Efficient estimation of emission probabilities in profile hidden Markov models

Bioinformatics. 2003 Dec 12;19(18):2359-68. doi: 10.1093/bioinformatics/btg328.


Motivation: Profile hidden Markov models provide a sensitive method for performing sequence database search and aligning multiple sequences. One of the drawbacks of the hidden Markov model is that the conserved amino acids are not emphasized, but signal and noise are treated equally. For this reason, the number of estimated emission parameters is often enormous. Focusing the analysis on conserved residues only should increase the accuracy of sequence database search.

Results: We address this issue with a new method for efficient emission probability (EEP) estimation, in which amino acids are divided into effective and ineffective residues at each conserved alignment position. A practical study with 20 protein families demonstrated that the EEP method is capable of detecting family members from other proteins with sensitivity of 98% and specificity of 99% on the average, even if the number of free emission parameters was decreased to 15% of the original. In the database search for TIM barrel sequences, EEP recognizes the family members nearly as accurately as HMMER or Blast, but the number of false positive sequences was significantly less than that obtained with the other methods.

Availability: The algorithms written in C language are available on request from the authors.

Publication types

  • Comparative Study
  • Evaluation Study
  • Research Support, Non-U.S. Gov't
  • Validation Study

MeSH terms

  • Algorithms*
  • Amino Acids / chemistry
  • Amino Acids / classification
  • Databases, Protein
  • Enzymes / chemistry
  • Enzymes / classification
  • False Positive Reactions
  • Markov Chains
  • Models, Genetic
  • Models, Statistical
  • Proteins / chemistry*
  • Proteins / classification
  • Reproducibility of Results
  • Sensitivity and Specificity
  • Sequence Alignment / methods*
  • Sequence Analysis, Protein / methods*


  • Amino Acids
  • Enzymes
  • Proteins