AIMC Topic: Protein Structure, Secondary

Clear Filters Showing 21 to 30 of 156 articles

Metastable alpha-rich and beta-rich conformations of small Aβ42 peptide oligomers.

Proteins
Probing the structures of amyloid-β (Aβ) peptides in the early steps of aggregation is extremely difficult experimentally and computationally. Yet, this knowledge is extremely important as small oligomers are the most toxic species. Experiments and s...

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...

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...

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...

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...

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...

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...

Scaffolding protein functional sites using deep learning.

Science (New York, N.Y.)
The binding and catalytic functions of proteins are generally mediated by a small number of functional residues held in place by the overall protein structure. Here, we describe deep learning approaches for scaffolding such functional sites without n...

Automated Protein Secondary Structure Assignment from C Positions Using Neural Networks.

Biomolecules
The assignment of secondary structure elements in protein conformations is necessary to interpret a protein model that has been established by computational methods. The process essentially involves labeling the amino acid residues with H (Helix), E ...