scholarly journals Extraction of local hydrogen exchange data from HDX CAD MS measurements by deconvolution of isotopic distributions of fragment ions

2006 ◽  
Vol 17 (11) ◽  
pp. 1543-1551 ◽  
Author(s):  
Rinat R. Abzalimov ◽  
Igor A. Kaltashov
Keyword(s):  
Hydrogen Exchange ◽  
Fragment Ions ◽  
Exchange Data

Der Einfluß der Kettenlänge auf die massenspektrometrigche Fragmentierung höherer 1.ω-Diphenylalkane / The Influence of the Chain Length on the Mass Spectrometric Fragmentation of Higher 1, ω-Diphenylalkanes

1979 ◽  
Vol 34 (12) ◽  
pp. 1750-1764 ◽  
Author(s):  
Dietmar Kuck ◽  
Hans-Friedrich Grützmacher
Keyword(s):  
Gas Phase ◽  
Chain Length ◽  
Hydrogen Exchange ◽  
Molecular Ions ◽  
Mass Spectrometric ◽  
Exchange Reactions ◽  
Fragment Ions ◽  
Competitive Reactions ◽  
Solvated Ions ◽  
Mass Spectrometric Fragmentation

The mass spectrometric fragmentation of 1,ω-diphenylalkanes (2 ≤ ω ≤ 22) has been studied in order to elucidate the effect of the chain length on the reactions of unstable and metastable molecular ions with respect to the occurrence of internally solvated ions in the gas phase. The unstable molecular ions of all 1,ω-diphenylalkanes react predominantly by formation of C7H7+ and C7H8+ ions, the latter ones are also formed by metastable molecular ions. Neither the variation of the relative abundances of these fragment ions with the chain length nor the fragmentation of specifically deuterated molecular ions indicate any internal solvation in the reacting ions. Inspite of the localized activated C-H bonds at the benzylic positions, these “activated” H atoms are not involved in intra­molecular hydrogen exchange reactions or the formation of C7H8+ ions (with the exception of ω = 3). Especially the higher homologues (co ≿ 12) react very similarly to the molecular ions of 1-phenylalkanes, while the fragmentation of the lower homologues (co ≲ 6) is determined by specific (“vinculoselective”) competitive reactions, e.g. loss of C7H7 and C8H8.

scholarly journals SWOTein: A structure-based approach to predict stability Strengths and Weaknesses of prOTEINs

2020 ◽  
Author(s):  
Q. Hou ◽  
F. Pucci ◽  
F. Ancien ◽  
J.M. Kwasigroch ◽  
R. Bourgeas ◽  
...  
Keyword(s):  
Free Energy ◽  
Large Scale ◽  
Hydrogen Exchange ◽  
Energy Functions ◽  
Functional Regions ◽  
Apocytochrome B ◽  
Amino Acid Binding ◽  
The Individual ◽  
Structured Proteins ◽  
Exchange Data

AbstractMotivationAlthough structured proteins adopt their lowest free energy conformation in physiological conditions, the individual residues are generally not in their lowest free energy conformation. Residues that are stability weaknesses are often involved in functional regions, whereas stability strengths ensure local structural stability. The detection of strengths and weaknesses provides key information to guide protein engineering experiments aiming to modulate folding and various functional processes.ResultsWe developed the SWOTein predictor which identifies strong and weak residues in proteins on the basis of three types of statistical energy functions describing local interactions along the chain, hydrophobic forces and tertiary interactions. The large-scale comparison of the different types of strengths and weaknesses showed their complementarity and the enhancement of the information they provide. We applied SWOTein to apocytochrome b562 and found good agreement between predicted strengths and weaknesses and native hydrogen exchange data. Its application to an amino acid-binding protein identified the hinge at the basis of the conformational change. SWOTein is both fast and accurate and can be applied at small and large scale to analyze and modulate folding and molecular recognition processes.AvailabilityThe SWOTein webserver provides the list of predicted strengths and weaknesses and a protein structure visualization tool that facilitates the interpretation of the predictions. It is freely available for academic use at http://babylone.ulb.ac.be/SWOTein.

