“Challenging” internal loop motifs Ali Mokdad, M.D., Ph.D.
-
date post
20-Dec-2015 -
Category
Documents
-
view
218 -
download
0
Transcript of “Challenging” internal loop motifs Ali Mokdad, M.D., Ph.D.
![Page 1: “Challenging” internal loop motifs Ali Mokdad, M.D., Ph.D.](https://reader036.fdocuments.in/reader036/viewer/2022062516/56649d445503460f94a20e50/html5/thumbnails/1.jpg)
“Challenging” internal loop motifs
Ali Mokdad, M.D., Ph.D.
![Page 2: “Challenging” internal loop motifs Ali Mokdad, M.D., Ph.D.](https://reader036.fdocuments.in/reader036/viewer/2022062516/56649d445503460f94a20e50/html5/thumbnails/2.jpg)
![Page 3: “Challenging” internal loop motifs Ali Mokdad, M.D., Ph.D.](https://reader036.fdocuments.in/reader036/viewer/2022062516/56649d445503460f94a20e50/html5/thumbnails/3.jpg)
![Page 4: “Challenging” internal loop motifs Ali Mokdad, M.D., Ph.D.](https://reader036.fdocuments.in/reader036/viewer/2022062516/56649d445503460f94a20e50/html5/thumbnails/4.jpg)
Systematically finding internal loops
![Page 5: “Challenging” internal loop motifs Ali Mokdad, M.D., Ph.D.](https://reader036.fdocuments.in/reader036/viewer/2022062516/56649d445503460f94a20e50/html5/thumbnails/5.jpg)
![Page 6: “Challenging” internal loop motifs Ali Mokdad, M.D., Ph.D.](https://reader036.fdocuments.in/reader036/viewer/2022062516/56649d445503460f94a20e50/html5/thumbnails/6.jpg)
• The state-of-the-art RNA automatic alignment methods are based on SCFG (covariance models) and do not systematically use all available 3D structural information for alignment.
• The advantage of using SCFG is their capability to describe nested interactions (RNA 2D structures).
• These methods as they are currently applied work best for helical W.C. segments, but do not produce accurate alignments in non helical segments or in areas where tertiary interactions occur.
• With the ever growing library of accurate RNA 3D structures, it is now possible to use the 3D information to build better alignments.
Problem with current automatic alignment methods
![Page 7: “Challenging” internal loop motifs Ali Mokdad, M.D., Ph.D.](https://reader036.fdocuments.in/reader036/viewer/2022062516/56649d445503460f94a20e50/html5/thumbnails/7.jpg)
KnownStructure
Generation
Parsing
UUAUCCAUGGCGUCGCACAAAGGCCAACAAAAAUAGUUCUGGGAGCAG
![Page 8: “Challenging” internal loop motifs Ali Mokdad, M.D., Ph.D.](https://reader036.fdocuments.in/reader036/viewer/2022062516/56649d445503460f94a20e50/html5/thumbnails/8.jpg)
• We use SCFG models that are capable of describing not only W.C. interactions, but also all other families of edge-to-edge interactions observed in 3D structures.
• We program all isosteric subfamilies (figure below) into the SCFG to allow isosteric substitutions when aligning sequences.
• We also combine SCFG with Markov Random Fields (MRF) models, allowing for the alignment of areas where local crossing interactions occur, or where multiple interactions with a common nucleotide take place.
• SCFG/MRF are thus capable of generating clusters of bases at once (triples, quadruples, etc.), and are not limited to basepairs.
• The hybrid SCFG/MRF is capable of detecting areas of motif swaps in the alignments from sequence data alone.
• Eventually it may be possible to detect structural features of small motifs directly from sequence data.
