NovoBench: Benchmarking Deep Learning-based De Novo Peptide Sequencing Methods in Proteomics

TL;DR

NovoBench provides a comprehensive, standardized benchmark for evaluating deep learning methods in de novo peptide sequencing, addressing previous inconsistencies and expanding evaluation metrics to include PTMs, efficiency, and robustness.

Contribution

This work introduces the first unified benchmark for de novo peptide sequencing, integrating diverse datasets, models, and evaluation metrics to facilitate fair comparison and comprehensive analysis.

Findings

Current methods vary significantly in performance.

PTM identification is challenging but crucial.

Robustness varies with peptide length and noise.

Abstract

Tandem mass spectrometry has played a pivotal role in advancing proteomics, enabling the high-throughput analysis of protein composition in biological tissues. Many deep learning methods have been developed for \emph{de novo} peptide sequencing task, i.e., predicting the peptide sequence for the observed mass spectrum. However, two key challenges seriously hinder the further advancement of this important task. Firstly, since there is no consensus for the evaluation datasets, the empirical results in different research papers are often not comparable, leading to unfair comparison. Secondly, the current methods are usually limited to amino acid-level or peptide-level precision and recall metrics. In this work, we present the first unified benchmark NovoBench for \emph{de novo} peptide sequencing, which comprises diverse mass spectrum data, integrated models, and comprehensive evaluation metrics. Recent impressive methods, including DeepNovo, PointNovo, Casanovo, InstaNovo, AdaNovo and $\pi$-HelixNovo are integrated into our framework. In addition to amino acid-level and peptide-level precision and recall, we evaluate the models' performance in terms of identifying post-tranlational modifications (PTMs), efficiency and robustness to peptide length, noise peaks and missing fragment ratio, which are important influencing factors while seldom be considered. Leveraging this benchmark, we conduct a large-scale study of current methods, report many insightful findings that open up new possibilities for future development.