AI Medical Compendium Topic:
Amino Acid Sequence

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Improving prediction of protein secondary structure, backbone angles, solvent accessibility and contact numbers by using predicted contact maps and an ensemble of recurrent and residual convolutional neural networks.

Bioinformatics (Oxford, England) Jul 15, 2019
MOTIVATION: Sequence-based prediction of one dimensional structural properties of proteins has been a long-standing subproblem of protein structure prediction. Recently, prediction accuracy has been significantly improved due to the rapid expansion o...
Computational Biology Proteins Amino Acid Sequence Neural Networks, Computer Solvents Protein Structure, Secondary
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The PSIPRED Protein Analysis Workbench: 20 years on.

Nucleic acids research Jul 2, 2019
The PSIPRED Workbench is a web server offering a range of predictive methods to the bioscience community for 20 years. Here, we present the work we have completed to update the PSIPRED Protein Analysis Workbench and make it ready for the next 20 year...
Protein Conformation, alpha-Helical Protein Conformation, beta-Strand Sequence Alignment Sequence Homology, Amino Acid Protein Interaction Domains and Motifs Models, Molecular Software Internet Protein Binding Gene Ontology Amino Acid Sequence Proteins Binding Sites History, 21st Century Molecular Sequence Annotation
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PrankWeb: a web server for ligand binding site prediction and visualization.

Nucleic acids research Jul 2, 2019
PrankWeb is an online resource providing an interface to P2Rank, a state-of-the-art method for ligand binding site prediction. P2Rank is a template-free machine learning method based on the prediction of local chemical neighborhood ligandability cent...
Binding Sites Thermodynamics Protein Interaction Domains and Motifs Amino Acid Sequence Ligands Datasets as Topic Protein Conformation, alpha-Helical Protein Conformation, beta-Strand Machine Learning Software Protein Binding Proteins Internet Benchmarking Humans
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NetGO: improving large-scale protein function prediction with massive network information.

Nucleic acids research Jul 2, 2019
Automated function prediction (AFP) of proteins is of great significance in biology. AFP can be regarded as a problem of the large-scale multi-label classification where a protein can be associated with multiple gene ontology terms as its labels. Bas...
Machine Learning Software Animals Computational Biology Humans Sequence Analysis, Protein Prokaryotic Cells Plants Amino Acid Sequence Molecular Sequence Annotation Sequence Alignment Gene Ontology Models, Molecular Proteins Sequence Homology, Amino Acid Databases, Protein Protein Interaction Mapping Internet Structure-Activity Relationship Benchmarking
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DeepCrystal: a deep learning framework for sequence-based protein crystallization prediction.

Bioinformatics (Oxford, England) Jul 1, 2019
MOTIVATION: Protein structure determination has primarily been performed using X-ray crystallography. To overcome the expensive cost, high attrition rate and series of trial-and-error settings, many in-silico methods have been developed to predict cr...
Computational Biology Proteins Deep Learning Amino Acid Sequence Crystallization
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Bastion3: a two-layer ensemble predictor of type III secreted effectors.

Bioinformatics (Oxford, England) Jun 1, 2019
MOTIVATION: Type III secreted effectors (T3SEs) can be injected into host cell cytoplasm via type III secretion systems (T3SSs) to modulate interactions between Gram-negative bacterial pathogens and their hosts. Due to their relevance in pathogen-hos...
Algorithms Computational Biology Machine Learning Software Bacterial Proteins Amino Acid Sequence Gram-Negative Bacteria
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[Prediction of protein subcellular localization based on multilayer sparse coding].

Sheng wu gong cheng xue bao = Chinese journal of biotechnology Apr 25, 2019
In order to provide a theoretical basis for better understanding the function and properties of proteins, we proposed a simple and effective feature extraction method for protein sequences to determine the subcellular localization of proteins. First,...
Amino Acid Sequence Protein Transport Subcellular Fractions Computational Biology Support Vector Machine Proteins Algorithms
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Identifying short disorder-to-order binding regions in disordered proteins with a deep convolutional neural network method.

Journal of bioinformatics and computational biology Feb 1, 2019
Molecular recognition features (MoRFs) are key functional regions of intrinsically disordered proteins (IDPs), which play important roles in the molecular interaction network of cells and are implicated in many serious human diseases. Identifying MoR...
Intrinsically Disordered Proteins Humans Binding Sites Neural Networks, Computer Deep Learning Membrane Proteins Protein Binding Databases, Protein Computational Biology Amino Acid Sequence
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Using two-dimensional convolutional neural networks for identifying GTP binding sites in Rab proteins.

Journal of bioinformatics and computational biology Feb 1, 2019
Deep learning has been increasingly and widely used to solve numerous problems in various fields with state-of-the-art performance. It can also be applied in bioinformatics to reduce the requirement for feature extraction and reach high performance. ...
rab GTP-Binding Proteins Guanosine Triphosphate Databases, Protein Amino Acids Neural Networks, Computer Humans Amino Acid Sequence Deep Learning Binding Sites Computational Biology
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DeepDom: Predicting protein domain boundary from sequence alone using stacked bidirectional LSTM.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing Jan 1, 2019
Protein domain boundary prediction is usually an early step to understand protein function and structure. Most of the current computational domain boundary prediction methods suffer from low accuracy and limitation in handling multi-domain types, or ...
Amino Acid Sequence Computational Biology Databases, Protein Protein Domains Neural Networks, Computer Algorithms Deep Learning
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