Name
Title | ||
|---|---|---|
Weak-binding molecules are not drugs? - toward a systematic strategy for finding effective weak-binding drugs. |
Abstract | ||
|---|---|---|
Designing maximally selective ligands that act on individual drug targets with high binding affinity has been the central dogma of drug discovery and development for the past two decades. However, many low-affinity drugs that aim for several targets at the same time are found more effective than the high-affinity binders when faced with complex disease conditions, such as cancers, Alzheimer's disease and cardiovascular diseases. The aim of this study was to appreciate the importance and reveal the features of weak-binding drugs and propose an integrated strategy for discovering them. Weak-binding drugs can be characterized by their high dissociation rates and transient interactions with their targets. In addition, network topologies and dynamics parameters involved in the targets of weak-binding drugs also influence the effects of the drugs. Here, we first performed a dynamics analysis for 33 elementary subgraphs to determine the desirable topology and dynamics parameters among targets. Then, by applying the elementary subgraphs to the mitogen-activated protein kinase (MAPK) pathway, several optimal target combinations were obtained. Combining drug-target interaction prediction with molecular dynamics simulation, we got two potential weak-binding drug candidates, luteolin and tanshinone IIA, acting on these targets. Further, the binding affinity of these two compounds to their targets and the anti-inflammatory effects of them were validated through in vitro experiments. In conclusion, weak-binding drugs have real opportunities for maximum efficiency and may show reduced adverse reactions, which can offer a bright and promising future for new drug discovery. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1093/bib/bbw018 | BRIEFINGS IN BIOINFORMATICS |
Keywords | Field | DocType |
weak-binding drug,polypharmacology,mathematical modeling,systems pharmacology | Drug discovery,Biology,Ligand (biochemistry),Tanshinone IIA,Bioinformatics,Computational biology,Maximum efficiency,Drug,Binding drugs | Journal |
Volume | Issue | ISSN |
18 | 2 | 1467-5463 |
Citations | PageRank | References |
0 | 0.34 | 11 |
Authors | ||
10 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jinan Wang | 1 | 30 | 2.14 |
| Zihu Guo | 2 | 31 | 3.11 |
| Yingxue Fu | 3 | 20 | 2.93 |
| Ziyin Wu | 4 | 17 | 2.23 |
| Chao Huang | 5 | 30 | 3.75 |
| Chunli Zheng | 6 | 9 | 2.50 |
| Piar Ali Shar | 7 | 1 | 1.64 |
| Zhenzhong Wang | 8 | 0 | 0.34 |
| Wei Xiao | 9 | 0 | 0.34 |
| Yonghua Wang | 10 | 0 | 0.34 |