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Mayaro virus (MAYV) is the causative agent of Mayaro fever, which is characterized mainly by acute fever and long-term severe arthralgia, common manifestations of other arbovirus infections, making the correct diagnosis a challenge. Besides, MAYV infections have been reported in South America, especially in Brazil. However, the lack of vaccines or specific antiviral drugs to control these infections makes the search for new antivirals an urgent need. Herein, we evaluated the antiviral potential of synthetic ß-enaminoesters derivatives against MAYV replication and their pharmacokinetic and toxicological (ADMET) properties using in vitro and in silico strategies. For this purpose, Vero cells were infected with MAYV at an MOI of 0.1, treated with compounds (50 µM) for 24 h, and virus titers were quantified by plaque reduction assays. Compounds 2b (83.33%) and 2d (77.53%) exhibited the highest activity with inhibition rates of 83.33% and 77.53%, respectively. The most active compounds 2b (EC50 = 18.92 µM; SI > 52.85), and 2d (EC50 = 14.52 µM; SI > 68.87) exhibited higher potency and selectivity than the control drug suramin (EC50 = 38.97 µM; SI > 25.66). Then, we investigated the mechanism of action of the most active compounds. None of the compounds showed virucidal activity, neither inhibited virus adsorption, but compound 2b inhibited virus entry (62.64%). Also, compounds 2b and 2d inhibited some processes involved with the release of new virus particles. Finally, in silico results indicated good ADMET parameters of the most active compounds and reinforced their promising profile as drug candidates against MAYV.

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In the face of escalating antibiotic resistance, the quest for novel antimicrobial compounds is critical. Actinobacteria is known for producing a substantial fraction of bioactive molecules from microorganisms, nonetheless there is the challenge of metabolic redundancy in bioprospecting. New sources of natural products are needed to overcome these current challenges. Our present work proposes an unexplored potential of Neotropical social wasp-associated microbes as reservoirs of novel bioactive compounds. Using social wasp-associated Tsukamurella sp. strains 8F and 8J, we aimed to determine their biosynthetic potential for producing novel antibiotics and evaluated phylogenetic and genomic traits related to environmental and ecological factors that might be associated with promising bioactivity and evolutionary specialization. These strains were isolated from the cuticle of social wasps and subjected to comprehensive genome sequencing. Our genome mining efforts, employing antiSMASH and ARTS, highlight the presence of BGCs with minimal similarity to known compounds, suggesting the novelty of the molecules they may produce. Previous, bioactivity assays of these strains against bacterial species which harbor known human pathogens, revealed inhibitory potential. Further, our study focuses into the phylogenetic and functional landscape of the Tsukamurella genus, employing a throughout phylogenetic analysis that situates strains 8F and 8J within a distinct evolutionary pathway, matching with the environmental and ecological context of the strains reported for this genus. Our findings emphasize the importance of bioprospecting in uncharted biological territories, such as insect-associated microbes as reservoirs of novel bioactive compounds. As such, we posit that Tsukamurella sp. strains 8F and 8J represent promising candidates for the development of new antimicrobials.

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CDK2 plays a pivotal role in controlling the progression of the cell cycle and is a target for anticancer drugs. The last 30 years of structural studies focused on CDK2 provided the basis for understanding its inhibition and furnished the data to develop machine-learning models to study intermolecular interactions. This review addresses the application of computational models to estimate the inhibition of CDK2. It focuses on machine-learning models developed to predict binding affinity against CDK2 using the program SAnDReS. A search of previously published articles on PubMed showed machine-learning models built to evaluate CDK2 inhibition. BindingDB information for CDK2 furnished the data to generate updated machine-learning models to predict the inhibition of this enzyme. The application of SAnDReS to model CDK2-inhibitor interactions showed that this approach can build machine-learning models with superior predictive performance compared with classical and deep-learning scoring functions. Also, the innovative DOME analysis of the predictive performance of machine learning and universal scoring function indicates that this method is adequate to select computational models to address protein-ligand interactions. The available structural and functional data about CDK2 is a rich source of information to build machine-learning models to predict the inhibition of this protein target. SAnDReS can build superior models to predict pKi and outperform universal scoring functions, including one developed using deep learning.

