Binding Free Energy Estimation Research Articles

Identification of DPP-IV inhibitory peptides derived from buffalo colostrum: Mining through bioinformatics, in silico and in vitro approaches.

Bioactive peptides derived from foods provide physiological health benefits beyond nutrition. This study focused on profiling small peptide inhibitors against two key serine proteases, dipeptidyl peptidase-IV (DPP-IV) and prolyl oligopeptidase (POP). DPP-IV is a well-known protein involved in diverse pathways regulating inflammation, renal, cardiovascular physiology, and glucose homeostasis. POP is yet another key target protein for neurodegenerative disorders. The study evaluated peptide libraries of buffalo colostrum whey and fat globule membrane proteins derived from pepsin and pepsin-pancreatin digestion through in silico web tools and structure-based analysis by molecular docking and binding free-energy estimation, followed by in vitro assay for DPP-IV inhibition for the lead peptides. The bioinformatic study indicated 49 peptides presented motifs with DPP-IV inhibition while 5 peptides with sequences for POP inhibition. In the molecular docking interactions study, 22 peptides interacted with active site residues of DPP-IV and 3 peptides with that of POP. The synthesized peptides, SFVSEVPEL and LTFQHNF inhibited DPP-IV in vitro with an IC50 of 193.5 μM and 1.782 mM, respectively. The study revealed the key residues for inhibition of DPP-IV and POP thus affirming the DPP-IV inhibitory potential of milk-derived peptides.

Molecular dynamics in multidimensional space explains how mutations affect the association path of neomycin to a riboswitch

Riboswitches are messenger RNA (mRNA) fragments binding specific small molecules to regulate gene expression. A synthetic N1 riboswitch, inserted into yeast mRNA controls the translation of a reporter gene in response to neomycin. However, its regulatory activity is sensitive to single-point RNA mutations, even those distant from the neomycin binding site. While the association paths of neomycin to N1 and its variants remain unknown, recent fluorescence kinetic experiments indicate a two-step process driven by conformational selection. This raises the question of which step is affected by mutations. To address this, we performed all-atom two-dimensional replica-exchange molecular dynamics simulations for N1 and U14C, U14C[Formula: see text], U15A, and A17G mutants, ensuring extensive conformational sampling of both RNA and neomycin. The obtained neomycin association and binding paths, along with multidimensional free-energy profiles, revealed a two-step binding mechanism, consisting of conformational selection and induced fit. Neomycin binds to a preformed N1 conformation upon identifying a stable upper stem and U-turn motif in the riboswitch hairpin. However, the positioning of neomycin in the binding site occurs at different RNA-neomycin distances for each mutant, which may explain their different regulatory activities. The subsequent induced fit arises from the interactions of the neomycin's N3 amino group with RNA, causing the G9 backbone to rearrange. In the A17G mutant, the critical C6-A17/G17 stacking forms at a closer RNA-neomycin distance compared to N1. These findings together with estimated binding free energies coincide with experiments and elucidate why the A17G mutation decreases and U15A enhances N1 activity in response to neomycin.

Identification of compounds from natural Peruvian sources as potential inhibitors of SARS-CoV-2 Mpro mutations by virtual screening and computational simulations

Background The coronavirus disease (COVID-19) pandemic continues to be a public health problem worldwide. Several therapeutic targets of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) have been identified, whereas the main protease (Mpro) is necessary for virus replication. Since SARS- CoV-2 Mpro mutation rates are inherently high, searching for new inhibitors remains challenging. Herein, this work aimed to evaluate 84 natural compounds from Peruvian sources against different mutations on the Mpro target. Methods We applied virtual screening, all-atom molecular dynamics simulations, and binding free energy estimation by Molecular Mechanics/Generalized Born Surface Area (MM/GBSA). Results The virtual screening results helped us identify rutin as the top compound against different Mpro mutations. Likewise, the computational simulations demonstrated the high structural stability of the Mpro-rutin system. Conclusions his research evaluated the antiviral capacity of Peruvian sources against SARS-CoV-2 Mpro and its mutations, which could be important in preventing and treating SARS-CoV-2 infection.

