Predicting Binding Affinities for GPCR Ligands Using Free-Energy Perturbation

Profiling of antioxidant properties and identification of potential analgesic inhibitory activities of Allophylus villosus and Mycetia sinensis employing in vivo, in vitro, and computational techniques

ETHNOPHARMACOLOGICAL RELEVANCE

The traditional use of plants for medicinal purposes, called phytomedicine, has been known to provide relief from pain. In Bangladesh, the Chakma indigenous community has been using Allophylus villosus and Mycetia sinensis to treat various types of pain and inflammation.

AIM OF THE STUDY

The object of this research is to evaluate the effectiveness of these plants in relieving pain and their antioxidant properties using various approaches such as in vitro, in vivo, and computational techniques. Additionally, the investigation will also analyse the phytochemicals present in these plants.

MATERIALS AND METHODS

We conducted in vivo analgesic experiment on Swiss albino mice and in-silico inhibitory activities on COX-2 & 15-LOX-2 enzymes. Assessment of DPPH, Anti Radical Activities (ARA), FRAP, H2O2 Free Radical Scavenging, Reducing the power of both plants performed significant % inhibition with tolerable IC50. Qualitative screening of functional groups of phytochemicals was précised by FTIR and GC-MS analysis demonstrated phytochemical investigations.

RESULTS

The ethyl acetate (EtOAc) fractioned Mycetia sinensis extract as well as the ethanoic extract and all fractioned extracts of Allophylus villosus have reported a significant percentage (%) of writhing inhibition (p < 0.05) with the concentrated doses 250 mg as well as 500 mg among the Swiss albino mice for writhing observation of analgesic effect. In the silico observation, a molecular-docking investigation has performed according to GC-MS generated 43 phyto-compounds of both plants to screen their binding affinity by targeting COX-2 and 15-LOX-2 enzymes. Consequently, in order to assess and ascertain the effectiveness of the sorted phytocompounds, ADMET (Absorption, Distribution, Metabolism, Excretion, and Toxicity) investigation, DFT (Density-functional theory) by QM (Quantum mechanics), and MDS (Molecular dynamics simulation) were carried out. As the outcome, compounds like 5-(2,4-ditert-butylphenoxy)-5-oxopentanoic acid; 2,4-ditert-butylphenyl 5-hydroxypentanoate; 3,3-diphenyl-5-methyl-3H-pyrazole; 2-O-(6-methylheptan-2-yl) 1-O-octyl benzene-1,2-dicarboxylate and dioctan-3-yl benzene-1,2-dicarboxylate derived from the ethnic plant A. villosus and another ethnic plant M. sinensis extracts enchants magnificent analgesic inhibitions and performed more significant drug like activities with the targeted enzymes.

CONCLUSIONS

Phytocompounds from A. villosus & M. sinensis exhibited potential antagonist activity against human 15-lipoxygenase-2 and cyclooxygenase-2 proteins. The effective ester compounds from these plants performed more potential anti-nociceptive activity which could be used as a drug in future.

Hit-to-Lead Optimization of Heterocyclic Carbonyloxycarboximidamides as Selective Antagonists at Human Adenosine A3 Receptor

Antagonism of the human adenosine A3 receptor (hA3R) has potential therapeutic application. Alchemical relative binding free energy calculations of K18 and K32 suggested that the combination of a 3-(2,6-dichlorophenyl)-isoxazolyl group with 2-pyridinyl at the ends of a carbonyloxycarboximidamide group should improve hA3R affinity. Of the 25 new analogues synthesized, 37 and 74 showed improved hA3R affinity compared to K18 (and K32). This was further improved through the addition of a bromine group to the 2-pyridinyl at the 5-position, generating compound 39. Alchemical relative binding free energy calculations, mutagenesis studies and MD simulations supported the compounds' binding pattern while suggesting that the bromine of 39 inserts deep into the hA3R orthosteric pocket, so highlighting the importance of rigidification of the carbonyloxycarboximidamide moiety. MD simulations highlighted the importance of rigidification of the carbonyloxycarboximidamide, while suggesting that the bromine of 39 inserts deep into the hA3R orthosteric pocket, which was supported through mutagenesis studies 39 also selectively antagonized endogenously expressed hA3R in nonsmall cell lung carcinoma cells, while pharmacokinetic studies indicated low toxicity enabling in vivo evaluation. We therefore suggest that 39 has potential for further development as a high-affinity hA3R antagonist.

Ligand Binding Affinity Prediction for Membrane Proteins with Alchemical Free Energy Calculation Methods

Alchemical relative binding free energy (ΔΔG) calculations have shown high accuracy in predicting ligand binding affinity and have been used as important tools in computer-aided drug discovery and design. However, there has been limited research on the application of ΔΔG methods to membrane proteins despite the fact that these proteins represent a significant proportion of drug targets, play crucial roles in biological processes, and are implicated in numerous diseases. In this study, to predict the binding affinity of ligands to G protein-coupled receptors (GPCRs), we employed two ΔΔG calculation methods: thermodynamic integration (TI) with AMBER and the alchemical transfer method (AToM) with OpenMM. We calculated ΔΔG values for 53 transformations involving four class A GPCRs and evaluated the performance of AMBER-TI and AToM-OpenMM. In addition, we conducted tests using different numbers of windows and varying simulation times to achieve reliable ΔΔG results and to optimize resource utilization. Overall, both AMBER-TI and AToM-OpenMM show good agreement with the experimental data. Our results validate the applicability of AMBER-TI and AToM-OpenMM for optimization of lead compounds targeting membrane proteins.

Peptide Drug Design Using Alchemical Free Energy Calculation: An Application and Validation on Agonists of Ghrelin Receptor

With recent large-scale applications and validations, the relative binding free energy (RBFE) calculated using alchemical free energy methods has been proven to be an accurate measure to probe the binding of small-molecule drug candidates. On the other hand, given the flexibility of peptides, it is of great interest to find out whether sufficient sampling could be achieved within the typical time scale of such calculation, and a similar level of accuracy could be reached for peptide drugs. However, the systematic evaluation of such calculations on protein-peptide systems has been less reported. Most reported studies of peptides were restricted to a limited number of data points or lacking experimental support. To demonstrate the applicability of the alchemical free energy method for protein-peptide systems in a typical real-world drug discovery project, we report an application of the thermodynamic integration (TI) method to the RBFE calculation of ghrelin receptor and its peptide agonists. Along with the calculation, the synthesis and in vitro EC50 activity of relamorelin and 17 new peptide derivatives were also reported. A cost-effective criterion to determine the data collection time was proposed for peptides in the TI simulation. The average of three TI repeats yielded a mean absolute error of 0.98 kcal/mol and Pearson's correlation coefficient (R) of 0.77 against the experimental free energy derived from the in vitro EC50 activity, showing good repeatability of the proposed method and a slightly better agreement than the results obtained from the arbitrary time frames up to 20 ns. Although it is limited by having one target and a deduced binding pose, we hope that this study can add some insights into alchemical free energy calculation of protein-peptide systems, providing theoretical assistance to the development of peptide drugs.

Structures of synaptic vesicle protein 2A and 2B bound to anticonvulsants

Epilepsy is a common neurological disorder characterized by abnormal activity of neuronal networks, leading to seizures. The racetam class of anti-seizure medications bind specifically to a membrane protein found in the synaptic vesicles of neurons called synaptic vesicle protein 2 (SV2) A (SV2A). SV2A belongs to an orphan subfamily of the solute carrier 22 organic ion transporter family that also includes SV2B and SV2C. The molecular basis for how anti-seizure medications act on SV2s remains unknown. Here we report cryo-electron microscopy structures of SV2A and SV2B captured in a luminal-occluded conformation complexed with anticonvulsant ligands. The conformation bound by anticonvulsants resembles an inhibited transporter with closed luminal and intracellular gates. Anticonvulsants bind to a highly conserved central site in SV2s. These structures provide blueprints for future drug design and will facilitate future investigations into the biological function of SV2s.

