The Structural Validation by G-factor Regulates Boost Potentials Employed in Conformational Sampling of Proteins
The Structural Validation by G-factor Regulates Boost Potentials Employed in Conformational Sampling of Proteins
复制标题
G 因子的结构验证调节蛋白质构象采样中采用的增强电位
DOI：
10.1021/acs.jcim.2c00573
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发表时间：
2022
影响因子：
5.6
通讯作者：
Ryuhei Harada
中科院分区：
文献类型：
--
作者：
Takunori Yasuda;Rikuri Morita;Yasuteru Shigeta;Ryuhei Harada
蛋白质的自由能谱是评价其热力学性质不可缺少的基础。分子动力学（MD）模拟是一种计算自由电子激光的计算方法，但由于时间尺度有限，传统的MD模拟往往不能搜索到较宽的构象子空间，导致计算结果不可靠。为了搜索一个宽的子空间，可以对蛋白质系统施加一个外部偏差，而有偏的采样往往会引起一个强烈的扰动，可能会破坏蛋白质结构，这表明外部偏差的强度应该得到适当的调节。这种调节可能具有挑战性，并且经常使用经验参数来施加最佳偏差。为了解决这个问题，有几种方法通过参考系统能量来调节外部偏置。在这里，我们专注于这种调节的蛋白质结构信息。在这项研究中，一个行之有效的结构指标（G因子）被用来获得结构信息。基于G因子，提出了一种调整偏置采样的方案，称为基于G因子的外部偏置限制器（GERBIL）。在GERBIL中，通过有偏抽样期间的G因子对配置进行了结构验证。作为有偏采样的例子，在GERBIL（aMD-GERBIL）中采用加速MD（aMD）模拟，由此通过增加升压电位的强度来重复执行aMD模拟。此外，aMD模拟采样的配置通过其G因子值进行结构验证，并且当采样的配置被视为低质量（塌陷）结构时，aMD-GERBIL停止增加助推潜力的强度。这种结构验证被视为提升潜力的“制动器”。为了演示，aMD-GERBIL被应用于球状蛋白（核糖结合蛋白和麦芽糖结合蛋白），以促进它们的大振幅开-闭转变，并成功地识别它们的结构域运动。
Free energy landscapes (FELs) of proteins are indispensable for evaluating thermodynamic properties. Molecular dynamics (MD) simulation is a computational method for calculating FELs; however, conventional MD simulation frequently fails to search a broad conformational subspace due to its accessible timescale, which results in the calculation of an unreliable FEL. To search a broad subspace, an external bias can be imposed on a protein system, and biased sampling tends to cause a strong perturbation that might collapse the protein structures, indicating that the strength of the external bias should be properly regulated. This regulation can be challenging, and empirical parameters are frequently employed to impose an optimal bias. To address this issue, several methods regulate the external bias by referring to system energies. Herein, we focused on protein structural information for this regulation. In this study, a well-established structural indicator (theG-factor) was used to obtain structural information. Based on theG-factor, we proposed a scheme for regulating biased sampling, which is referred to as aG-factor-based external bias limiter (GERBIL). With GERBIL, the configurations were structurally validated by theG-factor during biased sampling. As an example of biased sampling, an accelerated MD (aMD) simulation was adopted in GERBIL (aMD-GERBIL), whereby the aMD simulation was repeatedly performed by increasing the strength of the boost potential. Furthermore, the configurations sampled by the aMD simulation were structurally validated by theirG-factor values, and aMD-GERBIL stopped increasing the strength of the boost potential when the sampled configurations were regarded as low-quality (collapsed) structures. This structural validation is regarded as a “Brake” of the boost potential. For demonstrations, aMD-GERBIL was applied to globular proteins (ribose binding and maltose-binding proteins) to promote their large-amplitude open–closed transitions and successfully identify their domain motions.