WASHINGTON UNIVERSITY Division of Biology and Biomedical Sciences Program in Biochemistry
Dissertation Examination Committee: Garland R. Marshall, Chairperson Thomas J. Baranski Carolyn J. Anderson Douglas F. Covey Kevin D. Moeller David Sept
Mimicking Reverse Turns with Cyclic Tetrapeptides by Sage Child Arbor
A dissertation presented to the Graduate School of Arts and Sciences of Washington University in partial fulfillments for the degree of Doctor of Philosophy
August 2008 Saint Louis, Missouri
UMI Number: 3332059 Copyright 2008 by Arbor, Sage Child
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ACKNOWLEDGEMENTS I would like to acknowledge the amazing learning environment I was immersed in during my time at Washington University. Garland Marshall in particular provided an exciting, collaborative, nurturing lab in which to learn and work. He was not only a wealth of academic knowledge but was a true role model in how to live a good, balanced life in general.
All the members of the Marshall lab contributed to the open environment, where ideas could be bounced back and forth, always coming back clearer. JW Feng, Chris Ho, Dan Kuster, Yat Tang, Rob Yang, and Xiaoming Zhang provided an ideal working atmosphere in our computation lab. Greg Borne, Abigail Fischer, Yaniv Barda, Yun Wu, and Weijun Zhang were all invaluable in their mentorship and guidance during my time doing peptide synthesis in our wetlab. Jeff Kao was instrumental in helping with the performance and analysis of my NMR data.
I would also like to thank my thesis committee Carolyn J. Anderson, Thomas J. Baranski, Douglas F. Covey, Kevin D. Moeller, and David Sept for the focus and direction they provided me. The entire DBBS division also made a real difference with their compassion for their students and continued consciousness that helping the students learn and get the research done is their mission.
1
Lastly, I would like to express to my fiancee (wife in 25 days:), family, and friends that they completed my graduate student life and made my time here immeasurably more enjoyable than if I had not had their support and love.
This work was funded in part by NIH Grant GM 68460. There was also fellowship support from the Division of Biology and Biological Sciences, Washington University in St. Louis, Kauffman Cancer Research Pathway, and Washington University Center for Computational Biology Pathway.
u
Table of Contents ACKNOWLEDGEMENTS
1
LIST OF FIGURES AND TABLES
5
ABBREVIATIONS
8
ABSTRACT OF THE DISSERTATION
10
INTRODUCTION
1
1.1 Secondary Protein Structure
3
1.2 Importance of Reverse Turns
7
1.3 The Entropic Cost of Binding and Conformational Flexibility
16
1.5 Computational Drug Design
19
1.6 Reverse-Turn Mimics
24
1.7 Thesis Outline
29
CHAPTER TWO EXPLORING AMINO ACID EFFECT ON CONFORMATIONS OF CYCLIC TETRAPEPTIDE REVERSE TURN MIMICS
31
2.1 Conformational Searches
38
2.2 Cyclic Peptide Synthesis
38
2.3 NMR Structural Analysis
44
2.4 Overlap of NMR structures with reverse-turn classes from PDB
56
CONFORMATIONAL PREDICTION OF CYCLIC TETRAPEPTIDE LIBRARY THAT MIMICS 54% OF REVERSE TURNS IN THE PDB 3.1 Validation of CTP Conformational Prediction
iii
62 63
3.2 PDB Reverse Turns
67
3.3 Overlaps of Ca-Q3 Reverse Turn Bonds
71
3.4 CTP Combinatorial Library
73
CONCLUSION
79
APPENDIX
84
REFERENCES
Ill
IV
List of Figures and Tables CHAPTER ONE Figure 1-1: Protein Data Bank Deposited Strutures (Xray + NMR)
1
Figure 1-2: Stable Secondary Structure Found in Proteins
6
Table 1-1: (|)A|/ Values of Helices and
ff-Sheets
7
Table 1-2: Stable Secondary Structure Found in Proteins
9
Figure 1-3: Pseudo-torsions vectors which represent the 9 clusters of reverse turns
13
Figure 1-4: Pseudo-torsions vectors of PDB turns are grouped based on their classical reverse turn type
14
Figure 1-5: Reverse turns in the PDB organized by the nine Tran clusters
14
Figure 1-6: Superimposition of the nine reverse turn clusters mean structures
15
Table 1-3: Lipinskis Rule of 5
19
Figure 1 -7: Ramachandran plots of Glycine(a), Alanine(b), and Proline(c)
27
CHAPTER TWO
Table
2-1: Cyclic
tetrapeptide
conformational
searches
and
conformations
NMR 36
Table 2-2: Cyclic tetrapeptides NMR chemical shifts Figure 2-1: NOESY NMR data for the cyclic tetrapeptides in CDC13 Table 2-3: : c[pro-Pro-pro-N-Methyl-L-Ala] mimics reverse turns
v
47 49-53 57
Figure 2-2: All trans crpro-Pro-pro-NMe-Ala] is overlapped (0.57A RMSD1 by all 4 Ca-Cff bonds in Tran et. al.'s cluster 6
58
Figure 2-3:c|"pro-Pro-pro-NMe-AlaT dynamics overlapped with multiple Tran et. al's reverse turn clusters
60
