KNOBLE: A knowledge-based approach for the design and synthesis of readily accessible small-molecule chemical probes to test protein binding

DOI: 10.1002/anie.200703323

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Angewandte Chemie - International Edition p. 9105 - 9109 (2007)

Update date:2022-07-29

Topics:

Authors:

Gerlach, Christof

Muenzel, Martin

Baum, Bernhard

Gerber, Hans-Dieter

Craan, Tobias

Diederich, Wibke E.

Klebe, Gerhard

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Article abstract of DOI:10.1002/anie.200703323

(Figure Presented) Divide and combine: KNOwledge Based Ligand Enumeration (KNOBLE) is a strategy for the design and synthesis of focused combinatorial libraries. This approach is based on the assembly of potential leads from fragments that are detected by

Full text of DOI:10.1002/anie.200703323

Angewandte

Chemie

DOI: 10.1002/anie.200703323

Drug Design

KNOBLE: A Knowledge-Based Approach for the Design and

Synthesis of Readily Accessible Small-Molecule Chemical Probes To

Test Protein Binding**

Christof Gerlach, Martin Münzel, Bernhard Baum, Hans-Dieter Gerber, Tobias Craan,

Wibke E. Diederich, and Gerhard Klebe*

In memory of Jörg Stürzebecher

At the beginning of the last decade, potent high-throughput

technologies stimulated the development of combinatorial

chemistry as a productive source of candidate molecules

suited for screening biological activity against putative drug

targets. However, the sheer increase of readily available test

compounds has not dramatically enhanced the efficiency of

lead discovery.^[1–3]

As a consequence, compound selection for testing is

moving increasingly from “optimally diverse” sets to targeted

compound libraries. As proteins occur in families character-

ized by conserved binding motifs, they can be addressed by

ligand skeletons exhibiting binding epitopes complementary

to the commonly exposed properties of the protein family.

Accordingly, efficient design of targeted compound libraries

starts with a central privileged skeleton that specifically

addresses the exposed binding motif of a protein family.^[4]

Using appropriate substituents or side chains of the central

privileged skeleton, individual members of a focused library

are equipped with the desired selectivity towards distinct

members of a protein target family.^[5–8]

The design of an appropriately focused combinatorial

library starts with the selection of a central fragment equipped

with linker groups allowing substitutions by standard syn-

thetic chemistry. Putative building blocks are selected and

virtually assembled in a computer simulation. The actual

retrieval of suitable side chains is guided by the shape and the

physicochemical properties of the binding pocket of the target

protein. Usually protein binding pockets, particularly those in

proteases, can be split into a set of distinct subpockets, which

can be examined separately. Once such a subpocket is

characterized and extracted, its description according to the

pseudocenter concept can be used for cavity comparisons in

the Cavbase approach.^[10–13]

In Cavbase, binding pockets are automatically detected,

and their physicochemical properties are encoded by five

generic descriptors that describe the molecular recognition

features in terms of hydrogen bonding and hydrophobic

interactions. More than 130000 cavities have been stored, and

their occupants are available through a hyperlink to the

database Relibase.^[14]Both databases store structural infor-

mation about crystallographically determined protein–ligand

complexes deposited with the protein databank (PDB).^[15]

Cavbase maps a predefined query cavity against the entire set

of stored entries. The result is a list of cavities ranked in order

of decreasing similarity to the query cavity.

With respect to the design of a targeted compound library,

a Cavbase similarity search returns not only matching

subcavities with similar properties but, most importantly,

the chemical structures of the molecules or molecular frag-

ments occupying these subcavities. The chemical structures of

the retrieved occupants are subjected to a search in chemical

catalogues to see whether they or their derivatives are

commercially available. The composition of the returned

chemicals is subsequently used as a guideline to design

synthetic routes to build a targeted compound library

following combinatorial chemistry principles.

As a case study to probe this new strategy, we selected the

family of serine proteases. These enzymes cleave polypeptide

chains with varying sequence selectivity. Along the reaction

pathway, a covalently attached acyl-enzyme intermediate is

formed, and the N-terminal part of the cleaved peptide chain

remains bound to the protein. Consequently, serine proteases

exhibit well-established recognition pockets on the unprimed

side to accommodate the amino acid side chains of the N-

terminal part of the peptidic substrate next to the cleavage

Herein, we present the novel strategy KNOBLE

(KNOwledge-Based Ligand Enumeration) to assemble a

focused combinatorial library using

a structure-based

approach. In all steps of design, the synthetic accessibility of

the starting materials and products is considered. To test the

concept, library members are synthesized and subjected to

biological testing. Our goal is the discovery by simple

chemistry of lead compounds that are potent enough to

allow determination of the crystal structure together with the

target protein. This structure can subsequently serve as a

starting point to embark upon in-depth structure-based ligand

optimization.

