Estimation Of Binding Affinities Research Articles

Thymoquinone, artemisinin, and thymol attenuate proliferation of lung cancer cells as Sphingosine kinase 1 inhibitors

Sphingosine-1-phosphate (S1P) formed via catalytic actions of sphingosine kinase 1 (SphK1) behaves as a pro-survival substance and activates downstream target molecules associated with various pathologies, including initiation, inflammation, and progression of cancer. Here, we aimed to investigate the SphK1 inhibitory potentials of thymoquinone (TQ), Artemisinin (AR), and Thymol (TM) for the therapeutic management of lung cancer. We implemented docking, molecular dynamics (MD) simulations, enzyme inhibition assay, and fluorescence measurement studies to estimate binding affinity and SphK1 inhibitory potential of TQ, AR, and TM. We further investigated the anti-cancer potential of these compounds on non–small cell lung cancer (NSCLC) cell lines (H1299 and A549), followed by estimation of mitochondrial ROS, mitochondrial membrane potential depolarization, and cleavage of DNA by comet assay. Enzyme activity and fluorescence binding studies suggest that TQ, AR, and TM significantly inhibit the activity of SphK1 with IC50 values of 35.52 µM, 42.81 µM, and 53.68 µM, respectively, and have an excellent binding affinity. TQ shows cytotoxic effect and anti-proliferative potentials on H1299 and A549 with an IC50 value of 27.96 µM and 54.43 µM, respectively. Detection of mitochondrial ROS and mitochondrial membrane potential depolarization shows promising TQ-induced oxidative stress on H1299 and A549 cell lines. Comet assay shows promising TQ-induced oxidative DNA damage. In conclusion, TQ, AR, and TM act as potential inhibitors for SphK1, with a strong binding affinity. In addition, the cytotoxicity of TQ is linked to oxidative stress due to mitochondrial ROS generation. Overall, our study suggests that TQ is a promising inhibitor of SphK1 targeting lung cancer therapy.

Biophysical principles predict fitness of SARS-CoV-2 variants

SARS-CoV-2 employs its spike protein's receptor binding domain (RBD) to enter host cells. The RBD is constantly subjected to immune responses, while requiring efficient binding to host cell receptors for successful infection. However, our understanding of how RBD's biophysical properties contribute to SARS-CoV-2's epidemiological fitness remains largely incomplete. Through a comprehensive approach, comprising large-scale sequence analysis of SARS-CoV-2 variants and the identification of a fitness function based on binding thermodynamics, we unravel the relationship between the biophysical properties of RBD variants and their contribution to viral fitness. We developed a biophysical model that uses statistical mechanics to map the molecular phenotype space, characterized by dissociation constants of RBD to ACE2, LY-CoV016, LY-CoV555, REGN10987, and S309, onto an epistatic fitness landscape. We validate our findings through experimentally measured and machine learning (ML) estimated binding affinities, coupled with infectivity data derived from population-level sequencing. Our analysis reveals that this model effectively predicts the fitness of novel RBD variants and can account for the epistatic interactions among mutations, including explaining the later reversal of Q493R. Our study sheds light on the impact of specific mutations on viral fitness and delivers a tool for predicting the future epidemiological trajectory of previously unseen or emerging low-frequency variants. These insights offer not only greater understanding of viral evolution but also potentially aid in guiding public health decisions in the battle against COVID-19 and future pandemics.

Computational Mutagenesis of Antibody Fragments: Disentangling Side Chains from ΔΔG Predictions.

The development of highly potent antibodies and antibody fragments as binding agents holds significant implications in fields such as biosensing and biotherapeutics. Their binding strength is intricately linked to the arrangement and composition of residues at the binding interface. Computational techniques offer a robust means to predict the three-dimensional structure of these complexes and to assess the affinity changes resulting from mutations. Given the interdependence of structure and affinity prediction, our objective here is to disentangle their roles. We aim to evaluate independently six side-chain reconstruction methods and ten binding affinity estimation techniques. This evaluation was pivotal in predicting affinity alterations due to single mutations, a key step in computational affinity maturation protocols. Our analysis focuses on a data set comprising 27 distinct antibody/hen egg white lysozyme complexes, each with crystal structures and experimentally determined binding affinities. Using six different side-chain reconstruction methods, we transformed each structure into its corresponding mutant via in silico single-point mutations. Subsequently, these structures undergo minimization and molecular dynamics simulation. We therefore estimate ΔΔG values based on the original crystal structure, its energy-minimized form, and the ensuing molecular dynamics trajectories. Our research underscores the critical importance of selecting reliable side-chain reconstruction methods and conducting thorough molecular dynamics simulations to accurately predict the impact of mutations. In summary, our study demonstrates that the integration of conformational sampling and scoring is a potent approach to precisely characterizing mutation processes in single-point mutagenesis protocols and crucial for computational antibody design.

