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Journal Article

Protein ranking: from local to global structure in the protein similarity network

MPS-Authors
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Weston,  J
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Elisseeff,  A
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

/persons/resource/persons84330

Zhou,  D
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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https://www.pnas.org/content/pnas/101/17/6559.full.pdf
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Citation

Weston, J., Elisseeff, A., Zhou, D., Leslie, C., & Noble, W. (2004). Protein ranking: from local to global structure in the protein similarity network. Proceedings of the National Academy of Sciences of the United States of America, 101(17), 6559-6563. doi:10.1073/pnas.0308067101.


Cite as: https://hdl.handle.net/11858/00-001M-0000-0013-F3CF-B
Abstract
Biologists regularly search databases of DNA or protein sequences for evolutionary or functional relationships to a given query sequence. We describe a ranking algorithm that exploits the entire network structure of similarity relationships among proteins in a sequence database by performing a diffusion operation on a pre-computed, weighted network. The resulting ranking algorithm, evaluated using a human-curated database of protein structures, is efficient and provides significantly better rankings than a local network search algorithm such as PSI-BLAST.