CHAPTER THREE Figure 3-1: CTPs in CSD conformation prediciton
64
Figure 3-2: RMSD Overlap (A) of Reverse Turns Ca-G3 bonds in the PDB with 9 Tran Reverse Turn Clusters
68
Table 3-1: Clustering of PDB reverse turns into the 9 Tran et al. clusters
69
Figure 3-3: Ramachandran plots showing the (j> and \\f values for each amino acid type in the cyclic tetrapeptide library
73
APPENDIX Figure A-l.l: HPLC Spectra of linear pro-Pro-pro-N-Methyl-Alanine
83
Figure A-1.2: Mass Spectra of linear pro-Pro-pro-N-Methyl-Alanine
84
Figure A-1.3: HPLC Spectra of c[pro-Pro-pro-N-Methyl-Alanine] and c[Propip-Pro-pipl
,
85
Figure A-1.4: Mass Spectra of cfpro-Pro-pro-N-Methyl-Alanine]
86
Figure A-2.1: HPLC Spectra of linear Pro-pip-Pro-pro
87
Figure A-2.2: Mass Spectra of linear Pro-pip-Pro-pro
88
Figure A-2.3: HPLC Spectra of crPro-pip-Pro-prol
89
vi
Figure A-2.4: Mass Spectra of c[Pro-pip-Pro-pro] at varying concentrations revealing ionic and not covalent dimer
90
Figure A-3.1: HPLC Spectra of linear pip-Pro-pip-Pro
92
Figure A-3.2; Mass Spectra of linear pip-Pro-pip-Pro
93
Figure A-3.3: Mass Spectra of c[pip-Pro-pip-Pro]
94
Figure A-4.1: HPLC Spectra of linear Ala-Pro-pip-Pro
95
Figure A-4.2: Mass Spectra of linear Ala-Pro-pip-Pro
96
Figure A-4.3: HPLC Spectra of c[Ala-Pro-pip-Prol
97
Figure A-4.4: Mass Spectra of c[Ala-Pro-pip-Pro]
98
Figure A-5: NOESY spectra of c[Pro-Pip-pro-Pro]
99
Figure A-6: NMR of c|"Ala-Pro-pip-Pro] indicating a hydrogen bond
100
Table A-l: CTPs that mimic all four of the Ca-Cfi bonds in the 9 Tran reverse turn clusters by overlap with their C-C or C-H bonds
vn
101
Abbreviations $
All money is given in U.S. dollars
Aib
a-aminoisobutyric acid
Ala
alanine
AMBER
assisted model building with energy refinement
B3LYP
Becke's 3-parameter hybrid functional with gradient corrections by the Lee, Yang, and Parr functional
CSD
Cambridge Structure Database
CTP
cyclic tetra peptide
DFT
density functional theory
DMF
N, N-dimethylformamide
GB/SA
generalized Born/surface area
Gly
glycine
HIV
human immunodeficiency virus
HPLC
high performance liquid chromatography
HRPT
Hypoxanthine-guanine phosphoribosyltransferase
MC
Monte Carlo
MCMM
Monte Carlo Multiple Minimum
MM2
Allingers Molecular Mechanics 2
MMFF
Merck Molecular Force Field
MS
mass spectrometry
NMA
N-methyl-alanine
viii
NMR
nuclear magnetic resonance
OPLS
optimized potential for liquid simulations
PDB
Protein Data Bank
Pip
L-Pipecolic Acid
Pip
D-pipecolic Acid
Pro
L-Proline
pro
D-proline
RGD
fibrinogen binding sequence (-Arg-Gly-Asp-)
RMSD
root-mean-square displacement
SAR
structure activity relationship
IX
ABSTRACT OF THE DISSERTATION
Mimicking Reverse Turns with Cyclic Tetrapeptides by Sage Child Arbor
Doctor of Philosophy in Biochemistry Washington University in St. Louis, 2008 Garland R. Marshall, Chairperson
Chemical mimics of the reverse turn structures in proteins have proven useful as therapeutics. This thesis describes the investigation of cyclic tetrapeptides (CTPs) which are found to be rigid and synthetically feasible turn mimics. CTPs rigidity were probed computationally and a few test examples made by solid phase peptide synthesis and then characterized by NOESY NMR. All reverse turns in the Protein Data Bank (PDB) were analyzed to determine conformational clustering based on the orientation of the four CocC/3 bonds. Combining the residues of glycine, L and D alanine, L and D proline, L and D N-methyl-alanine, and L and D pipecolic acid yielded a computational library of CTPs that mimicked over half the turns in the PDB, as determined by overlap of less than 0.65A RMSD with the four Ca-C/3 bonds of each of 9 turn clusters.
x
CHAPTER ONE INTRODUCTION The elucidation of protein structures in the last four decades has increased exponentially. From 1990 to 2007, the number of x-ray and NMR structures deposited in the PDB increased 100-fold from 450 to 44,292 (Figure l-l). Blocking a protein's interactions is a common route Figure 1-1 Protein Data Bank Deposited Strutures (Xray + NMR) 50000 45000 40000 S 35000 a. a> 30000
• #Structures deposited that year • Cumulative # deposited structures
% 25000 a. 2 20000 =1
£ 15000 10000
1111
5000 ,, ,-, _ n
N
# #
N
jn J1J-,•- • J J 1
n i
# ^
#
N
#
N
# ^
^
N
# ^
Year
1
^
111 ^
^
, / ^> ^
/
to developing therapeutics, and protein structural data has proven useful in this endeavor. While the stable fold that a protein adopts can be described by a trace of the polypeptide backbone, a much finer level of the protein's surface topology needs to be known to explain the strength and selectivity of macromolecular interactions.
