A novel approach for characterizing protein ligand complexes: molecular basis for specificity of small-molecule Bcl-2 inhibitors.
ABSTRACT The increasing diversity of small molecule libraries has been an important source for the development of new drugs and, more recently, for unraveling the mechanisms of cellular events-a process termed chemical genetics.(1) Unfortunately, the majority of currently available compounds are mechanism-based enzyme inhibitors, whereas most of cellular activity regulation proceeds on the level of protein-protein interactions. Hence, the development of small molecule inhibitors of protein-protein interactions is important. When screening compound libraries, low-micromolar inhibitors of protein interactions can be routinely found. The enhancement of affinities and rationalization of the binding mechanism require structural information about the protein-ligand complexes. Crystallization of low-affinity complexes is difficult, and their NMR analysis suffers from exchange broadening, which limits the number of obtainable intermolecular constraints. Here we present a novel method of ligand validation and optimization, which is based on the combination of structural and computational approaches. We successfully used this method to analyze the basis for structure-activity relationships of previously selected (2) small molecule inhibitors of the antiapoptotic protein Bcl-xL and identified new members of this inhibitor family.
Article: Hot spots and transient pockets: predicting the determinants of small-molecule binding to a protein-protein interface.[show abstract] [hide abstract]
ABSTRACT: Protein-protein interfaces are considered difficult targets for small-molecule protein-protein interaction modulators (PPIMs ). Here, we present for the first time a computational strategy that simultaneously considers aspects of energetics and plasticity in the context of PPIM binding to a protein interface. The strategy aims at identifying the determinants of small-molecule binding, hot spots, and transient pockets, in a protein-protein interface in order to make use of this knowledge for predicting binding modes of and ranking PPIMs with respect to their affinity. When applied to interleukin-2 (IL-2), the computationally inexpensive constrained geometric simulation method FRODA outperforms molecular dynamics simulations in sampling hydrophobic transient pockets. We introduce the PPIAnalyzer approach for identifying transient pockets on the basis of geometrical criteria only. A sequence of docking to identified transient pockets, starting structure selection based on hot spot information, RMSD clustering and intermolecular docking energies, and MM-PBSA calculations allows one to enrich IL-2 PPIMs from a set of decoys and to discriminate between subgroups of IL-2 PPIMs with low and high affinity. Our strategy will be applicable in a prospective manner where nothing else than a protein-protein complex structure is known; hence, it can well be the first step in a structure-based endeavor to identify PPIMs.Journal of Chemical Information and Modeling 11/2011; 52(1):120-33. · 4.68 Impact Factor
Article: Structure–anticancer activity relationships among 4-azolidinone-3-carboxylic acids derivatives[show abstract] [hide abstract]
ABSTRACT: The aim of present research was investigation of anticancer activity of 4-azolidinone-3-carboxylic acids derivatives, and studies of structure–activity relationships (SAR) aspects. Methods. Organic synthesis; spectral methods; anticancer screening was performed according to the US NCI protocol (Developmental Therapeutic Program). Results. The data of new 4-thiazolidinone-3-alkanecarboxylic acids derivatives in vitro anticancer activity were described. The most active compounds which belong to 5-arylidene-2,4- thia(imida)zolidinone-3-alkanecarboxylic acids; 5-aryl(heteryl)idenerhodanine-3-succinic acids derivatives were selected. Determination of some SAR aspects which allowed to determine directions in leadcompounds structure optimization, as well as desirable molecular fragments for design of potential anticancer agents based on 4-azolidinone scaffold were performed. 5-Arylidenehydantoin-3-acetic acids amides were identified as a new class of significant selective antileukemic agents. Possible pharmacophore scaffold of 5-ylidenerhodanine-3-succinic acids derivatives was suggested. Conclusions. The series of active compounds with high anticancer activity and/or selectivity levels were selected. Some SAR aspects were determined and structure design directions were proposed.Biopolymers and Cell. 01/2010; 26:136-145.
