Estimation Of Binding Affinities Research Articles

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.

Structure-based prediction of Ras-effector binding affinities and design of “branchegetic” interface mutations

Ras is a central cellular hub protein controlling multiple cell fates. How Ras interacts with a variety of potential effector proteins is relatively unexplored, with only some key effectors characterized in great detail. Here, we have used homology modeling based on X-ray and AlphaFold2 templates to build structural models for 54 Ras-effector complexes. These models were used to estimate binding affinities using a supervised learning regressor. Furthermore, we systematically introduced Ras "branch-pruning" (or branchegetic) mutations to identify 200 interface mutations that affect the binding energy with at least one of the model structures. The impacts of these branchegetic mutants were integrated into a mathematical model to assess the potential for rewiring interactions at the Ras hub on a systems level. These findings have provided a quantitative understanding of Ras-effector interfaces and their impact on systems properties of a key cellular hub.

Time Evolution of Ultrasmall Gold Nanoparticle-Protein Interactions.

To date, much effort has been devoted toward the study of protein corona formation onto large gold nanoparticles (GNPs). However, the protein corona concept breaks down for GNPs in the ultrasmall size regime (<3 nm), and, as a result, our understanding of ultrasmall GNP (usGNP)-protein interactions remains incomplete. Herein, we used anionic usGNPs and six different proteins as model systems to systematically investigate usGNP-protein interactions, with particular focus on the time evolution and long-term behavior of complex formation. The different proteins comprised chymotrypsin (Cht), trypsin (Try), thrombin (Thr), serum albumin (HSA), cytochrome c (Cyt c), and factor XII (FXII). We used a range of biochemical and biophysical methods to estimate binding affinities, determine the effects of usGNPs on protein structure and function, assess the reversibility of any protein structural and functional changes, and evaluate usGNP-protein complex stability. Among the main findings, we observed that prolonged (24 h)─but not short-term (10 min)─interactions between proteins and usGNPs permanently altered protein function, including enzyme activities (Try, Thr, and FXIIa), peroxidase-like activity (Cyt c), and ligand-binding properties (HSA). Remarkably, this occurred without any large-scale loss of the native global conformation, implying time-dependent effects of usGNPs on local protein conformation or dynamics. We also found that both short-(10 min) and long-term (24 h) interactions between proteins and usGNPs yielded short-lived complexes, i.e., there was no time-dependent "hardening" of the interactions at the binding interface as usually seen with large GNPs. The present study increases our fundamental understanding of nano-bio interactions in the ultrasmall size regime, which may assist the safe and effective translation of usGNPs into the clinic.

Validation of the Alchemical Transfer Method for the Estimation of Relative Binding Affinities of Molecular Series.

The accurate prediction of protein-ligand binding affinities is crucial for drug discovery. Alchemical free energy calculations have become a popular tool for this purpose. However, the accuracy and reliability of these methods can vary depending on the methodology. In this study, we evaluate the performance of a relative binding free energy protocol based on the alchemical transfer method (ATM), a novel approach based on a coordinate transformation that swaps the positions of two ligands. The results show that ATM matches the performance of more complex free energy perturbation (FEP) methods in terms of Pearson correlation but with marginally higher mean absolute errors. This study shows that the ATM method is competitive compared to more traditional methods in speed and accuracy and offers the advantage of being applicable with any potential energy function.

Modulatory effects of phytol on the antiemetic property of domperidone, possibly through the D2 receptor interaction pathway: in vivo and in silico studies.

The current study is designed to evaluate the antiemetic effect of the diterpenoid phytol (PHY) using in vivo and in silico studies. For this, emesis was induced in 4-day-old chicks by the oral administration of copper sulfate (CuSO4.5H2O) at 50mg/kg. To see the possible antiemetic mechanism of PHY, we used a number of reference drugs such as domperidone (80mg/kg), ondansetron (24mg/kg) and hyoscine (100mg/kg) as positive controls, while the vehicle served as a negative control group. PHY was administered orally at the doses of 50 and 75mg/kg. Both PHY and reference drugs were given alone or in combined groups to evaluate their synergistic or antagonistic effects on the chicks. Molecular docking of PHY and reference drugs was carried out against 5HT3, D2, D3, H1, NK1,and mAChRs (M1-M5) receptors for estimating binding affinity to the receptors. Drug-receptor interactions and active sites of the receptors were observed with the aid of different computational tools. The drug-likeness and pharmacokinetics of all the drugs were predicted through the SwissADME online database. The results suggest that PHY reduces the mean number of retches and increases latency dose-dependently in the birds. In the combination groups, PHY75 showed better antiemetic effects with domperidone and ondansetron. In addition, PHY exhibited the highest binding affinity with the D2 receptor (6CM4) (-7.3kcal/mol). In conclusion, PHY showed an antiemetic activity in chicks, possibly through the D2 receptor interaction pathway.

