SAINT

SAINT assigns confidence scores to protein-protein interactions derived from affinity purification–mass spectrometry (AP-MS) spectral count data to distinguish true biological interactions from background noise.

Key Features:

• Confidence Scoring: Assigns probabilistic confidence scores to individual protein-protein interactions based on AP-MS spectral count data.
• Label-free Spectral Count Support: Operates on label-free quantitative spectral count measurements produced by AP-MS experiments.
• Scale Applicability: Applies to datasets of varying sizes and complexities, accommodating high-throughput proteomics studies.
• Transparent Statistical Modeling: Uses explicit statistical distributions for true and false interactions to support reproducible interpretation.

Scientific Applications:

• AP-MS proteomics: Identification and prioritization of high-confidence protein-protein interactions from affinity purification–mass spectrometry datasets.
• Interaction network curation: Filtering low-confidence interactions to improve the quality of protein interaction networks.
• Downstream functional analysis: Facilitating pathway mapping and functional annotation by providing a filtered set of interactions.

Methodology:

Operates on label-free quantitative spectral count data from AP-MS, constructs separate statistical distributions for true and false protein-protein interactions, and computes the probability that each interaction is genuine to produce confidence scores.