Computational modelling of the antimicrobial peptides Cruzioseptin-4 extracted from the frog Cruziohyla calcarifer and Pictuseptin-1 extracted from the frog Boana picturata.

A computational study of the peptides Cruzioseptin-4 and Pictuseptin-1, identified in Cruziohyla calcarifer and Boana picturata respectively, has been carried out. The studies on Cruzioseptin-4 show that it is a cationic peptide with a chain of 23 amino acids that possess 52.17% of hydrophobic amino acids and a charge of + 1.2 at pH 7. Similarly, Pictuseptin-1 is a 22 amino acids peptide with a charge of + 3 at pH 7 and 45.45% of hydrophobic amino acids. Furthermore, the predominant secondary structure for both peptides is alpha-helical. The physicochemical properties were predicted using PepCalc and Bio-Synthesis; secondary structures using Jpred4 and PredictProtein; while molecular docking was performed using Autodock Vina. Geometry optimization of the peptides was done using the ONIOM hybrid method with the HF/6-31G basis set implemented in the Gaussian 09 program. Finally, the molecular docking study indicates that the viable mechanism of action for both peptides is through a targeted attack on the cell membrane of pathogens via electrostatic interactions with different membrane components, leading to cell lysis.

ElectroPredictor: An Application to Predict Mayr's Electrophilicity E through Implementation of an Ensemble Model Based on Machine Learning Algorithms.

Electrophilicity (E) is one of the most important parameters to understand the reactivity of an organic molecule. Although the theoretical electrophilicity index (ω) has been associated with E in a small homologous series, the use of w to predict E in a structurally heterogeneous set of compounds is not a trivial task. In this study, a robust ensemble model is created using Mayr's database of reactivity parameters. A combination of topological and quantum mechanical descriptors and different machine learning algorithms are employed for the model's development. The predictability of the model is assessed using different statistical parameters, and its validation is examined, including a training/test partition, an applicability domain, and a y-scrambling test. The global ensemble model presents a Q5-fold2 of 0.909 and a Qext2 of 0.912, demonstrating an excellent predictability performance of E values and showing that w is not a good descriptor for the prediction of E, especially for the case of neutral compounds. ElectroPredictor, a noncommercial Python application (https://github.com/mmoreno1/ElectroPredictor), is developed to predict E. QM9, a well-known large dataset containing 133885 neutral molecules, is used to perform a virtual screening (94.0% coverage). Finally, the 10 most electrophilic molecules are analyzed as possible new Mayr's electrophiles, which have not yet been experimentally tested. This study confirms the necessity to build an ensemble model using nonlinear machine learning algorithms, topographic descriptors, and separating molecules into charged and neutral compounds to predict E with precision.

Study of potential inhibition of the estrogen receptor α by cannabinoids using an in silico approach: Agonist vs antagonist mechanism.

Breast cancer is the main cancer type with more than 2.2 million cases in 2020, and is the principal cause of death in women; with 685000 deaths in 2020 worldwide. The estrogen receptor is involved at least in 70% of breast cancer diagnoses, and the agonist and antagonist properties of the drug in this receptor play a pivotal role in the control of this illness. This work evaluated the agonist and antagonist mechanisms of 30 cannabinoids by employing molecular docking and dynamic simulations. Compounds with docking scores < -8 kcal/mol were analyzed by molecular dynamic simulation at 300 ns, and relevant insights are given about the protein's structural changes, centered on the helicity in alpha-helices H3, H8, H11, and H12. Cannabicitran was the cannabinoid that presented the best relative binding-free energy (-34.96 kcal/mol), and based on rational modification, we found a new natural-based compound with relative binding-free energy (-44.83 kcal/mol) better than the controls hydroxytamoxifen and acolbifen. Structure modifications that could increase biological activity are suggested.

Searching glycolate oxidase inhibitors based on QSAR, molecular docking, and molecular dynamic simulation approaches.

Primary hyperoxaluria type 1 (PHT1) treatment is mainly focused on inhibiting the enzyme glycolate oxidase, which plays a pivotal role in the production of glyoxylate, which undergoes oxidation to produce oxalate. When the renal secretion capacity exceeds, calcium oxalate forms stones that accumulate in the kidneys. In this respect, detailed QSAR analysis, molecular docking, and dynamics simulations of a series of inhibitors containing glycolic, glyoxylic, and salicylic acid groups have been performed employing different regression machine learning techniques. Three robust models with less than 9 descriptors-based on a tenfold cross (Q2 CV) and external (Q2 EXT) validation-were found i.e., MLR1 (Q2 CV = 0.893, Q2 EXT = 0.897), RF1 (Q2 CV = 0.889, Q2 EXT = 0.907), and IBK1 (Q2 CV = 0.891, Q2 EXT = 0.907). An ensemble model was built by averaging the predicted pIC50 of the three models, obtaining a Q2 EXT = 0.933. Physicochemical properties such as charge, electronegativity, hardness, softness, van der Waals volume, and polarizability were considered as attributes to build the models. To get more insight into the potential biological activity of the compouds studied herein, docking and dynamic analysis were carried out, finding the hydrophobic and polar residues show important interactions with the ligands. A screening of the DrugBank database V.5.1.7 was performed, leading to the proposal of seven commercial drugs within the applicability domain of the models, that can be suggested as possible PHT1 treatment.

