MolPredictX: A Pioneer Mobile App Version for Online Biological Activity Predictions by Machine Learning Models.

MolPredictX is a free-access web tool in which it is possible to analyze the prediction of biological activity of chemical molecules. MolPredictX has been available online to the general public for just over a year and has now gone through its first update. We also developed its version for android, being the first free app capable of predicting biological activities. MolPredictX is available for free at https://www.molpredictX.ufpb.br/ , and its mobile application version can be obtained from Google Play.

Hepatitis C virus infection and Parkinson's disease: insights from a joint sex-stratified BioOptimatics meta-analysis.

Hepatitis C virus (HCV) infection poses a significant public health challenge and often leads to long-term health complications and even death. Parkinson's disease (PD) is a progressive neurodegenerative disorder with a proposed viral etiology. HCV infection and PD have been previously suggested to be related. This work aimed to identify potential biomarkers and pathways that may play a role in the joint development of PD and HCV infection. Using BioOptimatics-bioinformatics driven by mathematical global optimization-, 22 publicly available microarray and RNAseq datasets for both diseases were analyzed, focusing on sex-specific differences. Our results revealed that 19 genes, including MT1H, MYOM2, and RPL18, exhibited significant changes in expression in both diseases. Pathway and network analyses stratified by sex indicated that these gene expression changes were enriched in processes related to immune response regulation in females and immune cell activation in males. These findings suggest a potential link between HCV infection and PD, highlighting the importance of further investigation into the underlying mechanisms and potential therapeutic targets involved.

Design and implementation of a metagenomic analytical pipeline for respiratory pathogen detection.

OBJECTIVE: We developed an in-house bioinformatics pipeline to improve the detection of respiratory pathogens in metagenomic sequencing data. This pipeline addresses the need for short-time analysis, high accuracy, scalability, and reproducibility in a high-performance computing environment. RESULTS: We evaluated our pipeline using ninety synthetic metagenomes designed to simulate nasopharyngeal swab samples. The pipeline successfully identified 177 out of 204 respiratory pathogens present in the compositions, with an average processing time of approximately 4 min per sample (processing 1 million paired-end reads of 150 base pairs). For the estimation of all the 470 taxa included in the compositions, the pipeline demonstrated high accuracy, identifying 420 and achieving a correlation of 0.9 between their actual and predicted relative abundances. Among the identified taxa, 27 were significantly underestimated or overestimated, including only three clinically relevant pathogens. We also validated the pipeline by applying it to a clinical dataset from a study on metagenomic pathogen characterization in patients with acute respiratory infections and successfully identified all pathogens responsible for the diagnosed infections. These findings underscore the pipeline's effectiveness in pathogen detection and highlight its potential utility in respiratory pathogen surveillance.

Peptide hemolytic activity analysis using visual data mining of similarity-based complex networks.

Peptides are promising drug development frameworks that have been hindered by intrinsic undesired properties including hemolytic activity. We aim to get a better insight into the chemical space of hemolytic peptides using a novel approach based on network science and data mining. Metadata networks (METNs) were useful to characterize and find general patterns associated with hemolytic peptides, whereas Half-Space Proximal Networks (HSPNs), represented the hemolytic peptide space. The best candidate HSPNs were used to extract various subsets of hemolytic peptides (scaffolds) considering network centrality and peptide similarity. These scaffolds have been proved to be useful in developing robust similarity-based model classifiers. Finally, using an alignment-free approach, we reported 47 putative hemolytic motifs, which can be used as toxic signatures when developing novel peptide-based drugs. We provided evidence that the number of hemolytic motifs in a sequence might be related to the likelihood of being hemolytic.

PROTA: A Robust Tool for Protamine Prediction Using a Hybrid Approach of Machine Learning and Deep Learning.

