Uncovering Hidden Members and Functions of the Soil Microbiome Using De Novo Metaproteomics

The fundamental task in proteomic mass spectrometry is identifying peptides from their observed spectra. Where protein sequences are known, standard algorithms utilize these to narrow the list of peptide candidates. If protein sequences are unknown, a distinct class of algorithms must interpret spectra de novo. Despite decades of effort on algorithmic constructs and machine learning methods, de novo software tools remain inaccurate when used on environmentally diverse samples. Here we train a deep neural network on 5 million spectra from 55 phylogenetically diverse bacteria. This new model outperforms current methods by 25-100%. The diversity of organisms used for training also improves the generality of the model, and ensures reliable performance regardless of where the sample comes from. Significantly, it also achieves a high accuracy in long peptides which assist in identifying taxa from samples of unknown origin. With the new tool, called Kaiko, we analyze proteomics data from six natural soil isolates for which a proteome database did not exist. Without any sequence information, we correctly identify the taxonomy of these soil microbes as well as annotate thousands of peptide spectra.