Estimation of Ligand Binding Free Energy Using Multi-eGO

The\ncomputational study of ligand binding to a target protein provides\nmechanistic insight into the molecular determinants of this process\nand can improve the success rate of <i>in silico</i> drug\ndesign. All-atom molecular dynamics (MD) simulations can be used to\nevaluate the binding free energy, typically by thermodynamic integration,\nand to probe binding mechanisms, including the description of protein\nconformational dynamics. The advantages of MD come at a high computational\ncost, which limits its use. Such cost could be reduced by using coarse-grained\nmodels, but their use is generally associated with an undesirable\nloss of resolution and accuracy. To address the trade-off between\nspeed and accuracy of MD simulations, we describe the use of the recently\nintroduced multi-eGO atomic model for the estimation of binding free\nenergies. We illustrate this approach in the case of the binding of\nbenzene to lysozyme by both thermodynamic integration and metadynamics,\nshowing multiple binding/unbinding pathways of benzene. We then provide\nequally accurate results for the binding free energy of dasatinib\nand PP1 to Src kinase by thermodynamic integration. Finally, we show\nhow we can describe the binding of the small molecule 10074-G5 to\nAβ42 by single molecule simulations and by explicit titration\nof the ligand as a function of concentration. These results demonstrate\nthat multi-eGO has the potential to significantly reduce the cost\nof accurate binding free energy calculations and can be used to develop\nand benchmark <i>in silico</i> ligand binding techniques.