Münch Jan L, Schmauder Ralf, Paul Fabian, Habeck Michael
Institute of Physiology II, Jena University Hospital, Friedrich Schiller University, Jena 07743, Germany.
Department of Biochemistry and Molecular Biology, University of Chicago, Chicago, United States.
bioRxiv. 2025 May 5:2024.04.20.590387. doi: 10.1101/2024.04.20.590387.
Hidden Markov models (HMMs) for biomolecules suffer from various forms of parameter non-identifiability. This poses severe challenges to both maximum likelihood and Bayesian inference. However, Bayesian inference offers effective means of overcoming these pathologies. We study the role of prior distributions in the face of practical parameter non-identifiability in Bayesian inference applied to prototypical patch clamp data of ligand-gated ion channels. We advocate the use of minimally informative priors, as they increase the accuracy and decrease the uncertainty of the inference. For complex HMMs, stronger prior assumptions are needed to render the posterior . This can be achieved by confining the parameter space to physically motivated limits. Another beneficial assumption is finite cooperativity of ligand-binding and unbinding events, which introduces a bias towards non-cooperativity but still allows for a non-vanishing degree of cooperativity that is inferred from the data. Despite its vagueness, our prior renders the posterior for all datasets that we considered without imposing the assumption of non-cooperativity. Combining all prior factors allows for meaningful inferences with a dataset of a thousand times lower quality.
用于生物分子的隐马尔可夫模型（HMM）存在各种形式的参数不可识别性。这给最大似然估计和贝叶斯推理都带来了严峻挑战。然而，贝叶斯推理提供了克服这些问题的有效方法。我们研究了在应用于配体门控离子通道的典型膜片钳数据的贝叶斯推理中，面对实际参数不可识别性时先验分布的作用。我们提倡使用信息量最小的先验，因为它们提高了推理的准确性并降低了不确定性。对于复杂的HMM，需要更强的先验假设来使后验分布合理。这可以通过将参数空间限制在符合物理意义的范围内来实现。另一个有益的假设是配体结合和解离事件的有限协同性，这引入了一种偏向非协同性的偏差，但仍然允许从数据中推断出非零程度的协同性。尽管我们的先验分布模糊，但它在不施加非协同性假设的情况下，使我们考虑的所有数据集的后验分布都合理。结合所有先验因素，对于质量低至千分之一的数据集也能进行有意义的推理。
