Pitfalls of the Martini Model
Pitfalls of the Martini Model
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DOI：
10.1021/acs.jctc.9b00473
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发表时间：
2019-10-01
影响因子：
5.5
通讯作者：
Marrink, Siewert J.
中科院分区：
文献类型：
--
作者：
Alessandri, Riccardo;Souza, Paulo C. T.;Marrink, Siewert J.
粗粒度（CG）模型实现的计算和概念简化使其成为当前计算建模领域中普遍存在的工具。基于构建块的CG模型，例如Martini模型，具有允许广泛应用而无需每次重新参数化力场的关键优势。然而，这种方法有某些固有的局限性，我们在这项工作中详细研究了这一点。我们首先研究了不同粒径之间缺乏特定交叉伦纳德-琼斯参数的后果。我们表明，这种缺乏可能导致二聚化剖面中人为的高自由能垒。然后，我们从溶质分配行为和溶剂性质两方面来看，偏离标准键参数太远的影响。此外，我们还表明，过弱的键合力常数会导致人为诱导聚类的风险，这在设计蛋白质弹性网络模型时必须考虑到。这些结果对当前马提尼模型的使用具有启示意义，并为马提尼模型的再参数化提供了明确的方向。此外，我们的研究结果一般适用于任何其他基于构建块的力场的参数化。
The computational and conceptual simplifications realized by coarse-grain (CG) models make them a ubiquitous tool in the current computational modeling landscape. Building block based CG models, such as the Martini model, possess the key advantage of allowing for a broad range of applications without the need to reparametrize the force field each time. However, there are certain inherent limitations to this approach, which we investigate in detail in this work. We first study the consequences of the absence of specific cross Lennard-Jones parameters between different particle sizes. We show that this lack may lead to artificially high free energy barriers in dimerization profiles. We then look at the effect of deviating too far from the standard bonded parameters, both in terms of solute partitioning behavior and solvent properties. Moreover, we show that too weak bonded force constants entail the risk of artificially inducing clustering, which has to be taken into account when designing elastic network models for proteins. These results have implications for the current use of the Martini CG model and provide clear directions for the reparametrization of the Martini model. Moreover, our findings are generally relevant for the parametrization of any other building block based force field.