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.

Generalized cue reactivity in dopamine neurons after opioids.

Cue reactivity is the maladaptive neurobiological and behavioral response upon exposure to drug cues and is a major driver of relapse. The leading hypothesis is that dopamine release by addictive drugs represents a persistently positive reward prediction error that causes runaway enhancement of dopamine responses to drug cues, leading to their pathological overvaluation compared to non-drug reward alternatives. However, this hypothesis has not been directly tested. Here we developed Pavlovian and operant procedures to measure firing responses, within the same dopamine neurons, to drug versus natural reward cues, which we found to be similarly enhanced compared to cues predicting natural rewards in drug-naïve controls. This enhancement was associated with increased behavioral reactivity to the drug cue, suggesting that dopamine release is still critical to cue reactivity, albeit not as previously hypothesized. These results challenge the prevailing hypothesis of cue reactivity, warranting new models of dopaminergic function in drug addiction, and provide critical insights into the neurobiology of cue reactivity with potential implications for relapse prevention.

Dopamine and acetylcholine correlations in the nucleus accumbens depend on behavioral task states.

Dopamine in the nucleus accumbens ramps up as animals approach desired goals. These ramps have received intense scrutiny because they seem to violate long-held hypotheses on dopamine function. Furthermore, it has been proposed that they are driven by local acetylcholine release, i.e., that they are mechanistically separate from dopamine signals related to reward prediction errors. Here, we tested this hypothesis by simultaneously recording accumbal dopamine and acetylcholine signals in rats executing a task involving motivated approach. Contrary to recent reports, we found that dopamine ramps were not coincidental with changes in acetylcholine. Instead, we found that acetylcholine could be positively, negatively, or uncorrelated with dopamine depending on whether the task phase was determined by a salient cue, reward prediction error, or active approach, respectively. Our results suggest that accumbal dopamine and acetylcholine are largely independent but may combine to engage different postsynaptic mechanisms depending on the behavioral task states.

Role of UDP-N-acetylmuramic acid in the regulation of MurA activity revealed by molecular dynamics simulations.

The peptidoglycan biosynthesis pathway plays a vital role in bacterial cells, and facilitates peptidoglycan layer formation, a fundamental structural component of the bacterial cell wall. The enzymes in this pathway are candidates for antibiotic development, as most do not have mammalian homologues. The UDP-N-acetylglucosamine (UNAG) enolpyruvyl transferase enzyme (MurA) in the peptidoglycan pathway cytoplasmic step is responsible for the phosphoenolpyruvate (PEP)-UNAG catalytic reaction, forming UNAG enolpyruvate and inorganic phosphate. Reportedly, UDP-N-acetylmuramic acid (UNAM) binds tightly to MurA forming a dormant UNAM-PEP-MurA complex and acting as a MurA feedback inhibitor. MurA inhibitors are complex, owing to competitive binding interactions with PEP, UNAM, and UNAG at the MurA active site. We used computational methods to explore UNAM and UNAG binding. UNAM showed stronger hydrogen-bond interactions with the Arg120 and Arg91 residues, which help to stabilize the closed conformation of MurA, than UNAG. Binding free energy calculations using end-point computational methods showed that UNAM has a higher binding affinity than UNAG, when PEP is attached to Cys115. The unbinding process, simulated using τ-random acceleration molecular dynamics, showed that UNAM has a longer relative residence time than UNAG, which is related to several complex dissociation pathways, each with multiple intermediate metastable states. This prevents the loop from opening and exposing the Arg120 residue to accommodate UNAG and potential new ligands. Moreover, we demonstrate the importance of Cys115-linked PEP in closed-state loop stabilization. We provide a basis for evaluating novel UNAM analogues as potential MurA inhibitors. PUBLIC SIGNIFICANCE: MurA is a critical enzyme involved in bacterial cell wall biosynthesis and is involved in antibiotic resistance development. UNAM can remain in the target protein's active site for an extended time compared to its natural substrate, UNAG. The prolonged interaction of this highly stable complex known as the 'dormant complex' comprises UNAM-PEP-MurA and offers insights into antibiotic development, providing potential options against drug-resistant bacteria and advancing our understanding of microbial biology.

Different Effects of Peer Sex on Operant Responding for Social Interaction and Striatal Dopamine Activity.

