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What Can We Learn from the Evolution of Protein-Ligand Interactions to Aid the Design of New Therapeutics?

Overview of attention for article published in PLOS ONE, December 2012
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Title
What Can We Learn from the Evolution of Protein-Ligand Interactions to Aid the Design of New Therapeutics?
Published in
PLOS ONE, December 2012
DOI 10.1371/journal.pone.0051742
Pubmed ID
Authors

Alicia P. Higueruelo, Adrian Schreyer, G. Richard J Bickerton, Tom L. Blundell, Will R. Pitt

Abstract

Efforts to increase affinity in the design of new therapeutic molecules have tended to lead to greater lipophilicity, a factor that is generally agreed to be contributing to the low success rate of new drug candidates. Our aim is to provide a structural perspective to the study of lipophilic efficiency and to compare molecular interactions created over evolutionary time with those designed by humans. We show that natural complexes typically engage in more polar contacts than synthetic molecules bound to proteins. The synthetic molecules also have a higher proportion of unmatched heteroatoms at the interface than the natural sets. These observations suggest that there are lessons to be learnt from Nature, which could help us to improve the characteristics of man-made molecules. In particular, it is possible to increase the density of polar contacts without increasing lipophilicity and this is best achieved early in discovery while molecules remain relatively small.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 58 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 2 3%
Germany 1 2%
India 1 2%
Brazil 1 2%
Japan 1 2%
United States 1 2%
Unknown 51 88%

Demographic breakdown

Readers by professional status Count As %
Researcher 18 31%
Student > Ph. D. Student 14 24%
Student > Bachelor 6 10%
Student > Doctoral Student 4 7%
Other 3 5%
Other 9 16%
Unknown 4 7%
Readers by discipline Count As %
Agricultural and Biological Sciences 20 34%
Chemistry 18 31%
Biochemistry, Genetics and Molecular Biology 9 16%
Computer Science 2 3%
Social Sciences 1 2%
Other 3 5%
Unknown 5 9%