Pfam 33.1 (May 2024,
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
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安卓软件-免费软件站-雷霆加速免费永久-光粒加速器用不了了
- 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
安卓软件-免费软件站-雷霆加速免费永久-光粒加速器用不了了
Paste your protein sequence here to find matching Pfam entries.
Go
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.
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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).
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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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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 2024:
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 (2024)
doi: 10.1093/nar/gky995
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