Binding Analysis of Some Classical Acetylcholinesterase Inhibitors: Insights for a Rational Design Using Free Energy Perturbation Method Calculations with QM/MM MD Simulations

Effects of changing ions on the crystal design, non-covalent interactions, antimicrobial activity, and molecular docking of Cu(II) complexes with a pyridoxal-hydrazone ligand

The present work reports the influence of the presence of different ions (Cl-, Br-, NO3 -, or SO4 2-) on the formation and proprieties of Cu(II) complexes with pyridoxal-benzoylhydrazone (PLBHZ). Four new complexes were successfully synthesized, [CuCl2(PLBHZ)] (1), [CuBr2(PLBHZ)] (2), [CuCl(PLBHZ)H2O]⋅NO3⋅H2O (3), and [CuSO4(PLBHZ)H2O]⋅3H2O (4), and characterized by spectroscopic and physicochemical methods. A single-crystal X-ray study reveals the Schiff base coordinated to the metal center tridentate by the ONS-donor system, resulting in distorted square pyramidal coordination geometries. Noncovalent interactions were investigated by 3D Hirshfeld surface analysis by the d norm function, 2D fingerprint plots, and full interaction maps. The ion exchange is important in forming three-dimensional networks with π⋅⋅⋅π stacking interactions and intermolecular hydrogen bonds. The in vitro biological activity of the free ligand and metal complexes was evaluated against Gram-positive and Gram-negative bacterial strains and the free pyridoxal-hydrazone ligand showed higher activity than their Cu(II) complexes. Molecular docking was used to predict the inhibitory activity of the ligand and complexes against Gram-positive Staphylococcus aureus and Gram-negative Escherichia coli bacteria.

Five new biflavonoids with acetylcholinesterase inhibitory activity from Diphylleia sinensis

Five new biflavonoids, diphybiflavonoids A - E (1-5), were isolated from the roots and rhizomes of Diphylleia sinensis. Their structures were elucidated by extensive spectroscopic data, including UV, IR, HR-ESI-MS and 2D NMR. Their absolute configurations were determined by ECD spectra. All isolated compounds were evaluated for acetylcholinesterase (AChE) inhibitory activity. Compounds 1-4 exhibited the potent AChE inhibitory activities with IC50 values of 1.62, 2.10, 2.08, and 5.15 μM, respectively. The preliminary structure-activity relationship study indicated that the connection mode (C2-O-C4'''/C3-O-C3''' or C2-O-C3'''/C3-O-C4''') of biflavonoid subunits, and 3-hydroxy group of flavonol subunit were important structural factors for AChE inhibitory activity. Biflavonoids, containing a C2-O-C4'''/C3-O-C3''' or C2-O-C3'''/C3-O-C4''' linkage, can be a potentially useful platform for development of cholinesterase inhibitors.

Derivatives of 9-phosphorylated acridine as butyrylcholinesterase inhibitors with antioxidant activity and the ability to inhibit β-amyloid self-aggregation: potential therapeutic agents for Alzheimer's disease

We investigated the inhibitory activities of novel 9-phosphoryl-9,10-dihydroacridines and 9-phosphorylacridines against acetylcholinesterase (AChE), butyrylcholinesterase (BChE), and carboxylesterase (CES). We also studied the abilities of the new compounds to interfere with the self-aggregation of β-amyloid (Aβ42) in the thioflavin test as well as their antioxidant activities in the ABTS and FRAP assays. We used molecular docking, molecular dynamics simulations, and quantum-chemical calculations to explain experimental results. All new compounds weakly inhibited AChE and off-target CES. Dihydroacridines with aryl substituents in the phosphoryl moiety inhibited BChE; the most active were the dibenzyloxy derivative 1d and its diphenethyl bioisostere 1e (IC50 = 2.90 ± 0.23 µM and 3.22 ± 0.25 µM, respectively). Only one acridine, 2d, an analog of dihydroacridine, 1d, was an effective BChE inhibitor (IC50 = 6.90 ± 0.55 μM), consistent with docking results. Dihydroacridines inhibited Aβ42 self-aggregation; 1d and 1e were the most active (58.9% ± 4.7% and 46.9% ± 4.2%, respectively). All dihydroacridines 1 demonstrated high ABTS•+-scavenging and iron-reducing activities comparable to Trolox, but acridines 2 were almost inactive. Observed features were well explained by quantum-chemical calculations. ADMET parameters calculated for all compounds predicted favorable intestinal absorption, good blood-brain barrier permeability, and low cardiac toxicity. Overall, the best results were obtained for two dihydroacridine derivatives 1d and 1e with dibenzyloxy and diphenethyl substituents in the phosphoryl moiety. These compounds displayed high inhibition of BChE activity and Aβ42 self-aggregation, high antioxidant activity, and favorable predicted ADMET profiles. Therefore, we consider 1d and 1e as lead compounds for further in-depth studies as potential anti-AD preparations.

