Binding Free Energy Estimation Research Articles

Virtual screening of drugs targeting PD-L1 protein

Monoclonal antibody inhibitors targeting PD-1/PD-L1 immune checkpoints are gradually entering the market and have achieved certain positive effects in the treatments of various types of tumors. However, with the expansion of application, the limitations of antibody drugs and problems such as excessive homogenization of research gradually appear, making small-molecule inhibitors the new focus of researchers. This study aims to use ligand-based and structure-based binding activity prediction methods to achieve virtual screening of small-molecule inhibitors targeting PD-L1, thereby helping to accelerate the development of small molecule drugs. A dataset of PD-L1 small-molecule inhibitory activity from relevant research literature and patents is collected and activity judgment classification models with intensity prediction regression models are constructed based on different molecular featurization methods and machine learning algorithms. The two types of models filter 68 candidate compounds with high PD-L1 inhibitory activity from a large drug-like small molecule screening pool (ZINC15). Ten of these compounds not only have good drug similarities and pharmacokinetics, but also exhibit comparable binding affinities and similar mechanisms of action with previous reported hotspot compounds in molecular docking. This phenomenon is further verified in subsequent molecular dynamics simulation and the estimation of binding free energy. In this study, a virtual screening workflow integrating ligand-based method and structure-based method is developed, and potential PD-L1 small-molecule inhibitors are effectively screened from large compound databases, which is expected to help accelerate the application and expansion of tumor immunotherapy.

A computational study on the molecular mechanisms of panduratin A as a potential inhibitor on SARS-CoV-2 protein targets

Panduratin A from Boesebergia rotunda was recently reported as a potent anti-SARS-CoV-2 compound. However, the molecular mechanisms underlying the inhibition by Panduratin A and its target remained unclear. Molecular docking calculations were performed between panduratin A and five important proteins, i.e., main protease (Mpro), papain-like protease (PLpro), receptor binding domain (RBD) of spike proteins, RNA-dependent-RNA-polymerase (RdRp), and 2′-O-methyltransferase (MTase). The estimated binding free energy and the interaction networks extracted from the best docking mode for each complex suggested that MTase was the most probable target for panduratin A inhibition. To further validate the ability of panduratin A to inhibit MTase, molecular dynamics (MD) simulations and binding free energy calculations were performed for panduratin A-MTase complex, in comparison with another MTase complex with sinefungin as a positive control. Chemical features of panduratin A and sinefungin were compared for their contribution in MTase binding. It was found that both molecules could bind to the S-Adenosyl methionine (SAM) binding pocket and prevent the SAM entrance co-substrate, which could eventually halt the function of MTase. Despite a slightly weaker binding free energy, the equilibrated positional binding of panduratin A was found at a closer distance to the active sites. Therefore, this study proposed MTase as a possible target of panduratin A, along with the mechanisms of inhibition, prompting another future in vitro study as a verification.

Employing an immunoinformatics approach revealed potent multi-epitope based subunit vaccine for lymphocytic choriomeningitis virus

BackgroundLymphocytic choriomeningitis virus (LCMV) infects many individuals worldwide and causes severe infection in the immunosuppressant recipient, spontaneous abortion, and congenital disabilities in infants. ObjectivesThere is no specific vaccine or therapeutics available to protect against LCMV infection; thus, there is a need to design a potential vaccine to combat the virus by developing immunity in the population. Herein, we attempted to design a potent multi-epitope vaccine for LCMV using immunoinformatics methods. MethodsThe whole proteome of the virus was screened and mapped to extract immunodominant B-cell and T-cell epitopes which were fused with appropriate linkers (EAAAK, GGGS, AAY, GPGPG, and AAY), PADRE sequence (13aa) and an adjuvant (50 S ribosomal protein L7/L12) to formulate a multi-epitope vaccine ensemble. Codon adaptation and in silico cloning of the constructed vaccine were carried out using bioinformatics tools. The secondary and tertiary structure of the vaccine construct was predicted and refined. The physicochemical profile of the designed vaccine was analyzed, and the multi-epitope vaccine's potential to bind Toll-like receptors (TLR2 and TLR4) was evaluated through molecular docking and molecular dynamics simulations. Computational immune simulation of the designed vaccine antigen was performed using the C-ImmSim server. ResultsThe designed multi-epitope-based vaccine (613 aa) comprised 26 immunodominant (six B-cell, nine cytotoxic T lymphocytes, and 11 helper T lymphocytes) epitopes and is predicted antigenic, non-toxic, non-allergen, soluble, and topographically accessible with a suitable physicochemical profile. The designed vaccine is expected to cover a broad worldwide population (96.35 %) and stimulate a robust adaptive immune response against the virus upon administration. In silico cloning of the constructed vaccine in PET28a (+) vector ensured its optimal expression in the Escherichia coli system. Molecular docking, molecular dynamics simulation, and binding free energy estimation collectively support the stability and energetically favourable interaction of the modeled vaccine–TLR2/4 complexes. ConclusionThe designed multi-epitope vaccine in the present study could serve as a potential vaccine candidate to protect against LMCV infection; however, the experimental validation and safety testing of the vaccine is warranted to validate the study’s outcomes.

