Towards an automated computational pipeline to guide drug lead optimization[]

164 Pages·2015·6.07 MB·English

by  Shuai Liu

UC Irvine
UC Irvine Electronic Theses and Dissertations
Title
Towards an automated computational pipeline to guide drug lead optimization
Permalink
https://escholarship.org/uc/item/6t03g54n
Author
Liu, Shuai
Publication Date
2015

Peer reviewed|Thesis/dissertation
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University of California
UNIVERSITY OF CALIFORNIA,
IRVINE
Towards an automated computational pipeline to guide drug lead optimization
DISSERTATION
submitted in partial satisfaction of the requirements
for the degree of
DOCTOR OF PHILOSOPHY
in Pharmacological Sciences
by
Shuai Liu
Dissertation Committee:
Professor David Mobley, Chair
Professor Ray Luo
Professor Douglas Tobias
Professor Weian Zhao
2015
Chapter 1 (cid:13)c 2012 Springer
Chapter 2 (cid:13)c 2014 Springer
Chapter 3 (cid:13)c 2013 Springer
Chapter 4 (cid:13)c 2015 Amrican Chemical Society
All other materials (cid:13)c 2015 Shuai Liu
DEDICATION
This work is dedicated to my grandmother Baolan Zhang
ii
TABLE OF CONTENTS
Page
LIST OF FIGURES vi
LIST OF TABLES ix
ACKNOWLEDGMENTS x
CURRICULUM VITAE xii
ABSTRACT OF THE DISSERTATION xiii
1 Alchemical prediction of hydration free energies for SAMPL 1
1.1 Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.2 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
1.3 Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
1.3.1 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
1.3.2 General “standard” protocol . . . . . . . . . . . . . . . . . . . . . . . 5
1.3.3 MP2/SCRF charge model . . . . . . . . . . . . . . . . . . . . . . . . 7
1.3.4 Solvent background charge QM . . . . . . . . . . . . . . . . . . . . . 7
1.3.5 Updated LJ Parameters for GAFF . . . . . . . . . . . . . . . . . . . 8
1.3.6 OEAnte Parameterization . . . . . . . . . . . . . . . . . . . . . . . . 9
1.3.7 OPLS Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
1.3.8 Error analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
1.4 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
1.4.1 Accuracy of blind predictions . . . . . . . . . . . . . . . . . . . . . . 11
1.4.2 Accuracy in follow-up testing after SAMPL . . . . . . . . . . . . . . 14
1.4.3 Key trends in hydration free energies . . . . . . . . . . . . . . . . . . 16
1.4.4 Observations for specific charge models and methods . . . . . . . . . 18
1.5 Discussion and conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
2 Blind prediction of HIV integrase binding from the SAMPL4 challenge 27
2.1 Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
2.2 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28
2.2.1 SAMPL protein-ligand binding background . . . . . . . . . . . . . . . 28
2.2.2 HIV integrase background . . . . . . . . . . . . . . . . . . . . . . . . 29
2.3 SAMPL challenge preparation and logistics . . . . . . . . . . . . . . . . . . . 33
iii
2.3.1 Pre-challenge preparation . . . . . . . . . . . . . . . . . . . . . . . . 34
2.3.2 Virtual screening . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38
2.3.3 Pose prediction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39
2.3.4 Affinity prediction . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40
2.4 SAMPL Analysis Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41
2.4.1 Virtual screening . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41
2.4.2 Pose prediction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41
2.4.3 Affinity prediction . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46
2.4.4 Error analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47
2.5 Integrase Screening Results . . . . . . . . . . . . . . . . . . . . . . . . . . . 48
2.5.1 SAMPL analysis focused on the full set . . . . . . . . . . . . . . . . . 48
2.5.2 Post-SAMPL: Alternate isomers may not be non-binders . . . . . . . 51
2.6 Integrase Pose Prediction Results . . . . . . . . . . . . . . . . . . . . . . . . 53
2.7 Integrase Affinity Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56
2.8 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58
3 Lead Optimization Mapper: Automating free energy calculations for lead
optimization 64
3.1 Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64
3.2 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65
3.3 Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68
3.3.1 Design goals . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68
3.3.2 Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74
3.3.3 Our implementation is in Python . . . . . . . . . . . . . . . . . . . 82
3.4 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82
3.5 Discussion and conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . 86
4 Is Ring Breaking Feasible in Relative Binding Free Energy Calculations? 92
4.1 Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92
4.2 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93
4.3 Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 96
4.4 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101
4.5 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 107
4.6 Appendix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 109
5 Using MD simulations to calculate how solvents modulate solubility 116
5.1 Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 116
5.2 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 117
5.3 Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 119
5.3.1 Theory . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 119
5.3.2 Dataset selection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 121
5.3.3 Simulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 122
5.3.4 Other methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 124
5.4 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 125
5.5 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 131
iv
Bibliography 133
v
LIST OF FIGURES
Page
1.1 Computed versus experimental hydration free energies . . . . . . . . . . . . . 12
1.2 Statistics for the different approaches . . . . . . . . . . . . . . . . . . . . . . 13
1.3 Errors in the electrostatic potential . . . . . . . . . . . . . . . . . . . . . . . 24
2.1 Integrase (IN) functional structure and architecture . . . . . . . . . . . . . . 60
2.2 CalculatedmetricsforSAMPL4virtualscreeningstatistics, graphedinranked