Accuracy and precision of binding free energy prediction for a tacrine related lead inhibitor of acetylcholinesterase with an arsenal of supercomputerized molecular modelling methods: a comparative study

Nowadays, advanced computational chemistry methods offer various strategies for revealing prospective hit structures in drug development essentially through accurate binding free energy predictions. After the era of molecular docking and quantitative structure-activity relationships, much interest has been lately oriented to perturbed molecular dynamic approaches like replica exchange with solute tempering and free energy perturbation (REST/FEP) and the potential of the mean force with adaptive biasing and accelerated weight histograms (PMF/AWH). Both of these receptor-based techniques can exploit exascale CPU&GPU supercomputers to achieve high throughput performance. In this fundamental study, we have compared the predictive power of a panel of supercomputerized molecular modelling methods to distinguish the major binding modes and the corresponding binding free energies of a promising tacrine related potential antialzheimerics in human acetylcholinesterase. The binding free energies were estimated using flexible molecular docking, molecular mechanics/generalized Born surface area/Poisson-Boltzmann surface area (MM/GBSA/PBSA), transmutation REST/FEP with 12 x 5 ns/λ windows, annihilation FEP with 20 x 5 ns/λ steps, PMF with weight histogram analysis method (WHAM) and 40 x 5 ns samples, and PMF/AWH with 10 x 100 ns replicas. Confrontation of the classical approaches such as canonical molecular dynamics and molecular docking with alchemical calculations and steered molecular dynamics enabled us to show how large errors in ΔG predictions can be expected if these in silico methods are employed in the elucidation of a common case of enzyme inhibition.Communicated by Ramaswamy H. Sarma.

Electronic and structural study of T315I mutated form in DFG-out conformation of BCR-ABL inhibitors

In this work, the four main drugs for the treatment of chronic myeloid leukemia were analyzed, being imatinib, dasatinib, nilotinib and ponatinib followed by four derivative molecules of nilotinib and ponatinib. For these derivative molecules, the fluorine atoms were replaced by hydrogen and chlorine atoms in order to shade light to the structural effects on this set of inhibitors. Electronic studies were performed at density functional theory level with the B3LYP functional and 6-311+G(d,p) basis set. The frontier molecular orbitals, gap HOMO-LUMO, and NBO were analyzed and compared to docking studies for mutant T315I tyrosine kinase protein structure code 3IK3, in the DFG-out conformation. Structural similarities were pointed out, such as the presence of groups common to all inhibitors and modifications raised up on new generations of imatinib-based inhibitors. One of them is the trifluoromethyl group present in nilotinib and later included in ponatinib, in addition to the 1-methylpiperazin-1-ium group that is present in imatinib and ponatinib. The frontier molecular orbitals of imatinib and ponatinib are contributing to the same amino acid residues, and the ineffectiveness of imatinib against the T315I mutation was discussed.Communicated by Ramaswamy H. Sarma.

Molecular engineering and activity improvement of acetylcholinesterase inhibitors: Insights from 3D-QSAR, docking, and molecular dynamics simulation studies

The carbamate molecule rivastigmine was found to possess promising anti-acetylcholinesterase activity, enabling to target and occupy choline binding sites, and as a result, widely used to improve the treatment of Alzheimer's disease (AD). Higher dose of rivastigmine indicates rapid onset but more adverse effects, such as the large fluctuations in plasma concentration level and frequent incidence of gastrointestinal side effect. To solve the dilemma, we developed a three-dimensional quantitative structure-activity relationship (3D-QSAR), docking and molecular dynamics (MD) simulation strategy to construct a dismountable nanoplatform of inhibitor engineering, verification and application for improving the inhibitory activity per unit concentration. With the aid of 3D-QSAR method, we constructed a model by using 25 molecules reported, and verified well the rationality of these QSAR models by non-cross validation coefficient (r² = 0.902). Docking and MD results show that rivastigmine, as a control, does target exactly the binding sites of acetylcholinesterase, those already observed experimentally, in turn, confirming the reliability of the present 3D-QSAR results. The method suggests that groups with electron-donating chemical property can improve the inhibitory activity, and screens out two novel inhibitors L-1 and L-2 with more activity from database (about 8000 compounds). Moreover, L-1 and L-2 not only target exactly the same binding sites of acetylcholinesterase as the rivastigmine does, but also hold stronger binding energy, showing a more powerful inhibitory ability. More broadly, this work showcases an approach in the engineering of carbamate inhibitors to enhance their inhibitory activity using electron-donating groups, which simplifies the design process of complex bioactive molecules.

