Isolation window optimization using predicted libraries for deep and accurate proteome profiling by data-independent acquisition

In silico spectral library prediction of all possible peptides from whole organisms has a great potential for improving proteome profiling by data-independent acquisition and extending its scope of application. In combination with other recent improvements in the field, including sample preparation, peptide separation and data analysis, we aimed to uncover the full potential of such an advanced DIA strategy by isolation window optimization. The results demonstrate that the combination of high-quality in silico libraries, reproducible and high-resolution peptide separation using micro-pillar array columns as well as neural network supported data analysis enables the use of long MS scan cycles without impairing the quantification performance.