When rats are given discrete choices between social interactions with a peer and opioid or psychostimulant drugs, they choose social interaction, even after extensive drug self-administration experience. Studies show that like drug and nondrug food reinforcers, social interaction is an operant reinforcer and induces dopamine release. However, these studies were conducted with same-sex peers. We examined if peer sex influences operant social interaction and the role of estrous cycle and striatal dopamine in same- versus opposite-sex social interaction. We trained male and female rats (n = 13 responders/12 peers) to lever-press (fixed-ratio 1 [FR1] schedule) for 15 s access to a same- or opposite-sex peer for 16 d (8 d/sex) while tracking females' estrous cycle. Next, we transfected GRAB-DA2m and implanted optic fibers into nucleus accumbens (NAc) core and dorsomedial striatum (DMS). We then retrained the rats for 15 s social interaction (FR1 schedule) for 16 d (8 d/sex) and recorded striatal dopamine during operant responding for a peer for 8 d (4 d/sex). Finally, we assessed economic demand by manipulating FR requirements for a peer (10 d/sex). In male, but not female rats, operant responding was higher for the opposite-sex peer. Female's estrous cycle fluctuations had no effect on operant social interaction. Striatal dopamine signals for operant social interaction were dependent on the peer's sex and striatal region (NAc core vs DMS). Results indicate that estrous cycle fluctuations did not influence operant social interaction and that NAc core and DMS dopamine activity reflect sex-dependent features of volitional social interaction.

BrainPepPass: A Framework Based on Supervised Dimensionality Reduction for Predicting Blood-Brain Barrier-Penetrating Peptides.

Peptides that pass through the blood-brain barrier (BBB) not only are implicated in brain-related pathologies but also are promising therapeutic tools for treating brain diseases, e.g., as shuttles carrying active medicines across the BBB. Computational prediction of BBB-penetrating peptides (B3PPs) has emerged as an interesting approach because of its ability to screen large peptide libraries in a cost-effective manner. In this study, we present BrainPepPass, a machine learning (ML) framework that utilizes supervised manifold dimensionality reduction and extreme gradient boosting (XGB) algorithms to predict natural and chemically modified B3PPs. The results indicate that the proposed tool outperforms other classifiers, with average accuracies exceeding 94% and 98% in 10-fold cross-validation and leave-one-out cross-validation (LOOCV), respectively. In addition, accuracy values ranging from 45% to 97.05% were achieved in the independent tests. The BrainPepPass tool is available in a public repository for academic use (https://github.com/ewerton-cristhian/BrainPepPass).

Dysfunctional suction dynamics in newborns with ankyloglossia / Dinâmica de sucção disfuncional em recém-nascidos com anquiloglossia

ABSTRACT Purpose Compare infant suction in babies with and without ankyloglossia using a microprocessor-controlled pressure sensor coupled to a pacifier. Methods Fifty-five infants from 0 to 2 months of age underwent clinical examination for ankyloglossia, after which they were offered a silicone pacifier connected to the pressure acquisition device and suction activity was recorded. Thus, we extracted the frequency of sucks within a burst, the average suck duration, the burst duration, the number of sucks per burst, the maximum amplitude of sucks per burst and the inter-burst interval. Results The key difference in newborns with ankyloglossia in relation to control was that they perform longer bursts of suction activity. Conclusion The longer burst durations are likely a compensatory strategy and may underlie the pain reported by mothers during breastfeeding. We therefore propose a method for objectively quantifying some parameters of infant suction capacity and demonstrate its use in assisting the evaluation of ankyloglossia.

RESUMO Objetivo Comparar a sucção infantil em bebês com e sem anquiloglossia usando um sensor de pressão controlado por microprocessador acoplado a uma chupeta. Método Cinquenta e cinco lactentes de 0 a 2 meses de idade foram submetidos ao exame clínico de anquiloglossia, em seguida foi oferecido uma chupeta de silicone conectada ao dispositivo de aquisição de pressão e a atividade de sucção foi registrada. Assim, obtivemos dados sobre a frequência de sucções dentro de um período de sucções, a duração média da sucção, a duração da rajada, o número de sucções por rajada, a amplitude máxima das sucções por rajada e o intervalo entre rajadas. O teste t não pareado foi utilizado para comparações entre os grupos. Resultados A principal diferença dos recém-nascidos com anquiloglossia em relação aos do grupo controle é que eles realizam rajadas mais longas durante a atividade de sucção. Conclusão A duração mais longa das rajadas é provavelmente uma estratégia compensatória e pode estar por trás da dor relatada pelas mães durante a amamentação. Portanto, propomos um método para quantificar objetivamente alguns parâmetros da sucção infantil e demonstramos seu uso para auxiliar na avaliação da anquiloglossia.