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.

In silico study of tacrine and acetylcholine binding profile with human acetylcholinesterase: docking and electronic structure

Alzheimer disease (AD) is a neurodegenerative process, one of the most common and incident dementia in the population over 60 years. AD manifests the presence of complex biochemical processes involved in neuronal degeneration, such as the formation of senile plaques containing amyloid-β peptides, the development of intracellular neurofibrillary tangles, and the suppression of the acetylcholine neurotransmitter. In this way, we performed a set of theoretical tests of tacrine ligand and acetylcholine neurotransmitter against the human acetylcholinesterase enzyme. Molecular docking was used to understand the most important interactions of these molecules with the enzyme. Computational chemistry calculation was carried out using MP2, DFT, and semi-empirical methods, starting from molecular docking structures. We have also performed studies regarding the non-covalent interactions, electron localization function, molecular electrostatic potential and explicit water molecule influence. For Trp86 residue, we show two main interactions in accordance to the results of the literature for TcAChE. First, intermolecular interactions of the cation-π and sigma-π type were found. Second, close stacking interactions were stablished between THA+ and Trp86 residue on one side and with Tyr337 residue on the other side.

AromTool: predicting aromatic stacking energy using an atomic neural network model

Aromatic stacking exists widely and plays important roles in protein-ligand interactions. Computational tools to automatically analyze the geometry and accurately calculate the energy of stacking interactions are desired for structure-based drug design. Herein, we employed a Behler-Parrinello neural network (BPNN) to build predictive models for aromatic stacking interactions and further integrated it into an open-source Python package named AromTool for benzene-containing aromatic stacking analysis. Based on extensive testing, AromTool presents desirable precision in comparison to DFT calculations and excellent efficiency for high-throughput aromatic stacking analysis of protein-ligand complexes.

Natural spirocyclic alkaloids and polyphenols as multi target dementia leads

The U rhynchophylla, U tomentosa, Isatis indigotica Fortune, Voacanga Africana, herbal constituents, fungal extracts from Aspergillus duricaulis culture media, include spirooxindoles, polyphenols or bridged spirocyclic alkaloids. Their constituents exhibit specific and synergistic multiple neuroprotective properties including inhibiting of Aβ fibril induced cytotoxicity, NMDA receptor inhibition in mice models of Alzheimer's disease (AD). The pioneering research from Woodward to Waldmann has advanced the synthesis of spirocyclic alkaloids. Furthermore, the elucidation of the genetic analysis, biochemical pathways that links strictosidine to the alkaloids akuammicine, stemmadenine, tabersonine, catharanthine, will now enable the biotechnological generation, also stimulate synthesis of related bridged spirocyclic alkaloids for medicinal investigations. From the value of spirocyclic structures as multi target dementia leads, we hypothesise that simpler Lipinski-like natural/synthetic alkaloid analogues may likewise be discovered that provide neurocognitive enhancing activities against dementia and AD.

Binding interaction of protoberberine alkaloids against acetylcholinesterase (AChE) using molecular dynamics simulations and QM/MM calculations

Acetylcholinesterase (AChE) plays a vital role in Alzheimer's disease (AD), which is one of the most common causes of dementia. Discovering new effective inhibitors against AChE activity is seen to be one of the effective approaches to reduce the suffering from AD. Protoberberine alkaloids isolated from natural resources have previously been reported as potent AChE inhibitors. In order to gain insights into how these alkaloids could inhibit AChE, berberine, palmatine, and cyclanoline were selected to investigate in terms of binding orientation and their key interactions with AChE using molecular docking and molecular dynamics simulations and quantum chemical calculations. The results revealed that the molecular dynamics structures of palmatine and berberine indicated that their equilibrated structures did not occupy the gorge but they slightly moved away from the catalytic site (CAS). For cyclanoline, the binding mode was quite different from those of donepezil and the other protoberberine alkaloids: it preferred to stay deeper in the CAS site. Interaction energies and residual interaction energies confirmed that the key interactions for palmatine and berberine were π-π interactions with Trp286 and Tyr341 and H-bond interactions with Tyr124. Cyclanoline formed π-π interactions with Trp86 and H-bonds to the amino acids in the CAS site. The results suggested the importance of aromaticity in the core structure and the flexibility of the core structure or the substituents in order to fit into the narrow gorge. The HOMO, LUMO, bioavailability, drug-likeness and pharmacokinetics were also predicted. The results obtained will be useful for further AD drug development.