Understanding the impact of binding free energy and kinetics calculations in modern drug discovery

INTRODUCTION

For rational drug design, it is crucial to understand the receptor-drug binding processes and mechanisms. A new era for the use of computer simulations in predicting drug-receptor interactions at an atomic level has begun with remarkable advances in supercomputing and methodological breakthroughs.

AREAS COVERED

End-point free energy calculation methods such as Molecular Mechanics/Poisson Boltzmann Surface Area (MM/PBSA) or Molecular-Mechanics/Generalized Born Surface Area (MM/GBSA), free energy perturbation (FEP), and thermodynamic integration (TI) are commonly used for binding free energy calculations in drug discovery. In addition, kinetic dissociation and association rate constants (k off and k on ) play critical roles in the function of drugs. Nowadays, Molecular Dynamics (MD) and enhanced sampling simulations are increasingly being used in drug discovery. Here, the authors provide a review of the computational techniques used in drug binding free energy and kinetics calculations.

EXPERT OPINION

The applications of computational methods in drug discovery and design are expanding, thanks to improved predictions of the binding free energy and kinetic rates of drug molecules. Recent microsecond-timescale enhanced sampling simulations have made it possible to accurately capture repetitive ligand binding and dissociation, facilitating more efficient and accurate calculations of ligand binding free energy and kinetics.

Computational Chemistry in Structure-Based Solute Carrier Transporter Drug Design: Recent Advances and Future Perspectives

Solute carrier transporters (SLCs) are a class of important transmembrane proteins that are involved in the transportation of diverse solute ions and small molecules into cells. There are approximately 450 SLCs within the human body, and more than a quarter of them are emerging as attractive therapeutic targets for multiple complex diseases, e.g., depression, cancer, and diabetes. However, only 44 unique transporters (∼9.8% of the SLC superfamily) with 3D structures and specific binding sites have been reported. To design innovative and effective drugs targeting diverse SLCs, there are a number of obstacles that need to be overcome. However, computational chemistry, including physics-based molecular modeling and machine learning- and deep learning-based artificial intelligence (AI), provides an alternative and complementary way to the classical drug discovery approach. Here, we present a comprehensive overview on recent advances and existing challenges of the computational techniques in structure-based drug design of SLCs from three main aspects: (i) characterizing multiple conformations of the proteins during the functional process of transportation, (ii) identifying druggability sites especially the cryptic allosteric ones on the transporters for substrates and drugs binding, and (iii) discovering diverse small molecules or synthetic protein binders targeting the binding sites. This work is expected to provide guidelines for a deep understanding of the structure and function of the SLC superfamily to facilitate rational design of novel modulators of the transporters with the aid of state-of-the-art computational chemistry technologies including artificial intelligence.

Machine Learning Methods as a Cost-Effective Alternative to Physics-Based Binding Free Energy Calculations

The rank ordering of ligands remains one of the most attractive challenges in drug discovery. While physics-based in silico binding affinity methods dominate the field, they still have problems, which largely revolve around forcefield accuracy and sampling. Recent advances in machine learning have gained traction for protein-ligand binding affinity predictions in early drug discovery programs. In this article, we perform retrospective binding free energy evaluations for 172 compounds from our internal collection spread over four different protein targets and five congeneric ligand series. We compared multiple state-of-the-art free energy methods ranging from physics-based methods with different levels of complexity and conformational sampling to state-of-the-art machine-learning-based methods that were available to us. Overall, we found that physics-based methods behaved particularly well when the ligand perturbations were made in the solvation region, and they did not perform as well when accounting for large conformational changes in protein active sites. On the other end, machine-learning-based methods offer a good cost-effective alternative for binding free energy calculations, but the accuracy of their predictions is highly dependent on the experimental data available for training the model.

Accurate calculation of affinity changes to the close state of influenza A M2 transmembrane domain in response to subtle structural changes of adamantyl amines using free energy perturbation methods in different lipid bilayers

Experimental binding free energies of 27 adamantyl amines against the influenza M2(22-46) WT tetramer, in its closed form at pH 8, were measured by ITC in DPC micelles. The measured Kd's range is ~44 while the antiviral potencies (IC50) range is ~750 with a good correlation between binding free energies computed with Kd and IC50 values (r = 0.76). We explored with MD simulations (ff19sb, CHARMM36m) the binding profile of complexes with strong, moderate and weak binders embedded in DMPC, DPPC, POPC or a viral mimetic membrane and using different experimental starting structures of M2. To predict accurately differences in binding free energy in response to subtle changes in the structure of the ligands, we performed 18 alchemical perturbative single topology FEP/MD NPT simulations (OPLS2005) using the BAR estimator (Desmond software) and 20 dual topology calculations TI/MD NVT simulations (ff19sb) using the MBAR estimator (Amber software) for adamantyl amines in complex with M2(22-46) WT in DMPC, DPPC, POPC. We observed that both methods with all lipids show a very good correlation between the experimental and calculated relative binding free energies (r = 0.77-0.87, mue = 0.36-0.92 kcal mol-1) with the highest performance achieved with TI/MBAR and lowest performance with FEP/BAR in DMPC bilayers. When antiviral potencies are used instead of the Kd values for computing the experimental binding free energies we obtained also good performance with both FEP/BAR (r = 0.83, mue = 0.75 kcal mol-1) and TI/MBAR (r = 0.69, mue = 0.77 kcal mol-1).

Computational Workflow for Refining AlphaFold Models in Drug Design Using Kinetic and Thermodynamic Binding Calculations: A Case Study for the Unresolved Inactive Human Adenosine A3 Receptor

A structure-based drug design pipeline that considers both thermodynamic and kinetic binding data of ligands against a receptor will enable the computational design of improved drug molecules. For unresolved GPCR-ligand complexes, a workflow that can apply both thermodynamic and kinetic binding data in combination with alpha-fold (AF)-derived or other homology models and experimentally resolved binding modes of relevant ligands in GPCR-homologs needs to be tested. Here, as test case, we studied a congeneric set of ligands that bind to a structurally unresolved G protein-coupled receptor (GPCR), the inactive human adenosine A3 receptor (hA3R). We tested three available homology models from which two have been generated from experimental structures of hA1R or hA2AR and one model was a multistate alphafold 2 (AF2)-derived model. We applied alchemical calculations with thermodynamic integration coupled with molecular dynamics (TI/MD) simulations to calculate the experimental relative binding free energies and residence time (τ)-random accelerated MD (τ-RAMD) simulations to calculate the relative residence times (RTs) for antagonists. While the TI/MD calculations produced, for the three homology models, good Pearson correlation coefficients, correspondingly, r = 0.74, 0.62, and 0.67 and mean unsigned error (mue) values of 0.94, 1.31, and 0.81 kcal mol-1, the τ-RAMD method showed r = 0.92 and 0.52 for the first two models but failed to produce accurate results for the multistate AF2-derived model. With subsequent optimization of the AF2-derived model by reorientation of the side chain of R1735.34 located in the extracellular loop 2 (EL2) that blocked ligand's unbinding, the computational model showed r = 0.84 for kinetic data and improved performance for thermodynamic data (r = 0.81, mue = 0.56 kcal mol-1). Overall, after refining the multistate AF2 model with physics-based tools, we were able to show a strong correlation between predicted and experimental ligand relative residence times and affinities, achieving a level of accuracy comparable to an experimental structure. The computational workflow used can be applied to other receptors, helping to rank candidate drugs in a congeneric series and enabling the prioritization of leads with stronger binding affinities and longer residence times.

