Efficient Sampling of Protein Folding Funnels using HMMSTR, and Pathway Generation using Probabilistic Roadmaps

Efficient Sampling of Protein Folding Funnels using HMMSTR, and Pathway Generation using Probabilistic Roadmaps. Girdhar, Y. Ph.D. Thesis, 4, 2005.

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Classical techniques for simulating molecular motion such as Molecular Dynamics and Monte Carlo simulations only generate one pathway at a time and have extremely high computational cost. We present a biologically significant and effi- cient way to predict protein folding pathways using HMMSTR and Probabilistic Roadmaps (PRM), with several order of magnitudes lower computational cost. We show how to perform unbiased sampling of the folding funnel to generate a PRM graph for protein chains of up to 36 residues. This biologically based sampling is achieved by enforcing protein-like local structures using HMMSTR, a hidden Markov model for local sequence-structure prediction, thereby significantly reducing the size of the conformational space. We also show that there exist favored folding pathways (highways) that proteins take to reach their native fold or other compact folded states. We evalute our approach with three different proteins: a 36 residue long subdomain from Chicken Villin Headpiece -- 1VII(36), a 16-residue long β-hairpin from Protein G -- 2GB1(16), and a 28 residue long Fbp28Ww Domain from Mus Musculus -- 1E0L(28).

@phdthesis{
 title = {Efficient Sampling of Protein Folding Funnels using HMMSTR, and Pathway Generation using Probabilistic Roadmaps},
 type = {phdthesis},
 year = {2005},
 websites = {http://cim.mcgill.ca/~yogesh/publications/girdhar_MS_thesis.pdf},
 month = {4},
 institution = {Rensselaer Polytechnic Institute},
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 created = {2011-11-19T13:45:48.000Z},
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 last_modified = {2019-12-09T12:48:31.043Z},
 read = {false},
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 authored = {true},
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 citation_key = {Girdhar:2005:MSThesis},
 source_type = {mastersthesis},
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 private_publication = {false},
 abstract = {Classical techniques for simulating molecular motion such as Molecular Dynamics and Monte Carlo simulations only generate one pathway at a time and have extremely high computational cost. We present a biologically significant and effi- cient way to predict protein folding pathways using HMMSTR and Probabilistic Roadmaps (PRM), with several order of magnitudes lower computational cost. We show how to perform unbiased sampling of the folding funnel to generate a PRM graph for protein chains of up to 36 residues. This biologically based sampling is achieved by enforcing protein-like local structures using HMMSTR, a hidden Markov model for local sequence-structure prediction, thereby significantly reducing the size of the conformational space. We also show that there exist favored folding pathways (highways) that proteins take to reach their native fold or other compact folded states. We evalute our approach with three different proteins: a 36 residue long subdomain from Chicken Villin Headpiece -- 1VII(36), a 16-residue long β-hairpin from Protein G -- 2GB1(16), and a 28 residue long Fbp28Ww Domain from Mus Musculus -- 1E0L(28).},
 bibtype = {phdthesis},
 author = {Girdhar, Yogesh}
}

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