Challenges in homology search: HMMER3 and convergent evolution of coiled-coil regions

By Jaina Mistry, Robert D. Finn, Sean R. Eddy, Alex Bateman, Marco Punta. 2013

1,424 citations recorded by the source corpus.

Detection of protein homology via sequence similarity has important applications in biology, from protein structure and function prediction to reconstruction of phylogenies. Although current methods for aligning protein sequences are powerful, challenges remain, including problems with homologous overextension of alignments and with regions under convergent evolution. Here, we test the ability of the profile hidden Markov model method HMMER3 to correctly assign homologous sequences to >13 000 manually curated families from the Pfam database. We identify problem families using protein regions that match two or more Pfam families not currently annotated as related in Pfam. We find that HMMER3 E-value estimates seem to be less accurate for families that feature periodic patterns of compositional bias, such as the ones typically observed in coiled-coils. These results support the continued use of manually curated inclusion thresholds in the Pfam database, especially on the subset of families that have been identified as problematic in experiments such as these. They also highlight the need for developing new methods that can correct for this particular type of compositional bias.

Subfields: Molecular Biology

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Citations in the corpus

ECOD: An Evolutionary Classification of Protein Domains (2014, methodology): “ECOD domains were attributed to Pfam families by HMMER3 [31].”
The EMBL-EBI bioinformatics web and programmatic tools framework (2015, background): “such as HMMER 3 (30), R-COFFEE (31) and new data resources such as ENA Barcode and Geospatial databases (11).”
ViraMiner: Deep learning on raw DNA sequences for identifying viral genomes in human samples (2019, positive): “Another commonly used algorithm for viral discovery in metagenomic samples is HMMER3 [20], which applies profile Hidden Markov Models (pHMM) in relation to the vFams [21] database of viral family of proteins, that have been built by multiple sequence alignments from all viral proteins in RefSeq.”
VFDB 2019: a comparative pathogenomic platform with an interactive web interface (2018, methodology): “To enable protein domain searches for divergent VFs, we leveraged the comparative pathogenomic datasets of VFDB to generate conserved domain profiles for each component of all VFs using hidden Markov models implemented in hmmbuild from the HMMER3 package (11).”
Automated model building and protein identification in cryo-EM maps (2024, positive): “Within ModelAngelo, these probabilities are converted into HMM profiles and used for searches in HMMER3 34 as described above, but using a larger proteome, rather than only the sequences known to be present in the structure.”
Current challenges and best-practice protocols for microbiome analysis (2019, methodology): “Similarly, other tools including HMMER and PhyloPythiaS(þ) assign reads to bins by utilizing an support vector machine model trained on a reference database [101, 102].”
TBtools - an integrative toolkit developed for interactive analyses of big biological data. (2020, methodology): “Functions in TBtools are, in most cases, coded from scratch using Java, with the exception of a few cross-platform programs, including BLAST (Camacho et al., 2009), MEME (Bailey et al., 2009), HMMER (Mistry et al., 2013), MUSCLE (Edgar, 2004), trimAl (Capella-Gutie´rrez et al., 2009), IQ-TREE (Nguye”
CATH: increased structural coverage of functional space (2020, methodology): “For each FunFam, CATH provides sequence alignments (generated using MAFFT (9)), profile hidden Markov models (HMMs, generated using HMMER3 (10)), and a set of high-quality GO annotations from UniProt-GOA (11).”