Monte Carlo(MC)methods are important computational tools for molecular structure optimizations and predictions.When solvent effects are explicitly considered,MC methods become very expensive due to the large degree of...Monte Carlo(MC)methods are important computational tools for molecular structure optimizations and predictions.When solvent effects are explicitly considered,MC methods become very expensive due to the large degree of freedom associated with the water molecules and mobile ions.Alternatively implicit-solvent MC can largely reduce the computational cost by applying a mean field approximation to solvent effects and meanwhile maintains the atomic detail of the target molecule.The two most popular implicit-solvent models are the Poisson-Boltzmann(PB)model and the Generalized Born(GB)model in a way such that the GB model is an approximation to the PB model but is much faster in simulation time.In this work,we develop a machine learning-based implicit-solvent Monte Carlo(MLIMC)method by combining the advantages of both implicit solvent models in accuracy and efficiency.Specifically,the MLIMC method uses a fast and accurate PB-based machine learning(PBML)scheme to compute the electrostatic solvation free energy at each step.We validate our MLIMC method by using a benzene-water system and a protein-water system.We show that the proposed MLIMC method has great advantages in speed and accuracy for molecular structure optimization and prediction.展开更多
We herein review our studies on simulating the thermal unfolding Fourier transform infrared and two-dimensional infrared spectra of peptides. The peptide-water configuration ensembles, required forspectrum modeling, a...We herein review our studies on simulating the thermal unfolding Fourier transform infrared and two-dimensional infrared spectra of peptides. The peptide-water configuration ensembles, required forspectrum modeling, aregenerated at a series of temperatures using the GBOBC implicit solvent model and the integrated tempering sampling technique. The fluctuating vibrational Hamiltonians of the amide I vibrational band are constructed using the Frenkel exciton model. The signals are calculated using nonlinear exciton propagation. The simulated spectral features such as the intensity and ellipticity are consistent with the experimental observations. Comparing the signals for two beta-hairpin polypeptides with similar structures suggests that this technique is sensitive to peptide foldinz landscapes.展开更多
分子动力学模拟是研究包括RNA在内的生物大分子结构和功能的重要方法,但常规的显式溶剂模拟耗时较长,影响了其进一步应用。隐式溶剂模型通过用连续模型代替溶剂分子,能大大加速模拟速度,因此提高模拟效率。然而,现有的隐式溶剂模型都不...分子动力学模拟是研究包括RNA在内的生物大分子结构和功能的重要方法,但常规的显式溶剂模拟耗时较长,影响了其进一步应用。隐式溶剂模型通过用连续模型代替溶剂分子,能大大加速模拟速度,因此提高模拟效率。然而,现有的隐式溶剂模型都不能很好地描述核酸分子,尤其是RNA。在之前的研究中(已接收),我们提出了一个新的隐式溶剂模型,并分别测试了A型RNA双螺旋、28S r RNA和t RNA等多个系统,验证了该模型可以更好地计算隐式溶剂下的静电相互作用。由于上述系统均以稳定的结构作为起始,因此没有采集到大的构象变化。在本文中,我们将采用B型RNA双螺旋(B-RNA)作为测试系统来验证该模型是否能正确地采集到大尺度的构象转变过程。初步结果表明,在我们的模型下,B-RNA不仅能够正确地采集A型RNA双链构象,而且其搜索速度也比相应的显式模型快。展开更多
We review in this article our recent simulation works on modeling peptide T-jump and thermal unfolding Fourier transform infrared spectroscopy(FTIR) and two-dimensional infrared(2DIR) spectra. The theoretical and comp...We review in this article our recent simulation works on modeling peptide T-jump and thermal unfolding Fourier transform infrared spectroscopy(FTIR) and two-dimensional infrared(2DIR) spectra. The theoretical and computational techniques used,including Markov state model(MSM), integrated tempering sampling(ITS) and nonlinear exciton propagation(NEP), are first briefly introduced. The protocols for simulating the thermal unfolding as well as T-jump unfolding are then summarized in details. The simulated spectral features, such as the intensity and ellipticity, are demonstrated to agree well with the experimental observations.展开更多
