Protein Language Model-Aligned Spectra Embeddings for De Novo Peptide Sequencing

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.