Performance Assessment of Semiempirical Molecular Orbital Methods in Describing Halogen Bonding: Quantum Mechanical and Quantum Mechanical/Molecular Mechanical-Molecular Dynamics Study

Computational methods for calculation of protein-ligand binding affinities in structure-based drug design

Drug design is an expensive and time-consuming process. Any method that allows reducing the time the costs of the drug development project can have great practical value for the pharmaceutical industry. In structure-based drug design, affinity prediction methods are of great importance. The majority of methods used to predict binding free energy in protein-ligand complexes use molecular mechanics methods. However, many limitations of these methods in describing interactions exist. An attempt to go beyond these limits is the application of quantum-mechanical description for all or only part of the analyzed system. However, the extensive use of quantum mechanical (QM) approaches in drug discovery is still a demanding challenge. This chapter briefly reviews selected methods used to calculate protein-ligand binding affinity applied in virtual screening (VS), rescoring of docked poses, and lead optimization stage, including QM methods based on molecular simulations.

Accurate Binding Free Energy Method from End-State MD Simulations

Herein, we introduce a new strategy to estimate binding free energies using end-state molecular dynamics simulation trajectories. The method is adopted from linear interaction energy (LIE) and ANI-2x neural network potentials (machine learning) for the atomic simulation environment (ASE). It predicts the single-point interaction energies between ligand–protein and ligand–solvent pairs at the accuracy of the wb97x/6-31G* level for the conformational space that is sampled by molecular dynamics (MD) simulations. Our results on 54 protein–ligand complexes show that the method can be accurate and have a correlation of R = 0.87–0.88 to the experimental binding free energies, outperforming current end-state methods with reduced computational cost. The method also allows us to compare BFEs of ligands with different scaffolds. The code is available free of charge (documentation and test files) at https://github.com/otayfuroglu/deepQM.

Protein–ligand free energies of binding from full-protein DFT calculations: convergence and choice of exchange–correlation functional

Quantum mechanical binding free energies based on thousands of full-protein DFT calculations are tractable, reproducible and converge well.

Halogen bonding in halocarbon-protein complexes and computational tools for rational drug design

Introduction: Halogens have a prominent role in drug design. Often used as a mean to improve ADME properties, they are also becoming a tool in protein-ligand recognition given their ability to form a non-covalent interaction, termed halogen bond, where halogens act as electrophilic species interacting with electron-rich partners. Rational drug design of halogen-bonding lead molecules requires an accurate description of halocarbon-protein complexes by computational tools though not all methods are able to tackle this non-covalent interaction. Areas covered: The authors present a review of computational methodologies that can be used to properly describe halogen bonds in the context of protein-ligand complexes, providing also insights on how these methods can be used in the context of computer-aided drug design. Expert opinion: Although in the last few years many computational tools, ranging from fast screening methods to the more expensive QM calculations, have been developed to tackle the halogen bonding phenomenon, they are not yet standard in the literature. This will eventually change as official software distributions are including support for halogen bonding in their methods. Tackling desolvation of halogenated species seems to be a good strategy to improve the accuracy of computational methods, that will be more commonly used prior to laboratory work in the future.

Looking Back, Looking Forward at Halogen Bonding in Drug Discovery

Halogen bonding has emerged at the forefront of advances in improving ligand: receptor interactions. In particular the newfound ability of this extant non-covalent-bonding phenomena has revolutionized computational approaches to drug discovery while simultaneously reenergizing synthetic approaches to the field. Here we survey, via examples of classical applications involving halogen atoms in pharmaceutical compounds and their biological hosts, the unique advantages that halogen atoms offer as both Lewis acids and Lewis bases.

Relationships between hydrogen bonds and halogen bonds in biological systems

The recent recognition that halogen bonding (XB) plays important roles in the recognition and assembly of biological molecules has led to new approaches in medicinal chemistry and biomolecular engineering. When designing XBs into strategies for rational drug design or into a biomolecule to affect its structure and function, we must consider the relationship between this interaction and the more ubiquitous hydrogen bond (HB). In this review, we explore these relationships by asking whether and how XBs can replace, compete against or behave independently of HBs in various biological systems. The complex relationships between the two interactions inform us of the challenges we face in fully utilizing XBs to control the affinity and recognition of inhibitors against their therapeutic targets, and to control the structure and function of proteins, nucleic acids and other biomolecular scaffolds.

