title: Toward High-Throughput Predictive Modeling of Protein Binding/Unbinding Kinetics.
abstract: One of the unaddressed challenges in drug discovery is that drug potency determined in vitro is not a reliable indicator of drug activity in vivo. Accumulated evidence suggests that in vivo activity is more strongly correlated with the binding/unbinding kinetics than the equilibrium thermodynamics of protein-ligand interactions (PLIs). However, existing experimental and computational techniques are insufficient in studying the molecular details of kinetics processes of PLIs on a large scale. Consequently, we not only have limited mechanistic understanding of the kinetic processes but also lack a practical platform for high-throughput screening and optimization of drug leads on the basis of their kinetic properties. For the first time, we address this unmet need by integrating coarse-grained normal mode analysis with multitarget machine learning (MTML). To test our method, HIV-1 protease is used as a model system. We find that computational models based on the residue normal mode directionality displacement of PLIs can not only recapitulate the results from all-atom molecular dynamics simulations but also predict protein-ligand binding/unbinding kinetics accurately. When this is combined with energetic features, the accuracy of combined kon and koff prediction reaches 74.35%. Furthermore, our integrated model provides us with new insights into the molecular determinants of the kinetics of PLIs. We propose that the coherent coupling of conformational dynamics and thermodynamic interactions between the receptor and the ligand may play a critical role in determining the kinetic rate constants of PLIs. In conclusion, we demonstrate that residue normal mode directionality displacement can serve as a kinetic fingerprint to capture long-time-scale conformational dynamics of the binding/unbinding kinetics. When this is coupled with MTML, it is possible to screen and optimize compounds on the basis of their binding/unbinding kinetics in a high-throughput fashion. The further development of such computational tools will bridge one of the critical missing links between in vitro compound screening and in vivo drug activity.
authors.affiliation: Department of Computer Science, The Graduate Center, The City University of New York , 365 Fifth Avenue, New York, New York 10016, United States.
authors.forename: See Hong
authors.initials: SH
authors.lastname: Chiu
authors.affiliation: Department of Computer Science, The Graduate Center, The City University of New York , 365 Fifth Avenue, New York, New York 10016, United States.|Department of Computer Science, Hunter College, The City University of New York , 695 Park Avenue, New York, New York 10065, United States.
authors.forename: Lei
authors.initials: L
authors.lastname: Xie
mesh_terms: D019295:Computational Biology / Q000379:methods*; D055808:Drug Discovery; D007700:Kinetics; D008024:Ligands; D000069550:Machine Learning; D056004:Molecular Dynamics Simulation; D011485:Protein Binding; D011487:Protein Conformation; D012449:Safety; D055672:Static Electricity; D013816:Thermodynamics
chemical_list: D008024:Ligands