Sampling Protein Cryptic Site Formation via Advanced Molecular Dynamics Simulation Method

Tianming Qu, Lianqing Zheng, Wei Yang

Florida State University, Department of Chemistry

Employing computational methods, particularly molecular dynamics (MD) simulation techniques, to discover cryptic binding sites has attracted a great deal of attention in recent years. This is largely due to the increasing interest in identifying novel druggable binding targets to address the innovation deficiency crisis in drug discovery and to gain additional specificity benefits. In this poster, we will present a computational protocol that combines enhanced sampling MD simulations and water density fluctuations for the predictive sampling and characterization of cryptic site formation in enzyme systems. Our study demonstrated that the proposed algorithm is capable of predictively sampling cryptic site formation, driven by both local and global conformational changes. Furthermore, it was revealed that water fluctuations around proteins are closely correlated with protein dynamic motions, enabling accurate and robust characterization of cryptic binding sites. This protocol holds a great potential for advancing drug discovery efforts by uncovering novel, druggable binding targets.