This is
because the side chains that protrude from the main chain, and not the
backbone
itself,
are
utilized
for
binding
and
recognition.
Macromolecular interactions are often described as forming "lock and key" binding partners, where one protein's protrusions fit into a perfectly grooved cavity of its binding partner. Because of this, small molecules that have side-chain moieties which are presented in a 3-D topology that mimic the side-chain orientation from a stable protein fold, have come to be termed "privileged scaffolds" and are potentially useful as therapeutics. Compared to the small peptide segment alone which a privileged scaffold mimics, the scaffold is usually more rigid, thereby locking the side-chain moieties in the correct orientation and removing the entropic cost of binding that the flexible peptide alone would have.
Ninety percent of protein structure has been found to
contain three types of secondary structure: helix, sheet, and turn 3 . While there are subtle subtypes of these three secondary structures, molecular engineering of privileged scaffolds selective for these three have proven very valuable.
2
In 2006, Adams and Brantner found devlopment of a new pharmaceutical cost on average $868 million, and the range of estimates for various drugs to be from $500 million to $2 billion4. Despite extensive R&D by the pharmaceutical industry and academia todays drugs target only 500 of the more than 20,000 genes considered "druggable" in the human genome5.
When looking at to
the difficulty of developing pharmaceuticals, the privileged scaffold route offers the opportunity for large cost and time savings. Reverse turns in particular are a valuable target to mimic; due to the globular nature of long polypeptide chains, reverse turns are most often the secondary structure element available for interaction at the surface of proteins. This thesis develops a library of privileged scaffolds that mimic reverse turns; thereby, increasing the rapidity of discovery and decreasing the cost of drug design of inhibitors of protein/protein interactions.
1.1 Secondary Protein Structure The way in which polypeptide chains compact themselves quickly into unique globular folds has intrigued scientists for many decades. In 1968, Leventhal6 succinctly displayed the remarkable folding abilities of proteins by pointing out that, via very simple theoretical calculations, for a protein to go from one fold to another could take 3
longer than the age of the universe if done in a random manner due to the immense conformational space available to such a molecule. It became apparent that the polypeptide sequence must not only contain the information to select the final folded state, but also guide and limit the pathway to the folded protein7"9. In these compact folds, the protein's secondary structure has been found to exist in a helix, sheet, or turn conformation 1 . Both helices and sheets have repeating hydrogen-bonding patterns that stabilize these secondary structures. However both, if continued without reverse turns, are extended conformations and cannot alone account for the globular nature of larger proteins. Reverse turns allow the ends of helices and sheets to turn back towards the core of the globular structure increasing compactness, and thereby allowing for hydrophobic residues to be buried on the interior, away from bulk water. All three of the common secondary structures exist in multiple conformations, p-sheets can exist in either parallel or anti-parallel formations in which the two peptide chains that are hydrogen bonded to each other either run in the same or opposite directions respectively (FIGURE 1-2 a-b). In both forms the polypeptide chain is nearly fully extended. A common form of the anti-parallel (3-sheet, called phairpin, involves two strands that follow each other in primary sequence with a reverse turn in between them. While the anti-parallel
4
sheet can be connected by a reverse turn, a parallel (3-sheet cannot. This is because the main chain polypeptide must loop all the way back to the beginning (N-terminus) of the first sheet segment to run the second sheet segment in a parallel fashion when forming the hydrogen bond network. (B-sheets are longer in one direction than reverse turns and, therefore, other sheet, helix, or loop segments must be used to form a parallel (3-sheet. (3-sheets, like other secondary structures, can be defined by the successive torsions of its backbone atoms (Table 1! ) •
5
B
f* ^pFigure 1-2 : Stable secondary structures found in proteins. a ) A n t i - p a r a l l e l 13-
sheet, b)parallel (3-sheet, c)left handed helix, d)reverse turn.
Helices, unlike sheets, form a energetically favorable internal (local) hydrogen-bonding network. While sheets hydrogen bond with distal amino acids, residues in helices form local hydrogen bonds within +/- 4 residues on the polypeptide chain. By far the most common helix is the right-handed a-helix (FIGURE l-2c), but there are also other helices such as the tighter 3io-helix, and the left-handed (aL) helix which is a mirror image of a-helix. The a-helix has a repeating N-Hi+4 -> C=Oi hydrogen bond pattern, whereas the 3i 0 -helix has a N-Hi+3 -> C=Oi pattern. The 3 i0 -helix is often found at the end
of a-helices and acts as a transition to terminate them . These three helices can be described by the , \j/ values of their polypeptide backbone (Table 1-1).
Table 1 - 1 : j and v|/ torsion values for the common secondary protein structures 0-sheets and helices. Secondary Structure
*
¥
p-sheet
-135
135
aR-helix10
-55
-45
3io helix10
-60
-30
aL-helix
55
45.
Reverse turns are of particular interest for drug design, due to their location on the exterior of globular proteins. When the main chain of a polypeptide turns back on itself in three, four, and five residues, the segment is called a gamma, beta, or omega turn respectively, p-turns are much more common than the other turn types and will be the focus of the remainder of this thesis.
