Molecular Dynamics Simulations and Kinetic Measurements to Estimate and Predict Protein–Ligand Residence Times
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- 作者：Luca Mollica ; Isabelle Theret ; Mathias Antoine ; Françoise Perron-Sierra ; Yves Charton ; Jean-Marie Fourquez ; Michel Wierzbicki ; Jean A. Boutin ; Gilles Ferry ; Sergio Decherchi ; Giovanni Bottegoni ; Pierre Ducrot ; Andrea Cavalli
- 刊名：Journal of Medicinal Chemistry
- 出版年：2016
- 出版时间：August 11, 2016
- 年：2016
- 卷：59
- 期：15
- 页码：7167-7176
- 全文大小：451K
- 年卷期：0
- ISSN：1520-4804
文摘
Ligand–target residence time is emerging as a key drug discovery parameter because it can reliably predict drug efficacy in vivo. Experimental approaches to binding and unbinding kinetics are nowadays available, but we still lack reliable computational tools for predicting kinetics and residence time. Most attempts have been based on brute-force molecular dynamics (MD) simulations, which are CPU-demanding and not yet particularly accurate. We recently reported a new scaled-MD-based protocol, which showed potential for residence time prediction in drug discovery. Here, we further challenged our procedure’s predictive ability by applying our methodology to a series of glucokinase activators that could be useful for treating type 2 diabetes mellitus. We combined scaled MD with experimental kinetics measurements and X-ray crystallography, promptly checking the protocol’s reliability by directly comparing computational predictions and experimental measures. The good agreement highlights the potential of our scaled-MD-based approach as an innovative method for computationally estimating and predicting drug residence times.