PPI Focused Libraries by Ligand-based Approach

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.

The compound selection can be customized based on your requirements, cherry picking is available.

Please, contact us at orders@lifechemicals.com 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-molecule compounds from Life Chemicals HTS Compound Collection can target PPIs [1]. 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. Over 7,000 PPI-targeting structurally-diverse screening compounds were selected.

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 [2]:

The selected 7,000 drug-like screening 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 this Screening Set.

This PPI Screening Library contains potential inhibitors for the following protein-protein TimbalDB complexes (Fig. 3):

Figure 3. 2P2I targets

PPI Focused Library by the Rule of Four

This Screening Set of over 3,300 potential PPI modulators was created based on the study done by X. Morelli et al. [5]. 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

This Screening Set contains over 14,400 compounds capable of protein-protein interaction inhibition.

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]:

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 this screening Library. Also, PAINS, reactive groups, and in-house developed MedChem filters were applied to provide the resulting compound set.

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. 
2. Mabonga L, Kappo AP. Protein-protein interaction modulators: advances, successes and remaining challenges. Biophys Rev. 2019;11(4):559-581. 
3. 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.
4. 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
5. 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
6. 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.