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Protein Structure, Secondary

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LTPConstraint: a transfer learning based end-to-end method for RNA secondary structure prediction.

BMC bioinformatics
BACKGROUND: RNA secondary structure is very important for deciphering cell's activity and disease occurrence. The first method which was used by the academics to predict this structure is biological experiment, But this method is too expensive, causi...

Protein secondary structure assignment using residual networks.

Journal of molecular modeling
Proteins are constructed from amino acid sequences. Their structural classifications include primary, secondary, tertiary, and quaternary, with tertiary and quaternary structures influencing protein function. Because a protein's structure is inextric...

BAT-Net: An enhanced RNA Secondary Structure prediction via bidirectional GRU-based network with attention mechanism.

Computational biology and chemistry
BACKGROUND: RNA Secondary Structure (RSS) has drawn growing concern, both for their pivotal roles in RNA tertiary structures prediction and critical effect in penetrating the mechanism of functional non-coding RNA. Computational techniques that can r...

SENSDeep: An Ensemble Deep Learning Method for Protein-Protein Interaction Sites Prediction.

Interdisciplinary sciences, computational life sciences
PURPOSE: The determination of which amino acid in a protein interacts with other proteins is important in understanding the functional mechanism of that protein. Although there are experimental methods to detect protein-protein interaction sites (PPI...

Convolutional ProteinUnetLM competitive with long short-term memory-based protein secondary structure predictors.

Proteins
The protein secondary structure (SS) prediction plays an important role in the characterization of general protein structure and function. In recent years, a new generation of algorithms for SS prediction based on embeddings from protein language mod...

GeoPacker: A novel deep learning framework for protein side-chain modeling.

Protein science : a publication of the Protein Society
Atomic interactions play essential roles in protein folding, structure stabilization, and function performance. Recent advances in deep learning-based methods have achieved impressive success not only in protein structure prediction, but also in prot...

HelixGAN a deep-learning methodology for conditional de novo design of α-helix structures.

Bioinformatics (Oxford, England)
MOTIVATION: Protein and peptide engineering has become an essential field in biomedicine with therapeutics, diagnostics and synthetic biology applications. Helices are both abundant structural feature in proteins and comprise a major portion of bioac...

LambdaPP: Fast and accessible protein-specific phenotype predictions.

Protein science : a publication of the Protein Society
The availability of accurate and fast artificial intelligence (AI) solutions predicting aspects of proteins are revolutionizing experimental and computational molecular biology. The webserver LambdaPP aspires to supersede PredictProtein, the first in...

HN-PPISP: a hybrid network based on MLP-Mixer for protein-protein interaction site prediction.

Briefings in bioinformatics
MOTIVATION: Biological experimental approaches to protein-protein interaction (PPI) site prediction are critical for understanding the mechanisms of biochemical processes but are time-consuming and laborious. With the development of Deep Learning (DL...

RNA independent fragment partition method based on deep learning for RNA secondary structure prediction.

Scientific reports
The non-coding RNA secondary structure largely determines its function. Hence, accuracy in structure acquisition is of great importance. Currently, this acquisition primarily relies on various computational methods. The prediction of the structures o...