Lan V. Zhang

Towards a proteome-scale map of the human protein-protein interaction network.

Systematic mapping of protein–protein interactions, or ‘interactome’ mapping, was initiated in model organisms, starting with defined biological processes and then expanding to the scale of the proteome. Although far from complete, such maps have revealed global topological and dynamic features of interactome networks that relate to known biological properties, suggesting that a human interactome map will provide insight into development and disease mechanisms at a systems level. Here we describe an initial version of a proteome-scale map of human binary protein–protein interactions. Using a stringent, high-throughput yeast two-hybrid system, we tested pairwise interactions among the products of ∼8,100 currently available Gateway-cloned open reading frames and detected ∼2,800 interactions. This data set, called CCSB-HI1, has a verification rate of ∼78% as revealed by an independent co-affinity purification assay, and correlates significantly with other biological attributes. The CCSB-HI1 data set increases by ∼70% the set of available binary interactions within the tested space and reveals more than 300 new connections to over 100 disease-associated proteins. This work represents an important step towards a systematic and comprehensive human interactome project.

Evidence for dynamically organized modularity in the yeast protein-protein interaction network

In apparently scale-free protein–protein interaction networks, or ‘interactome’ networks, most proteins interact with few partners, whereas a small but significant proportion of proteins, the ‘hubs’, interact with many partners. Both biological and non-biological scale-free networks are particularly resistant to random node removal but are extremely sensitive to the targeted removal of hubs. A link between the potential scale-free topology of interactome networks and genetic robustness seems to exist, because knockouts of yeast genes encoding hubs are approximately threefold more likely to confer lethality than those of non-hubs. Here we investigate how hubs might contribute to robustness and other cellular properties for protein–protein interactions dynamically regulated both in time and in space. We uncovered two types of hub: ‘party’ hubs, which interact with most of their partners simultaneously, and ‘date’ hubs, which bind their different partners at different times or locations. Both in silico studies of network connectivity and genetic interactions described in vivo support a model of organized modularity in which date hubs organize the proteome, connecting biological processes—or modules —to each other, whereas party hubs function inside modules.

Predicting co-complexed protein pairs using genomic and proteomic data integration

BackgroundIdentifying all protein-protein interactions in an organism is a major objective of proteomics. A related goal is to know which protein pairs are present in the same protein complex. High-throughput methods such as yeast two-hybrid (Y2H) and affinity purification coupled with mass spectrometry (APMS) have been used to detect interacting proteins on a genomic scale. However, both Y2H and APMS methods have substantial false-positive rates. Aside from high-throughput interaction screens, other gene- or protein-pair characteristics may also be informative of physical interaction. Therefore it is desirable to integrate multiple datasets and utilize their different predictive value for more accurate prediction of co-complexed relationship.ResultsUsing a supervised machine learning approach – probabilistic decision tree, we integrated high-throughput protein interaction datasets and other gene- and protein-pair characteristics to predict co-complexed pairs (CCP) of proteins. Our predictions proved more sensitive and specific than predictions based on Y2H or APMS methods alone or in combination. Among the top predictions not annotated as CCPs in our reference set (obtained from the MIPS complex catalogue), a significant fraction was found to physically interact according to a separate database (YPD, Yeast Proteome Database), and the remaining predictions may potentially represent unknown CCPs.ConclusionsWe demonstrated that the probabilistic decision tree approach can be successfully used to predict co-complexed protein (CCP) pairs from other characteristics. Our top-scoring CCP predictions provide testable hypotheses for experimental validation.

Probabilistic paths for protein complex inference

Understanding how individual proteins are organized into complexes and pathways is a significant current challenge. We introduce new algorithms to infer protein complexes by combining seed proteins with a confidence-weighted network. Two new stochastic methods use averaging over a probabilistic ensemble of networks, and the new deterministic method provides a deterministic ranking of prospective complex members. We compare the performance of these algorithms with three existing algorithms. We test algorithm performance using three weighted graphs: a naive Bayes estimate of the probability of a direct and stable protein-protein interaction; a logistic regression estimate of the probability of a direct or indirect interaction; and a decision tree estimate of whether two proteins exist within a common protein complex. The best-performing algorithms in these trials are the new stochastic methods. The deterministic algorithm is significantly faster, whereas the stochastic algorithms are less sensitive to the weighting scheme.