Pfam
Pfam is a comprehensive, open-access database of protein families and domains that classifies proteins based on shared evolutionary origins and functional similarities, utilizing curated multiple sequence alignments and profile hidden Markov models (HMMs) to detect and annotate domains in novel sequences.[1] Established in 1998 at the Wellcome Trust Sanger Institute, Pfam has evolved into a cornerstone resource in bioinformatics, now maintained by the European Molecular Biology Laboratory's European Bioinformatics Institute (EMBL-EBI) and integrated within the InterPro consortium for enhanced protein analysis.[2][3] Its development reflects advances in computational biology, starting with manual curation of seed alignments to build probabilistic HMMs that model conserved regions, allowing sensitive detection of distant homologs beyond simple sequence similarity searches.[4] As of release 37.4 in June 2025, Pfam encompasses over 26,000 entries, including families, domains, repeats, and motifs, which collectively annotate domains in more than 80% of known protein sequences from UniProt Reference Proteomes.[3][5] These entries are organized into clans—groups of related families based on sequence, structure, or functional evidence—to resolve overlaps and provide hierarchical insights into protein evolution.[6] Pfam's models support applications in genome annotation, metagenomics, and experimental design by predicting domain architecture, functional sites, and evolutionary relationships, thereby aiding research into protein diversity and disease mechanisms.[7] Recent enhancements include the integration of artificial intelligence and machine learning, such as deep learning models in Pfam-N, to automate curation, improve model accuracy, and expand coverage of underrepresented protein classes, ensuring Pfam remains a dynamic tool for the post-genomic era.[5] Accessible via web interfaces, APIs, and downloadable files, Pfam facilitates interdisciplinary studies by linking to resources like UniProt, PDB, and GO annotations for multifaceted protein exploration.[3]Introduction
Definition and Scope
Pfam is a widely used bioinformatics resource that provides a comprehensive collection of curated protein families and domains, with each entry represented by multiple sequence alignments (MSAs) and profile hidden Markov models (HMMs) to facilitate the identification and annotation of protein sequences.[5] These models capture the conserved features of protein architectures, enabling researchers to classify proteins based on evolutionary relationships and functional similarities.[5] As of Pfam version 38.0, released in October 2025, the database encompasses over 26,000 entries, including manually curated Pfam-A families, which collectively annotate domains in more than 90% of known protein sequences from UniProt Reference Proteomes through integration with UniProtKB.[3][5] These families are organized into clans that group related families based on sequence, structure, or functional evidence. In contrast, the legacy Pfam-B entries, which consisted of automatically generated clusters from unassigned sequences, have been deprecated since earlier releases and are no longer maintained or updated.[8] The scope of Pfam is primarily focused on proteins from eukaryotic and prokaryotic organisms, encompassing domains typically longer than short peptides to emphasize structurally and functionally significant regions, while excluding non-protein entities such as nucleic acids or small molecules.[5] This targeted coverage ensures high-quality annotations for the majority of cellular proteins across diverse taxa, supporting broad applicability in genomic and proteomic studies.[9]Importance in Bioinformatics
Pfam plays a central role in bioinformatics by classifying proteins into families and domains based on evolutionary relationships, enabling researchers to infer functional, structural, and interaction properties from sequence similarities. Through curated multiple sequence alignments and profile hidden Markov models (HMMs), Pfam identifies conserved domains that reflect shared evolutionary histories, allowing predictions of protein function even for uncharacterized sequences. For instance, domain architectures—combinations of domains within a single protein—provide insights into molecular interactions, as interacting domains often co-occur in protein complexes. This classification supports the annotation of protein roles in biological processes, bridging sequence data to higher-level understanding of cellular mechanisms. Pfam contributes significantly to large-scale bioinformatics projects, particularly in genome annotation pipelines and metagenomic analyses. It is integrated into resources like UniProt, where Pfam domains annotate protein entries to facilitate functional inference across proteomes, and Ensembl, which uses Pfam for identifying and summarizing protein domains in gene models during vertebrate genome assembly. In metagenomics, Pfam enables the functional characterization of proteins from uncultured microbes by detecting domain signatures in environmental sequencing data, aiding the discovery of novel enzymes and metabolic pathways in microbial communities without the need for cultivation. The database's emphasis on multidomain proteins highlights its impact on revealing functional novelty through domain combinations, as diverse arrangements of known domains can generate proteins with emergent properties, such as specialized signaling or regulatory roles. This is crucial for evolutionary studies, where domain shuffling drives innovation in protein function. Pfam is extensively used in bioinformatics, with its core publications cited thousands of times annually, and it supports AI-driven tools like AlphaFold by providing alignment data that enhance structure prediction accuracy for domain-containing proteins.Methodology
Hidden Markov Models
Profile hidden Markov models (profile HMMs) serve as the foundational statistical framework for representing protein families in Pfam, modeling the probabilistic patterns observed in multiple sequence alignments of related proteins. These models are position-specific, capturing both conserved regions and variability across a family by incorporating evolutionary changes such as substitutions, insertions, and deletions. Unlike simpler sequence profiles, profile HMMs treat the alignment as a stochastic process, where each position in the model corresponds to a column in the alignment, allowing for flexible handling of gaps and indels that reflect biological divergence.[10] The architecture of a profile HMM in Pfam consists of a linear chain of states, beginning with a silent begin state that initiates the model and transitioning into the core model, which includes a series of match (M), insert (I), and delete (D) states for each position in the consensus length of the family. Match states emit amino acids with position-specific emission probabilities derived from the frequency of residues in the alignment, while insert states model additional residues with their own emission probabilities, and delete states are silent, allowing skips without emission. Transitions between states are governed by transition probabilities that dictate the likelihood of moving from one state to another, such as from a match to an insert or delete, enabling the model to accommodate variable-length sequences. The core model concludes with an end state, and non-homologous flanking regions are handled by additional states like N (N-terminal), J (joiner for multi-domain proteins), and C (C-terminal). These parameters—emission probabilities for the 20 amino acids at match and insert states, and transition probabilities across all state types—are estimated from the seed alignment using priors to account for sampling biases.[11][10] Profile HMMs provide significant advantages over traditional pairwise or profile-based alignments by offering higher sensitivity for detecting distant homologs, particularly those with sequence identities below 20%, where simple alignments often fail due to accumulated mutations and gaps. This enhanced detection arises from the model's ability to incorporate position-dependent gap penalties and substitution probabilities, which better mirror the evolutionary processes shaping protein domains, leading to more accurate identification of family membership even in highly diverged sequences. In Pfam, as of release 38.0 (October 2025), these models are constructed and searched using the HMMER3 software suite, where thehmmbuild command generates a profile HMM from a multiple sequence alignment in Stockholm format, incorporating family-specific annotations like accession numbers, and the hmmsearch command queries sequence databases to find significant matches based on probabilistic scores and curated thresholds.[11][12]