Pfam

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Pfam
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Content
DescriptionThe Pfam database provides alignments and hidden Markov models for protein domains.
Data types
captured
Protein families
Organisms all
Contact
Research center EBI
Primary citation PMID   19920124
Access
Data format Stockholm format
Website www.ebi.ac.uk/interpro/entry/pfam/#table
Download URL FTP
Miscellaneous
License GNU Lesser General Public License
Version37.0
Bookmarkable
entities
yes

Pfam is a database of protein families that includes their annotations and multiple sequence alignments generated using hidden Markov models. [1] [2] [3] The latest version of Pfam, 37.0, was released in June 2024 and contains 21,979 families. [4] It is currently provided through InterPro website.

Contents

Uses

The general purpose of the Pfam database is to provide a complete and accurate classification of protein families and domains. [5] Originally, the rationale behind creating the database was to have a semi-automated method of curating information on known protein families to improve the efficiency of annotating genomes. [6] The Pfam classification of protein families has been widely adopted by biologists because of its wide coverage of proteins and sensible naming conventions. [7]

It is used by experimental biologists researching specific proteins, by structural biologists to identify new targets for structure determination, by computational biologists to organise sequences and by evolutionary biologists tracing the origins of proteins. [8] Early genome projects, such as human and fly used Pfam extensively for functional annotation of genomic data. [9] [10] [11]

The InterPro website allows users to submit protein or DNA sequences to search for matches to families in the Pfam database. If DNA is submitted, a six-frame translation is performed, then each frame is searched. [12] Rather than performing a typical BLAST search, Pfam uses profile hidden Markov models, which give greater weight to matches at conserved sites, allowing better remote homology detection, making them more suitable for annotating genomes of organisms with no well-annotated close relatives. [13]

Pfam has also been used in the creation of other resources such as iPfam, which catalogs domain-domain interactions within and between proteins, based on information in structure databases and mapping of Pfam domains onto these structures. [14]

Features

For each family in Pfam one can:

Entries can be of several types: family, domain, repeat or motif. Family is the default class, which simply indicates that members are related. Domains are defined as an autonomous structural unit or reusable sequence unit that can be found in multiple protein contexts. Repeats are not usually stable in isolation, but rather are usually required to form tandem repeats in order to form a domain or extended structure. Motifs are usually shorter sequence units found outside of globular domains. [9]

The descriptions of Pfam families are managed by the general public using Wikipedia (see #Community curation).

As of release 29.0, 76.1% of protein sequences in UniprotKB matched to at least one Pfam domain. [15]

Creation of new entries

New families come from a range of sources, primarily the PDB and analysis of complete proteomes to find genes with no Pfam hit. [16]

For each family, a representative subset of sequences are aligned into a high-quality seed alignment. Sequences for the seed alignment are taken primarily from pfamseq (a non-redundant database of reference proteomes) with some supplementation from UniprotKB. [15] This seed alignment is then used to build a profile hidden Markov model using HMMER. This HMM is then searched against sequence databases, and all hits that reach a curated gathering threshold are classified as members of the protein family. The resulting collection of members is then aligned to the profile HMM to generate a full alignment.

For each family, a manually curated gathering threshold is assigned that maximises the number of true matches to the family while excluding any false positive matches. False positives are estimated by observing overlaps between Pfam family hits that are not from the same clan. This threshold is used to assess whether a match to a family HMM should be included in the protein family. Upon each update of Pfam, gathering thresholds are reassessed to prevent overlaps between new and existing families. [16]

Domains of unknown function

Domains of unknown function (DUFs) represent a growing fraction of the Pfam database. The families are so named because they have been found to be conserved across species, but perform an unknown role. Each newly added DUF is named in order of addition. Names of these entries are updated as their functions are identified. Normally when the function of at least one protein belonging to a DUF has been determined, the function of the entire DUF is updated and the family is renamed. Some named families are still domains of unknown function, that are named after a representative protein, e.g. YbbR. Numbers of DUFs are expected to continue increasing as conserved sequences of unknown function continue to be identified in sequence data. It is expected that DUFs will eventually outnumber families of known function. [16]

