The prediction of protein secondary structure is generally regarded as the first step of protein structure prediction. Secondary structure refers to regular protein local structural elements such as α helix and β sheet. Different amino acid residues have different propensities to form different secondary structure elements. According to the components of the secondary structure of the protein, the globular proteins can be divided into four folding types: all α protein, all β protein, α + β protein and α/β protein. Most of the algorithms for predicting the secondary structure of proteins are based on proteins with known three-dimensional structures and secondary structures, and have used artificial neural networks, genetic algorithms and other technologies to construct prediction methods.
The protein sequence predicted by SOPMA mainly contains Alpha helix, Extended strand, Beta turn and Random coil.
The algorithm that JPred used at the beginning was a large-scale comparison analysis, and later it used a neural network method.
GOR is a method based on information theory and Bayesian statistics. The name of the method is a combination of the first letters of the names of three inventors (Garnier, Osguthorpe, Robson). GOR treats the protein sequence as a series of information values. The GOR method not only considers the influence of the type of amino acid residues in the predicted position, but also the influence of the types of adjacent residues on the conformation of the position.
Prediction based on statistics and machine learning methods:
|Project name||Protein structure prediction|
|Product delivery mode||We will provide protein prediction results and related documents, pictures, etc.|
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