Elucidating the higher‐order structure of biopolymers by structural probing and mass spectrometry: MS3D
Identifieur interne : 000999 ( Main/Exploration ); précédent : 000998; suivant : 000A00Elucidating the higher‐order structure of biopolymers by structural probing and mass spectrometry: MS3D
Auteurs : Daniele Fabris [États-Unis] ; Eizadora T. Yu [États-Unis]Source :
- Journal of Mass Spectrometry [ 1076-5174 ] ; 2010-08.
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Abstract
Chemical probing represents a very versatile alternative for studying the structure and dynamics of substrates that are intractable by established high‐resolution techniques. The implementation of MS‐based strategies for the characterization of probing products has not only extended the range of applicability to virtually all types of biopolymers but has also paved the way for the introduction of new reagents that would not have been viable with traditional analytical platforms. As the availability of probing data is steadily increasing on the wings of the development of dedicated interpretation aids, powerful computational approaches have been explored to enable the effective utilization of such information to generate valid molecular models. This combination of factors has contributed to making the possibility of obtaining actual 3D structures by MS‐based technologies (MS3D) a reality. Although approaches for achieving structure determination of unknown targets or assessing the dynamics of known structures may share similar reagents and development trajectories, they clearly involve distinctive experimental strategies, analytical concerns and interpretation paradigms. This Perspective offers a commentary on methods aimed at obtaining distance constraints for the modeling of full‐fledged structures while highlighting common elements, salient distinctions and complementary capabilities exhibited by methods used in dynamics studies. We discuss critical factors to be addressed for completing effective structural determinations and expose possible pitfalls of chemical methods. We survey programs developed for facilitating the interpretation of experimental data and discuss possible computational strategies for translating sparse spatial constraints into all‐atom models. Examples are provided to illustrate how the concerted application of very diverse probing techniques can lead to the solution of actual biological systems. Copyright © 2010 John Wiley & Sons, Ltd.
Url:
- https://api.istex.fr/document/3A8C4565A7CFBC41E9EA08B1BB06F80204607263/fulltext/pdf
- http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3432860
DOI: 10.1002/jms.1762
Affiliations:
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The implementation of MS‐based strategies for the characterization of probing products has not only extended the range of applicability to virtually all types of biopolymers but has also paved the way for the introduction of new reagents that would not have been viable with traditional analytical platforms. As the availability of probing data is steadily increasing on the wings of the development of dedicated interpretation aids, powerful computational approaches have been explored to enable the effective utilization of such information to generate valid molecular models. This combination of factors has contributed to making the possibility of obtaining actual 3D structures by MS‐based technologies (MS3D) a reality. Although approaches for achieving structure determination of unknown targets or assessing the dynamics of known structures may share similar reagents and development trajectories, they clearly involve distinctive experimental strategies, analytical concerns and interpretation paradigms. This Perspective offers a commentary on methods aimed at obtaining distance constraints for the modeling of full‐fledged structures while highlighting common elements, salient distinctions and complementary capabilities exhibited by methods used in dynamics studies. We discuss critical factors to be addressed for completing effective structural determinations and expose possible pitfalls of chemical methods. We survey programs developed for facilitating the interpretation of experimental data and discuss possible computational strategies for translating sparse spatial constraints into all‐atom models. 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