由于模拟这些系统中的大自由度所需的大量采样,因此使用从头算方法对大型化学系统进行元动力学计算在计算上令人望而却步。为了解决这个计算瓶颈,我们在大规模并行云计算平台上使用了GPU增强的密度函数紧密绑定(DFTB)方法,以有效地计算生化系统的热力学和元动力学。为了首先验证我们的方法,我们计算了丙氨酸二肽的自由能表面,并表明我们的GPU增强的DFTB计算在质量上与计算密集型混合DFT基准一致,而经典力场给出了重大误差。最重要的是,我们表明,我们的GPU加速DFTB计算比以前的方法快得多两个数量级。为了进一步扩展我们的GPU增强型DFTB方法,我们还对remdesivir进行了10ns元动力学模拟,这对于常规的基于DFT的元动力学计算来说是遥不可及的。我们发现,从DFTB和经典力场获得的remdesivir的自由能表面显着不同,后者高估了高自由能状态的内部能量贡献。一起来看,我们的基准测试,分析,以及对大型生化系统的扩展强调了使用GPU增强的DFTB模拟来有效预测大型生化系统的自由能表面/热力学。
Metadynamics calculations of large chemical systems with ab initio methods are computationally prohibitive due to the extensive sampling required to simulate the large degrees of freedom in these systems. To address this computational bottleneck, we utilized a GPU-enhanced density functional tight binding (
DFTB) approach on a massively parallelized cloud computing platform to efficiently calculate the thermodynamics and metadynamics of biochemical systems. To first validate our approach, we calculated the free-energy surfaces of alanine dipeptide and showed that our GPU-enhanced
DFTB calculations qualitatively agree with computationally-intensive hybrid DFT benchmarks, whereas classical force fields give significant errors. Most importantly, we show that our GPU-accelerated
DFTB calculations are significantly faster than previous approaches by up to two orders of magnitude. To further extend our GPU-enhanced
DFTB approach, we also carried out a 10 ns metadynamics simulation of remdesivir, which is prohibitively out of reach for routine DFT-based metadynamics calculations. We find that the free-energy surfaces of remdesivir obtained from
DFTB and classical force fields differ significantly, where the latter overestimates the internal energy contribution of high free-energy states. Taken together, our benchmark tests, analyses, and extensions to large biochemical systems highlight the use of GPU-enhanced DFTB simulations for efficiently predicting the free-energy surfaces/thermodynamics of large biochemical systems.