Computational Modeling of the Interaction of Silver Clusters with Carbohydrates.

Silver nanoparticles are recognized for their numerous physical, biological, and pharmaceutical applications. In the present study, the interaction of silver clusters with monosaccharide molecules is examined to identify which molecule works better as a reducing agent in the application of a green synthesis approach. Geometry optimization of clusters containing one, three, and five silver atoms is performed along with the optimization of α-d-glucose, α-d-ribose, d-erythrose, and glyceraldehyde using density functional theory. Optimized geometries allow identifying the interaction formed in the silver cluster and monosaccharide complexes. An electron localization function analysis is performed to further analyze the interaction found and explain the reduction process in the formation of silver nanoparticles. The overall results indicate that glyceraldehyde presents the best characteristics to serve as the most efficient reducing agent.

In Silico Searching for Alternative Lead Compounds to Treat Type 2 Diabetes through a QSAR and Molecular Dynamics Study.

Free fatty acid receptor 1 (FFA1) stimulates insulin secretion in pancreatic ß-cells. An advantage of therapies that target FFA1 is their reduced risk of hypoglycemia relative to common type 2 diabetes treatments. In this work, quantitative structure-activity relationship (QSAR) approach was used to construct models to identify possible FFA1 agonists by applying four different machine-learning algorithms. The best model (M2) meets the Tropsha's test requirements and has the statistics parameters R2 = 0.843, Q2CV = 0.785, and Q2ext = 0.855. Also, coverage of 100% of the test set based on the applicability domain analysis was obtained. Furthermore, a deep analysis based on the ADME predictions, molecular docking, and molecular dynamics simulations was performed. The lipophilicity and the residue interactions were used as relevant criteria for selecting a candidate from the screening of the DiaNat and DrugBank databases. Finally, the FDA-approved drugs bilastine, bromfenac, and fenofibric acid are suggested as potential and lead FFA1 agonists.

QSAR Studies, Molecular Docking, Molecular Dynamics, Synthesis, and Biological Evaluation of Novel Quinolinone-Based Thiosemicarbazones against Mycobacterium tuberculosis.

In this study, a series of novel quinolinone-based thiosemicarbazones were designed in silico and their activities tested in vitro against Mycobacterium tuberculosis (M. tuberculosis). Quantitative structure-activity relationship (QSAR) studies were performed using quinolinone and thiosemicarbazide as pharmacophoric nuclei; the best model showed statistical parameters of R2 = 0.83; F = 47.96; s = 0.31, and was validated by several different methods. The van der Waals volume, electron density, and electronegativity model results suggested a pivotal role in antituberculosis (anti-TB) activity. Subsequently, from this model a new series of quinolinone-thiosemicarbazone 11a-e was designed and docked against two tuberculosis protein targets: enoyl-acyl carrier protein reductase (InhA) and decaprenylphosphoryl-ß-D-ribose-2'-oxidase (DprE1). Molecular dynamics simulation over 200 ns showed a binding energy of -71.3 to -12.7 Kcal/mol, suggesting likely inhibition. In vitro antimycobacterial activity of quinolinone-thiosemicarbazone for 11a-e was evaluated against M. bovis, M. tuberculosis H37Rv, and six different strains of drug-resistant M. tuberculosis. All compounds exhibited good to excellent activity against all the families of M. tuberculosis. Several of the here synthesized compounds were more effective than the standard drugs (isoniazid, oxafloxacin), 11d and 11e being the most active products. The results suggest that these compounds may contribute as lead compounds in the research of new potential antimycobacterial agents.

Effect of the Nucleophile's Nature on Chloroacetanilide Herbicides Cleavage Reaction Mechanism. A DFT Study.