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The compilation of ligand and structure-based molecular modeling methods has become an important practice in virtual screening applied to drug discovery. This systematic review addresses and ranks various virtual screening strategies to drive the selection of the optimal method for studies that have as their starting point a multi-ligand investigation and investigation based on the protein structure of a therapeutic target. This study shows examples of applications and an evaluation based on the objective and problematic of a series of virtual screening studies present in the ScienceDirect® database. The results showed that the molecular docking technique is widely used in scientific production, indicating that approaches that use protein structure as a starting point are the most promising strategy for drug discovery that relies on virtual screening-based research.

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Glioblastoma (GB) is the most common type of malignant tumor of the central nervous system, responsible for significant morbidity and with a 5-year overall relative survival of only 6.8%. Without advances in treatment in the last twenty years, the standard of care continues to be maximum safe resection, Temozolomide (TMZ), and radiotherapy. Many new trials are ongoing, and despite showing increased progression-free survival, these trials did not improve overall survival. They did not consider the adverse effects of these therapies. Therefore, an increasing number of bioprospecting studies have used snake venom molecules to search for new strategies to attack GB selectively without producing side effects. The present review aims to describe GB characteristics and current and new approaches for treatment considering their side effects. Besides, we focused on the antitumoral activity of snake venom proteins from the Viperidae family against GB, exploring the potential for drug design based on in vitro and in vivo studies. This review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines. In January 2024, a systematic search was performed in the PubMed, EMBASE, and Web of Science databases from January 2000 to December 2023. Search terms were selected based on the population/exposure/outcome (PEO) framework and combined using Boolean operators ("AND", "OR"). The search strategy used these terms: glioblastoma, glioma, high-grade glioma, WHO IV glioma, brain cancer, snake venom, Viperidae, and bioprospection. We identified 10 in vivo and in vitro studies with whole and isolated proteins from Viperidae venom that could have antitumor activity against glioblastoma. Studies in bioprospecting exploring the advantage of snake venom proteins against GB deserve to be investigated due to their high specificity, small size, inherent bioactivity, and few side effects to cross the blood-brain barrier (BBB) to reach the tumor microenvironment.

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The DockThor-VS platform (https://dockthor.lncc.br/v2/) is a free protein-ligand docking server conceptualized to facilitate and assist drug discovery projects to perform docking-based virtual screening experiments accurately and using high-performance computing. The DockThor docking engine is a grid-based method designed for flexible-ligand and rigid-receptor docking. It employs a multiple-solution genetic algorithm and the MMFF94S molecular force field scoring function for pose prediction. This engine was engineered to handle highly flexible ligands, such as peptides. Affinity prediction and ranking of protein-ligand complexes are performed with the linear empirical scoring function DockTScore. The main steps of the ligand and protein preparation are available on the DockThor Portal, making it possible to change the protonation states of the amino acid residues, and include cofactors as rigid entities. The user can also customize and visualize the main parameters of the grid box. The results of docking experiments are automatically clustered and ordered, providing users with a diverse array of meaningful binding modes. The platform DockThor-VS offers a user-friendly interface and powerful algorithms, enabling researchers to conduct virtual screening experiments efficiently and accurately. The DockThor Portal utilizes the computational strength of the Brazilian high-performance platform SDumont, further amplifying the efficiency and speed of docking experiments. Additionally, the web server facilitates and enhances virtual screening experiments by offering curated structures of potential targets and compound datasets, such as proteins related to COVID-19 and FDA-approved drugs for repurposing studies. In summary, DockThor-VS is a dynamic and evolving solution for docking-based virtual screening to be applied in drug discovery projects.

