Predicting Fold Novelty Based on ProtoNet Hierarchical Classification

Structural genomics projects aim to solve a large number of protein structures with the ultimate objective of representing the entire protein space. The computational challenge is to identify and prioritize a small set of proteins with new, currently unknown, superfamilies or folds. We develop a met...

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Main Authors: KIFER, Ilona, SASSON, Ori, Linial, Michal
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Language:English
Published: Institutional Knowledge at Singapore Management University 2005
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Online Access:https://ink.library.smu.edu.sg/sis_research/86
http://dx.doi.org/10.1093/bioinformatics/bti135
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spelling sg-smu-ink.sis_research-10852010-09-22T14:00:36Z Predicting Fold Novelty Based on ProtoNet Hierarchical Classification KIFER, Ilona SASSON, Ori Linial, Michal Structural genomics projects aim to solve a large number of protein structures with the ultimate objective of representing the entire protein space. The computational challenge is to identify and prioritize a small set of proteins with new, currently unknown, superfamilies or folds. We develop a method that assigns each protein a likelihood of it belonging to a new, yet undetermined, structural superfamily. The method relies on a variant of ProtoNet, an automatic hierarchical classification scheme of all protein sequences from SwissProt. Our results show that proteins that are remote from solved structures in the ProtoNet hierarchy are more likely to belong to new superfamilies. The results are validated against SCOP releases from recent years that account for about half of the solved structures known to date. We show that our new method and the representation of ProtoNet are superior in detecting new targets, compared to our previous method using ProtoMap classification. Furthermore, our method outperforms PSI-BLAST search in detecting potential new superfamilies. 2005-04-01T08:00:00Z text https://ink.library.smu.edu.sg/sis_research/86 info:doi/10.1093/bioinformatics/bti135 http://dx.doi.org/10.1093/bioinformatics/bti135 Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Bioinformatics Computer Sciences
institution Singapore Management University
building SMU Libraries
continent Asia
country Singapore
Singapore
content_provider SMU Libraries
collection InK@SMU
language English
topic Bioinformatics
Computer Sciences
spellingShingle Bioinformatics
Computer Sciences
KIFER, Ilona
SASSON, Ori
Linial, Michal
Predicting Fold Novelty Based on ProtoNet Hierarchical Classification
description Structural genomics projects aim to solve a large number of protein structures with the ultimate objective of representing the entire protein space. The computational challenge is to identify and prioritize a small set of proteins with new, currently unknown, superfamilies or folds. We develop a method that assigns each protein a likelihood of it belonging to a new, yet undetermined, structural superfamily. The method relies on a variant of ProtoNet, an automatic hierarchical classification scheme of all protein sequences from SwissProt. Our results show that proteins that are remote from solved structures in the ProtoNet hierarchy are more likely to belong to new superfamilies. The results are validated against SCOP releases from recent years that account for about half of the solved structures known to date. We show that our new method and the representation of ProtoNet are superior in detecting new targets, compared to our previous method using ProtoMap classification. Furthermore, our method outperforms PSI-BLAST search in detecting potential new superfamilies.
format text
author KIFER, Ilona
SASSON, Ori
Linial, Michal
author_facet KIFER, Ilona
SASSON, Ori
Linial, Michal
author_sort KIFER, Ilona
title Predicting Fold Novelty Based on ProtoNet Hierarchical Classification
title_short Predicting Fold Novelty Based on ProtoNet Hierarchical Classification
title_full Predicting Fold Novelty Based on ProtoNet Hierarchical Classification
title_fullStr Predicting Fold Novelty Based on ProtoNet Hierarchical Classification
title_full_unstemmed Predicting Fold Novelty Based on ProtoNet Hierarchical Classification
title_sort predicting fold novelty based on protonet hierarchical classification
publisher Institutional Knowledge at Singapore Management University
publishDate 2005
url https://ink.library.smu.edu.sg/sis_research/86
http://dx.doi.org/10.1093/bioinformatics/bti135
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