AIMC Topic: Amino Acid Sequence

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Predicting apoptosis protein subcellular localization by integrating auto-cross correlation and PSSM into Chou's PseAAC.

Journal of theoretical biology Sep 1, 2018
The prediction of subcellular localization of an apoptosis protein is still a challenging task, and existing methods mainly based on protein primary sequences. In this study, we propose a novel model called MACC-PSSM by integrating Moran autocorrelat...
Computational Biology Models, Biological Amino Acid Sequence Protein Transport Apoptosis Regulatory Proteins Support Vector Machine Apoptosis Databases, Protein
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Detecting Proline and Non-Proline Cis Isomers in Protein Structures from Sequences Using Deep Residual Ensemble Learning.

Journal of chemical information and modeling Aug 29, 2018
It has been long established that cis conformations of amino acid residues play many biologically important roles despite their rare occurrence in protein structure. Because of this rarity, few methods have been developed for predicting cis isomers f...
Proteins Amino Acid Sequence Proline Machine Learning
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Improved Peptide Retention Time Prediction in Liquid Chromatography through Deep Learning.

Analytical chemistry Aug 29, 2018
The accuracy of peptide retention time (RT) prediction model in liquid chromatography (LC) is still not sufficient for wider implementation in proteomics practice. Herein, we propose deep learning as an ideal tool to considerably improve this predict...
Humans Peptides Deep Learning HeLa Cells Chromatography, Reverse-Phase Proteomics Models, Chemical Amino Acid Sequence
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Antibacterial activity and its mechanisms of a recombinant Funme peptide against Cronobacter sakazakii in powdered infant formula.

Food research international (Ottawa, Ont.) Aug 13, 2018
Cronobacter sakazakii (Cs) is a typical foodborne bacterium that infect powdered infant formula (PIF) worldwide. In this study, a recombinant antimicrobial peptide, branded as Funme peptide (FP)was applied to protect PIF from Cs contamination. The re...
Cronobacter sakazakii Infant Formula Food Handling Biological Control Agents Cell Membrane Permeability Biofilms Microbial Sensitivity Tests Bacteria Food Contamination Humans Ampicillin Food Microbiology Anti-Bacterial Agents Amino Acid Sequence Infant Powders Antimicrobial Cationic Peptides Erythrocytes
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KELM-CPPpred: Kernel Extreme Learning Machine Based Prediction Model for Cell-Penetrating Peptides.

Journal of proteome research Aug 13, 2018
Cell-penetrating peptides (CPPs) facilitate the transport of pharmacologically active molecules, such as plasmid DNA, short interfering RNA, nanoparticles, and small peptides. The accurate identification of new and unique CPPs is the initial step to ...
Cell-Penetrating Peptides Sequence Analysis, Protein Machine Learning Software Eukaryotic Cells Animals Amino Acid Sequence Internet Datasets as Topic Benchmarking Cell Membrane Permeability Models, Statistical Amino Acids Humans Neural Networks, Computer
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Deep Neural Network Based Predictions of Protein Interactions Using Primary Sequences.

Molecules (Basel, Switzerland) Aug 1, 2018
Machine learning based predictions of proteinā»protein interactions (PPIs) could provide valuable insights into protein functions, disease occurrence, and therapy design on a large scale. The intensive feature engineering in most of these methods make...
Humans Neural Networks, Computer Protein Interaction Mapping Animals Protein Binding Amino Acid Sequence
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PIP-EL: A New Ensemble Learning Method for Improved Proinflammatory Peptide Predictions.

Frontiers in immunology Jul 31, 2018
Proinflammatory cytokines have the capacity to increase inflammatory reaction and play a central role in first line of defence against invading pathogens. Proinflammatory inducing peptides (PIPs) have been used as an antineoplastic agent, an antibact...
Amino Acid Sequence Inflammation Mediators Humans Animals Peptides Machine Learning Computational Biology Algorithms Immunotherapy Reproducibility of Results
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De novo profile generation based on sequence context specificity with the long short-term memory network.

BMC bioinformatics Jul 18, 2018
BACKGROUND: Long short-term memory (LSTM) is one of the most attractive deep learning methods to learn time series or contexts of input data. Increasing studies, including biological sequence analyses in bioinformatics, utilize this architecture. Ami...
ROC Curve Computational Biology Proteins Sequence Homology, Amino Acid Amino Acid Sequence Databases as Topic Neural Networks, Computer Deep Learning
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A Data Driven Model for Predicting RNA-Protein Interactions based on Gradient Boosting Machine.

Scientific reports Jun 22, 2018
RNA protein interactions (RPI) play a pivotal role in the regulation of various biological processes. Experimental validation of RPI has been time-consuming, paving the way for computational prediction methods. The major limiting factor of these meth...
Amino Acid Sequence RNA Protein Binding Computational Biology RNA-Binding Proteins Machine Learning
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Computational design of thermostabilizing point mutations for G protein-coupled receptors.

eLife Jun 21, 2018
Engineering of GPCR constructs with improved thermostability is a key for successful structural and biochemical studies of this transmembrane protein family, targeted by 40% of all therapeutic drugs. Here we introduce a comprehensive computational ap...
Humans Serotonin 5-HT2 Receptor Antagonists Protein Binding Protein Conformation, beta-Strand Models, Molecular Point Mutation Serotonin 5-HT2 Receptor Agonists Machine Learning Binding Sites Animals Protein Interaction Domains and Motifs Sequence Alignment Protein Engineering Protein Stability Protein Conformation, alpha-Helical Gene Expression Sequence Homology, Amino Acid Receptor, Serotonin, 5-HT2C Kinetics Thermodynamics Crystallography, X-Ray Amino Acid Sequence
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