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Protein Structure, Secondary

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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 ...

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...

Accurate prediction of protein torsion angles using evolutionary signatures and recurrent neural network.

Scientific reports
The amino acid sequence of a protein contains all the necessary information to specify its shape, which dictates its biological activities. However, it is challenging and expensive to experimentally determine the three-dimensional structure of protei...

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...

Secondary structure specific simpler prediction models for protein backbone angles.

BMC bioinformatics
MOTIVATION: Protein backbone angle prediction has achieved significant accuracy improvement with the development of deep learning methods. Usually the same deep learning model is used in making prediction for all residues regardless of the categories...

CoCoPRED: coiled-coil protein structural feature prediction from amino acid sequence using deep neural networks.

Bioinformatics (Oxford, England)
MOTIVATION: Coiled-coil is composed of two or more helices that are wound around each other. It widely exists in proteins and has been discovered to play a variety of critical roles in biology processes. Generally, there are three types of structural...

Predicting RNA Secondary Structure Using In Vitro and In Vivo Data.

Methods in molecular biology (Clifton, N.J.)
The new flow of high-throughput RNA secondary structure data coming from different techniques allowed the further development of machine learning approaches. We developed CROSS and CROSSalive, two algorithms trained on experimental data able to predi...

End-to-End Deep Learning Model to Predict and Design Secondary Structure Content of Structural Proteins.

ACS biomaterials science & engineering
Structural proteins are the basis of many biomaterials and key construction and functional components of all life. Further, it is well-known that the diversity of proteins' function relies on their local structures derived from their primary amino ac...

SSpro/ACCpro 6: almost perfect prediction of protein secondary structure and relative solvent accessibility using profiles, deep learning and structural similarity.

Bioinformatics (Oxford, England)
MOTIVATION: Accurately predicting protein secondary structure and relative solvent accessibility is important for the study of protein evolution, structure and an early-stage component of typical protein 3D structure prediction pipelines.

Protein-DNA Binding Residue Prediction via Bagging Strategy and Sequence-Based Cube-Format Feature.

IEEE/ACM transactions on computational biology and bioinformatics
Protein-DNA interactions play an important role in diverse biological processes. Accurately identifying protein-DNA binding residues is a critical but challenging task for protein function annotations and drug design. Although wet-lab experimental me...