In silico strategies for identifying therapeutic candidates against Acinetobacter baumannii: spotlight on the UDP-N-acetylmuramoyl-L-alanine-D-glutamate:meso-diaminopimelate ligase (MurE)

The opportunistic bacterium Acinetobacter baumannii, which belongs to ESKAPE group of pathogenic bacteria, is leading cause of infections associated with gram-negative bacteria. Acinetobacter baumannii causes severe diseases, such as VAP (ventilator-associated pneumonia), meningitis, and UTI (urinary tract infections) among the nosocomial infections contracted in hospitals. The high infection rate and growing resistance to the vast array of antibiotics makes it paramount to look for new therapeutic strategies against this pathogen. The most promising therapeutic targets are the proteins involved in the synthesis of peptidoglycan which is chief component of bacterial cell wall, MurE is one of those enzymes and is responsible for the addition of one unit of meso-diaminopimelic acid (meso-A2pm) to the nucleotide precursor, UDPMurNAc-L-Ala-D-Glu, and aids in the formation of crosslinker pentapeptide chain. The three-dimensional structure of MurE was modelled using homology modelling technique and then vHTS was performed using this model against Approved Drug Library on DrugRep server using AutoDock Vina. Out of 500 drug molecules, two were selected based on estimated binding affinity, interaction pattern, interacting residues, etc. The selected drug molecules are DB12887 (Tazemetostat) and DB13879 (Glecaprevir). Then, MD simulations were performed on native MurE and its complexes with ligands to examine their dynamical behaviour, stability, integrity, compactness, and folding properties. The protein-ligand complexes were then subjected to binding free energy calculations using the MM/PBSA-based binding free energy analysis and the values are −109.788 ± 8.03 and −152.753 ± 11.98 kcal for MurE-DB12887 and MurE-DB13879 complex, respectively. All the analysis performed on MD trajectories for the complexes of these ligands with protein provided plenty of dependable evidences to consider these molecules for inhibition of MurE enzyme from A. baumannii. Communicated by Ramaswamy H. Sarma

A purely physics-based computational binding affinity estimation from restrained umbrella sampling simulations

A purely physics-based computational binding affinity estimation from restrained umbrella sampling simulations

Affinity Capillary Electrophoresis as a Tool To Characterize Molecularly Imprinted Nanogels in Solution.

In this work, an innovative and accurate affinity capillary electrophoresis (ACE) method was set up to monitor the complexation of aqueous MIP nanogels (NGs) with model cancer-related antigens. Using α2,6'- and α2,3'-sialyllactose as oversimplified cancer biomarker-mimicking templates, NGs were synthesized and characterized in terms of size, polydispersity, and overall charge. A stability study was also carried out in order to select the best storage conditions and to ensure product quality. After optimization of capillary electrophoresis conditions, injection of MIP NGs resulted in a single, sharp, and efficient peak. The mobility shift approach was applied to quantitatively estimate binding affinity, in this case resulting in an association constant of K ≈ 106 M-1. The optimized polymers further displayed a pronounced discrimination between the two sialylated sugars. The newly developed ACE protocol has the potential to become a very effective method for nonconstrained affinity screening of NG in solution, especially during the NG development phase and/or for a final accurate quantitation of the observed binding.

Potent inhibitors targeting cyclin-dependent kinase 9 discovered via virtual high-throughput screening and absolute binding free energy calculations.