Using AlphaFold and Experimental Structures for the Prediction of the Structure and Binding Affinities of GPCR Complexes via Induced Fit Docking and Free Energy Perturbation

Free energy perturbation (FEP) remains an indispensable method for computationally assaying prospective compounds in advance of synthesis. However, before FEP can be deployed prospectively, it must demonstrate retrospective recapitulation of known experimental data where the subtle details of the atomic ligand-receptor model are consequential. An open question is whether AlphaFold models can serve as useful initial models for FEP in the regime where there exists a congeneric series of known chemical matter but where no experimental structures are available either of the target or of close homologues. As AlphaFold structures are provided without a bound ligand, we employ induced fit docking to refine the AlphaFold models in the presence of one or more congeneric ligands. In this work, we first validate the performance of our latest induced fit docking technology, IFD-MD, on a retrospective set of public experimental GPCR structures with 95% of cross-docks producing a pose with a ligand RMSD ≤ 2.5 Å in the top two predictions. We then apply IFD-MD and FEP on AlphaFold models of the somatostatin receptor family of GPCRs. We use AlphaFold models produced prior to the availability of any experimental structure from this family. We arrive at FEP-validated models for SSTR2, SSTR4, and SSTR5, with RMSE around 1 kcal/mol, and explore the challenges of model validation under scenarios of limited ligand data, ample ligand data, and categorical data.

Design, Synthesis and Molecular Docking Studies of Pyrazoline Derivatives as PI3K Inhibitors

AIM

Design, synthesis and molecular docking studies of quinoline/naphthalene containing pyrazoline derivatives as PI3K inhibitors.

BACKGROUND

Phosphatidylinositol 3-kinases (PI3Ks) belong to the family of enzymes, which are associated with various cellular functions such as cell growth, proliferation, differentiation etc. Overexpression or any changes in these functions may result in various abnormalities, which in turn cause cancer.

OBJECTIVES

To perform synthesis and molecular docking studies of quinoline/naphthalene containing pyrazoline derivatives as PI3K inhibitors.

METHODS

2-Chloroquinoline-3-carbaldehyde was synthesized by a reaction of acetanilide and POCl3. The latter was reacted with substituted acetophenones to synthesize chalcones, which were reacted with substituted phenyl hydrazines to yield pyrazoline derivatives (Series I). Similarly, pchloro benzaldehyde was reacted with 2-acetonapthone to yield chalcone with substituted phenyl hydrazines to yield pyrazoline derivatives (Series II).

RESULTS

The synthetic compounds were subjected to molecular modelling experiments using Schrodinger 2016 software and evaluated in silico for their PI3K binding affinities. All the compounds had better docking scores than AMG-319 (-4.36 Kcal/mol) and comparable docking scores with PI-103 (-6.83 Kcal/mol).

CONCLUSION

Compounds 5 and 3 had the best docking scores (-7.85 and -7.17 Kcal/mol, respectively). The synthesized compounds have better docking scores than the reference drug AMG-319. As a result, they might be used as lead molecules in investigating PI3K inhibitors.

Alchemical Free Energy Workflows for the Computation of Protein-Ligand Binding Affinities

Alchemical free energy methods can be used for the efficient computation of relative binding free energies during preclinical drug discovery stages. In recent years, this has been facilitated further by the implementation of workflows that enable non-experts to quickly and consistently set up the required simulations. Given the correct input structures, workflows handle the difficult aspects of setting up perturbations, including consistently defining the perturbable molecule, its atom mapping and topology generation, perturbation network generation, running of the simulations via different sampling methods, and analysis of the results. Different academic and commercial workflows are discussed, including FEW, FESetup, FEPrepare, CHARMM-GUI, Transformato, PMX, QLigFEP, TIES, ProFESSA, PyAutoFEP, BioSimSpace, FEP+, Flare, and Orion. These workflows differ in various aspects, such as mapping algorithms or enhanced sampling methods. Some workflows can accommodate more than one molecular dynamics (MD) engine and use external libraries for tasks. Differences between workflows can present advantages for different use cases, however a lack of interoperability of the workflows' components hinders systematic comparisons.

Synthesis and In Silico Studies of Quinazolinones as PARP-1 Inhibitors

BACKGROUND

Cancer is a leading threat to humankind, accounting for nearly one million deaths in 2018, and the expected number of cancer-related deaths in 2040 is more than 16 million. The most common causes of cancer deaths are lung, colorectal, stomach, liver and breast cancer, while the highest number of new cancer cases belong to lung, breast, colorectal, prostate, stomach and liver cancer.

INTRODUCTION

PARP-1 is an enzyme that plays an important role in DNA repair, cell propagation/survival and death due to its influence on numerous biological processes. Quinazolinones represent an important scaffold in medicinal chemistry and have a broad spectrum of biological activities.

METHODS

In this study, we have synthesized quinazolinones by reaction of 2-aminobenzamide and substituted aldehydes. Molecular docking studies of synthesized compounds were performed for their PARP-1 binding affinities using Schrodinger 2016 software. In silico ADME studies were also performed for the synthesized compounds using the QikProp tool of Schrodinger software.

RESULTS

Results of molecular docking studies indicated that synthesized quinazolinones had a good affinity towards active site of PARP-1 and compound 4 had the best docking score (-10.343). Results of ADME studies indicated the drug-like properties of synthesized compounds, which make them suitable drug candidates.

CONCLUSION

All the synthesized compounds have a better docking score than niraparib (-9.05). Further, the synthesized compounds have a favorable ADME profile. Therefore, they may serve as important leads in discovering PARP-1 inhibitors.

Alchemical Free Energy Calculations on Membrane-Associated Proteins

Membrane proteins have diverse functions within cells and are well-established drug targets. The advances in membrane protein structural biology have revealed drug and lipid binding sites on membrane proteins, while computational methods such as molecular simulations can resolve the thermodynamic basis of these interactions. Particularly, alchemical free energy calculations have shown promise in the calculation of reliable and reproducible binding free energies of protein-ligand and protein-lipid complexes in membrane-associated systems. In this review, we present an overview of representative alchemical free energy studies on G-protein-coupled receptors, ion channels, transporters as well as protein-lipid interactions, with emphasis on best practices and critical aspects of running these simulations. Additionally, we analyze challenges and successes when running alchemical free energy calculations on membrane-associated proteins. Finally, we highlight the value of alchemical free energy calculations calculations in drug discovery and their applicability in the pharmaceutical industry.

Quantification of Phytochemicals and Metal Ions as well as the Determination of Volatile Compounds, Antioxidant, Antimicrobial and Antacid Activities of the Mimosa pudica L. Leaf: Exploration of Neglected and Under-Utilized Part

Mimosa pudica L. (MP) is well-known plant in traditional medicinal system, especially in India. Unfortunately, leaves of MP are less explored. To determine the food and nutritional value of the neglected part of Mimosa pudica L. (MP), that is MP leaves, phytochemicals and metal ions of MP were quantified by newly developed HPLC and ICPOES-based methods. The content of phytochemicals observed using HPLC analysis for chlorogenic acid, catechin, and epicatechin was 141.823 (±8.171), 666.621 (±11.432), and 293.175 (±12.743) μg/g, respectively. Using GC/MS/MS analysis, fatty acid like oleic acid were identified. In ICP-OES analysis, a significant content of Na, K, Ca, Cu, Fe, Mg, Mn, and Zn was observed. The observed TPC and TFC for MP leaf extracts was 44.327 (±1.041) mg GAE/ g of wt. and 214.217 (±4.372) mg QCE/ g of wt., respectively. The DPPH assay depicted a strong antioxidant activity of MP leaf extracts with IC50 values of 0.796 (±0.081) mg/mL and a TEAC value of 0.0356 (±0.0003). A significant antacid activity (666 mg MP+400 mg CaCO3 >400 mg CaCO3 ≫666 mg Gelusil) of MP leaves was noticed. The methanolic extract of MP leaves demonstrated anti-microbial activity against Staphylococcus aureus (15±2mm), Pseudomonas aeruginosa (12±2mm) and Escherichia coli (10±2mm). In silico studies confirmed the in vitro results obtained for antioxidant, antiacid, and anti-microbial activities. In addition, in silico studies revealed the anti-cancerous and anti-inflammatory potential of the MP leaves. In summary, this study demonstrated the medicinal significance of MP leaves and the conversion of agro-waste or the under-utilized part of MP into pharmaceutical potent materials. Consequently, the present study highlighted that MP leaves alone have medicinal importance with good nutritional utility and possess large promise in the pharma industry along with improving bio-valorization and the environment.

