A generative, probabilistic model of local protein structure

Wouter Krogh Boomsma; Kanti V. Mardia; Charles C. Taylor; Jesper Ferkinghoff-Borg; Anders Krogh; Thomas Hamelryck

doi:10.1073/pnas.0801715105

A generative, probabilistic model of local protein structure

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

97 Citations (Scopus)

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.

Original language	English
Journal	Proceedings of the National Academy of Science of the United States of America
Volume	105
Issue number	26
Pages (from-to)	8932-8937
Number of pages	5
ISSN	0027-8424
DOIs	https://doi.org/10.1073/pnas.0801715105
Publication status	Published - 2008

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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.",

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note = "Keywords: Amino Acid Motifs; Models, Molecular; Models, Statistical; Proteins",

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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

SN - 0027-8424

VL - 105

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EP - 8937

JO - Proceedings of the National Academy of Science of the United States of America

JF - Proceedings of the National Academy of Science of the United States of America

IS - 26

ER -

A generative, probabilistic model of local protein structure

Abstract

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