secondary structure

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Related to Protein secondary structure: alpha helix

sec·on·dar·y struc·ture

the localized arrangement in space of regions of a biopolymer; often these types of structures are regular and recurring along one dimension, for example, the α-helix often found in proteins.

secondary structure

n.
The three-dimensional structure of the polypeptide backbone of a protein (such as alpha helix, beta sheet, and coil patterns), or the sugar-phosphate backbone of a nucleic acid (such as the double helix pattern of DNA).
References in periodicals archive ?
Different tools for protein secondary structure predictions give different properties i.
Evaluation and improvement of multiple sequence methods for protein secondary structure prediction.
Predicting protein secondary structure content: A tandem neural network approach.
Cascaded Bidirectional Recurrent Neural Networks for Protein Secondary Structure Prediction.
For example, in bioinformatics, propositional methods would empirically seem sufficient to predict protein secondary structure because neural network approaches have time after time been the most successful in blind trials.
Specifically, [alpha]-helices and [beta]-sheets in the protein secondary structures can affect digestibility of feed proteins (Dyson and Wright, 1993).
Protein structural variation, due to nsSNP, was assessed through PSIPRED (protein structure prediction), which determine protein secondary structure prediction (Jones, 1999).
Protein structure, which is essential for function, can be described at different levels than the complete, all-atom representation of x, y, z coordinates with more topology-oriented descriptions, such as protein secondary structure, where each amino acid typically is being put into one of the mutually exclusive conformational categories (from a small number of possible conformational states).
Double prediction method (DPM) (11), Discrimination of protein secondary structure class (DSC) (12), GOR4 (13), Hierarchical neural network (HNN) (14), PHD (15), Predator (16), SIMPA96 (17), Self-optimized prediction method with alignment (SOPMA) (18) and Sec.
The research measures protein secondary structure changes by FTIR spectroscopy, protein density profile by neutron reflectometry, protein adsorption kinetics by FTIR spectroscopy, and protein exchange by a thin gap instrument.
Among the topics are concepts of similarity in bioinformatics, predicting and visualizing DNA structural properties from sequence, and comparing 20 common prediction algorithms using a neural network to predict protein secondary structure.
It is expected that the orientation of the protein secondary structures in the draw direction induces the enhancement of mechanical properties.