Abstract: Protein-protein interactions (PPIs) are fundamental to cellular regulation, governing complex biological processes through the assembly of stable or transient molecular machineries. Affinity purification coupled with mass spectrometry (AP-MS) has emerged as the gold standard for characterizing these interactomes. However, as AP-MS datasets grow in heterogeneity and complexity—incorporating both data-dependent acquisition (DDA) and data-independent acquisition (DIA) modes as well as multi-bait and time-series experimental designs—researchers face significant challenges. These include high levels of non-specific binding, complex background noise, and the lack of standardized, end-to-end computational pipelines that integrate quality control with advanced structural prediction. In this study, APMSflow, a comprehensive, R/Shiny-based analytical platform was developed, specifically engineered for the systematic processing of AP-MS data. The workflow integrates several critical modules: 1) Quality assessment, utilizing PCA and peptide-level metrics to evaluate sample reproducibility and digestion efficiency; 2) Pre-processing, offering multiple normalization strategies (e.g., median, bait-based, or endogenous biotinylated protein-based) and sophisticated missing value imputation methods; 3) Differential analysis, implementing moderated t-tests via the Limma package and a novel “multi-bait background modeling” strategy to enhance interaction specificity without requiring independent negative controls; 4) Downstream integration, combining cluster-based functional enrichment (clusterProfiler) with structural bioinformatics analysis. Uniquely, APMSflow incorporates AlphaFold-Multimer to perform structural modeling and scoring of predicted protein pairs, transitioning from statistical association analyse to structural validation. Validation performed using published large-scale proteomics datasets (PXD020709) demonstrated that APMSflow effectively filters non-specific contaminants and identifies high-confidence interactors. The platform successfully captured the temporal dynamics of the EGFR interactome, categorizing proteins into transient or stable interaction clusters based on their abundance profiles across time points. By applying stringent CV-based filtering and specialized handling of bait-specific proteins (addressing “NA” values in control groups), APMSflow recovered biologically relevant early-transient signaling components that are often missed by conventional pipelines. The integration of AlphaFold-Multimer further provided structural evidence for candidate protein complexes, streamlining the prioritization of targets for biochemical validation. APMSflow addresses a critical gap in the proteomics community by providing an accessible, standardized, and robust tool for protein interactome analysis. It lowers the technical barrier for wet-lab researchers while ensuring experimental reproducibility across studies. While current iterations focus on protein-level quantification, future updates aim to incorporate post-translational modification (PTM) site-specific analysis and more advanced protein complex prediction algorithms. APMSflow represents a significant step toward the automated, structure-aware interpretation of the dynamic protein interaction landscape, facilitating the discovery of novel regulatory mechanisms in systems biology. The tool is freely accessible as a web application at: https://humility3238.shinyapps.io/apmsflow/.