Insight into the Interaction Mechanism of Nicotine, NNK, and NNN with Cytochrome P450 2A13 Based on Molecular Dynamics Simulation.

Tobacco smoke contains various cancer-causing toxic substances, including nicotine and nitrosamines 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanone (NNK) and N'-nitrosonornicotine (NNN). The cytochrome 2A13 is involved in nicotine metabolism and in the activation of the pro-carcinogenic agents NNK and NNN, by means of α-hydroxylation reactions. Despite the significance of cytochrome 2A13 in the biotransformation of these molecules, its conformational mechanism and the molecular basis involved in the process are not fully understood. In this study, we used molecular dynamics and principal component analysis simulations for an in-depth analysis of the essential protein motions involved in the interaction of cytochrome 2A13 with its substrates. We also evaluated the interaction of these substrates with the amino acid residues in the binding pocket of cytochrome 2A13. Furthermore, we quantified the nature of these chemical interactions from free energy calculations using the Molecular Mechanics/Generalized Born Surface Area method. The ligands remained favorably oriented toward compound I (cytochrome P450 O═FeIV state), to undergo α-hydroxylation. The hydrogen bond with asparagine 297 was essential to maintaining the substrates in a favorable catalytic orientation. The plot of first principal motion vs second principal motion revealed that the enzyme's interaction with nicotine and NNK involved different conformational subgroups, whereas the conformational subgroups in the interaction with NNN are more similar. These results provide new mechanistic insights into the mode of interaction of the substrates with the active site of cytochrome 2A13, in the presence of compound I, which is essential for α-hydroxylation.

Computational Investigation of Bisphosphate Inhibitors of 3-Deoxy-d-manno-octulosonate 8-phosphate Synthase.

The synthase, 3-deoxy-d-manno-octulosonate 8-phosphate (KDO8P), is a key enzyme for the lipopolysaccharide (LPS) biosynthesis of gram-negative bacteria and a potential target for developing new antimicrobial agents. In this study, computational molecular modeling methods were used to determine the complete structure of the KDO8P synthase from Neisseria meningitidis and to investigate the molecular mechanism of its inhibition by three bisphosphate inhibitors: BPH1, BPH2, and BPH3. Our results showed that BPH1 presented a protein-ligand complex with the highest affinity, which is in agreement with experimental data. Furthermore, molecular dynamics (MD) simulations showed that BPH1 is more active due to the many effective interactions, most of which are derived from its phosphoenolpyruvate moiety. Conversely, BPH2 exhibited few hydrogen interactions during the MD simulations with key residues located at the active sites of the KDO8P synthase. In addition, we hydroxylated BPH2 to create the hypothetical molecule named BPH3, to investigate the influence of the hydroxyl groups on the affinity of the bisphosphate inhibitors toward the KDO8P synthase. Overall, we discuss the main interactions between the KDO8P synthase and the bisphosphate inhibitors that are potential starting points for the design of new molecules with significant antibiotic activities.

Investigating molecular descriptors in cell-penetrating peptides prediction with deep learning: Employing N, O, and hydrophobicity according to the Eisenberg scale.

Cell-penetrating peptides comprise a group of molecules that can naturally cross the lipid bilayer membrane that protects cells, sharing physicochemical and structural properties, and having several pharmaceutical applications, particularly in drug delivery. Investigations of molecular descriptors have provided not only an improvement in the performance of classifiers but also less computational complexity and an enhanced understanding of membrane permeability. Furthermore, the employment of new technologies, such as the construction of deep learning models using overfitting treatment, promotes advantages in tackling this problem. In this study, the descriptors nitrogen, oxygen, and hydrophobicity on the Eisenberg scale were investigated, using the proposed ConvBoost-CPP composed of an improved convolutional neural network with overfitting treatment and an XGBoost model with adjusted hyperparameters. The results revealed favorable to the use of ConvBoost-CPP, having as input nitrogen, oxygen, and hydrophobicity together with ten other descriptors previously investigated in this research line, showing an increase in accuracy from 88% to 91.2% in cross-validation and 82.6% to 91.3% in independent test.