[*] Dr. C. Gerlach, M. Münzel, B. Baum, H.-D. Gerber, T. Craan,

Dr. W. E. Diederich, Prof. Dr. G. Klebe

Institut für Pharmazeutische Chemie

Philipps-Universität Marburg

Marbacher Weg 6, 35032 Marburg (Germany)

Fax: (+49)6421-282-8994

E-mail: klebe@mailer.uni-marburg.de

[**] The DFG-Graduiertenkolleg “Proteinfunktion auf atomarer Ebene”

(C.G.) and the DPhG “Stiftung zur Förderung des wissenschaftli-

chen Nachwuchses” (W.E.D.) are gratefully acknowledged for their

financial support.

Supporting information for this article is available on the WWW

under http://www.angewandte.org or from the author.

Angew. Chem. Int. Ed. 2007, 46, 9105 –9109

ꢀ 2007 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim

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Communications

site. Furthermore, all serine proteases bind their substrates on

the unprimed side through hydrogen bonds to the peptide

backbone.

subpockets and ligand occupants across a protein family is

easily possible.

Once a particular ligand fragment had been selected as a

putative building block for the construction of a combinato-

rial library, a search in the Sigma–Aldrich catalogue was

performed using the program JME.^[16]For the different

subpockets, these searches retrieved possible reaction com-

ponents, which are given in the Supporting Information. The

list of commercially available starting materials was then

evaluated with respect to attached functional groups that are

suited to connect the individual building blocks by a generally

applicable synthetic route. In the synthesis design, we decided

to focus on ester, amide, and sulfonamide bond formation as

well as on nucleophilic substitution reactions. Three frag-

ments, (S)-prolinol, (S)-2-(hydroxymethyl)piperidine, and

(S)-valinol, were finally selected as central moieties to

address the S2 pocket (Scheme 1).

We picked thrombin as a reference for this case study. This

enzyme exposes three well-defined binding subpockets to

recognize the N-terminal substrate. Using these cavities as

queries for a Cavbase search revealed about 4000 hits in each

case. For the S1 subpocket, 646 cavities from other serine

proteases were matched; for the S3 pocket, 550 such cavities

were found. In case of the S2 pocket, only 274 entries from

other serine proteases were retrieved, thus indicating the

structural uniqueness of S2 in thrombin. This selectivity-

determining pocket is rather tight and spatially restricted by

the thrombin-specific 60 loop.

Closer analysis of the matching S3 pockets highlights the

hydrophobic character of this site, which is strongly deter-

mined by a highly conserved tryptophan residue forming the

floor of the pocket (e.g. acrosin, cathepsin G, factor VIIa,

factor IXa, factor Xa, factor XIa, chymase, trypsin, C1s-

elastase, tryptase, t-plasminogen activator, protein C, and

granzyme A). Similarly, the search retrieved S1 pockets from,

for example, cathepsin G, factor VIIa, factor IXa, factor XIa,

trypsin, tryptase, protein C, factor Xa, t-plasminogen activa-

tor, enteropeptidase, and urokinase.

Subsequently, the actual occupants were extracted in

terms of ligand fragments. As a first attempt, we decided to

design a compound library for thrombin. Thus, in each step, a

comparison with the thrombin reference subpocket was

performed to retrieve only fragments that potentially exhibit

a similar interaction pattern with the target protein. Evaluat-

ing the occupants of the S2 pocket matches highlights the

unique shape of this pocket in thrombin: among the 100 best-

ranked solutions, 98 examples are found in other thrombin

structures.

To further diversify the central P2 moiety, a glycine spacer

was attached between P2 and P3. The functional groups of

this additional spacer are appropriate to address the non-

specific backbone recognition site exposed by all serine

proteases. We chose ester bond formation to connect the

central moiety with a group propitious to address the S1

pocket. Finally, to place a suitable building block into S3, an

amide bond was formed using either the amino functionality

in the P2 central moiety or the glycine spacer. Alternatively,

bond formation through nucleophilic substitution was envis-

aged. In Scheme 1, carboxylic acid derivatives suited to

address the S1 pocket are listed together with carboxylic and

sulfonic acid derivatives as well as the chloro derivatives to

address the S3 pocket. The selected P1 to P3 building blocks

were assembled on the computer following Scheme 2 and

docked into the binding pocket of thrombin using the

combinatorial module of FlexX.^[17,18]Two libraries, with and

without the glycine spacer, were assembled, each comprising

507 possible members. For each docking run, the 10 best

solutions were stored and rescored using DrugScoreCSD.^[19]

