Core chemotype diversification in the HIV-1 entry inhibitor class using field-based bioisosteric replacement

Article abstract of DOI:10.1016/j.bmcl.2015.10.080

Demand remains for new inhibitors of HIV-1 replication and the inhibition of HIV-1 entry is an extremely attractive therapeutic approach. Using field-based bioisosteric replacements, we have further extended the chemotypes available for development in the HIV-1 entry inhibitor class. Moreover, using field-based disparity analysis of the compounds, 3D structure-activity relationships were derived that will be useful in the further development of these inhibitors towards clinical utility.

Full text of DOI:10.1016/j.bmcl.2015.10.080

Bioorganic & Medicinal Chemistry Letters 26 (2016) 228–234
Contents lists available at ScienceDirect
Bioorganic & Medicinal Chemistry Letters
journal homepage: www.elsevier.com/locate/bmcl
Core chemotype diversification in the HIV-1 entry inhibitor class
using field-based bioisosteric replacement
Marina Tuyishime a, Rae Lawrence b, Simon Cocklin a,
⇑
a Department of Biochemistry & Molecular Biology, Drexel University College of Medicine, Rooms 10302-10306, 245 N. 15th Street, Philadelphia, PA 19102, USA
b Cresset, New Cambridge House, Bassingbourn Road, Litlington, Cambridgeshire, UK
a r t i c l e i n f o
a b s t r a c t
Article history:
Demand remains for new inhibitors of HIV-1 replication and the inhibition of HIV-1 entry is an extremely
attractive therapeutic approach. Using field-based bioisosteric replacements, we have further extended
the chemotypes available for development in the HIV-1 entry inhibitor class. Moreover, using field-based
disparity analysis of the compounds, 3D structure–activity relationships were derived that will be useful
in the further development of these inhibitors towards clinical utility.
Received 11 September 2015
Revised 23 October 2015
Accepted 26 October 2015
Available online 27 October 2015
Ó 2015 Elsevier Ltd. All rights reserved.
Keywords:
Computer-aided drug design
HIV-1 envelope protein
Field-based
Bioisosteric replacement
Scaffold-hopping
SAR analysis
Structure–activity landscape
Antiviral
Inhibiting the process of entry of HIV-1 into susceptible cells
remains an elusive but extremely attractive intervention point
for the development of novel anti-HIV therapies. HIV-1 entry into
cells is solely orchestrated by the Env complex on the surface of
the virion. The Env complex is organized on the virion surface as
trimeric spikes, composed of three gp120 molecules non-cova-
lently linked to three gp41 molecules. Recently, two structures of
a soluble, cleaved HIV-1 Env trimer from a clade A founder virus
has been solved providing additional and much needed informa-
tion on the quaternary organization of the Env complex.1–3
HIV-1 infection usually occurs only after two sequential and
specific binding steps. The first interaction is between gp120 and
CD4 antigen present on CD4+ T cells, monocyte/macrophages,
and other immune and nonimmune cells. This interaction results
in a series of conformational rearrangements in gp120 that permits
the second binding event to occur. This second interaction occurs
between gp120 and a member of the chemokine receptor subfam-
ily, within the large G protein–coupled family of receptors, mainly
CCR5 and/or CXCR4. This interaction also promotes considerable
rearrangement in gp120 and transduction of this conformational
signal to gp41. This then elicits the exposure of the fusion peptide
Figure 1. Overlaid field point representation of compounds BMS-377806,
BMS488043, BMS-626529, and SC11 from the derived binding mode (Forge, UK).
Red field points highlight energy minima for a positively charged probe, blue for a
negative probe. Yellow spheres represent attractive van der Waals minima for a
neutral probe and gold spheres represent hydrophobic centroids. Oxygen atoms are
shown in red, nitrogen in blue. The size of the points is related to the strength of a
potential interaction (i.e., absolute value of the field strength at that point in space).
⇑
Corresponding author. Tel.: +1 215 762 7234 (O), +1 215 762 4979 (lab); fax: +1
215 762 4452.
