Multiscale Computational Framework for the Liquid-Liquid Phase Separation of Intrinsically Disordered Proteins.

The reversible assembly of intrinsically disordered proteins (IDPs) to form membraneless organelles (MLOs) is a fundamental process involved in the spatiotemporal regulation in living cells. MLOs formed via liquid-liquid phase separation (LLPS) serve as molecule-enhancing hubs to regulate cell functions. Owing to the complexity and dynamic nature of the protein assembly via a network of weak inter- and intra-molecular interactions, it is challenging to describe and predict the LLPS behavior. We have developed a multiscale computational model for IDPs, using the fused in sarcoma (FUS) protein and its variants as illustrative examples. To simplify the description of protein, FUS is represented as a linear chain of stickers interspaced by spacers, as inspired by the associative polymer theory. Low-complexity aromatic-rich kinked segments (LARKS) available in FUS were identified using LARKSdb and represented as "stickers". The pairwise potential energies of each pair of stickers and their ß-sheet-forming propensity were estimated via molecular docking and all atomistic molecular dynamics (AA-MD) simulations. Subsequently, FUS chains were randomly positioned in a cubic lattice as coarse-grained (CG) beads, with the bead assignment based on the Kuhn length estimation of stickers and spacers. Stochastic FUS movements were modeled by Monte Carlo (MC) simulations. In addition to the Metropolis algorithm, discretized pair potential distributions between stickers were considered in the move acceptance criteria. The chosen pair potential represents one of the possible binding energy states, with its probability determined by the frequency of the binding energy distribution histogram. The fluctuations of averaged radial distribution functions (RDFs) in successive MC trial move intervals of equilibrated lattice MC simulations were used to indicate the dynamic nature of assembly/disassembly of the protein chains. This multiscale computational framework provides an economical and efficient way of predicting and describing the LLPS behavior of IDPs.

Submillisecond Atomistic Molecular Dynamics Simulations Reveal Hydrogen Bond-Driven Diffusion of a Guest Peptide in Protein-RNA Condensate.

Liquid-liquid phase separation mediated by proteins and/or nucleic acids is believed to underlie the formation of many distinct condensed phases, or membraneless organelles, within living cells. These condensates have been proposed to orchestrate a variety of important processes. Despite recent advances, the interactions that regulate the dynamics of molecules within a condensate remain poorly understood. We performed accumulated 564.7 µs all-atom molecular dynamics (MD) simulations (system size ∼200k atoms) of model condensates formed by a scaffold RNA oligomer and a scaffold peptide rich in arginine (Arg). These model condensates contained one of three possible guest peptides: the scaffold peptide itself or a variant in which six Arg residues were replaced by lysine (Lys) or asymmetric dimethyl arginine (ADMA). We found that the Arg-rich peptide can form the largest number of hydrogen bonds and bind the strongest to the scaffold RNA in the condensate, relative to the Lys- and ADMA-rich peptides. Our MD simulations also showed that the Arg-rich peptide diffused more slowly in the condensate relative to the other two guest peptides, which is consistent with a recent fluorescence microscopy study. There was no significant increase in the number of cation-π interactions between the Arg-rich peptide and the scaffold RNA compared to the Lys-rich and ADMA-rich peptides. Our results indicate that hydrogen bonds between the peptides and the RNA backbone, rather than cation-π interactions, play a major role in regulating peptide diffusion in the condensate.

Ultrahigh-Throughput Directed Evolution of Polymer-Degrading Enzymes Using Yeast Display.

