Computational fragment-based drug design to explore the hydrophobic sub-pocket of the mitotic kinesin Eg5 allosteric binding site

Oguievetskaia, Ksénia; Martin-Chanas, Laetitia; Vorotyntsev, Artem; Doppelt-Azeroual, Olivia; Brotel, Xavier; Adcock, Stewart A.; Brevern, Alexandre G. de; Delfaud, François; Moriaud, Fabrice

doi:10.1007/s10822-009-9286-z

Cited by 10 publications

(8 citation statements)

References 35 publications

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“…Thus, MED-SMA is an approach that may improve the efficiency and effectiveness of early stage drug discovery steps, involving the initial lead selection, improving poor leads, or, multivariate optimization, as it was used in a previous study. 17 This study demonstrates that MED-SuMo is a particularly well suited tool to both annotate protein structures and to enable structural functional classification. Finally, its effectiveness at dealing with the entire PDB shows that MED-SuMo is well-suited to large-scale applications.…”

Section: Discussionmentioning

confidence: 82%

“…The full surface database which contains whole surfaces of the protein structures and the MED-Portions database also containing small protein regions but where characterized by chemical fragments detected in the ligands or peptides from the PDB. 16,17 The original version of SuMo is available on the internet, 61 but the latest improvements are only included in the MED-SuMo software distributed by MEDIT SA. 62 These improvements concern the definition and conception of protein databases and as well as the heuristic itself.…”

Section: Med-sumo Algorithmmentioning

confidence: 99%

“…[9][10][11] The combination of knowledge from 3D protein structures with hundreds of thousands of small-molecules can be used for structure and ligand-based drug design. [12][13][14][15][16][17] For example, Crespo and Fernandez used the protein structure of a imatinib-resistant mutant 18 to improve the anticancer drug by promoting stronger intermolecular nonbonded interactions than those bound by the original drug. In the same way, the resolution of the HIV-1 reverse transcriptase complex structure explained the potential application of anti-HIV drugs against resistance mutations.…”

Section: Introductionmentioning

confidence: 99%

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Fast and automated functional classification with MED‐SuMo: An application on purine‐binding proteins

Doppelt-Azeroual

Delfaud²,

Moriaud³

et al. 2010

Protein Science

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Ligand-protein interactions are essential for biological processes, and precise characterization of protein binding sites is crucial to understand protein functions. MED-SuMo is a powerful technology to localize similar local regions on protein surfaces. Its heuristic is based on a 3D representation of macromolecules using specific surface chemical features associating chemical characteristics with geometrical properties. MED-SMA is an automated and fast method to classify binding sites. It is based on MED-SuMo technology, which builds a similarity graph, and it uses the Markov Clustering algorithm. Purine binding sites are well studied as drug targets. Here, purine binding sites of the Protein DataBank (PDB) are classified. Proteins potentially inhibited or activated through the same mechanism are gathered. Results are analyzed according to PROSITE annotations and to carefully refined functional annotations extracted from the PDB. As expected, binding sites associated with related mechanisms are gathered, for example, the Small GTPases. Nevertheless, protein kinases from different Kinome families are also found together, for example, Aurora-A and CDK2 proteins which are inhibited by the same drugs. Representative examples of different clusters are presented. The effectiveness of the MED-SMA approach is demonstrated as it gathers binding sites of proteins with similar structure-activity relationships. Moreover, an efficient new protocol associates structures absent of cocrystallized ligands to the purine clusters enabling those structures to be associated with a specific binding mechanism. Applications of this classification by binding mode similarity include target-based drug design and prediction of cross-reactivity and therefore potential toxic side effects.

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Section: Discussionmentioning

confidence: 82%

Section: Med-sumo Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Fast and automated functional classification with MED‐SuMo: An application on purine‐binding proteins

Doppelt-Azeroual

Delfaud²,

Moriaud³

et al. 2010

Protein Science

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“…They applied their approach to distinct relevant target families such as GPCR and kinases. They also used this approach to study the allosteric binding site of the mitotic kinesin Eg5 [127].…”

Section: De Novo Designmentioning

confidence: 99%

Fragment-Based Drug Design: Computational and Experimental State of the Art

Hoffer¹,

Renaud²,

Horvath

2011

CCHTS

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Fragment-based screening is an emerging technology which is used as an alternative to high-throughput screening (HTS), and often in parallel. Fragment screening focuses on very small compounds. Because of their small size and simplicity, fragments exhibit a low to medium binding affinity (mM to µM) and must therefore be screened at high concentration in order to detect binding events. Since some issues are associated with high-concentration screening in biochemical assays, biophysical methods are generally employed in fragment screening campaigns. Moreover, these techniques are very sensitive and some of them can give precise information about the binding mode of fragments, which facilitates the mandatory hit-to-lead optimization. One of the main advantages of fragment-based screening is that fragment hits generally exhibit a strong binding with respect to their size, and their subsequent optimization should lead to compounds with better pharmacokinetic properties compared to molecules evolved from HTS hits. In other words, fragments are interesting starting points for drug discovery projects. Besides, the chemical space of low-complexity compounds is very limited in comparison to that of drug-like molecules, and thus easier to explore with a screening library of limited size. Furthermore, the "combinatorial explosion" effect ensures that the resulting combinations of interlinked binding fragments may cover a significant part of "drug-like" chemical space. In parallel to experimental screening, virtual screening techniques, dedicated to fragments or wider compounds, are gaining momentum in order to further reduce the number of compounds to test. This article is a review of the latest news in both experimental and in silico virtual screening in the fragment-based discovery field. Given the specificity of this journal, special attention will be given to fragment library design.

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“…[10] This approach was recently applied to design inhibitors of the mitotic kinesin Eg5. [11] We have previously shown that a combination of two commonly used computational approaches -pocket identification and pharmacophore screening algorithms -could be used to search the PDB for aromatic cages, i.e. sites chemically similar to known methyl-lysine binding elements, and identify novel putative sites involved in epigenetic signalling.…”

Section: Introductionmentioning

confidence: 99%

Finding Inspiration in the Protein Data Bank to Chemically Antagonize Readers of the Histone Code

Campágna-Slater¹,

Schapira²

2010

Molecular Informatics

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Members of the Royal family of proteins are readers of the histone code that contain aromatic cages capable of recognizing specific sequences and lysine methylation states on histone tails. These binding modules play a key role in epigenetic signalling, and are part of a larger group of epigenetic targets that are becoming increasingly attractive for drug discovery. In the current study, pharmacophore representations of the aromatic cages forming the methyl-lysine (Me-Lys) recognition site were used to search the Protein Data Bank (PDB) for ligand binding pockets possessing similar chemical and geometrical features in unrelated proteins. The small molecules bound to these sites were then extracted from the PDB, and clustered based on fragments binding to the aromatic cages. The compounds collected are numerous and structurally diverse, but point to a limited set of preferred chemotypes; these include quaternary ammonium, sulfonium, and primary, secondary and tertiary amine moieties, as well as aromatic, aliphatic or orthogonal rings, and bicyclic systems. The chemical tool-kit identified can be used to design antagonists of the Royal family and related proteins.

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Computational fragment-based drug design to explore the hydrophobic sub-pocket of the mitotic kinesin Eg5 allosteric binding site

Cited by 10 publications

References 35 publications

Fast and automated functional classification with MED‐SuMo: An application on purine‐binding proteins

Fast and automated functional classification with MED‐SuMo: An application on purine‐binding proteins

Fragment-Based Drug Design: Computational and Experimental State of the Art

Finding Inspiration in the Protein Data Bank to Chemically Antagonize Readers of the Histone Code

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