Discovery of novel inhibitor via molecular dynamics simulations against D-alanyl-D-alanine carboxypeptidase of Enterobacter cloacae

Antibiotics resistance by bacterial pathogens is a major concern to public health worldwide resulting in high health care costs and rising mortality. Subtractive proteomics prioritized D-alanyl-D-alanine carboxypeptidas (DacB) enzyme from Enterobacter cloacae ATCC 13047 as a potential candidate for drugs designing to block pathogen cell wall biosynthesis. Virtual screening of an antibacterial library against the target unraveled a hit compound (2-[(1-methylsulfonylpiperidin-3-yl)methyl]-6-(1H-pyrazol-4-yl) pyrazine) showing high affinity and stability with the target. The N-methyl-N-propyl-methanesulfonamide of the compound is seen as a closed affinity towards domain involving strong hydrogen bonds with Ser41, Lys44, Ser285, and Asn287. The 4-methyl-1H-pyrazole is posed towards the open cavity of domain I and II and formed hydrophobic and hydrophilic contacts. The system is highly stable with average carbon-alpha deviations of 1.69 Å over trajectories of 400-ns. Three vital residues projected: Arg437, Arg438 and Leu400 from enzyme pocket via Radial distribution function (RDF) assay, which actively engaged the inhibitor. Further confirmation is done by estimating binding free energies, which confirms the very low delta energy of −7.24 kcal/mol in Generalized Born (GB) method and −7.4363 kcal/mol in Poisson-Boltzmann (PB) method. WaterSwap calculations were performed that revealed the energies highly converged, an agreement on good system stability. Lastly, three DacB mutants were created to investigate the role of functional active residues and a decline in binding affinity of the residues was noticed. These computational results provide a gateway for experimentalists to further confirm their efficacy both in-vitro and in-vivo. Communicated by Ramaswamy H. Sarma

PopShift: A thermodynamically sound approach to estimate binding free energies by accounting for ligand-induced population shifts from a ligand-free MSM

PopShift: A thermodynamically sound approach to estimate binding free energies by accounting for ligand-induced population shifts from a ligand-free MSM

PopShift: A Thermodynamically Sound Approach to Estimate Binding Free Energies by Accounting for Ligand-Induced Population Shifts from a Ligand-Free Markov State Model.

Obtaining accurate binding free energies from in silico screens has been a long-standing goal for the computational chemistry community. However, accuracy and computational cost are at odds with one another, limiting the utility of methods that perform this type of calculation. Many methods achieve massive scale by explicitly or implicitly assuming that the target protein adopts a single structure, or undergoes limited fluctuations around that structure, to minimize computational cost. Others simulate each protein-ligand complex of interest, accepting lower throughput in exchange for better predictions of binding affinities. Here, we present the PopShift framework for accounting for the ensemble of structures a protein adopts and their relative probabilities. Protein degrees of freedom are enumerated once, and then arbitrarily many molecules can be screened against this ensemble. Specifically, we use Markov state models (MSMs) as a compressed representation of a protein's thermodynamic ensemble. We start with a ligand-free MSM and then calculate how addition of a ligand shifts the populations of each protein conformational state based on the strength of the interaction between that protein conformation and the ligand. In this work we use docking to estimate the affinity between a given protein structure and ligand, but any estimator of binding affinities could be used in the PopShift framework. We test PopShift on the classic benchmark pocket T4 Lysozyme L99A. We find that PopShift is more accurate than common strategies, such as docking to a single structure and traditional ensemble docking─producing results that compare favorably with alchemical binding free energy calculations in terms of RMSE but not correlation─and may have a more favorable computational cost profile in some applications. In addition to predicting binding free energies and ligand poses, PopShift also provides insight into how the probability of different protein structures is shifted upon addition of various concentrations of ligand, providing a platform for predicting affinities and allosteric effects of ligand binding. Therefore, we expect PopShift will be valuable for hit finding and for providing insight into phenomena like allostery.

Computational exploration of FOXM1 inhibitors for glioblastoma: an integrated virtual screening and molecular dynamics simulation study

