A generative, probabilistic model of local protein structure

Research output: Contribution to journal › Journal article › Research › peer-review

Standard

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

Harvard

Boomsma, WK, Mardia, KV, Taylor, CC, Ferkinghoff-Borg, J, Krogh, A & Hamelryck, T 2008, 'A generative, probabilistic model of local protein structure', Proceedings of the National Academy of Science of the United States of America, vol. 105, no. 26, pp. 8932-8937. https://doi.org/10.1073/pnas.0801715105

APA

Boomsma, W. K., Mardia, K. V., Taylor, C. C., Ferkinghoff-Borg, J., Krogh, A., & Hamelryck, T. (2008). A generative, probabilistic model of local protein structure. Proceedings of the National Academy of Science of the United States of America, 105(26), 8932-8937. https://doi.org/10.1073/pnas.0801715105

Vancouver

Boomsma WK, Mardia KV, Taylor CC, Ferkinghoff-Borg J, Krogh A, Hamelryck T. A generative, probabilistic model of local protein structure. Proceedings of the National Academy of Science of the United States of America. 2008;105(26):8932-8937. https://doi.org/10.1073/pnas.0801715105

Author

Boomsma, Wouter Krogh ; Mardia, Kanti V. ; Taylor, Charles C. ; Ferkinghoff-Borg, Jesper ; Krogh, Anders ; Hamelryck, Thomas. / A generative, probabilistic model of local protein structure. In: Proceedings of the National Academy of Science of the United States of America. 2008 ; Vol. 105, No. 26. pp. 8932-8937.

Bibtex

@article{3ca5dfb0dbff11dd9473000ea68e967b,
title = "A generative, probabilistic model of local protein structure",
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.",
author = "Boomsma, {Wouter Krogh} and Mardia, {Kanti V.} and Taylor, {Charles C.} and Jesper Ferkinghoff-Borg and Anders Krogh and Thomas Hamelryck",
note = "Keywords: Amino Acid Motifs; Models, Molecular; Models, Statistical; Proteins",
year = "2008",
doi = "10.1073/pnas.0801715105",
language = "English",
volume = "105",
pages = "8932--8937",
journal = "Proceedings of the National Academy of Sciences of the United States of America",
issn = "0027-8424",
publisher = "The National Academy of Sciences of the United States of America",
number = "26",

}

RIS

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