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Peptides have emerged as promising therapeutic agents. However, their potential is hindered by hemotoxicity. Understanding the hemotoxicity of peptides is crucial for developing safe and effective peptide-based therapeutics. Here, we employed chemical space complex networks (CSNs) to unravel the hemotoxicity tapestry of peptides. CSNs are powerful tools for visualizing and analyzing the relationships between peptides based on their physicochemical properties and structural features. We constructed CSNs from the StarPepDB database, encompassing 2004 hemolytic peptides, and explored the impact of seven different (dis)similarity measures on network topology and cluster (communities) distribution. Our findings revealed that each CSN extracts orthogonal information, enhancing the motif discovery and enrichment process. We identified 12 consensus hemolytic motifs, whose amino acid composition unveiled a high abundance of lysine, leucine, and valine residues, while aspartic acid, methionine, histidine, asparagine and glutamine were depleted. Additionally, physicochemical properties were used to characterize clusters/communities of hemolytic peptides. To predict hemolytic activity directly from peptide sequences, we constructed multi-query similarity searching models (MQSSMs), which outperformed cutting-edge machine learning (ML)-based models, demonstrating robust hemotoxicity prediction capabilities. Overall, this novel in silico approach uses complex network science as its central strategy to develop robust model classifiers, to characterize the chemical space and to discover new motifs from hemolytic peptides. This will help to enhance the design/selection of peptides with potential therapeutic activity and low toxicity.

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Tuberculosis (TB) is a world health challenge the treatment of which is impacted by the rise of drug-resistant strains. Thus, there is an urgent need for new antitubercular compounds and novel approaches to improve current TB therapy. The zebrafish animal model has become increasingly relevant as an experimental system. It has proven particularly useful during early development for aiding TB drug discovery, supporting both the discovery of new insights into mycobacterial pathogenesis and the evaluation of therapeutical toxicity and efficacy in vivo. In this review, we summarize the past two decades of zebrafish-Mycobacterium marinum research and discuss its contribution to the field of bioactive antituberculosis therapy development.

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Microorganisms can induce diseases with significant clinical implications for human health. Multidrug-resistant microorganisms have been on the rise worldwide over the past few decades, and no new antibiotics have been introduced to the market in a considerable amount of time. Such situation highlights the urgency of discovering new antimicrobial drugs to address this pressing issue. Therefore, the objective of this study was to identify bioactive compounds against 15 species of bacteria and 5 species of fungi of clinical relevance through in vitro screening of 58 synthetic compounds from four chemical classes of our internal library of synthetic compounds. Our findings highlight arylpiperazines 18, 20, 26, 27, and 29, and the aminothiazole 50, as potent broad-spectrum antimicrobials (MICs = 12.5 - 15.6 mg.mL-1) against clinically relevant bacteria and fungi. Additionally, these compounds displayed low cytotoxicity against various host cells and a favorable in vitro pharmacokinetic profile for oral administration. Indeed, all six showed adequate lipophilicity, high gastrointestinal permeability, metabolic stability in human and mouse liver microsomes, and satisfactory aqueous solubility. Thus, they emerge as promising starting points for hit-to-lead studies towards new antibacterial and antifungal agents, especially against Staphylococcus epidermidis, Staphylococcus aureus, Lactobacillus paracasei and Candida orthopsilosis.

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Aryl Hydrocarbon Receptor (AHR) signaling is crucial for regulating the biotransformation of xenobiotics and physiological processes like inflammation and immunity. Meanwhile, Thalassophryne nattereri Peptide (TnP), a promising anti-inflammatory candidate from toadfish venom, demonstrates therapeutic effects through immunomodulation. However, its influence on AHR signaling remains unexplored. This study aimed to elucidate TnP's molecular mechanisms on the AHR-cytochrome P450, family 1 (CYP1) pathway upon injury-induced inflammation in wild-type (WT) and Ahr2-knockdown (KD) zebrafish larvae through transcriptomic analysis and Cyp1a reporters. TnP, while unable to directly activate AHR, potentiated AHR activation by the high-affinity ligand 6-Formylindolo [3,2-b]carbazole (FICZ), implying a role as a CYP1A inhibitor, confirmed by in vitro studies. This interplay suggests TnP's ability to modulate the AHR-CYP1 complex, prompting investigations into its influence on biotransformation pathways and injury-induced inflammation. Here, the inflammation model alone resulted in a significant response on the transcriptome, with most differentially expressed genes (DEGs) being upregulated across the groups. Ahr2-KD resulted in an overall greater number of DEGs, as did treatment with the higher dose of TnP in both WT and KD embryos. Genes related to oxidative stress and inflammatory response were the most apparent under inflamed conditions for both WT and KD groups, e.g., Tnfrsf1a, Irf1b, and Mmp9. TnP, specifically, induces the expression of Hspa5, Hsp90aa1.2, Cxcr3.3, and Mpeg1.2. Overall, this study suggests an interplay between TnP and the AHR-CYP1 pathway, stressing the inflammatory modulation through AHR-dependent mechanisms. Altogether, these results may offer new avenues in novel therapeutic strategies, such as based on natural bioactive molecules, harnessing AHR modulation for targeted and sustained drug effects in inflammatory conditions.