In this study, a comprehensive investigation of a set of phytochemicals to identify potential inhibitors for the Forkhead box protein M1 (FOXM1) was conducted. FOXM1 is overexpressed in glioblastoma (GBM) cells and plays a crucial role in cell cycle progression, proliferation, and invasion. FOXM1 inhibitors have shown promising results in preclinical studies, and ongoing clinical trials are assessing their efficacy in GBM patients. However, there are limited studies on the identification of novel compounds against this attractive therapeutic target. To address this, the NPACT database containing 1,574 phytochemicals was used, employing a hierarchical multistep docking approach, followed by an estimation of relative binding free energy. By fixing user-defined XP-dock and MM-GBSA cut-off scores of −6.096 and −37.881 kcal/mol, the chemical space was further narrowed. Through exhaustive analysis of molecular binding interactions and various pharmacokinetics profiles, we identified four compounds, namely NPACT00002, NPACT01454, NPACT00856, and NPACT01417, as potential FOXM1 inhibitors. To assess the stability of protein-ligand binding in dynamic conditions, 100 ns Molecular dynamics (MD) simulations studies were performed. Furthermore, Molecular mechanics with generalized Born and surface area solvation (MM-GBSA) based binding free energy estimations of the entire simulation trajectories revealed a strong binding affinity of all identified compounds towards FOXM1, surpassing that of the control drug Troglitazone. Based on extensively studied multistep docking approaches, we propose that these molecules hold promise as FOXM1 inhibitors for potential therapeutic applications in GBM. However, experimental validation will be necessary to confirm their efficacy as targeted therapies. Communicated by Ramaswamy H. Sarma

What to Make of Zero: Resolving the Statistical Noise from Conformational Reorganization in Alchemical Binding Free Energy Estimates with Metadynamics Sampling.

We introduce the self-relative binding free energy (self-RBFE) approach to evaluate the intrinsic statistical variance of dual-topology alchemical binding free energy estimators. The self-RBFE is the relative binding free energy between a ligand and a copy of the same ligand, and its true value is zero. Nevertheless, because the two copies of the ligand move independently, the self-RBFE value produced by a finite-length simulation fluctuates and can be used to measure the variance of the model. The results of this validation provide evidence that a significant fraction of the errors observed in benchmark studies reflect the statistical fluctuations of unconverged estimates rather than the models' accuracy. Furthermore, we find that ligand reorganization is a significant contributing factor to the statistical variance of binding free energy estimates and that metadynamics-accelerated conformational sampling of the torsional degrees of freedom of the ligand can drastically reduce the time to convergence.

Structure-guided screening of protein-protein interaction for the identification of Myc-Max heterodimer complex modulators

De-regulation of oncogenic myelocytomatosis (c-Myc or Myc) transcription factor is one of the most common molecular anomalies encountered in human cancers, and it is typically linked to many aggressive malignancies including breast, lung, cervix, colon glioblastomas, and other haematological organs. The Myc belongs to the basic helix-loop-helix zipper protein family (bHLH-ZIP), and its dimerization with another principal interactor protein partner Myc-associated factor X (Max) is essentially required for cellular transformation, cell growth and proliferation, and transcriptional activation. Intermolecular interactions have been evaluated between hetero-dimer Myc-Max protein, which identified protein-protein interaction (PPI) specific modulators using highly précised molecular docking study followed by long-range interaction stability analyzed through molecular dynamic (MD) simulation. Moreover, ADME profile analyses have been estimated for screened hit compounds. MM-GBSA-based binding free energy (ΔG) estimations have been performed for all screened hit compounds obtained from multi-step molecular docking-based virtual screening technique. According to the employed various rigorous multi-chemometric techniques, four identified inhibitors/modulators appear to have a considerable number of intermolecular contacts with hotspot residues in the hetero-dimer interface region of the Myc-Max PPI complex. However, identified hit compounds might need further structural optimization or extensive biophysical analyses for better understanding of the molecular mechanism for exhibiting the Myc-Max PPI interface binding stability. Communicated by Ramaswamy H. Sarma

Identification of novel compounds and repurposing of FDA drugs for 1-deoxy-D-xylulose 5-phosphate reductoisomerase enzyme of Plasmodium falciparum to combat malaria resistance

The rise of Plasmodium falciparum resistance to Artemisinin-based combination therapies (ACTs) is a significant concern in the fight against malaria. This situation calls for the search for novel anti-malarial candidates. 1-deoxy-D-xylulose 5-phosphate reductoisomerase (IspC) is a potential target involved in various cellular processes in P. falciparum (Pf). We screened ∼0.69 billion novel compounds from the ZINC20 library and repurposed ∼1400 FDA drugs using computational drug discovery methods against PfIspC. Following our computational pipeline, we found five novel ZINC20 compounds (Z-2, Z-3, Z-10, Z-13, and Z-14) and three FDA drugs (Aliskiren, Ceftolozane, and Ombitasvir) that showed striking docking energy (ranging from −8.405 to −10.834 kcal/mol), and strong interactions with key binding site residues (Ser269, Ser270, Ser306, Asn311, Lys312, and Met360) of PfIspC. The novel anti-malarial compounds also exhibited favorable pharmacokinetics and physicochemical properties. Furthermore, through molecular dynamics simulation, we observed the stable dynamics of PfIspC–inhibitor complexes and the influence of inhibitor binding on the protein's conformational arrangements. Notably, the binding free energy estimation confirmed high binding affinity (varied from −11.68 to −33.16 kcal/mol) of these compounds for PfIspC. Our findings could contribute to the ongoing efforts in combating malaria and invite experimental-lab researchers for validation.

