Paper Info
Title
Comprehensive statistical method for protein fold recognition
Abstract
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 Conventionally, Boltzmann statistics dictate that the optimal alignment can give an estimate of the lowest free energy of the sequence conformation imposed by the structural model. The alignment is optimized for a scoring function that is interpreted as a free energy of an amino acid in a structural environment. Near-optimal alignments are ignored, regardless of how likely they might be compared to the optimal alignment. Here we investigate an alternative view. A structure model can be seen as a statistical representation of an ensemble of similar structures. The optimal alignment is always the most probable, but sub-optimal alignments may have comparable probabilities. These sub-optimal 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 an estimate of sequence-model compatibility. 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.
Year
DOI
Venue
2000
10.1145/332306.332347
RECOMB
Keywords
Field
DocType
summing probability,comparable probability,optimal alignment probability,sequence-to-structure alignments conventionally,optimal alignment,comprehensive statistical method,recognition method,scoring function,near-optimal alignment,sub-optimal alignment,total probability,free energy,score function,hidden markov model,comparative genomics,fold recognition,protein structure,structure alignment,amino acid
Structural alignment,Pattern recognition,Threading (protein sequence),Artificial intelligence,Bioinformatics,Hidden Markov model,Maxwell–Boltzmann statistics,Mathematics,Law of total probability,Protein structure
Conference
ISBN
Citations 
PageRank 
1-58113-186-0
0
0.34
References 
Authors
0
5
Name
Order
Citations
PageRank
Jadwiga R. Bienkowska1165.96
Lihua Yu200.34
Sophia Zarakhovich300.34
Robert G. Rogers, Jr.400.68
Temple F. Smith513973.26