Due to the crucial regulatory mechanism of cyclin-dependent kinase 9 (CDK9) in mRNA transcription, the development of kinase inhibitors targeting CDK9 holds promise as a potential treatment strategy for cancer. A structure-based virtual screening approach has been employed for the discovery of potential novel CDK9 inhibitors. First, compounds with kinase inhibitor characteristics were identified from the ZINC15 database via virtual high-throughput screening. Next, the predicted binding modes were optimized by molecular dynamics simulations, followed by precise estimation of binding affinities using absolute binding free energy calculations based on the free energy perturbation scheme. The binding mode of molecule 006 underwent an inward-to-outward flipping, and the new binding mode exhibited binding affinity comparable to the small molecule T6Q in the crystal structure (PDB ID: 4BCF), highlighting the essential role of molecular dynamics simulation in capturing a plausible binding pose bridging docking and absolute binding free energy calculations. Finally, structural modifications based on these findings further enhanced the binding affinity with CDK9. The results revealed that enhancing the molecule's rigidity through ring formation, while maintaining the major interactions, reduced the entropy loss during the binding process and, thus, enhanced binding affinities.

Identification of natural antiviral drug candidates against Tilapia Lake Virus: Computational drug design approaches.

Tilapia Lake Virus (TiLV) is a disease that affects tilapia fish, causing a high rate of sudden death at any stage in their life cycle. Unfortunately, there are currently no effective antiviral drugs or vaccines to prevent or control the progression of this disease. Researchers have discovered that the CRM1 protein plays a critical function in the development and spreading of animal viruses. By inhibiting CRM1, the virus's spread in commercial fish farms can be suppressed. With this in mind, this study intended to identify potential antiviral drugs from two different tropical mangrove plants from tropical regions: Heritiera fomes and Ceriops candolleana. To identify promising compounds that target the CRM1 protein, a computer-aided drug discovery approach is employed containing molecular docking, ADME (absorption, distribution, metabolism and excretion) analysis, toxicity assessment as well as molecular dynamics (MD) simulation. To estimate binding affinities of all phytochemicals, molecular docking is used and the top three candidate compounds with the highest docking scores were selected, which are CID107876 (-8.3 Kcal/mol), CID12795736 (-8.2 Kcal/mol), and CID12303662 (-7.9 Kcal/mol). We also evaluated the ADME and toxicity properties of these compounds. Finally, MD simulation was conducted to analyze the stability of the protein-ligand complex structures and confirm the suitability of these compounds. The computational study demonstrated that the phytochemicals found in H. fomes and C. candolleana could potentially serve as important inhibitors of TiLV, offering practical utility. However, further in vivo investigations are necessary to investigate and potentially confirm the effectiveness of these compounds as antiviral drugs against the virus TiLV.

ResBiGAAT: Residual Bi-GRU with attention for protein-ligand binding affinity prediction

Protein-ligand interaction plays a crucial role in drug discovery, facilitating efficient drug development and enabling drug repurposing. Several computational algorithms, such as Graph Neural Networks and Convolutional Neural Networks, have been proposed to predict the binding affinity using the three-dimensional structure of ligands and proteins. However, there are limitations due to the need for experimental characterization of the three-dimensional structure of protein sequences, which is still lacking for some proteins. Moreover, these models often suffer from unnecessary complexity, resulting in extraneous computations. This study presents ResBiGAAT, a novel deep learning model that combines a deep Residual Bidirectional Gated Recurrent Unit with two-sided self-attention mechanisms. ResBiGAAT leverages protein and ligand sequence-level features and their physicochemical properties to efficiently predict protein-ligand binding affinity. Through rigorous evaluation using 5-fold cross-validation, we demonstrate the performance of our proposed approach. The model exhibits competitive performance on an external dataset, highlighting its generalizability. Our publicly available web interface, located at resbigaat.streamlit.app, allows users to conveniently input protein and ligand sequences to estimate binding affinity.

CurvAGN: Curvature-based Adaptive Graph Neural Networks for Predicting Protein-Ligand Binding Affinity

Accurately predicting the binding affinity between proteins and ligands is crucial for drug discovery. Recent advances in graph neural networks (GNNs) have made significant progress in learning representations of protein-ligand complexes to estimate binding affinities. To improve the performance of GNNs, there frequently needs to look into protein-ligand complexes from geometric perspectives. While the “off-the-shelf” GNNs could incorporate some basic geometric structures of molecules, such as distances and angles, through modeling the complexes as homophilic graphs, these solutions seldom take into account the higher-level geometric attributes like curvatures and homology, and also heterophilic interactions.To address these limitations, we introduce the Curvature-based Adaptive Graph Neural Network (CurvAGN). This GNN comprises two components: a curvature block and an adaptive attention guided neural block (AGN). The curvature block encodes multiscale curvature informaton, then the AGN, based on an adaptive graph attention mechanism, incorporates geometry structure including angle, distance, and multiscale curvature, long-range molecular interactions, and heterophily of the graph into the protein-ligand complex representation. We demonstrate the superiority of our proposed model through experiments conducted on the PDBbind-V2016 core dataset.

