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Assignment of protein sequences to existing domain and family classification systems: Pfam and the PDB.

ABSTRACT: Automating the assignment of existing domain and protein family classifications to new sets of sequences is an important task. Current methods often miss assignments because remote relationships fail to achieve statistical significance. Some assignments are not as long as the actual domain definitions because local alignment methods often cut alignments short. Long insertions in query sequences often erroneously result in two copies of the domain assigned to the query. Divergent repeat sequences in proteins are often missed.We have developed a multilevel procedure to produce nearly complete assignments of protein families of an existing classification system to a large set of sequences. We apply this to the task of assigning Pfam domains to sequences and structures in the Protein Data Bank (PDB). We found that HHsearch alignments frequently scored more remotely related Pfams in Pfam clans higher than closely related Pfams, thus, leading to erroneous assignment at the Pfam family level. A greedy algorithm allowing for partial overlaps was, thus, applied first to sequence/HMM alignments, then HMM-HMM alignments and then structure alignments, taking care to join partial alignments split by large insertions into single-domain assignments. Additional assignment of repeat Pfams with weaker E-values was allowed after stronger assignments of the repeat HMM. Our database of assignments, presented in a database called PDBfam, contains Pfams for 99.4% of chains >50 residues.The Pfam assignment data in PDBfam are available at http://dunbrack2.fccc.edu/ProtCid/PDBfam, which can be searched by PDB codes and Pfam identifiers. They will be updated regularly.

SUBMITTER: Xu Q

PROVIDER: S-EPMC3476341 | biostudies-literature | 2012 Nov

REPOSITORIES: biostudies-literature

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Assignment of protein sequences to existing domain and family classification systems: Pfam and the PDB.

Xu Qifang Q Dunbrack Roland L RL

Bioinformatics (Oxford, England) 20120831 21

<h4>Motivation</h4>Automating the assignment of existing domain and protein family classifications to new sets of sequences is an important task. Current methods often miss assignments because remote relationships fail to achieve statistical significance. Some assignments are not as long as the actual domain definitions because local alignment methods often cut alignments short. Long insertions in query sequences often erroneously result in two copies of the domain assigned to the query. Diverge ...[more]

PMID: 22942020

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Project description:BACKGROUND: The SET (Su(var)3-9, Enhancer-of-zeste, Trithorax) domain is an evolutionarily conserved sequence of approximately 130-150 amino acids, and constitutes the catalytic site of lysine methyltransferases (KMTs). KMTs perform many crucial biological functions via histone methylation of chromatin. Histone methylation marks are interpreted differently depending on the histone type (i.e. H3 or H4), the lysine position (e.g. H3K4, H3K9, H3K27, H3K36 or H4K20) and the number of added methyl groups (i.e. me1, me2 or me3). For example, H3K4me3 and H3K36me3 are associated with transcriptional activation, but H3K9me2 and H3K27me3 are associated with gene silencing. The substrate specificity and activity of KMTs are determined by sequences within the SET domain and other regions of the protein. RESULTS: Here we identified 49 SET-domain proteins from the recently sequenced Brassica rapa genome. We performed sequence similarity and protein domain organization analysis of these proteins, along with the SET-domain proteins from the dicot Arabidopsis thaliana, the monocots Oryza sativa and Brachypodium distachyon, and the green alga Ostreococcus tauri. We showed that plant SET-domain proteins can be grouped into 6 distinct classes, namely KMT1, KMT2, KMT3, KMT6, KMT7 and S-ET. Apart from the S-ET class, which has an interrupted SET domain and may be involved in methylation of nonhistone proteins, the other classes have characteristics of histone methyltransferases exhibiting different substrate specificities: KMT1 for H3K9, KMT2 for H3K4, KMT3 for H3K36, KMT6 for H3K27 and KMT7 also for H3K4. We also propose a coherent and rational nomenclature for plant SET-domain proteins. Comparisons of sequence similarity and synteny of B. rapa and A. thaliana SET-domain proteins revealed recent gene duplication events for some KMTs. CONCLUSION: This study provides the first characterization of the SET-domain KMT proteins of B. rapa. Phylogenetic analysis data allowed the development of a coherent and rational nomenclature of this important family of proteins in plants, as in animals. The results obtained in this study will provide a base for nomenclature of KMTs in other plant species and facilitate the functional characterization of these important epigenetic regulatory genes in Brassica crops.

Project description:BackgroundThe identification of protein families is of outstanding practical importance for in silico protein annotation and is at the basis of several bioinformatic resources. Pfam is possibly the most well known protein family database, built in many years of work by domain experts with extensive use of manual curation. This approach is generally very accurate, but it is quite time consuming and it may suffer from a bias generated from the hand-curation itself, which is often guided by the available experimental evidence.ResultsWe introduce a procedure that aims to identify automatically putative protein families. The procedure is based on Density Peak Clustering and uses as input only local pairwise alignments between protein sequences. In the experiment we present here, we ran the algorithm on about 4000 full-length proteins with at least one domain classified by Pfam as belonging to the Pseudouridine synthase and Archaeosine transglycosylase (PUA) clan. We obtained 71 automatically-generated sequence clusters with at least 100 members. While our clusters were largely consistent with the Pfam classification, showing good overlap with either single or multi-domain Pfam family architectures, we also observed some inconsistencies. The latter were inspected using structural and sequence based evidence, which suggested that the automatic classification captured evolutionary signals reflecting non-trivial features of protein family architectures. Based on this analysis we identified a putative novel pre-PUA domain as well as alternative boundaries for a few PUA or PUA-associated families. As a first indication that our approach was unlikely to be clan-specific, we performed the same analysis on the P53 clan, obtaining comparable results.ConclusionsThe clustering procedure described in this work takes advantage of the information contained in a large set of pairwise alignments and successfully identifies a set of putative families and family architectures in an unsupervised manner. Comparison with the Pfam classification highlights significant overlap and points to interesting differences, suggesting that our new algorithm could have potential in applications related to automatic protein classification. Testing this hypothesis, however, will require further experiments on large and diverse sequence datasets.

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Dataset Information

Assignment of protein sequences to existing domain and family classification systems: Pfam and the PDB.

Publications

Assignment of protein sequences to existing domain and family classification systems: Pfam and the PDB.

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