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Enhance the performance of current scoring functions with the aid of 3D protein-ligand interaction fingerprints

Overview of attention for article published in BMC Bioinformatics, July 2017
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Title
Enhance the performance of current scoring functions with the aid of 3D protein-ligand interaction fingerprints
Published in
BMC Bioinformatics, July 2017
DOI 10.1186/s12859-017-1750-5
Pubmed ID
Authors

Jie Liu, Minyi Su, Zhihai Liu, Jie Li, Yan Li, Renxiao Wang

Abstract

In structure-based drug design, binding affinity prediction remains as a challenging goal for current scoring functions. Development of target-biased scoring functions provides a new possibility for tackling this problem, but this approach is also associated with certain technical difficulties. We previously reported the Knowledge-Guided Scoring (KGS) method as an alternative approach (BMC Bioinformatics, 2010, 11, 193-208). The key idea is to compute the binding affinity of a given protein-ligand complex based on the known binding data of an appropriate reference complex, so the error in binding affinity prediction can be reduced effectively. In this study, we have developed an upgraded version, i.e. KGS2, by employing 3D protein-ligand interaction fingerprints in reference selection. KGS2 was evaluated in combination with four scoring functions (X-Score, ChemPLP, ASP, and GoldScore) on five drug targets (HIV-1 protease, carbonic anhydrase 2, beta-secretase 1, beta-trypsin, and checkpoint kinase 1). In the in situ scoring test, considerable improvements were observed in most cases after application of KGS2. Besides, the performance of KGS2 was always better than KGS in all cases. In the more challenging molecular docking test, application of KGS2 also led to improved structure-activity relationship in some cases. KGS2 can be applied as a convenient "add-on" to current scoring functions without the need to re-engineer them, and its application is not limited to certain target proteins as customized scoring functions. As an interpolation method, its accuracy in principle can be improved further with the increasing knowledge of protein-ligand complex structures and binding affinity data. We expect that KGS2 will become a practical tool for enhancing the performance of current scoring functions in binding affinity prediction. The KGS2 software is available upon contacting the authors.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 61 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 61 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 11 18%
Researcher 11 18%
Student > Master 11 18%
Student > Bachelor 6 10%
Professor > Associate Professor 5 8%
Other 9 15%
Unknown 8 13%
Readers by discipline Count As %
Chemistry 14 23%
Pharmacology, Toxicology and Pharmaceutical Science 11 18%
Computer Science 7 11%
Biochemistry, Genetics and Molecular Biology 7 11%
Agricultural and Biological Sciences 5 8%
Other 7 11%
Unknown 10 16%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. 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 20 July 2017.
All research outputs
#17,906,525
of 22,990,068 outputs
Outputs from BMC Bioinformatics
#5,963
of 7,309 outputs
Outputs of similar age
#225,812
of 314,952 outputs
Outputs of similar age from BMC Bioinformatics
#67
of 92 outputs
Altmetric has tracked 22,990,068 research outputs across all sources so far. This one is in the 19th percentile – i.e., 19% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,309 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one is in the 13th percentile – i.e., 13% of its peers scored the same or lower than it.
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