Improved Calculation Of Electrostatic Energy Term In Force Field For RNA Molecules

Force fields are becoming more and more important in scientific research.The research methods using molecular force field simulation have the advantage of lower cost and accurate observation of detailed information at each moment in chemical reaction compared with traditional experiments.RNA is not only a carrier of biological genetic information,it plays many important roles in the physiology of animals and humans,and researchers are finding more and more other functions in scientific experiments.At present,force fields for RNA typically employ a point charge model of electrostatics,which does not provide a realistic quantum-mechanical picture,and the prediction effect is poor.In reality,electron distributions around nuclei are not spherically symmetric and are geometry dependent,which has defined by quantum chemical topology theory.Force field,which takes multipole moments and anisotropy of electron cloud into account,is described,and its applicability to modeling the behavior of RNA molecules is investigated.A 10-nucleotide RNA?PDB code:2MVY?is selected as representative system for study,contains five elements of C,H,O,N,P,and 15 types of atom-atom interactions.Fragments?phosphate,sugar,base,phosphate-sugar,sugar-base,nucleotide?are separated and then“capped”according to the principle of restoring its chemical environment in RNA as much as possible and the atom-atom electrostatic interaction energy is calculated using atomic multipole moments.The minimum convergence internuclear distance of the electrostatic energy for 15 atomic interaction types was obtained.Transferability of the multipole moments model is also investigated.The experiment proved that the model has good transferability.The study of the torsion angle of RNA small molecule fragments is also one of the important research directions for optimizing force field for RNA molecular.16 small molecules cut and capped?according to the principle of restoring its chemical environment in RNA as much as possible?from 2MVY,and energy minima of each molecules are obtained.Geometries were optimized at the HF/6-31G?d,p?,B3LYP/apc-1,and MP2/cc-pVDZ levels of theory using Gaussian09.The number of energy minima is related to the size and the flexibility of the molecule.The torsion angles of?,?,?,?,?,?and?of RNA were analyzed.Multipole moments of atoms occurring in the common fragment[HO-P?O₃?-CH₂-]of 30 phosphate-sugar-phosphate minima were calculated at the first two levels of theory mentioned above.The energy minima always have extreme value despite levels of theory.Moreover,we explored the transferability of properties between different minima.The atomic multipole moments are highly transferable between different minima,and the standard deviations are small.The works above have proved availability and importance of atomic multipole moments in force field for RNA molecular.However,using of integral methods to calculate the atomic multipole moments is expensive,especially when using higher level of theory.Using machine learning to predict the atomic multipole moments in RNA molecular can greatly reduce the computational expense.In this work,the method of fully connected neural network was used for research and prediction of atomic multipole moments,and good experimental results were obtained.In the improved experiment,the structure reject hydrogen atom was used to predict the atomic multipole moments,and the experimental results were greatly improved,which proves the redundancy of the position information of the H atom,and indicates that the influence of H atom on the distribution of RNA electron cloud is insignificant,and it can be ignored,which also proves the rationality of using hydrogen atom to cap the structure in our research process simultaneously.