Name
Title | ||
|---|---|---|
Binding energy landscape analysis helps to discriminate true hits from high-scoring decoys in virtual screening. |
Abstract | ||
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Although virtual screening through molecular clocking has been widely applied in lead discovery, it is still challenging to distinguish true hits from high-scoring decoys because of the difficulty in accurately predicting protein ligand binding affinities. Following the successful application of energy landscape analysis to both protein folding and biomolecular binding studies, we attempted to use protein ligand binding energy landscape analysis to recognize true binders from high-scoring decoys. Two parameters describing the binding energy landscape were used for this purpose. The energy gap, defined as the difference between the binding energy of the native binding mode and the average binding energy of other binding modes in the "denatured binding phase", was used to describe the thermodynamic stability of binding, and the number of local binding wells in the landscapes was used to account for the kinetic accessibility. These parameters, together with the docking score, were combined using logistic regression to investigate their capability to discriminate true ligands from high-scoring decoys. Inhibitors and the noninhibitors of two enzyme systems, neuraminidase and cyclooxygenase-2. were used to test their discrimination capability. Using a five-fold cross-validation, the areas under the receiver operator characteristic curves (AUCs) from the best linear combinations of parameters reached 0.878 for neuraminidase and 0.776 for cyclooxygenase-2. To make a more independent test, inhibitors and high-scoring decoys in a directory of useful decoys (DUD), the largest and most comprehensive public data set for benchmarking virtual screen programs by far, were used as independent test sets to test the discrimination capability of these parameters. The AUCs of the best linear combinations of parameters for the independent test sets were 0.750 for neuraminidase and 0.855 for cyclooxygenase-2. Furthermore, combining these two parameters with the docking scoring function improved the enrichment ratio to 200-300% compared to that using the scoring function alone. This study suggests that incorporating information from binding energy landscape analysis can significantly increase the success rate of virtual screening. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1021/ci900463u | JOURNAL OF CHEMICAL INFORMATION AND MODELING |
Field | DocType | Volume |
Docking (molecular),Protein folding,Binding energy,Docking (dog),Binding protein,Ligand (biochemistry),Combinatorial chemistry,Chemistry,Artificial intelligence,Computational biology,Virtual screening,Energy landscape | Journal | 50 |
Issue | ISSN | Citations |
10 | 1549-9596 | 3 |
PageRank | References | Authors |
0.42 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Dengguo Wei | 1 | 3 | 0.42 |
| Hao Zheng | 2 | 3 | 0.42 |
| Naifang Su | 3 | 17 | 0.94 |
| Minghua Deng | 4 | 171 | 19.45 |
| Luhua Lai | 5 | 369 | 33.78 |