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Anacardic acids are natural compounds found in various plant families, such as Anacardiaceae, Geraniaceae, Ginkgoaceae, and Myristicaceae, among others. Several activities have been reported regarding these compounds, including antibacterial, antioxidant, anticancer, anti-inflammatory, and antiviral activities, showing the potential therapeutic applicability of these compounds. From a chemical point of view, they are structurally made up of salicylic acids substituted by an alkyl chain containing unsaturated bonds, which can vary in number and position, determining their bioactivity and differentiating them from the various existing forms. Our work aimed to explore the potential of anacardic acids, based on studies that address the bioactivity of these compounds, as well as to establish a greater understanding of the structure-activity relationship of these compounds through in silico methods, with a focus on the elucidation of a possible drug target through the application of computer-aided drug design, CADD.

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Peptides are bioactive molecules whose functional versatility in living organisms has led to successful applications in diverse fields. In recent years, the amount of data describing peptide sequences and function collected in open repositories has substantially increased, allowing the application of more complex computational models to study the relations between the peptide composition and function. This work introduces AMP-Detector, a sequence-based classification model for the detection of peptides' functional biological activity, focusing on accelerating the discovery and de novo design of potential antimicrobial peptides (AMPs). AMP-Detector introduces a novel sequence-based pipeline to train binary classification models, integrating protein language models and machine learning algorithms. This pipeline produced 21 models targeting antimicrobial, antiviral, and antibacterial activity, achieving average precision exceeding 83%. Benchmark analyses revealed that our models outperformed existing methods for AMPs and delivered comparable results for other biological activity types. Utilizing the Peptide Atlas, we applied AMP-Detector to discover over 190,000 potential AMPs and demonstrated that it is an integrative approach with generative learning to aid in de novo design, resulting in over 500 novel AMPs. The combination of our methodology, robust models, and a generative design strategy offers a significant advancement in peptide-based drug discovery and represents a pivotal tool for therapeutic applications.

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The development of new treatments for neglected tropical diseases (NTDs) remains a major challenge in the 21st century. In most cases, the available drugs are obsolete and have limitations in terms of efficacy and safety. The situation becomes even more complex when considering the low number of new chemical entities (NCEs) currently in use in advanced clinical trials for most of these diseases. Natural products (NPs) are valuable sources of hits and lead compounds with privileged scaffolds for the discovery of new bioactive molecules. Considering the relevance of biodiversity for drug discovery, a chemoinformatics analysis was conducted on a compound dataset of NPs with anti-trypanosomatid activity reported in 497 research articles from 2019 to 2024. Structures corresponding to different metabolic classes were identified, including terpenoids, benzoic acids, benzenoids, steroids, alkaloids, phenylpropanoids, peptides, flavonoids, polyketides, lignans, cytochalasins, and naphthoquinones. This unique collection of NPs occupies regions of the chemical space with drug-like properties that are relevant to anti-trypanosomatid drug discovery. The gathered information greatly enhanced our understanding of biologically relevant chemical classes, structural features, and physicochemical properties. These results can be useful in guiding future medicinal chemistry efforts for the development of NP-inspired NCEs to treat NTDs caused by trypanosomatid parasites.