Visual inspection of the docking solutions was performed

using the graphical interface to evaluate the generated

docking modes in terms of per-atom scoring contributions.^[20]

For synthetic simplicity we decided to use the 3-chloro-

benzyl and 4-cyanophenyl portion from the list of best-scored

solutions to address the S1 pocket (apart from ligands

exhibiting an amidino or guanidino portion at this site,

which involve more synthesis steps). Selection of synthesis

candidates for S2 and S3 was guided by synthetic feasibility

and reactivity differences of the starting materials. The finally

selected 2-pyridylacetic acid, p-fluorobenzoic acid, and the

tert-butyloxy carbonyl moiety for S3 showed up in several of

the best-scored hits. From the list of best-scored derivatives,

compounds 2–6 were selected, synthesized (Scheme 3), and

subjected to a photometric enzyme kinetics assay.^[21]They

showed inhibition in the micromolar range (see Scheme 3 and

the Supporting Information). Subsequently, we succeeded in

diffusing 5 into crystals of thrombin, from which the complex

structure could be determined (Figure 2). This structure can

serve as a starting point to embark upon structure-based lead

optimization.

Ligand fragments with an isopropyl group, a small

aliphatic or heteroaliphatic ring such as a pyrrolidine

moiety, or partially aromatic building blocks were preferen-

tially detected (Figure 1).

In thrombin and many other members of the trypsin-like

serine proteases, the S1 subpocket is dominated by the

aspartate189 residue, which is embedded in a highly con-

served hydrophobic environment. The comparison of binding

pockets reveals ligand building blocks with comparable

pysicochemical properties, while the exact degree of similarity

in the amino acid sequence of the surrounding protein is much

less important. Nevertheless, besides the popular basic

residues derived from benzamidine, guanidine, or amino-

pyridine building blocks, halogen-substituted aromatic moi-

eties were also suggested; these interact with a conserved

tyrosine residue at the floor of the S1 pocket (Figure 1). The

most pronounced chemical diversity of occupants is proposed

by the Cavbase search for the S3 pocket. For this pocket,

possible side chains originate from other members of the

serine protease family (Figure 1).

Thus, we see the strength of this approach, as not only

well-known thrombin inhibitors are used to create the

combinatorial library. Cavbase provides easy access to an

entire class of proteins, and simple “hopping” between

9106 www.angewandte.org

ꢀ 2007 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim

Angew. Chem. Int. Ed. 2007, 46, 9105 –9109

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Angewandte

Chemie

Figure 1. Fragments found by subcavity matching. For comparison, the crystallographically determined binding mode of melagatran (green, PDB

code 1k22) in thrombin^[9]is shown in the upper row for the S1 (left), S2 (center), and S3 (right) pocket together with three examples retrieved

from occupants in other proteins (second to fourth rows). These protein surfaces display similar physicochemical properties (blue: H-bond donor,

red: H-bond acceptor, green: donor or acceptor, white and orange: areas for interaction with aliphatic and aromatic groups, respectively). In the

first row, the physicochemical properties for the melagatran complex are shown. In the following rows, the physicochemical properties of the

subcavities in the other proteins matched by the algorithm are displayed. The adopted binding geometry (cyan, orange, yellow) and chemical

formulas of the fragments occupying the corresponding subpockets are given.

As thrombin is a well-established target, the next opti-

mization steps appear evident, that is, comparing the present

complex with that of melagatran, a potent thrombin inhibitor.

Firstly, the ester oxygen atom is not ideal, as it lacks the donor

facility to form an H-bond to the neighboring serine residue.

Melagatran uses its amide NH group at this position to

establish such a contact. Secondly, our ester chain linking the

P1 and P2 occupant appears to be too long and should be

reduced by one member. In 5, the ester carbonyl oxygen atom

squeezes into a hydrogen bond to Gly216. This interaction

prevents the glycine portion of 5 from forming the usual dual-

ladder hydrogen-bond contact. In the case of melagatran, this

H-bond network is well-established. Accordingly, the next

synthetic step is to replace the four-membered P1–P2 ester

linkage with a three-membered amide linker. This change will

create additional space to allow the glycine building block to

participate in the dual-ladder contact to Gly216.