A new serotonin 5-HT6 receptor antagonist with procognitive activity - Importance of a halogen bond interaction to stabilize the binding

Serotonin 5-HT6 receptor has been proposed as a promising therapeutic target for cognition enhancement though the development of new antagonists is still needed to validate these molecules as a drug class for the treatment of Alzheimer's disease and other pathologies associated with memory deficiency. As part of our efforts to target the 5-HT6 receptor, new benzimidazole-based compounds have been designed and synthesized. Site-directed mutagenesis and homology models show the importance of a halogen bond interaction between a chlorine atom of the new class of 5-HT6 receptor antagonists identified herein and a backbone carbonyl group in transmembrane domain 4. In vitro pharmacological characterization of 5-HT6 receptor antagonist 7 indicates high affinity and selectivity over a panel of receptors including 5-HT2B subtype and hERG channel, which suggests no major cardiac issues. Compound 7 exhibited in vivo procognitive activity (1 mg/kg, ip) in the novel object recognition task as a model of memory deficit.

Probing Origin of Binding Difference of inhibitors to MDM2 and MDMX by Polarizable Molecular Dynamics Simulation and QM/MM-GBSA Calculation

Binding abilities of current inhibitors to MDMX are weaker than to MDM2. Polarizable molecular dynamics simulations (MD) followed by Quantum mechanics/molecular mechanics generalized Born surface area (QM//MM-GBSA) calculations were performed to investigate the binding difference of inhibitors to MDM2 and MDMX. The predicted binding free energies not only agree well with the experimental results, but also show that the decrease in van der Walls interactions of inhibitors with MDMX relative to MDM2 is a main factor of weaker bindings of inhibitors to MDMX. The analyses of dihedral angles based on MD trajectories suggest that the closed conformation formed by the residues M53 and Y99 in MDMX leads to a potential steric clash with inhibitors and prevents inhibitors from arriving in the deep of MDMX binding cleft, which reduces the van der Waals contacts of inhibitors with M53, V92, P95 and L98. The calculated results using the residue-based free energy decomposition method further prove that the interaction strength of inhibitors with M53, V92, P95 and L98 from MDMX are obviously reduced compared to MDM2. We expect that this study can provide significant theoretical guidance for designs of potent dual inhibitors to block the p53-MDM2/MDMX interactions.

Enhanced semiempirical QM methods for biomolecular interactions

Recent successes and failures of the application of 'enhanced' semiempirical QM (SQM) methods are reviewed in the light of the benefits and backdraws of adding dispersion (D) and hydrogen-bond (H) correction terms. We find that the accuracy of SQM-DH methods for non-covalent interactions is very often reported to be comparable to dispersion-corrected density functional theory (DFT-D), while computation times are about three orders of magnitude lower. SQM-DH methods thus open up a possibility to simulate realistically large model systems for problems both in life and materials science with comparably high accuracy.

A comparative study of trypsin specificity based on QM/MM molecular dynamics simulation and QM/MM GBSA calculation

Hydrogen bonding and polar interactions play a key role in identification of protein-inhibitor binding specificity. Quantum mechanics/molecular mechanics molecular dynamics (QM/MM MD) simulations combined with DFT and semi-empirical Hamiltonian (AM1d, RM1, PM3, and PM6) methods were performed to study the hydrogen bonding and polar interactions of two inhibitors BEN and BEN1 with trypsin. The results show that the accuracy of treating the hydrogen bonding and polar interactions using QM/MM MD simulation of PM6 can reach the one obtained by the DFT QM/MM MD simulation. Quantum mechanics/molecular mechanics generalized Born surface area (QM/MM-GBSA) method was applied to calculate binding affinities of inhibitors to trypsin and the results suggest that the accuracy of binding affinity prediction can be significantly affected by the accurate treatment of the hydrogen bonding and polar interactions. In addition, the calculated results also reveal the binding specificity of trypsin: (1) the amidinium groups of two inhibitors generate favorable salt bridge interaction with Asp189 and form hydrogen bonding interactions with Ser190 and Gly214, (2) the phenyl of inhibitors can produce favorable van der Waals interactions with the residues His58, Cys191, Gln192, Trp211, Gly212, and Cys215. This systematic and comparative study can provide guidance for the choice of QM/MM MD methods and the designs of new potent inhibitors targeting trypsin.

Insights from QM/MM Modeling the 3D Structure of the 2009 H1N1 Influenza A Virus Neuraminidase and Its Binding Interactions with Antiviral Drugs

Control of drug release through the inhibition of neuraminidase-1 has been identified as a potential target for the treatment of H1N1 influenza; however, the drug binding mode of neuraminidase is not yet completely understood. In this work, we propose a model for a neuraminidase-1 complex based on four known X-ray structures of drug/neuraminidase-1 complexes. Specifically, H1N1 neuraminidase-1 complexed with 4 drugs (zanamivir, laninamivir, laninamivir octanoate and oseltamivir) was first investigated using a combined quantum mechanical and molecular mechanical (QM/MM) approach. Based on these structures, a model for the H1N1 neuraminidase-1 complex was proposed and simulated using the same computational protocol. Implications to drug/H1N1 neuraminidase-1 binding modes are discussed. From our simulations, the predicted binding free energies of the four drugs are in good agreement with the experimental results, with the correlation coefficient being 0.84.