E-mail address: scocklin@drexelmed.edu (S. Cocklin).
http://dx.doi.org/10.1016/j.bmcl.2015.10.080
0960-894X/Ó 2015 Elsevier Ltd. All rights reserved.

M. Tuyishime et al. / Bioorg. Med. Chem. Lett. 26 (2016) 228–234
229
Table 1
Specificity and potency of compounds against HIV-1JR-CSF, HIV-1B41, HIV-1HxBc2 Env, and AMLV pseudotyped HIV-1. N.A. = not active; N.D. = not determined
Compound
IC50 JR-CSF (lM)
IC50 B41 (lM)
IC50 HxBc2 (
l
M)
IC50 AMLV (lM)
SC12
N
N
N
NH
O
N
0.008 0.002
0.006 0.003
0.08 0.02
N.A.
N
O
O
N
N
SC14
N
N
N
O
N
2.5 0.4
3.8 0.4
3.8 1.6
N.A.
NH
N
O
NH
O
O
SC15
N
N
N
O
NH
N
0.003 0.001
0.007 0.001
0.009 0.0011
N.A.
O
N
O
O
SC27
N
N
HN
N
13.0 9.0
3.9 0.7
36 21
N.A
O
N
N
N
O
O
O
O
SC28
N
O
0.096 0.019
0.085 0.03
0.069 0.014
N.A.
N
N
HN
N
NH
O
N
O
SC45
O
N
N
HN
N
0.224 0.017
0.35 0.03
0.38 0.03
N.A.
N
O
HN
N
O
O

230
M. Tuyishime et al. / Bioorg. Med. Chem. Lett. 26 (2016) 228–234
within the N-terminus of gp41, which through additional conforma-
tional rearrangements in gp41 facilitates fusion between the viral
and cellular membranes and release of the viral core into the cell.
Several groups are actively involved in the development of
small molecules targeted to gp120 that disrupt the Env molecular
machine to stop HIV-1 entry into cells.4–13 Despite this only one
chemotype, developed by Bristol Myers Squibb, has successfully
made it to clinical trials. The newest compound in the drug class,
BMS-663068, a phosphonooxymethyl prodrug of BMS-626529,14
recently performed favourably in a Phase IIb clinical study, high-
lighting the potential utility of these Env-directed entry inhibitor
class of compounds (presented at the 22nd Conference on Retro-
viruses and Opportunistic Infections [CROI]).
Our group recently described the computational design of new
compounds designed to act through a common binding site to that
of the Bristol Myers Squibb piperazine-based entry inhibitors, of
which BMS-663068/BMS626529 are members. Our most potent
compounds, SC11 and SC26, both contain a dipyrrolodine core
scaffold, and specifically inhibit HIV-1JR-CSF at 0.8 and 2 nM,
respectively.15
inhibitors, we first used FieldTemplater (Forge, Cresset)16–26 to
determine the most likely 3D conformation adopted by
BMS-377806,12 BMS-488043,27 BMS-626529,28 and SC11/SC2615
upon binding to the HIV-1 Env target (Fig. 1). This FieldTem-
plater-derived 3D conformation was then used as input into Spark
(Cresset, UK). Spark searches a database of up to 600,000 fragments
to find bioisosteres that exhibit similar shape and electronic prop-
erties as the region of interest when placed in the context of the
final molecule. To maximize the likelihood of identifying interest-
ing potential replacements, we performed bioisosteric searches of
the piperazine groups of BMS-377806, BMS-488043, and
BMS-626529, in addition to the dipyrrolodine group of compounds
SC11/SC26. The results of each search were analyzed and common
structures were identified. From this analysis, four different core
chemotypes were chosen for investigation based upon diversity
and BIF% scores (a factor that indicates how good the replacement
is in the context of the conformation of the entire molecule).
Compounds containing core pyrrolo-pyrazole, azetidine, tetrahy-
dropyridine, azabicyclo-hexane and diazaspiro-decane groups
were then synthesized. First, a common head group to be used in
all of the molecules was synthesized, compound 6, according to
Supplemental Scheme 1. This was subsequently used in the
synthesis of compounds SC12, SC14, SC15, SC27, SC28, and SC45
as outlined in Supplemental Schemes 1–7.