In this study, the degradation mechanism of chloroacetanilide herbicides in the presence of four different nucleophiles, namely: Br-, I-, HS-, and S2O3-2, was theoretically evaluated using the dispersion-corrected hybrid functional wB97XD and the DGDZVP as a basis set. The comparison of computed activation energies with experimental data shows an excellent correlation (R2 = 0.98 for alachlor and 0.97 for propachlor). The results suggest that the best nucleophiles are those where a sulfur atom performs the nucleophilic attack, whereas the other species are less reactive. Furthermore, it was observed that the different R groups of chloroacetanilide herbicides have a negligible effect on the activation energy of the process. Further insights into the mechanism show that geometrical changes and electronic rearrangements contribute 60% and 40% of the activation energy, respectively. A deeper analysis of the reaction coordinate was conducted, employing the evolution chemical potential, hardness, and electrophilicity index, as well as the electronic flux. The charge analysis shows that the electron density of chlorine increases as the nucleophilic attack occurs. Finally, NBO analysis indicates that the nucleophilic substitution in chloroacetanilides is an asynchronous process with a late transition state for all models except for the case of the iodide attack, which occurs through an early transition state in the reaction.

The Role of Organic Small Molecules in Pain Management.

In this review, a timeline starting at the willow bark and ending in the latest discoveries of analgesic and anti-inflammatory drugs will be discussed. Furthermore, the chemical features of the different small organic molecules that have been used in pain management will be studied. Then, the mechanism of different types of pain will be assessed, including neuropathic pain, inflammatory pain, and the relationship found between oxidative stress and pain. This will include obtaining insights into the cyclooxygenase action mechanism of nonsteroidal anti-inflammatory drugs (NSAID) such as ibuprofen and etoricoxib and the structural difference between the two cyclooxygenase isoforms leading to a selective inhibition, the action mechanism of pregabalin and its use in chronic neuropathic pain, new theories and studies on the analgesic action mechanism of paracetamol and how changes in its structure can lead to better characteristics of this drug, and cannabinoid action mechanism in managing pain through a cannabinoid receptor mechanism. Finally, an overview of the different approaches science is taking to develop more efficient molecules for pain treatment will be presented.

Novel antimicrobial cruzioseptin peptides extracted from the splendid leaf frog, Cruziohyla calcarifer.

Antimicrobial peptides (AMPs) constitute part of a broad range of bioactive compounds present on diverse organisms, including frogs. Peptides, produced in the granular glands of amphibian skin, constitute a component of their innate immune response, providing protection against pathogenic microorganisms. In this work, two novel cruzioseptins peptides, cruzioseptin-16 and -17, extracted from the splendid leaf frog Cruziohyla calcarifer are presented. These peptides were identified using molecular cloning and tandem mass spectrometry. Later, peptides were synthetized using solid-phase peptide synthesis, and their minimal inhibitory concentration and haemolytic activity were tested. Furthermore, these two cruzioseptins plus three previously reported (CZS-1, CZS-2, CZS-3) were computationally characterized. Results show that cruzioseptins are 21-23 residues long alpha helical cationic peptides, with antimicrobial activity against E. coli, S. aureus, and C. albicans and low haemolytic effect. Docking results agree with the principal action mechanism of cationic AMPs that goes through cell membrane disruption due to electrostatic interactions between cationic residues in the cruzioseptins and negative phosphate groups in the pathogen cell membrane. An action mechanism through enzymes inhibition was also tried, but no conclusive results about this mechanism were obtained.

In Silico Screening of the DrugBank Database to Search for Possible Drugs against SARS-CoV-2.

Coronavirus desease 2019 (COVID-19) is responsible for more than 1.80 M deaths worldwide. A Quantitative Structure-Activity Relationships (QSAR) model is developed based on experimental pIC50 values reported for a structurally diverse dataset. A robust model with only five descriptors is found, with values of R2 = 0.897, Q2LOO = 0.854, and Q2ext = 0.876 and complying with all the parameters established in the validation Tropsha's test. The analysis of the applicability domain (AD) reveals coverage of about 90% for the external test set. Docking and molecular dynamic analysis are performed on the three most relevant biological targets for SARS-CoV-2: main protease, papain-like protease, and RNA-dependent RNA polymerase. A screening of the DrugBank database is executed, predicting the pIC50 value of 6664 drugs, which are IN the AD of the model (coverage = 79%). Fifty-seven possible potent anti-COVID-19 candidates with pIC50 values > 6.6 are identified, and based on a pharmacophore modelling analysis, four compounds of this set can be suggested as potent candidates to be potential inhibitors of SARS-CoV-2. Finally, the biological activity of the compounds was related to the frontier molecular orbitals shapes.

A computational study of the reaction mechanism involved in the fast cleavage of an unconstrained amide bond assisted by an amine intramolecular nucleophilic attack.