基金supported in part by NIH grant GM126189NSF grants DMS-2052983,DMS-1761320+3 种基金IIS-1900473NASA grant 80NSSC21M0023Michigan Economic Development Corporation,MSU Foundation,Bristol-Myers Squibb 65109,and Pfizersupported in part by NSF grants DMS1819193 and DMS-2110922。
文摘Monte Carlo(MC)methods are important computational tools for molecular structure optimizations and predictions.When solvent effects are explicitly considered,MC methods become very expensive due to the large degree of freedom associated with the water molecules and mobile ions.Alternatively implicit-solvent MC can largely reduce the computational cost by applying a mean field approximation to solvent effects and meanwhile maintains the atomic detail of the target molecule.The two most popular implicit-solvent models are the Poisson-Boltzmann(PB)model and the Generalized Born(GB)model in a way such that the GB model is an approximation to the PB model but is much faster in simulation time.In this work,we develop a machine learning-based implicit-solvent Monte Carlo(MLIMC)method by combining the advantages of both implicit solvent models in accuracy and efficiency.Specifically,the MLIMC method uses a fast and accurate PB-based machine learning(PBML)scheme to compute the electrostatic solvation free energy at each step.We validate our MLIMC method by using a benzene-water system and a protein-water system.We show that the proposed MLIMC method has great advantages in speed and accuracy for molecular structure optimization and prediction.
基金supported by the National Natural Science Foundation of China(Grant No.21203178)the National Natural Science Foundation of China(Grant No.21373201)+2 种基金the National Natural Science Foundation of China(Grant No.21433014)the Science and Technological Ministry of China(Grant No.2011YQ09000505)“Strategic Priority Research Program”of the Chinese Academy of Sciences(Grant Nos.XDB10040304 and XDB100202002)
文摘We herein review our studies on simulating the thermal unfolding Fourier transform infrared and two-dimensional infrared spectra of peptides. The peptide-water configuration ensembles, required forspectrum modeling, aregenerated at a series of temperatures using the GBOBC implicit solvent model and the integrated tempering sampling technique. The fluctuating vibrational Hamiltonians of the amide I vibrational band are constructed using the Frenkel exciton model. The signals are calculated using nonlinear exciton propagation. The simulated spectral features such as the intensity and ellipticity are consistent with the experimental observations. Comparing the signals for two beta-hairpin polypeptides with similar structures suggests that this technique is sensitive to peptide foldinz landscapes.
文摘分子动力学模拟是研究包括RNA在内的生物大分子结构和功能的重要方法,但常规的显式溶剂模拟耗时较长,影响了其进一步应用。隐式溶剂模型通过用连续模型代替溶剂分子,能大大加速模拟速度,因此提高模拟效率。然而,现有的隐式溶剂模型都不能很好地描述核酸分子,尤其是RNA。在之前的研究中(已接收),我们提出了一个新的隐式溶剂模型,并分别测试了A型RNA双螺旋、28S r RNA和t RNA等多个系统,验证了该模型可以更好地计算隐式溶剂下的静电相互作用。由于上述系统均以稳定的结构作为起始,因此没有采集到大的构象变化。在本文中,我们将采用B型RNA双螺旋(B-RNA)作为测试系统来验证该模型是否能正确地采集到大尺度的构象转变过程。初步结果表明,在我们的模型下,B-RNA不仅能够正确地采集A型RNA双链构象,而且其搜索速度也比相应的显式模型快。
基金supported by the National Natural Science Foundation of China(21373201,21433014)the“Strategic Priority Research Program”of the Chinese Academy of Sciences(XDB10040304,XDB20010000)
文摘We review in this article our recent simulation works on modeling peptide T-jump and thermal unfolding Fourier transform infrared spectroscopy(FTIR) and two-dimensional infrared(2DIR) spectra. The theoretical and computational techniques used,including Markov state model(MSM), integrated tempering sampling(ITS) and nonlinear exciton propagation(NEP), are first briefly introduced. The protocols for simulating the thermal unfolding as well as T-jump unfolding are then summarized in details. The simulated spectral features, such as the intensity and ellipticity, are demonstrated to agree well with the experimental observations.
