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Mechanisms of DNA Recombination and Genome Rearrangements: Intersection between Homologous Recombination, DNA Replication and DNA Repair

Overview of attention for book
Mechanisms of DNA Recombination and Genome Rearrangements: Intersection between Homologous…
Elsevier
Attention for Chapter: Integrating Experimental and In Silico HTS in the Discovery of Inhibitors of Protein–Nucleic Acid Interactions
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Article details
Chapter title
Integrating Experimental and In Silico HTS in the Discovery of Inhibitors of Protein–Nucleic Acid Interactions
Book title
Mechanisms of DNA Recombination and Genome Rearrangements: Intersection between Homologous Recombination, DNA Replication and DNA Repair
Published in
Methods in enzymology, January 2018
DOI 10.1016/bs.mie.2017.11.036
Pubmed ID
Book ISBNs
978-0-12-813979-0
Authors

Quinn Li, Laura Folly da Silva Constantino, M. Ashley Spies

Abstract

Discovery of novel tool compounds and drug leads against a range of unorthodox protein targets has pushed both experimental screening methodologies as well as the field of structure-based design to the limit in recent years. Increasingly, it has been recognized that some of the most desirable targets for the development of small-molecule effectors are actually protein-protein and protein-nucleic acid interactions. There are numerous nontrivial challenges to pursuing small-molecule lead compounds directed toward PPIs and PNIs: relatively shallow cavities, large surface areas that are natively complexed to macromolecules, complex patterns of interstitial waters, a paucity of "hot spots," large conformational changes upon ligand binding, etc. Although there have been some notable successes targeting PPIs in the last decade, there has been distinctly less success in the realm of targeting PNIs. This chapter focuses on an approach, successfully applied by our group to address the challenge of gaining traction on the PPI target RAD52, which is a protein that binds both single-stranded and double-stranded DNA, and is an anticancer target for certain types of cancer. There are many approaches to tackling the difficult problems of finding effective small molecules that disrupt PPIs and PNIs, but the methods presented here offer a series of elegant solutions, which integrate experimental HTS, biophysical methods, docking, and molecular dynamics in a powerful way. Additionally, the structural knowledge gained from these studies provides a means for rationally understanding what features lead to ligand affinity in these fascinating and highly unorthodox pockets.

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The data shown below were collected from the profiles of 2 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley demographics

Mendeley demographics

The data shown below were compiled from readership statistics for 17 Mendeley readers of this research output. Click here to see the associated Mendeley record.
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Geographical breakdown

Geographical breakdown
Country Count As %
Unknown 17 100%

Demographic breakdown

Readers by professional status
Readers by professional status Count As %
Researcher 5 29%
Student > Ph. D. Student 4 24%
Student > Doctoral Student 2 12%
Other 1 6%
Student > Master 1 6%
Other 2 12%
Unknown 2 12%
Readers by discipline
Readers by discipline Count As %
Agricultural and Biological Sciences 4 24%
Biochemistry, Genetics and Molecular Biology 3 18%
Medicine and Dentistry 2 12%
Chemistry 2 12%
Immunology and Microbiology 1 6%
Other 2 12%
Unknown 3 18%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 2. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 15 March 2018.
All research outputs
#14,969,772
of 23,026,672 outputs
Outputs from Methods in enzymology
#1,711
of 2,610 outputs
Outputs of similar age
#255,771
of 442,370 outputs
Outputs of similar age from Methods in enzymology
#70
of 149 outputs
Altmetric has tracked 23,026,672 research outputs across all sources so far. This one is in the 32nd percentile – i.e., 32% of other outputs scored the same or lower than it.
So far Altmetric has tracked 2,610 research outputs from this source. They receive a mean Attention Score of 3.7. This one is in the 32nd percentile – i.e., 32% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 442,370 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 39th percentile – i.e., 39% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 149 others from the same source and published within six weeks on either side of this one. This one is in the 45th percentile – i.e., 45% of its contemporaries scored the same or lower than it.