The present study has been performed to demonstrate the

feasibility of the outlined knowledge-based approach to

combinatorial ligand design. It exploits the available struc-

tural information regarding experimentally characterized

binding geometries of molecular building blocks of ligands

hosted in subpockets of other proteins. These latter proteins

have to show pronounced similarity to subpockets present in

Angew. Chem. Int. Ed. 2007, 46, 9105 –9109

ꢀ 2007 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim

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Communications

Scheme 1. Chemical formulas of the building blocks for the three

subpockets.

Scheme 3. Synthesis of compounds 2–6. K_ivalues for 2b, 4a, 4b, 4c,

5, and 6 for thrombin inhibition are given in square brackets. Reagents

and reaction conditions are given in the Supporting Information.

Figure 2. Superposition of the crystal structure of 5 (P1: 3-chlorobenzyl

portion in blue; P2: prolinol moiety in brown, glycine spacer in dark

gray; P3: tert-butyl moiety in green) with melagatran (extracted from

PDB code 1k22). Melagatran (gray) forms by its P1–P2 amide-bond

H-bonds (light green) to Ser195 and Glu192. Furthermore, it involves

Gly216 in a double hydrogen bond. In contrast, in compound 5, the

formation of an optimal H-bond network between protein and bound

ligand is perturbed by interaction between the ester carbonyl oxygen

atom and the Gly216 NHgroup (magenta).

Scheme 2. Synthetic route for the assembly of the virtual combinatorial

library by means of FlexX^C.

the target protein for which a putative ligand is to be

developed. We recently applied Cavbase subpocket matching

to select the most promising side chains of non-natural amino

acids in the design and synthesis of peptidomimetic SARS

protease inhibitors.^[12]

Computational approaches are frequently accused of

suggesting synthesis candidates that are difficult to make or

much to elaborate as first candidates to validate a vague

design hypothesis. In contrast, the concept presented herein

tries to consider at a very early stage the synthetic feasibility

of the assembled molecules. Computational design and

combinatorial docking are consulted to select the most

prospective candidates from the multiplicity of possible

library members. Their synthesis is straightforward and is

based on commercially available starting materials.

Once likely lead candidates are detected, their metabolic

stability must be taken into account. For example, ester bonds

used to connect individual building blocks are accepted for

reasons of synthetic accessibility but must be removed owing

to their likely metabolic instability. Nevertheless, the major

9108 www.angewandte.org

ꢀ 2007 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim

Angew. Chem. Int. Ed. 2007, 46, 9105 –9109

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Angewandte

Chemie

[9] F. Dullweber, M. T. Stubbs, D. Musil, J. Stürzebecher, G. Klebe,

advantage of this approach is that it is based on binding

geometries actually found and confirmed in experimentally

determined crystal structures.

J. Mol. Biol. 2001 , 313 , 593.

[10] S. Schmitt, D. Kuhn, G. Klebe, J. Mol. Biol. 2002 , 323 , 387.

[11] N. Weskamp, D. Kuhn, E. Hüllermeier, G. Klebe, Bioinformatics

2004 , 20 , 1522.

[12] S. I. Al-Gharabli, S. T. A. Shah, S. Weik, M. F. Schmidt, J. R.

Mestres, D. Kuhn, G. Klebe, R. Hilgenfeld, J. Rademann,

ChemBioChem 2006 , 7 , 1048.

[13] D. Kuhn, N. Weskamp, S. Schmitt, E. Hüllermeier, G. Klebe, J.

Mol. Biol. 2006 , 359 , 1023.

[14] M. Hendlich, A. Bergner, J. Günther, G. Klebe, J. Mol. Biol.

2003 , 326 , 607.

[15] H. M. Berman, J. Westbrook, Z. Feng, G. Gilliland, T. N. Bhat,

H. Weissig, I. N. Shindyalov, P. E. Bourne, Nucleic Acids Res.

2000 , 28 , 235.

[16] P. Ertl, Novartis Institutes for BioMedical Research Basel,

Switzerland, 2004.

Received: July 24, 2007

Published online: October 23, 2007

Keywords: combinatorial chemistry · drug design ·

ligand design · thrombin

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Angew. Chem. Int. Ed. 2007, 46, 9105 –9109

ꢀ 2007 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim

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Article Doi

KNOBLE: A knowledge-based approach for the design and synthesis of readily accessible small-molecule chemical probes to test protein binding

DOI: 10.1002/anie.200703323

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Authors:

Article abstract of DOI:10.1002/anie.200703323

Full text of DOI:10.1002/anie.200703323

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