Protamines play a critical role in DNA compaction and stabilization in sperm cells, significantly influencing male fertility and various biotechnological applications. Traditionally, identifying these proteins is a challenging and time-consuming process due to their species-specific variability and complexity. Leveraging advancements in computational biology, we present PROTA, a novel tool that combines machine learning (ML) and deep learning (DL) techniques to predict protamines with high accuracy. For the first time, we integrate Generative Adversarial Networks (GANs) with supervised learning methods to enhance the accuracy and generalizability of protamine prediction. Our methodology evaluated multiple ML models, including Light Gradient-Boosting Machine (LIGHTGBM), Multilayer Perceptron (MLP), Random Forest (RF), eXtreme Gradient Boosting (XGBOOST), k-Nearest Neighbors (KNN), Logistic Regression (LR), Naive Bayes (NB), and Radial Basis Function-Support Vector Machine (RBF-SVM). During ten-fold cross-validation on our training dataset, the MLP model with GAN-augmented data demonstrated superior performance metrics: 0.997 accuracy, 0.997 F1 score, 0.998 precision, 0.997 sensitivity, and 1.0 AUC. In the independent testing phase, this model achieved 0.999 accuracy, 0.999 F1 score, 1.0 precision, 0.999 sensitivity, and 1.0 AUC. These results establish PROTA, accessible via a user-friendly web application. We anticipate that PROTA will be a crucial resource for researchers, enabling the rapid and reliable prediction of protamines, thereby advancing our understanding of their roles in reproductive biology, biotechnology, and medicine.

Bioinformatic Analysis from a Descriptive Profile of miRNAs in Chronic Migraine.

Chronic migraines have been described chiefly only from a clinical perspective. However, searching for reliable molecular markers has allowed for the discovery of the expression of different genes mainly associated with inflammation, neuro-vascularization, and pain-related pathways. The interest in microRNAs (miRs) that can regulate the expression of these genes has gained significant relevance since multiple miRs could play a key role in regulating these events. In this study, miRs were searched in samples from patients with chronic migraine, and the inclusion criteria were carefully reviewed. Different bioinformatic tools, such as miRbase, targetscan, miRPath, tissue atlas, and miR2Disease, were used to analyze the samples. Our findings revealed that some of the miRs were expressed more (miR-197, miR-101, miR-92a, miR-375, and miR-146b) and less (miR-133a/b, miR-134, miR-195, and miR-340) than others. We concluded that, during chronic migraine, common pathways, such as inflammation, vascularization, neurodevelopment, nociceptive pain, and pharmacological resistance, were associated with this disease.

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.

Exploring the potential of structure-based deep learning approaches for T cell receptor design.

Deep learning methods, trained on the increasing set of available protein 3D structures and sequences, have substantially impacted the protein modeling and design field. These advancements have facilitated the creation of novel proteins, or the optimization of existing ones designed for specific functions, such as binding a target protein. Despite the demonstrated potential of such approaches in designing general protein binders, their application in designing immunotherapeutics remains relatively underexplored. A relevant application is the design of T cell receptors (TCRs). Given the crucial role of T cells in mediating immune responses, redirecting these cells to tumor or infected target cells through the engineering of TCRs has shown promising results in treating diseases, especially cancer. However, the computational design of TCR interactions presents challenges for current physics-based methods, particularly due to the unique natural characteristics of these interfaces, such as low affinity and cross-reactivity. For this reason, in this study, we explored the potential of two structure-based deep learning protein design methods, ProteinMPNN and ESM-IF1, in designing fixed-backbone TCRs for binding target antigenic peptides presented by the MHC through different design scenarios. To evaluate TCR designs, we employed a comprehensive set of sequence- and structure-based metrics, highlighting the benefits of these methods in comparison to classical physics-based design methods and identifying deficiencies for improvement.

Computational Screening to Predict MicroRNA Targets in the Flavivirus 3' UTR Genome: An Approach for Antiviral Development.

MicroRNAs (miRNAs) are molecules that influence messenger RNA (mRNA) expression levels by binding to the 3' untranslated region (3' UTR) of target genes. Host miRNAs can influence flavivirus replication, either by inducing changes in the host transcriptome or by directly binding to viral genomes. The 3' UTR of the flavivirus genome is a conserved region crucial for viral replication. Cells might exploit this well-preserved region by generating miRNAs that interact with it, ultimately impacting viral replication. Despite significant efforts to identify miRNAs capable of arresting viral replication, the potential of all these miRNAs to interact with the flavivirus 3' UTR is still poorly characterised. In this context, bioinformatic tools have been proposed as a fundamental part of accelerating the discovery of interactions between miRNAs and the 3' UTR of viral genomes. In this study, we performed a computational analysis to reveal potential miRNAs from human and mosquito species that bind to the 3' UTR of flaviviruses. In humans, miR-6842 and miR-661 were found, while in mosquitoes, miR-9-C, miR-2945-5p, miR-11924, miR-282-5p, and miR-79 were identified. These findings open new avenues for studying these miRNAs as antivirals against flavivirus infections.

The Molecular Comorbidity Network of Periodontal Disease.

