As the simplest hydrogen-bonded alcohol,liquid methanol has attracted intensive experimental and theoretical interest.However,theoretical investigations on this system have primarily relied on empirical intermolecular...As the simplest hydrogen-bonded alcohol,liquid methanol has attracted intensive experimental and theoretical interest.However,theoretical investigations on this system have primarily relied on empirical intermolecular force fields or ab initio molecular dynamics with semilocal density functionals.Inspired by recent studies on bulk water using increasingly accurate machine learning force fields,we report a new machine learning force field for liquid methanol with a hybrid functional revPBE0 plus dispersion correction.Molecular dynamics simulations on this machine learning force field are orders of magnitude faster than ab initio molecular dynamics simulations,yielding the radial distribution functions,selfdiffusion coefficients,and hydrogen bond network properties with very small statistical errors.The resulting structural and dynamical properties are compared well with the experimental data,demonstrating the superior accuracy of this machine learning force field.This work represents a successful step toward a first-principles description of this benchmark system and showcases the general applicability of the machine learning force field in studying liquid systems.展开更多
Aim To optimize purification conditions of recombinant hirudin 3 in thefermentation broth and characterize the product. Methods Reambinant hirudin 3 was isolated andpurified from the fermentation broth by three column...Aim To optimize purification conditions of recombinant hirudin 3 in thefermentation broth and characterize the product. Methods Reambinant hirudin 3 was isolated andpurified from the fermentation broth by three column chromatography steps with macroporous resin,DEAE cellulose DES2 and preparative RP-HPLC, respectively, and the optimal conditions were obtained.Purity of the product was determined by SDS-PAGE and analytical RP-HPLC. The molecular weight wasdetermined by mass spec-trometry. The structure of the product was analyzed by peptide map.ResultsThe product with purity of 95.4786% was obtained after three purification steps in the optimumconditions with a total yield of 39%. The molecular weight of the product was 6 913.32 ± 6.55 Da,coincident to the theoretical molecular weight of r-hirudin 3. The structure of the product wascoincident to r-hirudin 3 either. Conclusion The optimized purification steps can be successfullyemployed for purification of r-hirudin 3 from E. coli using batch-type approaches. The productobtained with high purity was confirmed to be r-hirudin 3.展开更多
基金supported by the CAS Project for Young Scientists in Basic Research(YSBR-005)the National Natural Science Foundation of China(22325304,22221003 and 22033007)We acknowledge the Supercomputing Center of USTC,Hefei Advanced Computing Center,Beijing PARATERA Tech Co.,Ltd.,for providing high-performance computing services。
文摘As the simplest hydrogen-bonded alcohol,liquid methanol has attracted intensive experimental and theoretical interest.However,theoretical investigations on this system have primarily relied on empirical intermolecular force fields or ab initio molecular dynamics with semilocal density functionals.Inspired by recent studies on bulk water using increasingly accurate machine learning force fields,we report a new machine learning force field for liquid methanol with a hybrid functional revPBE0 plus dispersion correction.Molecular dynamics simulations on this machine learning force field are orders of magnitude faster than ab initio molecular dynamics simulations,yielding the radial distribution functions,selfdiffusion coefficients,and hydrogen bond network properties with very small statistical errors.The resulting structural and dynamical properties are compared well with the experimental data,demonstrating the superior accuracy of this machine learning force field.This work represents a successful step toward a first-principles description of this benchmark system and showcases the general applicability of the machine learning force field in studying liquid systems.
文摘Aim To optimize purification conditions of recombinant hirudin 3 in thefermentation broth and characterize the product. Methods Reambinant hirudin 3 was isolated andpurified from the fermentation broth by three column chromatography steps with macroporous resin,DEAE cellulose DES2 and preparative RP-HPLC, respectively, and the optimal conditions were obtained.Purity of the product was determined by SDS-PAGE and analytical RP-HPLC. The molecular weight wasdetermined by mass spec-trometry. The structure of the product was analyzed by peptide map.ResultsThe product with purity of 95.4786% was obtained after three purification steps in the optimumconditions with a total yield of 39%. The molecular weight of the product was 6 913.32 ± 6.55 Da,coincident to the theoretical molecular weight of r-hirudin 3. The structure of the product wascoincident to r-hirudin 3 either. Conclusion The optimized purification steps can be successfullyemployed for purification of r-hirudin 3 from E. coli using batch-type approaches. The productobtained with high purity was confirmed to be r-hirudin 3.
