Molecular Modeling of the M3 Acetylcholine Muscarinic Receptor and Its Binding Site

The present study reports the results of a combined computational and site mutagenesis study designed to provide new insights into the orthosteric binding site of the human M3 muscarinic acetylcholine receptor. For this purpose a three-dimensional structure of the receptor at atomic resolution was built by homology modeling, using the crystallographic structure of bovine rhodopsin as a template. Then, the antagonist N-methylscopolamine was docked in the model and subsequently embedded in a lipid bilayer for its refinement using molecular dynamics simulations. Two different lipid bilayer compositions were studied: one component palmitoyl-oleyl phosphatidylcholine (POPC) and two-component palmitoyl-oleyl phosphatidylcholine/palmitoyl-oleyl phosphatidylserine (POPC-POPS). Analysis of the results suggested that residues F222 and T235 may contribute to the ligand-receptor recognition. Accordingly, alanine mutants at positions 222 and 235 were constructed, expressed, and their binding properties determined. The results confirmed the role of these residues in modulating the binding affinity of the ligand

Automatic discovery of 100-miRNA signature for cancer classification using ensemble feature selection

Lopez-Rincon A, Martinez-Archundia M, Martinez-Ruiz GU, Schönhuth A, Tonda A. Automatic discovery of 100-miRNA signature for cancer classification using ensemble feature selection. BMC Bioinformatics. 2019;20(1): 480

Machine Learning-Based Ensemble Recursive Feature Selection of Circulating miRNAs for Cancer Tumor Classification

Lopez-Rincon A, Mendoza-Maldonado L, Martinez-Archundia M, et al. Machine Learning-Based Ensemble Recursive Feature Selection of Circulating miRNAs for Cancer Tumor Classification. Cancers. 2020;12(7): 1785.Circulating microRNAs (miRNA) are small noncoding RNA molecules that can be detected in bodily fluids without the need for major invasive procedures on patients. miRNAs have shown great promise as biomarkers for tumors to both assess their presence and to predict their type and subtype. Recently, thanks to the availability of miRNAs datasets, machine learning techniques have been successfully applied to tumor classification. The results, however, are difficult to assess and interpret by medical experts because the algorithms exploit information from thousands of miRNAs. In this work, we propose a novel technique that aims at reducing the necessary information to the smallest possible set of circulating miRNAs. The dimensionality reduction achieved reflects a very important first step in a potential, clinically actionable, circulating miRNA-based precision medicine pipeline. While it is currently under discussion whether this first step can be taken, we demonstrate here that it is possible to perform classification tasks by exploiting a recursive feature elimination procedure that integrates a heterogeneous ensemble of high-quality, state-of-the-art classifiers on circulating miRNAs. Heterogeneous ensembles can compensate inherent biases of classifiers by using different classification algorithms. Selecting features then further eliminates biases emerging from using data from different studies or batches, yielding more robust and reliable outcomes. The proposed approach is first tested on a tumor classification problem in order to separate 10 different types of cancer, with samples collected over 10 different clinical trials, and later is assessed on a cancer subtype classification task, with the aim to distinguish triple negative breast cancer from other subtypes of breast cancer. Overall, the presented methodology proves to be effective and compares favorably to other state-of-the-art feature selection methods

Machine learning-based ensemble recursive feature selection of circulating miRNAs for cancer tumor classification

Circulating microRNAs (miRNA) are small noncoding RNA molecules that can be detected in bodily fluids without the need for major invasive procedures on patients. miRNAs have shown great promise as biomarkers for tumors to both assess their presence and to predict their type and subtype. Recently, thanks to the availability of miRNAs datasets, machine learning techniques have been successfully applied to tumor classification. The results, however, are difficult to assess and interpret by medical experts because the algorithms exploit information from thousands of miRNAs. In this work, we propose a novel technique that aims at reducing the necessary information to the smallest possible set of circulating miRNAs. The dimensionality reduction achieved reflects a very important first step in a potential, clinically actionable, circulating miRNA-based precision medicine pipeline. While it is currently under discussion whether this first step can be taken, we demonstrate here that it is possible to perform classification tasks by exploiting a recursive feature elimination procedure that integrates a heterogeneous ensemble of high-quality, state-of-the-art classifiers on circulating miRNAs. Heterogeneous ensembles can compensate inherent biases of classifiers by using different classification algorithms. Selectin

