A generative, probabilistic model of local protein structure
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A generative, probabilistic model of local protein structure. / Boomsma, Wouter Krogh; Mardia, Kanti V.; Taylor, Charles C.; Ferkinghoff-Borg, Jesper; Krogh, Anders; Hamelryck, Thomas.
In: Proceedings of the National Academy of Science of the United States of America, Vol. 105, No. 26, 2008, p. 8932-8937.Research output: Contribution to journal › Journal article › Research › peer-review
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TY - JOUR
T1 - A generative, probabilistic model of local protein structure
AU - Boomsma, Wouter Krogh
AU - Mardia, Kanti V.
AU - Taylor, Charles C.
AU - Ferkinghoff-Borg, Jesper
AU - Krogh, Anders
AU - Hamelryck, Thomas
N1 - Keywords: Amino Acid Motifs; Models, Molecular; Models, Statistical; Proteins
PY - 2008
Y1 - 2008
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.
AB - 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.
U2 - 10.1073/pnas.0801715105
DO - 10.1073/pnas.0801715105
M3 - Journal article
C2 - 18579771
VL - 105
SP - 8932
EP - 8937
JO - Proceedings of the National Academy of Sciences of the United States of America
JF - Proceedings of the National Academy of Sciences of the United States of America
SN - 0027-8424
IS - 26
ER -
ID: 9541260