nearest-neighbor cooperativity observed on longer ssDNA, glutamate does not enhance these nearest-neighbor cooperativities. Therefore, we show that SSB possesses four types of cooperative interactions, with clear differences in the forces stabilizing nearest-neighbor versus non-nearest-neighbor cooperativity.

Dataset Information

Reverse Nearest Neighbor Search on a Protein-Protein Interaction Network to Infer Protein-Disease Associations.

Publications

Reverse Nearest Neighbor Search on a Protein-Protein Interaction Network to Infer Protein-Disease Associations.

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