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albanese_dissertation.out
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\BOOKMARK [1][-]{Doc-Start}{Dedication}{}% 1
\BOOKMARK [1][-]{Doc-Start}{Biographical Sketch}{}% 2
\BOOKMARK [1][-]{chapter*.1}{Acknowledgements}{}% 3
\BOOKMARK [1][-]{chapter*.2}{Table of Contents}{}% 4
\BOOKMARK [1][-]{chapter*.2}{List of Tables}{}% 5
\BOOKMARK [1][-]{chapter*.2}{List of Figures}{}% 6
\BOOKMARK [0][-]{chapter.1}{Introduction}{}% 7
\BOOKMARK [1][-]{section.1.1}{Perspective}{chapter.1}% 8
\BOOKMARK [1][-]{section.1.2}{Synopsis}{chapter.1}% 9
\BOOKMARK [0][-]{chapter.2}{Predicting the selectivity of small molecule kinase inhibitors}{}% 10
\BOOKMARK [1][-]{section.2.1}{Gloss}{chapter.2}% 11
\BOOKMARK [1][-]{section.2.2}{Abstract}{chapter.2}% 12
\BOOKMARK [1][-]{section.2.3}{Introduction}{chapter.2}% 13
\BOOKMARK [2][-]{subsection.2.3.1}{Free energy methods can aid structure-based drug design}{section.2.3}% 14
\BOOKMARK [2][-]{subsection.2.3.2}{Selectivity is an important consideration in drug design}{section.2.3}% 15
\BOOKMARK [2][-]{subsection.2.3.3}{The use of physical modeling to predict selectivity is relatively unexplored}{section.2.3}% 16
\BOOKMARK [2][-]{subsection.2.3.4}{Kinases are an important and particularly challenging model system for selectivity predictions}{section.2.3}% 17
\BOOKMARK [2][-]{subsection.2.3.5}{The correlation coefficient measures how useful predictions are in achieving selectivity}{section.2.3}% 18
\BOOKMARK [1][-]{section.2.4}{Results}{chapter.2}% 19
\BOOKMARK [2][-]{subsection.2.4.1}{Alchemical free energy methods can be used to predict compound selectivity}{section.2.4}% 20
\BOOKMARK [2][-]{subsection.2.4.2}{Correlation in force field errors can significantly enhance accuracy of selectivity predictions}{section.2.4}% 21
\BOOKMARK [2][-]{subsection.2.4.3}{Poor selectivity is achieved for the closely related kinases CDK2/CDK9}{section.2.4}% 22
\BOOKMARK [2][-]{subsection.2.4.4}{Greater selectivity is achieved for more distantly related kinases CDK2/ERK2}{section.2.4}% 23
\BOOKMARK [2][-]{subsection.2.4.5}{FEP+ calculations show smaller than expected errors for S predictions}{section.2.4}% 24
\BOOKMARK [2][-]{subsection.2.4.6}{Correlation of forcefield errors accelerates selectivity optimization}{section.2.4}% 25
\BOOKMARK [2][-]{subsection.2.4.7}{Expending more effort to reduce statistical error can be beneficial in selectivity optimization}{section.2.4}% 26
\BOOKMARK [1][-]{section.2.5}{Discussion and Conclusions}{chapter.2}% 27
\BOOKMARK [1][-]{section.2.6}{Methods}{chapter.2}% 28
\BOOKMARK [2][-]{subsection.2.6.1}{Numerical model of selectivity optimization speedup}{section.2.6}% 29
\BOOKMARK [2][-]{subsection.2.6.2}{Numerical model of impact of statistical error on selectivity optimization}{section.2.6}% 30