Quinoline Hydrazone Derivatives as New Antibacterials Against Multidrug Resistant Strains.

To develop novel antimicrobial agents a series of 2(4)-hydrazone derivatives of quinoline were designed, synthesized and tested. QSAR models of the antibacterial activity of quinoline derivatives were developed by the OCHEM web platform using different machine learning methods. A virtual set of quinoline derivatives was verified with a previously published classification model of anti-E. coli activity and screened using the regression model of anti-S. aureus activity. Selected and synthesized 2(4)-hydrazone derivatives of quinoline exhibited antibacterial activity against the standard and antibiotic-resistant S. aureus and E. coli strains in the range from 15 to 30 mm by the diameter of growth inhibition zones. Molecular docking showed the complex formation of the studied compounds into the catalytic domain of dihydrofolate reductase with an estimated binding affinity from -8.4 to -9.4 kcal/mol.

New Thiazole Carboxamide Derivatives as COX Inhibitors: Design, Synthesis, Anticancer Screening, In Silico Molecular Docking, and ADME Profile Studies.

The goal of this work was to create and test a new series of thiazole carboxamide derivatives for their cyclooxygenase (COX) suppressor and anticancer effects. The compounds were characterized using 1H, 13C NMR, and HRMS spectrum analysis, and their selectivity toward COX-1 and COX-2 was assessed using an in vitro COX inhibition assay kit. Cytotoxicity was assessed using an MTS assay against a panel of cancer and normal cell lines. The docking studies were aided by the Prime MM-GBSA method for estimating binding affinities. The density functional theory (DFT) analysis was performed to assess compound chemical reactivity, which was calculated by computing the border orbital energy of both HOMO and LUMO orbitals, as well as the HOMO-LUMO energy gap. For ADME-T analysis, the QiKProp module was employed. Furthermore, using human X-ray crystal structures, molecular docking studies were carried out to discover the probable binding patterns of these drugs within both COX-1 and COX-2 isozymes. The results demonstrated that the most effective compound against the COX-1 enzyme was 2b with an IC50 of 0.239 μM. It also showed potent activity against COX-2 with an IC50 value of 0.191 μM and a selectivity ratio of 1.251. The highest selectivity ratio was 2.766 for compound 2a against COX-2 with an IC50 dose of 0.958 μM relating to the celecoxib ratio of 23.8 and its IC50 against COX-2 of 0.002 μM. Compound 2j also showed good selectivity toward COX-2 (1.507) with an IC50 value of 0.957 μM. All compounds showed negligible cytotoxic activity against the evaluated normal cell lines, and the IC50 values were more than 300 μM, except for compound 2b, whose IC50 values were 203.71 ± 1.89 and 116.96 ± 2.05 μM against LX-2 and Hek293t cell lines, respectively. Moreover, compound 2b showed moderate anticancer activity against COLO205 and B16F1 cancer cell lines with IC50 values of 30.79 and 74.15 μM, respectively.

Protein-Ligand Binding Affinity Prediction Exploiting Sequence Constituent Homology.

Molecular docking is a commonly used approach for estimating binding conformations and their resultant binding affinities. Machine learning has been successfully deployed to enhance such affinity estimations. Many methods of varying complexity have been developed making use of some or all the spatial and categorical information available in these structures. The evaluation of such methods has mainly been carried out using datasets from PDBbind. Particularly the CASF 2007, 2013 and 2016 datasets with dedicated test sets. This work demonstrates that only a small number of simple descriptors is necessary to efficiently estimate binding affinity for these complexes without the need to know the exact binding conformation of a ligand. The developed approach of using a small number of ligand and protein descriptors in conjunction with gradient boosting trees demonstrates high performance on the CASF datasets. This includes the commonly used benchmark CASF2016 where it appears to perform better than any other approach. This methodology is also useful for datasets where the spatial relationship between the ligand and protein is unknown as demonstrated using a large ChEMBL derived dataset. Code and data uploaded to https://github.com/abbiAR/PLBAffinity. Supplementary data are available at Bioinformatics online.

Dynamic applicability domain (dAD): compound-target binding affinity estimates with local conformal prediction.

