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PPI Fragment Library

Fragments able to mimic protein structural motifs and hot-spot residues

3 600 compounds

Protein-protein interactions regulate most aspects of life cycle thereof being the most attractive and perspective target for contemporary drug development. This field is still not well explored and there are no common rules for proteins involved in this type of interaction. Such intricate biological systems cannot be cost-efficiently tackled using conventional high-throughput screening methods and algorithms. However, fragment-based approach showed fruitful results in search of new PPI inhibitors. We created new library of PPI fragments with dedicated design that consists of systematical knowledge of common PPI inhibitors and selection by privileged structural motifs.

Using our PPI Fragments for hit finding you receive multiple benefits allowing you to save on time and costs in lead generation. Each fragment from the library can be easily followed with available in stock analogues or synthesis of new derivatives through our REAL Database technology.

Most popular Library Formats available for immediate supply

Option
Catalog ID
Compounds
Plates
Amount
Format

Option

1

Catalog ID

PPIF-3600-Y-10

Compounds

3 600

Plates

12

Amount

10 µL of 10 mM DMSO stock solutions

Plates and formats

384-well microplates, Eco qualified Labcyte LP0200

Option

2

Catalog ID

PPIF-3600-X-50

Compounds

3 600

Plates

45

Amount

50 µL of 10 mM DMSO stock solutions

Plates and formats

96-well plates, Greiner, first and last columns empty

Option

3

Catalog ID

PPIF-3600-X-100

Compounds

3 600

Plates

45

Amount

100 µL of 10 mM DMSO stock solutions

Plates and formats

96-well microplates, 80 cmpds per plate

Key features

Recent researches in the field of PPI fragments revealed the requirement of higher molecular weight molecules compared to common fragments due to a larger contact surface area. Therefore “Rule of four” restriction were used to extract initial PPI fragments subset, as they are typically larger and more lipophilic . Additionally, machine-learning method (decision tree) with number of validated descriptors was used to refine the library.

The “hot-spots” concept has been applied, implying the use of “key” amino acid residues involved in PPIs. A significant number of the selected fragments contains groups/moieties which correspond to these hot-spots. Additionally, library was enriched with the molecules having alpha helix-like structure, able to mimic protein motifs. Also since hydrogen bonds often play a crucial role in PPIs the preference was set for the molecules bearing at least one H-bond donor (> 70 %) and one H-bond acceptor (100%).

Examples of the molecules

Molecular Properties

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