Cyclic Peptide Design Guided by Residual Dipolar Couplings, J

Subscriber access provided by Iowa State University | Library

Article

Cyclic Peptide Design Guided by Residual Dipolar Couplings, J-Couplings and Intramolecular Hydrogen Bond Analysis Kathleen A. Farley, Ye Che, Armando Navarro-Vázquez, Chris Limberakis, Dennis Anderson, Jiangli Yan, Michael Shapiro, Veerabahu Shanmugasundaram, and Roberto R. Gil J. Org. Chem., Just Accepted Manuscript • DOI: 10.1021/acs.joc.8b02811 • Publication Date (Web): 03 Jan 2019 Downloaded from http://pubs.acs.org on January 7, 2019

Just Accepted “Just Accepted” manuscripts have been peer-reviewed and accepted for publication. They are posted online prior to technical editing, formatting for publication and author proofing. The American Chemical Society provides “Just Accepted” as a service to the research community to expedite the dissemination of scientific material as soon as possible after acceptance. “Just Accepted” manuscripts appear in full in PDF format accompanied by an HTML abstract. “Just Accepted” manuscripts have been fully peer reviewed, but should not be considered the official version of record. They are citable by the Digital Object Identifier (DOI®). “Just Accepted” is an optional service offered to authors. Therefore, the “Just Accepted” Web site may not include all articles that will be published in the journal. After a manuscript is technically edited and formatted, it will be removed from the “Just Accepted” Web site and published as an ASAP article. Note that technical editing may introduce minor changes to the manuscript text and/or graphics which could affect content, and all legal disclaimers and ethical guidelines that apply to the journal pertain. ACS cannot be held responsible for errors or consequences arising from the use of information contained in these “Just Accepted” manuscripts.

is published by the American Chemical Society. 1155 Sixteenth Street N.W., Washington, DC 20036 Published by American Chemical Society. Copyright © American Chemical Society. However, no copyright claim is made to original U.S. Government works, or works produced by employees of any Commonwealth realm Crown government in the course of their duties.

Page 1 of 30 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

The Journal of Organic Chemistry

Cyclic Peptide Design Guided by Residual Dipolar Couplings, JCouplings and Intramolecular Hydrogen Bond Analysis Kathleen A. Farley,†,* Ye Che,† Armando Navarro-Vázquez,‡ Chris Limberakis,† Dennis Anderson,†, ‖ Jiangli Yan,†,o Michael Shapiro,†,^ Veerabahu Shanmugasundaram,†,& and Roberto R. Gil§,*

†Medicinal

Sciences, Pfizer Worldwide R&D, Eastern Point Road, Groton, CT 06340, USA

§Department

of Chemistry, Carnegie Mellon University, Pittsburgh, PA 15213, USA

‡Departamento

de Química Fundamental, CCEN, Universidade Federal de Pernambuco, Cidade

Universitária, Recife, PE 50740-560, Brazil Current Address: Lumigen Instrument Center, Wayne State University, Detroit, MI 48202

‖

^Current address: Niantic, CT 06357 oCurrent

address: Boston, MA 02124

&Current

address: Celgene Corp., 200 Cambridgepark Dr., Cambridge, MA 02140

*To whom correspondence should be addressed: Kathleen A. Farley, Email: [email protected] Roberto R. Gil, Email: [email protected]

ACS Paragon Plus Environment

The Journal of Organic Chemistry 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

ABSTRACT GRAPHIC


Page 2 of 30

Page 3 of 30 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


ABSTRACT: Cyclic peptides have long tantalized drug designers with their potential ability to combine the best attributes of antibodies and small molecules. An ideal cyclic peptide drug candidate would be able to recognize a protein surface like an antibody while achieving the oral bioavailability of a small molecule. It has been hypothesized that such cyclic peptides balance permeability and solubility using their solvent-dependent conformational flexibility. Herein we report a conformational deconvolution NMR methodology that combines residual dipolar couplings, J-couplings and intramolecular hydrogen bond analysis along with conformational analysis using molecular dynamics simulations and density functional theory calculations, for studying cyclic peptide conformations in both low-dielectric solvent (chloroform) and high-dielectric solvent (DMSO) to experimentally study the solvent-dependent conformational change hypothesis. Taken together, the combined experimental and computational approaches can illuminate conformational ensembles of cyclic peptides in solution and help identify design opportunities for better permeability. KEYWORDS Nuclear magnetic resonance, residual dipolar coupling, temperature-dependent chemical shift, density functional theory, molecular dynamics simulation



INTRODUCTION Interest in macrocyclic peptides and their novel architectures has increased significantly as their potential for interacting with a variety of targets including GPCRs,1-2 proteases,3 kinases,4 ATPases,5 and others has been recognized.6-9 However, achieving good cellular permeability and bioavailability for cyclic peptides is anything but straightforward due to larger molecular weights and increased polar surface areas. One prominent exception is the naturally-occurring cyclic undecapeptide, cyclosporin A, an orally active immunosuppressive drug that forms a ternary complex with cyclophilin and calcineurin and thereby inhibits T-cell activation.10 Cyclosporin A has an unexpectedly high permeability and bioavailability. Previous nuclear magnetic resonance (NMR),11-14 X-ray,15 and computational studies16 indicate that these properties in part originate from conformational flexibility that allows formation of intramolecular hydrogen bonds (IMHBs) that shield polarity during passage across nonpolar environments such as cell membranes.14, 17 The design of cell-permeable cyclic peptides remains a challenge due to the poorly understood relationship between structure and desirable pharmacokinetic properties, such as passive permeability, solubility and clearance.18-21 In an effort to determine conformational determinants of cellular permeability and bioavailability for cyclic peptides, a model system based on the cyclo[(Leu)4-Pro-Tyr], has been widely used by Lokey and coworkers,22-23 Pfizer24 and other groups to investigate the impact of stereochemical configuration, - and N-methylation, -branching and conformational flexibility on properties such as permeability, microsomal stability and oral bioavailability. These studies suggest that the solvent-dependent conformational behavior allows for a balance between aqueous solubility and cell permeability, highlighting structural flexibility as an important consideration in the design of cellpermeable cyclic peptides.


Page 4 of 30

Page 5 of 30 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


To gain further insight into the conformational properties of cyclic peptides, we report here for the first time the elucidation of solution conformations for one of the cyclic hexapeptide leads, cyclo-[Leu-D-LeuLeu-Leu-D-Pro-Tyr] (1), using residual dipolar couplings (RDCs), 3JNH-Hα scalar couplings, and NMR temperature coefficients (Tc) in both chloroform and DMSO. These solvents were selected to mimic cell membrane and aqueous solution, respectively since water solubility for this peptide is limited. Previous studies on this cyclic hexapeptide lead have focused on determining the structure using NOE’s in chloroform.22-23 While NOE based structural determination of peptides and proteins is a traditional technique, it commonly makes use of restricted molecular dynamics which yields a best fit to a spatially averaged virtual structure rather than an ensemble of the true conformers. Going beyond classical NOE and J-coupling analysis,26 RDCs27-28 can furnish very rich information about cyclic peptides.29 First, the structure of the backbone is rigidified in comparison to linear peptides, so analysis of the 1DNH and 1DCH couplings can yield easily interpreted information about the relative orientation of peptidic planes without the distance limitation of NOE’s. Due to the planarity of the peptide bond, the orientation of the adjacent carbonyl group can be surmised once the orientation of the NH moiety is determined. This information when combined with variable temperature chemical shift studies, furnish valuable data about the orientation of the NH moieties.30 Bringing this data together allows for the identification of the hydrogen bonding pattern in the peptide; an essential aspect governing the conformation of the molecule and a key factor in the design of new drug candidates. Analyzing the conformation of a small peptide in both a low and high polarity solvent, such as chloroform and DMSO, can yield design insights to improve molecular permeability while maintaining the bioactivity. This design strategy can be used to study the molecular flexibility of a peptide which is defined in some degree by IMHBs. We are proposing a new strategy for determining multiple conformations of a small cyclic peptide in two solvents and using the variations observed in the structures as a template in drug design. In addition, we report the application of replica-exchange with solute tempering (REST) molecular dynamics simulations31 for