1.2 Importance of Reverse Turns
Venkatachalam originally identified (3-turns in 1968 based on model-building studies11. He defined the four amino acids, i to i+3, as 7
a reverse turn based on the ( and y torsions (Figure l-2d) of the i+1 and i+2 residues, classifying turn types I, I I , and I I I along with their mirror images I', I I ' , and I I I ' . Because the amide bond between the i+1 and i+2 residues can be considered rigid, and these middle two residues § and y values also defined the turn type, Venkatachalam was able to define the six turn types that had a hydrogen bond between the C=Oi and N-Hi+3. In 1973 Lewis et. al. 12 redefined (3-turns based on crystal structures available at the time as having their Cai and Ca i+3
within 7 A of each other, but not necessarily requiring a hydrogen
bond between the C=Oi and N-Hi+3 (in fact 25% of turns were found not to have this hydrogen bond). This resulted in 10 distinct turn types I, I', I I , IF, I I I , I I I ' , IV, V, VI, and VIII 1 2 . More recently Hutchingson and Thornton 13 have used 9 turn types I, I', I I , I I ' , IV, V i a l , VIa2, VIb, and VIII, along with a miscellaneous type IV which together cover all known (3-turns (Table 1-2). The two most common reverse turns, type I and I I , were shown to account for 49% of reverse turns in the PDB, while the two least observed, type I I ' and VI, make up only 3.7% of turns. However, type IV turns, comprising 32% of all (3-turns, are comprised of turns that do not fit into any of the other classical turn types. While researchers have historically defined reverse turns by the different ) and y angles in their roughly 180° turn of the polypeptide
8
backbone, this is not how other macromolecules differentiate one turn from another. The side chains of all four residues protrude from the main chain of each turn type with a different surface topology and are what is accessible for other macromolecules to recognize.
Table 1-2: p-turn t y p e s , frequencies, and t o r s i o n s 1 3 . B-turn Turn Type
Frequency of Turn Type
I
i+l
y i+i
j bond) so the distance between Ca's was considered rigid (at ~3.6 A) and a pseudo-torsion angle measured between each C(3-Ca-Ca-C(3 (Figure l-3a). It should be noted that this assumption only applies for turns with all transamide bonds, while the classical turn type VI (representing 1.5% of PDB13) is defined as having a c/s-amide bond between i+1 and i+2 with a shorter distance and, therefore, these turns were not fit 14 . This analysis resulted in four pseudo-torsions (Figure l-3b), the last being between Cai+3-Ca i that was within 7 A as dictated by the reverse-turn definition. This last pseudo-torsion was, therefore, based on a broader set of Ca-Ca distances than the other three. A plot of these pseudotorsions as lines with pseudo-torsioni- pseudo-torsion2 = x and pseudo-torsion3 - pseudo-torsion4 = y can be made (Figure l-3c) to
11
graphically see that all the turns in each cluster are in fact similar in side-chain orientation (Figure l-3d). A similar overlap to Figure l-3d, but one in which the reverse turns are grouped by classical turn type, shows that the classical turn types do not group the orientations of the four Ca-C|3 bonds as precisely as the 9 clusters of Tran et al. (Figure 1-4). When all the turns were overlapped according to their classified cluster, it was clearly seen that the goal of overlapping the Ca-C(3 bonds was achieved (Figure 1-5).
12
Figure 1-3: Pseudo-torsions vectors which represent the 9 clusters of reverse turns, (a) A reverse turn to highlight the rigid peptide bond between Ca's, ( b ) the pseudo-torsions measured from C3rCarCai+i-C3i+i, (c) example of pseudo-torsion vector plotted, ( d ) pseudo-torsion vectors of PDB turns overlapped based on which of the 9 clusters they were assigned to. The pseudotorsion vectors are colored according to conventional p-turns type definition. (Type I-black, Type II-red, Type I'-green, Type H'-dark blue, Type IV-yellow, Type VHI-grey, Type Vial-magenta, , Type VIa2-light blue and Type VIb-thin red). Reprint from Tran et al., 2005 2 . (a)
(b)
(c) +180 64
1
1
1 1 1
1
62
-t — r — r i i i 1 - 1 - 1 ' •i i i •180 ei 63 +180
(d)
BBB • • 1 ^^J 11• 1HI • • •Hi 1 w^^^^^^
M
5
B^B^l
•••
•PV|
HI
f**1**11"
/ 8
6
jj
j JHK
H^V i>,«
y in 9 : yttfc
' •>•• iriMti-
r^pp'
Hot c l u s t e r e d
g ^ i f •••]"'" * |
/jjPjf •
13
WJi - • •
Figure 1-4: Pseudo-torsions vectors of PDB turns are grouped based on their classical reverse turn type showing significant variability of Ca-Cp orientations within classical turn types. Reprint from Tran et al., 2005 2 . Type
Type IV
r;
jype
vja
J_ -
!
_-
s ?
1 Type
Tipe
!I
VIXI
Type VJt
ifc-! • I
!
m
Pr-f
*
\
Figure 1-5: Reverse turns in the PDB organized by the nine Tran clusters. Similar Ca-C3 side chain orientations are shown by green-yellow bonds. Tran clusters 1 (top left) through 9 (bottom right) are ordered from left to right. Reprint from Tran et al., 2005 2 .