\BOOKMARK [2][-]{subsection.2.6.3}{Binding Site Similarity analysis}{section.2.6}% 31
\BOOKMARK [2][-]{subsection.2.6.4}{Extracting the binding free energy G from reported experimental data}{section.2.6}% 32
\BOOKMARK [2][-]{subsection.2.6.5}{Structure Preparation}{section.2.6}% 33
\BOOKMARK [2][-]{subsection.2.6.6}{Free Energy Calculations}{section.2.6}% 34
\BOOKMARK [2][-]{subsection.2.6.7}{Statistical Analysis of FEP+ calculations}{section.2.6}% 35
\BOOKMARK [2][-]{subsection.2.6.8}{Quantification of the correlation coefficient }{section.2.6}% 36
\BOOKMARK [2][-]{subsection.2.6.9}{Calculating the marginal distribution of speedup}{section.2.6}% 37
\BOOKMARK [1][-]{section.2.7}{Acknowledgments}{chapter.2}% 38
\BOOKMARK [1][-]{section.2.8}{Funding}{chapter.2}% 39
\BOOKMARK [1][-]{section.2.9}{Disclosures}{chapter.2}% 40
\BOOKMARK [1][-]{section.2.10}{Author Contributions}{chapter.2}% 41
\BOOKMARK [0][-]{chapter.3}{Predicting the impact of clinically-observed kinase mutations using physical modeling}{}% 42
\BOOKMARK [1][-]{section.3.1}{Gloss}{chapter.3}% 43
\BOOKMARK [1][-]{section.3.2}{Abstract}{chapter.3}% 44
\BOOKMARK [1][-]{section.3.3}{Introduction}{chapter.3}% 45
\BOOKMARK [2][-]{subsection.3.3.1}{The long tail of rare kinase mutations frustrates prediction of drug resistance}{section.3.3}% 46
\BOOKMARK [2][-]{subsection.3.3.2}{Alchemical free-energy methods can predict inhibitor binding affinities}{section.3.3}% 47
\BOOKMARK [2][-]{subsection.3.3.3}{Alchemical approaches can predict the impact of protein mutations on free energy}{section.3.3}% 48
\BOOKMARK [2][-]{subsection.3.3.4}{Assessing the potential for physical modeling to predict resistance to FDA-approved TKIs}{section.3.3}% 49
\BOOKMARK [1][-]{section.3.4}{Results}{chapter.3}% 50
\BOOKMARK [2][-]{subsection.3.4.1}{A benchmark of pIC50s for predicting mutational resistance}{section.3.4}% 51
\BOOKMARK [2][-]{subsection.3.4.2}{Most mutations do not significantly reduce TKI potency}{section.3.4}% 52
\BOOKMARK [2][-]{subsection.3.4.3}{FEP+ predicts affinity changes for clinical Abl mutants}{section.3.4}% 53
\BOOKMARK [2][-]{subsection.3.4.4}{FEP+ accurately classifies affinity changes for Abl mutants}{section.3.4}% 54
\BOOKMARK [2][-]{subsection.3.4.5}{How reliant are classification results on choice of cutoff?}{section.3.4}% 55
\BOOKMARK [2][-]{subsection.3.4.6}{Bayesian analysis can estimate the true error}{section.3.4}% 56
\BOOKMARK [2][-]{subsection.3.4.7}{How transferable is FEP+ across the six TKIs?}{section.3.4}% 57
\BOOKMARK [2][-]{subsection.3.4.8}{Understanding the origin of mispredictions}{section.3.4}% 58
\BOOKMARK [2][-]{subsection.3.4.9}{How strongly is accuracy affected for docked TKIs?}{section.3.4}% 59
\BOOKMARK [1][-]{section.3.5}{Discussion and Conclusions}{chapter.3}% 60
\BOOKMARK [1][-]{section.3.6}{Methods}{chapter.3}% 61
\BOOKMARK [2][-]{subsection.3.6.1}{System preparation}{section.3.6}% 62
