AIMC Topic: Protein Structure, Secondary

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Protein secondary structure detection in intermediate-resolution cryo-EM maps using deep learning.

Nature methods Jul 29, 2019
Although structures determined at near-atomic resolution are now routinely reported by cryo-electron microscopy (cryo-EM), many density maps are determined at an intermediate resolution, and extracting structure information from these maps is still a...
Humans Neural Networks, Computer Models, Molecular Cryoelectron Microscopy Protein Structure, Secondary Software Deep Learning Proteins
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DeepACLSTM: deep asymmetric convolutional long short-term memory neural models for protein secondary structure prediction.

BMC bioinformatics Jun 17, 2019
BACKGROUND: Protein secondary structure (PSS) is critical to further predict the tertiary structure, understand protein function and design drugs. However, experimental techniques of PSS are time consuming and expensive, and thus it's very urgent to ...
Proteins Protein Domains Protein Structure, Secondary Algorithms Deep Learning Models, Theoretical Neural Networks, Computer
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Analysis and prediction of human acetylation using a cascade classifier based on support vector machine.

BMC bioinformatics Jun 17, 2019
BACKGROUND: Acetylation on lysine is a widespread post-translational modification which is reversible and plays a crucial role in some biological activities. To better understand the mechanism, it is necessary to identify acetylation sites in protein...
Rats Support Vector Machine Protein Processing, Post-Translational Mice Computational Biology Amino Acid Sequence Acetylation Lysine Proteins Databases, Protein Humans Animals Protein Structure, Secondary Molecular Sequence Annotation Position-Specific Scoring Matrices Gene Ontology
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Auto-encoding NMR chemical shifts from their native vector space to a residue-level biophysical index.

Nature communications Jun 7, 2019
Chemical shifts (CS) are determined from NMR experiments and represent the resonance frequency of the spin of atoms in a magnetic field. They contain a mixture of information, encompassing the in-solution conformations a protein adopts, as well as th...
Protein Structure, Secondary Nuclear Magnetic Resonance, Biomolecular Magnetic Resonance Spectroscopy Magnetic Resonance Imaging Proteins Neural Networks, Computer Amino Acids Models, Molecular Biophysical Phenomena
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MCP: A multi-component learning machine to predict protein secondary structure.

Computers in biology and medicine May 12, 2019
The Gene or DNA sequence in every cell does not control genetic properties on its own; Rather, this is done through the translation of DNA into protein and subsequent formation of a certain 3D structure. The biological function of a protein is tightl...
Protein Structure, Secondary Proteins Models, Molecular Machine Learning
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NetSurfP-2.0: Improved prediction of protein structural features by integrated deep learning.

Proteins Mar 9, 2019
The ability to predict local structural features of a protein from the primary sequence is of paramount importance for unraveling its function in absence of experimental structural information. Two main factors affect the utility of potential predict...
Databases, Protein Protein Structure, Secondary Deep Learning Computational Biology Proteome
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Mechanism of glucocerebrosidase activation and dysfunction in Gaucher disease unraveled by molecular dynamics and deep learning.

Proceedings of the National Academy of Sciences of the United States of America Feb 26, 2019
The lysosomal enzyme glucocerebrosidase-1 (GCase) catalyzes the cleavage of a major glycolipid glucosylceramide into glucose and ceramide. The absence of fully functional GCase leads to the accumulation of its lipid substrates in lysosomes, causing G...
Catalytic Domain Hydrogen Bonding Protein Structure, Secondary Mutant Proteins Enzyme Activation Protein Interaction Maps Glucosylceramidase Gaucher Disease Saposins Molecular Dynamics Simulation Humans Deep Learning
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Improving Protein Gamma-Turn Prediction Using Inception Capsule Networks.

Scientific reports Oct 24, 2018
Protein gamma-turn prediction is useful in protein function studies and experimental design. Several methods for gamma-turn prediction have been developed, but the results were unsatisfactory with Matthew correlation coefficients (MCC) around 0.2-0.4...
Neural Networks, Computer Algorithms Proteins Amino Acid Sequence Computational Biology Protein Structure, Secondary
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Single-sequence-based prediction of protein secondary structures and solvent accessibility by deep whole-sequence learning.

Journal of computational chemistry Oct 14, 2018
Predicting protein structure from sequence alone is challenging. Thus, the majority of methods for protein structure prediction rely on evolutionary information from multiple sequence alignments. In previous work we showed that Long Short-Term Bidire...
Solvents Protein Structure, Secondary Deep Learning Proteins
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A New Method for Recognizing Cytokines Based on Feature Combination and a Support Vector Machine Classifier.

Molecules (Basel, Switzerland) Aug 11, 2018
Research on cytokine recognition is of great significance in the medical field due to the fact cytokines benefit the diagnosis and treatment of diseases, but the current methods for cytokine recognition have many shortcomings, such as low sensitivity...
Computational Biology Cytokines Support Vector Machine Amino Acids Humans Databases, Protein Reproducibility of Results Protein Structure, Secondary Algorithms Sensitivity and Specificity
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