Protein secondary structure prediction using the three-dimensional profile method

Prediction of Protein Secondary Structure Using Nonlinear Method

This paper presents the use of neural networks for the prediction of protein Secondary Structure. We propose a pre-processing stage based on the method of Cascaded Nonlinear Components Analysis (CNLPCA), in order to get a dimensional reduction of the data which may consider its nonlinearity. Then, the reduced data are placed in predictor networks and its results are combined. For the verificati...

Prediction of Protein Three-Dimensional Structure

Protein secondary structure prediction.

The past year has seen a consolidation of protein secondary structure prediction methods. The advantages of prediction from an aligned family of proteins have been highlighted by several accurate predictions made 'blind', before any X-ray or NMR structure was known for the family. New techniques that apply machine learning and discriminant analysis show promise as alternatives to neural networks.

Nonlinear Dimensional Reduction in Protein Secondary Structure Prediction

A pentapeptide-based method for protein secondary structure prediction.

We present a new method for protein secondary structure prediction, based on the recognition of well-defined pentapeptides, in a large databank. Using a databank of 635 protein chains, we obtained a success rate of 68.6%. We show that progress is achieved when the databank is enlarged, when the 20 amino acids are adequately grouped in 10 sets and when more pentapeptides are attributed one of th...