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Argentini A, Staes A, Grüning B, Mehta S, Easterly C, Griffin TJ, Jagtap P, Impens F, Martens L. Update on the moFF Algorithm for Label-Free Quantitative Proteomics. J Proteome Res 2018; 18:728-731. [DOI: 10.1021/acs.jproteome.8b00708] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Andrea Argentini
- VIB-UGent Center for Medical Biotechnology, VIB, 9000 Ghent, Belgium
- Department of Biochemistry, Ghent University, 9000 Ghent, Belgium
| | - An Staes
- VIB-UGent Center for Medical Biotechnology, VIB, 9000 Ghent, Belgium
- Department of Biochemistry, Ghent University, 9000 Ghent, Belgium
| | - Björn Grüning
- Bioinformatics Group, Department of Computer Science, University of Freiburg, Freiburg, Baden-Württemberg 79110, Germany
| | - Subina Mehta
- Department of Biochemistry, Molecular Biology, and Biophysics, University of Minnesota Twin Cities, Minneapolis 55455, United States
| | - Caleb Easterly
- Department of Biochemistry, Molecular Biology, and Biophysics, University of Minnesota Twin Cities, Minneapolis 55455, United States
| | - Timothy J. Griffin
- Department of Biochemistry, Molecular Biology, and Biophysics, University of Minnesota Twin Cities, Minneapolis 55455, United States
| | - Pratik Jagtap
- Department of Biochemistry, Molecular Biology, and Biophysics, University of Minnesota Twin Cities, Minneapolis 55455, United States
| | - Francis Impens
- VIB-UGent Center for Medical Biotechnology, VIB, 9000 Ghent, Belgium
- Department of Biochemistry, Ghent University, 9000 Ghent, Belgium
| | - Lennart Martens
- VIB-UGent Center for Medical Biotechnology, VIB, 9000 Ghent, Belgium
- Department of Biochemistry, Ghent University, 9000 Ghent, Belgium
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Zaia J, Khatri K, Klein J, Shao C, Sheng Y, Viner R. Complete Molecular Weight Profiling of Low-Molecular Weight Heparins Using Size Exclusion Chromatography-Ion Suppressor-High-Resolution Mass Spectrometry. Anal Chem 2016; 88:10654-10660. [PMID: 27709905 DOI: 10.1021/acs.analchem.6b03081] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Low-molecular weight heparins (LMWH) prepared by partial depolymerization of unfractionated heparin are used globally to treat coagulation disorders on an outpatient basis. Patent protection for several LMWH has expired and abbreviated new drug applications have been approved by the Food and Drug Administration. As a result, reverse engineering of LMWH for biosimilar LMWH has become an active global endeavor. Traditionally, the molecular weight distributions of LMWH preparations have been determined using size exclusion chromatography (SEC) with optical detection. Recent advances in liquid chromatography-mass spectrometry methods have enabled exact mass measurements of heparin saccharides roughly up to degree-of-polymerization 20, leaving the high molecular weight half of the LMWH preparation unassigned. We demonstrate a new LC-MS system capable of determining the exact masses of complete LMWH preparations, up to dp30. This system employed an ion suppressor cell to desalt the chromatographic effluent online prior to the electrospray mass spectrometry source. We expect this new capability will impact the ability to define LMWH mixtures favorably.
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Affiliation(s)
- Joseph Zaia
- Center for Biomedical Mass Spectrometry, Department of Biochemistry, Boston University Medical Campus , 670 Albany Street, Boston, Massachusetts 02118, United States.,Bioinformatics Program, Boston University , Boston, Massachusetts 02115, United States
| | - Kshitij Khatri
- Center for Biomedical Mass Spectrometry, Department of Biochemistry, Boston University Medical Campus , 670 Albany Street, Boston, Massachusetts 02118, United States
| | - Joshua Klein
- Center for Biomedical Mass Spectrometry, Department of Biochemistry, Boston University Medical Campus , 670 Albany Street, Boston, Massachusetts 02118, United States.,Bioinformatics Program, Boston University , Boston, Massachusetts 02115, United States
| | - Chun Shao
- Center for Biomedical Mass Spectrometry, Department of Biochemistry, Boston University Medical Campus , 670 Albany Street, Boston, Massachusetts 02118, United States
| | - Yuewei Sheng
- Center for Biomedical Mass Spectrometry, Department of Biochemistry, Boston University Medical Campus , 670 Albany Street, Boston, Massachusetts 02118, United States
| | - Rosa Viner
- Thermo Fisher Scientific , San Jose, California 95134, United States
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Dittwald P, Valkenborg D. BRAIN 2.0: time and memory complexity improvements in the algorithm for calculating the isotope distribution. JOURNAL OF THE AMERICAN SOCIETY FOR MASS SPECTROMETRY 2014; 25:588-94. [PMID: 24519333 PMCID: PMC3953541 DOI: 10.1007/s13361-013-0796-5] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/12/2013] [Revised: 11/27/2013] [Accepted: 11/27/2013] [Indexed: 06/03/2023]
Abstract
Recently, an elegant iterative algorithm called BRAIN (Baffling Recursive Algorithm for Isotopic distributioN calculations) was presented. The algorithm is based on the classic polynomial method for calculating aggregated isotope distributions, and it introduces algebraic identities using Newton-Girard and Viète's formulae to solve the problem of polynomial expansion. Due to the iterative nature of the BRAIN method, it is a requirement that the calculations start from the lightest isotope variant. As such, the complexity of BRAIN scales quadratically with the mass of the putative molecule, since it depends on the number of aggregated peaks that need to be calculated. In this manuscript, we suggest two improvements of the algorithm to decrease both time and memory complexity in obtaining the aggregated isotope distribution. We also illustrate a concept to represent the element isotope distribution in a generic manner. This representation allows for omitting the root calculation of the element polynomial required in the original BRAIN method. A generic formulation for the roots is of special interest for higher order element polynomials such that root finding algorithms and its inaccuracies can be avoided.
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Affiliation(s)
- Piotr Dittwald
- College of Inter-faculty Individual Studies in Mathematics and Natural Sciences, University of Warsaw, Warsaw, Poland
- Institute of Informatics, University of Warsaw, Warsaw, Poland
| | - Dirk Valkenborg
- Applied Bio and Molecular Systems, VITO, Mol, Belgium
- Interuniversity Institute for Biostatistics and Statistical Bioinformatics, Hasselt University, Diepenbeek, Belgium
- Center for Proteomics, Antwerp, Belgium
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