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BACKGROUND: Esophageal cancer (EC) remains a global health challenge, often diagnosed at advanced stages, leading to high mortality rates. Current diagnostic tools for EC are limited in their efficacy. This study aims to harness the potential of microRNAs (miRNAs) as novel, noninvasive diagnostic biomarkers for EC. Our objective was to determine the diagnostic accuracy of miRNAs, particularly in distinguishing miRNAs associated with EC from control miRNAs. METHODS: We applied machine learning (ML) techniques in WEKA (Waikato Environment for Knowledge Analysis) and TensorFlow Keras to a dataset of miRNA sequences and gene targets, assessing the predictive power of several classifiers: naïve Bayes, multilayer perceptron, Hoeffding tree, random forest, and random tree. The data were further subjected to InfoGain feature selection to identify the most informative miRNA sequence and gene target descriptors. The ML models' abilities to distinguish between miRNA implicated in EC and control group miRNA was then tested. RESULTS: Of the tested WEKA classifiers, the top 3 performing ones were random forest, Hoeffding tree, and naïve Bayes. The TensorFlow Keras neural network model was subsequently trained and tested, the model's predictive power was further validated using an independent dataset. The TensorFlow Keras gave an accuracy 0.91. The WEKA best algorithm (naïve Bayes) model yielded an accuracy of 0.94. CONCLUSIONS: The results demonstrate the potential of ML-based miRNA classifiers in diagnosing EC. However, further studies are necessary to validate these findings and explore the full clinical potential of this approach.
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Biomarcadores Tumorais , Neoplasias Esofágicas , Aprendizado de Máquina , MicroRNAs , MicroRNAs/genética , Neoplasias Esofágicas/genética , Neoplasias Esofágicas/diagnóstico , Humanos , Biomarcadores Tumorais/genética , Redes Neurais de Computação , Teorema de BayesRESUMO
Today, colorectal cancer (CRC) diagnosis is performed using colonoscopy, which is the current, most effective screening method. However, colonoscopy poses risks of harm to the patient and is an invasive process. Recent research has proven metabolomics as a potential, non-invasive detection method, which can use identified biomarkers to detect potential cancer in a patient's body. The aim of this study is to develop a machine-learning (ML) model based on chemical descriptors that will recognize CRC-associated metabolites. We selected a set of metabolites found as the biomarkers of CRC, confirmed that they participate in cancer-related pathways, and used them for training a machine-learning model for the diagnostics of CRC. Using a set of selective metabolites and random compounds, we developed a range of ML models. The best performing ML model trained on Stage 0-2 CRC metabolite data predicted a metabolite class with 89.55% accuracy. The best performing ML model trained on Stage 3-4 CRC metabolite data predicted a metabolite class with 95.21% accuracy. Lastly, the best-performing ML model trained on Stage 0-4 CRC metabolite data predicted a metabolite class with 93.04% accuracy. These models were then tested on independent datasets, including random and unrelated-disease metabolites. In addition, six pathways related to these CRC metabolites were also distinguished: aminoacyl-tRNA biosynthesis; glyoxylate and dicarboxylate metabolism; glycine, serine, and threonine metabolism; phenylalanine, tyrosine, and tryptophan biosynthesis; arginine biosynthesis; and alanine, aspartate, and glutamate metabolism. Thus, in this research study, we created machine-learning models based on metabolite-related descriptors that may be helpful in developing a non-invasive diagnosis method for CRC.
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BACKGROUND: The current standard for Parkinson's disease (PD) diagnosis is often imprecise and expensive. However, the dysregulation patterns of microRNA (miRNA) hold potential as a reliable and effective non-invasive diagnosis of PD. METHODS: We use data mining to elucidate new miRNA biomarkers and then develop a machine-learning (ML) model to diagnose PD based on these biomarkers. RESULTS: The best-performing ML model, trained on filtered miRNA dysregulated in PD, was able to identify miRNA biomarkers with 95.65% accuracy. Through analysis of miRNA implicated in PD, thousands of descriptors reliant on gene targets were created that can be used to identify novel biomarkers and strengthen PD diagnosis. CONCLUSIONS: The developed ML model based on miRNAs and their genomic pathway descriptors achieved high accuracies for the prediction of PD.
