predicting the 3d structure of rna motifs ali mokdad – ucsf may 28, 2007
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Predicting the 3D Structure of RNA motifs Ali Mokdad – UCSF May 28, 2007. Predicting RNA structure. Existing RNA folding algorithms (mfold, sfold, pfold, Dynalign) determine the locations of cWW helices. - PowerPoint PPT PresentationTRANSCRIPT
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Predicting the 3D Structure
of RNA motifs
Ali Mokdad – UCSF
May 28, 2007
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Predicting RNA structure• Existing RNA folding algorithms (mfold, sfold, pfold,
Dynalign) determine the locations of cWW helices.
• Internal loops, hairpin loops, and junctions are represented as “bulges” or unstructured areas between these helices.
• Many of these “bulges” have stable 3D structures that in many cases allow the whole molecule to carry its function.
• Many long-range interactions in the same RNA molecule, or interactions between RNA and other molecules occur at these locations.
• If we can determine the structures of these areas, we can target them with drugs, and we can better understand their mechanisms and functions...
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Predicting 3D structure of RNA loops
• RNA loops are mostly made of non-WC BPs.
• These non-WC BPs are less common than helical WC BPs, but they still make a good portion (ca. 1/3).
• To complicate things, the non-WC BPs are not all homogeneous, instead they belong to any of a dozen or so geometric types*.
• As a results, their 3D-structures eluded computational prediction for so long.
*Leontis & Westhof, RNA 2001, v7: 499-512
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Isostericity-based structure prediction
• Comparative Sequence Analysis has been very successful in predicting cis WC BPs.
• The problem with applying that onto non-WC BPs is that their allowed patterns of substitutions are more diverse and less obvious than cis WC substitutions.
• To some extent these patterns were not even known until recently*
*Leontis, Stombaugh, & Westhof, NAR 2002, v30: 3497-531
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ISFOLD: a small first stepto solve a big problem
• ISFOLD looks in sequence alignments for patterns similar to the known isosteric substitution patterns of base pairs.
• This similarity can be scored and ranked, and based on it predictions of individual BP occurrences (their types and locations) can be made.
• Such structural predictions are, of course, as good as the sequence alignments are.
• For the best results, the alignments should be highly accurate and large, but also divergent enough to show substitutions in places of interest.
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CSA example from 5S rRNA• CSA looks in sequence alignments for canonical “mutual compensating
mutations” (C=G, G=C, A–U, U–A, and G/U & U/G) that covary in two alignment positions (or columns).
??
cWW cWW cWW
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cWStWS
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ISFOLD GUI
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ISFOLD predictions for the 5S rRNA BPs
All Correct predictions!
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ISFOLD predictions for the whole motif
Note: #26 is 75%A & 25%C
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Summary of all 5S ISFOLD predictions
Also: discovered 2 mistakes in original classification of BPs from crystal structure
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ISFOLD can also use mutation data
(An example from viroids)
(Viable and lethal mutations determined experimentally)
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Predicting Loop E motif in Viroids
Published model* Without mutation data With mutation data
(My viroids alignment is low quality)*Zhong et al, J Virol 2006, v80: 8566-81
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Conclusions• This software predicts not just cWW BPs, but all types of BPs
from sequence alignments.
• ISFOLD does 2 tasks:1. Predicts which 2 nucleotides are interacting to form a BP (location).
2. Predicts which specific type of interaction is most probably formed.
• Good results when based on good alignments (5S rRNA).
• Results dramatically improved when mutation data is used.
• The higher the quality of the alignment, the better the predictions.
• By good alignment quality I mean 3 things: 1. Large number of sequences
2. Enough variability between sequences
3. But not too much variability that might mean complete change of the 3D motif (motif swap).
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ISFOLD:Mission is NOT accomplished
• ISFOLD as it is now is only the beginning, it provides a framework that can be added upon in the future…
• One thing to consider is that RNA recurrent motifs (such as internal and hairpin loops) occur as whole units – groups of individual BPs tend to occur together.
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ISFOLD:Mission is NOT accomplished
• ISFOLD as it is now is only the beginning, it provides a framework that can be added upon in the future…
• One thing to consider is that RNA recurrent motifs (such as internal and hairpin loops) occur as whole units – groups of individual BPs tend to occur together.
• When a good structural library of observed recurrent motifs becomes available (like SCOR database), ISFOLD could be modified to study whole motifs at once (specific stacks of BPs can be scored together)
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