受体配体结合位点预测的不确定性量化。

ArXiv Pub Date : 2024-11-15
Nanjie Chen, Dongliang Yu, Dmitri Beglov, Mark Kon, Julio Enrique Castrillon-Candas
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引用次数: 0

摘要

蛋白质对接位点预测领域的最新进展凸显了传统刚性对接算法(如 PIPER)的局限性,这些算法往往忽略了关键的随机因素,如溶剂引起的波动。由于高维随机能量流形的复杂性和低规则性,这些疏忽会导致在确定可行的对接位点时出现误差。为了解决这个问题,我们的研究引入了一个新模型,在这个模型中,配体和受体的分子形状使用多变量卡尔胡宁-洛厄夫(KL)展开来表示。作为 PIPER 的插件,我们开发的科学计算软件增强了该平台,为排名靠前的结合位点的能量流形提供了稳健的不确定性测量。我们的研究结果表明,排名靠前的结合位点随机能量流形的不确定性较低,与实际对接位点非常接近。相反,不确定性较高的结合位点与较差的最佳对接位置相关。这种区别不仅验证了我们的方法,还为蛋白质对接预测设定了新标准,对未来的分子相互作用研究和药物开发具有重大意义。
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Uncertainty quantification of receptor ligand binding sites prediction.

Recent advancements in protein docking site prediction have highlighted the limitations of traditional rigid docking algorithms, like PIPER, which often neglect critical stochastic elements such as solvent-induced fluctuations. These oversights can lead to inaccuracies in identifying viable docking sites due to the complexity of high-dimensional, stochastic energy manifolds with low regularity. To address this issue, our research introduces a novel model where the molecular shapes of ligands and receptors are represented using multi-variate Karhunen-Lo `eve (KL) expansions. This method effectively captures the stochastic nature of energy manifolds, allowing for a more accurate representation of molecular interactions.Developed as a plugin for PIPER, our scientific computing software enhances the platform, delivering robust uncertainty measures for the energy manifolds of ranked binding sites. Our results demonstrate that top-ranked binding sites, characterized by lower uncertainty in the stochastic energy manifold, align closely with actual docking sites. Conversely, sites with higher uncertainty correlate with less optimal docking positions. This distinction not only validates our approach but also sets a new standard in protein docking predictions, offering substantial implications for future molecular interaction research and drug development.

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