Keeping pace with a growing interest in various aspects of protein-protein interactions (PPI), Life Chemicals presents a proprietary collection of potential PPI modulators obtained by ligand-based approach for high throughput screening projects in drug discovery:
- PPI Focused Library by Machine Learning (Decision Tree) Method
- PPI Focused Library by 2D Similarity Search vs. Timbal, 2P2I and iPPIDB datasets
- PPI Focused Library by the Rule of Four
- PPI Focused Library by 2D Similarity Search vs. Binding DB, Pubmed DB, ChEMBL DB
These PPI Focused Libraries do not overlap. Compound cherry-picking is available. Custom compound selection based on specific parameters can be performed on request.
The compound selection can be customized based on your requirements, cherry picking is available.
Please, contact us at firstname.lastname@example.org for any additional information and price quotations.
For a pre plated set based on this Screening Library, please explore our Pre-plated Focused Libraries.
PPI Focused Library by Machine Learning (Decision Tree) Method
A machine learning method (decision tree, DT) was used to predict which small-moleculecompounds can target PPIs . This method is recognized to be a useful tool to identify PPI active molecules. The DT method is based on a cross-validation protocol to balance the enrichment, sensitivity, and specificity of the learning data set.
Comparing unique physicochemical features of non-PPI and PPI inhibitors, several descriptors showing a correlation for PPI binders in a specified range of values were found (Fig. 1):
- RDF 070m (≤ 3.31) - 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)
Figure 1. Distribution of descriptors values in the PPI Focused Library by Machine Learning (Decision Tree) Method
Other filters that were applied to the entire Life Chemicals HTS Compound Collection :
The selected 6,800 compounds were included in the Life Chemicals PPI Machine Learning Method Library (Fig. 2), with PAINS, toxic and reactive compounds being excluded.
Figure 2. Principal component analysis (PCA) showing the accumulation of compounds best matching our parameters.
PPI Focused Library by 2D Similarity Search vs. Timbal, 2P2I and iPPIDB datasets
Over 3,000 drug-like screening compounds were extracted from the Life Chemicals HTS Compound Collection by 2D fingerprint similarity search towards the reference set of 18,936 compounds from the TimbalDB (http://bigd.big.ac.cn/databasecommons/database/id/3188), 2P2IDB (http://2p2idb.inserm.fr/) and iPPIDB (https://ippidb.pasteur.fr/) with Tanimoto 85 % threshold [3-4]. All reactive and inactive compounds were excluded from the library.
The Library contained inhibitors for the following protein-protein TimbalDB complexes (Fig. 3):
- Annexin A2/S100-A10
- Bcl-2 and Bcl-XL with BAX; BAK and BID
- BetaCatenin/Tcf4 & Tcf3
- CD80/CD28 (or CTLA-4)
- Clathrin/adaptor & accessory proteins
- SOD1 dimer
- TNFa trimer or TNFa/TNFR
- Transthyretin tetramer
- Tubulin dimer
- UL30(Pol)/UL42 subunits of HSV type 1 DNA polymerase
- XIAP/Caspase9 or SMAC (BIR3 domanin)
Figure 3. 2P2I targets
PPI Focused Library by the Rule of Four
This Library was created based on the study done by X. Morelli et al. . They have proposed ‘Rule-of-Four’ (Ro4) to describe the chemical space based on a general analysis of the molecular descriptors of selected known PPI-inhibitors. By X. Morelli’s definition, a molecule belongs to this space if it obeys the following properties: MW ≥ 400 Da, cLogP ≥ 4, H-bond acceptors (HBA) ≥ 4, number of rings ≥ 4. Also, the PPI library has designed on selected sp3-enriched compounds to ensure their complexity and, therefore, 3D-diversity.
The rule was used as a filter to accelerate the identification of potential PPI inhibitors. Its application resulted in the Library of over 3,300 potential PPI binding compounds. All the compounds were passed through MedChem filters.
PPI Focused Library by 2D Similarity Search vs. Binding DB, Pubmed DB, ChEMBL DB
A reference set (≥40K) of small organic molecules with activity in PPI-related assays towards the following protein complexes was collected Using Binding DB, Pubmed DB, and ChEMBL DB [5-6]:
- Menin/Histone-lysine N-methyltransferase MLL
- Importin subunit beta-1/Snurportin-1
- Runt-related transcription factor 1/Core-binding factor subunit beta
- MIF/CD74 (Macrophage migration inhibitory factor and HLA-DR antigens-associated invariant chain)
- Peroxisome proliferator-activated receptor gamma/Nuclear receptor corepressor 2
- Voltage-gated N-type calcium channel alpha-1B subunit/Amyloid beta A4 precursor protein-binding family A member 1
- Peroxisome proliferator-activated receptor gamma/Nuclear receptor coactivator 2
- Peroxisome proliferator-activated receptor gamma/Nuclear receptor coactivator 1
- Ras and Rab interactor 1/Tyrosine-protein kinase ABL1
- Peroxisome proliferator-activated receptor gamma/Nuclear receptor coactivator 3
- Cyclin-dependent kinases regulatory subunit 1/S-phase kinase-associated protein 2
- Tumor suppressor p53/oncoprotein Mdm2
- Perilipin-1/ABHD5 (Lipase co-activator protein, abhydrolase domain containing 5 (ABHD5) with perilipin-1 (PLIN1))
- Annexin A2/S100-A10
After filtering and merging their activity data (Activity < 10 μM), the resulting 20,000 unique compounds were obtained and further used as a basis for the library design. The MDL public keys and the Tanimoto similarity cut-off 80 % were applied to the Life Chemicals HTS Compound Collection that enabled picking up almost 14,400 compounds for the Library. Also, PAINS, reactive groups, and in-house developed MedChem filters were applied to provide the resulting compound set.
- 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.
- Mabonga L, Kappo AP. Protein-protein interaction modulators: advances, successes and remaining challenges. Biophys Rev. 2019;11(4):559-581.
- Higueruelo A.P., Jubb H., Blunde T.L. TIMBAL v2: update of a database holding small molecules modulating protein-protein interactions. (Oxford) Database. 2013; 2013: bat039.
- Bosc, N., Muller, C., Hoffer L., et al. Fr-PPIChem: An Academic Compound Library Dedicated to Protein–Protein Interactions. ACS Chem. Biol. 2020, 15, 6, 1566–1574. DOI: 10.1021/acschembio.0c00179
- 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
- Ran X., Gestwicki J.E. Inhibitors of Protein-Protein Interactions (PPIs): An Analysis of Scaffold Choices and Buried Surface Area. Curr Opin Chem Biol. 2018; 44: 75–86.