A streamlined platform for analyzing tera-scale DDA and DIA mass spectrometry data enables highly sensitive immunopeptidomics

Abstract

Integrating data-dependent acquisition (DDA) and data-independent acquisition (DIA) approaches can enable highly sensitive mass spectrometry, especially for imunnopeptidomics applications. Here we report a streamlined platform for both DDA and DIA data analysis. The platform integrates deep learning-based solutions of spectral library search, database search, and de novo sequencing under a unified framework, which not only boosts the sensitivity but also accurately controls the specificity of peptide identification. Our platform identifies 5-30% more peptide precursors than other state-of-the-art systems on multiple benchmark datasets. When evaluated on immunopeptidomics datasets, we identify 1.7-4.1 and 1.4-2.2 times more peptides from DDA and DIA data, respectively, than previously reported results. We also discover six T-cell epitopes from SARS-CoV-2 immunopeptidome that might represent potential targets for COVID-19 vaccine development. The platform supports data formats from all major instruments and is implemented with the distributed high-performance computing technology, allowing analysis of tera-scale datasets of thousands of samples for clinical applications.

AlphaPeptDeep: A modular deep learning framework to predict peptide properties for proteomics

“…Our workflow identified 36,947 unique sequences. PEAKS-Online 53 is a very recently published tool which combines searching a public library, direct-DIA, and de novo sequencing. It identified 30,733 unique sequences within the same length range.…”

“…HLA DIA results of PEAKS-Online were downloaded from the PEAKS-Online publication. 53 Only results from RAW files '20200317_QE_HFX2_LC3_DIA_RA957_R01.raw' and '20200317_QE_HFX2_LC3_DIA_RA957_R02.raw' from RA957 were used to compare different methods.…”

Discovering and Validating Neoantigens by Mass Spectrometry-based Immunopeptidomics and Deep Learning

“…For each individual patient, HLA-I peptides were retrieved from HLA-I immunoprecipitation assays followed by MS experiments on the patient’s sample. The MS data was searched against the standard Swiss-Prot human protein database using PEAKS Xpro 11,23 with no-enzyme-specific cleavage and false discovery rate (FDR) of 1%. The identified peptides were subsequently checked for their HLA-I characteristic length distribution; those with lengths <8 or >14 amino acids were filtered out.…”

“…In this paper, we re-analyzed MS data from previous studies and did not perform the HLA or MS experiments ourselves. The MS data was searched against the standard Swiss-Prot human protein database (version June 15, 2020) using PEAKS Xpro 11,23 (version 10.6) with no-enzyme-specific cleavage and FDR of 1%. Precursor mass and fragment ion mass tolerances were set as 15.0 ppm and 0.05 Da, respectively; M(Oxidation) and NQ(Deamidation) were set as variable modifications.…”

“…Majority of current in silico methods focus on predicting the binding affinity of class 1 and class 2 peptide-HLA (pHLA) complexes or, given the rise of recent mass spectrometry (MS) based immunopeptidomics, the entire pHLA presentation pathways [6][7][8][9][10][11] . The peptides are then ranked by their HLA binding/presentation scores, their expression levels, and other criteria, based on the assumption that if a mutated peptide is presented in large amounts on the cancer cell surface, it has a better chance to trigger T cell responses.…”