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PROTEIN-PROTEIN INTERACTIONSTARGETED AND FOCUSED LIBRARIES

FULLY INTEGRATED RESEARCH AND MANUFACTURING PLATFORM FOR LIFE SCIENCES AND INDUSTRIES

HTS Compounds

Targeted and Focused Libraries

Building Blocks

Fragment Libraries

Custom Synthesis

Computational Chemistry

Early Drug Discovery

Primary Drug Trials

Drug Safety • General toxicity• Safety pharmacology (ICH S7A and S7B) Specific Pharmacological Activity

• Antihypertensive• Anti-inflammatory• Anti-ischaemic• Antiarrhythmic

Drug Bioavailability Studies

In vitro and in vivo ADMET tests

FRAMEWORK OF PRODUCTSAND SERVICES

GLP PRIMARY DRUG TRIAL SERVICES -KEY PRECLINICALS

Fully Integrated Solutions for Drug Discovery

• Collection of HTS Compounds • Targeted Libraries• Fragment Libraries• Custom synthesis• Computational chemistry• Early drug discovery

Advanced building blocks

• Off-the-shelf building blocks• Tangible building blocks• Innovative design of building blocks• Custom synthesis of building blocksand

intermediates

Fine Chemicals in Multigram Scale

• IR-Dyes• APIs• Fine reagents for organic synthsis• Scale-up synthesis (up to 100 kg)• Process optimization

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It is generally known that protein-protein interactions (PPI) are involved in important biological processes in living organisms, and their regulation can be crucial for treatment of numerous diseases. Low molecular weight PPI inhibitors that are able to selectively and potently modulate protein–protein interactions have recently reached clinical trials.

Keeping pace with a growing interest to various aspects of PPI, Life Chemicals has prepared the following Libraries of potential PPI inhibitors by ligand-based approach:

• PPI Focused Library by Machine Learning (Decision Tree) Method• PPI Focused Library by 2D Similarity Search vs Timbal DB• PPI Focused Library by 2D Similarity Search vs Binding DB and Pubmed DB• PPI Focused Library by the “Rule of Four”

PPI FOCUSED LIBRARIESLigand-based Approach

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To predict compounds that can affect PPI, a machine learning method (decision tree, DT) was used [1]. This method is recognized to be a useful tool to identify PPI inhibitors. The DT method is based on a cross-validation protocol to provide the balance between enrichment, sensitivity and specificity of the learning data set. By means of comparison of unique physicochemical features of PPI and non-PPI inhibitors, several descriptors showing a correlation for PPI inhibitors in a specified range of values were found:

• RDF 070m (≤ 3.31) is a shape-based descriptor that defines a radial distribution function of an ensemble of atoms in a spherical volume with the radius of 7 Å

• UI (> 4.13) - an unsaturation index directly linked to the number of multiple bonds, containing double, triple and aromatic bonds

• SHP2 (≤ 0.30) – an average shape profile index of order 2 deduced from the distance distribution of the geometry matrix

• Mor11m (> - 0.1) - a descriptor calculated by summing atom weights viewed by a different angular scattering function (signal 11 / weighted by atomic masses)

Other filters that were applied to the entire Life Chemicals Stock Collection [1]:

• ClogP = 1.5 – 4.5• TPSA = 75 – 120• MW ≤ 475• HBD = 0 – 4• HBA = 4 – 9• PAINS filters

Resulting compounds were included in The Life Chemicals PPI Machine Learning Method Library that finally comprised about 2,600 molecules (Fig. 1).

PPI FOCUSED LIBRARYMachine Learning (Decision Tree) Method

A. Principal component analysis (PCA) showing accumulation of compounds best matching our parameters (see the list of the parameters above).

B. Distribution of compounds within allowed values of descriptors. The plot was built to validate the method: red points correspond to the compounds obtained from Timbal database with molecular weight lower than 450, and green – to The Life Chemicals PPI

Inhibitors Machine Learning Method Library. All descriptors were calculated with PyChem.

Fig. 1.

A. B.

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About 1,500 compounds were extracted from The Life Chemicals Stock HTS Collection by 2D fingerprint similarity search towards Timbal DB [2] with Tanimoto 85 % threshold.

