@inproceedings{
 title = {[poster] Efficient sampling of protein folding pathways using HMMSTR and probabilistic roadmaps},
 type = {inproceedings},
 year = {2005},
 keywords = {poster},
 pages = {222-223},
 websites = {http://cim.mcgill.ca/~yogesh/publications/csb2005.pdf},
 month = {8},
 id = {7df7be77-fdc5-3bae-94bf-88db740067b2},
 created = {2011-11-19T13:45:48.000Z},
 file_attached = {true},
 profile_id = {2331788d-b144-3e67-ab8c-4abd7ab569c5},
 last_modified = {2020-03-04T11:53:05.097Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 citation_key = {Girdhar:CSB:2005},
 source_type = {inproceedings},
 folder_uuids = {a08eb1a8-df79-49b6-a3fe-c57f8c286952},
 private_publication = {false},
 abstract = {We present a method for constructing thousands of compact protein conformations from fragments and then connecting these structures to form a network of physically plausible folding pathways. This is the first attempt to merge the previous successes in fragment assembly methods with probabilistic roadmap (PRM) methods. Previous PRM methods have used the knowledge of the true structure to sample conformational space. Our method uses only the amino acid sequence to bias the conformational sampling. Conformational sampling is done using HMMSTR, a hidden Markov model for local sequence-structure correlations. We then build a PRM graph and find paths that have the the lowest energy climb. We find that favored folding pathways exist, corresponding to deep valleys in the energy landscape. We describe the pathways for three small proteins with different secondary structure content in the context of a folding funnel model.},
 bibtype = {inproceedings},
 author = {Girdhar, Yogesh and Bystroff, Chris and Akella, Srinivas},
 doi = {10.1109/CSBW.2005.59},
 booktitle = {Computational Systems Bioinformatics Conference, 2005. Workshops and Poster Abstracts. IEEE}
}

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