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The prediction of possible lead compounds from already-known drugs that may present DPP-4 inhibition activity imply a advantage in the drug development in terms of time and cost to find alternative medicines for the treatment of Type 2 Diabetes Mellitus (T2DM). The inhibition of dipeptidyl peptidase-4 (DPP-4) has been one of the most explored strategies to develop potential drugs against this condition. A diverse dataset of molecules with known experimental inhibitory activity against DPP-4 was constructed and used to develop predictive models using different machine-learning algorithms. Model M36 is the most promising one based on the internal and external performance showing values of Q2CV = 0.813, and Q2EXT = 0.803. The applicability domain evaluation and Tropsha's analysis were conducted to validate M36, indicating its robustness and accuracy in predicting pIC50 values for organic molecules within the established domain. The physicochemical properties of the ligands, including electronegativity, polarizability, and van der Waals volume were relevant to predict the inhibition process. The model was then employed in the virtual screening of potential DPP4 inhibitors, finding 448 compounds from the DrugBank and 9 from DiaNat with potential inhibitory activity. Molecular docking and molecular dynamics simulations were used to get insight into the ligand-protein interaction. From the screening and the favorable molecular dynamic results, several compounds including Skimmin (pIC50 = 3.54, Binding energy = -8.86 kcal/mol), bergenin (pIC50 = 2.69, Binding energy = -13.90 kcal/mol), and DB07272 (pIC50 = 3.97, Binding energy = -25.28 kcal/mol) seem to be promising hits to be tested and optimized in the treatment of T2DM. This results imply a important reduction in cost and time on the application of this drugs because all the information about the its metabolism is already available.

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Machine learning interatomic potentials (MLIPs) are one of the main techniques in the materials science toolbox, able to bridge ab initio accuracy with the computational efficiency of classical force fields. This allows simulations ranging from atoms, molecules, and biosystems, to solid and bulk materials, surfaces, nanomaterials, and their interfaces and complex interactions. A recent class of advanced MLIPs, which use equivariant representations and deep graph neural networks, is known as universal models. These models are proposed as foundation models suitable for any system, covering most elements from the periodic table. Current universal MLIPs (UIPs) have been trained with the largest consistent data set available nowadays. However, these are composed mostly of bulk materials' DFT calculations. In this article, we assess the universality of all openly available UIPs, namely MACE, CHGNet, and M3GNet, in a representative task of generalization: calculation of surface energies. We find that the out-of-the-box foundation models have significant shortcomings in this task, with errors correlated to the total energy of surface simulations, having an out-of-domain distance from the training data set. Our results show that while UIPs are an efficient starting point for fine-tuning specialized models, we envision the potential of increasing the coverage of the materials space toward universal training data sets for MLIPs.

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INTRODUCTION: Despite the availability of around 30 antiseizure medications, 1/3 of patients with epilepsy fail to become seizure-free upon pharmacological treatment. Available medications provide adequate symptomatic control in two-thirds of patients, but disease-modifying drugs are still scarce. Recently, though, new paradigms have been explored. AREAS COVERED: Three areas are reviewed in which a high degree of innovation in the search for novel antiseizure and antiepileptogenic medications has been implemented: development of novel screening approaches, search for novel therapeutic targets, and adoption of new drug discovery paradigms aligned with a systems pharmacology perspective. EXPERT OPINION: In the past, worldwide leaders in epilepsy have reiteratively stated that the lack of progress in the field may be explained by the recurrent use of the same molecular targets and screening procedures to identify novel medications. This landscape has changed recently, as reflected by the new Epilepsy Therapy Screening Program and the introduction of many in vitro and in vivo models that could possibly improve our chances of identifying first-in-class medications that may control drug-resistant epilepsy or modify the course of disease. Other milestones include the study of new molecular targets for disease-modifying drugs and exploration of a systems pharmacology perspective to design new drugs.

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Designing and developing inhibitors against the epigenetic target DNA methyltransferase (DNMT) is an attractive strategy in epigenetic drug discovery. DNMT1 is one of the epigenetic enzymes with significant clinical relevance. Structure-based de novo design is a drug discovery strategy that was used in combination with similarity searching to identify a novel DNMT inhibitor with a novel chemical scaffold and warrants further exploration. This study aimed to continue exploring the potential of de novo design to build epigenetic-focused libraries targeted toward DNMT1. Herein, we report the results of an in-depth and critical comparison of ligand- and structure-based de novo design of screening libraries focused on DNMT1. The newly designed chemical libraries focused on DNMT1 are freely available on GitHub.