Harnessing Brazilian biodiversity database: identification of flavonoids as potential inhibitors of SARS-CoV-2 main protease using computational approaches and all-atom molecular dynamics simulation.

SARS-CoV-2 (Severe Acute Respiratory Syndrome Coronavirus 2) is the etiological agent responsible for the global outbreak of COVID-19 (Coronavirus Disease 2019). The main protease of SARS-CoV-2, Mpro, is a key enzyme that plays a vital role in mediating viral replication and transcription. In this study, a comprehensive computational approach was employed to investigate the binding affinity, selectivity, and stability of natural product candidates as potential new antivirals acting on the viral polyprotein processing mediated by SARS-CoV-2 Mpro. A library of 288 flavonoids extracted from Brazilian biodiversity was screened to select potential Mpro inhibitors. An initial filter based on Lipinski's rule of five was applied, and 204 compounds that did not violate any of the Lipinski rules were selected. The compounds were then docked into the active site of Mpro using the GOLD program, and the poses were subsequently re-scored using MM-GBSA (Molecular Mechanics Generalized Born Surface Area) binding free energy calculations performed by AmberTools23. The top five flavonoids with the best MM-GBSA binding free energy values were selected for analysis of their interactions with the active site residues of the protein. Next, we conducted a toxicity and drug-likeness analysis, and non-toxic compounds were subjected to molecular dynamics simulation and free energy calculation using the MM-PBSA (Molecular Mechanics Poisson-Boltzmann Surface Area) method. It was observed that the five selected flavonoids had lower MM-GBSA binding free energy with Mpro than the co-crystal ligand. Furthermore, these compounds also formed hydrogen bonds with two important residues, Cys145 and Glu166, in the active site of Mpro. Two compounds that passed the drug-likeness filter showed stable conformations during the molecular dynamics simulations. Among these, NuBBE_867 exhibited the best MM-PBSA binding free energy value compared to the crystallographic inhibitor. Therefore, this study suggests that NuBBE_867 could be a potential inhibitor against the main protease of SARS-CoV-2 and may be further examined to confirm our results.

Predicting cell-penetrating peptides using machine learning algorithms and navigating in their chemical space.

Cell-penetrating peptides (CPPs) are naturally able to cross the lipid bilayer membrane that protects cells. These peptides share common structural and physicochemical properties and show different pharmaceutical applications, among which drug delivery is the most important. Due to their ability to cross the membranes by pulling high-molecular-weight polar molecules, they are termed Trojan horses. In this study, we proposed a machine learning (ML)-based framework named BChemRF-CPPred (beyond chemical rules-based framework for CPP prediction) that uses an artificial neural network, a support vector machine, and a Gaussian process classifier to differentiate CPPs from non-CPPs, using structure- and sequence-based descriptors extracted from PDB and FASTA formats. The performance of our algorithm was evaluated by tenfold cross-validation and compared with those of previously reported prediction tools using an independent dataset. The BChemRF-CPPred satisfactorily identified CPP-like structures using natural and synthetic modified peptide libraries and also obtained better performance than those of previously reported ML-based algorithms, reaching the independent test accuracy of 90.66% (AUC = 0.9365) for PDB, and an accuracy of 86.5% (AUC = 0.9216) for FASTA input. Moreover, our analyses of the CPP chemical space demonstrated that these peptides break some molecular rules related to the prediction of permeability of therapeutic molecules in cell membranes. This is the first comprehensive analysis to predict synthetic and natural CPP structures and to evaluate their chemical space using an ML-based framework. Our algorithm is freely available for academic use at http://comptools.linc.ufpa.br/BChemRF-CPPred .

Unraveling the conformational dynamics of glycerol 3-phosphate dehydrogenase, a nicotinamide adenine dinucleotide-dependent enzyme of Leishmania mexicana.

