Hidden Markov Models That Use Predicted Local Structure for Fold Recognition: Alphabets of Backbone Geometry R Karchin, M Cline, Y Mandel- Gutfreund, K.

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Presentation transcript:

Hidden Markov Models That Use Predicted Local Structure for Fold Recognition: Alphabets of Backbone Geometry R Karchin, M Cline, Y Mandel- Gutfreund, K Karplus

Problem Parent fold may have low sequence similarity Most often, query is only sequence

Proposed Solution Try to predict local structure of target Use local structure prediction in fold recognition

Simple SAM Overview Profile HMM Database Search Multiple Alignment Query Sequence

Profile HMM Start Stop

Two-Track Profile HMM Start Stop AA Str AA Str AA Str AA Str AA Str AA Str AA Str

SAM with Local Structure Profile HMM Database Search Multiple Alignment Query Sequence Predicted local structure Start Stop AA Str AA Str AA Str AA Str AA Str AA Str AA Str Two-track HMM

Scoring Profile HMM P(residue|state) = θ(AA,state) Two-Track Profile HMM P(residue|state) = θ(AA,state)Φ(local,state) SAM Two-Track Profile HMM P(residue|state) = θ(AA,state)Φ 0.3 (local,state)

Good Structure Alphabets Intuitively, we want –Predictability –Conservation –Better fold recognition –Better alignment

Local Structure Alphabets DSSP Hydrogen Bonding DSSP-EHL STR DSSP with 6 different β-strands STRIDE H-Bond and Φ,φ angles STRIDE-EHL Protein Blocks Designed from SOM of Φ,φ angles ANG Partition of Ramachandran ALPHA Dihedral angle between Calphas TCO Dihedral angle between carbonyls

Predicting Local Structure of the Query Alphabets very dissimilar Use a neural network –Input: window of multiple alignment –Output: structure probabilities for a single residue

Predictability Evaluated on –Correct predictions (QN) –Overlap of structure segments (SOV) –Information gain Winners: –*-EHL for precision –STR and PB for most information

Conservation Used FSSP structural alignments Calculated mutual information between proteins Used only alignments with low sequence similarity Winners: –STR, PB

Fold Recognition Winners: All except PB (STRIDE-EHL leading, STR lagging)

Alignment Compared to DALI and CE alignments Evaluated using a shift score –1.0 perfect match –-0.2 is the worst (adjustable) Winners –STR, and STRIDE gets an odd one. –All alphabets improve alignment

Conclusions Local structure improves: –Fold recognition –Alignment when there’s little sequence similarity Alignment and fold recognition are very different problems