Having successfully demonstrated that scaffold-hopping of the
piperazine moeity can be achieved, in this study we sought to
extend the core chemotypes available for the entry inhibitor class
in the hopes of improving drug-like properties. To accomplish this
we performed computationally directed scaffold-hopping studies,
coupled to synthesis, antiviral potency analysis and computational
3D Quantitative Structure–Activity Relationship (QSAR).
After successful synthesis of each of the five novel-scaffold
derivatives, we then tested them for specificity and potency
against HIV-1. To do this we used the HIV-1 single round infection
assay. In this system, recombinant single-round infectious
envelope-pseudotyped luciferase-reporter HIV-1 viruses are
Due to the lack of structural information on the bioactive
conformation of our inhibitors and the BMS piperazine based
Figure 2. Disparity Matrix showing the pairwise comparisons for the compounds reported using IC50 HIV-1JR-CSF as activity. Red and green boxes correspond to decreases
and increases in activity between the pair, respectively. The darker the shading, the sharper the activity cliff (higher calculated Disparity). The matrix is symmetrical—for
every red box, there is a corresponding green box for the comparison going in the opposite direction.

M. Tuyishime et al. / Bioorg. Med. Chem. Lett. 26 (2016) 228–234
231
Panel A
Panel B
Figure 3. Comparison of field differences for a selection of molecules in which the negative electrostatics over the central moiety appears to be important for activity. The
field difference map displays the surfaces as relative values between pairs of molecules. The regions where the surface is displayed have stronger electrostatic fields than the
corresponding regions in the compared molecule. In Panel A, SC27 is compared to SC11; in the Panel B, SC15 is compared to SC14. Again, IC50 refers to IC50 HIV-1JR-CSF
.
produced by co-transfection of 293T cells with the envelope-defi-
cient HIV-1 NL4-3 vector (pNL4-3-LucR+Eꢀ; a gift of N. R. Landau,
New York University),29 which carries the luciferase-reporter gene;
and the HIV-1 JR-CSF, HIV-1 B41,30 HIV-1 HxBc2 or amphotropic
murine leukemia virus (AMLV) envelope expressing vector. These
recombinant viruses are then used to infect U87.CD4.CCR5 (JR-
CSF and B41 Envs) target cells or U87.CD4.CXCR4 (HxBc2 Env)
and infectivity is quantified by measuring luciferase activity in cell
lysates (Luciferase Assay System, Promega) using a microplate
luminometer (GloMax, Promega). The effect of the compounds on
the infection of target cells by AMLV Env pseudotyped recombi-
nant HIV-1 was used to determine the specificity of the com-
pounds to HIV-1 Env-mediated cell entry. The results of this
analysis are shown in Table 1.
The higher the calculated disparity corresponds to a sharper
change in activity between a pair of very similar molecules and
hence an activity cliff.
Activity Atlas (Cresset, UK) provides a probabilistic approach to
analyzing Structure Activity Relationships (SAR). Like all 3D SAR
approaches, comprehensive conformation hunting and correct
alignments are critical, thus, alignments were hand checked to
reduce noise arising from inadequate alignments. This is a key
and necessary step, since, being a probabilistic method, Activity
Atlas assumes that the alignments are correct. Activity Atlas helps
provide insight into what feature(s) the active compounds in a col-
lection have in common—in essence, it summarizes the Activity
Miner analysis to illuminate what activity cliffs reveal about the
nature of the SAR.
As can be seen from the results in Table 1, all compounds had
specific anti-HIV activity but their half maximum inhibitory
Compounds SC03, SC04, SC07, SC08, SC11 and SC26 from our
previous report,15 and compounds SC12, SC14, SC15, SC27, SC28
and SC45 from this analysis were input into Activity Miner. From
the pairwise comparisons (50% field–50% shape) a Disparity Matrix
was generated (Fig. 2) which provides an overview of the dataset.
