Sample Reduction Strategies for Protein Secondary Structure Prediction

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

Protein Secondary Structure Prediction Accuracy versus Reduction Methods

Predicting protein secondary structure is a key step in determining the 3D structure of a protein that determines its function. The Dictionary of Secondary Structure of Proteins (DSSP) uses eight classes to describe a protein. The DSSP database is a database of secondary structure assignments for all protein entries in the Protein Data Bank (PDB) with an algorithm designed to standardize these ...

Bidirectional Dynamics for Protein Secondary Structure Prediction

For certain categories of sequences, information from both the past and the future can be used for analysis and predictions at time t. This is the case for biological sequences where the nature and function of a region in a sequence may strongly depend on events located both upstream and downstream. We develop a new family of adaptive graphical model architectures for learning non-causal sequen...

Prediction of Protein Secondary Structure

In the wake of large-scale DNA sequencing projects, accurate tools are needed to predict protein structures. The problem of predicting protein structure from DNA sequence remains fundamentally unsolved even after more than three decades of intensive research. In this paper, fundamental theory of the protein structure will be presented as a general guide to protein secondary structure prediction...