Discrete molecular dynamics can predict helical prestructured motifs in disordered proteins

Abstract

Intrinsically disordered proteins (IDPs) lack a stable tertiary structure, but their short binding regions termed Pre-Structured Motifs (PreSMo) can form transient secondary structure elements in solution. Although disordered proteins are crucial in many biological processes and designing strategies to modulate their function is highly important, both experimental and computational tools to describe their conformational ensembles and the initial steps of folding are sparse. Here we report that discrete molecular dynamics (DMD) simulations combined with replica exchange (RX) method efficiently samples the conformational space and detects regions populating α-helical conformational states in disordered protein regions. While the available computational methods predict secondary structural propensities in IDPs based on the observation of protein-protein interactions, our ab initio method rests on physical principles of protein folding and dynamics. We show that RX-DMD predicts α-PreSMos with high confidence confirmed by comparison to experimental NMR data. Moreover, the method also can dissect α-PreSMos in close vicinity to each other and indicate helix stability. Importantly, simulations with disordered regions forming helices in X-ray structures of complexes indicate that a preformed helix is frequently the binding element itself, while in other cases it may have a role in initiating the binding process. Our results indicate that RX-DMD provides a breakthrough in the structural and dynamical characterization of disordered proteins by generating the structural ensembles of IDPs even when experimental data are not available.

Figure 1. RX-DMD (replica exchange discrete molecular dynamics simulations) predicts prestructured motifs (PreSMos) with high confidence.

Secondary structure propensities and a.a. torsion angles were collected using DSSP from conformations of KID (a.a. 101–160) generated by RX-DMD. Regions determined as PreSMos by NMR experiments are labeled with green boxes. (A) Probability of amino acids being in a helix conformation in a simulation is shown. Data from 5,000 frames at 0.5886 temperature unit are averaged and normalized. Plots for other temperatures are shown in Figure S1 in File S1. (B) Continuous helices observed in various KID conformations are depicted over frames from the same temperature and RX-DMD simulation.

Figure 2. The different temperatures of RX-DMD simulations reveal the details of PreSMo formation and stability.

(A–C) Secondary structure propensities and a.a. torsion angles were collected and plotted for conformations of LEF1 (a.a. 1–86). Regions determined as PreSMos by NMR experiments are labeled with green boxes. (D) Continuous helices observed in various LEF1 conformations are depicted over frames corresponding to conformations at 0.5886 temperature unit.

Figure 3. The even distribution of Gly torsion angles may be a characteristic future of disordered proteins.

Ramachandran diagrams of (A) KID and (B) LEF1 conformations plotted using torsion angles determined for every residue in frames at 0.5886 temperature unit, using DSSP.

Figure 4. The energy landscape of IDPs is different from that of proteins with stable structures.

Density of States (DoS) are calculated along the reaction coordinates R_g and energy. (A) As an ordered polypeptide, an approximately 100-residue long segment of the MRP1 (a.a. 711–821) was used. (B–D) Disordered segments of dynein (a.a. 198–237), protein phosphatase inhibitor (a.a. 9–164), and FlgM (a.a. 1–97). Those conformations of the latter two proteins that were found in and around the minima were clustered based on RMSD. The centroids of the most populated clusters are shown at the bottom (green and blue, respectively). Densities are colored according to the bar at the top.