Beam search decoder for enhancing sequence decoding speed in single-molecule peptide sequencing data.

Publisher: Public Library of Science Country of Publication: United States NLM ID: 101238922 Publication Model: eCollection Cited Medium: Internet ISSN: 1553-7358 (Electronic) Linking ISSN: 1553734X NLM ISO Abbreviation: PLoS Comput Biol Subsets: MEDLINE

Original Publication: San Francisco, CA : Public Library of Science, [2005]-

Proteomics* ; Peptides*/chemistry; Proteins/chemistry ; Amino Acid Sequence ; Amino Acids/chemistry

Next-generation single-molecule protein sequencing technologies have the potential to significantly accelerate biomedical research. These technologies offer sensitivity and scalability for proteomic analysis. One auspicious method is fluorosequencing, which involves: cutting naturalized proteins into peptides, attaching fluorophores to specific amino acids, and observing variations in light intensity as one amino acid is removed at a time. The original peptide is classified from the sequence of light-intensity reads, and proteins can subsequently be recognized with this information. The amino acid step removal is achieved by attaching the peptides to a wall on the C-terminal and using a process called Edman Degradation to remove an amino acid from the N-Terminal. Even though a framework (Whatprot) has been proposed for the peptide classification task, processing times remain restrictive due to the massively parallel data acquisicion system. In this paper, we propose a new beam search decoder with a novel state formulation that obtains considerably lower processing times at the expense of only a slight accuracy drop compared to Whatprot. Furthermore, we explore how our novel state formulation may lead to even faster decoders in the future.
Competing Interests: The authors have declared that no competing interests exist.
(Copyright: © 2023 Kipen, Jaldén. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Brief Bioinform. 2012 Sep;13(5):586-614. (PMID: 22373723)
Nat Methods. 2022 Jul;19(7):774-779. (PMID: 35534633)
PLoS Comput Biol. 2023 May 30;19(5):e1011157. (PMID: 37253025)
Trends Biochem Sci. 2021 Aug;46(8):661-672. (PMID: 33653632)
Trends Biochem Sci. 2020 Jan;45(1):76-89. (PMID: 31676211)
Nat Biotechnol. 2018 Oct 22;:. (PMID: 30346938)
Nat Methods. 2021 Jun;18(6):604-617. (PMID: 34099939)
Nat Nanotechnol. 2018 Sep;13(9):786-796. (PMID: 30190617)
Eur J Biochem. 1967 Mar;1(1):80-91. (PMID: 6059350)
Nature. 2023 Jan;613(7945):794-797. (PMID: 36690758)
PLoS Comput Biol. 2015 Feb 25;11(2):e1004080. (PMID: 25714988)
Genome Biol. 2018 Jul 13;19(1):90. (PMID: 30005597)

0 (Peptides)
0 (Proteins)
0 (Amino Acids)

Date Created: 20231107 Date Completed: 20231120 Latest Revision: 20231129

20240628

PMC10656014

10.1371/journal.pcbi.1011345

37934778