For each pair, the comparison was color-coded by color and shade,
with red and green meaning a decrease or increase in activity,
respectively. The shading corresponds to the disparity: darker
shading indicates a higher disparity, and thus, a steeper activity
cliff.
concentrations (IC50) showed noticibale variation. To better
understand the factors driving activity of these HIV-1 entry inhibi-
tors, including those compounds previously reported by our group,
we assessed them computationally using Activity Miner and Activ-
ity Atlas (Cresset, UK). Activity Miner provides an interface for the
rapid assessment of structure–activity landscapes for a set of
aligned compounds in the context of activity cliffs. It achieves this
by comparing pairs of molecules to find where small changes in the
molecule’s structure or local properties results in a large change in
activity. In the software, this notion of disparity is expressed as:
As can be clearly seen in Figure 2, the highest ranked activity
cliff is between molecules SC11 (pIC50 = 9.1) and SC27
(pIC50 = 4.890). This pair has a 3D Similarity of 0.844 (quite high),
D
Activity
combined with a
DpIC50 = 4.21. When the field differences are
Disparity /
1 ꢀ Similarity
examined, as shown in Figure 3, it can be observed that the

232
M. Tuyishime et al. / Bioorg. Med. Chem. Lett. 26 (2016) 228–234
electron density spanning the dipyrollidine in SC11 is absent in the
central diazaspiro-decane group of SC27. This suggests that elec-
tron density in the center of the molecule may be important for
activity.
This observation is conserved in the comparisons of SC15 and
SC26 to SC27, with the higher potency compounds having this
region of electron density. Furthermore, this observation is even
borne out in the comparison of SC15 to SC14. SC14 and SC15 share
a core azetidine moiety but differ in its orientation. Again, the
reduction of negativity in the core region is seen in the lower
potency compound SC14 but maintained in the highly active
SC15 compound.
simultaneous scaffold modifications, it is more convenient and illu-
minative to summarize the pair-wise information using Activity
Atlas (Cresset, UK).
Activity Atlas analyzes SAR by employing a Bayesian approach
to qualitatively attain a global overview of available 3D structure
and activity information. Effectively, this approach automates
and summarizes the pair comparisons that could be examined
and exploited for SAR understanding in Activity Miner. The aim
of Activity Atlas is to: determine what electrostatic/hydrophobic/
shape features active molecules in the data set have in common;
to understand what the activity cliffs suggest about the SAR; and
finally, to be able to assess how well the compounds cover avail-
able chemical (or feature) space.
Comparison of SC11 to its parental compound SC04 (IC50
HIV-1JR-CSF = 100
l
M), highlights key field differences that could
Once the Activity Atlas calculated average field contributions to
activity for the data set are plotted, one can look at new molecules
in the context of these averages to assess suitability for
progression.
explain the corresponding 5-order of magnitude difference in
activity (Fig. 4). Most striking are the differences between the ‘head
regions’ of the compounds. The acenaphthene head group of SC04
shows an intact
p
-cloud, as well as positivity associated with the
Examining three of the compounds in the dataset with high, mid,
and low potencies in the context of the average field contributions
(Fig. 5, Panel A), we see that one of the most active compounds
(SC26), shows a field pattern that aligns well with the average elec-
in-plane hydrogens that is not observed in the azaindole group of
the more active SC11 (Fig. 4, Panel B).
Evaluating the complex structure–activity relationship for this
set of compounds, which have only loosely related 2D structure
based on Tanimoto similarity scores (based on Merck Atom Pairs),
resulted in a number of comparisons to examine in Activity Miner.
The visualization of single-pair comparisons provides plenty of
insight for molecule pairs with only minor structural or featural
changes resulting in activity cliffs. However, when examining more
complex datasets containing multiple chemotypes with multiple
trostatics. SC14, which has mid-range activity (IC50 JR-CSF = 2.5 lM),
has a field point pattern which aligns acceptably in some areas, but
doesn’t meet the negative electrostatic directionality associated
with the carbonyl group shown on the right. Finally, SC04, with
IC50 of 100 lM, shows reasonable field matching across the core,
but the acenaphthene ‘head region’ lacks much of the fields present
in the higher potency molecules (Fig. 5, Panel B).
