The Pfam protein families database in 2019

By Sara El-Gebali, Jaina Mistry, Alex Bateman, Sean R. Eddy, Aurélien Luciani, Simon C. Potter, Matloob Qureshi, Lorna J. Richardson, Gustavo A. Salazar, A. Smart and 6 more. 2018

4,015 citations recorded by the source corpus.

Abstract 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.

Subfields: Information Systems and Management, Molecular Biology, Spectroscopy, Computational Biology & Bioinformatics

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Citations in the corpus

BERTology Meets Biology: Interpreting Attention in Protein Language Models (2020, methodology): “In particular, we use the BERT-Base model from the TAPE repository [60], which was pretrained on masked language modeling of amino acids over a dataset of 31 million protein sequences [22].”
Evolutionary context-integrated deep sequence modeling for protein engineering (2020, methodology): “However, as these models are trained on massive sequences such as those in UniProt [31] and Pfam [32] , the learned representations only capture general context for a wide spectrum of proteins but may not be specific to the protein to be engineered.”
Evaluating Protein Transfer Learning with TAPE (2019, methodology): “We use Pfam [35], a database of thirty-one million protein domains used extensively in bioinformatics, as the pretraining corpus for TAPE.”
MGnify: the microbiome analysis resource in 2020 (2019, methodology): “The resultant protein sequences undergo functional predictions as described for raw reads, producing KO, InterPro, Pfam and GO term annotations.”
Profile Prediction: An Alignment-Based Pre-Training Task for Protein Sequence Models (2020, methodology): “To evaluate our pre-training task we make use of the TAPE benchmark: a set of five standardized protein sequence prediction tasks with associated datasets plus a large unlabeled pre-training dataset derived from Pfam (Rao et al., 2019).”
Evolutionary velocity with protein language models (2021, positive): “, 2019), trained with a different model architecture on the Pfam database of protein families (El-Gebali et al., 2019), closely reproduced the ESM1b evo-velocity results (Spearman r = 0.”
Intrinsic-Extrinsic Convolution and Pooling for Learning on 3D Protein Structures (2021, methodology): “Previously, the SOTA was dominated by methods based on hand-crafted features, usually extracted from multi-sequence alignment tools (Altschul et al., 1990) or annotated databases (El-Gebali et al., 2019).”
IMG/VR v3: an integrated ecological and evolutionary framework for interrogating genomes of uncultivated viruses (2020, methodology): “PFAM affiliation were obtained from the IMG/M database, marker VOGs were obtained from the taxonomic classification pipeline (see above), while for VPFs, a comparison of all IMG/VR UViGs predicted proteins to the VPF database was performed using hmmsearch v3.”