In Silico Investigation on the Molecular Behavior and Structural Stability of the Rosette Nanotubes as the Drug Vehicles for Paclitaxel, an Anti-Cancer Drug

Most anticancer drugs affect healthy cells in addition to cancer cells, causing severe side effects. Targeted delivery by nano-based drug delivery systems (NDDS) can reduce these severe side effects while maintaining therapeutic efficacy. This work introduced rosette nanotube (RNT) as a potential drug vehicle for paclitaxel (PTX) due to its self-assembling property, biocompatibility, amphiphilicity, and low toxicity. Molecular dynamics (MD) simulations aided with molecular mechanics Poisson Boltzmann surface area (MMPBSA) analysis are used here to investigate the molecular behavior and the loading energetics of each type of RNT (K1, xK1, and iEt-xK1) with PTX. Analysis showed that the most probable configuration of PTX is on either end of each RNT. The binding free energies (−117.74 to −69.29 kJ/mol) when PTX is closer to one end were stronger than when it is in the inner channel (−53.51 to −40.88 kJ/mol). The latter alludes to the encapsulation of the PTX by each RNT. Thus, loading is possible by encapsulation during the self-assembly process given the favorable estimated binding free energies. Based on the results, RNT has potential as a drug vehicle for PTX, which warrants further investigation.

Standard Binding Free-Energy Calculations: How Far Are We from Automation?

Recent success stories suggest that in silico protein-ligand binding free-energy calculations are approaching chemical accuracy. However, their widespread application remains limited by the extensive human intervention required, posing challenges for the neophyte. As such, it is critical to develop automated workflows for estimating protein-ligand binding affinities with minimum personal involvement. Key human efforts include setting up and tuning enhanced-sampling or alchemical-transformation algorithms as a preamble to computational binding free-energy estimations. Additionally, preparing input files, bookkeeping, and postprocessing represent nontrivial tasks. In this Perspective, we discuss recent progress in automating standard binding free-energy calculations, featuring the development of adaptive or parameter-free algorithms, standardization of binding free-energy calculation workflows, and the implementation of user-friendly software. We also assess the current state of automated standard binding free-energy calculations and evaluate the limitations of existing methods. Last, we outline the requirements for future algorithms and workflows to facilitate automated free-energy calculations for diverse protein-ligand complexes.

Structure-based design of new N-benzyl-piperidine derivatives as multitarget-directed AChE/BuChE inhibitors for Alzheimer's disease.

The pathogenic complexity of Alzheimer's disease (AD) demands the development of multitarget-directed agents aiming at improving actual pharmacotherapy. Based on the cholinergic hypothesis and considering the well-established role of butyrylcholinesterase (BuChE) in advanced stages of AD, the chemical structure of the acetylcholinesterase (AChE) inhibitor drug donepezil (1) was rationally modified for the design of new N-benzyl-piperidine derivatives (4a-d) as potential multitarget-direct AChE and BuChE inhibitors. The designed analogues were further studied through the integration of in silico and in vitro methods. ADMET predictions showed that 4a-d are anticipated to be orally bioavailable, able to cross the blood-brain barrier and be retained in the brain, and to have low toxicity. Computational docking and molecular dynamics indicated the formation of favorable complexes between 4a-d and both cholinesterases. Derivative 4a presented the lowest binding free energyestimation due to interaction with key residues from both target enzymes (-36.69 ± 4.47 and -32.23 ± 3.99 kcal/mol with AChE and BuChE, respectively). The in vitro enzymatic assay demonstrated that 4a was the most potent inhibitor of AChE (IC50 2.08 ± 0.16 µM) and BuChE (IC50 7.41 ± 0.44 µM), corroborating the in silico results and highlighting 4a as a novel multitarget-directed AChE/BuChE inhibitor.