14
S
Lastly, an average structure for each cluster was determined as the turn whose atoms differ least from the rest as measured by RMSD. Figure 1-6:
Superimposition of the nine clusters mean structures. Overlaps
When the Ca-C3's of the 9 average structures are overlapped the
based on Cai, Ca i+i, Ca \+2. difference between the clusters is The colors represent the following clusters: 1magenta, 2-red, 3-yellow, 4-green, 5-orange, 6-dark blue, 7-white, 8-light blue and 9-grey. Reprint from Tran e t a l . , 20052.
clearly seen (Figure 1-6). Ninety percent of the reverse turns in Tran et. al.'s data set were classified into one of nine clusters, having a maximum RMSD of 0.65A from the average cluster structure. The remaining 10% of turns did not fit into any of the nine clusters. The result of this analysis was 9 clusters, which describe 90% of the reverse turns analyzed in the PDB,
each of which can be described by an average structure of a reverse turn. Compared to Hutchinson &Thorton's classical turn-type classification, which resulted in 31.8% of turns being in the unclassified type IV group, Trans et al.'s clustering left only 10% of turns unassigned which was a marked improvement. The 9 average structures based off these clusters can be the basis for a next generation of reverse-turn mimetics that focus on the Ca-C(3 bond
15
orientations and the surface topology of the reverse turn and not on the geometry of the less important peptide backbone.
1.3 The Entropic Cost of Binding and Conformational
Flexibility
Macromolecules bind with each other based on a variety of physical interactions. Ligands often have enthalpic gains and entropic hindrances when binding. These can be described by the Gibbs free energy (AG), which is a thermodynamic parameter that measures the "useful" work a system can output in an isothermal, isobaric thermodynamic system (Eq 1).
AG = AH -TAS
(Eq. 1)
Enthalpic interactions, described by AH, involve how well the macromolecules physically compliment each other. Proteins are often described as fitting as a "lock and key" with each other, filling each others grooves with the others protrusions. This allows many weak van der Waals interactions to additively create significant enthalpic gains upon binding. Trying to mimic this enthalpic effect when making pharmaceuticals has proven difficult in that small drug-like molecules
16
do not have sufficient surface area to mimic a protein's interacting surface to achieve a similar magnitude enthalpic gain. Binding partners are also usually reciprocally compatible in other ways, such as by an acidic residue in one protein interacting with a basic residue in the other, hydrogen-bond donors in one macromolecule interacting with hydrogen-bond acceptors in the other, or hydrophobic patches from each interacting favorably. The small drug-like molecules often make up for some of that enthalpic loss by having less of an entropic (AS) penalty upon binding compared to many natural ligands. When a ligand binds a macromolecule, there is an entropic penalty due to constraining the conformational flexibility of the ligand and its binding partner as well as loss of rotational and translational degrees of freedom between ligand and receptor. However, some natural peptides and many pharmaceuticals are often cyclized to reduce flexibility and the entropic cost of binding. For example, somatostatins are a family of natural cyclic peptides produced in both a 14-mer and 28-mer form by a variety of normal human cells as well as tumor cells. Octreotide is a cyclic octapeptide analog of somatostatin found to be 20 times more potent in vivo 15 and to has even less entropic penalty upon binding due to its more rigid structure 16 . The entropic "cost" of binding is often harder to predict than enthalpic interactions since the former
17
often has to be dealt with by canonical statistical mechanics, whereas the latter can rely on basic Newtonian physical interactions. For example, upon binding of a ligand, many bound water molecules may be released to bulk solvent thereby creating an entropic benefit. It can be generally stated that a rigidified ligand that presents its interacting chemical moieties in as complimentary a way as the natural ligand will have less of an entropic penalty upon binding. Pharmaceuticals that are orally available generally conform to "Lipinski's rule of 5" which defines characteristics that "drug-like" molecules exhibit. The Lipinski rule of 5 got its name from the cutoff values for the 4 parameters describing drug-likeness, all of which are close to or multiples of 5 (Table 1-3)17. These rules describe a low molecular weight, low hydrophobicity, and the hydrogen-bonding acceptor and donator attributes. It is usually desirable for a pharmaceutical to stay in the aqueous environment of the blood and cell (hence hydrophilicity), but it also needs to cross lipid bilayers which limits the number of hydrogen-bond donors and acceptors it can have. If a compound has values in the unallowed drug-like space for two of the four rules, it is likely to have poor oral absorption or permeability.
18
Table 1-3: Lipinski's Rule of 5 (if 2 of these statements are applicable the compound will likely have poor absorption or permeability) 17 More than 5 H-bond donors (expressed as the sum of OHs and NHs). Molecular Weight > 500. The LogP is over 5 (or MLogP is
over 4.15). There are more than 10 H-bond acceptors (expressed as the sum of Ns and Os).
