基于有效连接性轮廓的蛋白质结构随机重建
Stochastic reconstruction of protein structures from effective connectivity profiles
摘要：
我们讨论了一种随机的方法来重建天然结构的蛋白质的知识的“有效连接”，这是一个一维的结构配置文件构建为一个线性组合的特征向量的接触地图的目标结构。的结构配置文件是用来偏置搜索的构象空间对目标结构的Monte Carlo计划上操作的C_α-链的均匀，有限的厚度。因此，结构信息通过有效连接进入折叠动力学，但相互作用不限于形成天然接触的氨基酸对，从而产生不依赖于最小挫折假设的自由能景观。此外，有效的连接向量可以更容易地预测从蛋白质的氨基酸序列比相应的接触地图，从而表明，这里提出的随机协议可以有效地结合其他当前的方法来预测天然结构。
英文摘要：
We discuss a stochastic approach for reconstructing the native structures of proteins from the knowledge of the "effective connectivity", which is a one-dimensional structural profile constructed as a linear combination of the eigenvectors of the contact map of the target structure. The structural profile is used to bias a search of the conformational space towards the target structure in a Monte Carlo scheme operating on a C_alpha-chain of uniform, finite thickness. Structure information thus enters the folding dynamics via the effective connectivity, but the interaction is not restricted to pairs of amino acids that form native contacts, resulting in a free energy landscape which does not rely on the assumption of minimal frustration. Moreover, effective connectivity vectors can be predicted more readily from the amino acid sequence of proteins than the corresponding contact maps, thus suggesting that the stochastic protocol presented here could be effectively combined with other current methods for predicting native structures.
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