Periodontal disease, a multifactorial inflammatory condition affecting the supporting structures of the teeth, has been increasingly recognized for its association with various systemic diseases. Understanding the molecular comorbidities of periodontal disease is crucial for elucidating shared pathogenic mechanisms and potential therapeutic targets. In this study, we conducted comprehensive literature and biological database mining by utilizing DisGeNET2R for extracting gene-disease associations, Romin for integrating and modeling molecular interaction networks, and Rentrez R libraries for accessing and retrieving relevant information from NCBI databases. This integrative bioinformatics approach enabled us to systematically identify diseases sharing associated genes, proteins, or molecular pathways with periodontitis. Our analysis revealed significant molecular overlaps between periodontal disease and several systemic conditions, including cardiovascular diseases, diabetes mellitus, rheumatoid arthritis, and inflammatory bowel diseases. Shared molecular mechanisms implicated in the pathogenesis of these diseases and periodontitis encompassed dysregulation of inflammatory mediators, immune response pathways, oxidative stress pathways, and alterations in the extracellular matrix. Furthermore, network analysis unveiled the key hub genes and proteins (such as TNF, IL6, PTGS2, IL10, NOS3, IL1B, VEGFA, BCL2, STAT3, LEP and TP53) that play pivotal roles in the crosstalk between periodontal disease and its comorbidities, offering potential targets for therapeutic intervention. Insights gained from this integrative approach shed light on the intricate interplay between periodontal health and systemic well-being, emphasizing the importance of interdisciplinary collaboration in developing personalized treatment strategies for patients with periodontal disease and associated comorbidities.

Unlocking epitope similarity: A comparative analysis of the American manatee (Trichechus manatus) IgA and human IgA through an immuno-informatics approach.

The American manatee (Trichechus manatus), experiencing population declines due to various threats, is the focus of conservation efforts that include the capture, rehabilitation, and release of orphaned calves when their mothers are unable to care for them. These efforts are compromised by the use of commercially available milk substitutes that lack essential components found in natural manatee breast milk, particularly immunoglobulin A (IgA). IgA plays a crucial role in nurturing the immune mucosal system and fostering a healthy microbiota. However, research on IgA in non-maternally fed manatees is limited due to the lack of species-specific reagents. To address this gap, our study employs immuno-informatics analysis to compare IgA sequences from manatees with those from other species, aiming to explore epitope similarity and sharing. We compared the protein sequence of manatee IgA with available IgA sequences, assessing similarity at the sequence, 3D structures, and epitope levels. Our findings reveal that human IgA exhibits the highest similarity in terms of sequence and 3D structure. Additionally, epitope analysis shows high conservation, identity, and similarity of predicted epitopes compared to human IgA. Future studies should focus on functional analysis using human IgA polyclonal reagents to detect manatee IgA in breast milk. Our findings highlight the potential of comparative analysis in advancing the understanding of immunology in non-human animals and overcoming challenges associated with the scarcity of species-specific reagents.

Identification of potential new T cell activation molecules: a Bioinformatic Approach.

T-cell activation is central for the initiation of T cell mediated adaptive immune response and is the result of the close communication between the Antigen Presenting Cell (APC) and the T lymphocyte. Although T-cell activation is currently well understood, and many intracellular pathways are well characterized, nevertheless new players are constantly identified, and this complements the known protein interactome. In this work we aimed to identify new proteins involved in T cell activation. We reviewed and analyzed results of microarray gene expression datasets reported in the public database GEO-NCBI. Using data from GSE136625, GSE50971, GSE13887, GSE11989 and GSE902 we performed different comparisons using R and other bioinformatic tools including GEO2R and we report here upregulated genes that have no previous reports in immune related functions and with potential participation upon T-cell activation. Our results indicate that RND3, SYT10, IgSF6 and PIN1 are potential new T-cell activation molecules.

In Silico Analysis of the Missense Variants of Uncertain Significance of CTNNB1 Gene Reported in GnomAD Database.