enhancing the conformational sampling of cyclic peptides in both solvents to help predict the solventdependent conformational behavior, which taken together can be applied to the prospective design of cell-permeable cyclic peptides. RESULTS and DISCUSSION Computational Analysis of Cyclic Peptide 1 It is very difficult to predict the structures of cyclic peptides and the impact of different solvents on conformational ensembles owing to their rugged conformational free energy landscape, which may prevent sampling of all thermodynamically relevant conformations. Here, we explored the conformational space of cyclo-[Leu1-D-Leu2-Leu3-Leu4-D-Pro5-Tyr6] cyclic peptide 1 as shown in Figure 1 using replica-exchange with solute tempering (REST) molecular dynamics simulations in either CHCl3 or DMSO solvent boxes. The REST-enhanced sampling method31 scales the contribution of solute-solvent and solvent-solvent energies to the total energy function. The scaling factors depend on replica temperature and are deliberately selected to strengthen solvent interactions at elevated temperatures. As a result, REST keeps the solvent effectively “cold” in all replicas, whereas the solute is simulated at a higher effective temperature, thereby focusing sampling efforts to most efficiently traverse the relevant conformational space. Overall, cyclic peptide 1 is more flexible in DMSO than in CHCl3, as can be seen in the sampling of backbone (,)-torsional angles (Figure 1), especially for residues Leu1 and D-Leu2.


Page 6 of 30

Page 7 of 30 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


Figure 1. The chemical structure of cyclo-[Leu1-D-Leu2-Leu3-Leu4-D-Pro5-Tyr6] (1) and the distribution of backbone (,)-torsional angles of 1 in either CHCl3 (filled circles) or DMSO (open circles) solutions based on 0.96 µs REST molecular dynamic simulations. A well-established design hypothesis for improving passive membrane permeability of cyclic peptide is to form internal hydrogen bonds in the membrane. We further analyzed and grouped all structures of cyclic peptides based on backbone IMHB patterns (Figure 2). In CHCl3, the cyclic peptide tries to maximize the number of IMHBs, with 6.0 % and 90.5 % of the conformational ensemble having 4 and 3 IMHBs, respectively. On the other hand, in the more polar solvent, e.g. DMSO, fewer numbers of IMHBs



were observed, with only 1.0 % and 27.7 % of the cyclic peptide having 4 and 3 IMHBs and more than half of the conformational ensembles (58.0 %) associated with only 2 IMHBs. Among all IMHB patterns in the cyclic peptide, the IMHB (14), an IMHB from the backbone NH group of residue Leu1 to the backbone CO group of residue Leu4, is dominated in both solvents. This is consistent with the earlier observation32 that a bi-peptide motif, D-Pro-amino acid, is an efficient reverse-turn constraint. In cyclic peptide 1, the bi-peptide motif, D-Pro5-Tyr6, effectively constrains the peptide conformation to a type II’ -turn at one end, with the IMHB (14) observed 99.6 % and 89.8 % percentage of the simulation times in CHCl3 and DMSO, respectively. Other IMHBs are strongly influenced by the solvent environment. For example, IMHBs, 31 and 25, are more prevalent in CHCl3; while 41 is more populated in DMSO.

Figure 2. The analysis of IMHBs of cyclic peptide 1 in either CHCl3 (filled columns) or DMSO (open columns) solutions based on 0.96 µs REST molecular dynamic simulations. (a) The distribution of total number of IMHBs, with 90.5% and 58.0% of cyclic peptide 1 having 3 or 2 IMHBs in CHCl3 or DMSO, respectively. (b) The distribution of observed backbone IMHBs of cyclic peptide 1, with 14, 31 and


Page 8 of 30

Page 9 of 30 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


25 more populated in CHCl3 and 14 and 41 more populated in DMSO, respectively (ab: indicating an IMHB from the NH group of residue a to the CO group of residue b). NMR Characterization of Cyclic Peptide 1 in CDCl3 Our strategy, in combination with IMHBs pattern obtained from variable temperature NMR studies of the backbone NH signals, is applied here as a proof of concept to the well-studied cyclic peptide 1. Both 1J CH

and 1JNH’s were collected for peptide 1 using compressed CDCl3-swollen PMMA gels.33 For the

proton-carbon and proton-nitrogen J-couplings, two J-scaled Bird (JSB) HC-HSQC spectra were collected. The first JSB HC-HSQC was collected on the peptide in CDCl3 (isotropic media) and the one-bond couplings (1J) were determined. The second JSB HC-HSQC was collected on the peptide in PMMA gel (anisotropic media) and the total one-bond couplings (1T) were determined. The RDC values (1D) were determined using the formula: 1D = 1T – 1J as illustrated in Figure 3. The proton-nitrogen J-couplings were collected in a similar fashion. The full spectra are shown in the SI. Overall, eleven RDCs (1JCH and 1J ) NH

in a range of -45 to +58 Hz were measured for this compound in CDCl3 as shown in Table 1.

Additionally five 3JNH-Hα scalar couplings were collected from the 1H spectrum, Table 1. Temperature coefficients for the NH protons show strong evidence for at least two IMHBs involving the NHs at Leu1 and Leu2.



Page 10 of 30

Figure 3. An expansion of the J-scaled Bird HC-HSQC spectrum for cyclic peptide 1 is shown in CDCl3 for D-Pro5. The full spectrum is shown in the SI. Table 1. Experimental 1DCH and 1DNH values for cyclic peptide 1 in CDCl3 and DMSO-d6. The measured values using JSB HC-HSQC and HN-HSQC spectra of cyclic peptide 1 in a compressed PMMA gel for CDCl3 and in a compressed poly-HEMA gel for DMSO-d6.

In CDCl3

Residue

In DMSO-d6

NH

3J NH-Hα

∆/∆T

NH

3J NH-Hα

∆/∆T

(Hz)

(Hz)

(Hz)[a]

(-ppb/K)[b]

(Hz)

(Hz)

(Hz)[a]

(-ppb/K)[b]

Leu1

57.6

-20.2

8.9

2.1

-18.7

6.9

8.1

0.3

D-Leu2

-24.0

20.1

8.0

0.9

-9.4

2.2

6.9

11.8

Leu3

-18.8

-0.5

6.3

3.2

-20.5

8.0

8.1

6.6

Leu4

34.3

-26.1

8.8

3.6

-15.5

8.6

8.8

5.0

D-Pro5

-44.7

-

-

-

-18.9

-

-

-

Tyr6

-14.0

13.5

6.5

3.7

-20.7

14.6

8.2

7.4

1D

CH

1D

1D

CH

1D

[a] Experimental 3JNH-Hα from dihedral angles of the best-fit RDC structure. [b] Temperature coefficient of amide proton chemical shift


