Sven Degroeve1, Lennart Martens. 1. Department of Medical Protein Research, VIB, Ghent 9000, Belgium and Department of Biochemistry, Ghent University, Ghent 9000, Belgium.
Abstract
MOTIVATION: Tandem mass spectrometry provides the means to match mass spectrometry signal observations with the chemical entities that generated them. The technology produces signal spectra that contain information about the chemical dissociation pattern of a peptide that was forced to fragment using methods like collision-induced dissociation. The ability to predict these MS(2) signals and to understand this fragmentation process is important for sensitive high-throughput proteomics research. RESULTS: We present a new tool called MS(2)PIP for predicting the intensity of the most important fragment ion signal peaks from a peptide sequence. MS(2)PIP pre-processes a large dataset with confident peptide-to-spectrum matches to facilitate data-driven model induction using a random forest regression learning algorithm. The intensity predictions of MS(2)PIP were evaluated on several independent evaluation sets and found to correlate significantly better with the observed fragment-ion intensities as compared with the current state-of-the-art PeptideART tool. AVAILABILITY: MS(2)PIP code is available for both training and predicting at http://compomics.com/.
MOTIVATION: Tandem mass spectrometry provides the means to match mass spectrometry signal observations with the chemical entities that generated them. The technology produces signal spectra that contain information about the chemical dissociation pattern of a peptide that was forced to fragment using methods like collision-induced dissociation. The ability to predict these MS(2) signals and to understand this fragmentation process is important for sensitive high-throughput proteomics research. RESULTS: We present a new tool called MS(2)PIP for predicting the intensity of the most important fragment ion signal peaks from a peptide sequence. MS(2)PIP pre-processes a large dataset with confident peptide-to-spectrum matches to facilitate data-driven model induction using a random forest regression learning algorithm. The intensity predictions of MS(2)PIP were evaluated on several independent evaluation sets and found to correlate significantly better with the observed fragment-ion intensities as compared with the current state-of-the-art PeptideART tool. AVAILABILITY: MS(2)PIP code is available for both training and predicting at http://compomics.com/.
Authors: Lewis Y Geer; Sanford P Markey; Jeffrey A Kowalak; Lukas Wagner; Ming Xu; Dawn M Maynard; Xiaoyu Yang; Wenyao Shi; Stephen H Bryant Journal: J Proteome Res Date: 2004 Sep-Oct Impact factor: 4.466
Authors: Chandrasegaran Narasimhan; David L Tabb; Nathan C Verberkmoes; Melissa R Thompson; Robert L Hettich; Edward C Uberbacher Journal: Anal Chem Date: 2005-12-01 Impact factor: 6.986
Authors: Henry Lam; Eric W Deutsch; James S Eddes; Jimmy K Eng; Nichole King; Stephen E Stein; Ruedi Aebersold Journal: Proteomics Date: 2007-03 Impact factor: 3.984
Authors: Jeroen Crappé; Elvis Ndah; Alexander Koch; Sandra Steyaert; Daria Gawron; Sarah De Keulenaer; Ellen De Meester; Tim De Meyer; Wim Van Criekinge; Petra Van Damme; Gerben Menschaert Journal: Nucleic Acids Res Date: 2014-12-15 Impact factor: 16.971
Authors: Axel Leppert; Gefei Chen; Danai Lianoudaki; Chloe Williams; Xueying Zhong; Jonathan D Gilthorpe; Michael Landreh; Jan Johansson Journal: Protein Sci Date: 2022-08 Impact factor: 6.993
Authors: Eric W Deutsch; Yasset Perez-Riverol; Robert J Chalkley; Mathias Wilhelm; Stephen Tate; Timo Sachsenberg; Mathias Walzer; Lukas Käll; Bernard Delanghe; Sebastian Böcker; Emma L Schymanski; Paul Wilmes; Viktoria Dorfer; Bernhard Kuster; Pieter-Jan Volders; Nico Jehmlich; Johannes P C Vissers; Dennis W Wolan; Ana Y Wang; Luis Mendoza; Jim Shofstahl; Andrew W Dowsey; Johannes Griss; Reza M Salek; Steffen Neumann; Pierre-Alain Binz; Henry Lam; Juan Antonio Vizcaíno; Nuno Bandeira; Hannes Röst Journal: J Proteome Res Date: 2018-10-11 Impact factor: 4.466