Beyond annotation transfer by homology: Novel protein-function prediction methods to assist drug discovery

Yanay Ofran, Marco Punta, Reinhard Schneider, Burkhard Rost

Research output: Contribution to journalReview articlepeer-review

73 Scopus citations

Abstract

Every entirely sequenced genome reveals 100s to 1000s of protein sequences for which the only annotation available is 'hypothetical protein'. Thus, in the human genome and in the genomes of pathogenic agents there could be 1000s of potential, unexplored drug targets. Computational prediction of protein function can play a role in studying these targets. We shall review the challenges, research approaches and recently developed tools in the field of computational function-prediction and we will discuss the ways these issues can change the process of drug discovery.

Original languageEnglish
Pages (from-to)1475-1482
Number of pages8
JournalDrug Discovery Today
Volume10
Issue number21
DOIs
StatePublished - 1 Nov 2005
Externally publishedYes

Bibliographical note

Funding Information:
Thanks to Jinfeng Liu, Rajesh Nair, Andrew Kernytsky and Kazimierz Wrzeszczynski (Columbia University, USA) for their help in preparing this manuscript. This work was supported by the grants (RO1-GM64633–01) from the National Institutes of Health (NIH), and (RO1-LM07329–01) from the National Library of Medicine (NLM). Last, but not least, thanks to the GeneOntology team of Michael Ashburner (Cambridge, UK) for their gargantuan effort, to Amos Bairoch (SIB, Geneva, Switzerland), Rolf Apweiler (EBI, Hinxton, UK), Phil Bourne (San Diego University, USA) and their crews for maintaining excellent databases and to all experimentalists who enable computational biology by making their data publicly available.

Funding

Thanks to Jinfeng Liu, Rajesh Nair, Andrew Kernytsky and Kazimierz Wrzeszczynski (Columbia University, USA) for their help in preparing this manuscript. This work was supported by the grants (RO1-GM64633–01) from the National Institutes of Health (NIH), and (RO1-LM07329–01) from the National Library of Medicine (NLM). Last, but not least, thanks to the GeneOntology team of Michael Ashburner (Cambridge, UK) for their gargantuan effort, to Amos Bairoch (SIB, Geneva, Switzerland), Rolf Apweiler (EBI, Hinxton, UK), Phil Bourne (San Diego University, USA) and their crews for maintaining excellent databases and to all experimentalists who enable computational biology by making their data publicly available.

FundersFunder number
National Institutes of HealthRO1-LM07329–01
National Institute of General Medical SciencesR01GM064633
U.S. National Library of MedicineR01LM007329

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