\BOOKMARK [2][-]{subsection.3.6.2}{Force field parameter assignment}{section.3.6}% 63
\BOOKMARK [2][-]{subsection.3.6.3}{Prime \(MM-GBSA\)}{section.3.6}% 64
\BOOKMARK [2][-]{subsection.3.6.4}{Alchemical free energy perturbation calculations using FEP+}{section.3.6}% 65
\BOOKMARK [2][-]{subsection.3.6.5}{Obtaining G from pIC50 benchmark set data}{section.3.6}% 66
\BOOKMARK [2][-]{subsection.3.6.6}{Assessing prediction performance}{section.3.6}% 67
\BOOKMARK [2][-]{subsection.3.6.7}{Data availability}{section.3.6}% 68
\BOOKMARK [2][-]{subsection.3.6.8}{Code availability}{section.3.6}% 69
\BOOKMARK [1][-]{section.3.7}{Acknowledgments}{chapter.3}% 70
\BOOKMARK [1][-]{section.3.8}{Author Contributions}{chapter.3}% 71
\BOOKMARK [1][-]{section.3.9}{Competing Interests}{chapter.3}% 72
\BOOKMARK [0][-]{chapter.4}{Enabling high-throughput biophysical experiments on clinically-observed mutations}{}% 73
\BOOKMARK [1][-]{section.4.1}{Gloss}{chapter.4}% 74
\BOOKMARK [1][-]{section.4.2}{Abstract}{chapter.4}% 75
\BOOKMARK [1][-]{section.4.3}{Introduction}{chapter.4}% 76
\BOOKMARK [1][-]{section.4.4}{Results}{chapter.4}% 77
\BOOKMARK [2][-]{subsection.4.4.1}{Construct boundary choice impacts Abl kinase domain expression}{section.4.4}% 78
\BOOKMARK [2][-]{subsection.4.4.2}{Screen of 96 kinases finds 52 with useful levels of automated E. coli expression}{section.4.4}% 79
\BOOKMARK [2][-]{subsection.4.4.3}{High-expressing kinases are folded with a well-formed ATP binding site}{section.4.4}% 80
\BOOKMARK [2][-]{subsection.4.4.4}{Fluorescence-based thermostability assay}{section.4.4}% 81
\BOOKMARK [2][-]{subsection.4.4.5}{ATP-competitive inhibitor binding fluorescence assay}{section.4.4}% 82
\BOOKMARK [2][-]{subsection.4.4.6}{Expressing clinically-derived Src and Abl mutants}{section.4.4}% 83
\BOOKMARK [1][-]{section.4.5}{Discussion}{chapter.4}% 84
\BOOKMARK [1][-]{section.4.6}{Methods}{chapter.4}% 85
\BOOKMARK [2][-]{subsection.4.6.1}{Semi-automated selection of kinase construct sequences for E. coli expression}{section.4.6}% 86
\BOOKMARK [2][-]{subsection.4.6.2}{Mutagenesis protocol}{section.4.6}% 87
\BOOKMARK [2][-]{subsection.4.6.3}{Expression testing}{section.4.6}% 88
\BOOKMARK [2][-]{subsection.4.6.4}{Fluorescence-based thermostability assay}{section.4.6}% 89
\BOOKMARK [2][-]{subsection.4.6.5}{ATP-competitive inhibitor binding fluorescence assay}{section.4.6}% 90
\BOOKMARK [2][-]{subsection.4.6.6}{Large Scale expression and purification protocol for MK14}{section.4.6}% 91
\BOOKMARK [1][-]{section.4.7}{Author Contributions}{chapter.4}% 92
\BOOKMARK [1][-]{section.4.8}{Acknowledgments}{chapter.4}% 93
\BOOKMARK [0][-]{chapter.5}{Conclusion}{}% 94
\BOOKMARK [0][-]{section*.51}{Appendices}{}% 95
\BOOKMARK [0][-]{Appendix.1.A}{Supplemental Figures from Chapter 2}{}% 96
\BOOKMARK [0][-]{table.caption.61}{Bibliography}{}% 97