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 (the G-factor) was used to obtain structural information. Based on the G-factor, we proposed a scheme for regulating biased sampling, which is referred to as a G-factor-based external bias limiter (GERBIL). With GERBIL, the configurations were structurally validated by the G-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 their G-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.

中文翻译：

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G 因子的结构验证正确调节蛋白质构象采样中施加的增强电位

蛋白质的自由能景观 (FEL) 对于评估热力学性质是必不可少的。分子动力学（MD）模拟是一种计算 FEL 的计算方法；然而，传统的 MD 模拟由于其可访问的时间尺度而经常无法搜索广泛的构象子空间，这导致计算不可靠的 FEL。为了搜索广阔的子空间，可以对蛋白质系统施加外部偏差，并且有偏差的采样往往会引起强烈的扰动，可能会破坏蛋白质结构，这表明应该适当调节外部偏差的强度。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。在此处，我们专注于该法规的蛋白质结构信息。在这项研究中，一个成熟的结构指标（G-因子）用于获得结构信息。基于G因子，我们提出了一种用于调节偏置采样的方案，称为基于G因子的外部偏置限制器 (GERBIL)。使用 GERBIL，配置在有偏采样期间通过G因子进行结构验证。作为偏置采样的一个例子，在 GERBIL (aMD-GERBIL) 中采用了加速 MD (aMD) 模拟，从而通过增加升压电位的强度来重复执行 aMD 模拟。此外，通过 aMD 模拟采样的配置在结构上由他们的G- 因子值，当采样配置被视为低质量（塌陷）结构时，aMD-GERBIL 停止增加升压潜力的强度。这种结构验证被视为提升潜力的“刹车”。为了演示，将 aMD-GERBIL 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换并成功识别它们的域运动。