AIMC Topic: Membrane Proteins

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An Improved Topology Prediction of Alpha-Helical Transmembrane Protein Based on Deep Multi-Scale Convolutional Neural Network.

IEEE/ACM transactions on computational biology and bioinformatics Feb 3, 2022
Alpha-helical proteins ( αTMPs) are essential in various biological processes. Despite their tertiary structures are crucial for revealing complex functions, experimental structure determination remains challenging and costly. In the past decades, va...
Neural Networks, Computer Membrane Proteins Databases, Protein
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Computed structures of core eukaryotic protein complexes.

Science (New York, N.Y.) Dec 10, 2021
Protein-protein interactions play critical roles in biology, but the structures of many eukaryotic protein complexes are unknown, and there are likely many interactions not yet identified. We take advantage of advances in proteome-wide amino acid coe...
Saccharomyces cerevisiae Proteins DNA Repair Deep Learning Ligases Protein Biosynthesis Computer Simulation Protein Interaction Maps Multiprotein Complexes Saccharomyces cerevisiae Ribosomes Membrane Proteins Computational Biology Acyltransferases Ubiquitin Evolution, Molecular Proteome Homologous Recombination Models, Molecular Protein Interaction Mapping Chromosome Segregation Protein Conformation
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MemDis: Predicting Disordered Regions in Transmembrane Proteins.

International journal of molecular sciences Nov 12, 2021
Transmembrane proteins (TMPs) play important roles in cells, ranging from transport processes and cell adhesion to communication. Many of these functions are mediated by intrinsically disordered regions (IDRs), flexible protein segments without a wel...
Reproducibility of Results Computational Biology Membrane Proteins Databases, Protein Neural Networks, Computer Data Mining Intrinsically Disordered Proteins Protein Conformation Models, Molecular Internet Amino Acid Sequence
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TopProperty: Robust Metaprediction of Transmembrane and Globular Protein Features Using Deep Neural Networks.

Journal of chemical theory and computation Oct 18, 2021
Transmembrane proteins (TMPs) are critical components of cellular life. However, due to experimental challenges, the number of experimentally resolved TMP structures is severely underrepresented in databases compared to their cellular abundance. Pred...
Neural Networks, Computer Algorithms Computational Biology Membrane Proteins Databases, Protein Protein Structure, Secondary
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iMPT-FDNPL: Identification of Membrane Protein Types with Functional Domains and a Natural Language Processing Approach.

Computational and mathematical methods in medicine Oct 11, 2021
Membrane protein is an important kind of proteins. It plays essential roles in several cellular processes. Based on the intramolecular arrangements and positions in a cell, membrane proteins can be divided into several types. It is reported that the ...
Protein Domains Membrane Proteins Humans Support Vector Machine Algorithms Databases, Protein Computational Biology Natural Language Processing
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Highly accurate protein structure prediction for the human proteome.

Nature Jul 22, 2021
Protein structures can provide invaluable information, both for reasoning about biological processes and for enabling interventions such as structure-based drug development or targeted mutagenesis. After decades of effort, 17% of the total residues i...
Datasets as Topic Protein Conformation Diacylglycerol O-Acyltransferase Computational Biology Protein Folding Humans Glucose-6-Phosphatase Proteome Reproducibility of Results Membrane Proteins Deep Learning Models, Molecular
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Accurate prediction of protein structures and interactions using a three-track neural network.

Science (New York, N.Y.) Jul 15, 2021
DeepMind presented notably accurate predictions at the recent 14th Critical Assessment of Structure Prediction (CASP14) conference. We explored network architectures that incorporate related ideas and obtained the best performance with a three-track ...
Neural Networks, Computer Computer Simulation Deep Learning Crystallography, X-Ray Receptors, G-Protein-Coupled Protein Conformation Membrane Proteins Models, Molecular Cryoelectron Microscopy Proteins Amino Acid Sequence Protein Folding Databases, Protein Multiprotein Complexes ADAM Proteins Protein Subunits Sphingosine N-Acyltransferase
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Loop-mediated isothermal amplification (LAMP) and machine learning application for early pregnancy detection using bovine vaginal mucosal membrane.

Biochemical and biophysical research communications Jul 10, 2021
An early and accurate pregnancy diagnosis method is required to improve the reproductive performance of cows. Here we developed an easy pregnancy detection method using vaginal mucosal membrane (VMM) with application of Reverse Transcription-Loop-med...
Ubiquitins Mucous Membrane Vagina CA-125 Antigen Cattle RNA-Binding Proteins Molecular Diagnostic Techniques Membrane Proteins Nucleic Acid Amplification Techniques Adaptor Proteins, Signal Transducing Gene Expression Female Reproducibility of Results Sensitivity and Specificity Cytokines Animals Biomarkers Pregnancy Machine Learning
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In-Pero: Exploiting Deep Learning Embeddings of Protein Sequences to Predict the Localisation of Peroxisomal Proteins.

International journal of molecular sciences Jun 15, 2021
Peroxisomes are ubiquitous membrane-bound organelles, and aberrant localisation of peroxisomal proteins contributes to the pathogenesis of several disorders. Many computational methods focus on assigning protein sequences to subcellular compartments,...
Software Amino Acid Sequence Algorithms Protein Transport Reproducibility of Results Peroxisomes Deep Learning Mitochondrial Proteins Membrane Proteins
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Regression plane concept for analysing continuous cellular processes with machine learning.

Nature communications May 5, 2021
Biological processes are inherently continuous, and the chance of phenotypic discovery is significantly restricted by discretising them. Using multi-parametric active regression we introduce the Regression Plane (RP), a user-friendly discovery tool e...
Drosophila melanogaster Cell Physiological Phenomena Humans Biological Phenomena Machine Learning Animals Cell Differentiation Cell Line, Tumor Supervised Machine Learning Carcinoma, Hepatocellular Cell Cycle Membrane Proteins
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