{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Abdul-Khalek N"],"funding":["Elitenetzwerk Bayern","Bundesministerium f?r Bildung und Forschung","European Research Council","Karl Pedersens og Hustrus Industrifond"],"pagination":["2709-2726"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC12150320"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["24(6)"],"pubmed_abstract":["Identifying detectable peptides, known as flyers, is key in mass spectrometry-based proteomics. Peptide detectability is strongly related to peptide sequences and their resulting physicochemical properties. Moreover, the high variability in MS data challenges the development of a generic model for detectability prediction, underlining the need for customizable tools. We present Pfly, a deep learning model developed to predict peptide detectability based solely on peptide sequence. Pfly is a versatile and reliable state-of-the-art tool, offering high performance, accessibility, and easy customizability for end-users. This adaptability allows researchers to tailor Pfly to specific experimental conditions, improving accuracy and expanding applicability across various research fields. Pfly is "],"journal":["Journal of proteome research"],"pubmed_title":["To Fly, or Not to Fly, That Is the Question: A Deep Learning Model for Peptide Detectability Prediction in Mass Spectrometry."],"pmcid":["PMC12150320"],"funding_grant_id":["DI-201907020","031L0305A","F-6-M5613.6.K-NW-2021-411/1/1","101077037"],"pubmed_authors":["Picciani M","Abdul-Khalek N","Wimmer R","Shouman O","Overgaard MT","Wilhelm M","Gregersen Echers S"],"additional_accession":[]},"is_claimable":false,"name":"To Fly, or Not to Fly, That Is the Question: A Deep Learning Model for Peptide Detectability Prediction in Mass Spectrometry.","description":"Identifying detectable peptides, known as flyers, is key in mass spectrometry-based proteomics. Peptide detectability is strongly related to peptide sequences and their resulting physicochemical properties. Moreover, the high variability in MS data challenges the development of a generic model for detectability prediction, underlining the need for customizable tools. We present Pfly, a deep learning model developed to predict peptide detectability based solely on peptide sequence. Pfly is a versatile and reliable state-of-the-art tool, offering high performance, accessibility, and easy customizability for end-users. This adaptability allows researchers to tailor Pfly to specific experimental conditions, improving accuracy and expanding applicability across various research fields. Pfly is ","dates":{"release":"2025-01-01T00:00:00Z","publication":"2025 Jun","modification":"2026-06-02T02:34:47.159Z","creation":"2026-05-25T03:07:02.851Z"},"accession":"S-EPMC12150320","cross_references":{"pubmed":["40344201"],"doi":["10.1021/acs.jproteome.4c00973"]}}