CTNNB1 pathogenic variants are related to the improper functioning of the WNT/ß-catenin pathway, promoting the development of different types of cancer of somatic origin. Bioinformatics analyses of genetic variation are a great tool to understand the possible consequences of these variants on protein structure and function and their probable implication in pathologies. The objective of this study is to describe the impact of the missense variants of uncertain significance (VUS) of the CTNNB1 gene on structure and function of the ß-catenin protein. The CTNNB1 variants were obtained from the GnomAD v2.1.1 database; subsequently, a bioinformatic analysis was performed using the VarSome, UCSC Genome Browser, UniProt, the Kinase Library database, and DynaMut2 platforms to evaluate clinical significance, gene conservation, consensus sites for post-translational modifications, and the dynamics and stability of proteins. The GnomAD v2.1.1 database included 826 variants of the CTNNB1 gene, of which 385 were in exons and exon/intron boundaries. Among these variants, 214 were identified as missense, of which 146 were classified as VUS. Notably, 12 variants were in proximity to consensus sites for post-translational modifications (PTMs). The in silico analysis showed a slight tendency towards probably pathogenic for c.59C>T (p.Ala20Val) and c.983T>C (p.Met328Thr) missense VUS. These findings provide possible functional implications of these variants in some types of cancer.

Immunoinformatic approach for multi-epitope vaccine design against Staphylococcus aureus based on hemolysin proteins.

Staphylococcus aureus is a common bacterium that causes a variety of infections in humans. This microorganism produces several virulence factors, including hemolysins, which contribute to its disease-causing ability. The treatment of S. aureus infections typically involves the use of antibiotics. However, the emergence of antibiotic-resistant strains has become a major concern. Therefore, vaccination against S. aureus has gained attention as an alternative approach. Vaccination has the advantage of stimulating the immune system to produce specific antibodies that can neutralize bacteria and prevent infection. However, developing an effective vaccine against S. aureus has proven to be challenging. This study aimed to use in silico methods to design a multi-epitope vaccine against S. aureus infection based on hemolysin proteins. The designed vaccine contained four B-cell epitopes, four CTL epitopes, and four HTL epitopes, as well as the ribosomal protein L7/L12 and pan-HLA DR-binding epitope, included as adjuvants. Furthermore, the vaccine was non-allergenic and non-toxic with the potential to stimulate the TLR2-, TLR-4, and TLR-6 receptors. The predicted vaccine exhibited a high degree of antigenicity and stability, suggesting potential for further development as a viable vaccine candidate. The population coverage of the vaccine was 94.4 %, indicating potential widespread protection against S. aureus. Overall, these findings provide valuable insights into the design of an effective multi-epitope vaccine against S. aureus infection and pave the way for future experimental validations.

Protein Language Models and Machine Learning Facilitate the Identification of Antimicrobial Peptides.

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.

OmicScope unravels systems-level insights from quantitative proteomics data.

Shotgun proteomics analysis presents multifaceted challenges, demanding diverse tool integration for insights. Addressing this complexity, OmicScope emerges as an innovative solution for quantitative proteomics data analysis. Engineered to handle various data formats, it performs data pre-processing - including joining replicates, normalization, data imputation - and conducts differential proteomics analysis for both static and longitudinal experimental designs. Empowered by Enrichr with over 224 databases, OmicScope performs Over Representation Analysis (ORA) and Gene Set Enrichment Analysis (GSEA). Additionally, its Nebula module facilitates meta-analysis from independent datasets, providing a systems biology approach for enriched insights. Complete with a data visualization toolkit and accessible as Python package and a web application, OmicScope democratizes proteomics analysis, offering an efficient and high-quality pipeline for researchers.

Proteins and Peptides Studied In Silico and In Vivo for the Treatment of Diabetes Mellitus: A Systematic Review.

Bioinformatics has expedited the screening of new efficient therapeutic agents for diseases such as diabetes mellitus (DM). The objective of this systematic review (SR) was to understand naturally occurring proteins and peptides studied in silico and subsequently reevaluated in vivo for treating DM, guided by the question: which peptides or proteins have been studied in silico for the treatment of diabetes mellitus? The RS protocol was registered in the International Prospective Register of Systematic Reviews database. Articles meeting the eligibility criteria were selected from the PubMed, ScienceDirect, Scopus, Web of Science, Virtual Health Library (VHL), and EMBASE databases. Five studies that investigated peptides or proteins analyzed in silico and in vivo were selected. Risk of bias assessment was conducted using the adapted Strengthening the Reporting of Empirical Simulation Studies (STRESS) tool. A diverse range of assessed proteins and/or peptides that had a natural origin were investigated in silico and corresponding in vivo reevaluation demonstrated reductions in glycemia and/or insulin, morphological enhancements in pancreatic ß cells, and alterations in the gene expression of markers associated with DM. The in silico studies outlined offer crucial insights into therapeutic strategies for DM, along with promising leads for screening novel therapeutic agents in future trials.

