Fragment based approach

A new high speed and accurate approach of structure based drug design is offered to molecular modelers, crystallographers and medicinal chemists

Fragment based applications

MEDIT is offering a new computational drug design protocol combining local similarity of protein surfaces and a fragment-based approach [1-6]. It is based on MED-SuMo and brings together their respective advantages in an attractive way. The protocol is intended for fragment library design, lead discovery and lead optimization. Lead Discovery and Lead optimization applications are performed with MED-Hybridise.

Fragment-based drug discovery has emerged in the last decade and is in contrast to conventional high throughput screening (HTS) where fully built, “drug-sized” chemical compounds are screened for activity. Small chemical structures or fragments (100-250 Da) that intrinsically have weaker binding affinity (100 mM to 10 nM range) are screened to probe the complete binding site, and then to identify larger molecules based on one or multiple binding fragments.

Obtaining experimental structural information on fragments or ligands complexed to a target protein is a key element and also a major limitation to the number and types of target that are amenable to fragment-based drug discovery. Consequently, computational methods play a key role in deriving structural information for designing compounds that fit a particular site on a given protein

MED-SuMo enables populating binding sites by searching and retrieving MED-portions chemical moieties from the MED-SuMo fragment database. This database of MED-Portions , where a MED-Portion is a new structural object encoding protein-fragment binding sites, is generated with MED-Fragmentor. a collected pool of MED-Portion chemical moities is shown below in the case of a DFG-out protein kinase pocket:

Fig.1: The collected pool of MED-Portion chemical moities in the case of a DFG-out protein kinase query pocket. chemical moities are colored according to their subpocket.



Binding sites superimposition with MED-SuMo leads to ligands alignments

In our fragment-based approach protocol, we do not hybridise ligands like is was done in a pioneering work published in 2004 [7] (Figure 2.) because the retrieved ligands are found to be much more likely to have strong bumps (steric clashes) with the query protein than the smaller MED-Portion chemical moieties [1]. After a bump count between the retrieved ligands and the query protein it can be concluded that this sort of starting material is not suitable for further study, since too many bumps are present.


Fig.2: superposition of p38 MAP kinase protein structures 1DI9, 1A9U, 1BMK in the MED-SuMo interface ; potential hybridization cases: (1DI9 & 1A9U) and   (1DI9 & 1BMK). The superposition of p38 MAP kinase structures (PDB codes 1DI9, 1A9U, 1BMK)  leads to the superposition of their co-cristallized ligand. The ligands can be hybridised to generate new ligands [7]. MED-SuMo can automatically generate similar alignment of the protein kinases and aligned ligands can be exported in PDB or SD files for post treatments like hybridisation.

Our strategy: MED-Portion chemical moieties hopping instead of ligand hopping from one target to another


Similar protein surfaces are likely to bind the same MED-Portion chemical moiety

MED-Portion chemical moieties have smaller interaction surfaces than ligands and therefore target hopping is more likely to occur

Many interfamily hits occur: GPCR binding sites can be successfully populated [1]


This work is based on two publications:

Computational fragment-based drug design to explore the hydrophobic sub-pocket of the mitotic kinesin Eg5 allosteric binding site. Oguievetskaia K, Martin-Chanas L, Vorotyntsev A, Doppelt-Azeroual O, Brotel X, Adcock SA, de Brevern AG, Delfaud F, Moriaud F. J Comput Aided Mol Des. 2009 Jun 17. 
PMID: 19533373

Computational fragment-based approach at PDB scale by protein local similarity. Moriaud F, Doppelt-Azeroual O, Martin L, Oguievetskaia K, Koch K, Vorotyntsev A, Adcock SA, Delfaud F. J Chem Inf Model. 2009 Feb;49(2):280-94. 
PMID: 19434830


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