期刊
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
卷 110, 期 11, 页码 4273-4278出版社
NATL ACAD SCIENCES
DOI: 10.1073/pnas.1301814110
关键词
thermodynamics; flexible docking; metastable states; transition states
资金
- GE Healthcare Life Sciences, Ltd.
- State Key Program of Basic Research of China [2009CB918500, 2009CB918502]
- National Natural Science Foundation of China [20721003, 20720102040, 21173076, 81222046, 81230076]
- Shanghai Committee of Science and Technology [11D22260600]
- 863 Hi-Tech Program of China [2012AA020308]
- National Science and Technology Major Projec [2009ZX09501-001]
- Ministry of Education
- Chinese Academy of Sciences
- Program for New Century Excellent Talents in University [NCET-10-0378]
- Center for Theoretical Biological Physics
- National Science Foundation (NSF) [PHY-0822283, MCB-1214457, MCB-0818353]
- Cancer Prevention and Research Institute of Texas
- National Institutes of Health [R01-GM067801]
- Welch Foundation [Q-1512]
- International Workstation for Protein Folding and Drug Design, Shanghai Institute of Materia Medica, Chinese Academy of Sciences
- Direct For Biological Sciences
- Div Of Molecular and Cellular Bioscience [1214457] Funding Source: National Science Foundation
- Division Of Physics
- Direct For Mathematical & Physical Scien [1308264] Funding Source: National Science Foundation
Drug-target residence time (t = 1/k(off), where k(off) is the dissociation rate constant) has become an important index in discovering better- or best-in-class drugs. However, little effort has been dedicated to developing computational methods that can accurately predict this kinetic parameter or related parameters, k(off) and activation free energy of dissociation (Delta G(off)(not equal)). In this paper, energy landscape theory that has been developed to understand protein folding and function is extended to develop a generally applicable computational framework that is able to construct a complete ligand-target binding free energy landscape. This enables both the binding affinity and the binding kinetics to be accurately estimated. We applied this method to simulate the binding event of the anti-Alzheimer's disease drug (-)-Huperzine A to its target acetylcholinesterase (AChE). The computational results are in excellent agreement with our concurrent experimental measurements. All of the predicted values of binding free energy and activation free energies of association and dissociation deviate from the experimental data only by less than 1 kcal/mol. The method also provides atomic resolution information for the (-)-Huperzine A binding pathway, which may be useful in designing more potent AChE inhibitors. We expect this methodology to be-widely applicable to drug discovery and development.
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