Battling BTK mutants with noncovalent inhibitors that overcome Cys481 and Thr474 mutations in Waldenström macroglobulinemia therapy: structural mechanistic insights on the role of fenebrutinib.

Recently, the non-covalent Bruton tyrosine kinase (BTK) inhibitor fenebrutinib was presented as a therapeutic option with strong inhibitory efficacy against a single (C481S) and double (T474S/C481S) BTK variant in the treatment of Waldenström macroglobulinemia (WM). However, the molecular events surrounding its inhibition mechanism towards this variant remain unresolved. Herein, we employed in silico methods such as molecular dynamic simulation coupled with binding free energy estimations to explore the mechanistic activity of the fenebrutinib on (C481S) and (T474S/C481S) BTK variant, at a molecular level. Our investigations reveal that amino acid arginine contributed immensely to the total binding energy, this establishing the cruciality of amino acid residues, Arg132 and Arg156 in (C481S) and Arg99, Arg137, and Arg132 in (T474S/C481S) in the binding of fenebrutinib towards both BTK variants. The structural orientations of fenebrutinib within the respective hydrophobic pockets allowed favorable interactions with binding site residues, accounting for its superior binding affinity by 24.5% and relative high hydrogen bond formation towards (T474S/C481S) when compared with (C481S) BTK variants. Structurally, fenebrutinib impacted the stability, flexibility, and solvent accessible surface area of both BTK variants, characterized by various alterations observed in the bound and unbound structures, which proved enough to disrupt their biological function. Findings from this study, therefore, provide insights into the inhibitory mechanism of fenebrutinib at the atomistic level and reveal its high selectivity towards BTK variants. These insights could be key in designing and developing BTK mutants' inhibitors to treat Waldenström macroglobulinemia (WM).

Computational molecular interaction between SARS-CoV-2 main protease and theaflavin digallate using free energy perturbation and molecular dynamics

Our objective was to identify the molecule which can inhibit SARS-CoV-2 main protease and can be easily procured. Natural products may provide such molecules and can supplement the current custom chemical synthesis-based drug discovery for this objective. A combination of docking approaches, scoring functions, classical molecular dynamic simulation, binding pose metadynamics, and free energy perturbation calculations have been employed in this study. Theaflavin digallate has been observed in top-scoring compounds after the three independent virtual screening simulations of 598435 compounds (unique 27256 chemical entities). The main protease-theaflavin digallate complex interacts with critical active site residues of the main protease in molecular dynamics simulation independent of the explored computational framework, simulation time, initial structure, and force field used. Theaflavin digallate forms approximately three hydrogen bonds with Glutamate166 of main protease, primarily through hydroxyl groups in the benzene ring of benzo(7)annulen-6-one, along with other critical residues. Glu166 is the most critical amino acid for main protease dimerization, which is necessary for catalytic activity. The estimated binding free energy, calculated by Amber and Schrodinger MMGBSA module, reflects a high binding free energy between theaflavin digallate and main protease. Binding pose metadynamics simulation shows the highly persistent H-bond and a stable pose for the theaflavin digallate-main protease complex. Using method control, experimental controls, and test set, alchemical transformation studies confirm high relative binding free energy of theaflavin digallate with the main protease. Computational molecular interaction suggests that theaflavin digallate can inhibit the main protease of SARS-CoV-2.