Enzymes that degrade synthetic polymers have attracted intense interest for eco-friendly plastic recycling. However, because enzymes did not evolve for the cleavage of abiotic polymers, directed evolution strategies are needed to enhance activity for plastic degradation. Previous directed evolution efforts relied on polymer degradation assays that were limited to screening ∼104 mutants. Here, we report a high-throughput yeast surface display platform to rapidly evaluate >107 enzyme mutants for increased activity in cleaving synthetic polymers. In this platform, individual yeast cells display distinct mutants, and enzyme activity is detected by a change in fluorescence upon the cleavage of a synthetic probe resembling a polymer of interest. Highly active mutants are isolated by fluorescence activated cell sorting and identified through DNA sequencing. To demonstrate this platform, we performed directed evolution of a polyethylene terephthalate (PET)-depolymerizing enzyme, leaf and branch compost cutinase (LCC). We identified activity-boosting mutations that substantially increased the kinetics of degradation of solid PET films. Biochemical assays and molecular dynamics (MD) simulations of the most active variants suggest that the H218Y mutation improves the binding of the enzyme to PET. Overall, this evolution platform increases the screening throughput of polymer-degrading enzymes by 3 orders of magnitude and identifies mutations that enhance kinetics for depolymerizing solid substrates.

The Ion-Dipole Correction of the 3DRISM Solvation Model to Accurately Compute Water Distributions around Negatively Charged Biomolecules.

The 3D reference interaction site model (3DRISM) provides an efficient grid-based solvation model to compute the structural and thermodynamic properties of biomolecules in aqueous solutions. However, it remains challenging for existing 3DRISM methods to correctly predict water distributions around negatively charged solute molecules. In this paper, we first show that this challenge is mainly due to the orientation of water molecules in the first solvation shell of the negatively charged solute molecules. To properly consider this orientational preference, position-dependent two-body intramolecular correlations of solvent need to be included in the 3DRISM theory, but direct evaluations of these position-dependent two-body intramolecular correlations remain numerically intractable. To address this challenge, we introduce the Ion-Dipole Correction (IDC) to the 3DRISM theory, in which we incorporate the orientation preference of water molecules via an additional solute-solvent interaction term (i.e., the ion-dipole interaction) while keeping the formulism of the 3DRISM equation unchanged. We prove that this newly introduced IDC term is equivalent to an effective direct correlation function which can effectively consider the orientation effect that arises from position dependent two-body correlations. We first quantitatively validate our 3DRISM-IDC theory combined with the PSE3 closure on Cl-, [ClO]- (a two-site anion), and [NO2]- (a three-site anion). For all three anions, we show that our 3DRISM-IDC theory significantly outperforms the 3DRISM theory in accurately predicting the solvation structures in comparison to MD simulations, including RDFs and 3D water distributions. Furthermore, we have also demonstrated that the 3DRISM-IDC can improve the accuracy of hydration free-energy calculation for Cl-. We further demonstrate that our 3DRISM-IDC theory yields significant improvements over the 3DRISM theory when applied to compute the solvation structures for various negatively charged solute molecules, including adenosine triphosphate (ATP), a short peptide containing 19 residues, a DNA hairpin containing 24 nucleotides, and a riboswitch RNA molecule with 77 nucleotides. We expect that our 3DRISM-IDC-PSE3 solvation model holds great promise to be widely applied to study solvation properties for nucleic acids and other biomolecules containing negatively charged functional groups.

TAPS: A traveling-salesman based automated path searching method for functional conformational changes of biological macromolecules.

Locating the minimum free energy paths (MFEPs) between two conformational states is among the most important tasks of biomolecular simulations. For example, knowledge of the MFEP is critical for focusing the effort of unbiased simulations that are used for the construction of Markov state models to the biologically relevant regions of the system. Typically, existing path searching methods perform local sampling around the path nodes in a pre-selected collective variable (CV) space to allow a gradual downhill evolution of the path toward the MFEP. Despite the wide application of such a strategy, the gradual path evolution and the non-trivial a priori choice of CVs are also limiting its overall efficiency and automation. Here we demonstrate that non-local perpendicular sampling can be pursued to accelerate the search, provided that all nodes are reordered thereafter via a traveling-salesman scheme. Moreover, path-CVs can be computed on-the-fly and used as a coordinate system, minimizing the necessary prior knowledge about the system. Our traveling-salesman based automated path searching method achieves a 5-8 times speedup over the string method with swarms-of-trajectories for two peptide systems in vacuum and solution, making it a promising method for obtaining initial pathways when investigating functional conformational changes between a pair of structures.