Premium
Protein fold recognition by total alignment probability
Author(s) -
Bienkowska Jadwiga R.,
Yu Lihua,
Zarakhovich Sophia,
Rogers Robert G.,
Smith Temple F.
Publication year - 2000
Publication title -
proteins: structure, function, and bioinformatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.699
H-Index - 191
eISSN - 1097-0134
pISSN - 0887-3585
DOI - 10.1002/1097-0134(20000815)40:3<451::aid-prot110>3.0.co;2-j
Subject(s) - hidden markov model , sequence (biology) , pattern recognition (psychology) , fold (higher order function) , structural alignment , computer science , probability distribution , multiple sequence alignment , algorithm , sequence alignment , set (abstract data type) , markov chain , artificial intelligence , mathematics , statistics , machine learning , biology , peptide sequence , genetics , gene , programming language
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 40% more accurate than fold recognition by the optimal alignment probability. Proteins 2000;40:451–462. © 2000 Wiley‐Liss, Inc.