In silico Prediction of Pranlukast as a Stabilizer of PD-L1 Homodimers.

INTRODUCTION: Tumors can be targeted by modulating the immune response of the patient. Programmed cell death protein 1 (PD-1) and programmed cell death ligand 1 (PD-L1) are critical immune checkpoints in cancer biology. The efficacy of certain cancer immunotherapies has been achieved by targeting these molecules using monoclonal antibodies. METHOD: Small-molecule drugs have also been developed as inhibitors of the PD-1/PD-L1 axis, with a mechanism of action that is distinct from that of antibodies: they induce the formation of PD-L1 homodimers, causing their stabilization, internalization, and subsequent degradation. Drug repurposing is a strategy in which new uses are sought after for approved drugs, expediting their clinical translation based on updated findings. In this study, we generated a pharmacophore model that was based on reported small molecules that targeted PD-L1 and used it to identify potential PD-L1 inhibitors among FDA-approved drugs. RESULTS: We identified 12 pharmacophore-matching compounds, but only 4 reproduced the binding mode of the reference inhibitors in docking experiments. Further characterization by molecular dynamics showed that pranlukast, an antagonist of leukotriene receptors that is used to treat asthma, generated stable and energyfavorable interactions with PD-L1 homodimers and induced homodimerization of recombinant PD-L1. CONCLUSION: Our results suggest that pranlukast inhibits the PD-1/PD-L1 axis, meriting its repurposing as an antitumor drug.

Ursolic acid interaction with transcription factors BRAF, V600E, and V600K: a computational approach towards new potential melanoma treatments.

CONTEXT: Melanoma is one of the cancers with the highest mortality rate for its ability to metastasize. Several targets have undergone investigation for the development of drugs against this pathology. One of the main targets is the kinase BRAF (RAF, rapidly accelerated fibrosarcoma). The most common mutation in melanoma is BRAFV600E and has been reported in 50-90% of patients with melanoma. Due to the relevance of the BRAFV600E mutation, inhibitors to this kinase have been developed, vemurafenib-OMe and dabrafenib. Ursolic acid (UA) is a pentacyclic triterpene with a privileged structure, the pentacycle scaffold, which allows to have a broad variety of biological activity; the most studied is its anticancer capacity. In this work, we reported the interaction profile of vemurafenib-OMe, dabrafenib, and UA, to define whether UA has binding capacity to BRAFWT, BRAFV600E, and BRAFV600K. Homology modeling of BRAFWT, V600E, and V600K; molecular docking; and molecular dynamics simulations were carried out and interactions and residues relevant to the binding of the inhibitors were obtained. We found that UA, like the inhibitors, presents hydrogen bond interactions, and hydrophobic interactions of van der Waals, and π-stacking with I463, Q530, C532, and F583. The ΔG of ursolic acid in complex with BRAFV600K (- 63.31 kcal/mol) is comparable to the ΔG of the selective inhibitor dabrafenib (- 63.32 kcal/mol) in complex to BRAFV600K and presents a ΔG like vemurafenib-OMe with BRAFWT and V600E. With this information, ursolic acid could be considered as a lead compound for design cycles and to optimize the binding profile and the selectivity towards mutations for the development of new selective inhibitors for BRAFV600E and V600K to new potential melanoma treatments. METHODS: The homology modeling calculations were executed on the public servers I-TASSER and ROBETTA, followed by molecular docking calculations using AutoGrid 4.2.6, AutoDockGPU 1.5.3, and AutoDockTools 1.5.6. Molecular dynamics and metadynamics simulations were performed in the Desmond module of the academic version of the Schrödinger-Maestro 2020-4 program, utilizing the OPLS-2005 force field. Ligand-protein interactions were evaluated using Schrödinger-Maestro program, LigPlot + , and PLIP (protein-ligand interaction profiler). Finally, all of the protein figures presented in this article were made in the PyMOL program.

The smallest electrochemical bubbles.