Article: In silico Methods for Designing Antagonists to Anti-apoptotic Members of Bcl-2 Family Proteins.[show abstract] [hide abstract]
ABSTRACT: Designing antagonists to anti-apoptotic proteins of Bcl-2 family has become an important strategy in cancer chemotherapy. Using experimental techniques and computational methods, a few numbers of lead inhibitors to the antiapoptotic proteins have been reported in the literature and a few of them are under clinical trials. In this review, the lead inhibitors designed using in silico methodologies are exclusively covered, systematically organized and critically evaluated. An orchestrated in silico strategy for screening and identifying efficient antagonists to the anti-apoptotic proteins has also been brought into fore.Mini Reviews in Medicinal Chemistry 06/2012; 12(11):1144-53. · 2.53 Impact Factor
A Novel Approach for Characterizing Protein Ligand
Complexes: Molecular Basis for Specificity of Small-Molecule
Alexey A. Lugovskoy,†,‡Alexei I. Degterev,§Amr F. Fahmy,‡Pei Zhou,‡
John D. Gross,‡Junying Yuan,§and Gerhard Wagner*,‡,⊥
Contribution from the Committee on Higher Degrees in Biophysics, HarVard UniVersity,
Cambridge, Massachusetts 02138, Department of Biochemistry and Molecular Pharmacology,
HarVard Medical School, 240 Longwood AVenue, Boston, Massachusetts 02115, and Department
of Cell Biology, HarVard Medical School, 240 Longwood AVenue, Boston, Massachusetts 02115
Received May 21, 2001. Revised Manuscript Received November 29, 2001
Abstract: The increasing diversity of small molecule libraries has been an important source for the
development of new drugs and, more recently, for unraveling the mechanisms of cellular eventssa process
termed chemical genetics.1Unfortunately, the majority of currently available compounds are mechanism-
based enzyme inhibitors, whereas most of cellular activity regulation proceeds on the level of protein-
protein interactions. Hence, the development of small molecule inhibitors of protein-protein interactions is
important. When screening compound libraries, low-micromolar inhibitors of protein interactions can be
routinely found. The enhancement of affinities and rationalization of the binding mechanism require structural
information about the protein-ligand complexes. Crystallization of low-affinity complexes is difficult, and
their NMR analysis suffers from exchange broadening, which limits the number of obtainable intermolecular
constraints. Here we present a novel method of ligand validation and optimization, which is based on the
combination of structural and computational approaches. We successfully used this method to analyze the
basis for structure-activity relationships of previously selected2small molecule inhibitors of the antiapoptotic
protein Bcl-xL and identified new members of this inhibitor family.
Apoptosis is a process of tightly regulated energy-dependent
cellular suicide, and it plays a critical part in the homeostasis
of multicellular organisms.3,4Inhibition of apoptosis has been
shown to contribute to the processes of tumorogenesis and
development of chemoresistance.3-10In recent years molecular
mechanisms of apoptosis have been investigated, and the
members of the Bcl-2 family have emerged as key regulators
of apoptotic pathways. The levels of the antiapoptotic Bcl-2
family proteins are often elevated in a variety of tumors, which
plays a major role in chemoresistance and contributes to poor
cancer prognosis.3,6,9On the other hand, proapoptotic family
members, such as Bax,11,12Noxa,13and PUMA,14are tran-
scriptionally activated by the tumor suppressor p53. Further-
more, recent genetic studies have demonstrated that inactivation
of Bax may directly lead to tumorogenesis.11,15
Homo- and heterodimerization of Bcl-2 family members
through their BH3 domains is the key mechanism regulating
the function of these proteins.16-21Synthetic BH3 domain-
containing peptide induces apoptosis in oocyte lysates, cultured
cells, and in vivo xenografts of human leukemia HL-60
cells.18,22,23Recently Degterev et al.2have selected a series of
†Committee on Higher Degrees in Biophysics.