Identification by molecular dynamic simulation and in vitro validation of SISB-A1, N-[1-(4-bromophenyl)-3-methyl-1H-pyrazol-5-yl]-2-[(2-oxo-4-phenyl-2H-chromen-7-yl) oxy], as an inhibitor of the AblT315I mutant kinase to combat imatinib resistance in chronic myeloid leukemia.

The discovery of imatinib, a specific inhibitor of Abl kinase, revolutionized the therapeutic approach to chronic myeloid leukemia (CML); however, its efficacy can be impeded by the emergence of novel mutations within the kinase domain, particularly AblT315I, that lead to the development of drug resistance. It therefore remains necessary to identify specific inhibitors that can effectively target imatinib-resistant CML harboring the AblT315I mutation. A natural product library sourced from the ZINC database was screened against the experimental structure of AblT315I kinase to identify compounds that selectively target the mutated kinase. The top-scoring compound was empirically tested for inhibition of AblT315I kinase using a luminescence-based kit and for impact on cellular proliferation using the BaF3-BCR-ABL-T315I stable cell line. Computational docking and molecular dynamic simulations identified the compound SISB-A1, N-[1-(4-bromophenyl)-3-methyl-1H-pyrazol-5-yl]-2-[(2-oxo-4-phenyl-2H-chromen-7-yl)oxy] acetamide, to effectively bind the catalytic domain of the mutant AblT315I kinase. Moreover, SISB-A1 exhibited greater preference than imatinib for amino acid residues of the mutant kinase's active site, including isoleucine 315. MMPBSA-based Gibbs binding free energy estimation predicted SISB-A1 to have a free energy of -51.5 versus -65.0kcal/mol for the conventional AblT315I inhibitor ponatinib. Cell proliferation assays showed SISB-A1 to have a GI50 of 164.0nM against the ABL-T315I stable cell line, whereas imatinib had a GI50 of 5035nM. The IC50 value obtained for SISB-A1 against the AblT315I kinase was 197.9nM. The results indicate SISB-A1 to have a notable ability to bind the catalytic domain of the AblT315I mutant kinase and effectively suppress its activity, thereby surpassing the associated resistance to imatinib. Continued advancement of this lead compound has the potential to yield innovative therapeutics for imatinib-resistant CML.

Combined Experimental and Computational Study of V-Substituted Lindqvist Polyoxotungstate: Screening by Docking for Potential Antidiabetic Activity.

In the current work, a novel vanadotungstate compound, (C6H9N2)4[V2W4O19]·2H2O (1), is isolated by a simple stepwise synthesis method and characterized by a combined experimental and computational study. Molecular docking is conducted for the first time for this kind of substituted Lindqvist polyoxometalates to elucidate for potential antidiabetic activity. Hence, the modeling results revealed a significant docking score of the reported compound to bind to the active sites of α-glucosidase with the lowest binding energy of -5.7 kcal/mol, where the standard drug acarbose (ACB) had -4.6 kcal/mol binding energy. The stability of binding was enhanced by strong H-bonding, van der Waals, and electrostatic interactions occurring in the three-dimensional (3D) supramolecular network of polyanionic vanadotungstate subunits templated with organic moieties as shown by X-ray diffraction and Hirshfeld analyses. Furthermore, density functional theory (DFT) calculations supported with photophysical measurements are also discussed to predict the most chemical and biological reactivity. In this view, the complete description of electronic and biological features of (1) is enhanced by determination of the highest occupied molecular orbital (HOMO)/least unoccupied molecular orbital (LUMO) energy, electronic density, ionization potential, electron affinity, etc. These chemical descriptors, intermolecular interactions, docking score, and binding free energy estimation are essential in understanding the reactivity of this bioactive compound offering potential inhibition of the α-glucosidase enzyme.

Discovery of Potential Noncovalent Inhibitors of Dehydroquinate Dehydratase from Methicillin-Resistant Staphylococcus aureus through Computational-Driven Drug Design.