Page 11 of 30 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


The chloroform RDC data was fit to each member of a conformational ensemble that consisted of 1,000 DFT-optimized structures that were generated in a solvent dependent manner based on the RESTenhanced molecular dynamic simulations. The RDC data was first fit using the RDC module of MSpin software.34 The best fit corresponded to a low energy structure with 3 IMHBs: 14, 25 and 31 as shown in Figure 4a and 4b. In this module, the Cornilescu Q-factor (Q)35 was used to judge the goodness of the fit for each conformation. The conformation with the best fit had a Q= 0.034. The distance of the IMHBs for the best-fit structure were 2.1, 2.0, and 2.1 Å, respectively, for the three IMHB’s as delineated above. In good agreement, the Tc of the corresponding amide protons are 2.1, 0.9, 3.2 ppb/K, as shown in Table 1. In addition, the experimental RDC’s correlated well with the back calculated values as shown in Figure 4a. Since it is also possible that the structure could have more than a single backbone conformation in solution, the conformational ensemble was also probed using a recently published approach36 as implemented in the StereoFitter program.37 Here the simplest conformational ensemble that explain the observed NMR data is chosen38-40 based on the Akaike Information Criterion (AIC).41 In this approach, sub-ensembles containing increasing number of conformers are picked up from the molecular modeling clustered ensembles until a minimum is found in the AIC function. Since the AIC criterion is based on the χ2 score function, different NMR observables can be mixed in a simple way provided that corresponding uncertainties are well estimated as shown in Equation 1. 2

i i i i  Dexp   J exp   Dcalc  J calc AIC    2(n  1)         D J  2

i

2

(1)

i

In StereoFitter, the molecular alignment tensor and corresponding 1DNH back-calculated RDCs are obtained by a SVD42 single-tensor fitting43 on the superimposed cluster structures. The 3JHα-NH couplings were computed using the equation proposed by Pardi et al.44-45 A general dipolar coupling uncertainty



of 1.5 Hz was estimated by analysis of previous data collected in compressible gels.46 For the σJ uncertainty, a value of 0.8 Hz was employed as reported previously.44 Using the multiple conformation approach, the eleven RDCs and five scaler couplings for the peptide in CDCl3 were fit. The best AIC fit corresponded to a two conformer ensemble as shown in Figure 4c and 4d. The two conformers present similar geometries and exhibit the same three IMHBs as observed previously: 14, 25 and 31. The two conformers are present at a ratio of 61:39 and yielded an AIC of 24.7 and a χ2 of 22.7. In addition, the experimental RDC’s correlated well with the back calculated values as shown in Figure 4c. The two conformers overlay extensively along the backbone of the peptide and only have small differences on the sidechains where some movement would be expected in solution. These conformers are consistent with the REST molecular dynamic simulation done in CHCl3, as such structures accounts for 85.1 % of all conformations observed. Hence we can conclude that on the basis of the present NMR evidence, peptide 1 presents as a single conformation of the macrocycle in solution, similar to that reported by NOE analysis in chloroform.24 Examination of the proton spectra for cyclic peptide 1 in CDCl3 and in the gel did not show a significant shift in the chemical shifts or coupling constants suggesting that the gel did not significantly affect the conformation in CDCl3.


Page 12 of 30

Page 13 of 30 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


Figure 4. (a) Experimental versus calculated backbone 1DCH (open circles) and 1DNH (filled circles) couplings in CDCl3 for the RDC-refined structural model of cyclic peptide 1 from MSpin. The quality factor Q = 0.034 and the correlation factor R2 = 0.99881 of the RDC-refined structure represents the fact that all RDCs are very close to the experimental error. (b) The RDC refined NMR conformation of cyclic peptide 1 from MSpin with three IMHBs (14, 25 and 31) highlighted. (c) Experimental versus calculated backbone 1DCH (open circles) and 1DNH (filled circles) couplings in CDCl3 for the RDC and Jcoupling refined structural model of cyclic peptide 1 using Stereofitter. The correlation factor is R2 = 0.99957 and the AIC = 24.70 using both RDC and J-coupling data. (d) The RDC and J-coupling refined NMR conformations from Stereofitter with three IMHBs (14, 25 and 31) highlighted. The IMHBs are the same for both conformers.



NMR Characterization of Cyclic Peptide 1 in DMSO-d6 A similar procedure was employed for the analysis of 1 in the high-polarity DMSO-d6 solvent. For the measurement of RDCs in DMSO, poly-HEMA aligning gels were employed.47 Noteworthy, these gels allow the determination of RDCs using a single JSB-HSQC experiment since the isotropic and anisotropic solutions co-exist in different regions of the NMR tube.48 The signals from these two regions are very well resolved in the proton dimension (F2) of the HSQC experiments, saving 50% of the NMR time used to collect the CDCl3 data. This feature was nicely exploited when collecting proton-nitrogen RDCs (1DNH) at natural abundance. Eleven RDCs (1DCH and 1DNH) were measured within a range of -21 to 15 Hz (Table 1). Additionally, five 3JNH-Hα scalar couplings were collected from the 1H spectrum (Table 1). Temperature coefficients for the backbone NH protons show strong evidence for at least one IMHB involving the NH at Leu1. This differs from the IMHB pattern observed for CDCl3 where three IMHBs were observed (14, 25 and 31). Similarly, the DMSO-d6 RDC data were fit to a solvent specific file consisting of 1,000 DFT-optimized structures from the REST-enhanced molecular dynamic simulation using the RDC module of MSpin software. The single conformation with the best fit (Q=0.060 and χ2 of 32.9) contains two IMHBs, 14 and 41. However, this single conformation does not completely match the Tc experimental data from Table 1. The Tc for Leu1 is in excellent agreement with a strong IMHB for this amide, while the Tc for Leu4 is consistent with a weak interaction. It is therefore necessary to account for dynamical/conformational effects in the NMR analysis. This was done through the use of the AIC based approach described above. For the multiple conformation fit, eleven RDCs and five 3J scalar couplings were fit in Stereofitter. This analysis yielded two fits with similar AIC’s (6.90 versus 7.34) so both fits were examined more closely. The first fit for the DMSO data (AIC of 6.90 and a χ2 of 4.90) is a combination of two structures as shown in Figure 5a and 5b. The major conformation in DMSO at 64% contains two IMHBs: 14 (2.09 Å) and


Page 14 of 30

Page 15 of 30 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


41 (2.17 Å). The minor conformation at 36% contains only one IMHB, 41 (2.4 Å). This ensemble is not an improvement over the earlier analysis, the Tc for Leu1 is consistent with a strong IMHB49 but this IMHB is only observed in one conformer. Leu4 would not be expected to have a strong IMHB with the Tc observed for this residue but this IMHB is observed in both conformers. In addition, the minor conformation of this ensemble is more consistent with a “weak electrostatic” H-bond interaction that is not reflected by the Tc measured for Leu1.49 For these reasons, this ensemble was deemed a less optimal fit for the data.

Figure 5. (a) RDC-Refined NMR Structures of cyclic peptide 1 in DMSO with a less optimal fit with two IMHBs (14 and 41) highlighted in yellow and (b) the RDC-refined NMR structure of cyclic peptide 1 with one IMHB (41). (c) RDC-Refined NMR Structures of cyclic peptide 1 in DMSO with the best fit with one IMHB (14) highlighted in yellow and (d) the RDC-refined NMR structure of cyclic peptide 1 with two IMHBs (14 and 41) highlighted in yellow.



