ArticleBioinformatics (Oxford, England)2010
Machine learning based prediction for peptide drift times in ion mobility spectrometry.
Article in Bioinformatics (Oxford, England), 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed.
- Artificial Intelligence and Machine Learning in Mass Spectrometry-Based Proteomics and Metabolomics.Methods in molecular biology (Clifton, N.J.) · 2027Article
- Peptide Property Prediction for Mass Spectrometry Using AI: An Introduction to State of the Art Models.Proteomics · 2025Review
- Optimized Time-Segmented Acquisition Expands Peptide and Protein Identification in TIMS-TOF Pro Mass Spectrometry.Journal of proteome research · 2025Article
- Deep Learning Predicts Non-Normal Transmission Distributions in High-Field Asymmetric Waveform Ion Mobility (FAIMS) Directly from Peptide Sequence.Analytical chemistry · 2025Article
- A Deep Convolutional Neural Network for Prediction of Peptide Collision Cross Sections in Ion Mobility Spectrometry.Biomolecules · 2021Article
- Deep learning the collisional cross sections of the peptide universe from a million experimental values.Nature communications · 2021Article
- Effect of Phosphorylation on the Collision Cross Sections of Peptide Ions in Ion Mobility Spectrometry.Mass spectrometry (Tokyo, Japan) · 2021Article
- Fundamentals of Ion Mobility-Mass Spectrometry for the Analysis of Biomolecules.Methods in molecular biology (Clifton, N.J.) · 2020Review
- Ion Mobility Collision Cross Section Compendium.Analytical chemistry · 2017Review
- Investigation of the Complete Suite of the Leucine and Isoleucine Isomers: Toward Prediction of Ion Mobility Separation Capabilities.Analytical chemistry · 2017Article
- Ion mobility-mass spectrometry: time-dispersive instrumentation.Analytical chemistry · 2015Review
- Physicochemical and formulation developability assessment for therapeutic peptide delivery--a primer.The AAPS journal · 2015Review
- Conformational ordering of biomolecules in the gas phase: nitrogen collision cross sections measured on a prototype high resolution drift tube ion mobility-mass spectrometer.Analytical chemistry · 2014Article
- Increasing Confidence of LC-MS Identifications by Utilizing Ion Mobility Spectrometry.International journal of mass spectrometry · 2013Article
- Spectroscopic determination of aboveground biomass in grasslands using spectral transformations, Support Vector Machine and Partial Least Squares Regression.Sensors (Basel, Switzerland) · 2013Article
- Prediction of peptide drift time in ion mobility mass spectrometry from sequence-based features.BMC bioinformatics · 2013Article
- Mass spectrometry-based proteomics: existing capabilities and future directions.Chemical Society reviews · 2012Review
- Update of PROFEAT: a web server for computing structural and physicochemical features of proteins and peptides from amino acid sequence.Nucleic acids research · 2011Article
- Quantifying and predicting the promiscuity and isoform specificity of small-molecule cytochrome P450 inhibitors.Drug metabolism and disposition: the biological fate of chemicals · 2010Article
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Authors and funding
12 authors.
Funding
Abstract
motivationIon mobility spectrometry (IMS) has gained significant traction over the past few years for rapid, high-resolution separations of analytes based upon gas-phase ion structure, with significant potential impacts in the field of proteomic analysis. IMS coupled with mass spectrometry (MS) affords multiple improvements over traditional proteomics techniques, such as in the elucidation of secondary structure information, identification of post-translational modifications, as well as higher identification rates with reduced experiment times. The high throughput nature of this technique benefits from accurate calculation of cross sections, mobilities and associated drift times of peptides, thereby enhancing downstream data analysis. Here, we present a model that uses physicochemical properties of peptides to accurately predict a peptide's drift time directly from its amino acid sequence. This model is used in conjunction with two mathematical techniques, a partial least squares regression and a support vector regression setting.
resultsWhen tested on an experimentally created high confidence database of 8675 peptide sequences with measured drift times, both techniques statistically significantly outperform the intrinsic size parameters-based calculations, the currently held practice in the field, on all charge states (+2, +3 and +4). AVAILABILITY: The software executable, imPredict, is available for download from http:/omics.pnl.gov/software/imPredict.php CONTACT: rds@pnl.gov SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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