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The Laccase Engineering Database: a classification and analysis system for laccases and related multicopper oxidases.

Database : the journal of biological databases and curation | 2011

Laccases and their homologues form the protein superfamily of multicopper oxidases (MCO). They catalyze the oxidation of many, particularly phenolic substances, and, besides playing an important role in many cellular activities, are of interest in biotechnological applications. The Laccase Engineering Database (LccED, http://www.lcced.uni-stuttgart.de) was designed to serve as a tool for a systematic sequence-based classification and analysis of the diverse multicopper oxidase protein family. More than 2200 proteins were classified into 11 superfamilies and 56 homologous families. For each family, the LccED provides multiple sequence alignments, phylogenetic trees and family-specific HMM profiles. The integration of structures for 14 different proteins allows a comprehensive comparison of sequences and structures to derive biochemical properties. Among the families, the distribution of the proteins regarding different kingdoms was investigated. The database was applied to perform a comprehensive analysis by MCO- and laccase-specific patterns. The LccED combines information of sequences and structures of MCOs. It serves as a classification tool to assign new proteins to a homologous family and can be applied to investigate sequence-structure-function relationship and to guide protein engineering. Database URL: http://www.lcced.uni-stuttgart.de.

Pubmed ID: 21498547 RIS Download

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Hmmer (tool)

RRID:SCR_005305

Tool for searching sequence databases for homologs of protein sequences, and for making protein sequence alignments. It implements methods using probabilistic models called profile hidden Markov models (profile HMMs). Compared to BLAST, FASTA, and other sequence alignment and database search tools based on older scoring methodology, HMMER aims to be significantly more accurate and more able to detect remote homologs because of the strength of its underlying mathematical models. In the past, this strength came at significant computational expense, but in the new HMMER3 project, HMMER is now essentially as fast as BLAST.

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