Panel A
Panel B
Figure 4. (Panel A) Comparing the field differences between SC11 and SC04. The
p
-cloud associated with the fused rings remains intact, whereas in the more active SC11, that
electron density has been pulled out of the region. (Panel B) Comparison of the calculated electrostatic surfaces for SC11 and SC04. Notice the intact
acenaphthene, as well as the positive field associated with the in-plane hydrogens, which leaves them available for potential hydrogen bonding.
p-cloud of the

M. Tuyishime et al. / Bioorg. Med. Chem. Lett. 26 (2016) 228–234
233
Panel A
Panel B
Panel C
Figure 5. (Panel A) Compound SC11 showing the average electrostatics and average hydrophobic regions based on the Activity Atlas summary of active molecules in the
dataset. (Panel B) Assessing three compounds in the context of the average electrostatics associated with activity based on the dataset. The higher active molecules have
better correlation with the average fields for actives (shown in Panel A), while the less active compounds miss critical features of the SAR. Red and blue maps depict the
average positive and negative electrostatics associated with active molecules, based on the dataset. IC50 refers to IC50 HIV-1JR-CSF. (Panel C) Summary graphic of the activity
cliffs found by pairwise comparisons in Activity Miner and summarized in Activity Atlas. The maps in this graphic show the key features that drive activity, as determined
from the alignment of the reported compounds. Note that ‘more negative’ can also mean ‘less positive’, and vice versa.
When the activity cliff information is summarized using Activ-
ity Atlas (Fig. 5, Panel C), it is seen that there are regions with
specific electrostatic conditions, as well as hydrophobicity consid-
erations. In essence, these regions qualitatively express our 3D SAR
in a graphical fashion and provide a blueprint for the design of fur-
ther analogues.
inhibitors. Future studies in our group will include assessment of
the drug-like properties of these new chemotypes, modulation
and optimization of their potencies based upon SAR relationships
derived in this study and simultaneously against genetically dis-
tinct isolates, and detailed mechanism of action studies, including
structural studies.
In summary, we have extended our previous work using
computational means to diversify the core scaffolds in the entry
inhibitor class, identifying 5 new core chemotypes. Computational
analysis of these core chemotype diversified inhibitors has pro-
vided a blueprint that can be used in the design of next generation
entry inhibitors to improve the potency. Taken together, the results
from this study validate the combined use of field-based
bioisosteric replacement and field-based disparity analysis as a
means to further the potential utility of Env-targeted HIV-1 entry
Acknowledgment
This work was supported by NIH/NIAID Grant 1R21AI104354-
01A1 (Cocklin, PI) and W. W. Smith Charitable Trust Grant
#A1301 (Cocklin, PI). We’d like to thank Pavel Pugachev and John
P. Moore (Department of Microbiology and Immunology, Weill
Medical College of Cornell University, New York, NY 10021, USA)
for the generous gift of the B41 gp160 expression plasmid.

234
M. Tuyishime et al. / Bioorg. Med. Chem. Lett. 26 (2016) 228–234
Demers, G.; Yang, Z.; Zadjura, L.; Meanwell, N.; Colonno, R. Proc. Natl. Acad. Sci.
Supplementary data
U.S.A. 2003, 100, 11013.
13. Herschhorn, A.; Gu, C.; Espy, N.; Richard, J.; Finzi, A.; Sodroski, J. G. Nat. Chem.
Biol. 2014, 10, 845.
14. Nettles, R. E.; Schurmann, D.; Zhu, L.; Stonier, M.; Huang, S. P.; Chang, I.; Chien,
C.; Krystal, M.; Wind-Rotolo, M.; Ray, N.; Hanna, G. J.; Bertz, R.; Grasela, D. J.
Infect. Dis. 2012, 206, 1002.
Supplementary data associated with this article can be found, in
the online version, at http://dx.doi.org/10.1016/j.bmcl.2015.10.
080.