Bacteria resistance to antibiotics is a concerning global health problem; in this context, methicillin-resistant Staphylococcus aureus (MRSA) is considered as a high priority by the World Health Organization. Furthermore, patients with a positive result for COVID-19 received early antibiotic treatment, a fact that potentially encourages the increase in antibiotic resistance. Therefore, there is an urgency to develop new drugs with molecular mechanisms different from those of the actual treatments. In this context, enzymes from the shikimate pathway, a route absent in humans, such as dehydroquinate dehydratase (DHQD), are considered good targets. In this work, a computer-aided drug design strategy, which involved exhaustive virtual screening and molecular dynamics simulations with MM-PBSA analysis, as well as an in silico ADMETox characterization, was performed to find potential noncovalent inhibitors of DHQD from MRSA (SaDHQD). After filtering the 997 million compounds from the ZINC database, 6700 compounds were submitted to an exhaustive virtual screening protocol. From these data, four molecules were selected and characterized (ZINC000005753647 (1), ZINC000001720488 (2), ZINC000082049768 (3), and ZINC000644149506 (4)). The results indicate that the four potential inhibitors interacted with residues important for substrate binding and catalysis, with an estimated binding free energy like that of the enzyme's substrate. Their ADMETox-predicted properties suggest that all of them support the structural characteristics to be considered good candidates. Therefore, the four compounds reported here are excellent option to be considered for future in vitro studies to design new SaDHQD noncovalent inhibitors and contribute to the search for new drugs against MRSA.

Repurposing of antibacterial compounds for suppression of Mycobacterium tuberculosis dormancy reactivation by targeting resuscitation-promoting factors B

Tuberculosis infection has always been a global concern for public health, and the mortality rate has increased tremendously every year. The ability of the resuscitation Mycobacterium tuberculosis (Mtb) from the dormant state is one of the major reasons for the epidemic spread of tuberculosis infection, especially latent tuberculosis infection (LTBI). The element that encourages resuscitation, RpfB (resuscitation-promoting factors B), is mostly in charge of bringing Mtb out of slumber. This reason makes RpfB a promising target for developing tuberculosis drugs because of the effects of latent tuberculosis. Therefore, this work was executed using a computational three-level screening of the Selleckhem antibiotics database consisting of 462 antibiotics against the ligand binding region of the RpfB protein, followed by an estimation of binding free energy for ideal identification and confirmation of potential RpfB inhibitor. Subsequently, three antibiotic drug molecules, i.e., Amikacin hydrate (−66.87 kcal/mol), Isepamicin sulphate (−60.8 kcal/mol), and Bekanamycin (−46.89 kcal/mol), were selected on the basis of their binding free energy value for further computational studies in comparison to reference ligand, 4-benzoyl-2-nitrophenyl thiocyanate (NPT7). Based on the intermolecular interaction profiling, 200 ns molecular dynamic simulation (MD), post-simulation analysis and principal component analysis (PCA), the selected antibiotics showed substantial stability with the RpfB protein compared to the NPT7 inhibitor. Conclusively based on the computational results, the preferred drugs can be potent inhibitors of the RpfB protein, which can be further validated using in vivo research and in vitro enzyme inhibition to understand their therapeutic activity against tuberculosis infection. Communicated by Ramaswamy H. Sarma

Gefitinib derivatives and drug-resistance: A perspective from molecular dynamics simulations

Epidermal-growth factor receptor (EGFR) is a transmembrane tyrosine kinase (TK) with a significant role in cell survival. EGFR is upregulated in various cancer cells and known as a druggable target. Gefitinib is a first-line TK inhibitor used against metastatic non-small cell lung cancer (NSCLC). Despite initial clinical response, a conserved therapeutic effect could not be achieved due to the occurrence of resistance mechanisms. Point mutations in EGFR genes are one of the major causes of rendered tumor sensitivity. To aid in the development of more efficient TKIs, chemical structures of prevailing drugs and their target binding patterns are very important. The aim of the present study was to propose synthetically-accessible gefitinib congeners with enhanced binding fitness to clinically frequent EGFR mutants. Docking simulations of intended molecules identified 1-(4-(3-chloro-4-fluorophenylamino)-7-methoxyquinazolin-6-yl)-3-(oxazolidin-2-ylmethyl) thiourea (23) as a top-binder structure inside G719S, T790 M, L858R and T790 M/L858R-EGFR active sites. Superior docked complexes were subjected to the entire 400 ns molecular dynamics (MD) simulations. Analysis of data revealed the stability of mutant enzymes upon binding to molecule 23. All mutant complexes with the exception of a T790 M/L858R-EGFR, were majorly stabilized through cooperative hydrophobic contacts. Pairwise analysis of H-bonds proved Met793 as the conserved residue with stable H-bond participations as hydrogen bond donor (Frequency 63–96%). Amino acid decomposition analysis confirmed the probable role of Met793 in complex stabilization. Estimated binding free energies indicated the proper accommodation of molecule 23 inside target active sites. Pairwise energy decompositions of stable binding modes revealed the energetic contribution of key residues. Although wet lab experiments are required to unravel the mechanistic details of mEGFR inhibition, MD results provide structural basis for those events that are difficult to address experimentally. The outputs of the current study may assist to design small molecules with high potency to mEGFRs.

