Metadynamics Simulations Distinguish Short- and Long-Residence-Time Inhibitors of Cyclin-Dependent Kinase 8.

The duration of drug efficacy in vivo is a key aspect primarily addressed during the lead optimization phase of drug discovery. Hence, the availability of robust computational approaches that can predict the residence time of a compound at its target would accelerate candidate selection. Nowadays the theoretical prediction of this parameter is still very challenging. Starting from methods reported in the literature, we set up and validated a new metadynamics (META-D)-based protocol that was used to rank the experimental residence times of 10 arylpyrazole cyclin-dependent kinase 8 (CDK8) inhibitors for which target-bound X-ray structures are available. The application of reported methods based on the detection of the escape from the first free energy well gave a poor correlation with the experimental values. Our protocol evaluates the energetics of the whole unbinding process, accounting for multiple intermediates and transition states. Using seven collective variables (CVs) encoding both roto-translational and conformational motions of the ligand, a history-dependent biasing potential is deposited as a sum of constant-height Gaussian functions until the ligand reaches an unbound state. The time required to achieve this state is proportional to the integral of the deposited potential over the CV hyperspace. Average values of this time, for replicated META-D simulations, provided an accurate classification of CDK8 inhibitors spanning short, medium, and long residence times

Design and SAR Analysis of Covalent Inhibitors Driven by Hybrid QM/MM Simulations

Quantum mechanics/molecular mechanics (QM/MM) hybrid technique is emerging as a reliable computational method to investigate and characterize chemical reactions occurring in enzymes. From a drug discovery perspective, a thorough understanding of enzyme catalysis appears pivotal to assist the design of inhibitors able to covalently bind one of the residues belonging to the enzyme catalytic machinery. Thanks to the current advances in computer power, and the availability of more efficient algorithms for QM-based simulations, the use of QM/MM methodology is becoming a viable option in the field of covalent inhibitor design. In the present review, we summarized our experience in the field of QM/MM simulations applied to drug design problems which involved the optimization of agents working on two well-known drug targets, namely fatty acid amide hydrolase (FAAH) and epidermal growth factor receptor (EGFR). In this context, QM/MM simulations gave valuable information in terms of geometry (i.e., of transition states and metastable intermediates) and reaction energetics that allowed to correctly predict inhibitor binding orientation and substituent effect on enzyme inhibition. What is more, enzyme reaction modelling with QM/MM provided insights that were translated into the synthesis of new covalent inhibitor featured by a unique combination of intrinsic reactivity, on-target activity, and selectivity

Metadynamics Simulations Distinguish Short- and Long-Residence-Time Inhibitors of Cyclin-Dependent Kinase 8

The duration of drug efficacy in vivo is a key aspect primarily addressed during the lead optimization phase of drug discovery. Hence, the availability of robust computational approaches that can predict the residence time of a compound at its target would accelerate candidate selection. Nowadays the theoretical prediction of this parameter is still very challenging. Starting from methods reported in the literature, we set up and validated a new metadynamics (META-D)-based protocol that was used to rank the experimental residence times of 10 arylpyrazole cyclin-dependent kinase 8 (CDK8) inhibitors for which target-bound X-ray structures are available. The application of reported methods based on the detection of the escape from the first free energy well gave a poor correlation with the experimental values. Our protocol evaluates the energetics of the whole unbinding process, accounting for multiple intermediates and transition states. Using seven collective variables (CVs) encoding both roto-translational and conformational motions of the ligand, a history-dependent biasing potential is deposited as a sum of constant-height Gaussian functions until the ligand reaches an unbound state. The time required to achieve this state is proportional to the integral of the deposited potential over the CV hyperspace. Average values of this time, for replicated META-D simulations, provided an accurate classification of CDK8 inhibitors spanning short, medium, and long residence times

Free energy and kinetics in protein-ligand binding: experimental measurements and computational estimates

Virtually all biochemical activities are mediated by the organization and recognition of biological macromolecules. An accurate characterization of the thermodynamics and kinetics governing the formation of supramolecular complexes is required to deeply understand the molecular principles driving all biological interactions. Thermodynamics provides the driving force of protein-ligand binding and is quantified by the binding free energies or the equilibrium dissociation constants. Since the interacting partners are out of equilibrium in vivo, the thermodynamic description of binding needs to be complemented by the knowledge of the kinetic rates. Nowadays, various biophysical experimental techniques can determine thermodynamic and kinetic properties, which are still difficult to be efficiently predicted by computational methods mainly because of the limited force field accuracy and the high computational cost. During my Ph.D., I applied molecular dynamics (MD)-based methods to characterize the thermodynamics and kinetics of inter-molecular interactions. First, I worked on a new enhanced MD-based protocol to simulate protein-ligand dissociation events. This approach provides a realistic description of the evolution of the system to an external perturbation accounting for the natural forces driving the dissociation mechanisms. By applying this computational approach to two pharmaceutically relevant kinases, I was able to rank two series of compounds on unbinding kinetics and to get qualitative mechanistic and path information on the underlying unbinding events, providing additional valuable information to be used in the optimization of lead compounds. Then, I developed an innovative computational method to estimate free energies applicable to systems of arbitrary complexity. Despite the number of challenges to be overcome, the method is very promising being able to provide accurate free energy estimates. Therefore, computer simulations emerged as a valuable tool to obtain information on both the thermodynamic and kinetic aspects governing the formation of supramolecular complexes, which might be used in the rational optimization of lead compounds