Pedersen Kasper B, Borges-Araújo Luís, Stange Amanda D, Souza Paulo C T, Marrink Siewert J, Schiøtt Birgit
Department of Chemistry, Aarhus University, Langelandsgade 140, 8000 Aarhus C, Denmark.
Laboratoire de Biologie et Modélisation de la Cellule, CNRS, UMR 5239, Inserm, U1293, Université Claude Bernard Lyon 1, Ecole Normale Supérieure de Lyon, 46 Allée d'Italie, 69364 Lyon, France.
J Chem Theory Comput. 2024 Sep 5. doi: 10.1021/acs.jctc.4c00553.
Coarse-grained molecular dynamics simulations enable the modeling of increasingly complex systems at millisecond timescales. The transferable coarse-grained force field Martini 3 has shown great promise in modeling a wide range of biochemical processes, yet folded proteins in Martini 3 are not stable without the application of external bias potentials, such as elastic networks or Go̅-like models. We herein develop an algorithm, called OLIVES, which identifies native contacts with hydrogen bond capabilities in coarse-grained proteins and use it to implement a novel Go̅-like model for Martini 3. We show that the protein structure instability originates in part from the lack of hydrogen bond energy in the coarse-grained force field representation. By using realistic hydrogen bond energies obtained from literature ab initio calculations, it is demonstrated that protein stability can be recovered by the reintroduction of a coarse-grained hydrogen bond network and that OLIVES removes the need for secondary structure restraints. OLIVES is validated against known protein complexes and at the same time addresses the open question of whether there is a need for protein quaternary structure bias in Martini 3 simulations. It is shown that OLIVES can reduce the number of bias terms, hereby speeding up Martini 3 simulations of proteins by up to ≈30% on a GPU architecture compared to the established Go̅MARTINI Go̅-like model.
粗粒度分子动力学模拟能够在毫秒时间尺度上对日益复杂的系统进行建模。可转移的粗粒度力场Martini 3在对广泛的生化过程进行建模方面显示出巨大的潜力，然而，在Martini 3中，折叠蛋白在没有外部偏置势（如弹性网络或类Go̅模型）的情况下是不稳定的。我们在此开发了一种名为OLIVES的算法，该算法可识别粗粒度蛋白质中具有氢键能力的天然接触，并将其用于为Martini 3实现一种新型的类Go̅模型。我们表明，蛋白质结构的不稳定性部分源于粗粒度力场表示中氢键能量的缺乏。通过使用从文献从头算计算中获得的实际氢键能量，证明了通过重新引入粗粒度氢键网络可以恢复蛋白质稳定性，并且OLIVES消除了对二级结构限制的需求。OLIVES针对已知的蛋白质复合物进行了验证，同时解决了Martini 3模拟中是否需要蛋白质四级结构偏置这一悬而未决的问题。结果表明，与已建立的类Go̅MARTINI Go̅模型相比，在GPU架构上，OLIVES可以减少偏置项的数量，从而将蛋白质的Martini 3模拟速度提高约30%。
