An integrated computational proteomics method to extract protein targets for Fanconi Anemia studies

Abstract

Fanconi Anemia (FA) is a rare autosomal genetic disease with multiple birth defects and severe childhood complications for its patients. The lack of sequence homology of the entire FA Complementation Group proteins in such as FANCC, FANCG, FANCA makes them extremely difficult to characterize using conventional bioinformatics methods. In this work, we describe how to use computational methods to extract protein targets for FA, using protein interaction data set collected for FANC group C protein (FANCC). We first generated an initial set of 130 FA-interacting proteins as "FANCC seed proteins" by merging an in-house experimental set of FANCC Tandem Affinity Purification (TAP) Pulldown Proteomics data identified from Mass Spectrometry methods with publicly available human FANCC-interacting proteins. Next, we expanded the FANCC seed proteins using a nearest-neighbor method to generate a FANCC protein interaction subnetwork of 948 proteins in 903 protein interactions. We show that this network is statistically significant, with high indices of aggregation and separations. We also show a visualization of the network, support the evidence that many well-connected proteins exists in the network. Further, we developed and applied an interaction network protein scoring algorithm, which allows us to calculate a ranked list of significant FA proteins. Our result has been supporting further biological investigations of disease biologists on our team. We believe our method can be generalized to other disease biology studies with similar problems.