Increasing efforts are being made in the field of machine learning to advance the learning of robust and accurate models from experimentally measured data and enable more efficient drug discovery processes. The prediction of binding affinity is one of the most frequent tasks of compound bioactivity modelling. Learned models for binding affinity prediction are assessed by their average performance on unseen samples, but point predictions are typically not provided with a rigorous confidence assessment. Approaches such as the conformal predictor framework equip conventional models with a more rigorous assessment of confidence for individual point predictions. In this paper, we extend the inductive conformal prediction (ICP) framework for interaction data, in particular the compound-target binding affinity prediction task. The new framework is based on dynamically defined calibration sets that are specific for each testing pair and provides prediction assessment in the context of calibration pairs from its compound-target neighbourhood, enabling improved estimates based on the local properties of the prediction model. The effectiveness of the approach is benchmarked on several publicly available datasets and tested in realistic use-case scenarios with increasing levels of difficulty on a complex compound-target binding affinity space. We demonstrate that in such scenarios, novel approach combining applicability domain paradigm with conformal prediction framework, produces superior confidence assessment with valid and more informative prediction regions compared to other state-of-the-art conformal prediction approaches. Dataset and the code are available on GitHub (https://github.com/mlkr-rbi/dAD). Supplementary data are available at Bioinformatics online.

A computational perspective towards the identification of promising lead molecules against 6-hydroxy-methyl dihydropterin pyrophosphokinase (HPPK) from Acinetobacter baumannii

Acinetobacter baumannii is an ESKAPE pathogen that causes endocarditis, pneumonia, blood infections, urinary tract infections, and several other illnesses. In addition, it is mainly responsible for nosocomial infection-related mortality. Gram-negative A. baumannii bacterium (AYE Strain) has high MDR and XDR levels. Due to its function in synthesizing purines and amino acids, folic acid is a significant molecule necessary for the growth of bacteria. The metabolic pathway of folate production is therefore a potential therapeutic target to inhibit bacterial growth. In the current study, the three-dimensional model of 6-Hydroxy-methyl dihydropterinpyrophosphokinase (HPPK) was predicted and subsequently processed through a virtual high throughput screening (vHTS) against compounds from Enamine HTSC library, that could bind to its active site. Three lead candidates (Z73322064, Z354558542, and Z906123504) and a control molecule (7,8 dihydro-7,7-dimethyl-6-hydroxymethlypterin; Accession Number: DB02278) were identified using several screening criteria namely estimated binding affinity, estimated inhibition constant, drug-like properties, ADME properties, mode of binding, and interaction patterns of the screened compounds. The physiological behavior of ligand binding on the HPPK enzyme was then studied using molecular dynamics simulations of apo and ligand bound complexes. This study proposed the following three molecules: Z73322064, Z354338542, and Z906123504 as promising lead candidates against the substrate-binding site of the HPPK enzyme from A. baumannii using global, essential dynamics studies along with MM/PBSA based binding free energy analysis. Communicated by Ramaswamy H. Sarma

Phytochemical and biological characterization of aqueous extract of Vassobia breviflora on proliferation and viability of melanoma cells: involvement of purinergic pathway

ABSTRACT Vassobia breviflora belongs to the Solanaceae family, possessing biological activity against tumor cells and is a promising alternative for therapy. The aim of this investigation was to determine the phytochemical properties V. breviflora using ESI-ToF-MS. The cytotoxic effects of this extract were examined in B16-F10 melanoma cells and the relationship if any to purinergic signaling was involved. The antioxidant activity of total phenols, (2,2-diphenyl-1-picrylhydrazyl (DPPH), 2,2′-azino-bis (3-ethylbenzothiazoline-6-sulfonic acid (ABTS) was analyzed, as well as production of reactive oxygen species (ROS) and nitric oxide (NO) was determined. Genotoxicity was assessed by DNA damage assay. Subsequently, the structural bioactive compounds were docked against purinoceptors P2X7 and P2Y1 receptors. The bioactive compounds found in V. breviflora were N-methyl-(2S,4 R)-trans-4-hydroxy-L-proline, calystegine B, 12-O-benzoyl- tenacigenin A and bungoside B. In vitro cytotoxicity was demonstrated at concentration ranges of 0.1–10 mg/ml, and plasmid DNA breaks only at the concentration of 10 mg/ml. V. breviflora extracts affected hydrolysis by ectoenzymes, such as ectonucleoside triphosphate diphosphohydrolase (E-NTPDase) and ectoadenosine deaminase (E-ADA) which control levels of degradation and formation of nucleosides and nucleotides. In the presence of substrates ATP, ADP, AMP and adenosine, the activities of E-NTPDase, 5´-NT or E-ADA were significantly modulated by V. breviflora. N-methyl-(2S,4 R)-trans-4-hydroxy-L-proline presented higher binding affinity (according to receptor-ligand complex estimated binding affinity as evidenced by ∆G values) to bind to both P2X7 and P2Y1purinergic receptors. Our results suggest a putative interaction of V. breviflora bioactive compounds with growth inhibitory potential in B16-F10 melanoma and suggest that may be considered as promising compounds in melanoma and cancer treatment

