Investigating the Unbinding of Muscarinic Antagonists from the Muscarinic 3 Receptor.

Patient symptom relief is often heavily influenced by the residence time of the inhibitor-target complex. For the human muscarinic receptor 3 (hMR3), tiotropium is a long-acting bronchodilator used in conditions such as asthma or chronic obstructive pulmonary disease (COPD). The mechanistic insights into this inhibitor remain unclear; specifically, the elucidation of the main factors determining the unbinding rates could help develop the next generation of antimuscarinic agents. Using our novel unbinding algorithm, we were able to investigate ligand dissociation from hMR3. The unbinding paths of tiotropium and two of its analogues, N-methylscopolamin and homatropine methylbromide, show a consistent qualitative mechanism and allow us to identify the structural bottleneck of the process. Furthermore, our machine learning-based analysis identified key roles of the ECL2/TM5 junction involved in the transition state. Additionally, our results point to relevant changes at the intracellular end of the TM6 helix leading to the ICL3 kinase domain, highlighting the closest residue L482. This residue is located right between two main protein binding sites involved in signal transduction for hMR3's activation and regulation. We also highlight key pharmacophores of tiotropium that play determining roles in the unbinding kinetics and could aid toward drug design and lead optimization.

Combined Free-Energy Calculation and Machine Learning Methods for Understanding Ligand Unbinding Kinetics.

The determination of drug residence times, which define the time an inhibitor is in complex with its target, is a fundamental part of the drug discovery process. Synthesis and experimental measurements of kinetic rate constants are, however, expensive and time consuming. In this work, we aimed to obtain drug residence times computationally. Furthermore, we propose a novel algorithm to identify molecular design objectives based on ligand unbinding kinetics. We designed an enhanced sampling technique to accurately predict the free-energy profiles of the ligand unbinding process, focusing on the free-energy barrier for unbinding. Our method first identifies unbinding paths determining a corresponding set of internal coordinates (ICs) that form contacts between the protein and the ligand; it then iteratively updates these interactions during a series of biased molecular dynamics (MD) simulations to reveal the ICs that are important for the whole of the unbinding process. Subsequently, we performed finite-temperature string simulations to obtain the free-energy barrier for unbinding using the set of ICs as a complex reaction coordinate. Importantly, we also aimed to enable the further design of drugs focusing on improved residence times. To this end, we developed a supervised machine learning (ML) approach with inputs from unbiased "downhill" trajectories initiated near the transition state (TS) ensemble of the string unbinding path. We demonstrate that our ML method can identify key ligand-protein interactions driving the system through the TS. Some of the most important drugs for cancer treatment are kinase inhibitors. One of these kinase targets is cyclin-dependent kinase 2 (CDK2), an appealing target for anticancer drug development. Here, we tested our method using two different CDK2 inhibitors for the potential further development of these compounds. We compared the free-energy barriers obtained from our calculations with those observed in available experimental data. We highlighted important interactions at the distal ends of the ligands that can be targeted for improved residence times. Our method provides a new tool to determine unbinding rates and to identify key structural features of the inhibitors that can be used as starting points for novel design strategies in drug discovery.

Modelling the active SARS-CoV-2 helicase complex as a basis for structure-based inhibitor design.

The RNA helicase (non-structural protein 13, NSP13) of SARS-CoV-2 is essential for viral replication, and it is highly conserved among the coronaviridae family, thus a prominent drug target to treat COVID-19. We present here structural models and dynamics of the helicase in complex with its native substrates based on thorough analysis of homologous sequences and existing experimental structures. We performed and analysed microseconds of molecular dynamics (MD) simulations, and our model provides valuable insights to the binding of the ATP and ssRNA at the atomic level. We identify the principal motions characterising the enzyme and highlight the effect of the natural substrates on this dynamics. Furthermore, allosteric binding sites are suggested by our pocket analysis. Our obtained structural and dynamical insights are important for subsequent studies of the catalytic function and for the development of specific inhibitors at our characterised binding pockets for this promising COVID-19 drug target.

The structural basis for Z α1-antitrypsin polymerization in the liver.

