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Protein Fold Recognition by Total Alignment Probability by Jadwiga R. Bienkowska; Lihua Yu; Sophia Zarakhovich; Robert G. Rogers Jr.; Temple F. Smith is a Biochemistry, Genetics and Molecular Biology article available to read on EtoBox.

What is Protein Fold Recognition by Total Alignment Probability about?

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.

Who reads Protein Fold Recognition by Total Alignment Probability?

Author

Jadwiga R. Bienkowska; Lihua Yu; Sophia Zarakhovich; Robert G. Rogers Jr.; Temple F. Smith

Publisher

John Wiley and Sons; Wiley (John Wiley & Sons); Wiley-Liss Inc; Wiley (ISSN 0887-3585)

Published

2000

Language

EN

Field

Biochemistry, Genetics and Molecular Biology (Life Sciences)