AIMC Topic: Protein Structure, Secondary

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Membrane protein contact and structure prediction using co-evolution in conjunction with machine learning.

PloS one May 24, 2017
De novo membrane protein structure prediction is limited to small proteins due to the conformational search space quickly expanding with length. Long-range contacts (24+ amino acid separation)-residue positions distant in sequence, but in close proxi...
Algorithms Protein Folding Membrane Proteins Amino Acid Sequence Protein Structure, Secondary Models, Molecular Humans Machine Learning
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Improved prediction of protein-protein interactions using novel negative samples, features, and an ensemble classifier.

Artificial intelligence in medicine Mar 4, 2017
Computational methods are employed in bioinformatics to predict protein-protein interactions (PPIs). PPIs and protein-protein non-interactions (PPNIs) display different levels of development, and the number of PPIs is considerably greater than that o...
Reproducibility of Results Protein Interaction Maps Computational Biology Humans Natural Language Processing Amino Acid Sequence Protein Structure, Secondary Databases, Protein Structure-Activity Relationship Proteins
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Pretata: predicting TATA binding proteins with novel features and dimensionality reduction strategy.

BMC systems biology Dec 23, 2016
BACKGROUND: It is necessary and essential to discovery protein function from the novel primary sequences. Wet lab experimental procedures are not only time-consuming, but also costly, so predicting protein structure and function reliably based only o...
Chemical Phenomena Computational Biology TATA-Box Binding Protein Protein Structure, Secondary Software Support Vector Machine Protein Binding Amino Acid Sequence
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HEMEsPred: Structure-Based Ligand-Specific Heme Binding Residues Prediction by Using Fast-Adaptive Ensemble Learning Scheme.

IEEE/ACM transactions on computational biology and bioinformatics Oct 4, 2016
Heme is an essential biomolecule that widely exists in numerous extant organisms. Accurately identifying heme binding residues (HEMEs) is of great importance in disease progression and drug development. In this study, a novel predictor named HEMEsPre...
Machine Learning Software Computational Biology Databases, Protein Models, Molecular Algorithms Protein Binding Protein Structure, Secondary Heme Binding Sites
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Protein secondary structure prediction using a small training set (compact model) combined with a Complex-valued neural network approach.

BMC bioinformatics Sep 13, 2016
BACKGROUND: Protein secondary structure prediction (SSP) has been an area of intense research interest. Despite advances in recent methods conducted on large datasets, the estimated upper limit accuracy is yet to be reached. Since the predictions of ...
Protein Structure, Secondary Neural Networks, Computer Proteins Humans
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Predicting the errors of predicted local backbone angles and non-local solvent- accessibilities of proteins by deep neural networks.

Bioinformatics (Oxford, England) Aug 22, 2016
MOTIVATION: Backbone structures and solvent accessible surface area of proteins are benefited from continuous real value prediction because it removes the arbitrariness of defining boundary between different secondary-structure and solvent-accessibil...
Proteins Amino Acids Protein Structure, Secondary Solvents Neural Networks, Computer Computational Biology
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GGIP: Structure and sequence-based GPCR-GPCR interaction pair predictor.

Proteins Jun 8, 2016
G Protein-Coupled Receptors (GPCRs) are important pharmaceutical targets. More than 30% of currently marketed pharmaceutical medicines target GPCRs. Numerous studies have reported that GPCRs function not only as monomers but also as homo- or hetero-d...
Humans Protein Interaction Domains and Motifs Receptors, G-Protein-Coupled Protein Structure, Secondary Area Under Curve Protein Multimerization Databases, Protein Protein Binding Animals Amino Acid Sequence Mice Models, Molecular ROC Curve Binding Sites Support Vector Machine
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conSSert: Consensus SVM Model for Accurate Prediction of Ordered Secondary Structure.

Journal of chemical information and modeling Mar 15, 2016
Accurate prediction of protein secondary structure remains a crucial step in most approaches to the protein-folding problem, yet the prediction of ordered secondary structure, specifically beta-strands, remains a challenge. We developed a consensus s...
Consensus Support Vector Machine Protein Structure, Secondary Proteins
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Enhancing the Prediction of Transmembrane β-Barrel Segments with Chain Learning and Feature Sparse Representation.

IEEE/ACM transactions on computational biology and bioinformatics Feb 11, 2016
Transmembrane β-barrels (TMBs) are one important class of membrane proteins that play crucial functions in the cell. Membrane proteins are difficult wet-lab targets of structural biology, which call for accurate computational prediction approaches. H...
Protein Structure, Secondary Machine Learning Molecular Sequence Data Sequence Analysis, Protein Membrane Proteins Amino Acid Sequence Databases, Protein Algorithms Pattern Recognition, Automated Models, Chemical Computer Simulation
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Protein Secondary Structure Prediction Using Deep Convolutional Neural Fields.

Scientific reports Jan 11, 2016
Protein secondary structure (SS) prediction is important for studying protein structure and function. When only the sequence (profile) information is used as input feature, currently the best predictors can obtain ~80% Q3 accuracy, which has not been...
Databases, Protein Protein Structure, Secondary Computational Biology Proteins Neural Networks, Computer Reproducibility of Results
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