‡Department of Biochemistry and Molecular Pharmacology.
§Department of Cell Biology.
(1) Stockwell, B. R. Trends Biotechnol. 2000, 18, 449-55.
(2) Degterev, A.; Lugovskoy, A.; Cardone, M.; Mulley, B.; Wagner, G.;
Mitchison, T.; Yuan, J. Nat. Cell Biol. 2001, 3, 173-182.
(3) Rudin, C. M.; Thompson, C. B. Annu. ReV. Med. 1997, 48, 267-81.
(4) Thompson, C. B. Science 1995, 267, 1456-62.
(5) Chresta, C. M.; Hickman, J. A. Urol. Res. 1999, 27, 1-2.
(6) Decaudin, D.; Marzo, I. I.; Brenner, C.; Kroemer, G. Int. J. Oncol. 1998,
(7) Hager, J. H.; Hanahan, D. Ann. N.Y. Acad. Sci. 1999, 887, 150-63.
(8) Reed, J. C. Toxicol. Lett. 1995, 82-83, 155-8.
(9) Reed, J. C. Hematol. Oncol. Clin. N. Am. 1995, 9, 451-73.
(10) Wyllie, A. H.; Bellamy, C. O.; Bubb, V. J.; Clarke, A. R.; Corbet, S.;
Curtis, L.; Harrison, D. J.; Hooper, M. L.; Toft, N.; Webb, S.; Bird, C. C.
Br. J. Cancer 1999, 80 Suppl 1, 34-7.
(11) McCurrach, M. E.; Connor, T. M.; Knudson, C. M.; Korsmeyer, S. J.; Lowe,
S. W. Proc. Natl. Acad. Sci. U.S.A. 1997, 94, 2345-9.
(12) Matsuyama, S.; Schendel, S. L.; Xie, Z.; Reed, J. C. J. Biol. Chem. 1998,
(13) Oda, E.; Ohki, R.; Murasawa, H.; Nemoto, J.; Shibue, T.; Yamashita, T.;
Tokino, T.; Taniguchi, T.; Tanaka, N. Science 2000, 288, 1053-8.
(14) Nakano, K. a. V., K. H. Mol. Cell 2001, 7, 683-694.
(15) Zhang, L.; Yu, J.; Park, B. H.; Kinzler, K. W.; Vogelstein, B. Science 2000,
(16) Simonen, M.; Keller, H.; Heim, J. Eur. J. Biochem. 1997, 249, 85-91.
(17) Zha, J.; Harada, H.; Osipov, K.; Jockel, J.; Waksman, G.; Korsmeyer, S.
J. J. Biol. Chem. 1997, 272, 24101-4.
(18) Holinger, E. P.; Chittenden, T.; Lutz, R. J. J. Biol. Chem. 1999, 274, 13298-
(19) Minn, A. J.; Kettlun, C. S.; Liang, H.; Kelekar, A.; Vander Heiden, M. G.;
Chang, B. S.; Fesik, S. W.; Fill, M.; Thompson, C. B. Embo J. 1999, 18,
(20) Wang, K.; Gross, A.; Waksman, G.; Korsmeyer, S. J. Mol. Cell. Biol. 1998,
(21) Gross, A.; Jockel, J.; Wei, M. C.; Korsmeyer, S. J. Embo J. 1998, 17,
(22) Cosulich, S. C.; Worrall, V.; Hedge, P. J.; Green, S.; Clarke, P. R. Curr.
Biol. 1997, 7, 913-20.
(23) Wang, J. L.; Zhang, Z. J.; Choksi, S.; Shan, S.; Lu, Z.; Croce, C. M.;
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Published on Web 01/25/2002
1234 VOL. 124, NO. 7, 2002 9 J. AM. CHEM. SOC.
10.1021/ja011239y CCC: $22.00 © 2002 American Chemical Society
small molecule inhibitors (termed BH3Is) which specifically
antagonize the BH3 domain-mediated interaction between anti-
and proapoptotic members of the Bcl-2 family. BH3Is induce
apoptosis in a broad range of cells, in a manner which depends
on their ability to disrupt the BH3 domain mediated protein-
protein interactions. By using NMR titrations, we examined the
BH3I/Bcl-xL complex and demonstrated that BH3Is bind to the
same hydrophobic groove as the Bak BH3 peptide, hence, acting
as small molecule mimetics of the proapoptotic BH3 domain.