Genomics for Arbovirus Surveillance: Considerations for Routine Use in Public Health Laboratories.

The emergence and re-emergence of arthropod-borne viruses is a public health threat. For routine surveillance in public health laboratories, cost-effective and reproducible methods are essential. In this review, we address the technical considerations of high-throughput sequencing methods (HTS) for arbovirus surveillance in national health laboratories, focusing on pre-sequencing, sequencing, and post-sequencing approaches, underlining the importance of robust wet and dry laboratory workflows for reproducible analysis. We aim to provide insights for researchers and clinicians interested in arbovirus, diagnosis, and surveillance by discussing current advances in sequencing methods and bioinformatics pipelines applied to arboviruses.

Bioinformatics analysis to identify the relationship between human papillomavirus-associated cervical cancer, toll-like receptors and exomes: A genetic epidemiology study.

INTRODUCTION: Genetic variants may influence Toll-like receptor (TLR) signaling in the immune response to human papillomavirus (HPV) infection and lead to cervical cancer. In this study, we investigated the pattern of TLR expression in the transcriptome of HPV-positive and HPV-negative cervical cancer samples and looked for variants potentially related to TLR gene alterations in exomes from different populations. MATERIALS AND METHODS: A cervical tissue sample from 28 women, which was obtained from the Gene Expression Omnibus database, was used to examine TLR gene expression. Subsequently, the transcripts related to the TLRs that showed significant gene expression were queried in the Genome Aggregation Database to search for variants in more than 5,728 exomes from different ethnicities. RESULTS: Cancer and HPV were found to be associated (p<0.0001). TLR1(p = 0.001), TLR3(p = 0.004), TLR4(221060_s_at)(p = 0.001), TLR7(p = 0.001;p = 0.047), TLR8(p = 0.002) and TLR10(p = 0.008) were negatively regulated, while TLR4(1552798_at)(p<0.0001) and TLR6(p = 0.019) were positively regulated in HPV-positive patients (p<0.05). The clinical significance of the variants was statistically significant for TLR1, TLR3, TLR6 and TLR8 in association with ethnicity. Genetic variants in different TLRs have been found in various ethnic populations. Variants of the TLR gene were of the following types: TLR1(5_prime_UTR), TLR4(start_lost), TLR8(synonymous;missense) and TLR10(3_prime_UTR). The "missense" variant was found to have a risk of its clinical significance being pathogenic in South Asian populations (OR = 56,820[95%CI:40,206,80,299]). CONCLUSION: The results of this study suggest that the variants found in the transcriptomes of different populations may lead to impairment of the functional aspect of TLRs that show significant gene expression in cervical cancer samples caused by HPV.

Molecular Mimicry between Toxoplasma gondii B-Cell Epitopes and Neurodevelopmental Proteins: An Immunoinformatic Approach.

Epidemiological studies and meta-analyses have shown a strong association between high seroprevalence of Toxoplasma gondii (T. gondii) and schizophrenia. Schizophrenic patients showed higher levels of anti-Toxoplasma immunoglobulins M and G (IgM and IgG) when compared to healthy controls. Previously, in a rat model, we demonstrated that the progeny of mothers immunized with T. gondii lysates before gestation had behavioral and social impairments during adulthood. Therefore, we suggested that T. gondii infection can trigger autoreactivity by molecularly mimicking host brain proteins. Here, we aimed to identify the occurrence of antigenic mimicry between T. gondii epitopes and host brain proteins. Using a bioinformatic approach, we predicted T. gondii RH-88 B cell epitopes and compared them to human cell-surface proteins involved in brain development and differentiation (BrainS). Five different algorithms for B-cell-epitope prediction were used and compared, resulting in 8584 T. gondii epitopes. We then compared T. gondii predicted epitopes to BrainS proteins by local sequence alignments using BLASTP. T. gondii immunogenic epitopes significantly overlapped with 42 BrainS proteins. Among these overlapping proteins essential for brain development and differentiation, we identified HSP90 and NOTCH receptors as the proteins most likely to be targeted by the maternally generated pathogenic antibodies due to their topological overlap at the extracellular region of their sequence. This analysis highlights the relevance of pregestational clinical surveillance and screening for potential pathogenic anti-T. gondii antibodies. It also identifies potential targets for the design of vaccines that could prevent behavioral and cognitive impairments associated with pre-gestational T. gondii exposure.