Automatic discovery of 100-miRNA signature for cancer classification using ensemble feature selection

International audienc

The Advantage of Using Immunoinformatic Tools on Vaccine Design and Development for Coronavirus

After the outbreak of SARS-CoV-2 by the end of 2019, the vaccine development strategies became a worldwide priority. Furthermore, the appearances of novel SARS-CoV-2 variants challenge researchers to develop new pharmacological or preventive strategies. However, vaccines still represent an efficient way to control the SARS-CoV-2 pandemic worldwide. This review describes the importance of bioinformatic and immunoinformatic tools (in silico) for guide vaccine design. In silico strategies permit the identification of epitopes (immunogenic peptides) which could be used as potential vaccines, as well as nonacarriers such as: vector viral based vaccines, RNA-based vaccines and dendrimers through immunoinformatics. Currently, nucleic acid and protein sequential as well structural analyses through bioinformatic tools allow us to get immunogenic epitopes which can induce immune response alone or in complex with nanocarriers. One of the advantages of in silico techniques is that they facilitate the identification of epitopes, while accelerating the process and helping to economize some stages of the development of safe vaccines

Machine learning-based ensemble recursive feature selection of circulating miRNAs for cancer tumor classification

Circulating microRNAs (miRNA) are small noncoding RNA molecules that can be detected in bodily fluids without the need for major invasive procedures on patients. miRNAs have shown great promise as biomarkers for tumors to both assess their presence and to predict their type and subtype. Recently, thanks to the availability of miRNAs datasets, machine learning techniques have been successfully applied to tumor classification. The results, however, are difficult to assess and interpret by medical experts because the algorithms exploit information from thousands of miRNAs. In this work, we propose a novel technique that aims at reducing the necessary information to the smallest possible set of circulating miRNAs. The dimensionality reduction achieved reflects a very important first step in a potential, clinically actionable, circulating miRNA-based precision medicine pipeline. While it is currently under discussion whether this first step can be taken, we demonstrate here that it is possible to perform classification tasks by exploiting a recursive feature elimination procedure that integrates a heterogeneous ensemble of high-quality, state-of-the-art classifiers on circulating miRNAs. Heterogeneous ensembles can compensate inherent biases of classifiers by using different classification algorithms. Selectin

circulating

circulating miRNAs datasets and informatio

The β2-Subunit (AMOG) of Human Na+, K+-ATPase Is a Homophilic Adhesion Molecule

The β2 subunit of Na+, K+-ATPase was originally identified as the adhesion molecule on glia (AMOG) that mediates the adhesion of astrocytes to neurons in the central nervous system and that is implicated in the regulation of neurite outgrowth and neuronal migration. While β1 isoform have been shown to trans-interact in a species-specific mode with the β1 subunit on the epithelial neighboring cell, the β2 subunit has been shown to act as a recognition molecule on the glia. Nevertheless, none of the works have identified the binding partner of β2 or described its adhesion mechanism. Until now, the interactions pronounced for β2/AMOG are heterophilic cis-interactions. In the present report we designed experiments that would clarify whether β2 is a cell–cell homophilic adhesion molecule. For this purpose, we performed protein docking analysis, cell–cell aggregation, and protein–protein interaction assays. We observed that the glycosylated extracellular domain of β2/AMOG can make an energetically stable trans-interacting dimer. We show that CHO (Chinese Hamster Ovary) fibroblasts transfected with the human β2 subunit become more adhesive and make large aggregates. The treatment with Tunicamycin in vivo reduced cell aggregation, suggesting the participation of N-glycans in that process. Protein–protein interaction assay in vivo with MDCK (Madin-Darby canine kidney) or CHO cells expressing a recombinant β2 subunit show that the β2 subunits on the cell surface of the transfected cell lines interact with each other. Overall, our results suggest that the human β2 subunit can form trans-dimers between neighboring cells when expressed in non-astrocytic cells, such as fibroblasts (CHO) and epithelial cells (MDCK)

circulating

circulating miRNAs datasets and informatio