The second fit for the DMSO data in Stereofitter yielded a much better fit for all of the NMR data. The fit for this ensemble is a combination of two structures, as shown in Figure 5c and 5d. The major conformation at 52% contains one IMHB: 14 (2.26 Å). The minor conformation at 48% contains two IMHBs: 14 (2.21 Å) and 14 (2.00 Å). The best fit for this ensemble using Stereofitter gave an AIC of 7.34 and a χ2 of 5.34 using the eleven RDC measurements and five scaler couplings. The temperature coefficients observed for the two amide protons involved in IMHBs (Leu1 and Leu4) are in good agreement with the conformers determined using the RDC and scalar coupling data. The gravimetric plots for the fitting are shown in Figure 6. The plot does not change substantially when using only RDC values and using both RDC and scalar coupling, consistent with a good fit using one or both sets of data. In order to test if using more than two conformers would yield a better fit for the data, two additional methods were tried to determine if the fit could be improved. Since the number of three-conformer models from the 1000 structures file would amount to over 1.5 x 109 ensembles, we resorted to using smaller starting conformation files. Our first approach was to simply select a single low energy conformer from each of the possible IMHB pattern(s) depicted in Figure 2. When this small 12 member conformation file was fit to the RDC and coupling data using Stereofitter, the fit was poor yielding an AIC >160 despite using five conformations in the fit. Our second approach to potentially find a better fit to the data was to use only conformers from clusters that contain the highest population of IMHBs as determined from the Tc’s in DMSO-d6. This file contained 30 conformers all of which exhibited one or both of the IMHB’s suggested by the Tc’s, (14 and 41 from Figure 2b). The best fit for this strategy is a combination of four structures, which roughly fall into two conformations as shown in Figure 7. The major conformation at 65% contains two IMHBs: 14 and 41. One structure from the major conformation is present at 46% and the second structure at 19%. The minor conformation in DMSO contains one IMHB: 14. One structure from this


Page 16 of 30

Page 17 of 30 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


conformation is present at 26% and the second structure at 8%. The best fit for the DMSO data using Stereofitter with this method gave an AIC of 14.9 and a χ2 of 8.9 using the eleven RDC measurements and five scaler couplings. As observed previously, the temperature coefficients observed for the two amide protons involved in IMHBs (Leu1 and Leu4) are in good agreement with the two conformers determined using the RDC and scalar coupling data. This approach yielded an ensemble which is consistent with all of the NMR data, and gave a fit that yielded the same pattern of IMHBs as observed previously except for some difference in the relative populations. For these reasons, we are confident the conformers shown in Figure 5c and 5d are the best fit for the DMSO data and we will use this method in future peptide conformational analysis. Examination of the proton spectra for cyclic peptide 1 in DMSO-d6 and in the gel did not show a significant shift in the chemical shifts or coupling constants suggesting that the gel did not significantly affect the conformation in DMSO-d6.

Figure 6. Gravimetric plots for data fitting for the best AIC selected models. (a) Experimental versus calculated backbone 1DCH (open circles) and 1DNH (filled circles) using only RDC data with an AIC= 2.82; (b) Experimental versus calculated backbone 1DCH (open circles) and 1DNH (filled circles) using both RDC and J-coupling data with an AIC=7.34.



Figure 7. Alternate method for determining the RDC-Refined NMR Structures in DMSO (a) The RDCrefined NMR structure of cyclic peptide 1 with two IMHB (14 and 41) highlighted in yellow and (b) the RDC-refined NMR structure of cyclic peptide 1 with one IMHB (14) highlighted in yellow. Comparison of NOE/ROE derived Structures with RDC derived Structures Two groups have independently determined the conformation of cyclic peptide 1 in CDCl3 using NOE’s/ROE’s.24-25 The RDC derived CDCl3 structure shown in Figure 4b is a good fit with all of the measured backbone ROE cross peaks measured previously (see SI) and are considered to be equivalent. The second study also determined the ROE’s observed for peptide 1 in DMSO, but the structure in DMSO was not reported.25 One can speculate that the DMSO structure was not reported previously because the ROEs did not fit well to a single low energy conformer. Comparison of the backbone ROE’s determined previously with the RDC DMSO structure shown in Figure 5c and 5d shows several ROE violations which do not fit the RDC conformers (see SI). Fitting the NOE data to the 1000 structures DFT ensemble yielded a best fit structure with a very low χ2 value of 8.05 (a 0.2 Å uncertainty was assumed). This structure did not fit the observed IMHB pattern (14, 31 and 41) with distances of 2.06, 2.0, and 2.59 Å nor the observed RDC pattern, and would furnish a very high Q > 0.6. It is clear therefore that


Page 18 of 30

Page 19 of 30 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


assessing the structure of the peptide using restricted molecular dynamics based on reported ROE data only, would yield an incorrect structure. Additionally we think that the impact of spin diffusion on the measured ROE volumes is likely very significant and PANIC based approaches should be employed when using ROE data.50 Overlaying the RDC derived structure in DMSO with the crystal structure of a tri-Nmethylated analog25 shows excellent agreement with the two structures, Figure 8. For these reasons, we believe RDC derived conformational analysis combined with temperature coefficients and Jcouplings is a more robust and simple alternative in determining a solution conformation to NOE/ROE analysis in some cases. Using Peptide Structure to Impact Design A common technique to increase permeability in peptides is to mask some of the NH groups thru methylation. While this can be an effective technique, selecting which NH(s) to methylate is often done by trial and error and testing all of the possibilities by making a large library of peptides can be expensive and time consuming. Using the RDC structures in CDCl3 and DMSO-d6 mimicking the lipid bilayer and water respectively, a template for N-methylation can now be determined. Residues with no IMHB’s would make the best targets for N-methylation (e.g. Tyr6). An IMHB found in only one solvent (D-Leu2, Leu3, and Leu4) may have a mixed result with N-methylation and IMHB’s in common for both solvents should not be targeted for N-methylation (e.g. Leu1). The N-methylation of peptide 1 to improve permeability was explored previously.22 N-methylation of D-Leu2, Leu3 and Tyr6 yielded a peptide (Figure 8) that was more permeable than the parent compound. Using the RRCK cell-based permeability assay51, the in vitro cell permeability of peptide 1 increased from 1.8 x 106 cm/s to 4.9 x 106 cm/s after tri-methylation, and the oral bioavailability in rat also increased from only 2% to almost 30%.22 Another tri-methylated peptide was synthesized by methylation of Leu1, D-Leu2 and Tyr6. This peptide did not have increased permeability,22 which is probably not surprising since the IMHBs



observed in peptide 1 would be disrupted in this peptide. The permeability of this peptide was accessed using the PAMPA assay52 which showed a decreased permeability (%T = 5.7  0.09) when compared to the parent peptide (%T = 9.5  1.9).22 Synthesis of unnecessary peptides would be avoided in future peptide libraries using the templates provided by RDC conformational analysis. Using the RDC method described here, the previous methylation library of 32 peptides would be reduced to only 8 methylation targets reducing both the time and expense of making the larger library. Using a RDC structure from only one solvent as a design template would be possible for peptides that do not change conformations between solvents, but using both apolar and polar solvents is necessary for peptides that change conformation with the polarity of the solvent.

Figure 8. (a) The structure of a tri-N-methylated cyclic peptide that exhibited improved passive permeability and oral bioavailability than the parent cyclic peptide 1. The methylation sites are denoted in red. (b) The crystal structure of the tri-N-methylated cyclic peptide (carbon in green) overlaid with the RDC-refined structure of cyclic peptide 1 in DMSO with a backbone RMSD of 0.2 Å. Changing an amino acid residue is another strategy to modulate the physical properties of a peptide. Since the structure of the peptide has been determined, selecting residues to replace which will not affect the IHMB pattern or overall conformation is now possible. Replacing Tyr6 is one option since it is not involved in an IMHB, but the tyrosine hydroxyl is often used as a resin attachment point during


Page 20 of 30

Page 21 of 30 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


synthesis so this may not be practical. Following this strategy, D-Leu2, Leu3, and Pro5 are all potential residues for replacement. While we did not apply this strategy to this peptide since many of the permeations had already been synthesized, we have successfully applied this RDC structure method to several other drug discovery projects to improve the potency and other physical properties of lead compounds. These projects will be the subject of future publications. CONCLUSIONS We determined a new method for determining the conformation(s) of a small cyclic peptide in two solvents and used the variations observed in the conformations as a template in drug design. We also developed a computational method based on the enhanced conformational sampling of macrocyclic peptides in two solvents using replica-exchange simulation with solute tempering to help predict the solvent-dependent conformational behavior. The combination of enhanced conformational sampling with RDC conformations, J-Couplings and Intramolecular Hydrogen Bond Analysis were applied to the prospective design of cell-permeable cyclic peptides to reduce the necessity of synthesizing all of the potential permeations. EXPERIMENTAL SECTION General Experimental Methods for Synthesis, Purification and Characterization 1. Peptide Synthesis: Commercial CTC resin and Fmoc-amino acids were used without further purification. Cyclic peptide 1 was synthesized using standard Fmoc-solid phase peptide synthesis (SPPS) techniques see synthetic scheme in SI. 2. Peptide Purification: Waters 4000 system connected to a Phenomenex Luna C18, 10μm, 100 Å reversed-phase HPLC column (70 x 250 mm) eluting with a solvent gradient of A:B (40:60 to 20:80) over



