A detailed description of the events ruling ligand/protein interaction and an accurate estimation of the drug affinity to its target is of great help in speeding drug discovery strategies. We have developed a metadynamics-based approach, named funnel metadynamics, that allows the ligand to enhance the sampling of the target binding sites and its solvated states. This method leads to an efficient characterization of the binding free-energy surface and an accurate calculation of the absolute protein–ligand binding free energy. We illustrate our protocol in two systems, benzamidine/trypsin and SC-558/cyclooxygenase 2. In both cases, the X-ray conformation has been found as the lowest free-energy pose, and the computed protein–ligand binding free energy in good agreement with experiments. Furthermore, funnel metadynamics unveils important information about the binding process, such as the presence of alternative binding modes and the role of waters. The results achieved at an affordable computational cost make funnel metadynamics a valuable method for drug discovery and for dealing with a variety of problems in chemistry, physics, and material science.

Significance: A crucial factor for drug efficacy is not just the binding affinity, but also the mean residence time in the binding pocket, usually quantified by its inverse, k _off . This is an important parameter that regulates the time during which the drug is active. Whereas the calculation of the binding affinity is by now routine, the calculation of k _off has proven more challenging because the timescales involved far exceed the limits of standard molecular dynamics simulation. We propose a metadynamics-based strategy that allows reaching timescales of seconds, and estimate k _off along with unbinding pathways and associated dynamical bottlenecks. The protocol is exemplified for trypsin–benzamidine unbinding. This work is a step towards a more effective computer-based drug design.

The detailed understanding of the binding of small molecules to proteins is the key for the development of novel drugs or to increase the acceptance of substrates by enzymes. Nowadays, computer-aided design of protein–ligand binding is an important tool to accomplish this task. Current approaches typically rely on high-throughput docking essays or computationally expensive atomistic molecular dynamics simulations. Here, we present an approach to use the recently re-parametrized coarse-grained Martini model to perform unbiased millisecond sampling of protein–ligand interactions of small drug-like molecules. Remarkably, we achieve high accuracy without the need of any a priori knowledge of binding pockets or pathways. Our approach is applied to a range of systems from the well-characterized T4 lysozyme over members of the GPCR family and nuclear receptors to a variety of enzymes. The presented results open the way to high-throughput screening of ligand libraries or protein mutations using the coarse-grained Martini model.

Understanding the structural and energetic requisites of ligand binding toward its molecular target is of paramount relevance in drug design. In recent years, atomistic free energy calculations have proven to be a valid tool to complement experiments in characterizing the thermodynamic and kinetic properties of protein/ligand interaction. Here, we investigate, through a recently developed metadynamics-based protocol, the unbinding mechanism of an inhibitor of the pharmacologically relevant target p38 MAP kinase. We provide a thorough description of the ligand unbinding pathway identifying the most stable binding mode and other thermodynamically relevant poses. From our simulations, we estimated the unbinding rate as k_off = 0.020 ± 0.011 s^–1. This is in good agreement with the experimental value (k_off = 0.14 s^–1). Next, we developed a Markov state model that allowed identifying the rate-limiting step of the ligand unbinding process. Our calculations further show that the solvation of the ligand and that of the active site play crucial roles in the unbinding process. This study paves the way to investigations on the unbinding dynamics of more complex p38 inhibitors and other pharmacologically relevant inhibitors in general, demonstrating that metadynamics can be a powerful tool in designing new drugs with engineered binding/unbinding kinetics.

The widely used nonsteroidal anti-inflammatory drugs block the cyclooxygenase enzymes (COXs) and are clinically used for the treatment of inflammation, pain, and cancers. A selective inhibition of the different isoforms, particularly COX-2, is desirable, and consequently a deeper understanding of the molecular basis of selective inhibition is of great demand. Using an advanced computational technique we have simulated the full dissociation process of a highly potent and selective inhibitor, SC-558, in both COX-1 and COX-2. We have found a previously unreported alternative binding mode in COX-2 explaining the time-dependent inhibition exhibited by this class of inhibitors and consequently their long residence time inside this isoform. Our metadynamics-based approach allows us to illuminate the highly dynamical character of the ligand/protein recognition process, thus explaining a wealth of experimental data and paving the way to an innovative strategy for designing new COX inhibitors with tuned selectivity.