Many of the relevant electrochemical processes in the context of catalysis or energy conversion and storage, entail the production of gases. This often implicates the nucleation of bubbles at the interface, with the concomitant blockage of the electroactive area leading to overpotentials and Ohmic drop. Nanoelectrodes have been envisioned as assets to revert this effect, by inhibiting bubble formation. Experiments show, however, that nanobubbles nucleate and attach to nanoscale electrodes, imposing a limit to the current, which turns out to be independent of size and applied potential in a wide range from 3 nm to tenths of microns. Here we investigate the potential-current response for disk electrodes of diameters down to a single-atom, employing molecular simulations including electrochemical generation of gas. Our analysis reveals that nanoelectrodes of 1 nm can offer twice as much current as that delivered by electrodes with areas four orders of magnitude larger at the same bias. This boost in the extracted current is a consequence of the destabilization of the gas phase. The grand potential of surface nanobubbles shows they can not reach a thermodynamically stable state on supports below 2 nm. As a result, the electroactive area becomes accessible to the solution and the current turns out to be sensitive to the electrode radius. In this way, our simulations establish that there is an optimal size for the nanoelectrodes, in between the single-atom and ∼3 nm, that optimizes the gas production.

In Silico Analysis of the Molecular Interaction between Anthocyanase, Peroxidase and Polyphenol Oxidase with Anthocyanins Found in Cranberries.

Anthocyanins are bioactive compounds responsible for various physiological processes in plants and provide characteristic colors to fruits and flowers. Their biosynthetic pathway is well understood; however, the enzymatic degradation mechanism is less explored. Anthocyanase (ß-glucosidase (BGL)), peroxidase (POD), and polyphenol oxidase (PPO) are enzymes involved in degrading anthocyanins in plants such as petunias, eggplants, and Sicilian oranges. The aim of this work was to investigate the physicochemical interactions between these enzymes and the identified anthocyanins (via UPLC-MS/MS) in cranberry (Vaccinium macrocarpon) through molecular docking to identify the residues likely involved in anthocyanin degradation. Three-dimensional models were constructed using the AlphaFold2 server based on consensus sequences specific to each enzyme. The models with the highest confidence scores (pLDDT) were selected, with BGL, POD, and PPO achieving scores of 87.6, 94.8, and 84.1, respectively. These models were then refined using molecular dynamics for 100 ns. Additionally, UPLC-MS/MS analysis identified various flavonoids in cranberries, including cyanidin, delphinidin, procyanidin B2 and B4, petunidin, pelargonidin, peonidin, and malvidin, providing important experimental data to support the study. Molecular docking simulations revealed the most stable interactions between anthocyanase and the anthocyanins cyanidin 3-arabinoside and cyanidin 3-glucoside, with a favorable ΔG of interaction between -9.3 and -9.2 kcal/mol. This study contributes to proposing a degradation mechanism and seeking inhibitors to prevent fruit discoloration.

A contact-based analysis of local energetic frustration dynamics identifies key residues enabling RfaH fold-switch.

Fold-switching enables metamorphic proteins to reversibly interconvert between two highly dissimilar native states to regulate their protein functions. While about 100 proteins have been identified to undergo fold-switching, unveiling the key residues behind this mechanism for each protein remains challenging. Reasoning that fold-switching in proteins is driven by dynamic changes in local energetic frustration, we combined fold-switching simulations generated using simplified structure-based models with frustration analysis to identify key residues involved in this process based on the change in the density of minimally frustrated contacts during refolding. Using this approach to analyze the fold-switch of the bacterial transcription factor RfaH, we identified 20 residues that significantly change their frustration during its fold-switch, some of which have been experimentally and computationally reported in previous works. Our approach, which we developed as an additional module for the FrustratometeR package, highlights the role of local frustration dynamics in protein fold-switching and offers a robust tool to enhance our understanding of other proteins with significant conformational shifts.

A Theoretical Study of the Interaction of PARP-1 with Natural and Synthetic Inhibitors: Advances in the Therapy of Triple-Negative Breast Cancer.

In the current study, we have investigated the secondary metabolites present in ethnomedical plants used for medicinal purposes-Astilbe chinensis (EK1), Scutellaria barbata D. Don (EK2), Uncaria rhynchophylla (EK3), Fallugia paradoxa (EK4), and Curcuma zedoaria (Christm.) Thread (EK5)-and we have compared them with five compounds of synthetic origin for the inhibition of PARP-1, which is linked to abnormal DNA replication, generating carcinogenic cells. We have studied these interactions through molecular dynamics simulations of each interacting system under physiological conditions (pH, temperature, and pressure) and determined that the compounds of natural origin have a capacity to inhibit PARP-1 (Poly(ADP-ribose) Polymerase 1) in all the cases inspected in this investigation. However, it is essential to mention that their interaction energy is relatively lower compared to that of compounds of synthetic origin. Given that binding energy is mandatory for the generation of a scale or classification of which is the best interacting agent, we can say that we assume that compounds of natural origin, having a complexation affinity with PARP-1, induce cell apoptosis, a potential route for the prevention of the proliferation of carcinogenic cells.

