Sampling bottlenecks in de novo protein structure prediction.
Sampling bottlenecks in de novo protein structure prediction.
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DOI：
10.1016/j.jmb.2009.07.063
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发表时间：
2009-10-16
影响因子：
5.6
通讯作者：
Baker D
中科院分区：
文献类型：
--
作者：
Kim DE;Blum B;Bradley P;Baker D
从头蛋白质结构预测的主要障碍是构象采样：天然状态通常比非天然结构具有较低的自由能，但极难定位。使用 Rosetta 结构预测方法，可以对小蛋白质进行原子级精度的结构预测，但对于更大、更复杂的蛋白质，几乎从未对天然状态进行采样，并且尚不清楚需要增加多少计算能力才能成功预测此类蛋白质的结构。在本文中，我们开发了一种方法，基于将构象搜索问题重新表述为离散特征空间中的组合采样问题，来确定准确预测蛋白质结构需要多少计算机能力。我们发现许多蛋白质的构象采样受到关键“关键”特征的限制，这些关键特征通常是单个残基的主干扭转角，这些特征在无偏轨迹中很少被采样，并且当受到约束时会显着增加天然状态的采样。这些关键特征经常出现在有助于蛋白质功能的不太规则且可能紧张的蛋白质区域中。在许多蛋白质中，关键特征位于实验发现的折叠后期形成的区域，这表明计算机中的折叠与现实中的折叠之间存在对应关系。
The primary obstacle to de novo protein structure prediction is conformational sampling: the native state generally has lower free energy than non-native structures but is exceedingly difficult to locate. Structure predictions with atomic level accuracy have been made for small proteins using the Rosetta structure prediction methodology, but for larger and more complex proteins, the native state is virtually never sampled and it has been unclear how much of an increase in computing power would be required to successfully predict the structures of such proteins. In this paper we develop an approach to determining how much computer power is required to accurately predict the structure of a protein, based on a reformulation of the conformational search problem as a combinatorial sampling problem in a discrete feature space. We find that conformational sampling for many proteins is limited by critical “linchpin” features, often the backbone torsion angles of individual residues, which are sampled very rarely in unbiased trajectories and when constrained dramatically increase the sampling of the native state. These critical features frequently occur in less regular and likely strained regions of proteins that contribute to protein function. In a number of proteins, the linchpin features are in regions found experimentally to form late in folding, suggesting a correspondence between folding in silico and in reality.
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影响因子：
5.6
作者：
Simons, KT;Kooperberg, C;Baker, D
通讯作者：
Baker, D
影响因子：
64.5
作者：
Lewis, HA;Musunuru, K;Burley, SK
通讯作者：
Burley, SK
影响因子：
2.9
作者：
Misura, KMS;Baker, D
通讯作者：
Baker, D
影响因子：
2.9
作者：
Das, Rhiju;Bin Qian;Baker, David
通讯作者：
Baker, David
影响因子：
5.6
作者：
Solà, M;López-Hernández, E;Párraga, A
通讯作者：
Párraga, A