Qualitative Estimation of Protein–Ligand Complex Stability through Thermal Titration Molecular Dynamics Simulations

Abstract

The prediction of ligand efficacy has long been linked to thermodynamic properties such as the equilibrium dissociation constant, which considers both the association and the dissociation rates of a defined protein−ligand complex. In the last 15 years, there has been a paradigm shift, with an increased interest in the determination of kinetic properties such as the drug−target residence time since they better correlate with ligand efficacy compared to other parameters. In this article, we present thermal titration molecular dynamics (TTMD), an alternative computational method that combines a series of molecular dynamics simulations performed at progressively increasing temperatures with a scoring function based on protein−ligand interaction fingerprints for the qualitative estimation of protein−ligand-binding stability. The protocol has been applied to four different pharmaceutically relevant test cases, including protein kinase CK1δ, protein kinase CK2, pyruvate dehydrogenase kinase 2, and SARS-CoV-2 main protease, on a variety of ligands with different sizes, structures, and experimentally determined affinity values. In all four cases, TTMD was successfully able to distinguish between high-affinity compounds (low nanomolar range) and low-affinity ones (micromolar), proving to be a useful screening tool for the prioritization of compounds in a drug discovery campaign.

“Dual Anta-Inhibitors” of the A2A Adenosine Receptor and Casein Kinase CK1delta: Synthesis, Biological Evaluation, and Molecular Modeling Studies

“…The remaining poses were then prioritized following the available structure-activity information for each receptor. Indeed, several studies have already been performed in the past by several academic and industrial groups about CK1δ and ARs, including us, and the most important features to guarantee stable binding with the receptors have been elucidated [ 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 ].…”

Thermal Titration Molecular Dynamics (TTMD): Not Your Usual Post-Docking Refinement

“…Four different pharmaceutically relevant biological targets, including protein kinase CK1δ, protein kinase CK2, pyruvate dehydrogenase kinase 2, and SARS-CoV-2 main protease were investigated, with three different protein-ligand complexes for each target used in this work. Test cases were chosen among the same protein-ligand complexes used in the original paper [ 25 ], as well as the same simulation conditions, i.e., the adopted temperature ramp.…”

“…As thoroughly described in the work of Pavan et al [ 25 ], Thermal Titration Molecular Dynamics (TTMD) is an alternative enhanced sampling MD approach originally developed for the qualitative estimation of protein-ligand unbinding kinetics. The sampling method consists of a series of short classic MD simulations (defined as TTMD-steps), performed at progressively increasing temperature values.…”

“…In the current work, we present the first application of Thermal Titration Molecular Dynamics (TTMD) as an alternative MD-based post-docking refinement tool. Recently developed as an enhanced sampling MD protocol for the qualitative estimation of protein-ligand unbinding kinetics [ 25 ], TTMD evaluates the persistence of the native binding mode in a certain protein-ligand complex of interest by combining a series of short MD simulations performed at progressively increasing temperatures with a scoring function based on protein-ligand interaction fingerprints [ 26 ]. The protocol has been applied to twelve different protein-ligand complexes from four different pharmaceutically relevant biological targets, including protein kinase CK1δ, protein kinase CK2, pyruvate dehydrogenase kinase 2, and SARS-CoV-2 main protease.…”

Targeting the I7L Protease: A Rational Design for Anti-Monkeypox Drugs?

“…Initially, docking poses were filtered based on the similarity of their interaction pattern with the top-scoring pose for the positive control TTP-6171. Specifically, the recently developed IFP CS [ 43 , 76 , 77 ] scoring function was used to perform the comparison between the reference TTP-6171 pose and the query poses derived from the virtual screening. Each docking pose is encoded in a rx8 integer vector (where r is the number of protein residues and 8 is the number of possible protein–ligand interactions that are computed), and the cosine similarity between a reference pose and a query one is calculated.…”