Anti-angiogenic potential of bioactive phytochemicals from Helicteres isora targeting VEGFR-2 to fight cancer through molecular docking and molecular dynamics simulation

Cancer is one of the leading causes of death due to its very high rate of morbidity and mortality, and there is a constant demand of effective drugs for cancer therapy. Vascular endothelial growth factor receptor-2 (VEGFR-2) plays a significant role as central modulator of angiogenesis and is targeted frequently for developing anti-angiogenic agents to fight cancer. Helicteres isora L. (Malvaceae) is reported to possess diverse medicinal properties including anti-cancer potentials. In the current investigation, 38 bioactive phytochemicals of H. isora were screened virtually to evaluate their anti-angiogenic potentials targeting VEGFR-2. The study unveiled three potential candidates such as, Diosgenin (-9.8 Kcal//mol), Trifolin (-8.4 Kcal/mol) and Yohimbine (-8.1 Kcal/mol) that showed favorable pharmacokinetic, pharmacodynamic and toxicity properties with no significant side effects. Molecular dynamics simulation employing 100 ns revealed noteworthy structural stability and compactness for all the top three candidates. The MM/GBSA binding free energy estimation corroborated the docking interactions where Yohimbine (-30.47 Kcal/mol) scored better than Diosgenin (-27.54 Kcal/mol) and Trifolin (-29.58 Kcal/mol). Target class prediction revealed enzymes in most of the cases and some FDA approved drugs were found as structurally similar analogs for Trifolin and Yohimbine. These findings could lead to the development of novel and effective anti-angiogenic agents. Communicated by Ramaswamy H. Sarma

OBLICZENIOWA ZMIANA PRZEZNACZENIA LEKÓW ZATWIERDZONYCH PRZEZ FDA PRZECIWKO SPECYFICZNYM PATOGENOM ODPOWIEDZIALNYM ZA MASTITIS

Mastitis is the single most expensive disease among cattle in the dairy industry and environmental pathogens such as Staphylococcus aureus and Staphylococcus warneri are among the most common culprits. Recent studies had shed the light on how such pathogens utilize lipolysis mechanisms to evade their host's immune response. In this study, computational drug discovery approaches were deployed to investigate human FDA-approved drugs that hold the potential of serving as inhibitors of lipase activity in the former 2 pathogens. Comprehensive computational analysis involving molecular docking, nanoscale molecular dynamics, and in silico binding free energy estimation has shown that Drospirenone, a unique progestogen with anti-mineralocorticoid properties commonly used in human birth control pills holds potential inhibitory activity against the lipase of Staphylococcus warneri as it had shown to form several stable hydrophobic interactions and hydrogen bonds with the formers lipWY lipase enzyme.

Assessment of a Computational Approach to Predict Drug Resistance Mutations for HIV, HBV and SARS-CoV-2.

Viral resistance is a worldwide problem mitigating the effectiveness of antiviral drugs. Mutations in the drug-targeting proteins are the primary mechanism for the emergence of drug resistance. It is essential to identify the drug resistance mutations to elucidate the mechanism of resistance and to suggest promising treatment strategies to counter the drug resistance. However, experimental identification of drug resistance mutations is challenging, laborious and time-consuming. Hence, effective and time-saving computational structure-based approaches for predicting drug resistance mutations are essential and are of high interest in drug discovery research. However, these approaches are dependent on accurate estimation of binding free energies which indirectly correlate to the computational cost. Towards this goal, we developed a computational workflow to predict drug resistance mutations for any viral proteins where the structure is known. This approach can qualitatively predict the change in binding free energies due to mutations through residue scanning and Prime MM-GBSA calculations. To test the approach, we predicted resistance mutations in HIV-RT selected by (-)-FTC and demonstrated accurate identification of the clinical mutations. Furthermore, we predicted resistance mutations in HBV core protein for GLP-26 and in SARS-CoV-2 3CLpro for nirmatrelvir. Mutagenesis experiments were performed on two predicted resistance and three predicted sensitivity mutations in HBV core protein for GLP-26, corroborating the accuracy of the predictions.

