John Lhota1, Ruth Hauptman1, Thomas Hart1, Clara Ng1, Lei Xie2. 1. Hunter College High School, New York, NY 10128, U.S.A., Department of Computer Science, Hunter College, The City University of New York, New York, NY 10065, U.S.A., Department of Biological Sciences, Hunter College, The City University of New York New York, NY 10065, U.S.A. and The Graduate Center, The City University of New York, New York, NY 10016, U.S.A. 2. Hunter College High School, New York, NY 10128, U.S.A., Department of Computer Science, Hunter College, The City University of New York, New York, NY 10065, U.S.A., Department of Biological Sciences, Hunter College, The City University of New York New York, NY 10065, U.S.A. and The Graduate Center, The City University of New York, New York, NY 10016, U.S.A. Hunter College High School, New York, NY 10128, U.S.A., Department of Computer Science, Hunter College, The City University of New York, New York, NY 10065, U.S.A., Department of Biological Sciences, Hunter College, The City University of New York New York, NY 10065, U.S.A. and The Graduate Center, The City University of New York, New York, NY 10016, U.S.A.
Abstract
MOTIVATION: Similarity search is the foundation of bioinformatics. It plays a key role in establishing structural, functional and evolutionary relationships between biological sequences. Although the power of the similarity search has increased steadily in recent years, a high percentage of sequences remain uncharacterized in the protein universe. Thus, new similarity search strategies are needed to efficiently and reliably infer the structure and function of new sequences. The existing paradigm for studying protein sequence, structure, function and evolution has been established based on the assumption that the protein universe is discrete and hierarchical. Cumulative evidence suggests that the protein universe is continuous. As a result, conventional sequence homology search methods may be not able to detect novel structural, functional and evolutionary relationships between proteins from weak and noisy sequence signals. To overcome the limitations in existing similarity search methods, we propose a new algorithmic framework-Enrichment of Network Topological Similarity (ENTS)-to improve the performance of large scale similarity searches in bioinformatics. RESULTS: We apply ENTS to a challenging unsolved problem: protein fold recognition. Our rigorous benchmark studies demonstrate that ENTS considerably outperforms state-of-the-art methods. As the concept of ENTS can be applied to any similarity metric, it may provide a general framework for similarity search on any set of biological entities, given their representation as a network. AVAILABILITY AND IMPLEMENTATION: Source code freely available upon request CONTACT: : lxie@iscb.org.
MOTIVATION: Similarity search is the foundation of bioinformatics. It plays a key role in establishing structural, functional and evolutionary relationships between biological sequences. Although the power of the similarity search has increased steadily in recent years, a high percentage of sequences remain uncharacterized in the protein universe. Thus, new similarity search strategies are needed to efficiently and reliably infer the structure and function of new sequences. The existing paradigm for studying protein sequence, structure, function and evolution has been established based on the assumption that the protein universe is discrete and hierarchical. Cumulative evidence suggests that the protein universe is continuous. As a result, conventional sequence homology search methods may be not able to detect novel structural, functional and evolutionary relationships between proteins from weak and noisy sequence signals. To overcome the limitations in existing similarity search methods, we propose a new algorithmic framework-Enrichment of Network Topological Similarity (ENTS)-to improve the performance of large scale similarity searches in bioinformatics. RESULTS: We apply ENTS to a challenging unsolved problem: protein fold recognition. Our rigorous benchmark studies demonstrate that ENTS considerably outperforms state-of-the-art methods. As the concept of ENTS can be applied to any similarity metric, it may provide a general framework for similarity search on any set of biological entities, given their representation as a network. AVAILABILITY AND IMPLEMENTATION: Source code freely available upon request CONTACT: : lxie@iscb.org.
Authors: Jason Weston; Andre Elisseeff; Dengyong Zhou; Christina S Leslie; William Stafford Noble Journal: Proc Natl Acad Sci U S A Date: 2004-04-15 Impact factor: 11.205
Authors: S F Altschul; T L Madden; A A Schäffer; J Zhang; Z Zhang; W Miller; D J Lipman Journal: Nucleic Acids Res Date: 1997-09-01 Impact factor: 16.971