The serpinopathies are among a diverse set of conformational diseases that involve the aberrant self-association of proteins into ordered aggregates. α1-Antitrypsin deficiency is the archetypal serpinopathy and results from the formation and deposition of mutant forms of α1-antitrypsin as "polymer" chains in liver tissue. No detailed structural analysis has been performed of this material. Moreover, there is little information on the relevance of well-studied artificially induced polymers to these disease-associated molecules. We have isolated polymers from the liver tissue of Z α1-antitrypsin homozygotes (E342K) who have undergone transplantation, labeled them using a Fab fragment, and performed single-particle analysis of negative-stain electron micrographs. The data show structural equivalence between heat-induced and ex vivo polymers and that the intersubunit linkage is best explained by a carboxyl-terminal domain swap between molecules of α1-antitrypsin.

Cations in motion: QM/MM studies of the dynamic and electrostatic roles of H+ and Mg2+ ions in enzyme reactions.

Here we discuss current trends in the simulations of enzymatic reactions focusing on phosphate catalysis. The mechanistic details of the proton transfers coupled to the phosphate cleavage is one of the key challenges in QM/MM calculations of these and other enzyme catalyzed reactions. The lack of experimental information offers both an opportunity for computations as well as often unresolved controversies. We discuss the example of small GTPases including the important human Ras protein. The high dimensionality and chemical complexity of these reactions demand carefully chosen computational techniques both in terms of the underlying quantum chemical theory and the sampling of the conformational ensemble. We also point out the important role of Mg2+ ions, and recent advances in their transient involvement in the catalytic mechanisms.

Structural basis of second-generation HIV integrase inhibitor action and viral resistance.

Although second-generation HIV integrase strand-transfer inhibitors (INSTIs) are prescribed throughout the world, the mechanistic basis for the superiority of these drugs is poorly understood. We used single-particle cryo-electron microscopy to visualize the mode of action of the advanced INSTIs dolutegravir and bictegravir at near-atomic resolution. Glutamine-148→histidine (Q148H) and glycine-140→serine (G140S) amino acid substitutions in integrase that result in clinical INSTI failure perturb optimal magnesium ion coordination in the enzyme active site. The expanded chemical scaffolds of second-generation compounds mediate interactions with the protein backbone that are critical for antagonizing viruses containing the Q148H and G140S mutations. Our results reveal that binding to magnesium ions underpins a fundamental weakness of the INSTI pharmacophore that is exploited by the virus to engender resistance and provide a structural framework for the development of this class of anti-HIV/AIDS therapeutics.

Lanthanides compete with calcium for binding to cadherins and inhibit cadherin-mediated cell adhesion.

Lanthanides are rare-earth metals with a broad range of applications in biological research and medicine. In addition to their unique magnetic and spectroscopic properties, lanthanides are also effective mimics of calcium and can stimulate or inhibit the function of calcium-binding proteins. Cadherins are a large family of calcium-binding proteins that facilitate cell adhesion and play key roles in embryo development, tissue homeostasis and tumour metastasis. However, whether lanthanides can bind cadherins and functionally replace calcium binding has not been comprehensively explored. In this study, we investigated the effect of lanthanide binding on cadherin structure and function using terbium, which is a commonly used lanthanide for protein spectroscopy and a proposed anti-cancer agent. We demonstrate that terbium can compete with calcium for binding to calcium-binding sites in cadherins. Terbium binding to cadherins abolished their cell adhesive activity and rendered cadherins sensitive to proteolysis by trypsin. Molecular dynamics simulations indicate that replacement of calcium by terbium results in structural rearrangements and increases the flexibility of the cadherin ectodomain. These changes in structure and dynamics are likely to underlie the inability of lanthanide-bound cadherins to support cell adhesion. Taken together, our findings further knowledge on lanthanide interactions with calcium-binding proteins and provide new insight into the influence of metal chemistry on cadherin structure, dynamics and function.

Calculating Kinetic Rates and Membrane Permeability from Biased Simulations.

We present a simple approach to calculate the kinetic properties of lipid membrane crossing processes from biased molecular dynamics simulations. We demonstrate that by using biased simulations, one can obtain highly accurate kinetic information with significantly reduced computational time with respect to unbiased simulations. We describe how to conveniently calculate the transition rates to enter, cross, and exit the membrane in terms of the mean first passage times. To obtain free energy barriers and relaxation times from biased simulations only, we constructed Markov models using the dynamic histogram analysis method (DHAM). The permeability coefficients that are calculated from the relaxation times are found to correlate highly with experimentally evaluated values. We show that more generally, certain calculated kinetic properties linked to the crossing of the membrane layer (e.g., barrier height and barrier crossing rates) are good indicators of ordering drugs by permeability. Extending the analysis to a 2D Markov model provides a physical description of the membrane crossing mechanism.