AIMC Topic: Protein Structure, Secondary

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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...

PSSP-MVIRT: peptide secondary structure prediction based on a multi-view deep learning architecture.

Briefings in bioinformatics
The prediction of peptide secondary structures is fundamentally important to reveal the functional mechanisms of peptides with potential applications as therapeutic molecules. In this study, we propose a multi-view deep learning method named Peptide ...

EMNUSS: a deep learning framework for secondary structure annotation in cryo-EM maps.

Briefings in bioinformatics
Cryo-electron microscopy (cryo-EM) has become one of important experimental methods in structure determination. However, despite the rapid growth in the number of deposited cryo-EM maps motivated by advances in microscopy instruments and image proces...

AI-based spectroscopic monitoring of real-time interactions between SARS-CoV-2 and human ACE2.

Proceedings of the National Academy of Sciences of the United States of America
The novel coronavirus, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), invades a human cell via human angiotensin-converting enzyme 2 (hACE2) as the entry, causing the severe coronavirus disease (COVID-19). The interactions between hACE...

A Tour of Unsupervised Deep Learning for Medical Image Analysis.

Current medical imaging
BACKGROUND: Interpretation of medical images for the diagnosis and treatment of complex diseases from high-dimensional and heterogeneous data remains a key challenge in transforming healthcare. In the last few years, both supervised and unsupervised ...

Variable Length Character N-Gram Embedding of Protein Sequences for Secondary Structure Prediction.

Protein and peptide letters
BACKGROUND: The prediction of a protein's secondary structure from its amino acid sequence is an essential step towards predicting its 3-D structure. The prediction performance improves by incorporating homologous multiple sequence alignment informat...

OPUS-TASS: a protein backbone torsion angles and secondary structure predictor based on ensemble neural networks.

Bioinformatics (Oxford, England)
MOTIVATION: Predictions of protein backbone torsion angles (ϕ and ψ) and secondary structure from sequence are crucial subproblems in protein structure prediction. With the development of deep learning approaches, their accuracies have been significa...

SAINT: self-attention augmented inception-inside-inception network improves protein secondary structure prediction.

Bioinformatics (Oxford, England)
MOTIVATION: Protein structures provide basic insight into how they can interact with other proteins, their functions and biological roles in an organism. Experimental methods (e.g. X-ray crystallography and nuclear magnetic resonance spectroscopy) fo...

Graph neural representational learning of RNA secondary structures for predicting RNA-protein interactions.

Bioinformatics (Oxford, England)
MOTIVATION: RNA-protein interactions are key effectors of post-transcriptional regulation. Significant experimental and bioinformatics efforts have been expended on characterizing protein binding mechanisms on the molecular level, and on highlighting...

Brewery: deep learning and deeper profiles for the prediction of 1D protein structure annotations.

Bioinformatics (Oxford, England)
MOTIVATION: Protein structural annotations (PSAs) are essential abstractions to deal with the prediction of protein structures. Many increasingly sophisticated PSAs have been devised in the last few decades. However, the need for annotations that are...