A Comprehensive Review of Cholinesterase Modeling and Simulation

The present article reviews published efforts to study acetylcholinesterase and butyrylcholinesterase structure and function using computer-based modeling and simulation techniques. Structures and models of both enzymes from various organisms, including rays, mice, and humans, are discussed to highlight key structural similarities in the active site gorges of the two enzymes, such as flexibility, binding site location, and function, as well as differences, such as gorge volume and binding site residue composition. Catalytic studies are also described, with an emphasis on the mechanism of acetylcholine hydrolysis by each enzyme and novel mutants that increase catalytic efficiency. The inhibitory activities of myriad compounds have been computationally assessed, primarily through Monte Carlo-based docking calculations and molecular dynamics simulations. Pharmaceutical compounds examined herein include FDA-approved therapeutics and their derivatives, as well as several other prescription drug derivatives. Cholinesterase interactions with both narcotics and organophosphate compounds are discussed, with the latter focusing primarily on molecular recognition studies of potential therapeutic value and on improving our understanding of the reactivation of cholinesterases that are bound to toxins. This review also explores the inhibitory properties of several other organic and biological moieties, as well as advancements in virtual screening methodologies with respect to these enzymes.

Organoselenium Compounds as Acetylcholinesterase Inhibitors: Evidence and Mechanism of Mixed Inhibition

Acetylcholinesterase (AChE) inhibitors are actively used for the effective treatment of Alzheimer's disease. In recent years, the neuroprotective effects of organoselenium compounds such as ebselen and diselenides on the AChE activity have been investigated as potential therapeutic agents. In this work, we have carried out systematic kinetic and intrinsic fluorescence assays in combination with docking and molecular dynamics (MD) simulations to elucidate the molecular mechanism of the mixed inhibition of AChE by ebselen and diphenyl diselenide (DPDSe) molecules. Our MD simulations demonstrate significant heterogeneity in the binding modes and allosteric hotspots for DPDSe on AChE due to non-specific interactions. We have further identified that both ebselen and DPDSe can strongly bind around the peripheral anionic site (PAS), leading to non-competitive inhibition similar to other PAS-binding inhibitors. We also illustrate the entry of the DPDSe molecule into the gorge through a "side door", which offers an alternate entry point for AChE inhibitors as compared to the usual substrate entry point of the gorge. Together with results from experiments, these simulations provide mechanistic insights into the mixed type of inhibition for AChE using DPDSe as a promising inhibitor for AChE.

Computational Determination of Potential Inhibitors of SARS-CoV-2 Main Protease

The novel coronavirus (SARS-CoV-2) has infected several million people and caused thousands of deaths worldwide since December 2019. As the disease is spreading rapidly all over the world, it is urgent to find effective drugs to treat the virus. The main protease (Mpro) of SARS-CoV-2 is one of the potential drug targets. Therefore, in this context, we used rigorous computational methods, including molecular docking, fast pulling of ligand (FPL), and free energy perturbation (FEP), to investigate potential inhibitors of SARS-CoV-2 Mpro. We first tested our approach with three reported inhibitors of SARS-CoV-2 Mpro, and our computational results are in good agreement with the respective experimental data. Subsequently, we applied our approach on a database of ∼4600 natural compounds, as well as 8 available HIV-1 protease (PR) inhibitors and an aza-peptide epoxide. Molecular docking resulted in a short list of 35 natural compounds, which was subsequently refined using the FPL scheme. FPL simulations resulted in five potential inhibitors, including three natural compounds and two available HIV-1 PR inhibitors. Finally, FEP, the most accurate and precise method, was used to determine the absolute binding free energy of these five compounds. FEP results indicate that two natural compounds, cannabisin A and isoacteoside, and an HIV-1 PR inhibitor, darunavir, exhibit a large binding free energy to SARS-CoV-2 Mpro, which is larger than that of 13b, the most reliable SARS-CoV-2 Mpro inhibitor recently reported. The binding free energy largely arises from van der Waals interaction. We also found that Glu166 forms H-bonds to all of the inhibitors. Replacing Glu166 by an alanine residue leads to ∼2.0 kcal/mol decreases in the affinity of darunavir to SARS-CoV-2 Mpro. Our results could contribute to the development of potential drugs inhibiting SARS-CoV-2.