Solvated interaction energy: from small-molecule to antibody drug design.

Scoring functions are ubiquitous in structure-based drug design as an aid to predicting binding modes and estimating binding affinities. Ideally, a scoring function should be broadly applicable, obviating the need to recalibrate and refit its parameters for every new target and class of ligands. Traditionally, drugs have been small molecules, but in recent years biologics, particularly antibodies, have become an increasingly important if not dominant class of therapeutics. This makes the goal of having a transferable scoring function, i.e., one that spans the range of small-molecule to protein ligands, even more challenging. One such broadly applicable scoring function is the Solvated Interaction Energy (SIE), which has been developed and applied in our lab for the last 15years, leading to several important applications. This physics-based method arose from efforts to understand the physics governing binding events, with particular care given to the role played by solvation. SIE has been used by us and many independent labs worldwide for virtual screening and discovery of novel small-molecule binders or optimization of known drugs. Moreover, without any retraining, it is found to be transferrable to predictions of antibody-antigen relative binding affinities and as accurate as functions trained on protein-protein binding affinities. SIE has been incorporated in conjunction with other scoring functions into ADAPT (Assisted Design of Antibody and Protein Therapeutics), our platform for affinity modulation of antibodies. Application of ADAPT resulted in the optimization of several antibodies with 10-to-100-fold improvements in binding affinity. Further applications included broadening the specificity of a single-domain antibody to be cross-reactive with virus variants of both SARS-CoV-1 and SARS-CoV-2, and the design of safer antibodies by engineering of a pH switch to make them more selective towards acidic tumors while sparing normal tissues at physiological pH.

Modeling by statistical physics and interpretation of the olfactory process of the two enantiomers 3-mercapto-2-methylbutan-1-ol and 3-mercapto-2-methylpentan-1-ol on the OR2M3 human olfactory receptor

In the present paper, a putative adsorption process of two odorants thiols (3-mercapto-2-methylbutan-1-ol and 3-mercapto-2-methylpentan-1-ol) on the human olfactory receptor OR2M3 has been investigated via advanced models developed by a grand canonical formalism of statistical physics. For the two olfactory systems, a monolayer model with two types of energy (ML2E) has been selected to correlate with the experimental data. The physicochemical analysis of the statistical physics modeling results showed that the adsorption system of the two odorants was multimolecular. Furthermore, the molar adsorption energies were inferior to 22.7 kJ/mol, which confirmed the physisorption process of the adsorption of the two odorant thiols on OR2M3. In addition, quantitative characterizations of both odorants were determined via the olfactory receptor pore size distribution (RPSD) and the adsorption energy distribution (AED), which were spread out from 0.25 to 1.25 nm and from 5 to 35 kJ/mol, respectively. For thermodynamic characterization of the olfactory process, the adsorption entropy indicated the disorder of the adsorption systems of 3-mercapto-2-methylbutan-1-ol and 3-mercapto-2-methylpentan-1-ol on the human olfactory receptor OR2M3. Besides, the used model showed that the presence of copper ions increases the efficacy (olfactory response at saturation) of 3-mercapt-2-methylpentan-1-ol odorant activating OR2M3. The docking molecular simulation indicated that the 3-mercapto-2-methylpentan-1-ol molecule presented more binding affinities (17.15 kJ/mol) with olfactory receptor OR2M3 than 3-mercapto-2-methylbutan-1-ol (14.64 kJ/mol). On the other hand, the two estimated binding affinities of the two odorants belonged to the adsorption energies spectrum (AED) to confirm the physisorption nature of the olfactory adsorption process.

Targeting multi-drug-resistant Acinetobacter baumannii: a structure-based approach to identify the promising lead candidates against glutamate racemase.

