Protein Structure Validation Derives a Smart Conformational Search in a Physically Relevant Configurational Subspace
Protein Structure Validation Derives a Smart Conformational Search in a Physically Relevant Configurational Subspace
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DOI：
10.1021/acs.jcim.2c01173
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发表时间：
2022-11
影响因子：
5.6
通讯作者：
Takunori Yasuda;Rikuri Morita;Y. Shigeta;R. Harada
中科院分区：
文献类型：
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作者：
Takunori Yasuda;Rikuri Morita;Y. Shigeta;R. Harada
由于蛋白质通过其动态特性来执行生物功能，分子动力学(MD)模拟是研究其功能的一种复杂策略。轨迹分析提供了关于特定蛋白质作为自由能景观(FEL)的统计信息。然而，正常MD的时间标度比生物功能的时间标度短，导致构象采样统计不足，最终导致FEL计算不可靠。为了寻找广泛的构造子空间，在目标蛋白质上施加外部偏差作为有偏采样。然而，它的调节是具有挑战性的，因为扰动的最佳强度是未知的。此外，当施加不适当的外部偏向时，搜索物理上不相关的配置子空间。为了解决这个问题，我们最近提出了一种被称为G因子外部偏置限制器(沙鼠)的外部偏置调节方案。在沙土鼠中，由外部偏置产生的蛋白质构型通过一个指示器(G因子)在结构上进行验证，从而能够搜索物理上相关的子空间。除了有偏采样，无偏采样可能会搜索物理上不相关的配置子空间，因为从几个初始结构重复多个MD模拟往往会搜索过宽的配置子空间。对于这个问题，无偏抽样产生的构型的结构性质还没有被研究过。因此，我们确认了G因子是否筛选了无偏抽样产生的折叠(低质量)配置。为了解决这一问题，在沙土鼠中采用了离群点泛洪方法(OFLOOD)作为一种无偏抽样方法，称为OFLOOD-沙土鼠。OFLOOD通过对给定蛋白质很少出现的状态进行重采样来快速扩展配置子空间，并倾向于搜索过于宽泛的子空间。因此，我们认为沙土鼠可能会改进OFLOOD对物理上无关的构型子空间的过度构象搜索。作为演示，OFLOOD和OFLOOD-沙土鼠被应用于一种球状蛋白(T4溶菌酶)，并评估了它们的构象搜索质量。根据我们的评估，没有离群值验证的正常OFLOOD经常抽样低质量的配置，而有离群值验证的OFLOOD-沙鼠则密集抽样高质量的配置。总之，OFLOOD-沙鼠在物理上相关的构型子空间中进行了智能构象搜索，表明蛋白质结构验证在无偏采样方法和有偏采样方法中都有效。
Since proteins perform biological functions through their dynamic properties, molecular dynamics (MD) simulation is a sophisticated strategy for investigating their functions. Analyses of trajectories provide statistical information about a specific protein as a free-energy landscape (FEL). However, the timescale of normal MD is shorter than that of biological functions, resulting in statistically insufficient conformational sampling, finally leading to unreliable FEL calculation. To search for a broad configurational subspace, an external bias is imposed on a target protein as biased sampling. However, its regulation is challenging because the optimal strength of the perturbation is unknown. Furthermore, a physically irrelevant configurational subspace was searched when imposing an inappropriate external bias. To address this issue, we newly proposed an external biased regulation scheme known as the G-factor external bias limiter (GERBIL). In GERBIL, protein configurations generated by external bias are structurally validated by an indicator (G-factor), enabling the search for a physically relevant subspace. In addition to biased sampling, nonbiased sampling might search for a physically irrelevant configurational subspace because repeating multiple MD simulations from several initial structures tends to search for an overly broad configurational subspace. For this issue, the structural qualities of configurations generated by nonbiased sampling have not been investigated. Therefore, we confirmed whether the G-factor screened the collapsed (low-quality) configurations generated by nonbiased sampling. To address this issue, the outlier flooding method (OFLOOD) was adopted in GERBIL as a nonbiased sampling method, which is referred to as OFLOOD-GERBIL. OFLOOD rapidly expands a configurational subspace by resampling the rarely occurring states of a given protein and tends to search an overly broad subspace. Thus, we considered that GERBIL might improve the excessive conformational search of OFLOOD for a physically irrelevant configurational subspace. As a demonstration, OFLOOD and OFLOOD-GERBIL were applied to a globular protein (T4 lysozyme) and their conformational search qualities were assessed. Based on our assessment, normal OFLOOD without the outlier validation frequently sampled low-quality configurations, whereas OFLOOD-GERBIL with the outlier validation intensively sampled high-quality configurations. In conclusion, OFLOOD-GERBIL derives a smart conformational search in a physically relevant configurational subspace, indicating that protein structure validation works in both nonbiased and biased sampling methods.