Accurate Binding Free Energy Method from End-State MD Simulations.

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

A computational study to target necroptosis via RIPK1 inhibition

The human receptor-interacting serine/threonine-protein kinase 1 (RIPK1) is a critical necroptosis regulator implicated in cancer, psoriasis, ulcerative colitis, rheumatoid arthritis, Alzheimer’s disease, and multiple sclerosis. Currently, there are no specific RIPK1 antagonists in clinical practice. In this study, we took a target-based computational approach to identify blood-brain-barrier-permeable potent RIPK1 ligands with novel chemotypes. Using molecular docking, we virtually screened the Marine Natural Products (MNP) library of 14,492 small molecules. Initial 18 hits were subjected to detailed ADMET profiling for bioavailability, brain penetration, druglikeness, and toxicities and eventually yielded 548773-66-6 as the best ligand. RIPK1 548773-66-6 binding was validated through duplicated molecular dynamics (MD) simulations where the co-crystallized ligand L8D served as a reference. Trajectory analysis indicated negligible Root-Mean-Square-Deviations (RMSDs) of the best ligand 548773-66-6 relative to the protein backbone: 0.156 ± 0.043 nm and 0.296 ± 0.044 nm (mean ± standard deviations) in two individual simulations. Visual inspection confirmed that 548773-66-6 occupied the RIPK1 ligand-binding pocket associated with the kinase activation loop. Further computations demonstrated consistent hydrogen bond interactions of the ligand with the residue ASP156. Binding free energy estimation also supported stable interactions of 548773-66-6 and RIPK1. Together, our in silico analysis predicted 548773-66-6 as a novel ligand for RIPK1. Therefore, 548773-66-6 could be a viable lead for inhibiting necroptosis in central nervous system inflammatory disorders. Communicated by Ramaswamy H. Sarma

Identifying the Novel Inhibitors Against the Mycolic Acid Biosynthesis Pathway Target "mtFabH" of Mycobacterium tuberculosis.

Mycolic acids are the key constituents of mycobacterial cell wall, which protect the bacteria from antibiotic susceptibility, helping to subvert and escape from the host immune system. Thus, the enzymes involved in regulating and biosynthesis of mycolic acids can be explored as potential drug targets to kill Mycobacterium tuberculosis (Mtb). Herein, Kyoto Encyclopedia of Genes and Genomes is used to understand the fatty acid metabolism signaling pathway and integrative computational approach to identify the novel lead molecules against the mtFabH (β-ketoacyl-acyl carrier protein synthase III), the key regulatory enzyme of the mycolic acid pathway. The structure-based virtual screening of antimycobacterial compounds from ChEMBL library against mtFabH results in the selection of 10 lead molecules. Molecular binding and drug-likeness properties of lead molecules compared with mtFabH inhibitor suggest that only two compounds, ChEMBL414848 (C1) and ChEMBL363794 (C2), may be explored as potential lead molecules. However, the spatial stability and binding free energy estimation of thiolactomycin (TLM) and compounds C1 and C2 with mtFabH using molecular dynamics simulation, followed by molecular mechanics Poisson–Boltzmann surface area (MM/PBSA) indicate the better activity of C2 (ΔG = −14.18 kcal/mol) as compared with TLM (ΔG = −9.21 kcal/mol) and C1 (ΔG = −13.50 kcal/mol). Thus, compound C1 may be explored as promising drug candidate for the structure-based drug designing of mtFabH inhibitors in the therapy of Mtb.

Investigation of the tight binding mechanism of a new anticoagulant DD217 to factor Xa by means of molecular docking and molecular dynamics

A new promising drug candidate DD217 has been proposed recently as a potent anticoagulant acting on factor Xa (fXa) target. It exhibits the lowest concentration of doubling the prothrombin time among the known anticoagulants. In order to explain the efficacy of DD217 in terms of molecular interactions with its target we studied the hypothesis of the tight binding mechanism by means of molecular dynamics simulations and statistical analysis of the trajectory. The conducted analysis confirms the significant contributions to the MM/GBSA estimated binding free energy of the S4 pocket residues as well the crucial role of establishing the hydrogen bonds between the ligand and the backbone amides of Gly216 and Gly218 of the target. The simulation results support the hypothesis of the tight binding mechanism of DD217 to fXa.