Characterization of the molecular geometry of protein-com-
pound complexes is central to our understanding of structure-
activity relationships and subsequent chemical optimization.24
Ideally, this is achieved with experimental methods, such as
crystallography or NMR.25However, crystallization of low-
affinity complexes is difficult, and NMR analysis of such
complexes suffers from exchange broadening, limiting the
number of intermolecular constraints obtainable. Moreover,
current developments in the field of combinatorial chemistry
and chemical genetics require methods capable of analyzing
multiple interactions in a high-throughput format. Computational
techniques that use the structure of the free protein and the
topology of the compound present a tempting tool to facilitate
such efforts. Additionally, virtual screening approaches that can
be used to guide chemical modifications would be extremely
useful. However, the computed hypothetical complex structures
require experimental verification, ideally with less effort than
that of full experimental structure analysis. Therefore, use of
validated computational approaches can result in a rapid
assessment of the bound state and optimization of the ligand.26
The majority of the molecular modeling approaches27-31
utilize stochastic search procedures, such as Monte Carlo or
simulated annealing. Since these methods do not enumerate all
of the relative configurations of the molecules, they may fail to
yield the most favorable orientation. Therefore, an exhaustive
search of the conformational space at high resolution would be
preferable. Unfortunately, due to the fact that interaction
interfaces on proteins are relatively large, exhaustive searches
are usually computationally costly. Thus, there is a great need
for new creative computational approaches to address this
problem. Furthermore, ways to limit the search space with
experimental data would be desirable.
In this paper we present a novel method for ligand validation
and optimization based on a combination of structural and
computational approaches. We use NMR chemical shift per-
turbation as an efficient tool for rapid mapping of interaction
interfaces32,33and direct NMR-derived constraints to restrict the
conformational space for molecular modeling routines. As a
molecular modeling module, we utilized the novel program
TreeDock,34which is optimized to allow high-resolution
exhaustive enumeration of all relative orientations between
complex components. It uses the Lennard-Jones potential as the
scoring function to obtain the protein-compound complexes
based primarily on shape complementarity. The models of
complexes were validated through an independent set of NMR
We employed this method to analyze structure-activity
relationships in the BH3Is/Bcl-xL complexes. We found that
the free energies of the complexes calculated using the TreeDock
routine correlated well with in vitro Bcl-xL binding affinities
of the compounds. To validate our method further, we experi-
mentally tested the affinities of two close homologues of the
original compounds, which scored low in our algorithm, and
found that they did not bind to Bcl-xL. Finally, we performed
a virtual screening of BH3Is homologues in the Chemnavigator
(www.chemnavigator.com) and Chembridge (www.hit2lead.com)
compound libraries and identified an additional compound-
inhibitor of the Bcl-xL/BH3 interaction.
Results and Discussion
BH3Is Bind to and Stabilize an “Open-Cleft” Conforma-
tion of Bcl-xL. To understand the structural determinants of
(24) Tollenaere, J. P. Pharm. World Sci. 1996, 18, 56-62.
(25) Zheng, T. S. Nat. Cell. Biol. 2001, 3, E43-6.
(26) Blundell, T. L. Nature 1996, 384, 23-6.
(27) Verlinde, C. L.; Hol, W. G. Structure 1994, 2, 577-87.
(28) Strynadka, N. C.; Eisenstein, M.; Katchalski-Katzir, E.; Shoichet, B. K.;
Kuntz, I. D.; Abagyan, R.; Totrov, M.; Janin, J.; Cherfils, J.; Zimmerman,
F.; Olson, A.; Duncan, B.; Rao, M.; Jackson, R.; Sternberg, M.; James, M.