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Aprendizado Profundo , MicroRNAs , Doença de Parkinson , Humanos , Doença de Parkinson/diagnóstico , Doença de Parkinson/genética , Doença de Parkinson/metabolismo , MicroRNAs/genética , MicroRNAs/metabolismo , Aprendizado de Máquina , BiomarcadoresRESUMO
PURPOSE: Laryngeal cancer (LC) is the most common head and neck cancer, which often goes undiagnosed due to the inaccessible nature of current diagnosis methods in some parts of the world. Many recent studies have shown that microRNAs (miRNAs) are crucial biomarkers for a variety of cancers. METHODS: In this study, we create a decision tree model for the diagnosis of laryngeal cancer using a created series of miRNA attributes, such as sequence-based characteristics, predicted miRNA target genes, and gene pathways. This series of attributes is extracted from both differentially expressed blood-based miRNAs in laryngeal cancer and random, non-associated with cancer miRNAs. RESULTS: Several machine-learning (ML) algorithms were tested in the ML model, and the Hoeffding Tree classifier yields the highest accuracy (86.8%) in miRNAs-based recognition of laryngeal cancer. Furthermore, our model is validated with the independent laryngeal cancer datasets and can accurately diagnose laryngeal cancer with 86% accuracy. We also explored the biological relationships of the attributes used in our model to understand their relationship with cancer proliferation or suppression pathways. CONCLUSION: Our study demonstrates that the proposed model and an inexpensive miRNA testing strategy have the potential to serve as an additional method for diagnosing laryngeal cancer.
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Neoplasias Laríngeas , MicroRNAs , Humanos , MicroRNAs/genética , MicroRNAs/metabolismo , Neoplasias Laríngeas/diagnóstico , Neoplasias Laríngeas/genética , Biomarcadores , Algoritmos , Árvores de Decisões , Regulação Neoplásica da Expressão GênicaRESUMO
Purpose: Patients with diabetes mellitus have an elevated chance of developing cataracts, a degenerative vision-impairing condition often needing surgery. The process of the reduction of glucose to sorbitol in the lens of the human eye that causes cataracts is managed by the Aldose Reductase Enzyme (AR), and it is been found that AR inhibitors may mitigate the onset of diabetic cataracts. There exists a large pool of natural and synthetic AR inhibitors that can prevent diabetic complications, and the development of a machine-learning (ML) prediction model may bring new AR inhibitors with better characteristics into clinical use. Methods: Using known AR inhibitors and their chemical-physical descriptors we created the ML model for prediction of new AR inhibitors. The predicted inhibitors were tested by computational docking to the binding site of AR. Results: Using cross-validation in order to find the most accurate ML model, we ended with final cross-validation accuracy of 90%. Computational docking testing of the predicted inhibitors gave a high level of correlation between the ML prediction score and binding free energy. Conclusions: Currently known AR inhibitors are not used yet for patients for several reasons. We think that new predicted AR inhibitors have the potential to possess more favorable characteristics to be successfully implemented after clinical testing. Exploring new inhibitors can improve patient well-being and lower surgical complications all while decreasing long-term medical expenses.
