Evolutionary and Physiological Importance of Hub Proteins

It has been claimed that proteins with more interaction partners (hubs) are both physiologically more important (i.e., less dispensable) and, owing to an assumed high density of binding sites, slow evolving. Not all analyses, however, support these results, probably because of biased and less-than reliable global protein interaction data. Here we provide the first examination of these issues using a comprehensive literature-curated dataset of well-substantiated protein interactions in Saccharomyces cerevisiae. Whereas use of less reliable yeast two-hybrid data alone can reject the possibility that local connectivity correlates with measures of dispensability, in higher quality datasets a relatively robust correlation is observed. In contrast, local connectivity does not correlate with the rate of protein evolution even in reliable datasets. This perhaps surprising lack of correlation with evolutionary rate appears in part to arise from the fact that hub proteins do not have a higher density of residues associated with binding. However, hub proteins do have at least one other set of unusual features, namely rapid turnover and regulation, as manifest in high mRNA decay rates and a large number of phosphorylation sites. This, we suggest, is an adaptation to minimize unwanted activation of pathways that might be mediated by adventitious binding to hubs, were they to actively persist longer than required at any given time point. We conclude that hub proteins are more important for cellular growth rate and under tight regulation but are not slow evolving

Network rewiring is an important mechanism of gene essentiality change.

Gene essentiality changes are crucial for organismal evolution. However, it is unclear how essentiality of orthologs varies across species. We investigated the underlying mechanism of gene essentiality changes between yeast and mouse based on the framework of network evolution and comparative genomic analysis. We found that yeast nonessential genes become essential in mouse when their network connections rapidly increase through engagement in protein complexes. The increased interactions allowed the previously nonessential genes to become members of vital pathways. By accounting for changes in gene essentiality, we firmly reestablished the centrality-lethality rule, which proposed the relationship of essential genes and network hubs. Furthermore, we discovered that the number of connections associated with essential and non-essential genes depends on whether they were essential in ancestral species. Our study describes for the first time how network evolution occurs to change gene essentiality

Computational methods in cancer gene networking

In the past few years, many high-throughput techniques have been developed and applied to biological studies. These techniques such as “next generation” genome sequencing, chip-on-chip, microarray and so on can be used to measure gene expression and gene regulatory elements in a genome-wide scale. Moreover, as these technologies become more affordable and accessible, they have become a driving force in modern biology. As a result, huge amount biological data have been produced, with the expectation of increasing number of such datasets to be generated in the future. High-throughput data are more comprehensive and unbiased, but ‘real signals’ or biological insights, molecular mechanisms and biological principles are buried in the flood of data. In current biological studies, the bottleneck is no longer a lack of data, but the lack of ingenuity and computational means to extract biological insights and principles by integrating knowledge and high-throughput data. 

Here I am reviewing the concepts and principles of network biology and the computational methods which can be applied to cancer research. Furthermore, I am providing a practical guide for computational analysis of cancer gene networks

Hub characteristics extraction of human proteins using tumor protein P53 – A case study

This paper addresses the characteristic extraction of hub protein based on Tumor Protein P53 whose properties are already established and known to have key functionalities. These characteristics can throw some light in the direction of hub classification in a cost effective manner. Current methods in this line use Gene Ontology database or sequence homology which are time consuming and complex. The proposed method uses a 420 element vector for the characteristic filtering of hub character from HPRD database and has shown some positive results

Examination of the relationship between essential genes in PPI network and hub proteins in reverse nearest neighbor topology

Abstract Background 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). Results 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. Conclusions 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.http://deepblue.lib.umich.edu/bitstream/2027.42/78257/1/1471-2105-11-505.xmlhttp://deepblue.lib.umich.edu/bitstream/2027.42/78257/2/1471-2105-11-505-S1.DOChttp://deepblue.lib.umich.edu/bitstream/2027.42/78257/3/1471-2105-11-505.pdfPeer Reviewe

Multiple protein-domain conservation architecture as a non-deterministic confounder of linear B cell epitopes

Epitope prediction is a critical step to diagnostic and vaccine discovery. Despite existence of some parameters for epitope discovery, this area remains inconclusive and wanting-for new complementary or stand-alone tools. The phenomenon of multiple protein-domain conservation architecture (MPDCA) as used here refers to homologous motifs unveiled by multiple sequence alignments across strain-variants of the same protein aside of the conserved domains (CD) present within the same super family. Unpublished data suggests that MPDCA might be a confounder of epitope necessitating further investigation as a predictor of the same. The ease of determining MPDCA is appealing when considering protein-analysis; specifically epitope discovery. This study aimed to validate MPDCA as a predictive confounder of epitope. Using two-sets of surface viral glycoproteins of human immunodeficiency virus type I, HIV-1 (gp120) and Ebola virus, EBOV (gp1,2 preprotein) (selected because their CD-architecture has widely been studied, their sequences are available in public databases, and the same are well annotated), the MPDCAs among three different virus-strains in each-set, were compared to epitopes predicted by established tools (Bipred and DiscoTope). 4/6 (66.6%) of the linear epitopes confounded MPDCA, with 3/6 (50%) of these MPDCA’s confounding with the predicted linear epitopes (LE) at identities of > 50%, when compared to just 3/6 (50%) of the discontinuous epitopes (DE) that confounded with MPDCA at a < 50% identity. MPDCA is a non-deterministic confounder of Linear B cell epitopy. There is no causal relationship between the two, much as there is an evident co-occurrence. Therefore, MPDCA cannot accurately be used as an additional parameter to predict linear and or non-linear B cell epitopes

Protein Under-Wrapping Causes Dosage Sensitivity and Decreases Gene Duplicability

A fundamental issue in molecular evolution is how to identify the evolutionary forces that determine the fate of duplicated genes. The dosage balance hypothesis has been invoked to explain gene duplication patterns at the genomic level under the premise that a dosage imbalance among protein-complex subunits or interacting partners is often deleterious. Here we examine this hypothesis by investigating the molecular basis of dosage sensitivity. We focus on the extent of protein wrapping, which indicates how strongly the structural integrity of a protein relies on its interactive context. From this perspective, we predict that the duplicates of a highly under-wrapped protein or protein subunit should (1) be more sensitive to dosage imbalance and be less likely to be retained and (2) be more likely to survive from a whole-genome duplication (WGD) than from a non-WGD because a WGD causes little or no dosage imbalance. Our under-wrapping analysis of more than 12,000 protein structures strongly supports these predictions and further reveals that the effect of dosage sensitivity on gene duplicability decreases with increasing organismal complexity