Developing a molecular dynamics force field for both folded and disordered protein states

By Paul Robustelli, Stefano Piana, David E. Shaw. Proceedings of the National Academy of Sciences of the United States of America, 2018

920 citations recorded by the source corpus.

Significance Many proteins that perform important biological functions are completely or partially disordered under physiological conditions. Molecular dynamics simulations could be a powerful tool for the structural characterization of such proteins, but it has been unclear whether the physical models (force fields) used in simulations are sufficiently accurate. Here, we systematically compare the accuracy of a number of different force fields in simulations of both ordered and disordered proteins, finding that each force field has strengths and limitations. We then describe a force field that substantially improves on the state-of-the-art accuracy for simulations of disordered proteins without sacrificing accuracy for folded proteins, thus broadening the range of biological systems amenable to molecular dynamics simulations. Molecular dynamics (MD) simulation is a valuable tool for characterizing the structural dynamics of folded proteins and should be similarly applicable to disordered proteins and proteins with both folded and disordered regions. It has been unclear, however, whether any physical model (force field) used in MD simulations accurately describes both folded and disordered proteins. Here, we select a benchmark set of 21 systems, including folded and disordered proteins, simulate these systems with six state-of-the-art force fields, and compare the results to over 9,000 available experimental data points. We find that none of the tested force fields simultaneously provided accurate descriptions of folded proteins, of the dimensions of disordered proteins, and of the secondary structure propensities of disordered proteins. Guided by simulation results on a subset of our benchmark, however, we modified parameters of one force field, achieving excellent agreement with experiment for disordered proteins, while maintaining state-of-the-art accuracy for folded proteins. The resulting force field, a99SB-disp, should thus greatly expand the range of biological systems amenable to MD simulation. A similar approach could be taken to improve other force fields.

Subfields: Materials Chemistry, Molecular Biology, Spectroscopy

View Paper: https://www.semanticscholar.org/paper/a31d50441cf228458dc88eb02151c3d0d73d5fac

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Materials Chemistry

In Molecular Biology

In Spectroscopy

Citations in the corpus

AlphaFold2: A Role for Disordered Protein/Region Prediction? (2022, methodology): “The MD simulation protocols for the two force fields were almost identical, the primary difference was that the simulations using the Amber-99SB*-ILDNP [48–50] force field were performed at 310 K with the TIP3P water model [51] while the Amber99SB-disp [52] simulations were performed at 298.15 K wit”
Toward the solution of the protein structure prediction problem (2021, background): “Since then, technology has progressed through the use of more advanced computer architectures (122, 123) and force ﬁ elds (124 – 130), but molecular dynamics-based structure prediction still remains impractical for proteins of typical length.”
Scalable emulation of protein equilibrium ensembles with generative deep learning (2025, positive): “Differences in the simulation conditions of our own generated data are intentional, e.g. AMBER ff99sb-disp [32] was chosen to avoid spuriously-misfolded states produced by other force fields in the context of protein folding (Sec.”
Molecular dynamics simulation for all (2018, positive): “Which force field to use? The most common choices are various versions of AMBER, CHARMM, and OPLS (Abraham et al., 2015; Harder et al., 2016; Huang et al., 2017; Robustelli et al., 2018).”