Related papers: DPST: De Novo Peptide Sequencing with Amino-Acid-Aware Transformers

DPST: De Novo Peptide Sequencing with Amino-Acid-Aware Transformers

URL: http://arxiv.org/abs/2203.13132v1
Date: Wed, 23 Mar 2022 08:01:06 GMT
Title: DPST: De Novo Peptide Sequencing with Amino-Acid-Aware Transformers
Authors: Yan Yang and Zakir Hossain and Khandaker Asif and Liyuan Pan and Shafin Rahman and Eric Stone
Abstract summary: De novo peptide sequencing aims to recover amino acid sequences of a peptide from tandem mass spectrometry (MS) data. Existing approaches for de novo analysis enumerate MS evidence for all amino acid classes during inference. Our approach, DPST, circumvents these limitations with two key components.
Score: 11.527280359634524
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: De novo peptide sequencing aims to recover amino acid sequences of a peptide from tandem mass spectrometry (MS) data. Existing approaches for de novo analysis enumerate MS evidence for all amino acid classes during inference. It leads to over-trimming on receptive fields of MS data and restricts MS evidence associated with following undecoded amino acids. Our approach, DPST, circumvents these limitations with two key components: (1) A confidence value aggregation encoder to sketch spectrum representations according to amino-acid-based connectivity among MS; (2) A global-local fusion decoder to progressively assimilate contextualized spectrum representations with a predefined preconception of localized MS evidence and amino acid priors. Our components originate from a closed-form solution and selectively attend to informative amino-acid-aware MS representations. Through extensive empirical studies, we demonstrate the superiority of DPST, showing that it outperforms state-of-the-art approaches by a margin of 12% - 19% peptide accuracy.

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