Listening to the Data: Computational Approaches to Addiction and Learning.

Computational approaches hold great promise for identifying novel treatment targets and creating translational therapeutics for substance use disorders. From circuitries underlying decision-making to computationally derived neural markers of drug-cue reactivity, this review is a summary of the approaches to data presented at our 2023 Society for Neuroscience Mini-Symposium. Here, we highlight data- and hypothesis-driven computational approaches that recently afforded advancements in addiction and learning neuroscience. First, we discuss the value of hypothesis-driven algorithmic modeling approaches, which integrate behavioral, neural, and cognitive outputs to refine hypothesis testing. Then, we review the advantages of data-driven dimensionality reduction and machine learning methods for uncovering novel predictor variables and elucidating relationships in high-dimensional data. Overall, this review highlights recent breakthroughs in cognitive mapping, model-based analysis of behavior/risky decision-making, patterns of drug taking, relapse, and neuromarker discovery, and showcases the benefits of novel modeling techniques, across both preclinical and clinical data.

Comparative analysis of the reactivity of anthocyanidins, leucoanthocyanidins, and flavonols using a quantum chemistry approach.

Anthocyanidins, leucoanthocyanidins, and flavonols are natural compounds mainly known due to their reported biological activities, such as antiviral, antifungal, anti-inflammatory activities, and antioxidant activity. In the present study, we performed a comparative structural, conformational, electronic, and nuclear magnetic resonance analysis of the reactivity of the chemical structure of primary anthocyanidins, leucoanthocyanidins, and flavonoids. We focused our analysis on the following molecular questions: (i) differences in cyanidin catechols ( +)-catechin, leucocyanidin, and quercetin; (ii) the loss of hydroxyl presents in the R1 radical of leucoanthocyanidin in the functional groups linked to C4 (ring C); and (iii) the electron affinity of the 3-hydroxyl group (R7) in the flavonoids delphinidin, pelargonidin, cyanidin, quercetin, and kaempferol. We show unprecedented results for bond critical point (BCP) of leucopelargonidin and leucodelphirinidin. The BCP formed between hydroxyl hydrogen (R2) and ketone oxygen (R1) of kaempferol has the same degrees of covalence of quercetin. Kaempferol and quercetin exhibited localized electron densities between hydroxyl hydrogen (R2) and ketone oxygen (R1). Global molecular descriptors showed quercetin and leucocyanidin are the most reactive flavonoids in electrophilic reactions. Complementary, anthocyanidins are the most reactive in nucleophilic reactions, while the smallest gap occurs in delphinidin. Local descriptors indicate that anthocyanidins and flavonols are more prone to electrophilic attacks, while in leucoanthocyanidins, the most susceptible to attack are localized in the ring A. The ring C of anthocyanidins is more aromatic than the same found in flavonols and leucoanthocyanidins. METHODS: For the analysis of the molecular properties, we used the DFT to evaluate the formation of the covalent bonds and intermolecular forces. CAM-B3LYP functional with the def2TZV basis set was used for the geometry optimization. A broad analysis of quantum properties was performed using the assessment of the molecular electrostatic potential surface, electron localization function, Fukui functions, descriptors constructed from frontier orbitals, and nucleus independent chemical shift.

The role of the lateral orbitofrontal cortex in creating cognitive maps.

We use mental models of the world-cognitive maps-to guide behavior. The lateral orbitofrontal cortex (lOFC) is typically thought to support behavior by deploying these maps to simulate outcomes, but recent evidence suggests that it may instead support behavior by underlying map creation. We tested between these two alternatives using outcome-specific devaluation and a high-potency chemogenetic approach. Selectively inactivating lOFC principal neurons when male rats learned distinct cue-outcome associations, but before outcome devaluation, disrupted subsequent inference, confirming a role for the lOFC in creating new maps. However, lOFC inactivation surprisingly led to generalized devaluation, a result that is inconsistent with a complete mapping failure. Using a reinforcement learning framework, we show that this effect is best explained by a circumscribed deficit in credit assignment precision during map construction, suggesting that the lOFC has a selective role in defining the specificity of associations that comprise cognitive maps.

Advances in UDP-N-Acetylglucosamine Enolpyruvyl Transferase (MurA) Covalent Inhibition.

