CCBuilder 2.0: Powerful and accessible coiled-coil modeling.
CCBuilder 2.0: Powerful and accessible coiled-coil modeling.
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DOI:
10.1002/pro.3279
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发表时间:
2018-01
期刊:
影响因子:
--
通讯作者:
Woolfson DN
中科院分区:
文献类型:
--
作者:
Wood CW;Woolfson DN
The increased availability of user‐friendly and accessible computational tools for biomolecular modeling would expand the reach and application of biomolecular engineering and design. For protein modeling, one key challenge is to reduce the complexities of 3D protein folds to sets of parametric equations that nonetheless capture the salient features of these structures accurately. At present, this is possible for a subset of proteins, namely, repeat proteins. The α‐helical coiled coil provides one such example, which represents ≈ 3–5% of all known protein‐encoding regions of DNA. Coiled coils are bundles of α helices that can be described by a small set of structural parameters. Here we describe how this parametric description can be implemented in an easy‐to‐use web application, called CCBuilder 2.0, for modeling and optimizing both α‐helical coiled coils and polyproline‐based collagen triple helices. This has many applications from providing models to aid molecular replacement for X‐ray crystallography, in silico model building and engineering of natural and designed protein assemblies, and through to the creation of completely de novo “dark matter” protein structures. CCBuilder 2.0 is available as a web‐based application, the code for which is open‐source and can be downloaded freely. http://coiledcoils.chm.bris.ac.uk/ccbuilder2. We have created CCBuilder 2.0, an easy to use web‐based application that can model structures for a whole class of proteins, the α‐helical coiled coil, which is estimated to account for 3–5% of all proteins in nature. CCBuilder 2.0 will be of use to a large number of protein scientists engaged in fundamental studies, such as protein structure determination, through to more‐applied research including designing and engineering novel proteins that have potential applications in biotechnology.
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影响因子:
38.3
作者:
通讯作者:
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影响因子:
5.6
作者:
Grigoryan G;Degrado WF
通讯作者:
Degrado WF
影响因子:
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作者:
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作者:
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通讯作者:
Speed, T
DOI:
10.1126/science.aad8036
发表时间:
2016-05-06
期刊:
Science (New York, N.Y.)
影响因子:
--
作者:
Jacobs TM;Williams B;Williams T;Xu X;Eletsky A;Federizon JF;Szyperski T;Kuhlman B
通讯作者:
Kuhlman B