Clans

Over time both sequence and residue coverage have increased, and as families have grown, more evolutionary relationships have been discovered, allowing the grouping of families into clans. [8] Clans were first introduced to the Pfam database in 2005. They are groupings of related families that share a single evolutionary origin, as confirmed by structural, functional, sequence and HMM comparisons. [5] As of release 29.0, approximately one third of protein families belonged to a clan. [15] This portion has grown to around three-fourths by 2019 (version 32.0). [17]

To identify possible clan relationships, Pfam curators use the Simple Comparison Of Outputs Program (SCOOP) as well as information from the ECOD database. [17] ECOD is a semi-automated hierarchical database of protein families with known structures, with families that map readily to Pfam entries and homology levels that usually map to Pfam clans. [18]

History

Pfam was founded in 1995 by Erik Sonnhammer, Sean Eddy and Richard Durbin as a collection of commonly occurring protein domains that could be used to annotate the protein coding genes of multicellular animals. [6] One of its major aims at inception was to aid in the annotation of the C. elegans genome. [6] The project was partly driven by the assertion in ‘One thousand families for the molecular biologist’ by Cyrus Chothia that there were around 1500 different families of proteins and that the majority of proteins fell into just 1000 of these. [5] [19] Counter to this assertion, the Pfam database currently contains 16,306 entries corresponding to unique protein domains and families. However, many of these families contain structural and functional similarities indicating a shared evolutionary origin (see Clans). [5]

A major point of difference between Pfam and other databases at the time of its inception was the use of two alignment types for entries: a smaller, manually checked seed alignment, as well as a full alignment built by aligning sequences to a profile hidden Markov model built from the seed alignment. This smaller seed alignment was easier to update as new releases of sequence databases came out, and thus represented a promising solution to the dilemma of how to keep the database up to date as genome sequencing became more efficient and more data needed to be processed over time. A further improvement to the speed at which the database could be updated came in version 24.0, with the introduction of HMMER3, which is ~100 times faster than HMMER2 and more sensitive. [8]

Because the entries in Pfam-A do not cover all known proteins, an automatically generated supplement was provided called Pfam-B. Pfam-B contained a large number of small families derived from clusters produced by an algorithm called ADDA. [20] Although of lower quality, Pfam-B families could be useful when no Pfam-A families were found. Pfam-B was discontinued as of release 28.0, [21] then reintroduced in release 33.1 using a new clustering algorithm, MMSeqs2. [22]

Pfam was originally hosted on three mirror sites around the world to preserve redundancy. However between 2012 and 2014, the Pfam resource was moved to EMBL-EBI, which allowed for hosting of the website from one domain (xfam.org), using duplicate independent data centres. This allowed for better centralisation of updates, and grouping with other Xfam projects such as Rfam, TreeFam, iPfam and others, whilst retaining critical resilience provided by hosting from multiple centres. [23]

From circa 2014 to 2016, Pfam underwent a substantial reorganisation to further reduce manual effort involved in curation and allow for more frequent updates. [15] Circa 2022, Pfam was integrated into InterPro at the European Bioinformatics Institute. [24]

Community curation

Curation of such a large database presented issues in terms of keeping up with the volume of new families and updated information that needed to be added. To speed up releases of the database, the developers started a number of initiatives to allow greater community involvement in managing the database.

A critical step in improving the pace of updating and improving entries was to open up the functional annotation of Pfam domains to the Wikipedia community in release 26.0. [16] For entries that already had a Wikipedia entry, this was linked into the Pfam page, and for those that did not, the community were invited to create one and inform the curators, in order for it to be linked in. It is anticipated that while community involvement will greatly improve the level of annotation of these families, some will remain insufficiently notable for inclusion in Wikipedia, in which case they will retain their original Pfam description. Some Wikipedia articles cover multiple families, such as the Zinc finger article. An automated procedure for generating articles based on InterPro and Pfam data has also been implemented, which populates a page with information and links to databases as well as available images, then once an article has been reviewed by a curator it is moved from the Sandbox to Wikipedia proper. In order to guard against vandalism of articles, each Wikipedia revision is reviewed by curators before it is displayed on the Pfam website. Almost all cases of vandalism have been corrected by the community before they reach curators, however. [16]