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Amyotrophic lateral sclerosis (ALS) is a progressive, uncurable neurodegenerative disorder characterized by the degradation of motor neurons leading to muscle impairment, failure, and death. Senataxin, encoded by the SETX gene, is a human helicase protein whose mutations have been linked with ALS onset, particularly in its juvenile ALS4 form. Using senataxin's yeast homolog Sen1 as a model for study, it is suggested that senataxin's N-terminus interacts with RNA polymerase II, whilst its C-terminus engages in helicase activity. Senataxin is heavily involved in transcription regulation, termination, and R-loop resolution, enabled by recruitment and interactions with enzymes such as ubiquitin protein ligase SAN1 and ribonuclease H (RNase H). Senataxin also engages in DNA damage response (DDR), primarily interacting with the exosome subunit Rrp45. The Sen1 mutation E1597K, alongside the L389S and R2136H gain-of-function mutations to senataxin, is shown to cause negative structural and thus functional effects to the protein, thus contributing to a disruption in WT functions, motor neuron (MN) degeneration, and the manifestation of ALS clinical symptoms. This review corroborates and summarizes published papers concerning the structure and function of senataxin as well as the effects of their mutations in ALS pathology in order to compile current knowledge and provide a reference for future research. The findings compiled in this review are indicative of the experimental and therapeutic potential of senataxin and its mutations as a target in future ALS treatment/cure discovery, with some potential therapeutic routes also being discussed in the review.
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Esclerose Lateral Amiotrófica , Humanos , Esclerose Lateral Amiotrófica/metabolismo , Neurônios Motores/metabolismo , Regulação da Expressão Gênica , Mutação , DNA Helicases/genética , RNA Helicases/genética , RNA Helicases/metabolismo , Enzimas Multifuncionais/genética , Enzimas Multifuncionais/metabolismoRESUMO
We developed a machine-learning system for the selective diagnostics of adenocarcinoma (AD), squamous cell carcinoma (SQ), and small-cell carcinoma lung (SC) cancers based on their metabolomic profiles. The system is organized as two-stage binary classifiers. The best accuracy for classification is 92%. We used the biomarkers sets that contain mostly metabolites related to cancer development. Compared to traditional methods, which exclude hierarchical classification, our method splits a challenging multiclass task into smaller tasks. This allows a two-stage classifier, which is more accurate in the scenario of lung cancer classification. Compared to traditional methods, such a "divide and conquer strategy" gives much more accurate and explainable results. Such methods, including our algorithm, allow for the systematic tracking of each computational step.
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BACKGROUND: Insulin resistance is the decreased effectiveness of insulin receptor function during signaling of glucose uptake. Insulin receptors are regulated by endocytosis, a process that removes receptors from the cell surface to be marked for degradation or for re-use. OBJECTIVES: Our goal was to discover insulin-resistance-related genes that play key roles in endocytosis which could serve as potential biological targets to enhance insulin sensitivity. METHODS: The gene mutations related to insulin resistance were elucidated from ClinVar. These were used as the seed set. Using the GeneFriends program, the genes associated with this set were elucidated and used as an enriched set for the next step. The enriched gene set network was visualized by Cytoscape. After that, using the VisANT program, the most significant cluster of genes was identified. With the help of the DAVID program, the most important KEGG pathway corresponding to the gene cluster and insulin resistance was found. Eleven genes part of the KEGG endocytosis pathway were identified. Finally, using the ChEA3 program, seven transcription factors managing these genes were defined. RESULTS: Thirty-two genes of pathogenic significance in insulin resistance were elucidated, and then co-expression data for these genes were utilized. These genes were organized into clusters, one of which was singled out for its high node count of 58 genes and low p-value (p = 4.117 × 10-7). DAVID Pathways, a functional annotation tool, helped identify a set of 11 genes from a single cluster associated with the endocytosis pathway related to insulin resistance. These genes (AMPH, BIN1, CBL, DNM1, DNM2, DNM3, ITCH, SH3GL1, SH3GL2, SH3GL3, and SH3KBP1) are all involved in either clathrin-mediated endocytosis of the insulin receptor (IR) or clathrin-independent endocytosis of insulin-resistance-related G protein-coupled receptors (GPCR). They represent prime therapeutic targets to improve insulin sensitivity through modulation of transmembrane cell signaling. Using the ChEA3 database, we also found seven transcription factors (REST, MYPOP, CAMTA2, MYT1L, ZBTB18, NKX6-2, and CXXC5) that control the expression of these 11 genes. Inhibiting these key transcription factors would be another strategy to downregulate endocytosis. CONCLUSION: We believe that delaying removal of insulin receptors from the cell surface would prolong signaling of glucose uptake and counteract the symptoms of insulin resistance.
