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Lessons for fragment library design: analysis of output from multiple screening campaigns

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Abstract

Over the past 8 years, we have developed, refined and applied a fragment based discovery approach to a range of protein targets. Here we report computational analyses of various aspects of our fragment library and the results obtained for fragment screening. We reinforce the finding of others that the experimentally observed hit rate for screening fragments can be related to a computationally defined druggability index for the target. In general, the physicochemical properties of the fragment hits display the same profile as the library, as is expected for a truly diverse library which probes the relevant chemical space. An analysis of the fragment hits against various protein classes has shown that the physicochemical properties of the fragments are complementary to the properties of the target binding site. The effectiveness of some fragments appears to be achieved by an appropriate mix of pharmacophore features and enhanced aromaticity, with hydrophobic interactions playing an important role. The analysis emphasizes that it is possible to identify small fragments that are specific for different binding sites. To conclude, we discuss how the results could inform further development and improvement of our fragment library.

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Abbreviations

AK:

Adenosine kinase

CDK2:

Cyclin-dependent kinase 2

DNAG:

DNA gyrase

FAAH:

Fatty acid amide hydrolase

HSP70:

Human heat shock protein 70

HSP90:

Human heat shock protein 90

JNK3:

c-Jun N-terminal kinase 3

PDPK1:

3-Phosphoinositide-dependent protein kinase 1

PIN-1:

Peptidyl-prolyl cis/trans isomerase

PPI:

Protein–protein interaction

SeeDs:

Structural exploitation of experimental drug startpoints

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Acknowledgments

The authors thank many scientists at Vernalis to make the analysis possible. In particular, this analysis has benefit greatly from fruitful discussion and data mining by Heather Simmonite, Ben Davis and James Murray. We also express our gratitude to Chemical Computing Group and Schrodinger Inc., for answering our questions and helping us with the programs MOE and SiteMap to extract information needed for the analysis.

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Authors and Affiliations

  1. Vernalis (R&D) Ltd, Granta Park, Cambridge, CB21 6GB, UK

    I-Jen Chen & Roderick E. Hubbard

  2. YSBL and HYMS, University of York, York, YO10 5YW, UK

    Roderick E. Hubbard

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  1. I-Jen Chen
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Correspondence to Roderick E. Hubbard.

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Chen, IJ., Hubbard, R.E. Lessons for fragment library design: analysis of output from multiple screening campaigns. J Comput Aided Mol Des 23, 603–620 (2009). https://doi.org/10.1007/s10822-009-9280-5

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