Name
Title | ||
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DLIGAND2: an improved knowledge-based energy function for protein-ligand interactions using the distance-scaled, finite, ideal-gas reference state. |
Abstract | ||
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Performance of structure-based molecular docking largely depends on the accuracy of scoring functions. One important type of scoring functions are knowledge-based potentials derived from known three-dimensional structures of proteins and/or protein–ligand complex structures. This study seeks to improve a knowledge-based protein–ligand potential based on a distance-scale finite ideal-gas reference (DFIRE) state (DLIGAND) by expanding the representation of protein atoms from 13 mol2 atom types to 167 residue-specific atom types, and employing a recently updated dataset containing 12,450 monomer protein chains for training. We found that the updated version DLIGAND2 has a consistent improvement over DLIGAND in predicting binding affinities for either native complex structures or docking-generated poses. More importantly, DLIGAND2 has a 52% increase over DLIGAND in enrichment factors in top 1% predictions based on the DUD-E decoy set, and consistently improves over Autodock Vina and other statistical energy functions in all three benchmark tests. We further found that DLIGAND2 outperforms empirical and machine-learning methods compared for virtual screening on new targets that are not homologous to the DUD-E training set. Given the best performance as a parameter-free statistical potential and among the best in all performance measures, DLIGAND2 should be useful for re-assessing the poses generated by docking software, or acting as one term in other scoring functions. The program is available at
https://github.com/sysu-yanglab/DLIGAND2
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Year | DOI | Venue |
|---|---|---|
2019 | 10.1186/s13321-019-0373-4 | Journal of Cheminformatics |
Keywords | DocType | Volume |
Docking, Protein–ligand interaction, Knowledge-based energy function | Journal | 11 |
Issue | ISSN | Citations |
1 | 1758-2946 | 2 |
PageRank | References | Authors |
0.36 | 28 | 9 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Pin Chen | 1 | 2 | 0.70 |
| Yaobin Ke | 2 | 2 | 0.36 |
| Yutong Lu | 3 | 307 | 53.61 |
| Yunfei Du | 4 | 72 | 14.62 |
| Jiahui Li | 5 | 66 | 19.69 |
| Hui Yan | 6 | 2 | 2.05 |
| Huiying Zhao | 7 | 66 | 4.20 |
| Yaoqi Zhou | 8 | 2 | 0.36 |
| Yuedong Yang | 9 | 196 | 23.47 |