We present our latest studies on the development of a general coarse-grain (CG) framework for protein and RNA modeling. For peptide simulations, a general, transferable CG framework is developed based on the Gay-Berne potential and electrostatic point multipole. Instead of matching the overall effective forces produced by atomic models, the fundamental intermolecular forces such as electrostatic, repulsion-dispersion and solvation are represented explicitly at a CG level based on quantum mechanical principles. The detailed conformational distribution is comparable with those reported from recent all-atom simulations and experiments. For RNA 3-D structure predictions, we introduce a new reduced model by deriving the parameters from statistical analysis of 688 RNA experimental structures. By explicitly describing the electrostatic and hydrogen bond interactions, our CG model can capture the native structure of RNA even better than all-atom simulations with implicit water model. Using the CG potential and simulated annealing simulations, most of tested RNAs are successfully folded to within 5.0 � of native structures. In addition, with limited secondary structure information, the current model can successfully predict large size RNAs with complex structures, such as 5S ribosome. The results suggest that the new coarse-graining approach presented in this study has the potential to offer both accuracy and efficiency for biomolecular modeling.

Develop a general coarse-grained framework for protein and RNA modeling

244th National Meeting (2012)

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