N. Nat. Struct. Biol. 1996, 3, 233-9.
(29) Sternberg, M. J.; Gabb, H. A.; Jackson, R. M. Curr. Opin. Struct. Biol.
1998, 8, 250-6.
(30) Sternberg, M. J.; Aloy, P.; Gabb, H. A.; Jackson, R. M.; Moont, G.; Querol,
E.; Aviles, F. X. Ismb 1998, 6, 183-92.
(31) Zeng, J. Comb. Chem. High Throughput Screen. 2000, 3, 355-62.
(32) Markus, M. A.; Nakayama, T.; Matsudaira, P.; Wagner, G. Protein Sci.
1994, 3, 70-81.
(33) Shuker, S. B.; Hajduk, P. J.; Meadows, R. P.; Fesik, S. W. Science 1996,
(34) Fahmy, A.; Wagner, G. J. Am. Chem. Soc. 2002, 124, 1241-1250.
Figure 1. Structures and affinities toward Bcl-xL of the two classes of BH3Is previously described.2
A NewMethod To Analyze Protein−Com pound Interactions A R T I C L E S
J. AM. CHEM. SOC. 9 VOL. 124, NO. 7, 2002 1235
action among the previously identified BH3Is (Figure 1) we
decided to characterize the interface between individual com-
pounds and Bcl-xL. For this purpose we employed NMR
spectroscopy titration techniques, which are capable of detecting
interactions with affinities up to 10 mM.32Analyses of changes
in 2D15N/1H heteronuclear single quantum correlation spectra
(HSQC)35of15N-labeled Bcl-xL upon addition of the inhibitors
revealed that all seven BH3Is induced significant changes in
the Bcl-xL structure. These perturbations were similar to that
induced by Bak BH3 peptide (Figure 2 and data not shown),
which is known to facilitate the formation of the hydrophobic
groove between BH1, BH3, and BH2 domains of the protein.36
Therefore, a similar grove is formed upon additions of BH3Is.2
Since approximately a third of the protein amide proton
resonances changed upon addition of the molecules, we reasoned
that it would be beneficial to separate changes in chemical
environment due to the conformational switch from those due
to direct interactions with the compounds. We decided to take
advantage of the fact that BH3Is fall into two distinct structural
classes (Figure 1) with members within each class differing in
a single substituent and compared changes in spectra induced
(35) Bodenhausen, G.; Ruben, D. J. Chem. Phys. Lett. 1980, 69, 185-189.
(36) Sattler, M.; Liang, H.; Nettesheim, D.; Meadows, R. P.; Harlan, J. E.;
Eberstadt, M.; Yoon, H. S.; Shuker, S. B.; Chang, B. S.; Minn, A. J.;
Thompson, C. B.; Fesik, S. W. Science 1997, 275, 983-6.
Figure 2. NMR titration experiments. (A)1H-15N HSQC spectra of free Bcl-xL. (B)1H-15N HSQC spectra of Bcl-xL with 2-fold excess of Bak BH3
peptide. (C)1H-15N HSQC spectra of Bcl-xL with 2-fold excess of BH3I-1. (D)1H-15N HSQC spectra of Bcl-xL with 2-fold excess of BH3I-2. The
cross-peak positions in free Bcl-xL are indicated with “+” marks.
A R T I C L E SLugovskoy et al.
1236 J. AM. CHEM. SOC.9VOL. 124, NO. 7, 2002
by various compounds in each of the classes. Since compounds
that differ by a single substitution have similar biological
activity2and bind the same conformational state of Bcl-xL, the
only resonances affected differently between the spectra should
be in the immediate vicinity of the compound. Indeed, such
differential mappings resulted in identification of 8 residues
(N100, G102, I104, A106, F110, G111, G112, and R55)
between BH3I-1 and BH3I-1′′ and 4 residues (F110, A164,
A165, R168) between BH3I-2 and BH3I-2′ (Figure 3) as located
next to the altered substituents. To obtain a separate set of
constraints, we searched for NOE contacts between BH3I-1 and
Bcl-xL in a14N-filtered15N-edited NOESY-HSQC spectrum.
