Collective Dynamics Differentiates Functional Divergence in Protein Evolution

Protein evolution is most commonly studied by analyzing related protein sequences and generating ancestral sequences through Bayesian and Maximum Likelihood methods, and/or by resurrecting ancestral proteins in the lab and performing ligand binding studies to determine function. Structural and dynamic evolution have largely been left out of molecular evolution studies. Here we incorporate both structure and dynamics to elucidate the molecular principles behind the divergence in the evolutionary path of the steroid receptor proteins. We determine the likely structure of three evolutionarily diverged ancestral steroid receptor proteins using the Zipping and Assembly Method with FRODA (ZAMF). Our predictions are within ∼2.7 Å all-atom RMSD of the respective crystal structures of the ancestral steroid receptors. Beyond static structure prediction, a particular feature of ZAMF is that it generates protein dynamics information. We investigate the differences in conformational dynamics of diverged proteins by obtaining the most collective motion through essential dynamics. Strikingly, our analysis shows that evolutionarily diverged proteins of the same family do not share the same dynamic subspace, while those sharing the same function are simultaneously clustered together and distant from those, that have functionally diverged. Dynamic analysis also enables those mutations that most affect dynamics to be identified. It correctly predicts all mutations (functional and permissive) necessary to evolve new function and ∼60% of permissive mutations necessary to recover ancestral function

The secondary structure is predicted through multiple sequence alignment with modern day homologs.

<p>These secondary structural elements are then connected with loops in extended conformation to generate hundreds of conformations with high flexibility. Only a few are shown here. These structures all undergo a FRODA simulation which collapses them by adding attractive perturbations between all hydrophobic contact pairs (represented by arrows) into tightly packed structures with hydrophobic cores. A subset of hydrophobic residues are shown as spheres. After scoring, the collapsed structures they are ran in a restrained r-REMD simulation for 5 ns and then an unrestrained REMD simulation for 5 ns or until converged. The 3 ancestral structures are prediction to within 2.7 Å all atom RMSD of a similar experimentally determined structure. The final ensemble of restraint free generated structures are analyzed for dynamics using PCA.</p

3D structures of AncCR, AncGR1 and AncGR2.

<p>AncCR was within 2.5 Å all-atom RMSD from the experimentally determined AncCR. AncGR1 was within 2.9 Å all-atom RMSD from the experimentally determined crystal structure. AncGR2 was within 2.8 Å all-atom RMSD from the experimentally determined AncGR2. Included for reference is a cartoon figure with helices labeled for reference and the ligand is bound, represented in blue spheres.</p

The change in fluctuation along the most collective mode between AncCR, AncGR1 and AncGR2.

<p>The X, Y, Z, and Y27R mutation groups necessary to alter function toward cortisol binding specificity are noted in red, and those permissive W mutations necessary to reverse function and recover promiscuous binding are noted in purple. A cutoff of ±0.002 Ų is applied to differentiate mutations critical to altering dynamics as also used in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002428#pcbi-1002428-g004" target="_blank">Fig. 4</a>. The upper left region of the graph indicates mutations that most alter dynamics when comparing the function-altering mutation from AncGR1 (binding promiscuity) to AncGR2 (binding specificity to cortisol) whereas the lower right region of the plot indicates mutations that most alter dynamics when comparing AncCR and AncGR1, which do not diverge functionally.</p

The change in net fluctuations and correlations of the mutated residues for successive evolution of MR to GR proteins.

<p>(A) The change in net fluctuation between successive ancestral proteins, AncCR, AncGR1 and AncGR2 for mutated residues. Those residues identified as critical to alter-function are noted in red. The activation-function (AF) helix contains mutations 224 and 229. A cutoff (solid line) results in all critical mutations identified except for Y91C and L197M. Y27R is noted as critical to function but sites 65, 117, and 158 are false positives. (B) The cross correlation map with AncGR2 on the upper left and AncGR1 on the lower right. Circled in black are changes in the cross correlation associated with critical residues near the binding pocket. Squared in black are the changes in cross correlation due to critical mutation N26T forming a hydrogen bond with the AF-helix. Circles in white are additional changes in cross correlation not associated with critical mutations. (C) The cross correlations between the X and W mutations. The correlation between X and W mutations is higher for AncGR2, whereas AncGR1 X functional mutations are uncorrelated, increasing the flexibility in the binding pocket and allowing for promiscuous binding.</p

Union of Geometric Constraint-Based Simulations with Molecular Dynamics for Protein Structure Prediction

Although proteins are a fundamental unit in biology, the mechanism by which proteins fold into their native state is not well understood. In this work, we explore the assembly of secondary structure units via geometric constraint-based simulations and the effect of refinement of assembled structures using reservoir replica exchange molecular dynamics. Our approach uses two crucial features of these methods: i), geometric simulations speed up the search for nativelike topologies as there are no energy barriers to overcome; and ii), molecular dynamics identifies the low free energy structures and further refines these structures toward the actual native conformation. We use eight α-, β-, and α/β-proteins to test our method. The geometric simulations of our test set result in an average RMSD from native of 3.7 Å and this further reduces to 2.7 Å after refinement. We also explore the question of robustness of assembly for inaccurate (shifted and shortened) secondary structure. We find that the RMSD from native is highly dependent on the accuracy of secondary structure input, and even slightly shifting the location of secondary structure along the amino acid sequence can lead to a rapid decrease in RMSD to native due to incorrect packing