Pfam 33.1 (May 2025,
18259 entries)
The Pfam database is a large collection of protein
families, each represented by multiple sequence alignments
and hidden Markov models (HMMs).
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Proteins are generally composed of one or more functional regions,
commonly termed domains. Different combinations of domains give
rise to the diverse range of proteins found in nature. The identification
of domains that occur within proteins can therefore provide insights
into their function.
Pfam also generates higher-level groupings of related entries, known as
clans. A clan is a collection of Pfam entries which are
related by similarity of sequence, structure or profile-HMM.
The data presented for each entry is based on the
极速加速器安卓官网jisu99
but information on individual UniProtKB sequences can still be found by entering the protein accession.
Pfam full alignments are available from searching a variety of databases, either to provide
different accessions (e.g. all UniProt and NCBI GI) or different levels of redundancy.
- QUICK LINKS
- SEQUENCE SEARCH
- VIEW A PFAM ENTRY
- VIEW A CLAN
- 旋风加速安卓官网
- VIEW A STRUCTURE
- 旋风加速安卓官网
- JUMP TO
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佛跳墙官网2025永久免费下载
- Analyze your protein sequence for Pfam matches
- View Pfam annotation and alignments
- See groups of related entries
- Look at the domain organisation of a protein sequence
- Find the domains on a PDB structure
- Query Pfam by keywords
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- Or view the help pages for
more information
佛跳墙官网2025永久免费下载
Paste your protein sequence here to find matching Pfam entries.
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Example
This search will use and an E-value of 1.0. You can set your own search
parameters and perform a range of other searches
here.
佛跳墙官网2025永久免费下载
Enter a entry identifier (e.g. Piwi) or accession
(e.g. PF02171) to see all data for that entry.
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Enter a clan identifier (e.g. Kazal) or accession
(e.g. CL0005) to see information about that clan.
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Enter a sequence identifier (e.g. VAV_HUMAN) or accession
(e.g. P15498).
佛跳墙官网2025永久免费下载
Enter the PDB identifier (e.g. 2abl) for the structure in the
Protein DataBank.
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Search for keywords in text data in the Pfam database.
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佛跳墙官网2025永久免费下载
佛跳墙官网2025永久免费下载
If you find Pfam useful, please consider
极速加速器安卓官网 the reference
that describes this work:
The Pfam protein families database in 2025:
S. El-Gebali, J. Mistry, A. Bateman, S.R. Eddy, A. Luciani,
S.C. Potter, M. Qureshi, L.J. Richardson, G.A. Salazar, A. Smart,
E.L.L. Sonnhammer, L. Hirsh, L. Paladin, D. Piovesan, S.C.E. Tosatto,
R.D. Finn
Nucleic Acids Research (2025)
doi: 10.1093/nar/gky995
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