60 minutes at a flow rate of 60 mL/min. [solvent A: 0.1% TFA in water, solvent B: 0.1% TFA in acetonitrile/water (4:1)]. 3. Purity Check: The pure peptide was analyzed using a HP1090 system connected to a Phenomenex C18(2), 5 micron, 100 Å reversed-phase (4.6 x 150 mm) column eluting with a solvent gradient A:C (34:66 to 24:76), where solvent A: 0.1% TFA in water and solvent C:0.09% TFA in acetonitrile/water (4:1), over 20 minutes at a flow rate of 1.0 mL/min. 4. Mass Spectrometry Analysis: a) LRMS: A Thermo-LCQ Advantage system was used to collect mass data based on ESI. b) HRMS: Accurate mass spectrometry analyses were conducted on an Agilent 6220 TOF mass spectrometer (Agilent Technologies, Wilmington, DE) in positive or negative electrospray mode. The system was calibrated to greater than 1 ppm accuracy across the mass range prior to analyses according to manufacturer’s specifications. The samples were separated using UHPLC on an Agilent 1200 (Agilent Technologies, Wilmington, DE) system prior to mass spectrometric analysis. The resulting spectra were automatically lock mass corrected and the target mass ions and any confirming adducts (Na+, NH4+) were extracted and combined as a chromatogram. The mass accuracy was calculated for all observed isotopes against the theoretical mass ions derived from the chemical formula using MassHunter software (Agilent Technologies, Wilmington, DE).

CTC resin (20 g, 20 mmol) was suspended in DCM (~100 mL). To the suspension was added FmocTyr(tBu)-OH (9.18 g, 20 mmol) and DIPEA (17.47 mL, 100 mmol). The mixture was stirred gently at rt for 3 h. To the mixture was then added MeOH (20 mL), and resulting mixture was stirred at rt for 0.5 h. The reaction mixture was filtered, and the resulting solid was washed with DMF (3 x 200 mL), DCM (3 x 200 mL) and MeOH (3 x 200 mL). The solid was then dried in vacuo overnight to furnish 26 g of Fmoc-


Page 22 of 30

Page 23 of 30 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


Tyr(tBu)-CTC resin. The loading level of the resulting resin was established as 0.48 mmol/g via standard UV absorption. The Fmoc-Tyr(tBu)-CTC resin (12.5 g, 6 mmol) was swelled in DMF for 0.5 h. Then, 20% piperidine in DMF (3 resin volumes) was added to the peptidyl resin. The suspension was kept at rt for 0.5 h while a stream of nitrogen was bubbled through it. Then, the suspension was filtered and the resin was washed with DMF (5 x 200 mL) to afford P1. Fmoc-D-Pro-OH (4.04 g, 12 mmol), HBTU (4.32 g, 11.4 mmol), NMM (2.64 mL, 24 mmol) and DMF (~1.2 equi. resin volumes) were added to the resin. The resin mixture was then gently agitated under a nitrogen atmosphere. The amidation was deemed complete using the Kaiser ninhydrin test. After the reaction was complete, the suspension was filtered, and the resin was washed with DMF (5 x 200 mL) to deliver the Fmoc-D-Pro-Tyr(tBu)-CTC resin. Then, 20% piperidine in DMF (3 resin volumes) was added to the peptidyl resin. The suspension was kept at rt for 0.5 h while a stream of nitrogen was bubbled through it. The suspension was filtered and the resin was washed with DMF (5 x 200 mL). Fmoc-Leu-OH, Fmoc-Leu-OH, Fmoc-D-Leu-OH, and Fmoc-Leu-OH were added sequentially to the peptidyl resin using the amidation/deprotection procedure described above to deliver the fully elaborated resin bound linear peptide. After the final Fmoc deprotection, the resin was washed with DMF (3 x 200 mL), DCM (3 x 200 mL) and MeOH (3 x 200 mL). The resin was dried in vacuo overnight to afford the fully elaborated resin bound peptide P2. P2 was treated with 1% TFA/DCM (200 mL) for 0.5 h. The suspension was filtered. The filtrate was then neutralized with DIPEA to pH 7 to give crude P3. DCM (3000 mL), BOP (7.9 g, 18 mmol), DIPEA (5.25 mL, 30 mmol), HOBt (2.43 g, 18 mmol) were added to the mixture. The reaction mixture was then stirred overnight. DCM was removed under reduced pressure to afford crude cyclic peptide P4. A solution of TFA/H2O/DCM (95:2.5:2.5, 50 mL) was added to crude cyclic peptide P4. The mixture was stirred for 2.5 h. Diethyl ether (400 mL) was added to the filtrate resulting in precipitation. The



suspension was centrifuged, and the supernatant was decanted. The resulting peptide pellet was washed with diethyl ether (3 x 7-8 filtrate volumes), filtered, and dried in vacuo overnight to deliver the desired crude 1 (5.0 g). The crude product (5.0g) was then purified via reversed-phase HPLC described in the peptide purification section. Like fractions were collected and combined; however, the peptide did not have adequate purity, so it was re-purified using the same method. Like fractions were once again combined and lyophilized to deliver 1.52 g (36%) of 1 as a white solid. UV purity (220 nm) = 95.7% (retention time = 9.01 min, see purity section for conditions); LRMS (ESI-MS) m/z 713.4 (M+H)+; HRMS (ESI) m/z [M+Na] +

calculated for C38H60N6O7Na 735.4416, found 735.4418. 1H-NMR (500 MHz, CDCl3) δ 7.63 (d, J = 8.8 Hz,

1H), 7.45 (d, J = 8.0 Hz, 1H), 7.12 – 7.01 (m, 2H), 6.90 – 6.77 (m, 2H), 6.76 (d, J = 6.3 Hz, 1H), 6.08 (d, J = 6.5 Hz, 1H), 5.86 (d, J = 8.9 Hz, 1H), 4.74 (dt, J = 9.1, 6.9 Hz, 1H), 4.63 (ddd, J = 12.3, 8.8, 3.8 Hz, 1H), 4.49 (td, J = 6.8, 4.9 Hz, 1H), 4.40 (td, J = 8.5, 6.3 Hz, 1H), 4.24 (ddd, J = 10.5, 6.3, 4.4 Hz, 1H), 3.93 (dd, J = 7.7, 6.0 Hz, 1H), 3.69 (dt, J = 10.2, 7.0 Hz, 1H), 3.60 – 3.49 (m, 1H), 3.22 (dd, J = 14.6, 7.1 Hz, 1H), 3.10 (dd, J = 14.5, 5.0 Hz, 1H), 2.23 (dp, J = 12.9, 6.6 Hz, 1H), 2.06 (ddq, J = 19.5, 12.9, 6.1 Hz, 2H), 1.96 – 1.83 (m, 2H), 1.81 – 1.60 (m, 7H), 1.61 – 1.50 (m, 2H), 1.50 – 1.38 (m, 3H), 1.04 – 0.82 (m, 24H). 13C{1H} -NMR (126 MHz, CDCl3) δ 174.0, 173.8, 172.2, 171.8, 171.3, 171.2, 155.6, 130.4, 127.2, 116.2, 77.2, 61.3, 55.7, 53.4, 51.6, 51.0, 48.8, 48.1, 42.0, 40.5, 39.2, 37.2, 35.5, 29.3, 25.4, 25.3, 24.9, 24.8, 24.5, 23.6, 23.1, 23.1, 22.9, 22.8, 22.1, 21.5, 21.0. 1H-NMR

