Protein fold recognition by total alignment probability

Research output: Contribution to journalArticlepeer-review

12 Scopus citations


We present a protein fold-recognition method that uses a comprehensive statistical interpretation of structural Hidden Markov Models (HMMs). The structure/fold recognition is done by summing the probabilities of all sequence-to-structure alignments. The optimal alignment can be defined as the most probable, but suboptimal alignments may have comparable probabilities. These suboptimal alignments can be interpreted as optimal alignments to the 'other' structures from the ensemble or optimal alignments under minor fluctuations in the scoring function. Summing probabilities for all alignments gives a complete estimate of sequence-model compatibility. In the case of HMMs that produce a sequence, this reflects the fact that due to our indifference to exactly how the HMM produced the sequence, we should sum over all possibilities. We have built a set of structural HMMs for 188 protein structures and have compared two methods for identifying the structure compatible with a sequence: by the optimal alignment probability and by the total probability. Fold recognition by total probability was 49% more accurate than fold recognition by the optimal alignment probability. (C) 2000 Wiley-Liss, Inc.

Original languageEnglish
Pages (from-to)451-462
Number of pages12
JournalProteins: Structure, Function and Genetics
Issue number3
StatePublished - Aug 15 2000


  • Fold recognition
  • HMM
  • Protein structure
  • Suboptimal alignments
  • Viterbi algorithm

ASJC Scopus subject areas

  • Structural Biology
  • Biochemistry
  • Molecular Biology


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