Pfam is run by an international consortium of three groups. In the earlier releases of Pfam, family entries could only be modified at the Cambridge, UK site, limiting the ability of consortium members to contribute to site curation. In release 26.0, developers moved to a new system that allowed registered users anywhere in the world to add or modify Pfam families. [16]

See also

Related Research Articles

<span class="mw-page-title-main">Structural Classification of Proteins database</span> Biological database of proteins

The Structural Classification of Proteins (SCOP) database is a largely manual classification of protein structural domains based on similarities of their structures and amino acid sequences. A motivation for this classification is to determine the evolutionary relationship between proteins. Proteins with the same shapes but having little sequence or functional similarity are placed in different superfamilies, and are assumed to have only a very distant common ancestor. Proteins having the same shape and some similarity of sequence and/or function are placed in "families", and are assumed to have a closer common ancestor.

InterPro is a database of protein families, protein domains and functional sites in which identifiable features found in known proteins can be applied to new protein sequences in order to functionally characterise them.

Rfam is a database containing information about non-coding RNA (ncRNA) families and other structured RNA elements. It is an annotated, open access database originally developed at the Wellcome Trust Sanger Institute in collaboration with Janelia Farm, and currently hosted at the European Bioinformatics Institute. Rfam is designed to be similar to the Pfam database for annotating protein families.

<span class="mw-page-title-main">MicrobesOnline</span>

MicrobesOnline is a publicly and freely accessible website that hosts multiple comparative genomic tools for comparing microbial species at the genomic, transcriptomic and functional levels. MicrobesOnline was developed by the Virtual Institute for Microbial Stress and Survival, which is based at the Lawrence Berkeley National Laboratory in Berkeley, California. The site was launched in 2005, with regular updates until 2011.

Stockholm format is a multiple sequence alignment format used by Pfam, Rfam and Dfam, to disseminate protein, RNA and DNA sequence alignments. The alignment editors Ralee, Belvu and Jalview support Stockholm format as do the probabilistic database search tools, Infernal and HMMER, and the phylogenetic analysis tool Xrate. Stockholm format files often have the filename extension .sto or .stk.

<span class="mw-page-title-main">HMMER</span> Software package for sequence analysis

HMMER is a free and commonly used software package for sequence analysis written by Sean Eddy. Its general usage is to identify homologous protein or nucleotide sequences, and to perform sequence alignments. It detects homology by comparing a profile-HMM to either a single sequence or a database of sequences. Sequences that score significantly better to the profile-HMM compared to a null model are considered to be homologous to the sequences that were used to construct the profile-HMM. Profile-HMMs are constructed from a multiple sequence alignment in the HMMER package using the hmmbuild program. The profile-HMM implementation used in the HMMER software was based on the work of Krogh and colleagues. HMMER is a console utility ported to every major operating system, including different versions of Linux, Windows, and macOS.

SUPERFAMILY is a database and search platform of structural and functional annotation for all proteins and genomes. It classifies amino acid sequences into known structural domains, especially into SCOP superfamilies. Domains are functional, structural, and evolutionary units that form proteins. Domains of common Ancestry are grouped into superfamilies. The domains and domain superfamilies are defined and described in SCOP. Superfamilies are groups of proteins which have structural evidence to support a common evolutionary ancestor but may not have detectable sequence homology.

<span class="mw-page-title-main">Richard M. Durbin</span> British computational biologist

Richard Michael Durbin is a British computational biologist and Al-Kindi Professor of Genetics at the University of Cambridge. He also serves as an associate faculty member at the Wellcome Sanger Institute where he was previously a senior group leader.

A domain of unknown function (DUF) is a protein domain that has no characterised function. These families have been collected together in the Pfam database using the prefix DUF followed by a number, with examples being DUF2992 and DUF1220. As of 2019, there are almost 4,000 DUF families within the Pfam database representing over 22% of known families. Some DUFs are not named using the nomenclature due to popular usage but are nevertheless DUFs.

