Table of contents
1. Sample and data quality in interaction analysis - two sides of the same coin
3. Sample and data quality in interaction analysis - two sides of the same coin
5. Sample and data quality in interaction analysis - two sides of the same coin
6. Frequently asked questions and trends in interaction analysis.
7. Frequently asked questions and trends in interaction analysis.
8. Reliable characterization of a binding interaction requires high data quality at good level of method understanding
9. ITC –gold standard in interaction analysis.
10. Ease of use without compromising performance in a broad range of affinities. MicroCal™ PEAQ ITC
11. Inadequate maintenance and experimental setup one of the main causes of poor data quality. Make a sanity check of the raw ITC data
12. Fully integrated wash module ensures adequate maintenance
13. Quality of experiment design facilitated by Design of Experiment software.MicroCal PEAQ-ITC
14. High-resolution data on interaction profiles delivers multiple insights on samples’ behavior.
15. Reliable characterization of a binding interaction requires high sample quality.
16. Basic characterization of protein state and stability in solution is warranted for high quality of results
17. Multiple biophysical methods used for characterization of sample quality
20. Scientific network shares best practices and advocates for sample quality control
21. Reliable characterization of a binding interaction requires high sample quality.
22. Error in sample concentration and lack of understanding of sample state in solution affect quality of interaction analysis
23. N<1. Amount of protein competent for ligand binding can be directly established by ITC
24. N<1. Do not leave it entirely to mathematics! Characterize protein state in solution.
25. ITC flags for a complex mechanism of interaction for a series of ligands.
26. Assessing protein oligomerization state with benchtop instruments.
27. Assessing protein oligomerization state with benchtop instruments.
28. Latest development in DLS technology opens up for broader application in sample QC.
29. Case study examples
30. Case study 1. Early Drug Discovery.Project setbacks could have been avoided with early on control of sample quality.
31. Biosensor assay development prematuarly deemed unsuccessful
32. Major issues with protein quality uncovered with DLS and ITC clarifying lack of specific binding in biosensor assay.
33. Case study 2.DLS and ESI-MS help to identify and resolve issues with irreproducible assay results and bring protein kinase X project back on track.
34. Throubleshooting irreproducible data from biochemical and biophysical assays run with protein kinase X.
35. Protein stability profile established and optimized with DLS.
36. Controlling quality of ligands is as important.
37. Case study 3. Solubility issues with LMW ligands and positive controls impact quality of the binding data.
38. Strickingly different affinity of two isomeric ligands of protein X.
39. Incorrect concentration of LMW compound impacts quality of the binding data
40. Errors in ligand concentration will impact the enthalpy data.
41. Case study 5.Value of sample quality assurance in a large scale Structural Biology project
43. Project setback. Incomplete data from thermal shift binding assays, DSF.
44. Due diligence of the quality of constructs and protein batches used in the project..
45. Profiling protein stability and homogeneity in solution.
46. Optimization of PARPs homogeneity with DLS and DSC
47. Enabling X-ray structure determination along the way.
48. Project setbacks waived and milestones reached through sample quality controls and stability optimization with DLS, DSC and ITC.
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52. Thank you for attending