Abstract
Mass spectrometry-based proteomics focuses on identifying the peptide
that generates a tandem mass spectrum. Traditional methods rely on
protein databases but are often limited or inapplicable in certain contexts.
De novo peptide sequencing, which assigns peptide sequences to spectra
without prior information, is valuable for diverse biological applications;
however, owing to a lack of accuracy, it remains challenging to apply. Here
we introduce InstaNovo, a transformer model that translates fragment ion
peaks into peptide sequences. We demonstrate that InstaNovo outperforms
state-of-the-art methods and showcase its utility in several applications.
We also introduce InstaNovo+, a diffusion model that improves performance
through iterative refinement of predicted sequences. Using these models,
we achieve improved therapeutic sequencing coverage, discover novel
peptides and detect unreported organisms in diverse datasets, thereby
expanding the scope and detection rate of proteomics searches. Our models
unlock opportunities across domains such as direct protein s­ eq­ ue­ nc­ ing,
i­ mm­ unopeptidomics and exploration of the dark proteome.

Date: 2025.04.24 (THU) 18:00 ~
Presenter: Seokwoo Jo

조석우 학생의 주도로, MS-based Deep Learning De novo peptide sequencing model에 관한 저널클럽을 진행했습니다.
Proteomics 전반적인 실험 샘플링부터, Liquid Chromatography, Mass spectrometry, Spectrometry concept에 대한 세미나를 진행했습니다.