Prediction of functional specificity determinants from protein sequences using log-likelihood ratios | Zendy

Jimin Pei | Zendy; Wei Cai | Zendy; Lisa N. Kinch | Zendy; Nick V. Grishin | Zendy

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Prediction of functional specificity determinants from protein sequences using log-likelihood ratios

Author(s) -

Jimin Pei,

Wei Cai,

Lisa N. Kinch,

Nick V. Grishin

Publication year - 2005

Publication title -

bioinformatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 3.599

H-Index - 390

eISSN - 1367-4811

pISSN - 1367-4803

DOI - 10.1093/bioinformatics/bti766

Subject(s) - set (abstract data type) , sequence (biology) , multiple sequence alignment , tree (set theory) , computer science , trace (psycholinguistics) , sequence alignment , phylogenetic tree , protein family , position (finance) , protein sequencing , mutual information , value (mathematics) , measure (data warehouse) , biology , artificial intelligence , mathematics , computational biology , data mining , machine learning , genetics , peptide sequence , combinatorics , linguistics , philosophy , finance , gene , economics , programming language

A number of methods have been developed to predict functional specificity determinants in protein families based on sequence information. Most of these methods rely on pre-defined functional subgroups. Manual subgroup definition is difficult because of the limited number of experimentally characterized subfamilies with differing specificity, while automatic subgroup partitioning using computational tools is a non-trivial task and does not always yield ideal results.

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