Mycobacterium tuberculosis and Clostridium difficille interactomes: demonstration of rapid development of computational system for bacterial interactome prediction

Abstract

BackgroundProtein-protein interaction (PPI) networks (interactomes) of most organisms, except for some model organisms, are largely unknown. Experimental methods including high-throughput techniques are highly resource intensive. Therefore, computational discovery of PPIs can accelerate biological discovery by presenting "most-promising" pairs of proteins that are likely to interact. For many bacteria, genome sequence, and thereby genomic context of proteomes, is readily available; additionally, for some of these proteomes, localization and functional annotations are also available, but interactomes are not available. We present here a method for rapid development of computational system to predict interactome of bacterial proteomes. While other studies have presented methods to transfer interologs across species, here, we propose transfer of computational models to benefit from cross-species annotations, thereby predicting many more novel interactions even in the absence of interologs. Mycobacterium tuberculosis (Mtb) and Clostridium difficile (CD) have been used to demonstrate the work.ResultsWe developed a random forest classifier over features derived from Gene Ontology annotations and genetic context scores provided by STRING database for predicting Mtb and CD interactions independently. The Mtb classifier gave a precision of 94% and a recall of 23% on a held out test set. The Mtb model was then run on all the 8 million protein pairs of the Mtb proteome, resulting in 708 new interactions (at 94% expected precision) or 1,595 new interactions at 80% expected precision. The CD classifier gave a precision of 90% and a recall of 16% on a held out test set. The CD model was run on all the 8 million protein pairs of the CD proteome, resulting in 143 new interactions (at 90% expected precision) or 580 new interactions (at 80% expected precision). We also compared the overlap of predictions of our method with STRING database interactions for CD and Mtb and also with interactions identified recently by a bacterial 2-hybrid system for Mtb. To demonstrate the utility of transfer of computational models, we made use of the developed Mtb model and used it to predict CD protein-pairs. The cross species model thus developed yielded a precision of 88% at a recall of 8%. To demonstrate transfer of features from other organisms in the absence of feature-based and interaction-based information, we transferred missing feature values from Mtb orthologs into the CD data. In transferring this data from orthologs (not interologs), we showed that a large number of interactions can be predicted.ConclusionsRapid discovery of (partial) bacterial interactome can be made by using existing set of GO and STRING features associated with the organisms. We can make use of cross-species interactome development, when there are not even sufficient known interactions to develop a computational prediction system. Computational model of well-studied organism(s) can be employed to make the initial interactome prediction for the target organism. We ha...

Transferring knowledge of bacterial protein interaction networks to predict pathogen targeted human genes and immune signaling pathways: a case study on M. tuberculosis

“…However, there are very few experimental studies on protein interactions between bacteria and their host. From a computational view of point, M. tuberculosis H37Rv has been extensively studied in recent years in terms of drug resistance analysis [ 4 – 6 , 8 ] and PPI networks reconstruction [ 9 , 10 ]. In [ 9 , 10 ], interlogs are derived as M. tuberculosis H37Rv PPIs from the known PPIs of other source species.…”

“…From a computational view of point, M. tuberculosis H37Rv has been extensively studied in recent years in terms of drug resistance analysis [ 4 – 6 , 8 ] and PPI networks reconstruction [ 9 , 10 ]. In [ 9 , 10 ], interlogs are derived as M. tuberculosis H37Rv PPIs from the known PPIs of other source species. In [ 9 ], the known M. tuberculosis H37Rv PPIs are laid aside unused and instead the E. coli PPIs are used as training data to predict M. tuberculosis H37Rv PPIs.…”

“…In [ 9 ], the known M. tuberculosis H37Rv PPIs are laid aside unused and instead the E. coli PPIs are used as training data to predict M. tuberculosis H37Rv PPIs. In [ 10 ], the interlogs derived from lostridium difficile are used to expand the known M. tuberculosis H37Rv PPI networks , and the expanded PPI networks are further used as training data to train a random forest model for the discovery of novel M. tuberculosis H37Rv PPIs.…”

Research prioritization through prediction of future impact on biomedical science: a position paper on inference-analytics

“…Extensive research is being carried out both with high-throughput biotechnology and computational methods to discover PPIs [10-14]. Both these approaches provide hundreds or thousands of hypothesized PPIs.…”

“…Although PPIs form the basis of many biological phenomena, 90% of the estimated number of PPIs are currently unknown [ 7 - 9 ]. Extensive research is being carried out both with high-throughput biotechnology and computational methods to discover PPIs [ 10 - 14 ]. Both these approaches provide hundreds or thousands of hypothesized PPIs.…”