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Resistência à Insulina , Receptor de Insulina , Humanos , Receptor de Insulina/genética , Receptor de Insulina/metabolismo , Resistência à Insulina/genética , Endocitose/genética , Clatrina/metabolismo , Insulina/metabolismo , Fatores de Transcrição/metabolismo , Glucose , Proteínas de Homeodomínio , Proteínas de Ligação a DNA/metabolismo , Proteínas de Ligação ao Cálcio , TransativadoresRESUMO
Aflatoxin B1 (AFB1) is a fungal metabolite found in animal feeds and human foods. It is one of the most toxic and carcinogenic of aflatoxins and is classified as a Group 1 carcinogen. Dietary exposure to AFB1 and infection with chronic Hepatitis B Virus (HBV) make up two of the major risk factors for hepatocellular carcinoma (HCC). These two major risk factors raise the probability of synergism between the two agents. This review proposes some collaborative molecular mechanisms underlying the interaction between AFB1 and HBV in accelerating or magnifying the effects of HCC. The HBx viral protein is one of the main viral proteins of HBV and has many carcinogenic qualities that are involved with HCC. AFB1, when metabolized by CYP450, becomes AFB1-exo-8,9-epoxide (AFBO), an extremely toxic compound that can form adducts in DNA sequences and induce mutations. With possible synergisms that exist between HBV and AFB1 in mind, it is best to treat both agents simultaneously to reduce the risk by HCC.
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Aflatoxinas , Carcinoma Hepatocelular , Hepatite B Crônica , Neoplasias Hepáticas , Animais , Humanos , Carcinoma Hepatocelular/genética , Vírus da Hepatite B/metabolismo , Neoplasias Hepáticas/genética , Hepatite B Crônica/complicações , Aflatoxinas/toxicidade , Aflatoxina B1/toxicidade , Carcinógenos/toxicidade , Carcinogênese/induzido quimicamenteRESUMO
The Olig genes encode members of the basic helix-loop-helix (bHLH) family of transcription factors. Olig1, Olig2, and Olig3 are expressed in both the developing and mature central nervous system (CNS) and regulate cellular specification and differentiation. Over the past decade extensive studies have established functional roles of Olig1 and Olig2 in development as well as in cancer. Olig2 overexpression drives glioma proliferation and resistance to radiation and chemotherapy. In this review, we summarize the biological functions of the Olig family in brain cancer and how targeting Olig family genes may have therapeutic benefit.
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Several lines of evidence suggest that a characteristic of the neuropathology of Alzheimer's disease (AD) is the aggregation of the amyloid beta peptides (Aß), fragments of the human amyloid precursor protein (hAPP). The dominating species are the Aß40 and Aß42 fragments with 40 and 42 amino acids, respectively. Aß initially forms soluble oligomers that continue to expand to protofibrils, suggestively the neurotoxic intermediates, and thereafter turn into insoluble fibrils that are markers of the disease. Using the powerful tool of pharmacophore simulation, we selected small molecules not known to possess central nervous system (CNS) activity but that might interact with Aß aggregation, from the NCI Chemotherapeutic Agents Repository, Bethesda, MD. We assessed the activity of these compounds on Aß aggregation using the thioflavin T fluorescence correlation spectroscopy (ThT-FCS) assay. Förster resonance energy transfer-based fluorescence correlation spectroscopy (FRET-FCS) was used to characterize the dose-dependent activity of selected compounds at an early stage of Aß aggregation. Transmission electron microscopy (TEM) confirmed that the interfering substances block fibril formation and identified the macrostructures of Aß aggregates formed in their presence. We first found three compounds generating protofibrils with branching and budding never observed in the control. One compound generated a two-dimensional sheet structure and another generated a double-stranded filament. Importantly, these compounds generating protofibrils with altered macrostructure protected against Aß-induced toxicity in a cell model while showing no toxicity in a model of cognition in normal mice. The data suggest that the active compounds act as decoys turning the aggregation into nontoxic trajectories and pointing toward novel approaches to therapy.