Discovery of Palindrome Dual PPARγ-GPR40 Agonists for Treating Type 2 Diabetes.

This work describes a first attempt of palindromic design for dual compounds that act simultaneously on peroxisome proliferator-activated receptor gamma (PPARg) and G-protein-coupled receptor 40 (GPR40) for the treatment of type 2 diabetes. The compounds were synthesized by multi-step chemical reactions and the relative mRNA expression levels of PPARg, GPR40, and GLUT-4 were measured in cultured C2C12 muscle cells and RIN-m5f b-pancreatic cells. In addition, insulin secretion and GLUT-4 translocation were measured. Compound 2 displayed a moderate increase in the mRNA expression of PPARg and GPR40. However, the translocation of the GLUT-4 transporter was 400% with a similar effect to pioglitazone. The in vivo effect of compound 2 was determined at 25 mg/kg single dose using a normoglycemic and non-insulin dependent diabetes mellitus (NIDDM) rat models. Compound 2 showed basal plasma glucose in diabetic rats with feed intake, which is associated with the moderate release of insulin measured in cells. Surprisingly, the glucose does not decrease in normoglycemic rats. Compound 2 maintained significant interactions with the GPR40 and PPARg receptors during molecular dynamics. Altogether, the results demonstrate that compound 2, with a palindromic design, simultaneously activates PPARg and GPR40 receptors without inducing hypoglycemia.

In vitro and in silico evaluation of the anti-leishmania activity of synthetic chalcones.

Leishmaniasis is a group of neglected, vector-borne infectious diseases that affect millions of people around the world. The medications available for its treatment, especially in cases of visceral leishmaniasis, are old, outdated and have serious side effects. In this work, 10 chalcones were synthesised and evaluated in vitro against promastigotes and axenic amastigotes of Leishmania infantum. Compounds CP04 and CP06 were the most promising, respectively presenting IC50 values = 13.64 ± 0.25 and 11.19 ± 0.22 µM against promastigotes, and IC50 = 18.92 ± 0.05 and 22.42 ± 0.05 µM against axenic amastigotes. Only compound CP04 did not show cytotoxicity against peripheral blood mononuclear cells (PBMCs). Molecular docking studies conducted with sterol 14-alpha demethylase (CYP-51) (PDB: 3L4D) and trypanothione reductase (PDB: 5EBK) enzymes from L. infantum evidenced the great affinity of compound CP04 for these targets, presenting Moldock score values of -94.0758 and -50.5692 KJ/mol-1.

In Silico Screening of Therapeutic Targets as a Tool to Optimize the Development of Drugs and Nutraceuticals in the Treatment of Diabetes mellitus: A Systematic Review.

The Target-Based Virtual Screening approach is widely employed in drug development, with docking or molecular dynamics techniques commonly utilized for this purpose. This systematic review (SR) aimed to identify in silico therapeutic targets for treating Diabetes mellitus (DM) and answer the question: What therapeutic targets have been used in in silico analyses for the treatment of DM? The SR was developed following the guidelines of the Preferred Reporting Items Checklist for Systematic Review and Meta-Analysis, in accordance with the protocol registered in PROSPERO (CRD42022353808). Studies that met the PECo strategy (Problem, Exposure, Context) were included using the following databases: Medline (PubMed), Web of Science, Scopus, Embase, ScienceDirect, and Virtual Health Library. A total of 20 articles were included, which not only identified therapeutic targets in silico but also conducted in vivo analyses to validate the obtained results. The therapeutic targets most frequently indicated in in silico studies were GLUT4, DPP-IV, and PPARγ. In conclusion, a diversity of targets for the treatment of DM was verified through both in silico and in vivo reassessment. This contributes to the discovery of potential new allies for the treatment of DM.

Peptides Evaluated In Silico, In Vitro, and In Vivo as Therapeutic Tools for Obesity: A Systematic Review.

