Virtual Screening for Transition State Analogue Inhibitors of IRAP Based on Quantum Mechanically Derived Reaction Coordinates

Enhanced-Sampling Simulations for the Estimation of Ligand Binding Kinetics: Current Status and Perspective

The dissociation rate (k off) associated with ligand unbinding events from proteins is a parameter of fundamental importance in drug design. Here we review recent major advancements in molecular simulation methodologies for the prediction of k off. Next, we discuss the impact of the potential energy function models on the accuracy of calculated k off values. Finally, we provide a perspective from high-performance computing and machine learning which might help improve such predictions.

Drug discovery for enzymes

Enzymes are essential, physiological catalysts involved in all processes of life, including metabolism, cellular signaling and motility, as well as cell growth and division. They are attractive drug targets because of the presence of defined substrate-binding pockets, which can be exploited as binding sites for pharmaceutical enzyme inhibitors. Understanding the reaction mechanisms of enzymes and the molecular mode of action of enzyme inhibitors is indispensable for the discovery and development of potent, efficacious, and safe novel drugs. The combination of classical concepts of enzymology with new experimental and data analysis methods opens new routes for drug discovery.

Using Atomic Confining Potentials for Geometry Optimization and Vibrational Frequency Calculations in Quantum-Chemical Models of Enzyme Active Sites

Quantum-chemical studies of enzymatic reaction mechanisms sometimes use truncated active-site models as simplified alternatives to mixed quantum mechanics molecular mechanics (QM/MM) procedures. Eliminating the MM degrees of freedom reduces the complexity of the sampling problem, but the trade-off is the need to introduce geometric constraints in order to prevent structural collapse of the model system during geometry optimizations that do not contain a full protein backbone. These constraints may impair the efficiency of the optimization, and care must be taken to avoid artifacts such as imaginary vibrational frequencies. We introduce a simple alternative in which terminal atoms of the model system are placed in soft harmonic confining potentials rather than being rigidly constrained. This modification is simple to implement and straightforward to use in vibrational frequency calculations, unlike iterative constraint-satisfaction algorithms, and allows the optimization to proceed without constraint even though the practical result is to fix the anchor atoms in space. The new approach is more efficient for optimizing minima and transition states, as compared to the use of fixed-atom constraints, and also more robust against unwanted imaginary frequencies. We illustrate the method by application to several enzymatic reaction pathways where entropy makes a significant contribution to the relevant reaction barriers. The use of confining potentials correctly describes reaction paths and facilitates calculation of both vibrational zero-point and finite-temperature entropic corrections to barrier heights.

Macrocyclic peptidomimetics as inhibitors of insulin-regulated aminopeptidase (IRAP)

Macrocyclic analogues of the linear hexapeptide, angiotensin IV (AngIV) have proved to be potent inhibitors of insulin-regulated aminopeptidase (IRAP, oxytocinase, EC 3.4.11.3). Along with higher affinity, macrocycles may also offer better metabolic stability, membrane permeability and selectivity, however predicting the outcome of particular cycle modifications is challenging. Here we describe the development of a series of macrocyclic IRAP inhibitors with either disulphide, olefin metathesis or lactam bridges and variations of ring size and other functionality. The binding mode of these compounds is proposed based on molecular dynamics analysis. Estimation of binding affinities (ΔG) and relative binding free energies (ΔΔG) with the linear interaction energy (LIE) method and free energy perturbation (FEP) method showed good general agreement with the observed inhibitory potency. Experimental and calculated data highlight the cumulative importance of an intact N-terminal peptide, the specific nature of the macrocycle, the phenolic oxygen and the C-terminal functionality.

What Next for Quantum Mechanics in Structure-Based Drug Discovery?

There is significant potential for electronic structure methods to improve the quality of the predictions furnished by the tools of computer-aided drug design, which typically rely on empirically derived functions. In this perspective, we consider some recent examples of how quantum mechanics has been applied in predicting protein-ligand geometries, protein-ligand binding affinities and ligand strain on binding. We then outline several significant developments in quantum mechanics methodology likely to influence these approaches: in particular, we note the advent of more computationally expedient ab initio quantum mechanical methods that can provide chemical accuracy for larger molecular systems than hitherto possible. We highlight the emergence of increasingly accurate semiempirical quantum mechanical methods and the associated role of machine learning and molecular databases in their development. Indeed, the convergence of improved algorithms for solving and analyzing electronic structure, modern machine learning methods, and increasingly comprehensive benchmark data sets of molecular geometries and energies provides a context in which the potential of quantum mechanics will be increasingly realized in driving future developments and applications in structure-based drug discovery.

Recent Trends in Quantum Chemical Modeling of Enzymatic Reactions

The quantum chemical cluster approach is a powerful method for investigating enzymatic reactions. Over the past two decades, a large number of highly diverse systems have been studied and a great wealth of mechanistic insight has been developed using this technique. This Perspective reviews the current status of the methodology. The latest technical developments are highlighted, and challenges are discussed. Some recent applications are presented to illustrate the capabilities and progress of this approach, and likely future directions are outlined.

Identification of novel PAD4 inhibitors based on a pharmacophore model derived from transition state coordinates

1.4 Protein arginine deiminases 4 (PAD4) is an attractive target for the development of novel and selective inhibitors of Rheumatoid Arthritis (RA). F-amidine is known as mechanism-based inhibitor targeting PAD4 and used as inactivators by covalently modifying the active site Cys645. To identify novel structural inhibitors of PAD4, we investigated the flexibility of protein on basis of the transition state geometry of PAD4 inhibited by F-amidine from our previous QM/MM calculation. And a pharmacophore model was generated containing four features (ADHH) using five representative structures from molecular dynamic (MD) simulation on basis of the transition state geometry of PAD4 inhibited by F-amidine. We performed virtual screening using the pharmacophore model and molecular docking methods, resulting in the discovery of two molecules with K_D (dissociation equilibrium constant) values of 112μM and 218μΜ against PAD4 through Surface Plasmon Resonance (SPR) experiments. These two molecules could potentially serve as PAD4 inhibitors.