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lecture7 - Bioinformatics 2 Lecture 7 Aligning sequence to...

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Bioinformatics 2 -- Lecture 7 Aligning sequence to template. Adding loops manually.
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Homology modeling Sequences that have similar sequence have similar structure . Therefore we can model a sequence of unknown structure based on a homolog of known structure. Differences between homolog functions can be inferred. 2 also called “comparative modeling” Percent sequence identity 5% 25% 50% 90% trivial useful difficult forget it.
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Why do homology modeling? Predict functional differences. Predict binding sites. Predict mutational effects. Predict drug interactions. Design all of the above. 3 It’s easier than growing crystals. We have ~50K crystal structures, but millions of sequences.
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What an alignment means to a biologist ACDEFG .... HIKLMNPQRSTVWY ||:|| || :| | ||||: .CDDFGACDGHIYIM..QQSTVWF target template Biologist infers... The gene was extended by one residue at the N-terminus. The Phe is conserved. Four residue deletion occurred between G to H. A non-similar mutation Y->K occurred. . A two-residue insertion occurred between M and Q . A similar mutation F->Y occurred. . Given this alignment... Aligned positions share a common ancestral position.
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What an alignment means to a modeler ACDEFG .... HIKLMNPQRSTVWY ||:|| || :| | ||||: .CDDFGACDGHIYIM..QQSTVWF target template Modeler program should... Add Ala to the N-terminal Cys using energy minimization. Keep the conserved Phe sidechain and backbone. Cut out the four residue insertion and connect G to H. Switch non-similar sidechains Y->K. Possibly move backbone. Possibly pick another alignment . Cut at M-Q, insert two residues, Asn-Pro . Switch similar sidechains F->Y. Keep backbone fixed . Given this alignment...
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In what order do I model? Modeling philosophy: MAXIMIZE USE OF THE INFORMATION IN THE TEMPLATE This means... •Assign coordinates based on homology first. Identity first, then similarity . •Then make deletions . •Then add insertions . •Then build extensions . confidence HIGH LOW
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Specifically, How? identity == keep coordinates. no change. similarity == mutate sidechain deletions == remove residues, make new peptide bond, energy minimize.
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