Journal article
POOL server: machine learning application for functional site prediction in proteins
Bioinformatics (Oxford, England), v 28(15), pp 2078-2079
01 Aug 2012
PMID: 22661648
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
We present an automated web server for partial order optimum likelihood (POOL), a machine learning application that combines computed electrostatic and geometric information for high-performance prediction of catalytic residues from 3D structures. Input features consist of THEMATICS electrostatics data and pocket information from ConCavity. THEMATICS measures deviation from typical, sigmoidal titration behavior to identify functionally important residues and ConCavity identifies binding pockets by analyzing the surface geometry of protein structures. Both THEMATICS and ConCavity (structure only) do not require the query protein to have any sequence or structure similarity to other proteins. Hence, POOL is applicable to proteins with novel folds and engineered proteins. As an additional option for cases where sequence homologues are available, users can include evolutionary information from INTREPID for enhanced accuracy in site prediction.
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Details
- Title
- POOL server: machine learning application for functional site prediction in proteins
- Creators
- Srinivas Somarowthu - Northeastern UniversityMary Jo Ondrechen - Northeastern University
- Publication Details
- Bioinformatics (Oxford, England), v 28(15), pp 2078-2079
- Publisher
- Oxford Univ Press
- Number of pages
- 2
- Grant note
- MCB-0843603; MCB-1158176 / NSF; National Science Foundation (NSF)
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Biochemistry and Molecular Biology
- Web of Science ID
- WOS:000306686400024
- Scopus ID
- 2-s2.0-84865150047
- Other Identifier
- 991020837829704721
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InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Web of Science research areas
- Biochemical Research Methods
- Biotechnology & Applied Microbiology
- Computer Science, Interdisciplinary Applications
- Mathematical & Computational Biology
- Statistics & Probability