Bioinformatics has emerged as a valuable tool for screening drugs and understanding their effects. This systematic review aimed to evaluate whether in silico studies using anti-obesity peptides targeting therapeutic pathways for obesity, when subsequently evaluated in vitro and in vivo, demonstrated effects consistent with those predicted in the computational analysis. The review was framed by the question: "What peptides or proteins have been used to treat obesity in in silico studies?" and structured according to the acronym PECo. The systematic review protocol was developed and registered in PROSPERO (CRD42022355540) in accordance with the PRISMA-P, and all stages of the review adhered to these guidelines. Studies were sourced from the following databases: PubMed, ScienceDirect, Scopus, Web of Science, Virtual Heath Library, and EMBASE. The search strategies resulted in 1015 articles, of which, based on the exclusion and inclusion criteria, 7 were included in this systematic review. The anti-obesity peptides identified originated from various sources including bovine alpha-lactalbumin from cocoa seed (Theobroma cacao L.), chia seed (Salvia hispanica L.), rice bran (Oryza sativa), sesame (Sesamum indicum L.), sea buckthorn seed flour (Hippophae rhamnoides), and adzuki beans (Vigna angularis). All articles underwent in vitro and in vivo reassessment and used molecular docking methodology in their in silico studies. Among the studies included in the review, 46.15% were classified as having an "uncertain risk of bias" in six of the thirteen criteria evaluated. The primary target investigated was pancreatic lipase (n = 5), with all peptides targeting this enzyme demonstrating inhibition, a finding supported both in vitro and in vivo. Additionally, other peptides were identified as PPARγ and PPARα agonists (n = 2). Notably, all peptides exhibited different mechanisms of action in lipid metabolism and adipogenesis. The findings of this systematic review underscore the effectiveness of computational simulation as a screening tool, providing crucial insights and guiding in vitro and in vivo investigations for the discovery of novel anti-obesity peptides.

Size-dependent Curie temperature of Ni nanoparticles from spin-lattice dynamics simulations.

The magnetic properties of Ni nanoparticles (NPs) with diameter D are investigated using spin-lattice dynamics (SLD) simulations. Using exchange interactions fitted to ab-initio results we obtain a Curie temperature, T c , similar, but lower, than experiments. In order to reproduce quantitatively the bulk Curie temperature and the experimental results, the exchange energy has to be increased by 25% compared to the ab-initio value. During the simulated time, Ni NPs remain ferromagnetic down to the smallest sizes investigated here, containing around 500 atoms. The average magnetic moment of the NPs is slightly smaller than that determined experimentally. By considering a core-shell model for NPs, in which the shell atoms are assigned a larger magnetic moment, this discrepancy can be removed. T c is lower for a moving lattice than for a frozen lattice, as expected, but this difference decreases with NP size because smaller NPs include higher surface disorder which dominates the transition. For NPs, T c decreases with the NP diameter D by at most 10% at D = 2  nm, in agreement with several experiments, and unlike some modeling or theoretical scaling results which predict a considerably larger decrease. The decrease of T c is well described by finite-size scaling models, with a critical exponent that depends on the SLD settings for a frozen or moving lattice, and also depends on the procedure for determining T c . Extrapolating the inverse of the magnetization as function of temperature near T c gives a lower T c than the maximum of the susceptibility.

Advancing rational pesticide development against Drosophila suzukii: bioinformatics tools and applications-a systematic review.

CONTEXT: Drosophila suzukii (Matsumura, 1931) is a widespread agricultural pest responsible for significant damage to various soft-skinned fruit hosts. The revolutionary potential of bioinformatics in agriculture emerges from its ability to provide extensive information on pests, fungi, chemical resistance, implications of non-target species, and other critical aspects. This wealth of information allows researchers to engage in projects and applied research in diverse agricultural domains that face these challenges. In this context, bioinformatics tools play a fundamental role. The negative impact of pests on crops, resulting in substantial economic losses, has highlighted the importance of in silico methods. METHODS: To achieve this, we conducted a systematic search in scientific databases using as keywords "Drosophila suzukii," "biopesticides," "simulations computational," and "in-silico." After applying the filters of relevance and publication date, we organized the articles and prioritized those that directly addressed that matched the keywords and the use of bioinformatics tools. Additionally, we included studies focusing on in silico assays of biopesticides, such as molecular docking. Our review aimed to present a collection of recent literature on biopesticides against Drosophila suzukii, emphasizing bioinformatics methods. Through this work, we strive to contribute to the literature of new perspectives on the development and efficiency of biopesticides, along with to advance research that may improve pest control strategies. RESULTS: In the results of the systematic review, we found 2734 articles related to the selected keywords. Six of these articles directly address Drosophila suzukii and the use of bioinformatics tools in the search for alternatives in pest control. In the selected studies, we observed that two articles tend to focus on phylogenetic approaches, searching for gene sequences, amino acids, and constructing phylogenetic trees. The other three articles used molecular modeling and docking of receptors such as GABA and TRP with plant-derived and synthetic compounds to study intermolecular interactions. However, we identified gaps in these studies that could lead to further research in the biorational development of biopesticides using bioinformatics tools.