15. Tuyishime, M.; Danish, M.; Princiotto, A.; Mankowski, M. K.; Lawrence, R.;
Lombart, H. G.; Esikov, K.; Berniac, J.; Liang, K.; Ji, J.; Ptak, R. G.; Madani, N.;
Cocklin, S. Bioorg. Med. Chem. Lett. 2014, 24, 5439.
References and notes
16. Cheeseright, T.; Mackey, M.; Rose, S.; Vinter, A. J. Chem. Inf. Model. 2006, 46,
665.
17. Cheeseright, T.; Mackey, M.; Rose, S.; Vinter, A. Expert Opin. Drug Discov. 2007,
2, 131.
18. Cheeseright, T. J.; Holm, M.; Lehmann, F.; Luik, S.; Gottert, M.; Melville, J. L.;
Laufer, S. J. Med. Chem. 2009, 52, 4200.
19. Cheeseright, T. J.; Mackey, M. D.; Melville, J. L.; Vinter, J. G. J. Chem. Inf. Model.
2008, 48, 2108.
20. Cheeseright, T. J.; Mackey, M. D.; Scoffin, R. A. Curr. Comput. Aided Drug Des.
2011, 7, 190.
21. Collins, J. C.; Armstrong, A.; Chapman, K. L.; Cordingley, H. C.; Jaxa-Chamiec, A.
A.; Judd, K. E.; Mann, D. J.; Scott, K. A.; Tralau-Stewart, C. J.; Low, C. M. R.
MedChemComm 2013, 4, 1148.
22. Kirpotina, L. N.; Khlebnikov, A. I.; Schepetkin, I. A.; Ye, R. D.; Rabiet, M. J.; Jutila,
M. A.; Quinn, M. T. Mol. Pharmacol. 2010, 77, 159.
23. Low, C. M.; Buck, I. M.; Cooke, T.; Cushnir, J. R.; Kalindjian, S. B.; Kotecha, A.;
Pether, M. J.; Shankley, N. P.; Vinter, J. G.; Wright, L. J. Med. Chem. 2005, 48,
6790.
24. Low, C. M.; Vinter, J. G. J. Med. Chem. 2008, 51, 565.
25. Mackey, M. D.; Melville, J. L. J. Chem. Inf. Model. 2009, 49, 1154.
26. Webster, S. P.; Binnie, M.; McConnell, K. M.; Sooy, K.; Ward, P.; Greaney, M. F.;
Vinter, A.; Pallin, T. D.; Dyke, H. J.; Gill, M. I.; Warner, I.; Seckl, J. R.; Walker, B. R.
Bioorg. Med. Chem. Lett. 2010, 20, 3265.
27. Yang, Z.; Zadjura, L. M.; Marino, A. M.; D’Arienzo, C. J.; Malinowski, J.;
Gesenberg, C.; Lin, P. F.; Colonno, R. J.; Wang, T.; Kadow, J. F.; Meanwell, N. A.;
Hansel, S. B. J. Pharm. Sci. 2010, 99, 2135.
28. Nowicka-Sans, B.; Gong, Y. F.; McAuliffe, B.; Dicker, I.; Ho, H. T.; Zhou, N.;
Eggers, B.; Lin, P. F.; Ray, N.; Wind-Rotolo, M.; Zhu, L.; Majumdar, A.; Stock, D.;
Lataillade, M.; Hanna, G. J.; Matiskella, J. D.; Ueda, Y.; Wang, T.; Kadow, J. F.;
Meanwell, N. A.; Krystal, M. Antimicrob. Agents Chemother. 2012, 56, 3498.
29. Connor, R. I.; Chen, B. K.; Choe, S.; Landau, N. R. Virology 1995, 206, 935.
30. Pugach, P.; Ozorowski, G.; Cupo, A.; Ringe, R.; Yasmeen, A.; de Val, N.; Derking,
R.; Kim, H. J.; Korzun, J.; Golabek, M.; de Los Reyes, K.; Ketas, T. J.; Julien, J. P.;
Burton, D. R.; Wilson, I. A.; Sanders, R. W.; Klasse, P. J.; Ward, A. B.; Moore, J. P. J.
Virol. 2015, 89, 3380.
