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The last few years have witnessed significant changes in Pfam (https://pfam.xfam.org). The number of families has grown substantially to a total of 17,929 in release 32.0. New additions have been coupled with efforts to improve existing families, including refinement of domain boundaries, their classification into Pfam clans, as well as their functional annotation. We recently began to collaborate with the RepeatsDB resource to improve the definition of tandem repeat families within Pfam. We carried out a significant comparison to the structural classification database, namely the Evolutionary Classification of Protein Domains (ECOD) that led to the creation of 825 new families based on their set of uncharacterized families (EUFs). Furthermore, we also connected Pfam entries to the Sequence Ontology (SO) through mapping of the Pfam type definitions to SO terms. Since Pfam has many community contributors, we recently enabled the linking between authorship of all Pfam entries with the corresponding authors' ORCID identifiers. This effectively permits authors to claim credit for their Pfam curation and link them to their ORCID record.

In the last two years the Pfam database (http://pfam.xfam.org) has undergone a substantial reorganisation to reduce the effort involved in making a release, thereby permitting more frequent releases. Arguably the most significant of these changes is that Pfam is now primarily based on the UniProtKB reference proteomes, with the counts of matched sequences and species reported on the website restricted to this smaller set. Building families on reference proteomes sequences brings greater stability, which decreases the amount of manual curation required to maintain them. It also reduces the number of sequences displayed on the website, whilst still providing access to many important model organisms. Matches to the full UniProtKB database are, however, still available and Pfam annotations for individual UniProtKB sequences can still be retrieved. Some Pfam entries (1.6%) which have no matches to reference proteomes remain; we are working with UniProt to see if sequences from them can be incorporated into reference proteomes. Pfam-B, the automatically-generated supplement to Pfam, has been removed. The current release (Pfam 29.0) includes 16 295 entries and 559 clans. The facility to view the relationship between families within a clan has been improved by the introduction of a new tool., (© The Author(s) 2015. Published by Oxford University Press on behalf of Nucleic Acids Research.)

Pfam is a widely used database of protein families, currently containing more than 13,000 manually curated protein families as of release 26.0. Pfam is available via servers in the UK (http://pfam.sanger.ac.uk/), the USA (http://pfam.janelia.org/) and Sweden (http://pfam.sbc.su.se/). Here, we report on changes that have occurred since our 2010 NAR paper (release 24.0). Over the last 2 years, we have generated 1840 new families and increased coverage of the UniProt Knowledgebase (UniProtKB) to nearly 80%. Notably, we have taken the step of opening up the annotation of our families to the Wikipedia community, by linking Pfam families to relevant Wikipedia pages and encouraging the Pfam and Wikipedia communities to improve and expand those pages. We continue to improve the Pfam website and add new visualizations, such as the 'sunburst' representation of taxonomic distribution of families. In this work we additionally address two topics that will be of particular interest to the Pfam community. First, we explain the definition and use of family-specific, manually curated gathering thresholds. Second, we discuss some of the features of domains of unknown function (also known as DUFs), which constitute a rapidly growing class of families within Pfam.

Background: Profile hidden Markov models (profile-HMMs) are sensitive tools for remote protein homology detection, but the main scoring algorithms, Viterbi or Forward, require considerable time to search large sequence databases., Results: We have designed a series of database filtering steps, HMMERHEAD, that are applied prior to the scoring algorithms, as implemented in the HMMER package, in an effort to reduce search time. Using this heuristic, we obtain a 20-fold decrease in Forward and a 6-fold decrease in Viterbi search time with a minimal loss in sensitivity relative to the unfiltered approaches. We then implemented an iterative profile-HMM search method, JackHMMER, which employs the HMMERHEAD heuristic. Due to our search heuristic, we eliminated the subdatabase creation that is common in current iterative profile-HMM approaches. On our benchmark, JackHMMER detects 14% more remote protein homologs than SAM's iterative method T2K., Conclusions: Our search heuristic, HMMERHEAD, significantly reduces the time needed to score a profile-HMM against large sequence databases. This search heuristic allowed us to implement an iterative profile-HMM search method, JackHMMER, which detects significantly more remote protein homologs than SAM's T2K and NCBI's PSI-BLAST.

