@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",
}