SQM/COSMO Scoring Function: Reliable Quantum-Mechanical Tool for Sampling and Ranking in Structure-Based Drug Design

Quantum mechanical (QM) methods have been gaining importance in structure-based drug design where a reliable description of protein-ligand interactions is of utmost significance. However, strategies i. e. QM/MM, fragmentation or semiempirical (SQM) methods had to be pursued to overcome the unfavorable scaling of QM methods. Various SQM-based approaches have significantly contributed to the accuracy of docking and improvement of lead compounds. Parametrizations of SQM and implicit solvent methods in our laboratory have been instrumental to obtain a reliable SQM-based scoring function. The experience gained in its application for activity ranking of ligands binding to tens of protein targets resulted in setting up a faster SQM/COSMO scoring approach, which outperforms standard scoring methods in native pose identification for two dozen protein targets with ten thousand poses. Recently, SQM/COSMO was effectively applied in a proof-of-concept study of enrichment in virtual screening. Due to its superior performance, feasibility and chemical generality, we propose the SQM/COSMO approach as an efficient tool in structure-based drug design.

Donepezil Inhibits Acetylcholinesterase via Multiple Binding Modes at Room Temperature

Donepezil is a second generation acetylcholinesterase (AChE) inhibitor for treatment of Alzheimer's disease (AD). AChE is important for neurotransmission at neuromuscular junctions and cholinergic brain synapses by hydrolyzing acetylcholine into acetate and choline. In vitro data support that donepezil is a reversible, mixed competitive and noncompetitive inhibitor of AChE. The experimental fact then suggests a more complex binding mechanism beyond the molecular view in X-ray models resolved at cryogenic temperatures that show a unique binding mode of donepezil in the active site of the enzyme. Aiming at clarifying the mechanism behind that mixed competitive and noncompetitive nature of the inhibitor, we have applied molecular dynamics (MD) simulations and docking and free-energy calculations to investigate microscopic details and energetics of donepezil association for conditions of substrate-free and -bound states of the enzyme. Liquid-phase MD simulation at room temperature shows AChE transits between "open" and "closed" conformations to control accessibility to the active site and ligand binding. As shown by docking and free-energy calculations, association of donepezil involves its reversible axial displacement and reorientation in the active site of the enzyme, assisted by water molecules. Donepezil binds equally well the main-door anionic binding site PAS, the acyl pocket, and the catalytic site CAS by respectively adopting outward-inward-inward orientations regardless of substrate occupancy-the overall stability of that reaction process depends however on co-occupancy of the enzyme being preferential for its substrate-free state. All together, our findings support a physiologically relevant mechanism of AChE inhibition by donepezil involving multistable interactions modes at the molecular origin of the inhibitor's activity.

Binding Free Energy Calculation Using Quantum Mechanics Aimed for Drug Lead Optimization

The routine use of in silico tools is already established in drug lead design. Besides the use of molecular docking methods to screen large chemical libraries and thus prioritize compounds for purchase or synthesis, more accurate calculations of protein-ligand binding free energy has shown the potential to guide lead optimization, thus saving time and resources. Theoretical developments and advances in computing power have allowed quantum mechanical-based methods applied to calculations on biomacromolecules to be increasingly explored and used, with the purpose of providing a more accurate description of protein-ligand interactions and an enhanced level of accuracy in the calculation of binding affinities. It should be noted that the quantum mechanical formulation includes, in principle, all contributions to the energy, considering terms usually neglected in molecular mechanics force fields, such as electronic polarization, metal coordination, and covalent binding; moreover, quantum mechanical approaches are systematically improvable. By treating all elements and interactions on equal footing, and avoiding the need of system-dependent parameterizations, they provide a greater degree of transferability. In this review, we illustrate the increasing relevance of quantum mechanical methods for binding free energy calculation in the context of structure-based drug lead optimization, showing representative applications of the different approaches available.

User-Friendly Quantum Mechanics: Applications for Drug Discovery

Quantum mechanics (QM) methods provide a fine description of receptor-ligand interactions and of chemical reactions. Their use in drug design and drug discovery is increasing, especially for complex systems including metal ions in the binding sites, for the design of highly selective inhibitors, for the optimization of bi-specific compounds, to understand enzymatic reactions, and for the study of covalent ligands and prodrugs. They are also used for generating molecular descriptors for predictive QSAR/QSPR models and for the parameterization of force fields. Thanks to the continuous increase of computational power offered by GPUs and to the development of sophisticated algorithms, QM methods are becoming part of the standard tools used in computer-aided drug design (CADD). We present the most used QM methods and software packages, and we discuss recent representative applications in drug design and drug discovery.