Synthesis, crystal structure investigation and computational approach to discover potential hydrazide derivatives as a potent inhibitor of cyclooxygenase-2 enzyme.

This study reports the synthesis of two new hydrazide derivatives, namely, (E)-N'-(4- bromobenzylidene)-2-(4-isobutylphenyl)propanehydrazide (4a) and (E)-N'-benzylidene-2-(4-isobutylphenyl)propanehydrazide (4b), respectively. The compounds were synthesized by the reaction of benzaldehyde with Ibuprofen acid hydrazide. Their structures were confirmed by X-ray crystallography. To try to do a more detailed investigation, computational studies including Hirshfeld surface analyses, energy frameworks, density functional theory (DFT)optimizations, frontier orbital analyses, molecular electrostatic potential analyses, and natural bond orbital analyses of the studied compounds are performed. Moreover, molecular docking and dynamics simulations of complexes of the compounds with the cyclooxygenase-2 (COX-2) enzyme were performed to determine the anti-inflammatory potential of the compounds. These analyses predicted the compounds to show maximum chemical interactions and be dynamically stable during simulation time. Furthermore, estimation of binding free energies confirmed the high binding affinity of the compounds for the COX-2 enzyme.

Classical and Machine Learning Methods for Protein - Ligand Binding Free Energy Estimation.

Binding free energy estimation of drug candidates to their biomolecular target is one of the best quantitative estimators in computer-aided drug discovery. Accurate binding free energy estimation is still a challengeable task even after decades of research, along with the complexity of the algorithm, time-consuming procedures, and reproducibility issues. In this review, we have discussed the advantages and disadvantages of diverse free energy methods like Thermodynamic Integration (TI), Bennett's Acceptance Ratio (BAR), Free Energy Perturbation (FEP), and alchemical methods. Moreover, we discussed the possible application of the machine learning method in proteinligand binding free energy estimation.

Large Scale Study of Ligand-Protein Relative Binding Free Energy Calculations: Actionable Predictions from Statistically Robust Protocols.

The accurate and reliable prediction of protein–ligand binding affinities can play a central role in the drug discovery process as well as in personalized medicine. Of considerable importance during lead optimization are the alchemical free energy methods that furnish an estimation of relative binding free energies (RBFE) of similar molecules. Recent advances in these methods have increased their speed, accuracy, and precision. This is evident from the increasing number of retrospective as well as prospective studies employing them. However, such methods still have limited applicability in real-world scenarios due to a number of important yet unresolved issues. Here, we report the findings from a large data set comprising over 500 ligand transformations spanning over 300 ligands binding to a diverse set of 14 different protein targets which furnish statistically robust results on the accuracy, precision, and reproducibility of RBFE calculations. We use ensemble-based methods which are the only way to provide reliable uncertainty quantification given that the underlying molecular dynamics is chaotic. These are implemented using TIES (Thermodynamic Integration with Enhanced Sampling). Results achieve chemical accuracy in all cases. Ensemble simulations also furnish information on the statistical distributions of the free energy calculations which exhibit non-normal behavior. We find that the “enhanced sampling” method known as replica exchange with solute tempering degrades RBFE predictions. We also report definitively on numerous associated alchemical factors including the choice of ligand charge method, flexibility in ligand structure, and the size of the alchemical region including the number of atoms involved in transforming one ligand into another. Our findings provide a key set of recommendations that should be adopted for the reliable application of RBFE methods.

