Automated force-field parametrization guided by multisystem ensemble averages

RNA structure and dynamics play a fundamental role in many cellular processes. Molecular dynamics (MD) is a computational tool that can be used to investigate RNA structure and dynamics. However, its capability to predict and explain experimental data is limited by the accuracy of the employed potential energy functions, known as force fields. Recent works have shown that state-of-the-art force fields could predict unphysical RNA conformations that are not in agreement with experiments. The emerging strategy to overcome these limitations is to complement MD with experimental data included as restraints. We recently suggested a maximum entropy based method to enforce solution experiments in MD simulations by simultaneously adapting force-field corrections to multiple systems. We here push this idea further and develop a general scheme to fit arbitrary force-field parameters given a set of ensemble averages ranging from NMR data to native state populations. The key feature is the possibility to concurrently combine ensemble averages from multiple systems into a unique error function to be minimized, drastically enhancing corrections’ transferability. The method is applied to maximize native state populations of GAGA and UUCG tetraloops by refining torsional potentials alone.