A generative, probabilistic model of local protein structure

by: Wouter Boomsma, Kanti V Mardia, Charles C Taylor, Jesper Ferkinghoff-Borg, Anders Krogh, Thomas Hamelryck

Proceedings of the National Academy of Sciences, Vol. 105, No. 26. (1 July 2008), pp. 8932-8937.

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Abstract

Despite significant progress in recent years, protein structure prediction maintains its status as one of the prime unsolved problems in computational biology. One of the key remaining challenges is an efficient probabilistic exploration of the structural space that correctly reflects the relative conformational stabilities. Here, we present a fully probabilistic, continuous model of local protein structure in atomic detail. The generative model makes efficient conformational sampling possible and provides a framework for the rigorous analysis of local sequence-structure correlations in the native state. Our method represents a significant theoretical and practical improvement over the widely used fragment assembly technique by avoiding the drawbacks associated with a discrete and nonprobabilistic approach. 10.1073/pnas.0801715105

@article{citeulike:2951509, abstract = {Despite significant progress in recent years, protein structure prediction maintains its status as one of the prime unsolved problems in computational biology. One of the key remaining challenges is an efficient probabilistic exploration of the structural space that correctly reflects the relative conformational stabilities. Here, we present a fully probabilistic, continuous model of local protein structure in atomic detail. The generative model makes efficient conformational sampling possible and provides a framework for the rigorous analysis of local sequence-structure correlations in the native state. Our method represents a significant theoretical and practical improvement over the widely used fragment assembly technique by avoiding the drawbacks associated with a discrete and nonprobabilistic approach. 10.1073/pnas.0801715105}, author = {Boomsma, Wouter and Mardia, Kanti V. and Taylor, Charles C. and Ferkinghoff-Borg, Jesper and Krogh, Anders and Hamelryck, Thomas }, citeulike-article-id = {2951509}, doi = {http://dx.doi.org/10.1073/pnas.0801715105}, journal = {Proceedings of the National Academy of Sciences}, keywords = {prediction, protein, structure}, month = {July}, number = {26}, pages = {8932--8937}, posted-at = {2008-07-02 17:55:01}, priority = {2}, title = {A generative, probabilistic model of local protein structure}, url = {http://dx.doi.org/10.1073/pnas.0801715105}, volume = {105}, year = {2008} }

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TY - JOUR ID - citeulike:2951509 L3 - citeulike-article-id:2951509 N2 - Despite significant progress in recent years, protein structure prediction maintains its status as one of the prime unsolved problems in computational biology. One of the key remaining challenges is an efficient probabilistic exploration of the structural space that correctly reflects the relative conformational stabilities. Here, we present a fully probabilistic, continuous model of local protein structure in atomic detail. The generative model makes efficient conformational sampling possible and provides a framework for the rigorous analysis of local sequence-structure correlations in the native state. Our method represents a significant theoretical and practical improvement over the widely used fragment assembly technique by avoiding the drawbacks associated with a discrete and nonprobabilistic approach. 10.1073/pnas.0801715105 IS - 26 JF - Proceedings of the National Academy of Sciences EP - 8937 TI - A generative, probabilistic model of local protein structure VL - 105 SP - 8932 KW - prediction KW - protein KW - structure AU - Boomsma, Wouter AU - Mardia, Kanti AU - Taylor, Charles AU - Ferkinghoff-Borg, Jesper AU - Krogh, Anders AU - Hamelryck, Thomas PY - 2008/07/01/ UR - http://dx.doi.org/10.1073/pnas.0801715105 ER -

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