1.5 Computational Drug Design In order for a small molecule to be a good drug candidate, it requires both the correct topology to fit with its binding partner and reduced flexibility. A key to this combination is having the decreased rigidity lock the molecule in the correct orientation for recognition. X-Ray crystal and NMR structures of bound conformations have proven extremely helpful in the design of potential therapeutics over the last half century. When there are not crystal structures available, the conformational flexibility of ligands can still be predicted computationally and lead compounds can be found from low energy
19
structures. Research toward this end will be detailed in Chapters Two and Three. The ideal computational tool to determine conformational stability and preference often depends on the molecule being investigated and the amount of information known about it. For example, on the detailed end of the computational spectrum, quantum calculations can be done on very small molecules, or parts of molecules, and account for electron distribution and polarizability, thereby defining transition states with more precision than molecular mechanics can. Quantum chemical methods include both ab initio and density functional methods that are approximations of the former. Ab initio methods are based purely on theoretical principles and have no empirical parameters. Density functional theory (DFT) on the other hand, while often considered sem\-ab initio, has semi-empirical parameters derived from empirical data or the aforementioned ab initio methods. All quantum methods take significantly more computational processing power compared to molecular mechanics. Currently, with a high interest in biological therapeutics, molecular mechanics is often the best tool to characterize a ligand with significant flexibility. There is still a range of detailed methods than can be used within this realm. Perhaps, the most common is Monte Carlo conformational search in which specified rotatable bonds are
20
rotated at random and the resulting structures minimized. The best conformation can be thought of being elucidated as jumping down the energy potential well in this method while allowing for random jumps to discover new nearby local energy wells. The justifiable complaint with this method is the lack of assurance that the true low energy conformation has been found, since it is often used in situations where a more methodical approach would be computationally cumbersome. Therefore, the output of Monte Carlo searches can be analyzed to find criteria that suggest how well conformational space was covered; for example, how often each conformer was found, or if all expected rotamers were found. Simulated annealing is an augmented conformational search that repeatedly computationally heats and cools a molecule allowing all the bonds to escape local energy wells before cooling into a neighboring well in conformational space. If the molecule is small enough, a systematic conformational search can be done in which each free degree of rotation is turned through all 360 degrees at a given increment, for example, every 5 degrees. This method can become computationally untenable since the number of necessary conformations that require an energy evaluation increases as the power of the number of flexible bonds being searched. However, each conformation made is not minimized, which saves on computational
21
demand, and the result is an exhaustive search of conformational space. Molecular dynamics is a fourth method that allows molecules to "wiggle" computationally and do a Newtonian-guided dance to lower energy structures. Molecular dynamics simulations are expensive computationally, but maybe best describes a proteins fluctuations in a temporal fashion and, therefore, can add other interesting information such as possible peptide-folding pathways. All these computational conformational analyses result in a number of structures whose stability can be ranked by relative energy. The difference in Gibbs free energy is proportional to the equilibrium constant between the two conformers (Eq 2).
10
=
AG° = -RT In K
(Eq. 2)
Keq = e^G°/RT>
(Eq. 3)
p-([1-36kca|/mo|]/[(l-987cal/K*mol)*300K])
,pQ
*>.
As shown rearranged in Eq. 3 and then with an example in Eq. 4, AG° of 1.36 kcal/mol between two conformers correlates to one conformer being 10 times more abundant than the other at room temperature. Therefore, once a conformation can be determined that is reliably much greater than 1 kcal more stable than any other conformation, it
22
can be further investigated as the topology that will potentially bind as a drug. The computational methods mentioned above can be used to derive likely low-energy conformations with only the structure of the ligand and no other data. When there is more information for a system, other computational tools are often used following the aforementioned conformation searches. For example, a crystal structure of the protein target can be used in computational docking studies to verify binding mode and affinity relative to other known and possible ligands. Efficient computational tools, such as the program CHARLIE18 developed by Chris Ho and distributed via the TRIPOS software package, have been developed to design small molecules which mimic a ligand's bound conformations most important chemical moieties and bridges between them with rigid cyclic structures. Such tools can be used most effectively in the later stage of drug discovery after there is a known three-dimensional recognition motif, or pharmacophore. The ability to predict a chemical structure de novo, without any information outside of the chemical structure, should remain in high demand for the foreseeable future. Indeed, in recent decades, pharmaceutical companies have created ever larger libraries of unique chemical moieties. When screens reveal a low affinity hit, the conformation of those molecules need to be known to make the
23
most effective next group of lead compounds. Monte Carlo conformational search was used repeatedly in the research described herein. It is useful to understand what the magnitude of these energy differences mean.
1.6 Reverse-Turn
Mimics
A bottleneck in developing therapeutics is the fact that the structure-activity relationship (SAR) investigations are often started de novo for each molecular interaction of interest, for example, by highthroughput screens. A more efficient approach to drug design involves classifying privileged chemical structures so that the search for mimetics can start from a set of scaffolds that correctly orient chemical substituents to reproduce one of the molecular surfaces usually involved in protein/protein recognition. For example, once a new turn of potential therapeutic interest is classified, then the set of preexisting turn mimics for that turn class can quickly be tested as mimetics. In addition, a turn-recognition site whose structure is unknown can more quickly be determined by screening against a small set of privileged scaffold compounds that represent the set of possible reverse-turn motifs as compared to a much larger library of compounds that cover conformational space randomly.
24
As mentioned earlier, reverse turns, which have been found to make
up one-fourth
of the
residues in high-resolution
protein
structures in the PDB19, are often viewed as potential targets for therapeutic design20,
21
because they are generally found on the
exterior of proteins and often involved in macromolecule interactions3, 22
.