scholarly journals Revealing Unknown Protein Structures Using Computational Conformational Sampling Guided by Experimental Hydrogen-Exchange Data

2018 ◽  
Vol 19 (11) ◽  
pp. 3406
Author(s):  
Didier Devaurs ◽  
Dinler Antunes ◽  
Lydia Kavraki
Keyword(s):  
Protein Structure ◽  
Computational Methods ◽  
Hydrogen Exchange ◽  
Protein Structures ◽  
Conformational Space ◽  
Conformational Sampling ◽  
Computational Framework ◽  
Large Proteins ◽  
Unknown Protein ◽  
Exchange Data

Both experimental and computational methods are available to gather information about a protein’s conformational space and interpret changes in protein structure. However, experimentally observing and computationally modeling large proteins remain critical challenges for structural biology. Our work aims at addressing these challenges by combining computational and experimental techniques relying on each other to overcome their respective limitations. Indeed, despite its advantages, an experimental technique such as hydrogen-exchange monitoring cannot produce structural models because of its low resolution. Additionally, the computational methods that can generate such models suffer from the curse of dimensionality when applied to large proteins. Adopting a common solution to this issue, we have recently proposed a framework in which our computational method for protein conformational sampling is biased by experimental hydrogen-exchange data. In this paper, we present our latest application of this computational framework: generating an atomic-resolution structural model for an unknown protein state. For that, starting from an available protein structure, we explore the conformational space of this protein, using hydrogen-exchange data on this unknown state as a guide. We have successfully used our computational framework to generate models for three proteins of increasing size, the biggest one undergoing large-scale conformational changes.

scholarly journals Coarse-Grained Conformational Sampling of Protein Structure Improves the Fit to Experimental Hydrogen-Exchange Data

2017 ◽  
Vol 4 ◽  
Author(s):  
Didier Devaurs ◽  
Dinler A. Antunes ◽  
Malvina Papanastasiou ◽  
Mark Moll ◽  
Daniel Ricklin ◽  
...  
Keyword(s):  
Protein Structure ◽  
Hydrogen Exchange ◽  
Coarse Grained ◽  
Conformational Sampling ◽  
Exchange Data

Kinetic class analysis of hydrogen-exchange data

Biochemistry ◽  
1970 ◽  
Vol 9 (7) ◽  
pp. 1547-1553 ◽  
Author(s):  
Stuart L. Laiken ◽  
Morton P. Printz
Keyword(s):  
Hydrogen Exchange ◽  
Class Analysis ◽  
Kinetic Class ◽  
Exchange Data

FORMATION OF METASUBJECT COMPETENCIES IN THE COURSE “INFORMATION TECHNOLOGIES” BY MEANS OF THE BIG DATA PROCESSING LANGUAGE R

2019 ◽  
pp. 12-22
Author(s):  
D. M. Nazarov
Keyword(s):  
Data Processing ◽  
Information Technologies ◽  
Data Sets ◽  
Training Methods ◽  
Economic Knowledge ◽  
R Language ◽  
Currency Exchange ◽  
Business Informatics ◽  
Exchange Data ◽  
Processing Language

The article describes the training methods in the course “Information Technologies” for the future bachelors of the directions “Economics”, “Management”, “Finance”, “Business Informatics”, the development of metasubject competencies of the student while his use of tools for data processing by means of the language R. The metasubject essence of the work is to update traditional economic knowledge and skills through various presentation forms of the same data sets. As part of the laboratory work described in the article, future bachelors learn to use the basic tools of the R language and acquire specific skills and abilities in R-Studio using the example of processing currency exchange data. The description of the methods is presented in the form of the traditional Key-by-Key technology, which is widely used in teaching information technologies.

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