基于构象群体贝叶斯推断的自动正向模型参数化
Automatic Forward Model Parameterization with Bayesian Inference of Conformational Populations
AI总结：
本研究增强了BICePs算法以优化经验正向模型参数，提出两种新方法（冗余参数积分和变分最小化BICePs分数），并结合改进的异常值处理，成功优化了预测J耦合常数的Karplus参数，且框架可推广至任意可微正向模型。
摘要：
为了量化结构系综的理论预测与实验测量之间的一致程度，我们依赖于正向模型的准确性。这些模型是基于将特定分子性质与实验测量联系起来的经验关系，从分子构型生成可观测量的计算框架。构象群体贝叶斯推断（BICePs）是一种重加权算法，即使观测数据稀疏和/或存在噪声，它也能使模拟系综与系综平均的实验观测相协调。这是通过对实验约束下的构象群体后验分布进行采样，同时对由随机和系统误差引起的不确定性后验分布进行采样来实现的。在本研究中，我们增强了该算法以优化经验正向模型（FM）参数。我们引入并评估了两种优化FM参数的新方法。第一种方法将FM参数视为冗余参数，并在完整后验分布中对它们进行积分。第二种方法采用变分最小化一个称为BICePs分数的量，该分数报告了“开启”实验约束的自由能。这种技术结合了用于处理实验异常值的改进似然函数，促进了力场的验证和优化，如近期研究（Raddi等人，2023，2024）所示。使用这种方法，我们优化了调节Karplus关系的参数，这对于基于相互作用核之间的二面角准确预测J耦合常数至关重要。我们首先在一个玩具模型系统中验证了这种方法，然后针对人类泛素蛋白预测了六组Karplus参数。最后，我们证明了我们的框架可以自然地将优化推广到任何可微的正向模型...
英文摘要：
To quantify how well theoretical predictions of structural ensembles agree with experimental measurements, we depend on the accuracy of forward models. These models are computational frameworks that generate observable quantities from molecular configurations based on empirical relationships linking specific molecular properties to experimental measurements. Bayesian Inference of Conformational Populations (BICePs) is a reweighting algorithm that reconciles simulated ensembles with ensemble-averaged experimental observations, even when such observations are sparse and/or noisy. This is achieved by sampling the posterior distribution of conformational populations under experimental restraints as well as sampling the posterior distribution of uncertainties due to random and systematic error. In this study, we enhance the algorithm for the refinement of empirical forward model (FM) parameters. We introduce and evaluate two novel methods for optimizing FM parameters. The first method treats FM parameters as nuisance parameters, integrating over them in the full posterior distribution. The second method employs variational minimization of a quantity called the BICePs score that reports the free energy of `turning on` the experimental restraints. This technique, coupled with improved likelihood functions for handling experimental outliers, facilitates force field validation and optimization, as illustrated in recent studies (Raddi et al. 2023, 2024). Using this approach, we refine parameters that modulate the Karplus relation, crucial for accurate predictions of J-coupling constants based on dihedral angles between interacting nuclei. We validate this approach first with a toy model system, and then for human ubiquitin, predicting six sets of Karplus parameters. Finally, we demonstrate that our framework naturally generalizes optimization to any differentiable forward model...
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