Accurate prediction of protein enzymatic class by N-to-1 Neural Networks

BMC Bioinformatics

Research

Gianluca Pollastri¹  Viola Volpato¹  Alessandro Adelfio¹ 

[1] School of Computer Science and Informatics, University College Dublin, Ireland;Complex and Adaptive Systems Laboratory, University College Dublin, Ireland;

关键词: Hide Unit;    Enzyme Commission;    Enzyme Commission Number;    Secondary Structure Information;    Input Code;   

DOI  :  10.1186/1471-2105-14-S1-S11

来源: Springer

【 摘 要 】

We present a novel ab initio predictor of protein enzymatic class. The predictor can classify proteins, solely based on their sequences, into one of six classes extracted from the enzyme commission (EC) classification scheme and is trained on a large, curated database of over 6,000 non-redundant proteins which we have assembled in this work. The predictor is powered by an ensemble of N-to-1 Neural Network, a novel architecture which we have recently developed. N-to-1 Neural Networks operate on the full sequence and not on predefined features. All motifs of a predefined length (31 residues in this work) are considered and are compressed by an N-to-1 Neural Network into a feature vector which is automatically determined during training. We test our predictor in 10-fold cross-validation and obtain state of the art results, with a 96% correct classification and 86% generalized correlation. All six classes are predicted with a specificity of at least 80% and false positive rates never exceeding 7%. We are currently investigating enhanced input encoding schemes which include structural information, and are analyzing trained networks to mine motifs that are most informative for the prediction, hence, likely, functionally relevant.

【 授权许可】

Unknown   
© Volpato et al.; licensee BioMed Central Ltd. 2013. This article is published under license to BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.