A Transferable Coarse Grain Non-bonded Interaction Model For Amino Acids.
A Transferable Coarse Grain Non-bonded Interaction Model For Amino Acids.
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DOI：
10.1021/ct800441u
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发表时间：
2009-08-11
影响因子：
5.5
通讯作者：
Klein, Michael L.
中科院分区：
文献类型：
--
作者：
DeVane, Russell;Shinoda, Wataru;Moore, Preston B.;Klein, Michael L.
从基因组数据中产生的大量蛋白质序列大大超过了实验蛋白质结构测定方法的吞吐量，因此迫切需要准确的蛋白质结构预测工具。低分辨率，或粗粒度（CG）模型，已经成为计算蛋白质结构预测的主流，是目前最好的工具之一。追求高质量的广义CG模型提出了一个极具挑战性但流行的努力。在这一点上，一个基于CG的相互作用势在这里提出了天然存在的氨基酸。在目前的方法中，三到四个重原子和相关的氢被凝聚成一个单一的CG位点。位点-位点相互作用势的参数化依赖于实验数据，因此提供了一种既不基于全原子（AA）模拟也不基于实验蛋白质结构数据的新方法。具体来说，基于Lennard-Jones （LJ）式函数形式的分子间势使用包括表面张力和密度在内的热力学数据进行参数化。利用这种方法，已经开发了一个氨基酸电位数据集，用于多肽和蛋白质的建模。这里通过比较溶剂可及表面积（SASA）与AA表示和Decoys ' R ' Us提供的蛋白质诱饵数据集的排名来评估潜力。与其他现有的预测模型相比，该模型对这些属性的预测效果非常好。
The large quantity of protein sequences being generated from genomic data has greatly outpaced the throughput of experimental protein structure determining methods and consequently brought urgency to the need for accurate protein structure prediction tools. Reduced resolution, or coarse grained (CG) models, have become a mainstay in computational protein structure prediction perfoming among the best tools available. The quest for high quality generalized CG models presents an extremely challenging yet popular endeavor. To this point, a CG based interaction potential is presented here for the naturally occurring amino acids. In the present approach, three to four heavy atoms and associated hydrogens are condensed into a single CG site. The parameterization of the site-site interaction potential relies on experimental data thus providing a novel approach that is neither based on all-atom (AA) simulations nor experimental protein structural data. Specifically, intermolecular potentials, which are based on Lennard-Jones (LJ) style functional forms, are parameterized using thermodynamic data including surface tension and density. Using this approach, an amino acid potential dataset has been developed for use in modeling peptides and proteins. The potential is evaluated here by comparing the solvent accessible surface area (SASA) to AA representations and ranking of protein decoy data sets provided by Decoys ’R’ Us. The model is shown to perform very well compared to other existing prediction models for these properties.
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影响因子：
10.8
作者：
Khurana E;DeVane RH;Kohlmeyer A;Klein ML
通讯作者：
Klein ML
影响因子：
5.6
作者：
Keasar, C;Levitt, M
通讯作者：
Levitt, M
影响因子：
5.6
作者：
LEE, B;RICHARDS, FM
通讯作者：
RICHARDS, FM
DOI：
10.1016/s1359-0278(97)00024-2
发表时间：
1997-01-01
期刊：
FOLDING & DESIGN
影响因子：
--
作者：
Bahar, I;Atilgan, AR;Erman, B
通讯作者：
Erman, B
影响因子：
5.5
作者：
Han, Wei;Wan, Cheuk-Kin;Wu, Yun-Dong
通讯作者：
Wu, Yun-Dong