Protein Language Model-Aligned Spectra Embeddings for De Novo Peptide Sequencing
NaderiAlizadeh, N.; Dallago, C.; Soderblom, E. J.; Soderling, S. H.
Show abstract
We consider the problem of de novo peptide sequencing in tandem mass spectrometry, where the goal is to predict the underlying peptide sequence given a spectrums fragment peaks and precursor information. We present PLMNovo, a constrained learning framework that leverages pre-trained protein language models (PLMs) to guide the training process. In particular, we cast peptide-spectrum matching as a constrained optimization problem that enforces alignment between spectrum and peptide embeddings produced by a spectrum encoder and a PLM, respectively. We use a Lagrangian primal-dual algorithm to train the spectrum encoder and the peptide decoder by solving the proposed constrained learning problem, while optionally fine-tuning the pre-trained PLM. Through numerical experiments on established benchmarks, we demonstrate that PLMNovo outperforms several state-of-the-art deep learning-based de novo sequencing algorithms.
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