(500 MHz, DMSO-d6) δ 9.18 (s, 1H), 8.59 (d, J = 8.2 Hz, 1H), 8.55 (d, J = 8.1 Hz, 1H), 8.14 (d, J =

6.9 Hz, 1H), 7.52 (d, J = 8.1 Hz, 1H), 7.18 (d, J = 8.9 Hz, 1H), 7.08 – 6.93 (m, 2H), 6.76 – 6.53 (m, 2H), 4.70 (td, J = 8.8, 4.1 Hz, 1H), 4.43 (q, J = 7.4 Hz, 1H), 4.23 (q, J = 7.5 Hz, 1H), 4.12 (ddd, J = 11.4, 7.9, 3.9 Hz, 2H), 4.06 (ddd, J = 11.1, 8.0, 3.1 Hz, 1H), 3.54 – 3.51 (m, 2H), 3.13 (dd, J = 14.2, 3.2 Hz, 1H), 2.64 (dd, J = 14.2, 11.2 Hz, 1H), 2.00 – 1.90 (m, 1H), 1.90 – 1.75 (m, 2H), 1.57 (tdd, J = 14.2, 7.5, 4.1 Hz, 3H), 1.48 (dq, J


Page 24 of 30

Page 25 of 30 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


= 8.4, 4.9, 3.9 Hz, 7H), 1.39 (td, J = 8.6, 8.0, 4.1 Hz, 3H), 0.99 – 0.72 (m, 24H). 13C{1H} -NMR (126 MHz, DMSO-d6) δ 172.1, 171.8, 171.2, 170.7, 170.5, 169.5, 155.7, 129.8, 128.4, 114.9, 59.8, 54.9, 51.6, 51.5, 51.2, 47.7, 46.9, 42.1, 41.9, 40.1, 35.5, 28.3, 24.7, 24.4, 24.4, 24.1, 24.0, 23.25, 23.22, 22.54, 22.45, 22.43, 22.2, 22.0, 20.6. Computational Methods for Conformational Analysis To efficiently sample cyclic peptide 1 conformational ensembles in different solutions, replica exchange with solute tempering (REST) molecular dynamics were performed using the OPLS3 force field as implemented in Desmond as part of the Schrodinger software suite 2016-2. Isobar-isothermal (NPT) REST simulations of the cyclic peptide 1 in DMSO and CHCl3 were performed. The REST simulation systems included a single cyclic peptide 1 as the central group embedded in a DMSO or CHCl3 solvent box as the bath. The simulation box with an initial dimension of 37.7 Å x 33.3 Å x 32.6 Å contains either 275 DMSO or 256 CHCl3 solvent molecules, respectively. The target temperature for REST was set to 300 K. Eight replicas, with temperature of 300, 335, 375, 417, 464, 515, 571 and 633 K, were used to generate nearest-neighbor acceptance ratios around 30%. Each replica in a trajectory was simulated for 120 ns. In total, 0.96 µs was collected for REST. The last 100 ns of the simulations were used for the analysis of conformational ensembles in DMSO and CHCl3, with 1,000 coordinate files of cyclic peptide 1 were saved every 0.1 ns for further analysis. The 1,000 conformers selected from the REST simulations in either DMSO or CHCl3 were subjected to further geometry optimization by density functional theory (DFT) calculations using Jaguar as part of the Schrodinger software suite 2016-2. The DFT geometry optimizations were performed using the B3LYP functional with the 6-31G* basis set, and the solvent effects of DMSO or CHCl3 on the conformational energies were implicitly considered using the finite-element Poisson-Boltzmann solver. Thus, the DFT calculations were performed in either DMSO or CHCl3 on each initial structure from REST simulations



with a maximum of 50 optimization steps. Finally, DFT-optimized conformational ensembles were saved for further analysis and comparison based on experimental NMR measurements. The backbone (, ) torsional angles and backbone intramolecular hydrogen bonds (IMHBs), in respective solvents were extracted from the conformational ensembles for analysis. The following geometrical criteria were used to identify hydrogen bonds between backbone CO and NH groups: distance of (O…H) < 2.5 Å, angle of (N-H…O) > 120° and angle (C=O…H) > 90°. NMR methods All of the 1H and 13C assignments were made for the cyclic peptide 1 using a combination of the following NMR spectra: 1H, 13C, COSY, TOCSY, NOESY, HSQC, and HMBC. RDC’s were collected using the J-scaled F1-coupled BIRD-filtered (JSB) HSQC experiment53 using a J-scaling factor of 4. The NMR experiments were collected on either a Bruker Avance III NMR instrument operating at 600.13 MHz for 1H,

150.90 MHz for 13C and 92.12 MHz for 2H, using a 5 mm TCI helium cryoprobe probe equipped with a

z-axis gradient or a Bruker Avance III NMR instrument operating at 500.13 MHz for 1H, 125.76 MHz for 13C

and 76.77 MHz for 2H, using a 5 mm DCH helium cryoprobe probe with a z-axis gradient using

Topspin 3.2. Chemical shifts were referenced to residual solvent (CDCl3 or DMSO-d6) and all spectra were assigned using MestreNova software version 11.0.54 The cyclic peptide was dissolved in CDCl3 or DMSO-d6 at a concentration of 6 mg/150 µL for NMR assignment. Both samples dissolved rapidly in their respective solvents and neither sample showed signs of gelation during the analysis. For the cyclic peptide sample in CDCl3, PMMA gel sticks ~25 mm long with a diameter of 2mm and crosslink density of 0.3% were pre-swollen in CDCl3, inserted into a New Era Enterprises compression gel device and rinsed to remove residual monomer as previously described.33 The cyclic peptide was added to the gel and the plunger was repeatedly pumped to transfer the peptide into the gel. The PMMA gel exhibited a 2H quadrupolar splitting of 51 Hz when fully


Page 26 of 30

Page 27 of 30 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


compressed during spectral acquisition. For the cyclic peptide sample in DMSO-d6, poly-HEMA gel sticks ~25 mm long with a diameter of 2 mm and crosslink density of 0.3% were pre-swollen in DMSO-d6, inserted into a New Era Enterprises compression gel device and rinsed to remove residual monomer as previously described.47 The cyclic peptide was added to the gel and the plunger was repeatedly pumped to transfer the peptide into the gel. The poly-HEMA gel exhibited a quadrupolar splitting of 4.2 Hz for full compression during spectral acquisition. RDC-only fittings were first calculated using the RDC module of MSpin-2.3.34 Conformational models from the computational conformational ensembles were selected and scored using RDCs and 3JH-Cα-N-H couplings on the basis of the Akaike information criterion (AIC)34, 36, 46 in the Stereofitter program. The alignment tensor and populations were computed in StereoFitter by a combination of the SVD and Levenberg-Marquadt algorithms43 and the 3JH-Cα-N-H couplings were computed using a published Karplus-like equation.44-45 ACKNOWLEDGEMENTS The study was funded by Pfizer, Inc. The authors thank James Xu and Villa Zheng with the Chinese Peptide Company for peptide synthesis support and Dr. Jane Withka for many helpful conversations during the preparation of this manuscript. ANV thanks FACEPE (APQ-0507-1.06/15) for financial support.

Supporting Information is available for this manuscript: NMR spectra (proton, carbon, COSY, HSQC, HMBC), Temperature coefficient analysis, Stereofitter input, ROE/RDC comparison, DFT computational files.



