SIMULATIONS OF BIOMOLECULES

引入了人工神经网络来开发一种新的形式 基于势能的氢键势 水-水和肽-水体系的表面(PES)。潜力 能量函数有三个项，分别代表静电 相互作用、非键相互作用和极化。对于第一次 两项经典的1-6-12成对势能函数 使用。由于极化不是原子对属性，人为的 神经网络已经被用来描述复杂的 用势能面代替解析的成对势 能量函数。为了计算水的原子净电荷，一个 电势衍生电荷(PD)方法已被应用于一些 约束条件。由该方法得到的电荷与ab符合得很好。 初始静电势也表现出显著的一致性 实验偶极矩和四极矩与其他方法的比较 这是由其他人开发的。来测试势能 函数、分子动力学和蒙特卡罗模拟将 已执行。从模拟中，一些物理性质(热 蒸发量、汽化热和径向分布函数 不同的温度)将被计算并将与 实验数据。有机或生物小分子的水化能 可以用这个势来计算，然后与 实验数据。也是水的最低能量结构 二聚体、三聚体和四聚体将用这个势来计算 函数，并与实验数据进行了比较。

Artificial Neural Networks were introduced to develop a new form for the hydrogen bond potential based on the potential energy surfaces(PES) of water-water and peptide-water systems. The potential energy function has three terms which represent electrostatic interaction, nonbonded interaction and polarization. For the first two terms, classical 1-6-12 pairwise potential energy functions were used. Since the polarization is not an atom pair property, artificial Neural Networks have been applied to describe the complicated potential energy surface instead of analytical pairwise potential energy functions. To calculate the net atomic charges of water, a Potential Derived Charge(PD) method has been applied with some constraints. The charges from the method show good agreement with ab initio electrostatic potentials and also show prominent agreement with experimental dipole and quadrupole moments compared to other methods which have been developed by others. To test the potential energy function, Molecular Dynamics and Monte Carlo simulations will be performed. From the simulations, some physical properties (heat capacity, heat of vaporization and radial distribution function at various temperature) will be calculated and will be compared to experimental data. Hydration energy of organic or small biomolecules can be calculated with this potential and then compared to experimental data. Also minimum-energy structures for the water dimer, trimer and tetramer will be calculated with this potential function and compared to experimental data.