In Silico Studies of Indole Derivatives as Antibacterial Agents

Objectives

Molecular docking and QSAR studies of indole derivatives as antibacterial agents.

Methods

In this study, we used a multiple linear regressions (MLR) approach to construct a 2D quantitative structure activity relationship of 14 reported indole derivatives. It was performed on the reported antibacterial activity data of 14 compounds based on theoretical chemical descriptors to construct statistical models that link structural properties of indole derivatives to antibacterial activity. We have also performed molecular docking studies of same compounds by using Maestro module of Schrodinger. A set the molecular descriptors like hydrophobic, geometric, electronic and topological characters were calculated to represent the structural features of compounds. The conventional antibiotics sultamicillin and ampicillin were not used in the model development since their structures are different from those of the created compounds. Biological activity data was first translated into pMIC values (i.e. -log MIC) and used as a dependent variable in QSAR investigation.

Results

Compounds with high electronic energy and dipole moment were effective antibacterial agents against S. aureus, indole derivatives with lower κ₂ values were excellent antibacterial agents against MRSA standard strain, and compounds with lower R value and a high ²χ^v value were effective antibacterial agents against MRSA isolate.

Conclusion

Compounds 12 and 2 showed better binding score against penicillin binding protein 2 and penicillin binding protein 2a respectively.

Assessing the performance of docking, FEP, and MM/GBSA methods on a series of KLK6 inhibitors

Kallikrein 6 (KLK6) is an attractive drug target for the treatment of neurological diseases and for various cancers. Herein, we explore the accuracy and efficiency of different computational methods and protocols to predict the free energy of binding (ΔG_bind) for a series of 49 inhibitors of KLK6. We found that the performance of the methods varied strongly with the tested system. For only one of the three KLK6 datasets, the docking scores obtained with rDock were in good agreement (R² ≥ 0.5) with experimental values of ΔG_bind. A similar result was obtained with MM/GBSA (using the ff14SB force field) calculations based on single minimized structures. Improved binding affinity predictions were obtained with the free energy perturbation (FEP) method, with an overall MUE and RMSE of 0.53 and 0.68 kcal/mol, respectively. Furthermore, in a simulation of a real-world drug discovery project, FEP was able to rank the most potent compounds at the top of the list. These results indicate that FEP can be a promising tool for the structure-based optimization of KLK6 inhibitors.

Study of SQ109 analogs binding to mycobacterium MmpL3 transporter using MD simulations and alchemical relative binding free energy calculations

N-geranyl-N΄-(2-adamantyl)ethane-1,2-diamine (SQ109) is a tuberculosis drug that has high potency against Mycobacterium tuberculosis (Mtb) and may function by blocking cell wall biosynthesis. After the crystal structure of MmpL3 from Mycobacterium smegmatis in complex with SQ109 became available, it was suggested that SQ109 inhibits Mmpl3 mycolic acid transporter. Here, we showed using molecular dynamics (MD) simulations that the binding profile of nine SQ109 analogs with inhibitory potency against Mtb and alkyl or aryl adducts at C-2 or C-1 adamantyl carbon to MmpL3 was consistent with the X-ray structure of MmpL3 - SQ109 complex. We showed that rotation of SQ109 around carbon-carbon bond in the monoprotonated ethylenediamine unit favors two gauche conformations as minima in water and lipophilic solvent using DFT calculations as well as inside the transporter's binding area using MD simulations. The binding assays in micelles suggested that the binding affinity of the SQ109 analogs was increased for the larger, more hydrophobic adducts, which was consistent with our results from MD simulations of the SQ109 analogues suggesting that sizeable C-2 adamantyl adducts of SQ109 can fill a lipophilic region between Y257, Y646, F260 and F649 in MmpL3. This was confirmed quantitatively by our calculations of the relative binding free energies using the thermodynamic integration coupled with MD simulations method with a mean assigned error of 0.74 kcal mol^-1 compared to the experimental values.

Insight into potent TLR2 inhibitors for the treatment of disease caused by Mycoplasma pneumoniae based on machine learning approaches

Mycoplasma pneumoniae (MP) is one of the most common pathogens that causes acute respiratory tract infections. Children experiencing MP infection often suffer severe complications, lung injury, and even death. Previous studies have demonstrated that Toll-like receptor 2 (TLR2) is a potential therapeutic target for treating the MP-induced inflammatory response. However, the screening of natural compounds has received more attention for the treatment of bacterial infections to reduce the likelihood of bacterial resistance. Herein, we screened compounds by combining molecular docking and machine learning approaches to find potential lead compounds for treating MP infection. First, all compounds were docked with the TLR2 receptor protein to screen for potential candidates. To predict drug bioactivity, a machine learning model (random forest) was trained for TLR2 inhibitors to obtain the predictive model. The model achieved significant squared correlation coefficient (R²) values for the training set (0.85) and validation set (0.84) of compounds. The developed machine learning model was then used to predict the pIC50 values of the top 50 candidates from the Traditional Chinese compounds and Discovery Diversity sets of compounds. As a result, these compounds are capable of inhibiting the inflammatory response induced by MP. However, prior to bringing these compounds to market, it is necessary to verify these results with additional biological testing, including preclinical and clinical studies. Moreover, the present study provides a theoretical basis for the use of natural compounds as potential candidates to treat pneumonia caused by MP.

Insight into TLR4 receptor inhibitory activity via QSAR for the treatment of Mycoplasma pneumonia disease

Mycoplasma pneumoniae (MP) is one of the most common pathogenic organisms causing upper and lower respiratory tract infections, lung injury, and even death in young children. Toll-like receptors (TLRs) play an important role in innate immunity by allowing the host to recognize pathogens invading the body. Previous studies demonstrated that TLR4 is a potential therapeutic target for the treatment of MP pneumonia. Therefore, the present study aimed to screen biologically active ingredients that target the TLR4 receptor pathway. We first used molecular docking to screen out the active compounds inhibiting the TLR4 pathway, and then used regression and classification machine learning algorithms to establish a quantitative structure-activity relationship (QSAR) model to predict the biological activity of the screened compounds. A total of 78 molecules were used in QSAR modelling, which were retrieved from the ChEMBL database. The QSAR models had acceptable correlation coefficients of R ² on the training and testing dataset in the range of 0.96 to 0.91 and 0.93 to 0.76, respectively. The multiclass classification models showed accuracy on training and testing data within ranges of 1.0 to 0.70, 0.96 to 0.63, and log loss ranges from 0.27 to 8.63, respectively. In addition, molecular descriptors and fingerprints have been studied as structural elements involved in increased and decreased inhibitory activities. These results provide a quantitative analysis of QSAR and classification models applicable for high-throughput screening, as well as insights into the mechanisms of inhibition of TLR4 antagonists.

