Gridnev Dmitry K, Ojeda-May Pedro, Garcia Martin E
FIAS, Routh-Moufang-Strasse 1, 60438 Frankfurt, Germany.
Phys Rev E Stat Nonlin Soft Matter Phys. 2013 Jan;87(1):012714. doi: 10.1103/PhysRevE.87.012714. Epub 2013 Jan 18.
We propose a general method for predicting potentially good folders from a given number of amino acid sequences. Our approach is based on the calculation of the rate of convergence of each amino acid chain towards the native structure using only the very initial parts of the dynamical trajectories. It does not require any preliminary knowledge of the native state and can be applied to different kinds of models, including atomistic descriptions. We tested the method within both the lattice and off-lattice model frameworks and obtained several so far unknown good folders. The unbiased algorithm also allows one to determine the optimal folding temperature and takes at least 3-4 orders of magnitude fewer time steps than those needed to compute folding times.
我们提出了一种从给定数量的氨基酸序列预测潜在优质折叠结构的通用方法。我们的方法基于仅使用动力学轨迹的非常初始部分来计算每个氨基酸链向天然结构收敛的速率。它不需要对天然状态有任何初步了解,并且可以应用于不同类型的模型,包括原子描述。我们在晶格模型和非晶格模型框架内测试了该方法,并获得了几个迄今为止未知的优质折叠结构。这种无偏算法还允许确定最佳折叠温度,并且所需的时间步长比计算折叠时间所需的时间步长至少少3至4个数量级。