Allosteric changes modulate the enzymatic activity, leading to activation or inhibition of the molecular target. Understanding the induced fit accommodation mechanism of a ligand in its lowest-free energy state and the subsequent conformational changes induced in the protein are important questions for drug design. In the present study, molecular dynamics (MD) simulations, binding free energy calculations, and principal component analysis (PCA) were applied to analyze the glycerol-3-phosphate dehydrogenase of Leishmania mexicana (LmGPDH) conformational changes induced by its cofactor and substrate binding. GPDH is a nicotinamide adenine dinucleotide (NAD)-dependent enzyme, which has been reported as an interesting target for drug discovery and development against leishmaniasis. Despite its relevance for glycolysis and pentose phosphate pathways, the structural flexibility and conformational motions of LmGPDH in complex with NADH and dihydroxyacetone phosphate (DHAP) remain unexplored. Here, we analyzed the conformational dynamics of the enzyme-NADH complex (cofactor), and the enzyme-NADH-DHAP complex (adduct), mapped the hydrogen-bond interactions for the complexes and pointed some structural determinants of the enzyme that emerge from these contacts to NADH and DHAP. Finally, we proposed a consistent mechanism for the conformational changes on the first step of the reversible redox conversion of dihydroxyacetone phosphate to glycerol 3-phosphate, indicating key residues and interactions that could be further explored in drug discovery.

Targeting Peptidyl-prolyl Cis-trans Isomerase NIMA-interacting 1: A Structure-based Virtual Screening Approach to Find Novel Inhibitors.

BACKGROUND: Peptidyl-prolyl cis-trans isomerase NIMA-interacting 1 (Pin1) is an enzyme that isomerizes phosphorylated serine or threonine motifs adjacent to proline residues. Pin1 has important roles in several cellular signaling pathways, consequently impacting the development of multiple types of cancers. METHODS: Based on the previously reported inhibitory activity of pentacyclic triterpenoids isolated from the gum resin of Boswellia genus against Pin1, we designed a computational experiment using molecular docking, pharmacophore filtering, and structural clustering allied to molecular dynamics (MD) simulations and binding free energy calculations to explore the inhibitory activity of new triterpenoids against Pin1 structure. RESULTS: Here, we report different computational evidence that triterpenoids from neem (Azadirachta indica A. Juss), such as 6-deacetylnimbinene, 6-Oacetylnimbandiol, and nimbolide, replicate the binding mode of the Pin1 substrate peptide, interacting with high affinity with the binding site and thus destabilizing the Pin1 structure. CONCLUSIONS: Our results are supported by experimental data, and provide interesting structural insights into their molecular mechanism of action, indicating that their structural scaffolds could be used as a start point to develop new inhibitors against Pin1.

Using Accelerated Molecular Dynamics Simulation to elucidate the effects of the T198F mutation on the molecular flexibility of the West Nile virus envelope protein.

The envelope (E) protein is an important target for antibodies in flavivirus. Literature reports that the mutation T198F, located at the domain I-II hinge of the E protein, regulates viral breathing and increases the accessibility of a distal cryptic epitope located on the fusion loop, having a direct impact in the neutralization of West Nile virus (WNV). Our study aimed to describe, using accelerated molecular dynamics simulations, the effects of the T198F mutation in the flexibility of the E protein of WNV and to elucidate the mechanism that regulates epitope accessibility. The simulation results revealed that the mutation favors the formation of alternative hydrogen bonds, hampering the bending movement between domains I and II. We hypothesized that this is the mechanism by which the T198F mutation, located at the middle of the protein, locks the distal cryptc epitope near a single preferred conformation, rendering it more prone to recognition by antibodies.

Computational study of conformational changes in human 3-hydroxy-3-methylglutaryl coenzyme reductase induced by substrate binding.