Using Binding DB and Pubmed DB, a reference set of small organic molecules with activity in 28 PPI assays towards the following 7 targets was collected: toll-like receptor 4; Hepatitis C virus core protein (dimerization inhibition); Tyrosine-protein kinase TYRO11; runt-related transcription factor 1 isoform AML1c; core-binding factor beta subunit isoform 1; mitogen-activated protein kinase 2 (MAP2); mitogen-activated protein kinase 3 (MAP3) [3,4]. After filtering and merging their activity data, the resulting 10,000 unique compounds were obtained and further used as a basis for the library design. The MDL public keys and the Tanimoto similarity cut-off 90 % were applied to The Life Chemicals Stock Collection that enabled picking up almost 23,000 compounds.

PPI FOCUSED LIBRARY2D Similarity Search vs Timbal DB

PPI FOCUSED LIBRARY2D Similarity Search vs Binding DB and Pubmed DB

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This library was created on the basis of the study done by X. Morelli et al [5]. The analysis of 2P2I dataset (http://2p2idb.cnrs-mrs.fr) determined a group of structural and chemical features that were recognized as the “Rule of Four” (Fig. 2). It has been shown that a specific value range for AlogP/ClogP (ALOGP/CLOGP > 4), molecular weight (MW > 400), number of hydrogen bond acceptors (HDA > 4) and number of rings (Ring > 4) defines the properties of a PPI inhibitor. The rule was used as a filter to accelerate the process of identification of potential PPI inhibitors and their application resulted in the library containing 4,300 compounds (Fig. 3). All the compounds were passed through PAINS filters.

A group of proteins presented in the study that illustrates the concept of the “Rule of Four”.

Fig. 2.

Scatter plots prepared with SYBYL-X software that demonstrate distribution of compounds from the Library

according to the “Rule of Four” descriptor values.

THE “RULE OF FOUR” PPI Focused Library

Fig. 3.

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REFERENCES:

1. Designing focused chemical libraries enriched in protein-protein interaction inhibitors using machine learning methods. Reynès C, Host H, Camproux AC et al. PLoSComput Biol. 2010 Mar 5;6(3):e1000695. doi: 10.1371/journal.pcbi.1000695

2. http://www-cryst.bioc.cam.ac.uk/databases/timbal3. http://www.bindingdb.org4. http://www.ncbi.nlm.nih.gov/pccompound5. Morelli X, Bourgeas R, Roche P: Chemical and structural lessons from recent

successes in protein-protein interaction inhibition (2P2I). Curr Opin Chem Biol 2011,15:475-481

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PPI INHIBITORS LIBRARIES: Docking Search against PDZ Domain-containing ProteinsReceptor-based Approach

PDZ domains are well known protein-protein interaction modules involved in various key signaling pathways that regulate essential functions, such as clustering of ion channels, protein targeting, expression of membrane receptors and cell-cell communications. Inhibition of PDZ-peptide interactions can have important implications in treatment of cancer, cystic fibrosis, Parkinson’s disease, Alzheimer’s disease, schizophrenia, cerebral ischaemia, pain and other disorders of central nervous system.

Our study was focused on PDZ domains of PSD95 and MAGI1, as they were reported to play an important role in some types of cancer, neuropathic pain, reduction of consequences of stroke and human papilloma virus (HPV)-related diseases. Like many other PDZ domains, the structures of the PSD95 PDZ2 and MAGI1 PDZ1 domains were already described in literature. This allowed us to use structure-based virtual screening to computationally predict new PDZ ligands. The UNITY module of the SYBYL-X software package was used to screen a set of 50,000 diverse small-molecules obtained from The Life Chemicals Stock Collection pre-processed by means of special medicinal chemistry filters. These included PAINS and in-house developed structure filters aimed at discarding compounds with toxic and other unfavorable groups, Lipinski’s “Rule of Five” and Veber Rule.

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FIRST PDZ DOMAIN OF MAGI1(D1)

The Unity Query Model based on 2I04 PDB entry [1] was created as a superposition of features of the ligand structure (human papillomavirus (HPV) E6) and corresponding residues from the substrate binding groove of PDZ domain of MAGI1. Therefore, the final Unity model contained structural elements both from PDZ binding site residues and HPV E6 peptide pharmacophore (Fig. 1). The screening model included: nine hydrogen bond donor features (donor site), six hydrogen bond acceptor features (acceptor site) and two hydrophobic features (hydrophobic sites). Partial match constraints for hit compounds included at least one feature for donor and acceptor sites and two hydrophobic features (Fig. 2).

