CaltechAUTHORS
Published March 15, 2009 | Version Accepted Version
Journal Article Open

A global benchmark study using affinity-based biosensors

Creators

  • 1. ROR icon University of Utah
  • 2. ROR icon Humanigen (United States)
  • 3. ROR icon California Institute of Technology
  • 4. ROR icon Columbia University
  • 5. ROR icon St. Jude Children's Research Hospital
  • 6. ROR icon University of Zurich
  • 7. ROR icon University of Strasbourg
  • 8. ROR icon Merck (Germany)
  • 9. ROR icon Abbott (United States)
  • 10. ROR icon Institute for Molecular and Cellular Biology
  • 11. ROR icon Momenta Pharmaceuticals (United States)
  • 12. ROR icon XOMA (United States)
  • 13. ROR icon National University of Singapore
  • 14. ROR icon Weizmann Institute of Science
  • 15. ROR icon Thermo Fisher Scientific (United States)
  • 16. ROR icon AstraZeneca (United States)
  • 17. ROR icon GlaxoSmithKline (United Kingdom)
  • 18. ROR icon Medical College of Wisconsin
  • 19. ROR icon Alexion Pharmaceuticals (United States)
  • 20. ROR icon Diazyme (United States)
  • 21. ROR icon Eli Lilly (United States)
  • 22. ROR icon University of Navarra
  • 23. ROR icon Istituto Superiore di Sanità
  • 24. ROR icon Institut Pasteur
  • 25. ROR icon Georgia State University
  • 26. ROR icon PDL BioPharma (United States)
  • 27. ROR icon The University of Texas Health Science Center at San Antonio
  • 28. ROR icon Institut Européen de Chimie et Biologie
  • 29. ROR icon University of Southern Denmark
  • 30. ROR icon Novo Nordisk (Denmark)
  • 31. ROR icon Södertörn University
  • 32. ROR icon Temple University
  • 33. ROR icon United States Food and Drug Administration
  • 34. ROR icon University College London
  • 35. ROR icon University of Twente
  • 36. ROR icon Massachusetts Institute of Technology
  • 37. ROR icon Northern Illinois University
  • 38. ROR icon Institut Pasteur de Montevideo
  • 39. ROR icon University of Manchester
  • 40. ROR icon University of Maryland, Baltimore
  • 41. ROR icon University of Georgia
  • 42. ROR icon François Rabelais University
  • 43. ROR icon University of Michigan–Ann Arbor
  • 44. ROR icon Human Genome Sciences (United States)
  • 45. ROR icon University of Catania
  • 46. ROR icon Montana State University
  • 47. ROR icon Complutense University of Madrid
  • 48. ROR icon Bio-Rad (Israel)
  • 49. ROR icon Institute of Chemistry, Academia Sinica
  • 50. ROR icon The University of Texas Health Science Center at Houston
  • 51. ROR icon Fred Hutchinson Cancer Research Center
  • 52. ROR icon The Francis Crick Institute
  • 53. ROR icon University of Edinburgh
  • 54. ROR icon Acceleron Pharma (United States)
  • 55. ROR icon Biotechnology Research Institute
  • 56. ROR icon Health Sciences and Nutrition
  • 57. ROR icon Attana (Sweden)
  • 58. ROR icon University of Applied Sciences and Arts Northwestern Switzerland
  • 59. ROR icon Bio-Rad (United States)
  • 60. ROR icon Monsanto (United States)
  • 61. ROR icon Saarland University
  • 62. ROR icon Institute of Neuroimmunology of the Slovak Academy of Sciences
  • 63. ROR icon Bristol-Myers Squibb (United States)
  • 64. ROR icon Array BioPharma (United States)
  • 65. ROR icon L'Institut de Biologie et Technologies
  • 66. ROR icon Masaryk University
  • 67. ROR icon University of Connecticut Health Center
  • 68. ROR icon University of Southern California
  • 69. ROR icon Royal Melbourne Hospital
  • 70. ROR icon Regeneron (United States)
  • 71. ROR icon University of Münster
  • 72. ROR icon University of Salzburg
  • 73. ROR icon Los Alamos National Laboratory
  • 74. ROR icon Arizona State University
  • 75. ROR icon University of Washington
  • 76. ROR icon Bio-Rad (Canada)

Abstract

To explore the variability in biosensor studies, 150 participants from 20 countries were given the same protein samples and asked to determine kinetic rate constants for the interaction. We chose a protein system that was amenable to analysis using different biosensor platforms as well as by users of different expertise levels. The two proteins (a 50-kDa Fab and a 60-kDa glutathione S-transferase [GST] antigen) form a relatively high-affinity complex, so participants needed to optimize several experimental parameters, including ligand immobilization and regeneration conditions as well as analyte concentrations and injection/dissociation times. Although most participants collected binding responses that could be fit to yield kinetic parameters, the quality of a few data sets could have been improved by optimizing the assay design. Once these outliers were removed, the average reported affinity across the remaining panel of participants was 620 pM with a standard deviation of 980 pM. These results demonstrate that when this biosensor assay was designed and executed appropriately, the reported rate constants were consistent, and independent of which protein was immobilized and which biosensor was used.

Additional Information

© 2009 Elsevier B.V. Received 3 October 2008. Available online 27 November 2008. We thank KaloBios Pharmaceuticals for providing the purified Fab and GST–Ag, Biacore/GE Healthcare for providing sensor chips to help develop this model system, and Bio-Rad Laboratories for shipping all of the sample sets worldwide.

Attached Files

Accepted Version - nihms500790.pdf

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nihms500790.pdf

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Additional details

Identifiers

PMCID
PMC3793259
Eprint ID
15514
DOI
10.1016/j.ab.2008.11.021
Resolver ID
CaltechAUTHORS:20090901-094826364

Related works

Describes
10.1016/j.ab.2008.11.021 (DOI)

Dates

Created
2009-09-14
Created from EPrint's datestamp field
Updated
2023-03-16
Created from EPrint's last_modified field