The enzyme 3-hydroxy-3-methylglutaryl coenzyme A reductase (HMGR) is mainly involved in the regulation of cholesterol biosynthesis. HMGR catalyses the reduction of 3-hydroxy-3-methylglutaryl coenzyme A (HMG-CoA) to mevalonate at the expense of two NADPH molecules in a two-step reversible reaction. In the present study, we constructed a model of human HMGR (hHMGR) to explore the conformational changes of HMGR in complex with HMG-CoA and NADPH. In addition, we analysed the complete sequence of the Flap domain using molecular dynamics (MD) simulations and principal component analysis (PCA). The simulations revealed that the Flap domain plays an important role in catalytic site activation and substrate binding. The apo form of hHMGR remained in an open state, while a substrate-induced closure of the Flap domain was observed for holo hHMGR. Our study also demonstrated that the phosphorylation of Ser872 induces significant conformational changes in the Flap domain that lead to a complete closure of the active site, suggesting three principal conformations for the first stage of hHMGR catalysis. Our results were consistent with previous proposed models for the catalytic mechanism of hHMGR. Communicated by Ramaswamy H. Sarma.

Exploring the Potentiality of Natural Products from Essential Oils as Inhibitors of Odorant-Binding Proteins: A Structure- and Ligand-Based Virtual Screening Approach To Find Novel Mosquito Repellents.

Odorant-binding proteins (OBPs) are the main olfactory proteins of mosquitoes, and their structures have been widely explored to develop new repellents. In the present study, we combined ligand- and structure-based virtual screening approaches using as a starting point 1633 compounds from 71 botanical families obtained from the Essential Oil Database (EssOilDB). Using as reference the crystallographic structure of N,N-diethyl-meta-toluamide interacting with the OBP1 homodimer of Anopheles gambiae (AgamOBP1), we performed a structural and pharmacophoric similarity search to select potential natural products from the library. Thymol acetate, 4-(4-methyl phenyl)-pentanal, thymyl isovalerate, and p-cymen-8-yl demonstrated a favorable chemical correlation with DEET and also had high-affinity interactions with the OBP binding pocket that molecular dynamics simulations showed to be stable. To the best of our knowledge, this is the first study to evaluate on a large scale the potentiality of NPs from essential oils as inhibitors of the mosquito OBP1 using in silico approaches. Our results could facilitate the design of novel repellents with improved selectivity and affinity to the protein binding pocket and can shed light on the mechanism of action of these compounds against insect olfactory recognition.

Structural analysis of viral infectivity factor of HIV type 1 and its interaction with A3G, EloC and EloB.

BACKGROUND: The virion infectivity factor (Vif) is an accessory protein, which is essential for HIV replication in host cells. Vif neutralizes the antiviral host protein APOBEC3 through recruitment of the E3 ubiquitin ligase complex. METHODOLOGY: Fifty thousand Vif models were generated using the ab initio relax protocol of the Rosetta algorithm from sets of three- and nine-residue fragments using the fragment Monte Carlo insertion-simulated annealing strategy, which favors protein-like features, followed by an all-atom refinement. In the protocol, a constraints archive was used to define the spatial relationship between the side chains from Cys/His residues and zinc ions that formed the zinc-finger motif that is essential for Vif function. We also performed centroids analysis and structural analysis with respect to the formation of the zinc-finger, and the residue disposal in the protein binding domains. Additionally, molecular docking was used to explore details of Vif-A3G and Vif-EloBC interactions. Furthermore, molecular dynamics simulation was used to evaluate the stability of the complexes Vif-EloBC-A3G and Vif-EloC. PRINCIPAL FINDINGS: The zinc in the HCCH domain significantly alters the folding of Vif and changes the structural dynamics of the HCCH region. Ab initio modeling indicated that the Vif zinc-finger possibly displays tetrahedral geometry as suggested by Mehle et al. (2006). Our model also showed that the residues L146 and L149 of the BC-box motif bind to EloC by hydrophobic interactions, and the residue P162 of the PPLP motif is important to EloB binding. CONCLUSIONS/SIGNIFICANCE: The model presented here is the first complete three-dimensional structure of the Vif. The interaction of Vif with the A3G protein and the EloBC complex is in agreement with empirical data that is currently available in the literature and could therefore provide valuable structural information for advances in rational drug design.