ct0c00228_si_001.pdf (2.04 MB)
Fine-Tuning of the AMBER RNA Force Field with a New Term Adjusting Interactions of Terminal Nucleotides
journal contribution
posted on 2020-05-19, 21:03 authored by Vojtěch Mlýnský, Petra Kührová, Tomáš Kühr, Michal Otyepka, Giovanni Bussi, Pavel Banáš, Jiří ŠponerDetermination
of RNA structural-dynamic properties is challenging
for experimental methods. Thus, atomistic molecular dynamics (MD)
simulations represent a helpful technique complementary to experiments.
However, contemporary MD methods still suffer from limitations of
force fields (ffs), including imbalances in the nonbonded ff terms.
We have recently demonstrated that some improvement of state-of-the-art
AMBER RNA ff can be achieved by adding a new term for H-bonding called
gHBfix, which increases tuning flexibility and reduces risk of side-effects.
Still, the first gHBfix version did not fully correct simulations
of short RNA tetranucleotides (TNs). TNs are key benchmark systems
due to availability of unique NMR data, although giving too much weight
on improving TN simulations can easily lead to overfitting to A-form
RNA. Here we combine the gHBfix version with another term called tHBfix,
which separately treats H-bond interactions formed by terminal nucleotides.
This allows to refine simulations of RNA TNs without affecting simulations
of other RNAs. The approach is in line with adopted strategy of current
RNA ffs, where the terminal nucleotides possess different parameters
for terminal atoms than the internal nucleotides. Combination of gHBfix
with tHBfix significantly improves the behavior of RNA TNs during
well-converged enhanced-sampling simulations using replica exchange
with solute tempering. TNs mostly populate canonical A-form like states
while spurious intercalated structures are largely suppressed. Still,
simulations of r(AAAA) and r(UUUU) TNs show some residual discrepancies
with primary NMR data which suggests that future tuning of some other
ff terms might be useful. Nevertheless, the tHBfix has a clear potential
to improve modeling of key biochemical processes, where interactions
of RNA single stranded ends are involved.