UNITY search query for potential inhibitors of PDZ

d1 MAGI1 with HPV E6 binding pose. The query

was prepared by comparing pharmacophore model of HPV E6 and features

created on the base of the binding pocket residues

of MAGI1 PDZ1. Donor sites are colored in green, acceptor sites are colored in violet and hydrophobic

feature is brown.

Fig. 1.

Docking pose of a virtual hit molecule F2196-0085

that is characterized by three intermolecular

hydrogen bonds with the substrate binding groove of PDZ (His512, Phe464

and Glu520). Hydrophobic interactions are observed in the hydrophobic pocket P0 and the hydrophobic

pocket P1.QFit = 47.98.

Fig. 2.

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When designing this library, two docking models were developed and used for screening. The first docking model was based on 2KA9 PDB entry (Fig. 3) [2]. The query features were identified from the key residues of the PDZ domain responsible for ligand binding (Fig. 4).

UNITY query included the following (Fig. 5):

• 8 H - bond donor features, minimum 2 features match• 4 H - bond acceptor features, minimum 2 match• 1 hydrophobic feature match (tolerance is 2.2 Å)

Totally, at least 5 feature constraints were set to meet hit compound binding requirements.MOLCAD Surface (solvent-accessible surface) was created using Fast Connolly function with VdW ratio 1Å.

As a result, 1,100 compounds were identified as hits.

Cypin peptide bound to PSD95 PDZ2 (2KA9). PDZ domain surface is colored

by hydrophobicity from brown (hydrophobic) to

blue (hydrophilic).

Fig. 3.

Amino acid residues that are critical for the PDZ

domain-ligand interaction.

Fig. 4.

Unity search query with surface volume of the PSD95 PDZ2. Donor sites are colored in

green, acceptor sites are colored in violet and the hydrophobic feature is

brown.

Fig. 5.

SECOND PDZ DOMAIN OF PSD-95(2 screening models)

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Substrate binding grooveSubstrate binding groove

Conformational differences of PSD95 PDZ d2 in PDB entries 1QLC and 2KA9 PDB.

Fig. 6.

Example of a virtual hit compound F3225-8235 binding mode. QFit = 65.03.The compound forms two hydrogen bonds with Ile114and Asn120 of the PSD95 PDZ2 substrate binding site.

Fig. 7.

1QLCsurface representation of

PSD95 PDZ d2

2KA9surface representation of

PSD95 PDZ d2

The UNITY query contained:

• 8 H - bond donor features, minimum 1 feature must be matched• 4 H - bond acceptor features, minimum 2 must be matched• 1 hydrophobic feature which must be matched (tolerance is 2.5 Å)

At least 4 features must be matched by a hit compound. MOLCAD Surface (solvent-accessible surface) was created by Fast Connolly type with VdW ratio of 1 Å.

This study enabled us to identify 1,800 hits (Fig. 7).

The second screening model was based on 1QLC PDB entry [3]. It should be noted that there are some differences in the binding groove conformation of PDZ domain observed in 2KA9 and 1QLC (Fig. 6).

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REFERENCES:

1. Zhang Y., Dasgupta J., Ma R.Z., Banks L., Thomas M., Chen, X.S. Structures of a human papillomavirus (HPV) E6 polypeptide bound to MAGUK proteins: mechanisms of targeting tumor suppressors by a high-risk HPV oncoprotein. J. Virol. 2007, 81, pp. 3618–3626.

2. Wang W.N., Weng J.W., Zhang X., Liu M.L., Zhang M.J. Creating conformational entropy by increasing interdomain mobility in ligand binding regulation: a revisit to N-terminal tandem PDZ domains of PSD-95. J. Am. Chem. Soc., 2009, 131, pp. 787–796.

3. Tochio H., Hung F., Li M., Bredt D.S., Zhang M. Solution structure of the second pdz domain of postsynaptic density-95. J. Mol. Biol., 2000, 295 (2), pp. 225–237.

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