A knowledge-based halogen bonding scoring function for predicting protein-ligand interactions

Halogen bonding, a non-covalent interaction between the halogen σ-hole and Lewis bases, could not be properly characterized by majority of current scoring functions. In this study, a knowledge-based halogen bonding scoring function, termed XBPMF, was developed by an iterative method for predicting protein-ligand interactions. Three sets of pairwise potentials were derived from two training sets of protein-ligand complexes from the Protein Data Bank. It was found that two-dimensional pairwise potentials could characterize appropriately the distance and angle profiles of halogen bonding, which is superior to one-dimensional pairwise potentials. With comparison to six widely used scoring functions, XBPMF was evaluated to have moderate power for predicting protein-ligand interactions in terms of "docking power", "ranking power" and "scoring power". Especially, it has a rather satisfactory performance for the systems with typical halogen bonds. To the best of our knowledge, XBPMF is the first halogen bonding scoring function that is not dependent on any dummy atom, and is practical for high-throughput virtual screening. Therefore, this scoring function should be useful for the study and application of halogen bonding interactions like molecular docking and lead optimization.

Parametrization of the SCC-DFTB Method for Halogens

Parametrization of the approximative DFT method SCC-DFTB for halogen elements is presented. The new parameter set is intended to describe halogenated organic as well as inorganic molecules, and it is compatible with the established parametrization of SCC-DFTB for carbon, hydrogen, oxygen, and nitrogen. The performance of the parameter set is tested on a representative set of molecules and discussed.

Does electron-correlation has any role in the quantitative structure-activity relationships?

For developing quantitative structure-activity relationships (QSARs), quantum-mechanical molecular descriptors based on the state-of-the-art quantum-mechanical methods such as Hartree-Fock (HF) method and density-functional theory (DFT), are now routinely employed. The validity of these quantum-mechanical methods, however, rests on the accurate estimation of electron-correlation energy. This work analyses the role of electron-correlation, using correlation energy as a molecular descriptor, in the QSARs. In particular, QSAR models, for the mutagenic activity of a set of nitrated polycyclic aromatic hydrocarbons (nitro-PAHs), are examined for the role of electron-correlation through state-of-the-art external validation parameters such as concordance correlation coefficient and recently proposed predictive squared correlation coefficients, namely, QF1(2), QF2(2), and QF3(2) etc. The electron-correlation contribution to the highest occupied and lowest unoccupied molecular orbital (HOMO/LUMO) energies is also analyzed. QSAR models based on the semi-empirical quantum-mechanical methods like PM6 and RM1 are also compared. It is found that the models, developed using electron-correlation contribution of the quantum-mechanical descriptors, are not only robust but also relatively more predictive than those developed with the HF and DFT descriptors. The latter are found to be even less reliable than PM6 and RM1 descriptors based models, which show comparable robustness and predictivity with those developed using electron correlation based descriptors. The external predictivity of model based on semi-empirical descriptors can be improved if electron-correlation contribution of the quantum-mechanical descriptors is explicitly included in the model. This work reports the first-ever use of electron-correlation energy and its contribution to the HOMO/LUMO energies as molecular descriptors.

Halogen bonding at the ATP binding site of protein kinases: preferred geometry and topology of ligand binding

Halogenated ligands have been widely developed as potent, and frequently selective, inhibitors of protein kinases (PK). Herein, all structures of protein kinases complexed with a halogenated ligand, identified in the PDB, were analyzed in the context of eventual contribution of halogen bonding to protein-ligand interactions. Global inspection shows that two carbonyl groups of residues located in the hinge region are the most abundant halogen bond acceptors. In contrast to solution data, well-defined water molecules, located at sites conserved across most PK structures, are also involved in halogen bonding. Analysis of cumulative distributions of halogen-acceptor distances shows that structures displaying short contacts involving a halogen atom are overpopulated, contributing together to clearly defined maxima of 2.82, 2.91 and 2.94Å for chlorine, bromine and iodine, respectively. The angular preference of a halogen bond favors ideal topology (180°, 120°) for iodine. For bromine the distribution is much more dispersed, and no such preference was found for chlorine. This article is part of a Special Issue entitled: Inhibitors of Protein Kinases (2012).