Insight from atomistic molecular dynamics simulations into the supramolecular assembly of the aldo-keto reductase from Trypanosoma cruzi.

CONTEXT: Currently, Chagas disease represents an important public health problem affecting more than 8 million people worldwide. The vector of this disease is the Trypanosoma cruzi (Tc) parasite. Our research specifically focuses on the structure and aggregation states of the enzyme aldo-keto reductase of Tc (TcAKR) reported in this parasite. TcAKR belongs to the aldo-keto reductase (AKR) superfamily, enzymes that catalyze redox reactions involved in crucial biological processes. While most AKRs are found in monomeric forms, some have been reported to form dimeric and tetrameric structures. This is the case for some TcAKR. To better understand how TcAKR multimers form and remain stable, we conducted a comprehensive computational analysis using molecular dynamics (MD) simulations. Our approach to elucidating the aggregation states of TcAKR involved two strategies. Initially, we explored the dynamic behaviour of pre-assembled TcAKR dimers. Subsequently, we examined the self-aggregation of eight monomers. This investigation led to the identification of crucial residues that contribute to the stabilization of protein-protein interactions. It was also found that TcAKRs can form stable supramolecular assemblies, with each monomer typically surrounded by three first neighbours. These findings align with experimental reports of tetrameric or more complex supramolecular structures. Our computational studies could guide further experimental investigations aiming at drug development and assist in designing strategies to modulate aggregation. METHOD: Atomistic molecular dynamics simulations were carried out. The TcAKR 3D model structure was obtained by homology modelling using the Swiss Model for the TcAKR sequence (GenBank accession no. EU558869). Further, we checked the model with Alphafold2 and found a high degree of similarity between models. Several tools were used to build the dimers including CLUSPRO, GRAMM-Docking, Hdock, and Py-dock. Protein superstructures were built using the PACKMOL package. CHARMM-GUI was used to set up the simulation systems. GROMACS version 2020.5 was used to perform the simulations with the CHARMM36 force field for the protein and ions and the TIP3P model for water. Further analyses were performed using VMD, GROMACS, AMBER tools, MDLovoFit, bio3d, and in-house programs.

Deep learning for discriminating non-trivial conformational changes in molecular dynamics simulations of SARS-CoV-2 spike-ACE2.

Molecular dynamics (MD) simulations produce a substantial volume of high-dimensional data, and traditional methods for analyzing these data pose significant computational demands. Advances in MD simulation analysis combined with deep learning-based approaches have led to the understanding of specific structural changes observed in MD trajectories, including those induced by mutations. In this study, we model the trajectories resulting from MD simulations of the SARS-CoV-2 spike protein-ACE2, specifically the receptor-binding domain (RBD), as interresidue distance maps, and use deep convolutional neural networks to predict the functional impact of point mutations, related to the virus's infectivity and immunogenicity. Our model was successful in predicting mutant types that increase the affinity of the S protein for human receptors and reduce its immunogenicity, both based on MD trajectories (precision = 0.718; recall = 0.800; [Formula: see text] = 0.757; MCC = 0.488; AUC = 0.800) and their centroids. In an additional analysis, we also obtained a strong positive Pearson's correlation coefficient equal to 0.776, indicating a significant relationship between the average sigmoid probability for the MD trajectories and binding free energy (BFE) changes. Furthermore, we obtained a coefficient of determination of 0.602. Our 2D-RMSD analysis also corroborated predictions for more infectious and immune-evading mutants and revealed fluctuating regions within the receptor-binding motif (RBM), especially in the [Formula: see text] loop. This region presented a significant standard deviation for mutations that enable SARS-CoV-2 to evade the immune response, with RMSD values of 5Å in the simulation. This methodology offers an efficient alternative to identify potential strains of SARS-CoV-2, which may be potentially linked to more infectious and immune-evading mutations. Using clustering and deep learning techniques, our approach leverages information from the ensemble of MD trajectories to recognize a broad spectrum of multiple conformational patterns characteristic of mutant types. This represents a strategic advantage in identifying emerging variants, bypassing the need for long MD simulations. Furthermore, the present work tends to contribute substantially to the field of computational biology and virology, particularly to accelerate the design and optimization of new therapeutic agents and vaccines, offering a proactive stance against the constantly evolving threat of COVID-19 and potential future pandemics.