Computational investigation of potent inhibitors against SARS-CoV-2 2'-O-methyltransferase (nsp16): Structure-based pharmacophore modeling, molecular docking, molecular dynamics simulations and binding free energy calculations

The Coronavirus Disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, has created unprecedented public health and economic crises around the world. SARS-CoV-2 2'-O-methyltransferase (nsp16) adds a "cap" to viral RNA to maintain the stability of viral RNA, and inhibition of nsp16 activity may reduce viral proliferation, making this protein an attractive drug target. Here, we report the identification of several small molecule inhibitors of nsp16 by virtual screening. First, the nsp16-sinefungin complex (PDB ID: 6WKQ) was selected from the protein data bank. Asp6912, Cys6913, Asp6897 and Asp6928 were determined to be the key amino acids for sinefungin binding in the crystal structure of nsp16-sinefungin complex by molecular dynamics simulation. The complex structures in the stable binding trajectory of nsp16-sinefungin were than clustered through molecular dynamics RMSD analysis. Six clusters were generated, and six representative structures were selected to construct the pharmacophore based on the structure. These six pharmacophores were superimposed on the binding pocket to simplify and pick the common characteristics. The compounds obtained by the pharmacophore screening from Bionet and Chembiv databases were docked into the nsp16 active pocket. The candidate compounds were selected according to the molecular docking score and then screened by MM/GBSA. Finally, four candidate compounds were obtained. Four sets of 150ns molecular dynamics simulations were performed to determine whether candidate compounds could maintain stable interactions with key amino acids. The results of MD and MM/PBSA energy decomposition indicated that C1 and C2 could form a stable complex system with nsp16, and could form strong hydrogen bonds and salt bridges with the key amino acid Asp6897 and Asp6928. This study thus identifies and attempts to validate for the first time the potential inhibitory activities of C1 and C2 against nsp16, allowing the development of potent anti-COVID-19 drugs and unique treatment strategies.

An In-Silico Identification of Potential Flavonoids against Kidney Fibrosis Targeting TGFβR-1

Fibrosis is a hallmark of progressive kidney diseases. The overexpression of profibrotic cytokine, namely transforming growth factor β (TGF-β) due to excessive inflammation and tissue damage, induces kidney fibrosis. The inhibition of TGF-β signaling is markedly limited in experimental disease models. Targeting TGF-β signaling, therefore, offers a prospective strategy for the management of kidney fibrosis. Presently, the marketed drugs have numerous side effects, but plant-derived compounds are relatively safer and more cost-effective. In this study, TGFβR-1 was targeted to identify the lead compounds among flavonoids using various computational approaches, such as ADME/T (absorption, distribution, metabolism, and excretion/toxicity) analysis, molecular docking, and molecular dynamics simulation. ADME/T screening identified a total of 31 flavonoids with drug-like properties of 31 compounds, a total of 5 compounds showed a higher binding affinity to TGFβR-1, with Epicatechin, Fisetin, and Luteolin ranking at the top three (-13.58, -13.17, and -10.50 kcal/mol, respectively), which are comparable to the control drug linagliptin (-9.074 kcal/mol). The compounds also exhibited outstanding protein-ligand interactions. The molecular dynamic simulations revealed a stable interaction of these compounds with the binding site of TGFβR-1. These findings indicate that flavonoids, particularly Epicatechin, Fisetin, and Luteolin, may compete with the ligand-binding site of TGFβR-1, suggesting that these compounds can be further evaluated for the development of potential therapeutics against kidney fibrosis. Further, in-vitro and in-vivo studies are recommended to support the current findings.

Highly Chemoselective Synthesis of Purino[3,2-c]oxazoles via the Asymmetric Dearomative [3+2] Cycloaddition of Purines with Donor-Acceptor Oxiranes

A Ni(II)/bisoxazoline-catalyzed asymmetric dearomative [3+2] cycloaddition of substituted purines with donor-acceptor oxiranes was developed. This reaction, which proceeds via highly chemoselective C-C bond cleavage of the oxiranes, accesses chiral purino[3,2-c]oxazole compounds (≤99% ee after enrichment via crystallization). The electronic effects of the purine ring determine the reactivity of the substrate. The general applicability of this method was illustrated by gram-scale synthesis, the diverse transformations of the product, and the promising biological activities of selected derivatives.

Dual A1/A3 Adenosine Receptor Antagonists: Binding Kinetics and Structure-Activity Relationship Studies Using Mutagenesis and Alchemical Binding Free Energy Calculations

Drugs targeting adenosine receptors (AR) can provide treatment for diseases. We report the identification of 7-(phenylamino)-pyrazolo[3,4-c]pyridines L2-L10, A15, and A17 as low-micromolar to low-nanomolar A₁R/A₃R dual antagonists, with 3-phenyl-5-cyano-7-(trimethoxyphenylamino)-pyrazolo[3,4-c]pyridine (A17) displaying the highest affinity at both receptors with a long residence time of binding, as determined using a NanoBRET-based assay. Two binding orientations of A17 produce stable complexes inside the orthosteric binding area of A₁R in molecular dynamics (MD) simulations, and we selected the most plausible orientation based on the agreement with alanine mutagenesis supported by affinity experiments. Interestingly, for drug design purposes, the mutation of L250^6.51 to alanine increased the binding affinity of A17 at A₁R. We explored the structure-activity relationships against A₁R using alchemical binding free energy calculations with the thermodynamic integration coupled with the MD simulation (TI/MD) method, applied on the whole G-protein-coupled receptor-membrane system, which showed a good agreement (r = 0.73) between calculated and experimental relative binding free energies.

Are Deep Learning Structural Models Sufficiently Accurate for Free-Energy Calculations? Application of FEP+ to AlphaFold2-Predicted Structures

The availability of AlphaFold2 has led to great excitement in the scientific community─particularly among drug hunters─due to the ability of the algorithm to predict protein structures with high accuracy. However, beyond globally accurate protein structure prediction, it remains to be determined whether ligand binding sites are predicted with sufficient accuracy in these structures to be useful in supporting computationally driven drug discovery programs. We explored this question by performing free-energy perturbation (FEP) calculations on a set of well-studied protein-ligand complexes, where AlphaFold2 predictions were performed by removing all templates with >30% identity to the target protein from the training set. We observed that in most cases, the ΔΔG values for ligand transformations calculated with FEP, using these prospective AlphaFold2 structures, were comparable in accuracy to the corresponding calculations previously carried out using crystal structures. We conclude that under the right circumstances, AlphaFold2-modeled structures are accurate enough to be used by physics-based methods such as FEP in typical lead optimization stages of a drug discovery program.

Best practices for constructing, preparing, and evaluating protein-ligand binding affinity benchmarks [Article v0.1]

Free energy calculations are rapidly becoming indispensable in structure-enabled drug discovery programs. As new methods, force fields, and implementations are developed, assessing their expected accuracy on real-world systems (benchmarking) becomes critical to provide users with an assessment of the accuracy expected when these methods are applied within their domain of applicability, and developers with a way to assess the expected impact of new methodologies. These assessments require construction of a benchmark-a set of well-prepared, high quality systems with corresponding experimental measurements designed to ensure the resulting calculations provide a realistic assessment of expected performance when these methods are deployed within their domains of applicability. To date, the community has not yet adopted a common standardized benchmark, and existing benchmark reports suffer from a myriad of issues, including poor data quality, limited statistical power, and statistically deficient analyses, all of which can conspire to produce benchmarks that are poorly predictive of real-world performance. Here, we address these issues by presenting guidelines for (1) curating experimental data to develop meaningful benchmark sets, (2) preparing benchmark inputs according to best practices to facilitate widespread adoption, and (3) analysis of the resulting predictions to enable statistically meaningful comparisons among methods and force fields. We highlight challenges and open questions that remain to be solved in these areas, as well as recommendations for the collection of new datasets that might optimally serve to measure progress as methods become systematically more reliable. Finally, we provide a curated, versioned, open, standardized benchmark set adherent to these standards (PLBenchmarks) and an open source toolkit for implementing standardized best practices assessments (arsenic) for the community to use as a standardized assessment tool. While our main focus is free energy methods based on molecular simulations, these guidelines should prove useful for assessment of the rapidly growing field of machine learning methods for affinity prediction as well.