In-silico studies on wild orange (Citrus macroptera Mont.) compounds against COVID-19 pro-inflammation targets

One-fifth of COVID-19 patients suffer a severe course of COVID-19 (SARS-CoV-2) infection; however, the specific causes remain unclear. Despite numerous papers that have been flooded in different scientific journals clear clinical picture of COVID-19 aftermath persists to remain fuzzy. The survivors of severe COVID-19infection having defeated the virus are just the starting of an uncharted recovery path. Currently, there is no drug available that is safe to consume to combat this pandemic. However, researchers still struggling to find specific therapeutic solutions. The present study employed an in silico approach to assessing the inhibitory potential of the phytochemicals obtained from GC-MS analysis of Citrus macroptera against inflammatory proteins like COX-2, NMDAR and VCAM-1 which remains in a hyperactive state even after a patient is fully cured of this deadly mRNA virus. An extensive molecular docking investigation of the phyto-compounds at the active binding pockets of the inflammatory proteins revealed the promising inhibitory potential of the phytochemicals. Reasonable physicochemical attributes of the compounds following Lipinski’s rule of five, VEBER and PAINS analysis further established them as potential therapeutic candidates against aforesaid inflammatory proteins. MM-GBSA binding free energy estimation revealed that Limonene was the most promising candidate displaying the highest binding efficacy with the concerned VCAM-1 protein included in the present analysis. An interesting finding is the phytochemicals exhibited better binding energy scores with the concerned COX-2, VCAM-1 and NMDA receptor proteins than the conventional drugs that are specifically targeted against them. Our in silico results suggest that all the natural phyto-compounds derived from C. macroptera could be employed in Post covid inflammation complexities after appropriate pre-clinical and clinical trials for further scientific validation. Communicated by Ramaswamy H. Sarma

Identification of α-Glucosidase Inhibitors from Scutellaria edelbergii: ESI-LC-MS and Computational Approach.

The recent study investigated the in vitro anti-diabetic impact of the crude extract (MeOH) and subfractions ethyl acetate (EtOAc); chloroform; n-butanol; n-hexane; and aqueous fraction of S. edelbergii and processed the active EtOAc fraction for the identification of chemical constituents for the first time via ESI-LC-MS analysis through positive ionization mode (PIM) and negative ionization mode (NIM); the identified compounds were further validated through computational analysis via standard approaches. The crude extract and subfractions presented appreciable activity against the α-glucosidase inhibitory assay. However, the EtOAc fraction with IC50 = 0.14 ± 0.06 µg/mL revealed the maximum potential among the fractions used, followed by the MeOH and n-hexane extract with IC50 = 1.47 ± 0.14 and 2.18 ± 0.30 µg/mL, respectively. Moreover, the acarbose showed an IC50 = 377.26 ± 1.20 µg/ mL whereas the least inhibition was observed for the chloroform fraction, with an IC50 = 23.97 ± 0.14 µg/mL. Due to the significance of the EtOAc fraction, when profiled for its chemical constituents, it presented 16 compounds among which the flavonoid class was dominant, and offered eight compounds, of which six were identified in NIM, and two compounds in PIM. Moreover, five terpenoids were identified—three and two in NIM and PIM, respectively—as well as two alkaloids, both of which were detected in PIM. The EtOAc fraction also contained one phenol that was noticed in PIM. The detected flavonoids, terpenoids, alkaloids, and phenols are well-known for their diverse biomedical applications. The potent EtOAc fraction was submitted to computational analysis for further validation of α-glucosidase significance to profile the responsible compounds. The pharmacokinetic estimations and protein-ligand molecular docking results with the support of molecular dynamic simulation trajectories at 100 ns suggested that two bioactive compounds—dihydrocatalpol and leucosceptoside A—from the EtOAc fraction presented excellent drug-like properties and stable conformations; hence, these bioactive compounds could be potential inhibitors of alpha-glucosidase enzyme based on intermolecular interactions with significant residues, docking score, and binding free energy estimation. The stated findings reflect that S. edelbergii is a rich source of bioactive compounds offering potential cures for diabetes mellitus; in particular, dihydrocatalpol and leucosceptoside A could be excellent therapeutic options for the progress of novel drugs to overcome diabetes mellitus.

Relative Binding Free Energy Calculations for Ligands with Diverse Scaffolds with the Alchemical Transfer Method.

