Spotlite: Web Application and Augmented Algorithms for Predicting Co-Complexed Proteins from Affinity Purification – Mass Spectrometry Data

Protein–protein
interactions defined by affinity purification
and mass spectrometry (APMS) suffer from high false discovery rates.
Consequently, lists of potential interactions must be pruned of contaminants
before network construction and interpretation, historically an expensive,
time-intensive, and error-prone task. In recent years, numerous computational
methods were developed to identify genuine interactions from the hundreds
of candidates. Here, comparative analysis of three popular algorithms,
HGSCore, CompPASS, and SAINT, revealed complementarity in their classification
accuracies, which is supported by their divergent scoring strategies.
We improved each algorithm by an average area under a receiver operating
characteristics curve increase of 16% by integrating a variety of
indirect data known to correlate with established protein–protein
interactions, including mRNA coexpression, gene ontologies, domain–domain
binding affinities, and homologous protein interactions. Each APMS
scoring approach was incorporated into a separate logistic regression
model along with the indirect features; the resulting three classifiers
demonstrate improved performance on five diverse APMS data sets. To
facilitate APMS data scoring within the scientific community, we created
Spotlite, a user-friendly and fast web application. Within Spotlite,
data can be scored with the augmented classifiers, annotated, and
visualized (http://cancer.unc.edu/majorlab/software.php). The utility of the Spotlite platform to reveal physical, functional,
and disease-relevant characteristics within APMS data is established
through a focused analysis of the KEAP1 E3 ubiquitin ligase.