HMMER Profile Search
Hidden Markov Model Profile Search for Sequence Homology · Also known as: profile-hidden Markov model, HMM profile search, HMMER
HMMER profile search identifies distant protein sequence homologs using probabilistic models of protein families, known as profile Hidden Markov Models (HMMs). Developed by Eddy and colleagues, this method captures sequence variation patterns within protein families and detects homologs with far greater sensitivity than position-weight matrices or pairwise alignment.
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When to use it
Use HMMER to detect distant homologs undetectable by sequence similarity tools like BLAST, particularly for proteins with low sequence identity to known families. It is essential for genome annotation, identifying conserved domains, and discovering functional relationships. Avoid HMMER for single-member proteins lacking homologs.
Strengths & limitations
- Detects distant homologs missed by sequence similarity methods
- Position-specific scoring captures biologically important constraints
- Provides rigorous statistical significance assessment
- Widely available HMM databases (Pfam, InterPro) cover most known proteins
- Depends on quality and completeness of reference alignments
- Computationally more intensive than BLAST-like searches
- May generate false positives if HMM profiles are over-trained on limited data
- Difficult to interpret matches at marginal significance thresholds
Frequently asked
What E-value or bit score threshold should I use to call a significant HMM match?
Recommended E-value thresholds are <0.001 for confident domain hits or <1.0 for exploratory searches. Bit scores > 100 are typically significant for well-trained HMMs. However, thresholds should be calibrated against known true and false positives in your specific domain.
How do I interpret overlapping domain matches on the same protein?
Overlapping matches may indicate multidomain proteins with complex architecture. Prioritize non-overlapping matches or matches with higher scores. Cross-validate by checking domain order conservation in known orthologs and whether overlap is biologically meaningful.
Can HMMER detect novel protein folds not in existing databases?
No, HMMER searches rely on reference HMM profiles. To detect novel folds, de novo structure prediction tools or alignment-free methods are necessary. However, HMMER can identify conserved elements within novel proteins if they share domain homology with known families.
Sources
- Krogh, A., Brown, M., Mian, I. S., Sjölander, K., & Haussler, D. (1994). Hidden Markov models in computational biology: applications to protein modeling. Journal of Molecular Biology, 235(5), 1501-1531. DOI: 10.1006/jmbi.1994.1104 ↗
- Eddy, S. R. (1998). Profile hidden Markov models. Bioinformatics, 14(9), 755-763. DOI: 10.1093/bioinformatics/14.9.755 ↗
- Finn, R. D., Clements, J., & Eddy, S. R. (2011). HMMER web server: interactive sequence similarity searching. Nucleic Acids Research, 39(Web Server issue), W29-W37. DOI: 10.1093/nar/gkr367 ↗
How to cite this page
ScholarGate. (2026, June 3). Hidden Markov Model Profile Search for Sequence Homology. ScholarGate. https://scholargate.app/en/bioinformatics/hmmer-profile-search
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