Prioritizing missense mutations via a deep learning phosphorylation prediction model

Phosphorylation is a crucial post-translational modification mechanism that enhances proteomic diversity, and its malfunction has been confirmed to be associated with complex traits, especially brain disorders. One of the factors contributing to this malfunction is the missense mutations given that they may alter the peptides flanking the phosphorylated residues. However, the specific effects of these missense mutations on phosphorylation remain unclear. To ascertain these, a deep learning phosphorylation prediction model (DeepMEP), which is the first to be developed on a Chinese-brain-specific phosphorylation dataset (CBMAP), was established to bridge the phosphorylation and peptides. The impact of each missense mutation on phosphorylation was subsequently quantified based on the differences between the outputs of reference and mutant protein sequences. A permutation test adjusting for the confounding factors was finally employed to estimate the enrichment for high-impact mutations in disease-associated genomic loci. DeepMEP achieved superior predictive performance compared with other existing tools on both CBMAP and publicly available datasets. Enrichment analysis revealed the high-impact mutations were significantly enriched in GWAS signals for Alzheimer’s disease (AD) and Parkinson’s disease (PD). The corresponding genes of those missense mutations overlapping with GWAS included ABCA7, APOE, and PLCG2 for AD, and MMRN1 and TMEM175 for PD, which are disease-associated genes confirmed by other studies beyond GWAS. Our study demonstrated that DeepMEP effectively captured the impact of missense mutations on phosphorylation and highlighted an enrichment of high-impact mutations in AD- and PD-associated genomic loci.