Peptide-Spectra Matching with Weak Supervision

Sam Schoenholz
Sean Hackett
Laura Deming
Eugene Melamud
Navdeep Jaitly
Fiona McAllister
Jonathon O'Brien
George Dahl
Bryson Bennett
Daphne Koller


As in many other scientific domains, we face a fundamental problem when using machine learning to identify proteins from mass spectrometry data: large ground truth datasets mapping inputs to correct outputs are extremely difficult to obtain. Instead, we have access to imperfect hand-coded models crafted by domain experts. In this paper, we apply deep neural networks to an important step of the protein identification problem, the pairing of mass spectra with short sequences of amino acids called peptides. We train our model to differentiate between top scoring results from a state-of-the art classical system and hard-negative second and third place results. Our resulting model is much better at identifying peptides with spectra than the model used to generate its training data. In particular, we achieve a 43% improvement over standard matching methods and a 10% improvement over a combination of the matching method and an industry standard cross-spectra reranking tool. Importantly, in a more difficult experimental regime that reflects current challenges facing biologists, our advantage over the previous state-of-theart grows to 15% even after reranking. We believe this approach will generalize to other challenging scientific problems.