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Doença de Alzheimer , Peptídeos beta-Amiloides , Animais , Humanos , Camundongos , Peptídeos beta-Amiloides/metabolismo , Fragmentos de Peptídeos/metabolismo , Doença de Alzheimer/metabolismo , Microscopia Eletrônica de Transmissão , Precursor de Proteína beta-AmiloideRESUMO
Ovarian cancer (OC) is the second most common cancer of the female reproductive system. Due to the asymptomatic nature of early stages of OC and an increasingly poor prognosis in later stages, methods of screening for OC are much desired. Furthermore, screening and diagnosis processes, in order to justify use on asymptomatic patients, must be convenient and non-invasive. Recent developments in machine-learning technologies have made this possible via techniques in the field of metabolomics. The objective of this research was to use existing metabolomics data on OC and various analytic methods to develop a machine-learning model for the classification of potentially OC-related metabolite biomarkers. Pathway analysis and metabolite-set enrichment analysis were performed on gathered metabolite sets. Quantitative molecular descriptors were then used with various machine-learning classifiers for the diagnostics of OC using related metabolites. We elucidated that the metabolites associated with OC used for machine-learning models are involved in five metabolic pathways linked to OC: Nicotinate and Nicotinamide Metabolism, Glycolysis/Gluconeogenesis, Aminoacyl-tRNA Biosynthesis, Valine, Leucine and Isoleucine Biosynthesis, and Alanine, Aspartate and Glutamate Metabolism. Several classification models for the identification of OC using related metabolites were created and their accuracies were confirmed through testing with 10-fold cross-validation. The most accurate model was able to achieve 85.29% accuracy. The elucidation of biological pathways specific to OC using metabolic data and the observation of changes in these pathways in patients have the potential to contribute to the development of screening techniques for OC. Our results demonstrate the possibility of development of the machine-learning models for OC diagnostics using metabolomics data.
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Neoplasias Ovarianas , Humanos , Feminino , Neoplasias Ovarianas/metabolismo , Metabolômica/métodos , Redes e Vias Metabólicas , Biomarcadores Tumorais/metabolismo , Aprendizado de MáquinaRESUMO
Severe acute respiratory syndrome coronavirus 2 (SARSCoV2) induced cytokine storm is the major cause of COVID-19 related deaths. Patients have been treated with drugs that work by inhibiting a specific protein partly responsible for the cytokines production. This approach provided very limited success, since there are multiple proteins involved in the complex cell signaling disease mechanisms. We targeted five proteins: Angiotensin II receptor type 1 (AT1R), A disintegrin and metalloprotease 17 (ADAM17), Nuclear FactorKappa B (NFκB), Janus kinase 1 (JAK1) and Signal Transducer and Activator of Transcription 3 (STAT3), which are involved in the SARSCoV2 induced cytokine storm pathway. We developed machine-learning (ML) models for these five proteins, using known active inhibitors. After developing the model for each of these proteins, FDA-approved drugs were screened to find novel therapeutics for COVID19. We identified twenty drugs that are active for four proteins with predicted scores greater than 0.8 and eight drugs active for all five proteins with predicted scores over 0.85. Mitomycin C is the most active drug across all five proteins with an average prediction score of 0.886. For further validation of these results, we used the PyRx software to conduct protein-ligand docking experiments and calculated the binding affinity. The docking results support findings by the ML model. This research study predicted that several drugs can target multiple proteins simultaneously in cytokine storm-related pathway. These may be useful drugs to treat patients because these therapies can fight cytokine storm caused by the virus at multiple points of inhibition, leading to synergistically effective treatments.