SCFG/MRF models
![Page 9: “Challenging” internal loop motifs Ali Mokdad, M.D., Ph.D.](https://reader036.fdocuments.in/reader036/viewer/2022062516/56649d445503460f94a20e50/html5/thumbnails/9.jpg)
![Page 10: “Challenging” internal loop motifs Ali Mokdad, M.D., Ph.D.](https://reader036.fdocuments.in/reader036/viewer/2022062516/56649d445503460f94a20e50/html5/thumbnails/10.jpg)
![Page 11: “Challenging” internal loop motifs Ali Mokdad, M.D., Ph.D.](https://reader036.fdocuments.in/reader036/viewer/2022062516/56649d445503460f94a20e50/html5/thumbnails/11.jpg)
Programs
http://rna.bgsu.edu/FR3D• GUI ready, will be posted online within days• User manual sometime soon…• Appearing soon in J. Math. Biol
http://rna.bgsu.edu/ribostral• MATLAB and compiled version (PC) available• Full manual available• Appearing soon in Bioinformatics
![Page 12: “Challenging” internal loop motifs Ali Mokdad, M.D., Ph.D.](https://reader036.fdocuments.in/reader036/viewer/2022062516/56649d445503460f94a20e50/html5/thumbnails/12.jpg)
![Page 13: “Challenging” internal loop motifs Ali Mokdad, M.D., Ph.D.](https://reader036.fdocuments.in/reader036/viewer/2022062516/56649d445503460f94a20e50/html5/thumbnails/13.jpg)
![Page 14: “Challenging” internal loop motifs Ali Mokdad, M.D., Ph.D.](https://reader036.fdocuments.in/reader036/viewer/2022062516/56649d445503460f94a20e50/html5/thumbnails/14.jpg)
![Page 15: “Challenging” internal loop motifs Ali Mokdad, M.D., Ph.D.](https://reader036.fdocuments.in/reader036/viewer/2022062516/56649d445503460f94a20e50/html5/thumbnails/15.jpg)
![Page 16: “Challenging” internal loop motifs Ali Mokdad, M.D., Ph.D.](https://reader036.fdocuments.in/reader036/viewer/2022062516/56649d445503460f94a20e50/html5/thumbnails/16.jpg)
![Page 17: “Challenging” internal loop motifs Ali Mokdad, M.D., Ph.D.](https://reader036.fdocuments.in/reader036/viewer/2022062516/56649d445503460f94a20e50/html5/thumbnails/17.jpg)
![Page 18: “Challenging” internal loop motifs Ali Mokdad, M.D., Ph.D.](https://reader036.fdocuments.in/reader036/viewer/2022062516/56649d445503460f94a20e50/html5/thumbnails/18.jpg)
Ribostral• Full manual available …
• Inputs:
1. Fasta alignment file
2. A list of interactions taken from a 3D structure
![Page 19: “Challenging” internal loop motifs Ali Mokdad, M.D., Ph.D.](https://reader036.fdocuments.in/reader036/viewer/2022062516/56649d445503460f94a20e50/html5/thumbnails/19.jpg)
![Page 20: “Challenging” internal loop motifs Ali Mokdad, M.D., Ph.D.](https://reader036.fdocuments.in/reader036/viewer/2022062516/56649d445503460f94a20e50/html5/thumbnails/20.jpg)
![Page 21: “Challenging” internal loop motifs Ali Mokdad, M.D., Ph.D.](https://reader036.fdocuments.in/reader036/viewer/2022062516/56649d445503460f94a20e50/html5/thumbnails/21.jpg)
Individual BP score =c x (3I + 2NI – H – 2F – 2G1 – 3G2)
Where c is the correction coefficient:c = 100 / (3 x number of sequences)
Score calculation: BP 26/22 in Bacteria:26/22 is tWS CG in the crystal structure. There are:312 sequences with isosteric (I) substitutions25 heterosteric (H) substitutions13 forbidden (F) substitutionsCorrection coefficient c = 100 / (3x351) = 0.095Score = 0.095 x (3x312 – 25 – 2x13) = 83
![Page 22: “Challenging” internal loop motifs Ali Mokdad, M.D., Ph.D.](https://reader036.fdocuments.in/reader036/viewer/2022062516/56649d445503460f94a20e50/html5/thumbnails/22.jpg)
![Page 23: “Challenging” internal loop motifs Ali Mokdad, M.D., Ph.D.](https://reader036.fdocuments.in/reader036/viewer/2022062516/56649d445503460f94a20e50/html5/thumbnails/23.jpg)