Cholinesterase inhibitory isoquinoline alkaloids from Corydalis mucronifera

Eight previously undescribed isoquinoline alkaloids, mucroniferanines H-M, together with 16 known isoquinoline alkaloids, were isolated from Corydalis mucronifera Maxim.. The structures of the previously undescribed compounds were elucidated by interpretation of 1D and 2D NMR spectroscopic and HRMS data, and their absolute configurations were established by computational electronic circular dichroism (ECD) calculations and X-ray diffraction data. Mucroniferanine L is reported as the first natural amide bond linked isoquinoline alkaloid dimer. The isolated compounds were evaluated for AChE and BuChE inhibitory activities and mucroniferanine H showed significant activities with IC₅₀ values of 2.31 μM and 36.71 μM, respectively.

A protein interaction free energy model based on amino acid residue contributions: Assessment of point mutation stability of T4 lysozyme

Here we present a model to estimate the interaction free energy contribution of each amino acid residue of a given protein. Protein interaction energy is described in terms of per-residue interaction factors, μ. Multibody interactions are implicitly captured in μ through the combination of amino acid terms (γ) guided by local conformation indices (σ). The model enables construction of an interaction factor heat map for a protein in a given fold, allows prima facie assessment of the degree of residue-residue interaction, and facilitates a qualitative and quantitative evaluation of protein association properties. The model was used to compute thermal stability of T4 bacteriophage lysozyme mutants across seven sites. Qualitative assessment of mutational effects provides a straightforward rationale regarding whether a particular site primarily perturbs native or non-native states, or both. The presented model was found to be in good agreement with experimental mutational data (R 2 = 0.73) and suggests an approach by which to convert structure space into energy space.

Effective prediction model and determination of binding residues influential for inhibitors targeting HIV-1 integrase-LEDGF/p75 interface by employing solvent accessible surface area energy as key determinant

Development of a highly accurate prediction model for protein-ligand inhibition has been a major challenge in drug discovery. Herein, we describe a novel predictive model for the inhibition of HIV-1 integrase (IN)-LEDGF/p75 protein-protein interaction. The model was constructed using energy parameters approximated from molecular dynamics (MD) simulations and molecular mechanics Poisson-Boltzmann surface area (MM-PBSA) calculations. Chemometric analysis using partial least squares (PLS) regression revealed that solvent accessible surface area energy (ΔG_SASA) is the major determinant parameter contributing greatly to the prediction accuracy. PLS prediction model on the ΔG_SASA values collected from 41 complexes yielded a strong correlation between the predicted and the actual inhibitory activities (R² = 0.9666, RMSEC of pIC₅₀ values = 0.0890). Additionally, for the test set of 14 complexes, the model performed satisfactorily with very low pIC₅₀ errors (Q² = 0.5168, RMSEP = 0.3325). A strong correlation between the buried surface areas on the IN protein, when bound with IN-LEDGF/p75 inhibitors, and the respective ΔG_SASA values was also obtained. Furthermore, the current method could identify 'hot spots'of amino acid residues highly influential to the inhibitory activity prediction. This could present fruitful implications in binding site determination and future inhibitor developments targeting protein-protein interactions.Communicated by Ramaswamy H. Sarma.

Quantum Chemical Approaches in Structure-Based Virtual Screening and Lead Optimization

Today computational chemistry is a consolidated tool in drug lead discovery endeavors. Due to methodological developments and to the enormous advance in computer hardware, methods based on quantum mechanics (QM) have gained great attention in the last 10 years, and calculations on biomacromolecules are becoming increasingly explored, aiming to provide better accuracy in the description of protein-ligand interactions and the prediction of binding affinities. In principle, the QM formulation includes all contributions to the energy, accounting for terms usually missing in molecular mechanics force-fields, such as electronic polarization effects, metal coordination, and covalent binding; moreover, QM methods are systematically improvable, and provide a greater degree of transferability. In this mini-review we present recent applications of explicit QM-based methods in small-molecule docking and scoring, and in the calculation of binding free-energy in protein-ligand systems. Although the routine use of QM-based approaches in an industrial drug lead discovery setting remains a formidable challenging task, it is likely they will increasingly become active players within the drug discovery pipeline.