Acinetobacter baumannii, one of the critical ESKAPE pathogens, is a highly resilient, multi-drug-resistant, Gramnegative, rod-shaped, highly pathogenic bacteria. It is responsible for almost 1-2% of all hospital-borne infections in immunocompromised patients and causes community outbreaks. Because of its resilience and MDR characteristics, looking for new strategies to check the infections related to this pathogen becomes paramount. The enzymes involved in the peptidoglycan biosynthetic pathway are attractive and the most promising drug targets. They contribute to the formation of the bacterial envelope and help to maintain the rigidity and integrity of the cell. The MurI (glutamate racemase) is one of the crucial enzymes that aid in the formation of the pentapeptide responsible for the interlinkage of peptidoglycan chains. It converts L-glutamate to D-glutamate, which is required to synthesise the pentapeptide chain. In this study, the MurI protein of A. baumannii (strain AYE) was modelled and subjected to high-throughput virtual screening against the enamine-HTSC library, taking UDP-MurNAc-Ala binding site as the targeted site. Four ligandmolecules, Z1156941329 (N-(1-methyl-2-oxo-3,4-dihydroquinolin-6-yl)-1-phenyl-3,4-dihydro-1H-isoquinoline-2-carboxamide), Z1726360919 (1-[2-[3-(benzimidazol-1-ylmethyl)piperidin-1-yl]-2-oxo-1-phenylethyl]piperidin-2-one), Z1920314754 (N-[[3-(3-methylphenyl)phenyl]methyl]-8-oxo-2,7-diazaspiro[4.4]nonane-2-carboxamide) and Z3240755352 (4R)-4-(2,5-difluorophenyl)-1-(4-fluorophenyl)-1,3a,4,5,7,7a-hexahydro-6H-pyrazolo[3,4-b]pyridin-6-one), were identified to be the lead candidates based on Lipinski's rule of five, toxicity, ADME properties, estimated binding affinity and intermolecular interactions. The complexes of these ligands with the protein molecule were then subjected to MD simulations to scrutinise their dynamic behaviour, structural stability and effects on protein dynamics. The molecular mechanics/Poisson-Boltzmann surface area-based binding free energy analysis was also performed to compute the binding free energy of protein-ligand complexes, which offered the following values -23.32 ± 3.04 kcal/mol, -20.67 ± 2.91kcal/mol, -8.93 ± 2.90 kcal/mol and -26.73 ± 2.95 kcal/mol for MurI-Z1726360919, MurI-Z1156941329, MurI-Z3240755352 and MurI-Z3240755354 complexes respectively. Together, the results from various computational analyses utilised in this study proposed that Z1726360919, Z1920314754 and Z3240755352 could act as potential lead molecules to suppress the function of MurI protein from Acinetobacter baumannii.

Elucidating Atomistic Insight into the Dynamical Responses of the SARS-CoV-2 Main Protease for the Binding of Remdesivir Analogues: Leveraging Molecular Mechanics To Decode the Inhibition Mechanism.

To combat mischievous coronavirus disease followed by continuous upgrading of therapeutic strategy against the antibody-resistant variants, the molecular mechanistic understanding of protein-drug interactions is a prerequisite in the context of target-specific rational drug development. Herein, we attempt to decipher the structural basis for the inhibition of SARS-CoV-2 main protease (Mpro) through the elemental analysis of potential energy landscape and the associated thermodynamic and kinetic properties of the enzyme-inhibitor complexes using automated molecular docking calculations in conjunction with classical force field-based molecular dynamics (MD) simulations. The crux of the scalable all-atom MD simulations consummated in explicit solvent media is to capture the structural plasticity of the viral enzyme due to the binding of remdesivir analogues and ascertain the subtle interplay of noncovalent interactions in stabilizing specific conformational states of the receptor that controls the biomolecular processes related to the ligand binding and dissociation kinetics. To unravel the critical role of modulation of the ligand scaffold, we place further emphasis on the estimation of binding free energy as well as the energy decomposition analysis by employing the generalized Born and Poisson-Boltzmann models. The estimated binding affinities are found to vary between -25.5 and -61.2 kcal/mol. Furthermore, the augmentation of inhibitory efficacy of the remdesivir analogue crucially stems from the van der Waals interactions with the active site residues of the protease. The polar solvation energy contributes unfavorably to the binding free energy and annihilates the contribution of electrostatic interactions as derived from the molecular mechanical energies.