order. The statistics are as given in Table 2.1. Note that many submissions
have overlapping error bars, so ranked order is not necessarily indicative of
significantly better performance. . . . . . . . . . . . . . . . . . . . . . . . . . 61
(a) BEDROC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61
(b) AUC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61
(c) EF 10% . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61
2.3 Enrichmentplotsfortwoofthebest-performingvirtualscreeningsubmissions,
and one for which performance is close to random. Submission 164 was the
top performer by all metrics, and 135 was one of the other top performers. 198
is shown here as representative of more typical performance for comparison.
Error bars are shown in red and give an idea of the expected variation with
the composition of the set. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61
(a) Enrichment, 164 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61
(b) Enrichment, 135 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61
(c) Enrichment, 198 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61
2.4 Rankedperformanceonposeprediction,byvariousmetrics. (a)-(b),box/whisker
plots showing performance by ligand as judged by RMSD; (b) focuses only
on the subset of ligands placed within the correct site. (c) shows performance
judged by AUC, by ligand; and (d) shows performance by ligand as judged
by interaction fingerprint Tanimoto scores. (e) shows the number of ligands
placed into the correct binding site for each submission. For bar plots, normal
submissions are shown in blue, while control models (300, 301) are shown in
gray, as discussed in the text. . . . . . . . . . . . . . . . . . . . . . . . . . . 62
(a) RMSD . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62
(b) RMSD, correct site . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62
(c) AUC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62
(d) Fingerprint Tanimoto . . . . . . . . . . . . . . . . . . . . . . . . . . . 62
(e) Site identification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62
vi
2.5 Fraction of ligands with correct binding modes, versus RMSD cutoff. Shown
are the fraction of ligand binding modes predicted correctly within a cutoff
of x ˚A RMSD, where x is the horizontal axis. The scale is semi-log. Method
177 performed particularly well by this and other metrics, while 583 did not
perform particularly well here. In submission 177, most ligands are predicted
correctly within 10 ˚A RMSD, with a substantial fraction better 3 ˚A. In con-
trast, in 583, only a small number are predicted better than 10 ˚A RMSD.
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62
(a) 177 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62
(b) 583 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62
2.6 Representative statistics for the integrase affinity challenge. Shown are the
Pearson correlation coefficient, R, and the average unsigned error (AUE).
Normal submissions are shown in blue; control models are shown in gray, as
discussed in the text. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63
(a) Ranked AUE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63
(b) Ranked R . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63
2.7 Performance of submission 184 in the integrase affinity challenge . . . . . . . 63
3.1 Favorable mutations of 2-methylnaphthalene . . . . . . . . . . . . . . . . . . 88
3.2 Planned RBFE calculations for the FXa set . . . . . . . . . . . . . . . . . . 89
3.3 Planned calculations for the charge +1 subset of the FXa test set . . . . . . 90
3.4 Single topology, explicit intermediate free energy calculations . . . . . . . . . 91
4.1 Ring breaking rules in LOMAP . . . . . . . . . . . . . . . . . . . . . . . . . 94
4.2 Thermodynamic cycle closure errors . . . . . . . . . . . . . . . . . . . . . . . 95
4.3 Errors for mutating naphthalene to benzene . . . . . . . . . . . . . . . . . . 103
4.4 Errors for mutating decalin to cyclohexane . . . . . . . . . . . . . . . . . . . 104
4.5 Errors for mutating adamantane to bicyclo[3.3.1 . . . . . . . . . . . . . . . . 105
4.6 Simulation cycle . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 106
(a) absolute . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 106
(b) relative . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 106
4.7 Simulation cycle for adamantane-bicyclononane . . . . . . . . . . . . . . . . 107
(a) absolute . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 107
(b) relative . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 107
4.8 2D structure for naphthalene . . . . . . . . . . . . . . . . . . . . . . . . . . . 109
5.1 The average error in ln(cα1) by solvent . . . . . . . . . . . . . . . . . . . . . . 128
cζ
1
(a) Pairwise errors by solvent in GAFF . . . . . . . . . . . . . . . . . . . 128
(b) Pairwise errors by solvent in GAFF-DC . . . . . . . . . . . . . . . . . 128
(c) Pairwise errors by solvent in SMD . . . . . . . . . . . . . . . . . . . . 128
(d) Pairwise errors by solvent in SMD vac . . . . . . . . . . . . . . . . . 128
(e) Pairwise errors by solvent in UNIFAC . . . . . . . . . . . . . . . . . . 128
(f) Pairwise errors by solvent in UNIFAC mod . . . . . . . . . . . . . . . 128
5.2 Calculated solubility vs Experimental solubility . . . . . . . . . . . . . . . . 131
(a) GAFF . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 131
vii
(b) GAFF-DC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 131
(c) SMD . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 131
(d) SMD vac . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 131
(e) UNIFAC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 131
(f) UNIFAC mod . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 131
5.3 GAFF method vs all other methods . . . . . . . . . . . . . . . . . . . . . . . 132
(a) GAFF vs GAFF-DC . . . . . . . . . . . . . . . . . . . . . . . . . . . 132
(b) GAFF vs SMD . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 132
(c) GAFF vs SMD vac . . . . . . . . . . . . . . . . . . . . . . . . . . . . 132
(d) GAFF vs UNIFAC . . . . . . . . . . . . . . . . . . . . . . . . . . . . 132
(e) GAFF vs UNIFAC mod . . . . . . . . . . . . . . . . . . . . . . . . . 132
5.4 Test and training set errors . . . . . . . . . . . . . . . . . . . . . . . . . . . 132
viii