In the present work, the fast amide bond cleavage of [3-((1R,5S,7s)-3-azabicyclo[3.3.1]nonane-7-carbonyl)-3-azabicyclo[3.3.1]nonane-7-carboxylic acid (bi-ATDO)], through an intramolecular nucleophilic attack of an amine group is evaluated. First, six possible peptide bond cleavage mechanisms, two of them including a water molecule, are described at the ωB97XD/6-311 + G(d,p)//MP2/6-311 + G(d,p) level of theory. The reaction consisting of an intramolecular nitrogen nucleophilic attack followed by a proton transfer and the amide bond cleavage is determined as the most favorable mechanism. The activation free energy computed for the latter is 20.5 kcal mol-1 , which agrees with the reported experimental result of 24.8 kcal mol-1 . Inclusion of a water molecule to assist the first step of the reaction results in an activation free energy increase of about 17 kcal mol-1 . All the steps in the most favorable mechanism are studied more in detail employing intrinsic reaction coordinate as well as the reaction force and reaction electronic flux analysis.

Molecular modeling of four Dermaseptin-related peptides of the gliding tree frog Agalychnis spurrelli.

In this research, we present a preliminary computational study of four Dermaseptin-related peptides from the skin exudate of the gliding tree frog Agalychnis spurrelli. Experimentally, the amino acid sequence of these peptides was elucidated through molecular cloning and tandem mass spectrometry and synthetic peptides were assayed against E. coli, S. aureus, and C. albicans to determine their antimicrobial properties. With the sequences on hand, a computational study of the structures was carried out, obtaining their physicochemical properties, secondary structure, and their similarity to other known peptides. A molecular docking study of these peptides was also performed against cell membrane and several enzymes are known to be vital for the organisms. Results showed that Dermaseptin-related peptides are α-helical cationic peptides with an isoelectric point above 9.70 and a positive charge of physiological pH. Introducing theses peptides in a database, it was determined that their identity compared with known peptides range from 36 to 82% meaning these four Dermaseptins are novel peptides. This preliminary study of molecular docking suggests the mechanism of action of this peptide is not given by the inhibition of essential enzymatic pathways, but by cell lysis. Graphical abstract.

On the Reaction Mechanism of the 3,4-Dimethoxybenzaldehyde Formation from 1-(3',4'-Dimethoxyphenyl)Propene.

Lignin peroxidase (LiP) is an important enzyme for degrading aromatic hydrocarbons not only in nature but also in industry. In the presence of H2O2, this enzyme can easily decompose lignin and analogue compounds under mild conditions. In this reaction mechanism, LiP catalyzes the C-C cleavage of a propenyl side chain, being able to produce veratraldehyde (VAD) from 1-(3',4'-dimethoxyphenyl) propene (DMPP). One of the few and complete proposed mechanisms includes several non-enzymatic reactions. In this study, we performed a computational study to gain insight about the non-enzymatic steps involved in the reaction mechanism of VAD formation from DMPP using LiP as a catalyst. A kinetic characterization of the reaction using the reaction force and the reaction force constant concepts within the density functional theory (DFT) framework is proposed. All theoretical calculations for the reaction pathway were performed using the Minnesota Global Hybrid functional M06-2X and a 6-31++G(d,p) basis set. The complete reaction comprises seven steps (five steps not including LiP as a catalyst), which include radical species formation, bond transformation, water and oxygen addition, atom reordering, and deacetylation. The overall mechanism is an endothermic process with mixed activation energies depending on the four transition states. These results are the first attempt to fully understand the catalytic role of LiP in the degradation of lignin and its aromatic derivative compounds in terms of the electronic structure methods and future hybrid calculation approaches that we have recently been performing.

Computational study of vicarious nucleophilic substitution reactions.

Vicarious nucleophilic substitution reactions are a versatile way of introducing substituents into aromatic and heteroaromatic electron-deficient compounds. In this project, a kinetic study of these reactions by applying quantum mechanics concepts, such as reaction force, force constant, and electronic reaction flow was proposed. Furthermore, absolute theoretical scales of electrophilicity by applying density functional theory electronic indices were established to classify a series of five and six-membered nitroheteroarenes, and nitrobenzenes with substituents in ortho, meta and para positions. The theoretical model was validated by comparison with experimental kinetic results. Calculations using B3LYP/6-311G(d,p) level of theory allowed analysis of the reactivity patterns and the mechanisms of these chemical reactions. The theoretical scale properly accounts for the activating/deactivating effects promoted by the substituents and agrees with the ability of these substituents to accept or donate electrons, electron acceptor substituents are those that increase electrophilicity, and electron donors those that reduce it.