RT Journal Article
SR Electronic
T1 Deep Boosted Molecular Dynamics (DBMD): Accelerating molecular simulations with Gaussian boost potentials generated using probabilistic Bayesian deep neural network
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2023.03.25.534210
DO 10.1101/2023.03.25.534210
A1 Do, Hung N.
A1 Miao, Yinglong
YR 2023
UL http://biorxiv.org/content/early/2023/04/05/2023.03.25.534210.abstract
AB We have developed a new Deep Boosted Molecular Dynamics (DBMD) method. Probabilistic Bayesian neural network models were implemented to construct boost potentials that exhibit Gaussian distribution with minimized anharmonicity, thereby allowing for accurate energetic reweighting and enhanced sampling of molecular simulations. DBMD was demonstrated on model systems of alanine dipeptide and the fast-folding protein and RNA structures. For alanine dipeptide, 30ns DMBD simulations captured up to 83-125 times more backbone dihedral transitions than 1µs conventional molecular dynamics (cMD) simulations and were able to accurately reproduce the original free energy profiles. Moreover, DBMD sampled multiple folding and unfolding events within 300ns simulations of the chignolin model protein and identified low-energy conformational states comparable to previous simulation findings. Finally, DBMD captured a general folding pathway of three hairpin RNAs with the GCAA, GAAA, and UUCG tetraloops. Based on Deep Learning neural network, DBMD provides a powerful and generally applicable approach to boosting biomolecular simulations. DBMD is available with open source in OpenMM at https://github.com/MiaoLab20/DBMD/.Competing Interest StatementThe authors have declared no competing interest.