Abstract
Characterizing structural ensembles of intrinsically disordered proteins (IDPs) and intrinsically disordered regions (IDRs) of proteins is essential for studying structure–function relationships. Due to the different neutron scattering lengths of hydrogen and deuterium, selective labeling and contrast matching in small-angle neutron scattering (SANS) becomes an effective tool to study dynamic structures of disordered systems. However, experimental timescales typically capture measurements averaged over multiple conformations, leaving complex SANS data for disentanglement. We hereby demonstrate an integrated method to elucidate the structural ensemble of a complex formed by two IDRs. We use data from both full contrast and contrast matching with residue-specific deuterium labeling SANS experiments, microsecond all-atom molecular dynamics (MD) simulations with four molecular mechanics force fields, and an autoencoder-based deep learning (DL) algorithm. From our combined approach, we show that selective deuteration provides additional information that helps characterize structural ensembles. We find that among the four force fields, a99SB-disp and CHARMM36m show the strongest agreement with SANS and NMR experiments. In addition, our DL algorithm not only complements conventional structural analysis methods but also successfully differentiates NMR and MD structures which are indistinguishable on the free energy surface. Lastly, we present an ensemble that describes experimental SANS and NMR data better than MD ensembles generated by one single force field and reveal three clusters of distinct conformations. Our results demonstrate a new integrated approach for characterizing structural ensembles of IDPs.
| Original language | English |
|---|---|
| Article number | e4772 |
| Journal | Protein Science |
| Volume | 32 |
| Issue number | 10 |
| DOIs | |
| State | Published - Oct 2023 |
Funding
We would like to thank David Bell (FNLCR) and Yung-Ko Chen (Alicuu Technology Co., Ltd.) for helpful discussions. We would also like to thank Chris Layton (ORNL) and Daniel Dewey (ORNL) for their technical support. This work was performed at the Compute and Data Environment for Science (CADES) of the Oak Ridge National Laboratory (ORNL), which is funded by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC05-00OR22725. A portion of this research was performed at Oak Ridge National Laboratory's Spallation Neutron Source, sponsored by the U.S. Department of Energy, Office of Basic Energy Sciences. We acknowledge laboratory support by the Center for Structural Molecular Biology, funded by the Office of Biological and Environmental Research of the U.S. Department of Energy. The research was supported by the U.S. Department of Energy, Office of Science, Office of Advanced Scientific Computing Research, under contract numbers DE-AC05-00OR22725 and DE-SC0023490; the Joint Design of Advanced Computing Solutions for Cancer (JDACS4C) program established by the U.S. Department of Energy (DOE) and the National Cancer Institute (NCI) of the National Institutes of Health. It was performed under the auspices of the U.S. Department of Energy by Argonne National Laboratory under Contract DE-AC02-06-CH11357, Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344, Los Alamos National Laboratory under Contract DE-AC5206NA25396, Oak Ridge National Laboratory under Contract DE-AC05-00OR22725, and Frederick National Laboratory for Cancer Research under Contract HHSN261200800001E. This work was performed at the Compute and Data Environment for Science (CADES) of the Oak Ridge National Laboratory (ORNL), which is funded by the Office of Science of the U.S. Department of Energy under Contract No. DE‐AC05‐00OR22725. A portion of this research was performed at Oak Ridge National Laboratory's Spallation Neutron Source, sponsored by the U.S. Department of Energy, Office of Basic Energy Sciences. We acknowledge laboratory support by the Center for Structural Molecular Biology, funded by the Office of Biological and Environmental Research of the U.S. Department of Energy. The research was supported by the U.S. Department of Energy, Office of Science, Office of Advanced Scientific Computing Research, under contract numbers DE‐AC05‐00OR22725 and DE‐SC0023490; the Joint Design of Advanced Computing Solutions for Cancer (JDACS4C) program established by the U.S. Department of Energy (DOE) and the National Cancer Institute (NCI) of the National Institutes of Health. It was performed under the auspices of the U.S. Department of Energy by Argonne National Laboratory under Contract DE‐AC02‐06‐CH11357, Lawrence Livermore National Laboratory under Contract DE‐AC52‐07NA27344, Los Alamos National Laboratory under Contract DE‐AC5206NA25396, Oak Ridge National Laboratory under Contract DE‐AC05‐00OR22725, and Frederick National Laboratory for Cancer Research under Contract HHSN261200800001E.
| Funders | Funder number |
|---|---|
| CADES | |
| Center for Structural Molecular Biology | |
| Chris Layton | |
| Data Environment for Science | |
| National Institutes of Health | |
| U.S. Department of Energy | |
| National Cancer Institute | |
| Office of Science | |
| Basic Energy Sciences | |
| Advanced Scientific Computing Research | DE‐SC0023490, DE‐AC05‐00OR22725 |
| Advanced Scientific Computing Research | |
| Biological and Environmental Research | |
| Argonne National Laboratory | DE‐AC02‐06‐CH11357 |
| Argonne National Laboratory | |
| Lawrence Livermore National Laboratory | DE‐AC52‐07NA27344 |
| Lawrence Livermore National Laboratory | |
| Oak Ridge National Laboratory | |
| Los Alamos National Laboratory | DE‐AC5206NA25396 |
| Los Alamos National Laboratory | |
| Frederick National Laboratory for Cancer Research | HHSN261200800001E |
| Frederick National Laboratory for Cancer Research |
Keywords
- deep learning
- force field
- intrinsically disordered protein
- small-angle neutron scattering
- structural ensemble