AIMC Topic: Protein Structure, Secondary

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TopProperty: Robust Metaprediction of Transmembrane and Globular Protein Features Using Deep Neural Networks.

Journal of chemical theory and computation
Transmembrane proteins (TMPs) are critical components of cellular life. However, due to experimental challenges, the number of experimentally resolved TMP structures is severely underrepresented in databases compared to their cellular abundance. Pred...

Mapping the glycosyltransferase fold landscape using interpretable deep learning.

Nature communications
Glycosyltransferases (GTs) play fundamental roles in nearly all cellular processes through the biosynthesis of complex carbohydrates and glycosylation of diverse protein and small molecule substrates. The extensive structural and functional diversifi...

Testing machine learning techniques for general application by using protein secondary structure prediction. A brief survey with studies of pitfalls and benefits using a simple progressive learning approach.

Computers in biology and medicine
Many researchers have recently used the prediction of protein secondary structure (local conformational states of amino acid residues) to test advances in predictive and machine learning technology such as Neural Net Deep Learning. Protein secondary ...

Research on RNA secondary structure predicting via bidirectional recurrent neural network.

BMC bioinformatics
BACKGROUND: RNA secondary structure prediction is an important research content in the field of biological information. Predicting RNA secondary structure with pseudoknots has been proved to be an NP-hard problem. Traditional machine learning methods...

Improved protein relative solvent accessibility prediction using deep multi-view feature learning framework.

Analytical biochemistry
The accurate prediction of the relative solvent accessibility of a protein is critical to understanding its 3D structure and biological function. In this study, a novel deep multi-view feature learning (DMVFL) framework that integrates three differen...

Assigning secondary structure in proteins using AI.

Journal of molecular modeling
Knowledge about protein structure assignment enriches the structural and functional understanding of proteins. Accurate and reliable structure assignment data is crucial for secondary structure prediction systems. Since the 1980s, various methods bas...

A Multitask Deep-Learning Method for Predicting Membrane Associations and Secondary Structures of Proteins.

Journal of proteome research
Prediction of residue-level structural attributes and protein-level structural classes helps model protein tertiary structures and understand protein functions. Existing methods are either specialized on only one class of proteins or developed to pre...

Protein-Protein Interface Topology as a Predictor of Secondary Structure and Molecular Function Using Convolutional Deep Learning.

Journal of chemical information and modeling
To power the specific recognition and binding of protein partners into functional complexes, a wealth of information about the structure and function of the partners is necessarily encoded into the global shape of protein-protein interfaces and their...

RNA Backbone Torsion and Pseudotorsion Angle Prediction Using Dilated Convolutional Neural Networks.

Journal of chemical information and modeling
RNA three-dimensional structure prediction has been relied on using a predicted or experimentally determined secondary structure as a restraint to reduce the conformational sampling space. However, the secondary-structure restraints are limited to pa...