We present an extension of the alchemical transfer method (ATM) for the estimation of relative binding free energies of molecular complexes applicable to conventional, as well as scaffold-hopping, alchemical transformations. Named ATM-RBFE, the method is implemented in the free and open-source OpenMM molecular simulation package and aims to provide a simpler and more generally applicable route to the calculation of relative binding free energies than what is currently available. ATM-RBFE is based on sound statistical mechanics theory and a novel coordinate perturbation scheme designed to swap the positions of a pair of ligands such that one is transferred from the bulk solvent to the receptor binding site while the other moves simultaneously in the opposite direction. The calculation is conducted directly in a single solvent box with a system prepared with conventional setup tools, without splitting of electrostatic and nonelectrostatic transformations, and without pairwise soft-core potentials. ATM-RBFE is validated here against the absolute binding free energies of the SAMPL8 GDCC host-guest benchmark set and against protein-ligand benchmark sets that include complexes of the estrogen receptor ERα and those of the methyltransferase EZH2. In each case the method yields self-consistent and converged relative binding free energy estimates in agreement with absolute binding free energies and reference literature values, as well as experimental measurements.

Application of the alchemical transfer and potential of mean force methods to the SAMPL8 host-guest blinded challenge.

We report the results of our participation in the SAMPL8 GDCC Blind Challenge for host-guest binding affinity predictions. Absolute binding affinity prediction is of central importance to the biophysics of molecular association and pharmaceutical discovery. The blinded SAMPL series have provided an important forum for assessing the reliability of binding free energy methods in an objective way. In this challenge, we employed two binding free energy methods, the newly developed alchemical transfer method (ATM) and the well-established potential of mean force (PMF) physical pathway method, using the same setup and force field model. The calculated binding free energies from the two methods are in excellent quantitative agreement. Importantly, the results from the two methods were also found to agree well with the experimental binding affinities released subsequently, with R values of 0.89 (ATM) and 0.83 (PMF). These results were ranked among the best of the SAMPL8 GDCC challenge and second only to those obtained with the more accurate AMOEBA force field. Interestingly, the two host molecules included in the challenge (TEMOA and TEETOA) displayed distinct binding mechanisms, with TEMOA undergoing a dehydration transition whereas guest binding to TEETOA resulted in the opening of the binding cavity that remains essentially dry during the process. The coupled reorganization and hydration equilibria observed in these systems is a useful prototype for the study of these phenomena often observed in the formation of protein-ligand complexes. Given that the two free energy methods employed here are based on entirely different thermodynamic pathways, the close agreement between the two and their general agreement with the experimental binding free energies are a testament to the high quality and precision achieved by theory and methods. The study provides further validation of the novel ATM binding free energy estimation protocol and paves the way to further extensions of the method to more complex systems.

Computational assessment of phytochemicals of medicinal plants from Mexico as potential inhibitors of Salmonella enterica efflux pump AcrB protein

The AcrAB-TolC efflux pump (EP) confers multidrug resistance to Salmonella enterica, a major etiological agent of foodborne infections. Phytochemicals that inhibit the functions of AcrAB-TolC EP present ideal candidates for reversal of antibiotic resistance. Progressive technological advancements, have facilitated the development of computational methods that offer a rapid low-cost approach to screen and identify phytochemicals with inhibitory potential against EP. In this study, 71 phytochemicals derived from plants used for medicinal purposes in Mexico were screened for their potential as inhibitors of Salmonella AcrB protein using in silico approaches including molecular docking and molecular dynamics (MD) simulation. Consequently, naringenin, 5-methoxypsoralen, and licarin A were identified as candidate inhibitors of AcrB protein. The three phytochemicals bound distal/deep pocket (DP) and hydrophobic trap (HPT) residues of AcrB protein critical for interactions with inhibitors, with estimated binding free energies of −95.5 kJ/mol, −97.4 kJ/mol, and −143.8 kJ/mol for naringenin, 5-methoxypsoralen, and licarin A, respectively. Data from the 50 ns MD simulation study revealed stability of the protein-ligand complex and alterations in the AcrB protein DP conformation upon binding of phytochemicals to the DP and HPT regions. Based on the estimated binding free energy and interactions with three out of five residues lining the hydrophobic trap, licarin A demonstrated the highest inhibitory potential, supporting its further application as a candidate for overcoming drug resistance in pathogens. Communicated by Ramaswamy H. Sarma