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

Area of Science:

• Proteomics
• Bioinformatics
• Computational Biology

Background:

• Mass spectrometry (MS) advancements still result in a significant proportion of undetected proteins, creating the missing protein problem.
• Existing network-based protein recovery methods show unsatisfactory performance.
• Current methods often face ambiguity issues due to reliance on peptide information.

Purpose of the Study:

• To introduce PROTREC (PROtein RECovery), a novel network-based approach for predicting missing proteins (MPs).
• To demonstrate PROTREC's superior performance compared to existing methods like FCS, HE, and GSEA.
• To provide a direct probabilistic interpretation of protein presence, unlike p-value-based methods.

Main Methods:

• Developed a Bayesian-inspired, network-based approach named PROTREC.
• Validated PROTREC across diverse proteomics datasets and acquisition paradigms.
• PROTREC does not rely on peptide information, avoiding common ambiguity issues.

Main Results:

• PROTREC scores show a strong positive correlation with higher recovery rates of MPs across sample replicates.
• PROTREC significantly outperforms existing network-based methods (FCS, HE, GSEA).
• The PROTREC score is directly interpretable as the probability of a protein's actual presence.

Conclusions:

• PROTREC offers an exceptional and reliable solution for the missing protein problem in proteomics.
• The method's performance is robust across various validation strategies and datasets.
• PROTREC enhances the accuracy and interpretability of protein identification in MS-based studies.