Rigid compounds that have modifiable bonds overlapping Ca-CP
bonds in reverse turns (Figure 1-5), such as benzodiazapenes23"28, can be considered privileged mimetic scaffolds; they can bind to sites for reverse-turn recognition with the potential for high affinity due to their preorganization 23 " 25,28 . Turns have been found critical in a range of macromolecular recognition motifs, such as integrin or interferon-y binding in biological systems
ranging
from
cell
adhesion29
to
antiviral
agents30,
respectively. There has, therefore, been considerable effort to mimic reverse turns which has been accomplished by small molecules such as benzodiazepines,23"28 short linear peptides 31,32 , and cyclic peptides3, 24, 33-35_
The
u s e 0f
pr0|jne
nas
| o n g b e e n known to help a peptide
adopt a reverse-turn conformation 3 . For example, the classical type VI turn is defined as having a c/s-amide bond between residue i+1 and i+2, which proline facilitates in the i+2 position due to its substituted tertiary nitrogen. In a c/s-amide bond conformation, most amino acids C(3's have a steric clash with the preceeding amino acids Ca
25
substituted atoms.
Glycine doesn't have this problem due to a
unsubstituted Ca atom, and proline has a counteracting clash with its C5 when there is a preceding trans-amide bond. The Ramachandran plots of Hypoxanthine-guanine phosphoribosyltransferase (HPRT) (PDB code 1HMP) in Figure 1-7 shows the allowed $/y torsions for the 18 general L-amino acids, glycine, L-proline, and pre-proline residues. Each point represents a torsion of an amino acid in the HRPT crystal structure, while the shaded regions depict ty/y allowed contours of 500 high resolution protein crystal structures in the PDB as described by Lovell et. al. 36
26
Figure 1-7: Ramachandran plots of general L-amino acids, Glycine, Proline, and pre-Proline from PDB structure 1HMB is representative of allowed torsional space for differently substitutes amino acids. Reproduced from 1 .
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Email communique follow which permit the reproduction of material from three sources:
DPics of 9 reverse turn Tran clusters from Tran, T. T.; McKie, J.; Meutermans, W. D.; Bourne, G. T.; Andrews, P. R.; Smythe, M. L., Topological side-chain classification of beta-turns: ideal motifs for peptidomimetic development. J Comput AidedMol Des 2005,19, (8), 551-66.
2KTP and NMR pics from Arbor, S. K., J; Yun, W; Marshall, G, c[D-pro-Pro-D-pro-N-Methyl-Ala] adopts a rigid conformation that serves as a scaffold to mimic reverse-turn. Biopolymers Peptide Science, In Press 2007.
3)Ramachandran plots from Cock, P. Drawing Ramachandran (phi/psi) plots. http://www2.warwick.ac.Uk/fac/sci/moac/currentstudents/peter_cock/r/ramachandran/ (2006), /
105
1)Pics of 9 reverse turn Tran clusters from
r.
Werf van der, Nel, Springer SBM NL
to
[email protected],
date
Thu, Feb 7, 2008 at 4:37 AM
,. x subject J mailed-by
FW: reproduction of article _ ,cr . , . figures (Sage Arbor) springer.com
hlde
details Feb Reply n '
From: Werf van der, Nel, Springer SBM NL On Behalf Of Permissions Europe/NL Sent: donderdag 7 februari 2008 10:36 To: Straalen van, Berendina, Springer SBM NL Subject: RE: reproduction of article figures (Sage Arbor)
Dear Sir, With reference to your request (copy herewith) to reprint material on which Springer Science and Business Media controls the copyright, our permission is granted, free of charge, for the use indicated in your enquiry. This permission allows you non-exclusive reproduction rights throughout the World. permission includes use in an electronic form, provided that content is * password protected; * at intranet; excludes use in any other electronic form. Should you have a specific project in mind, please reapply for permission. requires a full credit (Springer/Kluwer Academic Publishers book/journal title, volume, year of publication, page, chapter/article title, name(s) of author(s), figure number(s), original copyright notice) to the publication in which the material was originally published, by adding: with kind permission of Springer Science and Business Media.
106
The material can only be used for the purpose of defending your dissertation, and with a maximum of 100 extra copies in paper. Permission free of charge on this occasion does not prejudice any rights we might have to charge for reproduction of our copyrighted material in the future.