Pfam is a comprehensive collection of protein domains and families, represented as multiple sequence alignments and as profile hidden Markov models. The current release of Pfam (22.0) contains 9318 protein families. Pfam is now based not only on the UniProtKB sequence database, but also on NCBI GenPept and on sequences from selected metagenomics projects. Pfam is available on the web from the consortium members using a new, consistent and improved website design in the UK (http://pfam.sanger.ac.uk/), the USA (http://pfam.janelia.org/) and Sweden (http://pfam.sbc.su.se/), as well as from mirror sites in France (http://pfam.jouy.inra.fr/) and South Korea (http://pfam.ccbb.re.kr/).

Pfam is a database of protein families that currently contains 7973 entries (release 18.0). A recent development in Pfam has enabled the grouping of related families into clans. Pfam clans are described in detail, together with the new associated web pages. Improvements to the range of Pfam web tools and the first set of Pfam web services that allow programmatic access to the database and associated tools are also presented. Pfam is available on the web in the UK (http://www.sanger.ac.uk/Software/Pfam/), the USA (http://pfam.wustl.edu/), France (http://pfam.jouy.inra.fr/) and Sweden (http://pfam.cgb.ki.se/).

Many sequence alignment programs use the BLOSUM62 score matrix to score pairs of aligned residues. Where did BLOSUM62 come from?

Pfam is a large collection of protein families and domains. Over the past 2 years the number of families in Pfam has doubled and now stands at 6190 (version 10.0). Methodology improvements for searching the Pfam collection locally as well as via the web are described. Other recent innovations include modelling of discontinuous domains allowing Pfam domain definitions to be closer to those found in structure databases. Pfam is available on the web in the UK (http://www.sanger.ac.uk/Software/Pfam/), the USA (http://pfam.wustl.edu/), France (http://pfam.jouy.inra.fr/) and Sweden (http://Pfam.cgb.ki.se/).

Pfam is a large collection of protein multiple sequence alignments and profile hidden Markov models. Pfam is available on the World Wide Web in the UK at http://www.sanger.ac.uk/Software/Pfam/, in Sweden at http://www.cgb.ki.se/Pfam/, in France at http://pfam.jouy.inra.fr/ and in the US at http://pfam.wustl.edu/. The latest version (6.6) of Pfam contains 3071 families, which match 69% of proteins in SWISS-PROT 39 and TrEMBL 14. Structural data, where available, have been utilised to ensure that Pfam families correspond with structural domains, and to improve domain-based annotation. Predictions of non-domain regions are now also included. In addition to secondary structure, Pfam multiple sequence alignments now contain active site residue mark-up. New search tools, including taxonomy search and domain query, greatly add to the functionality and usability of the Pfam resource.

Background: Profile hidden Markov models (profile-HMMs) are sensitive tools for remote protein homology detection, but the main scoring algorithms, Viterbi or Forward, require considerable time to search large sequence databases. Results: We have designed a series of database filtering steps, HMMERHEAD, that are applied prior to the scoring algorithms, as implemented in the HMMER package, in an effort to reduce search time. Using this heuristic, we obtain a 20-fold decrease in Forward and a 6-fold decrease in Viterbi search time with a minimal loss in sensitivity relative to the unfiltered approaches. We then implemented an iterative profile-HMM search method, JackHMMER, which employs the HMMERHEAD heuristic. Due to our search heuristic, we eliminated the subdatabase creation that is common in current iterative profile-HMM approaches. On our benchmark, JackHMMER detects 14% more remote protein homologs than SAM's iterative method T2K. Conclusions: Our search heuristic, HMMERHEAD, significantly reduces the time needed to score a profile-HMM against large sequence databases. This search heuristic allowed us to implement an iterative profile-HMM search method, JackHMMER, which detects significantly more remote protein homologs than SAM's T2K and NCBI's PSIBLAST. [ABSTRACT FROM AUTHOR]