Cell-free expression and characterization of multivalent rhamnose-binding lectins using biolayer interferometry.

Lectins are important biological tools for binding glycans, but recombinant protein expression poses challenges for some lectin classes, limiting the pace of discovery and characterization. To discover and engineer lectins with new functions, workflows amenable to rapid expression and subsequent characterization are needed. Here, we present bacterial cell-free protein synthesis as a means for efficient, small-scale expression of multivalent, disulfide bond-rich, rhamnose-binding lectins. Furthermore, we demonstrate that the cell-free expressed lectins can be directly coupled with bio-layer interferometry (BLI) analysis, either in solution or immobilized on the sensor, to measure interaction with carbohydrate ligands without purification. This workflow enables determination of lectin substrate specificity and estimation of binding affinity. Overall, we believe that this method will enable high-throughput expression, screening, and characterization of new and engineered multivalent lectins for applications in synthetic glycobiology.

Advanced investigation of a putative adsorption process of nine non key food odorants (non-KFOs) on the broadly tuned human olfactory receptor OR2W1: Statistical physics modeling and molecular docking study

In the present paper, statistical physics formalism was used to understand the olfactory perception via the investigation of dose-olfactory response curves of a putative adsorption process of nine non key food odorants (non-KFOs) on the broadly tuned human olfactory receptor OR2W1, in order to quantitative characterize the interactions between the nine studied non-KFOs, i. e., furfuryl sulfide, furfuryl disulfide, benzyl methyl disulfide, furfuryl methyl disulfide, benzyl methyl sulfide, 1-phenylethanethiol, benzyl mercaptan, furfuryl methyl sulfide and 3-phenylpropanol molecules and OR2W1 binding sites at a molecular level. Two advanced adsorption models have been proposed: the advanced monolayer monoenergy model (monolayer model with identical and independent olfactory receptor binding sites) (Model 1) and the advanced monolayer model with two independent types of olfactory receptor binding sites (Model 2). It was concluded that the monolayer monoenergy model was selected as the most adequate model to fit the experimental dose-olfactory response curves tabulated in literature. Actually, the numerical values of the three fitted physico-chemical parameters (RM1, n and C1) were obtained by a non-linear regression. Indeed, modeling results suggested that the number of docked non-KFOs per OR2W1 binding site n values (1.24 < n < 1.94) was always superior to 1, which indicated the non-parallel orientation of the studied odorants on the olfactory receptor and the multi-molecular adsorption mechanism. The estimated molar adsorption energy ΔEa values (ranged from 6.07 to 12.16 kJ/mol) for the nine olfactory systems confirmed the physical the exothermic characters of the adsorption process since ΔEa values were lower than 40 kJ/mol and positive. Furthermore, these estimated parameters were applied to characterize stereographically and energetically the interaction between the nine non-KFOs and OR2W1 through the determination of the human receptor binding site size distributions (RSDs) and the adsorption energy distributions (AEDs), which were spread out from 0.25 to 6.50 nm and from 0 to 22.50 kJ/mol, respectively. The docking computation between these nine non-KFOs and OR2W1 proved that the estimated binding affinities were belonged to the adsorption energies spectrum in general and the specific adsorption energy band or the molecular vibration modes limited spectrum (between 2.50 kJ/mol and 17 kJ/mol) (approximate olfactory band).

Towards a purely physics-based computational binding affinity estimation.

Towards a purely physics-based computational binding affinity estimation.

Discovery of new inhibitors of Mycobacterium tuberculosis EPSP synthase - A computational study

Tuberculosis (TB) is a highly infectious disease caused by the pathogen Mycobacterium tuberculosis (Mtb). EPSP Synthase (MtEPSPS), the enzyme responsible for the sixth step of the shikimate pathway, is a potential target for the development of new drugs for the treatment of TB, as it is essential in mycobacteria but absent in humans. In this work, we performed virtual screening using sets of molecules from two databases and three crystallographic structures of MtEPSPS. The initial hits obtained from molecular docking were filtered based on predicted binding affinity and interactions with binding site residues. Subsequently, molecular dynamics simulations were carried out to analyze the stability of protein-ligand complexes. We have found that MtEPSPS forms stable interactions with several candidates, including already approved pharmaceutical drugs such as Conivaptan and Ribavirin monophosphate. In particular, Conivaptan had the highest estimated binding affinity with the open conformation of the enzyme. The complex formed between MtEPSPS and Ribavirin monophosphate was also energetically stable as shown by RMSD, Rg and FEL analyses, and the ligand was stabilized by hydrogen bonds with important residues of the binding site. The findings reported in this work could serve as the basis of promising scaffolds for the discovery, design, and development of new anti-TB drugs.