GenPPI

Ab initio protein interaction network generator

The present work describes the new GenPPI software enhancements on determining homology between protein pairs, which is one of the principal bottlenecks in creating ab initio interaction networks from genomes and the primary step inferring neighborhood and phylogenetic profiles conserved for all genomes under analysis. This improvement was achieved using the Random Forest algorithm, working on biophysical features derived from ten amino acid propensity indexes used to calculate sixty features for each genome's proteins. We crafted a training data set of homolog and non-homolog proteins using nine full proteomes from critical bacteria to classify similar proteins with more than 65% amino acid identities via a machine learning test, an average result obtained from dozens of validations. Such a strategy resulted in more comprehensive and accurate protein interaction networks capable of analyzing genomes of different organisms. Coupled with the significant preprocessing increase, we implemented parallelism in GenPPI to address critical code bottlenecks. Our testing of the new GenPPI improvement using the bacterium Buchenera aphidicola yielded impressive results. We achieved an overlap of 62% with the interactions documented in the STRING, surpassing the previous GenPPI version.

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