Abstract: Two-dimensional gel electrophoresis was used to assess differential protein expression between euthermic and hibernating states in heart of Myotis lucifugus. A hibernation-induced protein was identified by mass spectrometry as a thioredoxin peroxidase-like protein known as PAG. Western blotting confirmed up-regulation (>2-fold) and RT-PCR also revealed up-regulation (>5-fold) of pag mRNA. Cloning revealed a highly conserved sequence suggesting a conserved function for PAG. Oxidative stress markers, p-IκB-α (Ser 32) and p-HSP27 (Ser 78/82), were also up-regulated in heart and skeletal muscle during hibernation. Although there are selected increases in gene/protein expression during hibernation, general translation inhibition occurs as part of metabolic rate depression. This was confirmed by elevated levels of the inactive forms of the eIF2α (Ser 51) in both heart and skeletal muscle (2- to 5-fold higher than in euthermia) and the eEF2 (Thr 51) in skeletal muscle (a 15-fold increase). This study suggests that hibernators may use up-regulation of specific proteins to counteract oxidative stress. [Copyright &y& Elsevier]

Summary: Sequence database searches are an essential part of molecular biology, providing information about the function and evolutionary history of proteins, RNA molecules and DNA sequence elements. We present a tool for DNA/DNA sequence comparison that is built on the HMMER framework, which applies probabilistic inference methods based on hidden Markov models to the problem of homology search. This tool, called nhmmer, enables improved detection of remote DNA homologs, and has been used in combination with Dfam and RepeatMasker to improve annotation of transposable elements in the human genome.Availability: nhmmer is a part of the new HMMER3.1 release. Source code and documentation can be downloaded from http://hmmer.org. HMMER3.1 is freely licensed under the GNU GPLv3 and should be portable to any POSIX-compliant operating system, including Linux and Mac OS/X.Contact: wheelert@janelia.hhmi.org [ABSTRACT FROM AUTHOR]

Aberrant constitutive expression of NF-κB subunits, reported in more than 90% of breast cancers and multiple other malignancies, plays pivotal roles in tumorigenesis. Higher RelB subunit expression was demonstrated in estrogen receptor alpha (ER)α-negative breast cancers versus ERα-positive ones, due in part to repression of RelB synthesis by ERα signaling. Notably, RelB promoted a more invasive phenotype in ERα-negative cancers via induction of the BCL2 gene. We report here that RelB reciprocally inhibits ERα synthesis in breast cancer cells, which contributes to a more migratory phenotype. Specifically, RelB is shown for the first time to induce expression of the zinc finger repressor protein Blimp1 (B-lymphocyte-induced maturation protein), the critical mediator of B- and T-cell development, which is transcribed from the PRDM1 gene. Blimp1 protein repressed ERα (ESR1) gene transcription. Commensurately higher Blimp1/PRDM1 expression was detected in ERα-negative breast cancer cells and primary breast tumors. Induction of PRDM1 gene expression was mediated by interaction of Bcl-2, localized in the mitochondria, with Ras. Thus, the induction of Blimp1 represents a novel mechanism whereby the RelB NFκ-B subunit mediates repression, specifically of ERα, thereby promoting a more migratory phenotype. [ABSTRACT FROM AUTHOR]

Fragile-X mental retardation is caused by loss of function of a single gene encoding the Fragile-X mental retardation protein, FMRP, an RNA-binding protein that harbors two KH-type and one RGG-type RNA-binding domains. Previous studies identified intramolecular G-quartet RNAs as high-affinity targets for the RGG box, but the relationship of RNA binding to FMRP function and mental retardation remains unclear. One severely affected patient harbors a missense mutation (I304N) within the second KH domain (KH2), and some evidence suggests this domain may be involved in the proposed role of FMRP in translational regulation. We now identify the RNA target for the KH2 domain as a sequence-specific element within a complex tertiary structure termed the FMRP kissing complex. We demonstrate that the association of FMRP with brain polyribosomes is abrogated by competition with the FMRP kissing complex RNA, but not by high-affinity G-quartet RNAs. We conclude that mental retardation associated with the I304N mutation, and likely the Fragile-X syndrome more generally, may relate to a crucial role for RNAs harboring the kissing complex motif as targets for FMRP translational regulation. [ABSTRACT FROM AUTHOR]