Data Mining and Knowledge Discovery
A Novel Method for Prediction of Protein Domain Using Distance-Based Maximal Entropy
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Part II--Advances in Neural Networks
SPlitSSI-SVM: An algorithm to reduce the misleading and increase the strength of domain signal
Computers in Biology and Medicine
Domain boundary prediction based on profile domain linker propensity index
Computational Biology and Chemistry
Prediction of protein domains from sequence information using support vector machines
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part III
| Hi-index | 3.84 |
Motivation: We describe a novel method for detecting the domain structure of a protein from sequence information alone. The method is based on analyzing multiple sequence alignments that are derived from a database search. Multiple measures are defined to quantify the domain information content of each position along the sequence and are combined into a single predictor using a neural network. The output is further smoothed and post-processed using a probabilistic model to predict the most likely transition positions between domains. Results: The method was assessed using the domain definitions in SCOP and CATH for proteins of known structure and was compared with several other existing methods. Our method performs well both in terms of accuracy and sensitivity. It improves significantly over the best methods available, even some of the semi-manual ones, while being fully automatic. Our method can also be used to suggest and verify domain partitions based on structural data. A few examples of predicted domain definitions and alternative partitions, as suggested by our method, are also discussed. Availability: An online domain-prediction server is available at http://biozon.org/tools/domains/