Alzheimer's Disease Immunotherapy and Mimetic Peptide Design for Drug Development: Mutation Screening, Molecular Dynamics, and a Quantum Biochemistry Approach Focusing on Aducanumab::Aß2-7 Binding Affinity.

Seven treatments are approved for Alzheimer's disease, but five of them only relieve symptoms and do not alter the course of the disease. Aducanumab (Adu) and lecanemab are novel disease-modifying antiamyloid-ß (Aß) human monoclonal antibodies that specifically target the pathophysiology of Alzheimer's disease (AD) and were recently approved for its treatment. However, their administration is associated with serious side effects, and their use is limited to early stages of the disease. Therefore, drug discovery remains of great importance in AD research. To gain new insights into the development of novel drugs for Alzheimer's disease, a combination of techniques was employed, including mutation screening, molecular dynamics, and quantum biochemistry. These were used to outline the interfacial interactions of the Aducanumab::Aß2-7 complex. Our analysis identified critical stabilizing contacts, revealing up to 40% variation in the affinity of the Adu chains for Aß2-7 depending on the conformation outlined. Remarkably, two complementarity determining regions (CDRs) of the Adu heavy chain (HCDR3 and HCDR2) and one CDR of the Adu light chain (LCDR3) accounted for approximately 77% of the affinity of Adu for Aß2-7, confirming their critical role in epitope recognition. A single mutation, originally reported to have the potential to increase the affinity of Adu for Aß2-7, was shown to decrease its structural stability without increasing the overall binding affinity. Mimetic peptides that have the potential to inhibit Aß aggregation were designed by using computational outcomes. Our results support the use of these peptides as promising drugs with great potential as inhibitors of Aß aggregation.

Microextraction of steroidal hormones from urine samples using natural deep eutectic solvents: insights into chemical interactions using molecular dynamics simulations.

Natural deep eutectic solvents (NADES) are gaining significant attention in analytical chemistry due to attractive physico-chemical properties associated with sustainable aspects. They have been successfully evaluated in different fields, and applications in sample preparation have increased in the last years. However, there is a limited knowledge related to chemical interactions and mechanism of intermolecular action with specific analytes. In this regard, for the first time, this study exploited a computational investigation using molecular dynamics (MD) predictions combined with experimental data for the extraction/determination of steroidal hormones (estriol, ß-estradiol, and estrone) in urine samples using NADES. The ultrasound-assisted liquid-liquid microextraction (UALLME) approach followed by high-performance liquid chromatography with diode array detection (HPLC-DAD) was employed using menthol:decanoic acid as extraction solvent. Experimental parameters were optimized through multivariate strategies, with the best conditions consisting of 3 min of extraction, 150 µL of NADES, and 3 mL of sample (tenfold diluted). According to molecular dynamics predictions confirmed by experimental data, a molar ratio that permitted the highest efficiency consisted of menthol:decanoic acid 2:1 v/v. Importantly, computational simulations revealed that van der Waals interactions were the most significant contributor to the interaction energy of analytes-NADES. Using the optimized conditions, limits of detection (LOD) ranged from 3 and 8 µg L-1, and precision (n = 3) varied from 8 to 19%. Intraday precision was evaluated at 3 concentrations: low (LOQ according to each analyte), medium (100 µg L-1), and high (750 µg L-1). Accuracy was successfully assessed through recoveries that ranged from 82 to 98%. In this case, molecular dynamics simulations proved to be an important tool for in-depth investigations of interaction mechanisms of DES with different analytes.

The Endocannabinoid Peptide RVD-Hemopressin Is a TRPV1 Channel Blocker.