Drug Discovery by Automated Adaptation of Chemical Structure and Identity

Computer-aided drug design offers the potential to dramatically reduce the cost and effort required for drug discovery. While screening-based methods are valuable in the early stages of hit identification, they are frequently succeeded by iterative, hypothesis-driven computations that require recurrent investment of human time and intuition. To increase automation, we introduce a computational method for lead refinement that combines concerted dynamics of the ligand/protein complex via molecular dynamics simulations with integrated Monte Carlo-based changes in the chemical formula of the ligand. This approach, which we refer to as ligand-exchange Monte Carlo molecular dynamics, accounts for solvent- and entropy-based contributions to competitive binding free energies by coupling the energetics of bound and unbound states during the ligand-exchange attempt. Quantitative comparison of relative binding free energies to reference values from free energy perturbation, conducted in vacuum, indicates that ligand-exchange Monte Carlo molecular dynamics simulations sample relevant conformational ensembles and are capable of identifying strongly binding compounds. Additional simulations demonstrate the use of an implicit solvent model. We speculate that the use of chemical graphs in which exchanges are only permitted between ligands with sufficient similarity may enable an automated search to capture some of the benefits provided by human intuition during hypothesis-guided lead refinement.

Do AutoML-Based QSAR Models Fulfill OECD Principles for Regulatory Assessment? A 5-HT1A Receptor Case

The drug discovery and development process requires a lot of time, financial, and workforce resources. Any reduction in these burdens might benefit all stakeholders in the healthcare domain, including patients, government, and companies. One of the critical stages in drug discovery is a selection of molecular structures with a strong affinity to a particular molecular target. The possible solution is the development of predictive models and their application in the screening process, but due to the complexity of the problem, simple and statistical models might not be sufficient for practical application. The manuscript presents the best-in-class predictive model for the serotonin 1A receptor affinity and its validation according to the Organization for Economic Co-operation and Development guidelines for regulatory purposes. The model was developed based on a database with close to 9500 molecules by using an automatic machine learning tool (AutoML). The model selection was conducted based on the Akaike information criterion value and 10-fold cross-validation routine, and later good predictive ability was confirmed with an additional external validation dataset with over 700 molecules. Moreover, the multi-start technique was applied to test if an automatic model development procedure results in reliable results.

Adenosine A2A receptor antagonists: from caffeine to selective non-xanthines

A long evolution of knowledge of the psychostimulant caffeine led in the 1960s to another purine natural product, adenosine and its A2A receptor. Adenosine is a short-lived autocrine/paracrine mediator that acts pharmacologically at four different adenosine receptors in a manner opposite to the pan-antagonist caffeine and serves as an endogenous allostatic regulator. Although detrimental in the developing brain, caffeine appears to be cerebroprotective in aging. Moderate caffeine consumption in adults, except in pregnancy, may also provide benefit in pain, diabetes, and kidney and liver disorders. Inhibition of A2A receptors is one of caffeine's principal effects and we now understand this interaction at the atomic level. The A2A receptor has become a prototypical example of utilizing high-resolution structures of GPCRs for the rational design of chemically diverse drug molecules. The previous focus on discovery of selective A2A receptor antagonists for neurodegenerative diseases has expanded to include immunotherapy for cancer, and clinical trials have ensued. LINKED ARTICLES: This article is part of a themed issue on Structure Guided Pharmacology of Membrane Proteins (BJP 75th Anniversary). To view the other articles in this section visit http://onlinelibrary.wiley.com/doi/10.1111/bph.v179.14/issuetoc.

Relative Binding Free-Energy Calculations at Lipid-Exposed Sites: Deciphering Hot Spots

Relative binding free-energy (RBFE) calculations are experiencing resurgence in the computer-aided drug design of novel small molecules due to performance gains allowed by cutting-edge molecular mechanic force fields and computer hardware. Application of RBFE to soluble proteins is becoming a routine, while recent studies outline necessary steps to successfully apply RBFE at the orthosteric site of membrane-embedded G-protein-coupled receptors (GPCRs). In this work, we apply RBFE to a congeneric series of antagonists that bind to a lipid-exposed, extra-helical site of the P2Y1 receptor. We find promising performance of RBFE, such that it may be applied in a predictive manner on drug discovery programs targeting lipid-exposed sites. Further, by the application of the microkinetic model, binding at a lipid-exposed site can be split into (1) membrane partitioning of the drug molecule followed by (2) binding at the extra-helical site. We find that RBFE can be applied to calculate the free energy of each step, allowing the uncoupling of observed binding free energy from the influence of membrane affinity. This protocol may be used to identify binding hot spots at extra-helical sites and guide drug discovery programs toward optimizing intrinsic activity at the target.

Progress in Free Energy Perturbation: Options for Evolving Fragments

One of the remaining bottlenecks in fragment-based drug design (FBDD) is the initial exploration and optimization of the identified hit fragments. There is a growing interest in computational approaches that can guide these efforts by predicting the binding affinity of newly designed analogues. Among others, alchemical free energy (AFE) calculations promise high accuracy at a computational cost that allows their application during lead optimization campaigns. In this review, we discuss how AFE could have a strong impact in fragment evolution, and we raise awareness on the challenges that could be encountered applying this methodology in FBDD studies.

Structure-Based Virtual Screening for Ligands of G Protein-Coupled Receptors: What Can Molecular Docking Do for You?

G protein-coupled receptors (GPCRs) constitute the largest family of membrane proteins in the human genome and are important therapeutic targets. During the last decade, the number of atomic-resolution structures of GPCRs has increased rapidly, providing insights into drug binding at the molecular level. These breakthroughs have created excitement regarding the potential of using structural information in ligand design and initiated a new era of rational drug discovery for GPCRs. The molecular docking method is now widely applied to model the three-dimensional structures of GPCR-ligand complexes and screen for chemical probes in large compound libraries. In this review article, we first summarize the current structural coverage of the GPCR superfamily and the understanding of receptor-ligand interactions at atomic resolution. We then present the general workflow of structure-based virtual screening and strategies to discover GPCR ligands in chemical libraries. We assess the state of the art of this research field by summarizing prospective applications of virtual screening based on experimental structures. Strategies to identify compounds with specific efficacy and selectivity profiles are discussed, illustrating the opportunities and limitations of the molecular docking method. Our overview shows that structure-based virtual screening can discover novel leads and will be essential in pursuing the next generation of GPCR drugs. SIGNIFICANCE STATEMENT: Extraordinary advances in the structural biology of G protein-coupled receptors have revealed the molecular details of ligand recognition by this large family of therapeutic targets, providing novel avenues for rational drug design. Structure-based docking is an efficient computational approach to identify novel chemical probes from large compound libraries, which has the potential to accelerate the development of drug candidates.

Deciphering conformational selectivity in the A2A adenosine G protein-coupled receptor by free energy simulations

Transmembranal G Protein-Coupled Receptors (GPCRs) transduce extracellular chemical signals to the cell, via conformational change from a resting (inactive) to an active (canonically bound to a G-protein) conformation. Receptor activation is normally modulated by extracellular ligand binding, but mutations in the receptor can also shift this equilibrium by stabilizing different conformational states. In this work, we built structure-energetic relationships of receptor activation based on original thermodynamic cycles that represent the conformational equilibrium of the prototypical A_2A adenosine receptor (AR). These cycles were solved with efficient free energy perturbation (FEP) protocols, allowing to distinguish the pharmacological profile of different series of A_2AAR agonists with different efficacies. The modulatory effects of point mutations on the basal activity of the receptor or on ligand efficacies could also be detected. This methodology can guide GPCR ligand design with tailored pharmacological properties, or allow the identification of mutations that modulate receptor activation with potential clinical implications.