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From: Sage Arbor [mailto:sagearbor(aqmail.com] Sent: zondag 3 februari 2008 0:15 To: Murphy, Catherine, Springer SBM NL Subject: reproduction of article figures
Hi, my name is Sage Arbor. I am writing up my thesis and would like to reproduce figures from the paper below, since much of my thesis work is based off this paper and it would be hard to have an adequate intro without the figures: Tran, T. T.; McKie, J.; Meutermans, W. D.; Bourne, G. T.; Andrews, P. R.; Smythe, M. L., Topological side-chain classification of beta-turns: ideal motifs for peptidomimetic development. J Comput AidedMol Des 2005, 19, (8), 551-66. Im not sure if you are the person I should contact. If not can you point me in the right direction. Thank Sage
you,
107
2 K T P and NMR pics from from James, Duncan - Chichester "
[email protected]" , date ,. mailed-by
Fri, Mar 7,2008 at 11:17 AM RE: Republication/Electronic Request Form wiley.com
hide details Mar 7
epy
Dear Sage Arbor Thank you for your request. Permission is hereby granted to reproduce the specified article for the use requested, [see quoted text at end of response] Please note however that we cannot allow you to place the Wiley PDF version of this article online. You should instead use a preprint version on your site, which may be the final version you submitted for publication. Any third party material is expressly excluded from this permission. If any material appears within the article with credit to another source, authorisation from that source must be obtained. This permission does not include the right to grant others permission to reproduce this material. Proper credit must be given to our publication. Credit must include the following components: Title of the Work, Author(s) and/or Editor(s) Name(s). Copyright year. Copyright John Wiley & Sons Limited. Reproduced with permission. Yours sincerely Duncan James
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email:
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Quoted Text follows describing journal article pertaining too Al 3_Book_Title: Biopolymers Peptide Science A40_Book_or_Journal: Journal Al 4_Book_Author: A15_Book_ISBN: A16_Journal_Month: 04 A17_Journal_Year: 2008 A18_Journal_Volume: 01 A19_Journal_Issue_Number: Biopolymers Peptide Science --> special issue in honor of Bruce Merrifield A20_Copy_Pages: entire article A21JVlaximum_Copies: 50 A22_Your_Publisher: myself A23_Your_Title: c[D-pro-Pro-D-pro-N-Methyl-Ala] adopts a rigid conformation that serves as a scaffold to mimic reverse-turns A24_Publication_Date: May 1, 2008
109
3^Ramachandran plots from From to date
Peter Cock
[email protected], Tue, Apr 8, 2008 at 2:59 PM „ ' T , ,.- . Re: FW: reproduce subject i. j wo ramachandran plots? mailed-by googlemail.com
, ., , , hide details 2:59 PM „ , „. „ . Reply (A, (4 hourso ago)
Hello Sage, Prof. Rodger has forwarded your email to me. I'm not sure why you didn't contact me directly as the author of the webpage and software concerned. I would be happy for you to use the R code or the figures I created with it in your thesis, provided you to cite the webpage (put the year as 2006): http://www2.warwick.ac.Uk/fac/sci/moac/currentstudents/peter_cock/r/ramachandran/ This can be shortened to: http://www2.warwick.ac.Uk/go/peter_cock/r/ramachandran/ Note that you may want to change the script slightly to produce a higher resolution image (or a PDF file), which would look better when printed. If you decided to use the code, then citing The R-Project as well might be appropriate. Peter
110
References 1. Cock, P. Drawing Ramachandran (phi/psi) plots. http://www2.warwick.ac.uk/fac/sci/moac/currentstudents/peter_cockyr/ramachandran/ (2006), 2. Tran, T. T.; McKie, J.; Meutermans, W. D.; Bourne, G. T.; Andrews, P. R.; Smythe, M. L., Topological side-chain classification of beta-turns: ideal motifs for peptidomimetic development. J Comput AidedMol Des 2005,19, (8), 551-66. 3. Rose, G. D.; Gierasch, L. M ; Smith, J. A., Turns in Peptides and Proteins. Adv. Protein Chem. 1985, 37, 1-109. 4. Adams, C. P.; Brantner, V. V., Estimating the cost of new drug development: is it really 802 million dollars? Health Aff (Millwood) 2006, 25, (2), 420-8. 5. Guttmacher, A. E.; Collins, F. S., Realizing the promise of genomics in biomedical research. Jama 2005, 294, (11), 1399-402. 6. Levinthal, C , Are there pathways to protein folding? J. Chim. Phys. 1968, 85, 2. 7. Honig, B., Protein folding: from the levinthal paradox to structure prediction. J Mol Biol 1999, 293, (2), 283-93. 8. Karplus, M., The Levinthal paradox: yesterday and today. Folding & Design 1997, 2, (4), S69-S75. 9. Zwanzig, R.; Szabo, A.; Bagchi, B., Levinthal's paradox. Proc. Natl. Acad. Sci. USA 1992, 89, (January), 20-22. 10. Toniolo, C ; Benedetti, E., The polypeptide 310-helix. Trends Biochem Sci 1991, 16, (9), 350-3. 11. Venkatachalam, C. M., Stereochemical Criteria for Polypeptides and Proteins. V. Conformation of a System of Three Linked Peptide Units. Biopolymers 1968, 6, 14251436. 12. Lewis, P. N.; Momany, F. A.; Scheraga, H. A., Chain Reversals in Proteins. Biochim. Biophys. Acta 1973, 303, 211-229. 13. Hutchinson, E. G.; Thornton, J. M., A revised set of potentials for beta-turn formation in proteins. Protein Sci 1994, 3, (12), 2207-16. 14. Smythe, M. L., Communication. In 2007. 15. Bauer, W.; Briner, U.; Doepmer, W.; Haller, R.; Huguenin, R.; Marbach, P.; Petcher, T. J.; Pless, SMS 201-995: a very potent and selective octapeptide analogue of somatostatin with prolonged action. Life Sci 1982, 31, (11), 1133-40. 16. Weckbecker, G.; Lewis, I.; Albert, R.; Schmid, H. A.; Hoyer, D.; Bruns, C , Opportunities in somatostatin research: biological, chemical and therapeutic aspects. Nat Rev Drug Discov 2003, 2, (12), 999-1017. 17. Lipinski, C. A.; Lombardo, F.; Dominy, B. W.; Feeney, P. J., Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Adv Drug Deliv Rev 1997, 23, 3-25. 18. Ho, C. M. W.; Marshall, G. R., SPLICE: A program to assemble partial query solutions from three-dimensional database searches into novel ligands. J. Comput.-Aided Mol. Design 1993, 7, 623-647.
Ill
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