Pfam: the protein families database

By Robert D. Finn, Alex Bateman, Jody Clements, Penny C. Coggill, Ruth Y. Eberhardt, Sean R. Eddy, Andreas Heger, Kirstie Hetherington, Liisa Holm, Jaina Mistry and 3 more. 2013

1,372 citations recorded by the source corpus.

Pfam, available via servers in the UK (http://pfam.sanger.ac.uk/) and the USA (http://pfam.janelia.org/), is a widely used database of protein families, containing 14 831 manually curated entries in the current release, version 27.0. Since the last update article 2 years ago, we have generated 1182 new families and maintained sequence coverage of the UniProt Knowledgebase (UniProtKB) at nearly 80%, despite a 50% increase in the size of the underlying sequence database. Since our 2012 article describing Pfam, we have also undertaken a comprehensive review of the features that are provided by Pfam over and above the basic family data. For each feature, we determined the relevance, computational burden, usage statistics and the functionality of the feature in a website context. As a consequence of this review, we have removed some features, enhanced others and developed new ones to meet the changing demands of computational biology. Here, we describe the changes to Pfam content. Notably, we now provide family alignments based on four different representative proteome sequence data sets and a new interactive DNA search interface. We also discuss the mapping between Pfam and known 3D structures.

Subfields: Molecular Biology, Spectroscopy

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Citations in the corpus

Generating functional protein variants with variational autoencoders (2020, methodology): “To create a multiple sequence alignment from the dataset, the full set of training and validation sequences were aligned using Clustal Omega [36] using the profile HMM of the bacterial luciferase family from PFAM [44] as an external profile alignment to guide the creation of the MSA.”
CheckM: assessing the quality of microbial genomes recovered from isolates, single cells, and metagenomes (2015, methodology): “Pfam and TIGRFAMs families were considered redundant if they matched the same genes in >90% of the finished IMG genomes, in which case only the Pfam gene was used as a marker.”
Using deep learning to annotate the protein universe (2019, background): “Broad protein families require muliple HMM profiles to model their diversity [12], while more than 22% of the highly-curated families in Pfam 32.”
Learning protein constitutive motifs from sequence data (2018, methodology): “(10) Fischer and Igel (2012). The naive approach is to run for each gradient iteration a full Markov Chain Monte Carlo (MCMC) simulation of the RBM until the samples reach equilibrium, then use these samples to compute the model average Ackley et al.”
DeepConv-DTI: Prediction of drug-target interactions via deep learning with convolution on protein sequences (2018, background): “For proteins whose domain information is not in Pfam [39], datasets for training, validation and test are not fully available.”
Integrating taxonomic, functional, and strain-level profiling of diverse microbial communities with bioBakery 3 (2020, background): “…annotations from different systems (GO terms (Ashburner et al., 2000) , KEGG modules (Kanehisa et al., 2014) , KO identifiers, Pfam accessions (Finn et al., 2014) , EC numbers (Bairoch, 2000) , and eggNOG accessions (Powell et al., 2014) ) associated with the UniProtKB entry.”
ProteinBERT: a universal deep-learning model of protein sequence and function (2021, methodology): “To this day, protein research is still dominated by classical sequence-similarity methods (such as BLAST [18] and hidden Markov models [19]), in contrast to domains such as computer vision which have become dominated by deep learning.”
DeepAffinity: Interpretable Deep Learning of Compound-Protein Affinity through Unified Recurrent and Convolutional Neural Networks (2018, background): “, 2016)) Pfam domains (Finn et al., 2014) (structural units) and 1’s are assigned based on k-hot encoding (Tabei and Yamanishi, 2013; Cheng et al.”
The extracellular contractile injection system is enriched in environmental microbes and associates with numerous toxins (2020, background): “…evolutionary similarity between eCIS and other contractile machinery, we removed any putative operons with genes annotated with phage-related pfams 61 (Supplementary Table 1), such as capsid genes (e.g., pfam03864, i.e., Phage major capsid protein E), within 10,000 base pairs of any genes in our…”
GTDB: an ongoing census of bacterial and archaeal diversity through a phylogenetically consistent, rank normalized and complete genome-based taxonomy (2021, background): “1 instead of v27 (19) starting with R06-RS202.”