Using neural networks and evolutionary information in decoy discrimination for protein tertiary structure prediction.

ABSTRACT:

Background

We present a novel method of protein fold decoy discrimination using machine learning, more specifically using neural networks. Here, decoy discrimination is represented as a machine learning problem, where neural networks are used to learn the native-like features of protein structures using a set of positive and negative training examples. A set of native protein structures provides the positive training examples, while negative training examples are simulated decoy structures obtained by reversing the sequences of native structures. Various features are extracted from the training dataset of positive and negative examples and used as inputs to the neural networks.

Results

Results have shown that the best performing neural network is the one that uses input info

SUBMITTER: Tan CW 

PROVIDER: S-EPMC2267779 | biostudies-literature | 2008 Feb

REPOSITORIES: biostudies-literature