1. Lyumkis, D.; Julien, J. P.; de Val, N.; Cupo, A.; Potter, C. S.; Klasse, P. J.; Burton, D.
R.; Sanders, R. W.; Moore, J. P.; Carragher, B.; Wilson, I. A.; Ward, A. B. Science
2013, 342, 1484.
2. Julien, J. P.; Cupo, A.; Sok, D.; Stanfield, R. L.; Lyumkis, D.; Deller, M. C.; Klasse, P.
J.; Burton, D. R.; Sanders, R. W.; Moore, J. P.; Ward, A. B.; Wilson, I. A. Science
2013, 342, 1477.
3. Pancera, M.; Zhou, T.; Druz, A.; Georgiev, I. S.; Soto, C.; Gorman, J.; Huang, J.;
Acharya, P.; Chuang, G. Y.; Ofek, G.; Stewart-Jones, G. B.; Stuckey, J.; Bailer, R. T.;
Joyce, M. G.; Louder, M. K.; Tumba, N.; Yang, Y.; Zhang, B.; Cohen, M. S.; Haynes,
B. F.; Mascola, J. R.; Morris, L.; Munro, J. B.; Blanchard, S. C.; Mothes, W.;
Connors, M.; Kwong, P. D. Nature 2014.
4. Courter, J. R.; Madani, N.; Sodroski, J.; Schon, A.; Freire, E.; Kwong, P. D.;
Hendrickson, W. A.; Chaiken, I. M.; Lalonde, J. M.; Smith, A. B., 3rd Acc. Chem.
Res. 2014.
5. Kwon, Y. D.; LaLonde, J. M.; Yang, Y.; Elban, M. A.; Sugawara, A.; Courter, J. R.;
Jones, D. M.; Smith, A. B., 3rd; Debnath, A. K.; Kwong, P. D. PLoS One 2014, 9,
e85940.
6. Li, Z.; Zhou, N.; Sun, Y.; Ray, N.; Lataillade, M.; Hanna, G. J.; Krystal, M.
Antimicrob. Agents Chemother. 2013, 57, 4172.
7. Lalonde, J. M.; Le-Khac, M.; Jones, D. M.; Courter, J. R.; Park, J.; Schon, A.;
Princiotto, A. M.; Wu, X.; Mascola, J. R.; Freire, E.; Sodroski, J.; Madani, N.;
Hendrickson, W. A.; Smith, A. B., 3rd ACS Med. Chem. Lett. 2013, 4, 338.
8. Curreli, F.; Choudhury, S.; Pyatkin, I.; Zagorodnikov, V. P.; Bulay, A. K.; Altieri,
A.; Kwon, Y. D.; Kwong, P. D.; Debnath, A. K. J. Med. Chem. 2012, 55, 4764.
9. Williams, D. H.; Adam, F.; Fenwick, D. R.; Fok-Seang, J.; Gardner, I.; Hay, D.;
Jaiessh, R.; Middleton, D. S.; Mowbray, C. E.; Parkinson, T.; Perros, M.; Pickford,
C.; Platts, M.; Randall, A.; Siddle, D.; Stephenson, P. T.; Tran, T. D.; Vuong, H.
Bioorg. Med. Chem. Lett. 2009, 19, 5246.
10. Zhao, Q.; Ma, L.; Jiang, S.; Lu, H.; Liu, S.; He, Y.; Strick, N.; Neamati, N.; Debnath,
A. K. Virology 2005, 339, 213.
11. Si, Z.; Madani, N.; Cox, J. M.; Chruma, J. J.; Klein, J. C.; Schon, A.; Phan, N.; Wang,
L.; Biorn, A. C.; Cocklin, S.; Chaiken, I.; Freire, E.; Smith, A. B., 3rd; Sodroski, J. G.
Proc. Natl. Acad. Sci. U.S.A. 2004, 101, 5036.
12. Lin, P. F.; Blair, W.; Wang, T.; Spicer, T.; Guo, Q.; Zhou, N.; Gong, Y. F.; Wang, H.
G.; Rose, R.; Yamanaka, G.; Robinson, B.; Li, C. B.; Fridell, R.; Deminie, C.;