Peptidoglycan is a cross-linked polymer responsible for maintaining the bacterial cell wall integrity and morphology in Gram-negative and Gram-positive bacteria. The peptidoglycan pathway consists of the enzymatic reactions held in three steps: cytoplasmic, membrane-associated, and periplasmic. The Mur enzymes (MurA-MurF) are involved in a cytoplasmic stage. The UDP-N-acetylglucosamine enolpyruvyl transferase (MurA) enzyme is responsible for transferring the enolpyruvate group from phosphoenolpyruvate (PEP) to UDP-N-acetylglucosamine (UNAG) to form UDP-N-acetylglucosamine enolpyruvate (EP-UNAG). Fosfomycin is a natural product analogous to PEP that acts on the MurA target enzyme via binding covalently to the key cysteine residue in the active site. Similar to fosfomycin, other MurA covalent inhibitors have been described with a warhead in their structure that forms a covalent bond with the molecular target. In MurA, the nucleophilic thiolate of Cys115 is pointed as the main group involved in the warhead binding. Thus, in this minireview, we briefly describe the main recent advances in the design of MurA covalent inhibitors.

Dopamine.

Dopamine was first described by George Barger, James Ewens, and Henry Dale in 1910 as an epinephrine-like monoamine compound. Initially believed to be a mere precursor of norepinephrine, it was mostly ignored for the next four decades (Figure 1A). However, in the 1950s Kathleen Montagu showed that dopamine occurred in the brain by itself, and a series of studies by Arvid Carlsson and collaborators demonstrated that dopamine is a bona fide neurotransmitter, a finding that would earn Carlsson the 2000 Nobel Prize in Physiology and Medicine. In a landmark experiment, he pharmacologically blocked all dopamine neurotransmission in rabbits, which rendered them completely paralyzed, and then fully recovered their behavior with an injection of the dopamine precursor L-DOPA, demonstrating that dopamine was essential for self-initiated movement (Figure 1B). A similar effect was quickly reproduced by Oleg Hornykiewicz and collaborators in human Parkinsonian patients. Within a few years, dopamine jumped from relative obscurity to being critical for life as we know it.

Virtual screening of natural products against 5-enolpyruvylshikimate-3-phosphate synthase using the Anagreen herbicide-like natural compound library.

The shikimate pathway enzyme 5-enolpyruvylshikimate-3-phosphate synthase (EPSPS) catalyzes a reaction involved in the production of amino acids essential for plant growth and survival. EPSPS is the main target of glyphosate, a broad-spectrum herbicide that acts as a competitive inhibitor concerning phosphoenolpyruvate (PEP), which is the natural substrate of EPSPS. In the present study, we introduce a natural compound library, named Anagreen, which is a compendium of herbicide-like compounds obtained from different natural product databases. Herein, we combined the structure- and ligand-based virtual screening strategies to explore Anagreen against EPSPS using the structure of glyphosate complexed with a T102I/P106S mutant of EPSPS from Eleusine indica (EiEPSPS) as a starting point. First, ligand-based pharmacophore screening was performed to select compounds with a similar pharmacophore to glyphosate. Then, structure-based pharmacophore modeling was applied to build a model which represents the molecular features of glyphosate. Then, consensus docking was performed to rank the best poses of the natural compounds against the PEP binding site, and then molecular dynamics simulations were performed to analyze the stability of EPSPS complexed with the selected ligands. Finally, we have investigated the binding affinity of the complexes using free energy calculations. The selected hit compound, namely AG332841, showed a stable conformation and binding affinity to the EPSPS structure and showed no structural similarity to the already known weed EPSPS inhibitors. Our computational study aims to clarify the inhibition of the mutant EiEPSPS, which is resistant to glyphosate, and identify new potential herbicides from natural products.

Host-Guest Inclusion Complexes of Natural Products and Nanosystems: Applications in the Development of Repellents.

Repellents are compounds that prevent direct contact between the hosts and the arthropods that are vectors of diseases. Several studies have described the repellent activities of natural compounds obtained from essential oils. In addition, these chemical constituents have been pointed out as alternatives to conventional synthetic repellents due to their interesting residual protection and low toxicity to the environment. However, these compounds have been reported with short shelf life, in part, due to their volatile nature. Nanoencapsulation provides protection, stability, conservation, and controlled release for several compounds. Here, we review the most commonly used polymeric/lipid nanosystems applied in the encapsulation of small organic molecules obtained from essential oils that possess repellent activity, and we also explore the theoretical aspects related to the intermolecular interactions, thermal stability, and controlled release of the nanoencapsulated bioactive compounds.

Biological Membrane-Penetrating Peptides: Computational Prediction and Applications.

