Alessia Ghidini

The dissociation of a ligand bound to a receptor is a rare event occurring on a timescale that is far longer than can be afforded by standard simulation methodologies. A number of specialized approaches have been designed to overcome this challenge. One of these, Random Acceleration Molecular Dynamics (RAMD), is a biased simulation method that is particularly effective at accelerating the dissociation of ligands bound to protein receptors. However, no theoretical framework is currently available to extract unbiased estimates from the RAMD simulation results. In this study, we develop a simple theory for its interpretation based on the assumption that the RAMD simulations can be approximated by an overdamped Langevin dynamics with an additional force of fixed magnitude changing direction randomly at exponentially distributed times. We show that the dynamics of the model is consistent with a Smoluchowski equation with an effective temperature and diffusion coefficient that depend quadratically on the magnitude of the random force. These observations explain why the RAMD simulations yield accelerated dynamics. Importantly, we derive an analytical expression for the unbiased escape time of a particle over a free-energy barrier based on the modified Smoluchowski equation. The theoretical framework was used to analyze RAMD simulations monitoring the dissociation of the trypsin–benzamidine protein–ligand complex. The results indicate that the theoretical framework is sound and could potentially be used to unbias the results from RAMD simulations.

ConspectusThis Account discusses recent progress and challenges in binding free energy computations, focusing on two classes of enhanced sampling techniques: alchemical transformations and path-based methods. Binding free energy is a crucial metric in drug discovery, as it measures the affinity of a ligand for its target receptor. Free energy and affinity guide the ranking and selection of potential drug candidates. The theoretical foundations of free energy calculations were established several decades ago, but their efficient application to drug-target binding remains a grand challenge in computational drug design. The main obstacles stem from sampling issues (as binding is a rare event), force field accuracy limitations, and simulation convergence. Alchemical transformations are now the most used methods for computing binding free energies in the pharmaceutical industry. However, while they efficiently calculate energy differences, the application of these methods is often limited to relative binding free energy calculations. Absolute and accurate (error < 1 kcal/mol) binding free energy predictions remain one of the great challenges for computational chemists and physicists. Another limitation of alchemical methods is that they lack the ability to provide mechanistic or kinetic insights into the binding process, crucial for optimizing lead compounds and designing novel therapies. Path-based methods offer, in principle, the possibility to accurately estimate absolute binding free energy while also providing insights into binding pathways and interactions.This Account explores recent advances in binding free energy methods for drug-target recognition and binding. In particular, we discuss the similarities and differences between alchemical and path-based approaches, highlighting recent innovations in both families of methods and providing perspectives from our group's contributions. We examine the foundational role of alchemical methods, which have been employed since the inception of free energy calculations, in both equilibrium and nonequilibrium contexts. We also emphasize the growing importance of path-based methods in drug discovery and their ability to predict binding and unbinding pathways, free energy profiles, and binding free energy estimates. In particular, the combination of path methods with machine learning has proven to be a powerful means for accurate path generation and free energy estimations. Building on our recent research, we discuss several path-based applications for drug discovery. Moreover, we focus on two semiautomatic protocols representing our group's state-of-the-art in free energy calculations. The first protocol is based on MetaDynamics simulation. From this, a recent innovation is instead based on nonequilibrium simulations combined with nonequilibrium estimators. We discuss in depth the advantages and drawbacks of equilibrium and nonequilibrium approaches to drug-target binding free energy predictions.

Integrin receptors mediate cell–cell interactions via the recognition of cell-adhesion glycoproteins, as well as via the interactions of cells with proteins of the extracellular matrix, and upon activation they transduce signals bi-directionally across the cell membrane. In the case of injury, infection, or inflammation, integrins of β₂ and α₄ families participate in the recruitment of leukocytes, a multi-step process initiated by the capturing of rolling leukocytes and terminated by their extravasation. In particular, α₄β₁ integrin is deeply involved in leukocyte firm adhesion preceding extravasation. Besides its well-known role in inflammatory diseases, α₄β₁ integrin is also involved in cancer, being expressed in various tumors and showing an important role in cancer formation and spreading. Hence, targeting this integrin represents an opportunity for the treatment of inflammatory disorders, some autoimmune diseases, and cancer. In this context, taking inspiration from the recognition motives of α₄β₁ integrin with its natural ligands FN and VCAM-1, we designed minimalist α/β hybrid peptide ligands, with our approach being associated with a retro strategy. These modifications are expected to improve the compounds’ stability and bioavailability. As it turned out, some of the ligands were found to be antagonists, being able to inhibit the adhesion of integrin-expressing cells to plates coated with the natural ligands without inducing any conformational switch and any activation of intracellular signaling pathways. An original model structure of the receptor was generated using protein–protein docking to evaluate the bioactive conformations of the antagonists via molecular docking. Since the experimental structure of α₄β₁ integrin is still unknown, the simulations might also shed light on the interactions between the receptor and its native protein ligands.

Recently, mixed opioid/NOP agonists came to the spotlight for their favorable functional profiles and promising outcomes in clinical trials as novel analgesics. This study reports on two novel chimeric peptides incorporating the fragment Tyr-c[D-Lys-Phe-Phe]Asp-NH₂ (RP-170), a cyclic peptide with high affinity for µ and κ opioid receptors (or MOP and KOP, respectively), conjugated with the peptide Ac-RYYRIK-NH₂, a known ligand of the nociceptin/orphanin FQ receptor (NOP), yielding RP-170-RYYRIK-NH₂ (KW-495) and RP-170-Gly₃-RYYRIK-NH₂ (KW-496). In vitro, the chimeric KW-496 gained affinity for KOP, hence becoming a dual KOP/MOP agonist, while KW-495 behaved as a mixed MOP/NOP agonist with low nM affinity. Hence, KW-495 was selected for further in vivo experiments. Intrathecal administration of this peptide in mice elicited antinociceptive effects in the hot-plate test; this action was sensitive to both the universal opioid receptor antagonist naloxone and the selective NOP antagonist SB-612111. The rotarod test revealed that KW-495 administration did not alter the mice motor coordination performance. Computational studies have been conducted on the two chimeras to investigate the structural determinants at the basis of the experimental activities, including any role of the Gly₃ spacer.

Molecular docking is a key method for structure-based drug design used to predict the conformations assumed by small drug-like ligands when bound to their target. However, the evaluation of molecular docking studies can be hampered by the lack of a free and easy to use platform for the complete analysis of results obtained by the principal docking programs. To this aim, we developed PacDOCK, a freely available and user-friendly web server that comprises a collection of tools for positional distance-based and interaction-based analysis of docking results, which can be provided in several file formats. PacDOCK allows a complete analysis of molecular docking results through root mean square deviation (RMSD) calculation, molecular visualization, and cluster analysis of docked poses. The RMSD calculation compares docked structures with a reference structure, also when atoms are randomly labelled, and their conformational and positional differences can be visualised. In addition, it is possible to visualise a ligand into the target binding pocket and investigate the key receptor–ligand interactions. Moreover, PacDOCK enables the clustering of docking results by identifying a restrained number of clusters from many docked poses. We believe that PacDOCK will contribute to facilitating the analysis of docking results to improve the efficiency of computer-aided drug design.