The design of new ligands as chemical modulators of G protein-coupled receptors (GPCRs) has benefited considerably during the last years of advances in both the structural and computational biology disciplines. Within the last area, the use of free energy calculation methods has arisen as a computational tool to predict ligand affinities to explain structure-affinity relationships and guide lead optimization campaigns. However, our comprehension of the structural determinants of ligands with different pharmacological profile is scarce, and knowledge of the chemical modifications associated with an agonistic or antagonistic profile would be extremely valuable. We herein report an original implementation of the thermodynamic cycles associated with free energy perturbation (FEP) simulations, to mimic the conformational equilibrium between active and inactive GPCRs, and establish a framework to describe pharmacological profiles as a function of the ligands selectivity for a given receptor conformation. The advantage of this method resides into its simplicity of use, and the only consideration of active and inactive conformations of the receptor, with no simulation of the transitions between them. This model can accurately predict the pharmacological profile of series of full and partial agonists as opposed to antagonists of the A_2A adenosine receptor, and moreover, how certain mutations associated with modulation of basal activity can influence this pharmacological profiles, which enables our understanding of such clinically relevant mutations.

Fragment-based design of selective GPCR ligands guided by free energy simulations

Fragment-based drug discovery relies on successful optimization of weakly binding ligands for affinity and selectivity. Herein, we explored strategies for structure-based evolution of fragments binding to a G protein-coupled receptor. Molecular dynamics simulations combined with rigorous free energy calculations guided synthesis of nanomolar ligands with up to >1000-fold improvements of binding affinity and close to 40-fold subtype selectivity.

Binding free energy calculation of human beta defensin 3 with negatively charged lipid bilayer using free energy perturbation method

Human β defensin type 3 (hBD-3) is a cationic peptide having strong antimicrobial activities even at high salt concentrations. The conserved sequence is believed to contribute to its unique antibacterial activities. To design novel drugs based on hBD-3, predicting the binding free energy contribution of each residue on hBD-3 with bacterial membrane is important. Firstly, the stable binding structure of hBD-3 dimer in analog form bound on POPG lipid bilayer was predicted using NAMD simulations, which was confirmed by RMSD, buried surface area, hydrogen bonds, distance map, and insertion depth map calculations. Then, free energy perturbation (FEP) method was applied to calculate the binding free energy of each residue by mutating it into Alanine. It was found that the positively charged residues on the tail region of hBD-3 contribute significantly to its binding with membrane. The result emphasized the importance of electrostatic interactions to hBD-3's binding with bacterial membrane.

Performance of Relative Binding Free Energy Calculations on an Automatically Generated Dataset of Halogen-Deshalogen Matched Molecular Pairs

In this study, we generated a matched molecular pair dataset of halogen/deshalogen compounds with reliable binding affinity data and structural binding mode information from public databases. The workflow includes automated system preparation and setup of free energy perturbation relative binding free energy calculations. We demonstrate the suitability of these datasets to investigate the performance of molecular mechanics force fields and molecular simulation algorithms for the purpose of in silico affinity predictions in lead optimization. Our datasets of a total of 115 matched molecular pairs show highly accurate binding free energy predictions with an average error of <1 kcal/mol despite the semi-automated calculation scheme. We quantify the accuracy of the optimized potential for liquid simulations (OPLS) force field to predict the effect of halogen addition to compounds, a commonly employed chemical modification in the design of drug-like molecules.

Scaffold Hopping Transformations Using Auxiliary Restraints for Calculating Accurate Relative Binding Free Energies

In silico screening of drug-target interactions is a key part of the drug discovery process. Changes in the drug scaffold via contraction or expansion of rings, the breaking of rings, and the introduction of cyclic structures from acyclic structures are commonly applied by medicinal chemists to improve binding affinity and enhance favorable properties of candidate compounds. These processes, commonly referred to as scaffold hopping, are challenging to model computationally. Although relative binding free energy (RBFE) calculations have shown success in predicting binding affinity changes caused by perturbing R-groups attached to a common scaffold, applications of RBFE calculations to modeling scaffold hopping are relatively limited. Scaffold hopping inevitably involves breaking and forming bond interactions of quadratic functional forms, which is highly challenging. A novel method for handling ring opening/closure/contraction/expansion and linker contraction/expansion is presented here. To the best of our knowledge, RBFE calculations on linker contraction/expansion have not been previously reported. The method uses auxiliary restraints to hold the atoms at the ends of a bond in place during the breaking and forming of the bonds. The broad applicability of the method was demonstrated by examining perturbations involving small-molecule macrocycles and mutations of proline in proteins. High accuracy was obtained using the method for most of the perturbations studied. The rigor of the method was isolated from the force field by validating the method using relative and absolute hydration free energy calculations compared to standard simulation results. Unlike other methods that rely on λ-dependent functional forms for bond interactions, the method presented here can be employed using modern molecular dynamics software without modification of codes or force field functions.

Ketamine Metabolite (2R,6R)-Hydroxynorketamine Interacts with μ and κ Opioid Receptors

Ketamine is an anesthetic, analgesic, and antidepressant whose secondary metabolite (2R,6R)-hydroxynorketamine (HNK) has N-methyl-d-aspartate-receptor-independent antidepressant activity in a rodent model. In humans, naltrexone attenuates its antidepressant effect, consistent with opioid pathway involvement. No detailed biophysical description is available of opioid receptor binding of ketamine or its metabolites. Using molecular dynamics simulations with free energy perturbation, we characterize the binding site and affinities of ketamine and metabolites in μ and κ opioid receptors, finding a profound effect of the protonation state. G-protein recruitment assays show that HNK is an inverse agonist, attenuated by naltrexone, in these receptors with IC₅₀ values congruous with our simulations. Overall, our findings are consistent with opioid pathway involvement in ketamine function.

G protein-coupled receptors: structure- and function-based drug discovery

As one of the most successful therapeutic target families, G protein-coupled receptors (GPCRs) have experienced a transformation from random ligand screening to knowledge-driven drug design. We are eye-witnessing tremendous progresses made recently in the understanding of their structure-function relationships that facilitated drug development at an unprecedented pace. This article intends to provide a comprehensive overview of this important field to a broader readership that shares some common interests in drug discovery.

Unravelling the molecular basis of AM-6494 high potency at BACE1 in Alzheimer's disease: an integrated dynamic interaction investigation

β-amyloid precursor protein cleaving enzyme1 (BACE1) has prominently been an important drug design target implicated in Alzheimer's disease pathway. The failure rate of most of the already tested drugs at different clinical phases remains a major concern. Recently, AM-6494 was reported as a novel potent, highly selective, and orally effective inhibitor against BACE1. AM-6494 displayed no alteration of skin/fur colour in animal studies, an adverse effect common to previous BACE1 inhibitors. However, the atomistic molecular mechanism of BACE1 inhibition by AM-6494 remains unclear. To elucidate the binding mechanism of AM-6494 relative to umibecestat (CNP-520) as well as the structural changes when bound to BACE1, advanced computational techniques such as accelerated MD simulation and principal component analysis have been utilised. The results demonstrated higher binding affinity of AM-6494 at BACE1 with van der Waals as dominant energy contributor compared to umibecestat. Conformational monitoring of the β-hairpin flap covering the active site revealed an effective flap closure when bound with AM-6494 compared to CNP-520, which predominantly alternates between semi-open and closed conformations. The observed effective flap closure of AM-6494 explains its higher inhibitory power towards BACE1. Besides the catalytic Asp32/228 dyad, Tyr14, Leu30, Tyr71 and Gly230 represent critical residues in the potency of these inhibitors at BACE1 binding interface. The findings highlighted in this research provide a basis to explain AM-6494 high inhibitory potency and might assist in the design of new inhibitors with improved selectivity and potency for BACE1.

Is Structure-Based Drug Design Ready for Selectivity Optimization?