Protein function prediction methods are techniques that bioinformatics researchers use to assign biological or biochemical roles to proteins. These proteins are usually ones that are poorly studied or predicted based on genomic sequence data. These predictions are often driven by data-intensive computational procedures. Information may come from nucleic acid sequence homology, gene expression profiles, protein domain structures, text mining of publications, phylogenetic profiles, phenotypic profiles, and protein-protein interaction. Protein function is a broad term: the roles of proteins range from catalysis of biochemical reactions to transport to signal transduction, and a single protein may play a role in multiple processes or cellular pathways.

<span class="mw-page-title-main">Sean Eddy</span> American professor at Harvard University

Sean Roberts Eddy is Professor of Molecular & Cellular Biology and of Applied Mathematics at Harvard University. Previously he was based at the Janelia Research Campus from 2006 to 2015 in Virginia. His research interests are in bioinformatics, computational biology and biological sequence analysis. As of 2016 projects include the use of Hidden Markov models in HMMER, Infernal Pfam and Rfam.

OMPdb is a dedicated database that contains beta barrel (β-barrel) outer membrane proteins from Gram-negative bacteria. Such proteins are responsible for a broad range of important functions, like passive nutrient uptake, active transport of large molecules, protein secretion, as well as adhesion to host cells, through which bacteria expose their virulence activity.

<span class="mw-page-title-main">ProtCID</span>

The Protein Common Interface Database (ProtCID) is a database of similar protein-protein interfaces in crystal structures of homologous proteins.

<span class="mw-page-title-main">Rolf Apweiler</span> German bioinformatician

Rolf Apweiler is a director of European Bioinformatics Institute (EBI) part of the European Molecular Biology Laboratory (EMBL) with Ewan Birney.

αr9 is a family of bacterial small non-coding RNAs with representatives in a broad group of α-proteobacteria from the order Hyphomicrobiales. The first member of this family (Smr9C) was found in a Sinorhizobium meliloti 1021 locus located in the chromosome (C). Further homology and structure conservation analysis have identified full-length Smr9C homologs in several nitrogen-fixing symbiotic rhizobia, in the plant pathogens belonging to Agrobacterium species as well as in a broad spectrum of Brucella species. αr9C RNA species are 144-158 nt long and share a well defined common secondary structure consisting of seven conserved regions. Most of the αr9 transcripts can be catalogued as trans-acting sRNAs expressed from well-defined promoter regions of independent transcription units within intergenic regions (IGRs) of the α-proteobacterial genomes.

<span class="mw-page-title-main">Alex Bateman</span> British bioinformatician

Alexander George Bateman is a computational biologist and Head of Protein Sequence Resources at the European Bioinformatics Institute (EBI), part of the European Molecular Biology Laboratory (EMBL) in Cambridge, UK. He has led the development of the Pfam biological database and introduced the Rfam database of RNA families. He has also been involved in the use of Wikipedia for community-based annotation of biological databases.

Julian John Thurstan Gough was a Group Leader in the Laboratory of Molecular Biology (LMB) of the Medical Research Council (MRC). He was previously a professor of bioinformatics at the University of Bristol.

<span class="mw-page-title-main">Christine Orengo</span> Professor of Bioinformatics

Christine Anne Orengo is a Professor of Bioinformatics at University College London (UCL) known for her work on protein structure, particularly the CATH database. Orengo serves as president of the International Society for Computational Biology (ISCB), the first woman to do so in the history of the society.

Major facilitator superfamily domain containing 3 (MFSD3) is a protein belonging to the MFS Pfam clan. It is an Atypical solute carrier located to the neuronal plasma membrane.

<span class="mw-page-title-main">Protein tandem repeats</span>

An array of protein tandem repeats is defined as several adjacent copies having the same or similar sequence motifs. These periodic sequences are generated by internal duplications in both coding and non-coding genomic sequences. Repetitive units of protein tandem repeats are considerably diverse, ranging from the repetition of a single amino acid to domains of 100 or more residues.

References

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