PROTREC: a probability-based approach for recovering missing proteins based on biological networks

Abstract

A novel network-based approach for predicting missing proteins (MPs) is proposed here. This approach, PROTREC (short for PROtein RECovery), dominates existing network-based methods - such as Functional Class Scoring (FCS), Hypergeometric Enrichment (HE), and Gene Set Enrichment Analysis (GSEA) - across a variety of proteomics datasets derived from different proteomics data acquisition paradigms: Higher PROTREC scores are much more closely correlated with higher recovery rates of MPs across sample replicates. The PROTREC score, unlike methods reporting p-values, can be directly interpreted as the probability that an unreported protein in a proteomic screen is actually present in the sample being screened. SIGNIFICANCE: Mass spectrometry (MS) has developed rapidly in recent years; however, an obvious proportion of proteins is still undetected, leading to missing protein problems. A few existing protein recovery methods are based on biological networks, but the performance is not satisfactory. We propose a new protein recovery method, PROTREC, a Bayesian-inspired approach based on biological networks, which shows exceptional performance across multiple validation strategies. It does not rely on peptide information, so it avoids the ambiguity issue that most protein assembly methods face.