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The objective of this research is, with the analysis of existing data of thyroid cancer (TC) metabolites, to develop a machine-learning model that can diagnose TC using metabolite biomarkers. Through data mining, pathway analysis, and machine learning (ML), the model was developed. We identified seven metabolic pathways related to TC: Pyrimidine metabolism, Tyrosine metabolism, Glycine, serine, and threonine metabolism, Pantothenate and CoA biosynthesis, Arginine biosynthesis, Phenylalanine metabolism, and Phenylalanine, tyrosine, and tryptophan biosynthesis. The ML classifications' accuracies were confirmed through 10-fold cross validation, and the most accurate classification was 87.30%. The metabolic pathways identified in relation to TC and the changes within such pathways can contribute to more pattern recognition for diagnostics of TC patients and assistance with TC screening. With independent testing, the model's accuracy for other unique TC metabolites was 92.31%. The results also point to a possibility for the development of using ML methods for TC diagnostics and further applications of ML in general cancer-related metabolite analysis.
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Bcl2-associated athanogene 3 (BAG3) is a multifunctional cochaperone responsible for protein quality control within cells. BAG3 interacts with chaperones HSPB8 and Hsp70 to transport misfolded proteins to the Microtubule Organizing Center (MTOC) and degrade them in autophagosomes in a process known as Chaperone Assisted Selective Autophagy (CASA). Mutations in the second conserved IPV motif of BAG3 are known to cause Dilated Cardiomyopathy (DCM) by inhibiting adequate removal of non-native proteins. The proline 209 to leucine (P209L) BAG3 mutant in particular causes the aggregation of BAG3 and misfolded proteins as well as the sequestration of essential chaperones. The exact mechanisms of protein aggregation in DCM are unknown. However, the similar presence of insoluble protein aggregates in Charcot-Marie-Tooth disease type 2 (CMT2) induced by the proline 182 to leucine (P182L) HSPB1 mutant points to a possible avenue for future research: IPV motif. In this review, we summarize the molecular mechanisms of CASA and the currently known pathological effects of mutated BAG3 in DCM. Additionally, we will provide insight on the importance of the IPV motif in protein aggregation by analyzing a potential association between DCM and CMT2.
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Cardiomiopatia Dilatada , Doença de Charcot-Marie-Tooth , Proteínas Adaptadoras de Transdução de Sinal/genética , Proteínas Adaptadoras de Transdução de Sinal/metabolismo , Proteínas Reguladoras de Apoptose/genética , Proteínas Reguladoras de Apoptose/metabolismo , Cardiomiopatia Dilatada/genética , Doença de Charcot-Marie-Tooth/genética , Humanos , Leucina , Prolina , Agregados ProteicosRESUMO
Successful control of the COVID-19 pandemic depends on vaccines that prevent transmission. The full-length Spike protein is highly immunogenic but the majority of antibodies do not target the virus: ACE2 interface. In an effort to affect the quality of the antibody response focusing it to the receptor-binding motif (RBM) we generated a series of conformationally-constrained immunogens by inserting solvent-exposed RBM amino acid residues into hypervariable loops of an immunoglobulin molecule. Priming C57BL/6 mice with plasmid (p)DNA encoding these constructs yielded a rapid memory response to booster immunization with recombinant Spike protein. Immune sera antibodies bound strongly to the purified receptor-binding domain (RBD) and Spike proteins. pDNA primed for a consistent response with antibodies efficient at neutralizing authentic WA1 virus and three variants of concern (VOC), B.1.351, B.1.617.2, and BA.1. We demonstrate that immunogens built on structure selection can be used to influence the quality of the antibody response by focusing it to a conserved site of vulnerability shared between wildtype virus and VOCs, resulting in neutralizing antibodies across variants.