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Mosquitoes can transmit several pathogenic viruses to humans, but their natural viral community is also composed of a myriad of other viruses such as insect-specific viruses (ISVs) and those that infect symbiotic microorganisms. Besides a growing number of studies investigating the mosquito virome, the majority are focused on few urban species, and relatively little is known about the virome of sylvatic mosquitoes, particularly in high biodiverse biomes such as the Brazilian biomes. Here, we characterized the RNA virome of 10 sylvatic mosquito species from Atlantic forest remains at a sylvatic-urban interface in Northeast Brazil employing a metatranscriptomic approach. A total of 16 viral families were detected. The phylogenetic reconstructions of 14 viral families revealed that the majority of the sequences are putative ISVs. The phylogenetic positioning and, in most cases, the association with a high RNA-dependent RNA polymerase amino acid divergence from other known viruses suggests that the viruses characterized here represent at least 34 new viral species. Therefore, the sylvatic mosquito viral community is predominantly composed of highly divergent viruses highlighting the limited knowledge we still have about the natural virome of mosquitoes in general. Moreover, we found that none of the viruses recovered were shared between the species investigated, and only one showed high identity to a virus detected in a mosquito sampled in Peru, South America. These findings add further in-depth understanding about the interactions and coevolution between mosquitoes and viruses in natural environments. IMPORTANCE: Mosquitoes are medically important insects as they transmit pathogenic viruses to humans and animals during blood feeding. However, their natural microbiota is also composed of a diverse set of viruses that cause no harm to the insect and other hosts, such as insect-specific viruses. In this study, we characterized the RNA virome of sylvatic mosquitoes from Northeast Brazil using unbiased metatranscriptomic sequencing and in-depth bioinformatic approaches. Our analysis revealed that these mosquitoes species harbor a diverse set of highly divergent viruses, and the majority comprises new viral species. Our findings revealed many new virus lineages characterized for the first time broadening our understanding about the natural interaction between mosquitoes and viruses. Finally, it also provided several complete genomes that warrant further assessment for mosquito and vertebrate host pathogenicity and their potential interference with pathogenic arboviruses.

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Biting midges (Culicoides) are vectors of many pathogens of medical and veterinary importance, but their viromes are poorly characterized compared to certain other hematophagous arthropods, e.g., mosquitoes and ticks. The goal of this study was to use metagenomics to identify viruses in Culicoides from Mexico. A total of 457 adult midges were collected in Chihuahua, northern Mexico, in 2020 and 2021, and all were identified as female Culicoides reevesi. The midges were sorted into five pools and homogenized. An aliquot of each homogenate was subjected to polyethylene glycol precipitation to enrich for virions, then total RNA was extracted and analyzed by unbiased high-throughput sequencing. We identified six novel viruses that are characteristic of viruses from five families (Nodaviridae, Partitiviridae, Solemoviridae, Tombusviridae, and Totiviridae) and one novel virus that is too divergent from all classified viruses to be assigned to an established family. The newly discovered viruses are phylogenetically distinct from their closest known relatives, and their minimal infection rates in female C. reevesi range from 0.22 to 1.09. No previously known viruses were detected, presumably because viral metagenomics had never before been used to study Culicoides from the Western Hemisphere. To conclude, we discovered multiple novel viruses in C. reevesi from Mexico, expanding our knowledge of arthropod viral diversity and evolution.

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Novel antibiotics are urgently needed to combat the antibiotic-resistance crisis. We present a machine-learning-based approach to predict antimicrobial peptides (AMPs) within the global microbiome and leverage a vast dataset of 63,410 metagenomes and 87,920 prokaryotic genomes from environmental and host-associated habitats to create the AMPSphere, a comprehensive catalog comprising 863,498 non-redundant peptides, few of which match existing databases. AMPSphere provides insights into the evolutionary origins of peptides, including by duplication or gene truncation of longer sequences, and we observed that AMP production varies by habitat. To validate our predictions, we synthesized and tested 100 AMPs against clinically relevant drug-resistant pathogens and human gut commensals both in vitro and in vivo. A total of 79 peptides were active, with 63 targeting pathogens. These active AMPs exhibited antibacterial activity by disrupting bacterial membranes. In conclusion, our approach identified nearly one million prokaryotic AMP sequences, an open-access resource for antibiotic discovery.

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