Alchemical free-energy calculations are now widely used to drive or maintain potency in small-molecule lead optimization with a roughly 1 kcal/mol accuracy. Despite this, the potential to use free-energy calculations to drive optimization of compound selectivity among two similar targets has been relatively unexplored in published studies. In the most optimistic scenario, the similarity of binding sites might lead to a fortuitous cancellation of errors and allow selectivity to be predicted more accurately than affinity. Here, we assess the accuracy with which selectivity can be predicted in the context of small-molecule kinase inhibitors, considering the very similar binding sites of human kinases CDK2 and CDK9 as well as another series of ligands attempting to achieve selectivity between the more distantly related kinases CDK2 and ERK2. Using a Bayesian analysis approach, we separate systematic from statistical errors and quantify the correlation in systematic errors between selectivity targets. We find that, in the CDK2/CDK9 case, a high correlation in systematic errors suggests that free-energy calculations can have significant impact in aiding chemists in achieving selectivity, while in more distantly related kinases (CDK2/ERK2), the correlation in systematic error suggests that fortuitous cancellation may even occur between systems that are not as closely related. In both cases, the correlation in systematic error suggests that longer simulations are beneficial to properly balance statistical error with systematic error to take full advantage of the increase in apparent free-energy calculation accuracy in selectivity prediction.

Two pronged approach for prevention and therapy of COVID-19 (Sars-CoV-2) by a multi-targeted herbal drug, a component of ayurvedic decoction

Introduction

SARS-CoV-2 a new virus of the zoonotic coronavirus family causes the disease COVID-19, which has become a global pandemic. One of the ways for prevention of COVID-19 is by disabling its spike protein which results in inhibiting its binding with angiotensin-converting enzyme 2 (ACE-2). The other alternative is to inhibit its replication once inside the body. The aim of this study was to explore the literature to identify whether there were any Ayurvedic remedies which contained ingredients which demonstrated this dual effect.

Methods

In silico studies were carried out to find the structures of the targets i.e. spike protein of the virus and its main protease (Mpro). Databases were searched to identify the composition of Ayurvedic decoctions used for respiratory ailments.

Results

We have found that two components out of 26 active ingredients of Ayurvedic decoctions are strong binders for spike protein as well as corresponding Mpro (3CL protease) which plays an essential role in mediating viral replication and transcription, making it an attractive antiviral drug target. Out of 26 components of Ayurvedic herbal decoction used for influenza, one compound was found to be most active. It is a well-known antioxidant, antinflammatory and hepatoprotective molecule.

Conclusion

The resultant compound could act as a repurposed drug or like other methoxyphenols, could be a good lead molecule for a potent drug for COVID-19.

Synthesis and molecular docking studies of quinoline derivatives as HIV non-nucleoside reverse transcriptase inhibitors

Quinoline moiety is an important scaffold in the field of drug discovery and drug development, with a wide range of pharmacological activities. Quinoline derivatives are potent inhibitors for reverse transcriptase, which is responsible for the conversion of single-stranded viral RNA into double-stranded viral DNA.In the present study, we have designed and synthesized 2 series, namely pyrazoline and pyrimidine containing quinoline derivatives as non nucleoside reverse transcriptase inhibitors (NNRTIs). Eleven compounds were synthesized and characterized by 1H and 13C NMR and mass spectrophotometry. The synthesized compounds were also docked on an HIV reverse transcriptase binding site (PDB: 4I2P); most of these compounds showed good binding interactions with the active domain of the receptor. Most of the compounds displayed a docking score higher than those of standard drugs. Among the synthesized quinoline derivatives, compound 4 exhibited the highest docking score (-10.675).

Rapid, accurate, precise and reproducible ligand-protein binding free energy prediction

A central quantity of interest in molecular biology and medicine is the free energy of binding of a molecule to a target biomacromolecule. Until recently, the accurate prediction of binding affinity had been widely regarded as out of reach of theoretical methods owing to the lack of reproducibility of the available methods, not to mention their complexity, computational cost and time-consuming procedures. The lack of reproducibility stems primarily from the chaotic nature of classical molecular dynamics (MD) and the associated extreme sensitivity of trajectories to their initial conditions. Here, we review computational approaches for both relative and absolute binding free energy calculations, and illustrate their application to a diverse set of ligands bound to a range of proteins with immediate relevance in a number of medical domains. We focus on ensemble-based methods which are essential in order to compute statistically robust results, including two we have recently developed, namely thermodynamic integration with enhanced sampling and enhanced sampling of MD with an approximation of continuum solvent. Together, these form a set of rapid, accurate, precise and reproducible free energy methods. They can be used in real-world problems such as hit-to-lead and lead optimization stages in drug discovery, and in personalized medicine. These applications show that individual binding affinities equipped with uncertainty quantification may be computed in a few hours on a massive scale given access to suitable high-end computing resources and workflow automation. A high level of accuracy can be achieved using these approaches.

Hit-to-lead and lead optimization binding free energy calculations for G protein-coupled receptors

We apply the hit-to-lead ESMACS (enhanced sampling of molecular dynamics with approximation of continuum solvent) and lead-optimization TIES (thermodynamic integration with enhanced sampling) methods to compute the binding free energies of a series of ligands at the A1 and A2A adenosine receptors, members of a subclass of the GPCR (G protein-coupled receptor) superfamily. Our predicted binding free energies, calculated using ESMACS, show a good correlation with previously reported experimental values of the ligands studied. Relative binding free energies, calculated using TIES, accurately predict experimentally determined values within a mean absolute error of approximately 1 kcal mol-1. Our methodology may be applied widely within the GPCR superfamily and to other small molecule-receptor protein systems.

CHARMM-GUI Free Energy Calculator for Absolute and Relative Ligand Solvation and Binding Free Energy Simulations

Alchemical free energy simulations have long been utilized to predict free energy changes for binding affinity and solubility of small molecules. However, while the theoretical foundation of these methods is well established, seamlessly handling many of the practical aspects regarding the preparation of the different thermodynamic end states of complex molecular systems and the numerous processing scripts often remains a burden for successful applications. In this work, we present CHARMM-GUI Free Energy Calculator (http://www.charmm-gui.org/input/fec) that provides various alchemical free energy perturbation molecular dynamics (FEP/MD) systems with input and post-processing scripts for NAMD and GENESIS. Four submodules are available: Absolute Ligand Binder (for absolute ligand binding FEP/MD), Relative Ligand Binder (for relative ligand binding FEP/MD), Absolute Ligand Solvator (for absolute ligand solvation FEP/MD), and Relative Ligand Solvator (for relative ligand solvation FEP/MD). Each module is designed to build multiple systems of a set of selected ligands at once for high-throughput FEP/MD simulations. The capability of Free Energy Calculator is illustrated by absolute and relative solvation FEP/MD of a set of ligands and absolute and relative binding FEP/MD of a set of ligands for T4-lysozyme in solution and the adenosine A_2A receptor in a membrane. The calculated free energy values are overall consistent with the experimental and published free energy results (within ∼1 kcal/mol). We hope that Free Energy Calculator is useful to carry out high-throughput FEP/MD simulations in the field of biomolecular sciences and drug discovery.

Boosting Free-Energy Perturbation Calculations with GPU-Accelerated NAMD

Harnessing the power of graphics processing units (GPUs) to accelerate molecular dynamics (MD) simulations in the context of free-energy calculations has been a longstanding effort toward the development of versatile, high-performance MD engines. We report a new GPU-based implementation in NAMD of free-energy perturbation (FEP), one of the oldest, most popular importance-sampling approaches for the determination of free-energy differences that underlie alchemical transformations. Compared to the CPU implementation available since 2001 in NAMD, our benchmarks indicate that the new implementation of FEP in traditional GPU code is about four times faster, without any noticeable loss of accuracy, thereby paving the way toward more affordable free-energy calculations on large biological objects. Moreover, we have extended this new FEP implementation to a code path highly optimized for a single-GPU node, which proves to be up to nearly 30 times faster than the CPU implementation. Through optimized GPU performance, the present developments provide the community with a cost-effective solution for conducting FEP calculations. The new FEP-enabled code has been released with NAMD 3.0.