According to this experiment, the benzene ring of the BH3I-1
class lies in the immediate vicinity of amide protons of Y65
and F107. Interestingly, the majority of these hydrophobic
residues are buried in the structure of free Bcl-xL,37but become
accessible to the ligand in the structure of Bcl-xL/Bak BH3
complex.36This change in residue accessibility is a direct
consequence of the cleft opening conformational change ob-
served upon binding of the BH3 peptide (Figure 4). Therefore,
we concluded that BH3Is bind to and stabilize an “open cleft”
conformation of Bcl-xL, similar to the Bak BH3 peptide.
Molecular Modeling of BH3Is/Bcl-xL Complexes Reveals
the Basis for Structure-Activity Relationship in the Com-
pound Series. Next, we decided to generate molecular models
of Bcl-xL/BH3Is complexes based on the structure of Bcl-xL/
Bak BH3 peptide complex36and obtained interface mapping
data. For this purpose we utilized a novel molecular modeling
routine TreeDock,34which samples exhaustively all of the
available conformational space with high (no atom moves more
than 1 Å in one step) resolution using the Lennard-Jones
potential as the only scoring function. The fact that BH3Is bind
to and stabilize the “open cleft” conformation of Bcl-xL, which
has been already structurally characterized,36allowed us to keep
the protein molecule rigid. We assumed that “open-cleft”
conformation of the Bcl-xL/Bak complex represents the protein
state of interest. The flexibility of a compound was explored
by virtue of docking multiple compound conformers (2-4 per
rotatable bond). In cases when structural data on the ligand-
binding state of the protein is unavailable, it is advisible to use
multiple protein states different by rotamers of few side chains
located on the characterized epitope (which is usually small for
protein/small molecule interaction). Here we used the following
In the first step, we choose all solvent-accessible atoms within
a 6 Å distance from differentially affected (see Figure 4) amide
protons on Bcl-xL as anchor points. This step is required to
restrict the spatially accessible space, enabling the use of a
systematic search routine. Next, each anchor point was brought
into contact with an atom on the compound as a docking point,
and the compound was rotated systematically in 3D excluding
the areas of van der Waals clashes, with energy being computed
for each nonclashing configuration. This procedure was repeated
until all possible pairs of anchor points and docking points were
In the second step of the algorithm we clustered the models
compliant with interface mapping data, which required all the
differentially affected amide protons of Bcl-xL to lie in the
vicinity of the compound. Eventually, we took the lowest energy
structure out of the cluster that satisfied the criteria. Once
identified, the docking point was kept the same for all
compounds in the series. The complexes of BH3I-1 and BH3I-2
with Bcl-xL modeled using this approach are presented in Figure
(37) Muchmore, S. W.; Sattler, M.; Liang, H.; Meadows, R. P.; Harlan, J. E.;
Yoon, H. S.; Nettesheim, D.; Chang, B. S.; Thompson, C. B.; Wong, S.
L.; Ng, S. L.; Fesik, S. W. Nature 1996, 381, 335-41.
Figure 3. Differential titration experiments. (A) An overlay of1H-15N HSQC spectra of Bcl-xL with 2-fold excess of BH3I-1 (black) and1H-15N HSQC
spectra of Bcl-xL with 2-fold excess of BH3I-1′′ (red). (B) An overlay of1H-15N HSQC spectra of Bcl-xL with 2-fold excess of BH3I-2 (black) and
1H-15N HSQC spectra of Bcl-xL with 2-fold excess of BH3I-2′ (red).
