AIMC Topic: Sequence Analysis, Protein

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Classification of G-protein coupled receptors based on a rich generation of convolutional neural network, N-gram transformation and multiple sequence alignments.

Amino acids Nov 18, 2017
Sequence classification is crucial in predicting the function of newly discovered sequences. In recent years, the prediction of the incremental large-scale and diversity of sequences has heavily relied on the involvement of machine-learning algorithm...
Models, Theoretical Databases, Protein Receptors, G-Protein-Coupled Amino Acid Sequence Sequence Alignment Sequence Analysis, Protein Machine Learning Algorithms Reproducibility of Results Neural Networks, Computer
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Template-based and free modeling of I-TASSER and QUARK pipelines using predicted contact maps in CASP12.

Proteins Nov 14, 2017
We develop two complementary pipelines, "Zhang-Server" and "QUARK", based on I-TASSER and QUARK pipelines for template-based modeling (TBM) and free modeling (FM), and test them in the CASP12 experiment. The combination of I-TASSER and QUARK successf...
Computational Biology Proteins Databases, Protein Humans Machine Learning Algorithms Protein Folding Sequence Alignment Sequence Analysis, Protein Models, Molecular Crystallography, X-Ray Protein Conformation
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Protein contact prediction by integrating deep multiple sequence alignments, coevolution and machine learning.

Proteins Oct 31, 2017
In this study, we report the evaluation of the residue-residue contacts predicted by our three different methods in the CASP12 experiment, focusing on studying the impact of multiple sequence alignment, residue coevolution, and machine learning on co...
Protein Folding Sequence Alignment Sequence Analysis, Protein Proteins Databases, Protein Models, Molecular Datasets as Topic Crystallography, X-Ray Protein Conformation Machine Learning Algorithms Computational Biology Humans
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Simultaneous refinement of inaccurate local regions and overall structure in the CASP12 protein model refinement experiment.

Proteins Oct 26, 2017
Advances in protein model refinement techniques are required as diverse sources of protein structure information are available from low-resolution experiments or informatics-based computations such as cryo-EM, NMR, homology models, or predicted resid...
Humans Machine Learning Algorithms Molecular Dynamics Simulation Crystallography, X-Ray Protein Conformation Computational Biology Proteins Models, Molecular Sequence Analysis, Protein Protein Interaction Domains and Motifs
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Machine learning to design integral membrane channelrhodopsins for efficient eukaryotic expression and plasma membrane localization.

PLoS computational biology Oct 23, 2017
There is growing interest in studying and engineering integral membrane proteins (MPs) that play key roles in sensing and regulating cellular response to diverse external signals. A MP must be expressed, correctly inserted and folded in a lipid bilay...
Subcellular Fractions Rhodopsin Structure-Activity Relationship HEK293 Cells Cell Membrane Lipid Bilayers Humans Sequence Analysis, Protein Machine Learning Ion Channels Drug Design
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A Computational-Based Method for Predicting Drug-Target Interactions by Using Stacked Autoencoder Deep Neural Network.

Journal of computational biology : a journal of computational molecular cell biology Sep 11, 2017
Identifying the interaction between drugs and target proteins is an important area of drug research, which provides a broad prospect for low-risk and faster drug development. However, due to the limitations of traditional experiments when revealing d...
Reproducibility of Results Molecular Docking Simulation Deep Learning Protein Binding Databases, Chemical Sequence Analysis, Protein
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Prediction of N-linked glycosylation sites using position relative features and statistical moments.

PloS one Aug 10, 2017
Glycosylation is one of the most complex post translation modification in eukaryotic cells. Almost 50% of the human proteome is glycosylated as glycosylation plays a vital role in various biological functions such as antigen's recognition, cell-cell ...
Algorithms Support Vector Machine Sequence Analysis, Protein Humans Computational Biology Databases, Protein Neural Networks, Computer Glycosylation Amino Acid Sequence
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Protein binding hot spots prediction from sequence only by a new ensemble learning method.

Amino acids Aug 1, 2017
UNLABELLED: Hot spots are interfacial core areas of binding proteins, which have been applied as targets in drug design. Experimental methods are costly in both time and expense to locate hot spot areas. Recently, in-silicon computational methods hav...
Databases, Protein Models, Molecular Sequence Analysis, Protein Machine Learning Algorithms
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A novel alignment-free method to classify protein folding types by combining spectral graph clustering with Chou's pseudo amino acid composition.

Journal of theoretical biology May 3, 2017
The present work employs pseudo amino acid composition (PseAAC) for encoding the protein sequences in their numeric form. Later this will be arranged in the similarity matrix, which serves as input for spectral graph clustering method. Spectral metho...
Sequence Analysis, Protein Proteins Protein Folding Machine Learning Sequence Alignment Algorithms
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Investigating Correlation between Protein Sequence Similarity and Semantic Similarity Using Gene Ontology Annotations.

IEEE/ACM transactions on computational biology and bioinformatics Apr 18, 2017
Sequence similarity is a commonly used measure to compare proteins. With the increasing use of ontologies, semantic (function) similarity is getting importance. The correlation between these measures has been applied in the evaluation of new semantic...
Databases, Protein Amino Acid Sequence Semantics Mice Humans Sequence Analysis, Protein Molecular Sequence Annotation Computational Biology Animals Gene Ontology Proteins Sequence Alignment
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