Neurotransmission is critical for brain function, allowing neurons to communicate through neurotransmitters and neuropeptides. RVD-hemopressin (RVD-Hp), a novel peptide identified in noradrenergic neurons, modulates cannabinoid receptors CB1 and CB2. Unlike hemopressin (Hp), which induces anxiogenic behaviors via transient receptor potential vanilloid 1 (TRPV1) activation, RVD-Hp counteracts these effects, suggesting that it may block TRPV1. This study investigates RVD-Hp's role as a TRPV1 channel blocker using HEK293 cells expressing TRPV1-GFP. Calcium imaging and patch-clamp recordings demonstrated that RVD-Hp reduces TRPV1-mediated calcium influx and TRPV1 ion currents. Molecular docking and dynamics simulations indicated that RVD-Hp interacts with TRPV1's selectivity filter, forming stable hydrogen bonds and van der Waals contacts, thus preventing ion permeation. These findings highlight RVD-Hp's potential as a therapeutic agent for conditions involving TRPV1 activation, such as pain and anxiety.

Permeation mechanisms of hydrogen peroxide and water through Plasma Membrane Intrinsic Protein aquaporins.

Hydrogen peroxide (H2O2) transport by aquaporins (AQP) is a critical feature for cellular redox signaling. However, the H2O2 permeation mechanism through these channels remains poorly understood. Through functional assays, two Plasma membrane Intrinsic Protein (PIP) AQP from Medicago truncatula, MtPIP2;2 and MtPIP2;3 have been identified as pH-gated channels capable of facilitating the permeation of both water (H2O) and H2O2. Employing a combination of unbiased and enhanced sampling molecular dynamics simulations, we investigated the key barriers and translocation mechanisms governing H2O2 permeation through these AQP in both open and closed conformational states. Our findings reveal that both H2O and H2O2 encounter their primary permeation barrier within the selectivity filter (SF) region of MtPIP2;3. In addition to the SF barrier, a second energetic barrier at the NPA (asparagine-proline-alanine) region that is more restrictive for the passage of H2O2 than for H2O, was found. This behavior can be attributed to a dissimilar geometric arrangement and hydrogen bonding profile between both molecules in this area. Collectively, these findings suggest mechanistic heterogeneity in H2O and H2O2 permeation through PIPs.

Unveiling crucial amino acids in the carbohydrate recognition domain of a viral protein through a structural bioinformatic approach.

Carbohydrate binding modules (CBMs) are protein domains that typically reside near catalytic domains, increasing substrate-protein proximity by constraining the conformational space of carbohydrates. Due to the flexibility and variability of glycans, the molecular details of how these protein regions recognize their target molecules are not always fully understood. Computational methods, including molecular docking and molecular dynamics simulations, have been employed to investigate lectin-carbohydrate interactions. In this study, we introduce a novel approach that integrates multiple computational techniques to identify the critical amino acids involved in the interaction between a CBM located at the tip of bacteriophage J-1's tail and its carbohydrate counterparts. Our results highlight three amino acids that play a significant role in binding, a finding we confirmed through in vitro experiments. By presenting this approach, we offer an intriguing alternative for pinpointing amino acids that contribute to protein-sugar interactions, leading to a more thorough comprehension of the molecular determinants of protein-carbohydrate interactions.

High-Throughput Molecular Modeling and Evaluation of the Anti-Inflammatory Potential of Açaí Constituents against NLRP3 Inflammasome.

The search for bioactive compounds in natural products holds promise for discovering new pharmacologically active molecules. This study explores the anti-inflammatory potential of açaí (Euterpe oleracea Mart.) constituents against the NLRP3 inflammasome using high-throughput molecular modeling techniques. Utilizing methods such as molecular docking, molecular dynamics simulation, binding free energy calculations (MM/GBSA), and in silico toxicology, we compared açaí compounds with known NLRP3 inhibitors, MCC950 and NP3-146 (RM5). The docking studies revealed significant interactions between açaí constituents and the NLRP3 protein, while molecular dynamics simulations indicated structural stabilization. MM/GBSA calculations demonstrated favorable binding energies for catechin, apigenin, and epicatechin, although slightly lower than those of MCC950 and RM5. Importantly, in silico toxicology predicted lower toxicity for açaí compounds compared to synthetic inhibitors. These findings suggest that açaí-derived compounds are promising candidates for developing new anti-inflammatory therapies targeting the NLRP3 inflammasome, combining efficacy with a superior safety profile. Future research should include in vitro and in vivo validation to confirm the therapeutic potential and safety of these natural products. This study underscores the value of computational approaches in accelerating natural product-based drug discovery and highlights the pharmacological promise of Amazonian biodiversity.