Peptides comprise a versatile class of biomolecules that present a unique chemical space with diverse physicochemical and structural properties. Some classes of peptides are able to naturally cross the biological membranes, such as cell membrane and blood-brain barrier (BBB). Cell-penetrating peptides (CPPs) and blood-brain barrier-penetrating peptides (B3PPs) have been explored by the biotechnological and pharmaceutical industries to develop new therapeutic molecules and carrier systems. The computational prediction of peptides' penetration into biological membranes has been emerged as an interesting strategy due to their high throughput and low-cost screening of large chemical libraries. Structure- and sequence-based information of peptides, as well as atomistic biophysical models, have been explored in computer-assisted discovery strategies to classify and identify new structures with pharmacokinetic properties related to the translocation through biomembranes. Computational strategies to predict the permeability into biomembranes include cheminformatic filters, molecular dynamics simulations, artificial intelligence algorithms, and statistical models, and the choice of the most adequate method depends on the purposes of the computational investigation. Here, we exhibit and discuss some principles and applications of these computational methods widely used to predict the permeability of peptides into biomembranes, exhibiting some of their pharmaceutical and biotechnological applications.

Neuroscience: Finding the middle ground of behavioral (in)flexibility.

We often perform actions even when they incur heavy costs or no longer serve any clear purpose. Two recent studies have independently identified heightened phasic dopamine release in the dorsomedial striatum as a predisposing factor for such behavioral inflexibility.

Synthesis, Characterization, Applications, and Toxicity of Green Synthesized Nanoparticles.

Nanotechnology is a cutting-edge area with numerous industrial applications. Nanoparticles are structures that have dimensions ranging from 1 - 100 nm, which significantly exhibit different mechanical, optical, electrical, and chemical properties when compared with their larger counterparts. Synthetic routes that use natural sources, such as plant extracts, honey, and microorganisms, are environmentally friendly and low-cost methods that can be used to obtain nanoparticles. These methods of synthesis generate products that are more stable and less toxic than those obtained using conventional methods. Nanoparticles formed by titanium dioxide, zinc oxide, silver, gold, and copper, as well as cellulose nanocrystals, are among the nanostructures obtained by green synthesis that have shown interesting applications in several technological industries. Several analytical techniques have also been used to analyze the size, morphology, hydrodynamics, diameter, and chemical functional groups involved in the stabilization of the nanoparticles as well as to quantify and evaluate their formation. Despite their pharmaceutical, biotechnological, cosmetic, and food applications, studies have detected their harmful effects on human health and the environment, and thus, caution must be taken in uses involving living organisms. The present review aims to present an overview of the applications, the structural properties, and the green synthesis methods that are used to obtain nanoparticles, and special attention is given to those obtained from metal ions. The review also presents the analytical methods used to analyze, quantify, and characterize these nanostructures.

First report on the Annona exsucca DC. Essential oil and in silico identification of potential biological targets of its major compounds.

In the present study, the essential oil (EO) of Annona exsucca DC. or Rollinia exsucca was extracted by hydrodistillation, and the identification and quantification of volatile compounds were performed by GC-MS and GC-FID. A. exsucca leaves were collected from the Magalhães Barata, northeast of the State of Pará (Brazil) in March and September of 2019. Moreover, we used computational approaches to evaluate possible biological targets for the major compounds of the EO. In the sample obtained in March, 50 compounds were identified, with hydrocarbon sesquiterpenes being the predominant ones with the content of 80.52%. In the sample collected in September, 58 compounds were identified, and the chemical class of hydrocarbon monoterpenes and sesquiterpenes were the dominant ones with contents of 43.36 and 31.29%, respectively. Computational methods demonstrated that some major compounds have potential biological activity against some strains of pathogenic bacteria, as well as against molecular targets involved in cancer development.

Assessment of host-guest molecular encapsulation of eugenol using ß-cyclodextrin.

Eugenol is a natural compound with well-known repellent activity. However, its pharmaceutical and cosmetic applications are limited, since this compound is highly volatile and thermolabile. Nanoencapsulation provides protection, stability, conservation, and controlled release for several compounds. Here, eugenol was included in ß-cyclodextrin, and the complex was characterized through X-ray diffraction analysis (XRD) and Fourier-transform infrared spectroscopy (FTIR). Additionally, we used molecular dynamics simulations to explore the eugenol-ß-cyclodextrin complex stability with temperature increases. Our computational result demonstrates details of the molecular interactions and conformational changes of the eugenol-ß-cyclodextrin complex and explains its stability between temperatures 27°C and 48°C, allowing its use in formulations that are subjected to varied temperatures.