当前位置：
X-MOL 学术
›
J. Chem. Inf. Model.
›
论文详情
Our official English website, www.x-mol.net, welcomes your
feedback! (Note: you will need to create a separate account there.)
Protein Structure Validation Derives a Smart Conformational Search in a Physically Relevant Configurational Subspace
Journal of Chemical Information and Modeling ( IF 5.6 ) Pub Date : 2022-11-30 , DOI: 10.1021/acs.jcim.2c01173 Takunori Yasuda 1 , Rikuri Morita 2 , Yasuteru Shigeta 2 , Ryuhei Harada 2
Journal of Chemical Information and Modeling ( IF 5.6 ) Pub Date : 2022-11-30 , DOI: 10.1021/acs.jcim.2c01173 Takunori Yasuda 1 , Rikuri Morita 2 , Yasuteru Shigeta 2 , Ryuhei Harada 2
Affiliation
|
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.
中文翻译：
蛋白质结构验证在物理相关的构型子空间中进行智能构象搜索
由于蛋白质通过其动态特性执行生物学功能，因此分子动力学 (MD) 模拟是研究其功能的复杂策略。轨迹分析提供有关特定蛋白质的统计信息作为自由能景观 (FEL)。然而，正常MD的时间尺度比生物功能的时间尺度短，导致构象采样统计不足，最终导致FEL计算不可靠。为了搜索广泛的构型子空间，将外部偏差作为偏差采样施加在目标蛋白质上。然而，它的调节具有挑战性，因为扰动的最佳强度是未知的。此外，在施加不适当的外部偏差时搜索物理上不相关的配置子空间。为了解决这个问题，我们新提出了一种称为 G 因子外部偏置限制器 (GERBIL) 的外部偏置调节方案。在 GERBIL 中，由外部偏差生成的蛋白质配置通过指标（G 因子）进行结构验证，从而能够搜索物理相关的子空间。除了偏置采样之外，非偏置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次 MD 模拟往往会搜索过于宽泛的配置子空间。对于此问题，尚未研究由无偏抽样生成的配置的结构质量。因此，我们确认 G 因子是否筛选了由无偏采样生成的折叠（低质量）配置。为了解决这个问题，GERBIL中采用异常值洪泛法（OFLOOD）作为一种无偏采样方法，简称OFLOOD-GERBIL。OFLOOD 通过重新采样给定蛋白质的罕见状态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 GERBIL 可能会改进 OFLOOD 对物理上不相关的配置子空间的过度构象搜索。作为演示，OFLOOD 和 OFLOOD-GERBIL 被应用于球状蛋白（T4 溶菌酶），并评估了它们的构象搜索质量。根据我们的评估，没有异常值验证的正常 OFLOOD 经常采样低质量配置，而具有异常值验证的 OFLOOD-GERBIL 密集采样高质量配置。综上所述，
更新日期：2022-11-30
中文翻译：
蛋白质结构验证在物理相关的构型子空间中进行智能构象搜索
由于蛋白质通过其动态特性执行生物学功能，因此分子动力学 (MD) 模拟是研究其功能的复杂策略。轨迹分析提供有关特定蛋白质的统计信息作为自由能景观 (FEL)。然而，正常MD的时间尺度比生物功能的时间尺度短，导致构象采样统计不足，最终导致FEL计算不可靠。为了搜索广泛的构型子空间，将外部偏差作为偏差采样施加在目标蛋白质上。然而，它的调节具有挑战性，因为扰动的最佳强度是未知的。此外，在施加不适当的外部偏差时搜索物理上不相关的配置子空间。为了解决这个问题，我们新提出了一种称为 G 因子外部偏置限制器 (GERBIL) 的外部偏置调节方案。在 GERBIL 中，由外部偏差生成的蛋白质配置通过指标（G 因子）进行结构验证，从而能够搜索物理相关的子空间。除了偏置采样之外，非偏置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次 MD 模拟往往会搜索过于宽泛的配置子空间。对于此问题，尚未研究由无偏抽样生成的配置的结构质量。因此，我们确认 G 因子是否筛选了由无偏采样生成的折叠（低质量）配置。为了解决这个问题，GERBIL中采用异常值洪泛法（OFLOOD）作为一种无偏采样方法，简称OFLOOD-GERBIL。OFLOOD 通过重新采样给定蛋白质的罕见状态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 GERBIL 可能会改进 OFLOOD 对物理上不相关的配置子空间的过度构象搜索。作为演示，OFLOOD 和 OFLOOD-GERBIL 被应用于球状蛋白（T4 溶菌酶），并评估了它们的构象搜索质量。根据我们的评估，没有异常值验证的正常 OFLOOD 经常采样低质量配置，而具有异常值验证的 OFLOOD-GERBIL 密集采样高质量配置。综上所述，
京公网安备 11010802027423号