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Anticorpos Neutralizantes , COVID-19 , SARS-CoV-2 , Glicoproteína da Espícula de Coronavírus , Animais , Anticorpos Neutralizantes/imunologia , Anticorpos Antivirais , COVID-19/prevenção & controle , Camundongos , Camundongos Endogâmicos C57BL , Pandemias/prevenção & controle , Glicoproteína da Espícula de Coronavírus/imunologiaRESUMO
BACKGROUND: Approximately 30% of Candida genus isolates are resistant to all currently available antifungal drugs and it is highly important to develop new treatments. Additionally, many current drugs are toxic and cause unwanted side effects. 1,3-beta-glucan synthase is an essential enzyme that builds the cell walls of Candida. OBJECTIVES: Targeting CaFKS1, a subunit of the synthase, could be used to fight Candida. METHODS: In the present study, a machine-learning model based on chemical descriptors was trained to recognise drugs that inhibit CaFKS1. The model attained 96.72% accuracy for classifying between active and inactive drug compounds. Descriptors for FDA-approved and other drugs were calculated, and the model was used to predict the potential activity of these drugs against CaFKS1. RESULTS: Several drugs, including goserelin and icatibant, were detected as active with high confidence. Many of the drugs, interestingly, were gonadotrophin-releasing hormone (GnRH) antagonists or agonists. A literature search found that five of the predicted drugs inhibit Candida experimentally. CONCLUSIONS: This study yields promising drugs to be repurposed to combat Candida albicans infection. Future steps include testing the drugs on fungal cells in vitro.
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Candida albicans , Candidíase , Antifúngicos/farmacologia , Antifúngicos/uso terapêutico , Candida , Candidíase/tratamento farmacológico , Candidíase/microbiologia , Humanos , Aprendizado de Máquina , Testes de Sensibilidade MicrobianaRESUMO
BACKGROUND: In the current COVID-19 pandemic, with an absence of approved drugs and widely accessible vaccines, repurposing existing drugs is vital to quickly developing a treatment for the disease. METHODS: In this study, we used a dataset consisting of sequences of viral proteins and chemical structures of pharmaceutical drugs for known drug-target interactions (DTIs) and artificially generated non-interacting DTIs to train a binary classifier with the ability to predict new DTIs. Random Forest (RF), deep neural network (DNN), and convolutional neural networks (CNN) were tested. The CNN and RF models were selected for the classification task. RESULTS: The models generalized well to the given DTI data and were used to predict DTIs involving SARS-CoV-2 nonstructural proteins (NSPs). We elucidated (with the CNN) 29 drugs involved in 82 DTIs with a 97% probability of interaction, 44 DTIs of which had a 99% probability of interaction, to treat COVID-19. The RF elucidated 6 drugs involved in 17 DTIs with a 90% probability of interacting. CONCLUSIONS: These results give new insight into possible inhibitors of the viral proteins beyond pharmacophore models and molecular docking procedures used in recent studies.
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Tratamento Farmacológico da COVID-19 , Aprendizado Profundo , Reposicionamento de Medicamentos , Humanos , Simulação de Acoplamento Molecular , Farmacologia em Rede , Pandemias , SARS-CoV-2 , Proteínas ViraisRESUMO
Mutant superoxide dismutase 1 (SOD1) may form cyclic structures due to its greater instability from aberrant demetallization and oxidation of cysteine bonds. This cyclic structure may allow SOD1 to form ion channels on membranes such as the mitochondrial membrane, causing imbalances in the concentration of intracellular ions as a potential mechanism for the progressive neuron death involved in amyotrophic lateral sclerosis (ALS). Using docking programs within modeling software, models of mutant SOD1 dimers and eventually ring oligomers were constructed based on known descriptions of such structures in addition to information on the orientation of the models associated with a membrane. The resulting structure consists of a ring of four demetallated mutant SOD1 dimers with cross-linked disulfide bonds. Stability of the octamer model was supported by the molecular dynamics simulations. Further analysis of the octamer model indicated that its inner- and outer-pore diameters were stable, matching the dimensions of known SOD1 ion channels.