Identification of phytochemical inhibitors targeting phosphate acetyltransferase of Mycoplasma genitalium: insights from virtual screening and molecular dynamics studies.

Mycoplasma genitalium (M. genitalium) has evolved as a superbug, and the developing antimicrobial resistancewith just a few treatment options available is an imminentconcern. Due to the emergence of antibiotic resistance, a new antibiotic class or medications are requiredto combat this pathogen. The phosphate acetyltransferase (PTA) enzyme can be a suitable drug target which is essential for M. genitalium survival and involves in acetate metabolism. To efficiently find potent inhibitors, structure-based drug design approaches targeting the PTA of M. genitalium have been established. In this study, the three most potent phytochemical inhibitors were predicted from virtual screening and these are sitostanyl ferulate, beta-sitosterol-beta-D-glucoside, and brassinolide, with binding energies of - 9.66, - 9.60, and - 9.48kcal/mol, respectively. The active site residues Thr-125, Arg-300, Ser-299, Tyr-272, and Lys-273 appear to be critical in binding the three predicted potentinhibitors. The results of the molecular dynamics study indicate that the three predicted phytochemical inhibitors have formed stable bonds with PTA. Molecular Mechanics Poisson-Boltzmann Surface Area (MM-PBSA) was utilized for the estimation of binding free energy of PTA-phytochemical complexes. Taken together, the findings of our computational work might aid in the development of possible potential drugsto treat and ameliorate the severity of M. genitalium infection.

STITCH, Physicochemical, ADMET, and In Silico Analysis of Selected Mikania Constituents as Anti-Inflammatory Agents

The Mikania genus has been known to possess numerous pharmacological activities. In the present study, we aimed to evaluate the interaction of 26 selected constituents of Mikania species with (i) cyclooxygenase 2 (COX 2), (ii) human neutrophil elastase (HNE), (iii) lipoxygenase (LOX), matrix metalloproteinase ((iv) MMP 2 and (v) MMP 9), and (vi) microsomal prostaglandin E synthase 2 (mPGES 2) inhibitors using an in silico approach. The 26 selected constituents of Mikania species, namely mikamicranolide, kaurenoic acid, stigmasterol, grandifloric acid, kaurenol, spathulenol, caryophyllene oxide, syringaldehyde, dihydrocoumarin, o-coumaric acid, taraxerol, melilotoside, patuletin, methyl-3,5-di-O-caffeoyl quinate, 3,3′,5-trihydroxy-4′,6,7-trimethoxyflavone, psoralen, curcumene, herniarin, 2,6-dimethoxy quinone, bicyclogermacrene, α-bisabolol, γ-elemene, provincialin, dehydrocostus lactone, mikanin-3-O-sulfate, and nepetin, were assessed based on the docking action with COX 2, HNE, LOX, MMP 2, MMP 9, and mPGES 2 using Discovery Studio (in the case of LOX, the Autodock method was utilized). Moreover, STITCH (Search Tool for Interacting Chemicals), physicochemical, drug-likeness, and ADMET (Absorption, Distribution, Metabolism, Excretion, and Toxicity) analyses were conducted utilizing the STITCH web server, the Mol-inspiration web server, and Discovery Studio, respectively. In the present study, STITCH analysis revealed only six ligands (dihydrocoumarin, patuletin, kaurenol, psoralen, curcumene, and nepetin) that showed interactions with human proteins. Physicochemical analysis showed that seventeen ligands complied well with Lipinski’s rule. ADMET analysis showed eleven ligands to possess hepatotoxic effects. Significantly, the binding free energy estimation displayed that the ligand methyl-3, 5-di-O-caffeoyl quinate revealed the highest binding energy for all the target enzymes, excluding LOX, suggesting that this may have efficacy as a non-steroidal anti-inflammatory drug (NSAID). The current study presents a better understanding of how Mikania is used as a traditional medicinal plant. Specifically, the 26 ligands of the Mikania plant are potential inhibitor against COX 2, HNE, LOX, MMP 2, MMP 9, and mPGES 2 for treatments for acute and/or chronic inflammatory diseases.