REFERENCES 1. Ermert, P.; Moehle, K.; Obrecht, D., CHAPTER 8. Macrocyclic Inhibitors of GPCR's, Integrins and Protein–Protein Interactions. In Macrocycles in Drug Discovery, The Royal Society of Chemistry: 2014; pp 283-338. 2. Boehm, M.; Beaumont, K.; Jones, R.; Kalgutkar, A. S.; Zhang, L.; Atkinson, K.; Bai, G.; Brown, J. A.; Eng, H.; Goetz, G. H.; Holder, B. R.; Khunte, B.; Lazzaro, S.; Limberakis, C.; Ryu, S.; Shapiro, M. J.; Tylaska, L.; Yan, J.; Turner, R.; Leung, S. S. F.; Ramaseshan, M.; Price, D. A.; Liras, S.; Jacobson, M. P.; Earp, D. J.; Lokey, R. S.; Mathiowetz, A. M.; Menhaji-Klotz, E., Discovery of Potent and Orally Bioavailable Macrocyclic Peptide-Peptoid Hybrid CXCR7 Modulators. J Med Chem 2017, 60 (23), 9653-9663. 3. Chua, K. C. H.; Pietsch, M.; Zhang, X.; Hautmann, S.; Chan, H. Y.; Bruning, J. B.; Gütschow, M.; Abell, A. D., Macrocyclic Protease Inhibitors with Reduced Peptide Character. Angewandte Chemie 2014, 126 (30), 7962-7965. 4. Aleem, S.; Georghiou, G.; Kleiner, R. E.; Guja, K.; Craddock, B. P.; Lyczek, A.; Chan, A. I.; GarciaDiaz, M.; Miller, W. T.; Liu, D. R.; Seeliger, M. A., Structural and Biochemical Basis for Intracellular Kinase Inhibition by Src-specific Peptidic Macrocycles. Cell Chem Biol 2016, 23 (9), 1103-1112. 5. Huss, M.; Wieczorek, H., Inhibitors of V-ATPases: old and new players. J Exp Biol 2009, 212 (Pt 3), 341-6. 6. Mason, J. M., Design and development of peptides and peptide mimetics as antagonists for therapeutic intervention. Future Medicinal Chemistry 2010, 2 (12), 1813-22. 7. Marsault, E.; Peterson, M. L., Macrocycles are great cycles: applications, opportunities, and challenges of synthetic macrocycles in drug discovery. J Med Chem 2011, 54 (7), 1961-2004. 8. Villar, E. A.; Beglov, D.; Chennamadhavuni, S.; Porco, J. A., Jr.; Kozakov, D.; Vajda, S.; Whitty, A., How proteins bind macrocycles. Nat Chem Biol 2014, 10 (9), 723-31. 9. Giordanetto, F.; Kihlberg, J., Macrocyclic drugs and clinical candidates: what can medicinal chemists learn from their properties? J Med Chem 2014, 57 (2), 278-95. 10. Jin, L.; Harrison, S. C., Crystal structure of human calcineurin complexed with cyclosporin A and human cyclophilin. Proc Natl Acad Sci U S A 2002, 99 (21), 13522-6. 11. Weber, C.; Wider, G.; Von Freyberg, B.; Traber, R.; Braun, W.; Widmer, H.; Wuethrich, K., NMR structure of cyclosporin A bound to cyclophilin in aqueous solution. Biochemistry 2002, 30 (26), 65636574. 12. Efimov, S. V.; Karataeva, F. K.; Aganov, A. V.; Berger, S.; Klochkov, V. V., Spatial structure of cyclosporin A and insight into its flexibility. Journal of Molecular Structure 2013, 1036, 298-304. 13. Koeck, M.; Kessler, H.; Seebach, D.; Thaler, A., Novel backbone conformation of cyclosporin A: the complex with lithium chloride. Journal of the American Chemical Society 1992, 114 (7), 2676-2686. 14. Klages, J.; Neubauer, C.; Coles, M.; Kessler, H.; Luy, B., Structure refinement of cyclosporin A in chloroform by using RDCs measured in a stretched PDMS-gel. Chembiochem 2005, 6 (9), 1672-8. 15. Mikol, V.; Kallen, J.; Pflugl, G.; Walkinshaw, M. D., X-ray structure of a monomeric cyclophilin Acyclosporin A crystal complex at 2.1 A resolution. J Mol Biol 1993, 234 (4), 1119-30. 16. Hu, G.; Wang, K.; Groenendyk, J.; Barakat, K.; Mizianty, M. J.; Ruan, J.; Michalak, M.; Kurgan, L., Human structural proteome-wide characterization of Cyclosporine A targets. Bioinformatics 2014, 30 (24), 3561-6. 17. Kallen, J.; Mikol, V.; Taylor, P.; Walkinshaw, M. D., X-ray structures and analysis of 11 cyclosporin derivatives complexed with cyclophilin A. J Mol Biol 1998, 283 (2), 435-49. 18. Craik, D. J.; Fairlie, D. P.; Liras, S.; Price, D., The future of peptide-based drugs. Chem Biol Drug Des 2013, 81 (1), 136-47. 19. Price, D. A.; Eng, H.; Farley, K. A.; Goetz, G. H.; Huang, Y.; Jiao, Z.; Kalgutkar, A. S.; Kablaoui, N. M.; Khunte, B.; Liras, S.; Limberakis, C.; Mathiowetz, A. M.; Ruggeri, R. B.; Quan, J. M.; Yang, Z.,


