Protein mass spectrometry is the major technique to characterize thousands of proteins in biological samples, and therefore is an important cornerstone for unraveling and understanding biological processes and their role in complex diseases. Although this experimental platform is increasingly used in biological and clinical research, the current instrumentation is very expensive, slow and requires experienced technical staff for their operation. One reason for this complexity is the usage of at least two stages of data acquisition to ensure high quality identification and quantification of proteins.
We seek to revolutionize this slow and costly process by stripping the second stage from the experiment and replace it with cutting-edge artificial intelligence algorithms, so-called deep learning models. These AIs will be trained to predict proteins on basis of extensive mostly unharvested information from the first stage. We envision that our method will open protein mass spectrometry to a broader spectrum of use-cases in research and industry, and ultimately to enable high-quality research with small mobile units.