A NewMethod To Analyze Protein−Com pound InteractionsA R T I C L E S
J. AM. CHEM. SOC. 9 VOL. 124, NO. 7, 2002 1237
Using the obtained models we were able to examine the
structure-activity relationship for the compounds. In our
previous study we found the order of the in vitro affinities and
in vivo activities to be BH3I-2′ > BH3I-2 > BH3I-2′′ > BH3I-1
> BH3-I-1′ > BH3I-1′′ > BH3I-1′′′.2The compounds scored
in exactly the same order in our algorithm, and calculated
energies correlated well with the in vitro affinities (Figure 6).
Inhibitors of the BH3I-1 class interact mostly with Phe61,
Leu94, Gly102, Ala106, Tyr 159, and the aliphatic part of Arg
103 side chain. The bromine group of BH3I-1 (Figure 7 on the
left in magenta) interacts with the C?1and H?1of Phe 61 and
Cγ1, Cδ1, and Cδ2of Leu94. When bromine is substituted by
chlorine (BH3I-1′) or hydrogen (BH3I-1′′), these interactions
are progressively weakened, resulting in a decrease in the
affinity. On the other hand, introduction of a dimethylamine
moiety (BH3I-1′′′) causes steric clashes between this group and
methyl groups (Cδ1and Cδ2) of Leu94, as well as the ring (Cδ1)
of Tyr65, making this compound a poor binder.
Inhibitors of the BH3I-2 class target a longer stretch of the
groove centered at residues Phe61, Arg64, Tyr65, Phe69, Leu72,
Val90, Ala106, and Phe110. The bromine substituent of BH3I-2
(Figure 7 on the right in red) interacts with the side chain of
Figure 4. BH3Is/Bcl-xL interaction interface. (A) Structure of free Bcl-
xL.37Location of the hydrophobic cleft is shown: BH1, dark blue; BH2,
green; BH3, red. (B) Structure of Bcl-xL in complex with the Bak BH3
peptide.36Location of BH1, BH2, and BH3 domains is shown: BH1, dark
blue; BH2, green; BH3, red. (C, D) Differential mapping of BH3I-1 and
BH3I-2 analogues binding. Residues differentially affected by the binding
of BH3I-1 and BH3I-1′′ chemicals are shown in green. Y65 and F107
forming a direct contact with BH3I-1 are shown in red. Residues
differentially affected by the binding of BH3I-2 and BH3I-2′ chemicals
are shown in gold. F110, which is differentially affected by either BH3I-1
and BH3I-1”, or BH3I-2 and BH3I-2′ is shown in cyan. Residues, such as
F107 (red), F110 (cyan), A164, A165, R168 (gold), etc., are buried in the
structure of free Bcl-xL (C) and are exposed in the structure of the Bcl-xL
complex with the Bak BH3 peptide (D).
Figure 5. Structural models of BH3I-1/Bcl-xL (on the left) and BH3I-2/
Bcl-xL (on the right) complexes.
Figure 6. A correlation plot between computed interaction energies in
BH3Is/Bcl-xL complexes and their affinities toward Bcl-xL. Data points
for BH3Is are shown in red, except for BH3I-1-SCH3, which is shown in
green. Only compounds that bind to Bcl-xL are shown.
Figure 7. The map of BH3Is binding moieties on the surface of Bcl-xL.
The mutual orientation of molecules is the same as in Figure 5. The
backbones of the compounds are shown in yellow. Bromine of BH3I-1 (on
the left) is colored in magenta. Essential chlorine of BH3I-2 (on the right)
is colored in cyan. Bromine of BH3I-2 (on the right) is colored in red.
Protein is colored according to normalized contribution of its atoms to BH3Is
binding, with white (RGB palette 0 0 0) meaning no interaction, and blue
(RGB palette 0 0 1) designating maximal interaction. Anchor points on the
protein are shown in red.
A R T I C L E SLugovskoy et al.
1238 J. AM. CHEM. SOC.9VOL. 124, NO. 7, 2002