Page 28 of 30

Page 29 of 30 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


Comparative pharmacokinetic profile of cyclosporine (CsA) with a decapeptide and a linear analogue. Org Biomol Chem 2017, 15 (12), 2501-2506. 20. Schwochert, J.; Turner, R.; Thang, M.; Berkeley, R. F.; Ponkey, A. R.; Rodriguez, K. M.; Leung, S. S.; Khunte, B.; Goetz, G.; Limberakis, C.; Kalgutkar, A. S.; Eng, H.; Shapiro, M. J.; Mathiowetz, A. M.; Price, D. A.; Liras, S.; Jacobson, M. P.; Lokey, R. S., Peptide to Peptoid Substitutions Increase Cell Permeability in Cyclic Hexapeptides. Org Lett 2015, 17 (12), 2928-31. 21. Wang, C. K.; Northfield, S. E.; Swedberg, J. E.; Colless, B.; Chaousis, S.; Price, D. A.; Liras, S.; Craik, D. J., Exploring experimental and computational markers of cyclic peptides: Charting islands of permeability. Eur J Med Chem 2015, 97, 202-13. 22. Rezai, T.; Bock, J. E.; Zhou, M. V.; Kalyanaraman, C.; Lokey, R. S.; Jacobson, M. P., Conformational flexibility, internal hydrogen bonding, and passive membrane permeability: successful in silico prediction of the relative permeabilities of cyclic peptides. J Am Chem Soc 2006, 128 (43), 14073-80. 23. Rezai, T.; Yu, B.; Millhauser, G. L.; Jacobson, M. P.; Lokey, R. S., Testing the conformational hypothesis of passive membrane permeability using synthetic cyclic peptide diastereomers. J Am Chem Soc 2006, 128 (8), 2510-1. 24. White, T. R.; Renzelman, C. M.; Rand, A. C.; Rezai, T.; McEwen, C. M.; Gelev, V. M.; Turner, R. A.; Linington, R. G.; Leung, S. S.; Kalgutkar, A. S.; Bauman, J. N.; Zhang, Y.; Liras, S.; Price, D. A.; Mathiowetz, A. M.; Jacobson, M. P.; Lokey, R. S., On-resin N-methylation of cyclic peptides for discovery of orally bioavailable scaffolds. Nat Chem Biol 2011, 7 (11), 810-7. 25. Lewis, I.; Schaefer, M.; Wagner, T.; Oberer, L.; Sager, E.; Wipfli, P.; Vorherr, T., A Detailed Investigation on Conformation, Permeability and PK Properties of Two Related Cyclohexapeptides. International Journal of Peptide Research and Therapeutics 2014, 21 (2), 205-221. 26. Kessler, H., Conformation and Biological Activity of Cyclic Peptides. Angewandte Chemie International Edition in English 1982, 21 (7), 512-523. 27. Huben, K.; Jewginski, M.; Pabis, A.; Paluch, P.; Luy, B.; Jankowski, S., The structure of cyclolinopeptide A in chloroform refined by RDC measurements. J Pept Sci 2014, 20 (11), 901-7. 28. Schmidts, V., Perspectives in the application of residual dipolar couplings in the structure elucidation of weakly aligned small molecules. Magn Reson Chem 2017, 55 (1), 54-60. 29. Rigling, C.; Kisunzu, J. K.; Duschmale, J.; Haussinger, D.; Wiesner, M.; Ebert, M. O.; Wennemers, H., Conformational Properties of a Peptidic Catalyst: Insights from NMR Spectroscopic Studies. J Am Chem Soc 2018, 140 (34), 10829-10838. 30. Wang, C. K.; Northfield, S. E.; Colless, B.; Chaousis, S.; Hamernig, I.; Lohman, R. J.; Nielsen, D. S.; Schroeder, C. I.; Liras, S.; Price, D. A.; Fairlie, D. P.; Craik, D. J., Rational design and synthesis of an orally bioavailable peptide guided by NMR amide temperature coefficients. Proc Natl Acad Sci U S A 2014, 111 (49), 17504-9. 31. Liu, P.; Kim, B.; Friesner, R. A.; Berne, B. J., Replica exchange with solute tempering: a method for sampling biological systems in explicit water. Proc Natl Acad Sci U S A 2005, 102 (39), 13749-54. 32. Chalmers, D. K.; Marshall, G. R., Pro-D-NMe-Amino Acid and D-Pro-NMe-Amino Acid: Simple, Efficient Reverse-Turn Constraints. Journal of the American Chemical Society 1995, 117 (22), 5927-5937. 33. Gayathri, C.; Tsarevsky, N. V.; Gil, R. R., Residual dipolar couplings (RDCs) analysis of small molecules made easy: fast and tuneable alignment by reversible compression/relaxation of reusable PMMA Gels. Chemistry-A European Journal 2010, 16 (12), 3622-6. 34. Navarro-Vazquez, A., MSpin-RDC. A program for the use of residual dipolar couplings for structure elucidation of small molecules. Magn Reson Chem 2012, 50 Suppl 1, S73-9. 35. Cornilescu, G.; Delaglio, F.; Bax, A., Protein backbone angle restraints from searching a database for chemical shift and sequence homology. J Biomol NMR 1999, 13 (3), 289-302.



36. Troche-Pesqueira, E.; Anklin, C.; Gil, R. R.; Navarro-Vazquez, A., Computer-Assisted 3D Structure Elucidation of Natural Products using Residual Dipolar Couplings. Angew Chem Int Ed Engl 2017, 56 (13), 3660-3664. 37. StereoFitter1.0-beta MestReLab Research S. L: Santiago de Compostela, Spain, 2018. 38. Erdelyi, M.; d'Auvergne, E.; Navarro-Vazquez, A.; Leonov, A.; Griesinger, C., Dynamics of the glycosidic bond: conformational space of lactose. Chemistry-A European Journal 2011, 17 (34), 9368-76. 39. Trigo-Mourino, P.; de la Fuente, M. C.; Gil, R. R.; Sanchez-Pedregal, V. M.; Navarro-Vazquez, A., Conformational analysis of the anti-obesity drug lorcaserin in water: how to take advantage of longrange residual dipolar couplings. Chemistry-A European Journal 2013, 19 (44), 14989-97. 40. Lancefield, C. S.; Slawin, A. M.; Westwood, N. J.; Lebl, T., The use of residual dipolar coupling for conformational analysis of structurally related natural products. Magn Reson Chem 2015, 53 (6), 467-75. 41. Akaike, H., A new look at the statistical model identification. IEEE Transactions on Automatic Control 1974, 19 (6), 716-723. 42. Losonczi, J. A.; Andrec, M.; Fischer, M. W.; Prestegard, J. H., Order matrix analysis of residual dipolar couplings using singular value decomposition. J Magn Reson 1999, 138 (2), 334-42. 43. Sanchez-Pedregal, V. M.; Santamaria-Fernandez, R.; Navarro-Vazquez, A., Residual dipolar couplings of freely rotating groups in small molecules. Stereochemical assignment and side-chain conformation of 8-phenylmenthol. Org Lett 2009, 11 (7), 1471-4. 44. Pardi, A.; Billeter, M.; Wüthrich, K., Calibration of the angular dependence of the amide protonCα proton coupling constants, 3JHNα, in a globular protein. Journal of Molecular Biology 1984, 180 (3), 741-751. 45. Navarro-Vazquez, A.; Santamaria-Fernandez, R.; Sardina, F. J., MSpin-JCoupling. A modular program for prediction of scalar couplings and fast implementation of Karplus relationships. Magn Reson Chem 2018, 56 (6), 505-512. 46. Navarro-Vazquez, A.; Gil, R. R.; Blinov, K., Computer-Assisted 3D Structure Elucidation (CASE-3D) of Natural Products Combining Isotropic and Anisotropic NMR Parameters. J Nat Prod 2018, 81 (1), 203210. 47. Gil-Silva, L. F.; Santamaria-Fernandez, R.; Navarro-Vazquez, A.; Gil, R. R., Collection of NMR Scalar and Residual Dipolar Couplings Using a Single Experiment. Chemistry-A European Journal 2016, 22 (2), 472-6. 48. Hellemann, E.; Teles, R. R.; Hallwass, F.; Barros, W., Jr.; Navarro-Vazquez, A.; Gil, R. R., Mechanical Behavior of Polymer Gels for RDCs and RCSAs Collection: NMR Imaging Study of Buckling Phenomena. Chemistry-A European Journal 2016, 22 (46), 16632-16635. 49. Cierpicki, T.; Otlewski, J., Amide proton temperature coefficients as hydrogen bond indicators in proteins. J Biol NMR 2001, 21 (3),249–261. 50. Hu H; Krishnamurthy K., Revisiting the initial rate approximation in kinetic NOE measurements. J Magn Reson 2006, 182 (1), 173–177. 51. Di, L.; Whitney-Pickett, C.; Umland, J. P.; Zhang, H.; Zhang, X.; Gebhard, D. F.; Lai, Y.; Federico, J. J., 3rd; Davidson, R. E.; Smith, R.; Reyner, E. L.; Lee, C.; Feng, B.; Rotter, C.; Varma, M. V.; Kempshall, S.; Fenner, K.; El-Kattan, A. F.; Liston, T. E.; Troutman, M. D., Development of a new permeability assay using low-efflux MDCKII cells. J Pharm Sci 2011, 100 (11), 4974-85. 52. Kansy, M.; Senner, F.; Gubernator, K., Physicochemical high throughput screening: parallel artificial membrane permeation assay in the description of passive absorption processes. J Med Chem 1998, 41 (7), 1007-10. 53. Thiele, C. M.; Bermel, W., Speeding up the measurement of one-bond scalar (1J) and residual dipolar couplings (1D) by using non-uniform sampling (NUS). J Magn Reson 2012, 216, 134-43. 54. Cobas, J. C.; Sardina, F. J., Nuclear magnetic resonance data processing. MestRe-C: A software package for desktop computers. Concepts in Magnetic Resonance 2003, 19A (2), 80-96.


Page 30 of 30

Cyclic Peptide Design Guided by Residual Dipolar Couplings, J

Recommend Documents