Development of a Hybrid-Resolution Force Field for Peptide Self-Assembly Simulations: Optimizing Peptide-Peptide and Peptide-Solvent Interactions

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.

Aqueous Circularly Polarized Luminescence Induced by Homopolypeptide Self-Assembly

Remarkable advances have been achieved in solution self-assembly of polypeptides from the perspective of nanostructures, mechanisms, and applications. Despite the intrinsic chirality of polypeptides, the promising generation of aqueous circularly polarized luminescence (CPL) based on their self-assembly has been rarely reported due to the weak fluorescence of most polypeptides and the indeterminate self-assembly mechanism. Here, we propose a facile strategy for achieving aqueous CPL based on the self-assembly of simple homopolypeptides modified with a terminal group featuring both twisted intramolecular charge transfer and aggregation-induced emission properties. A morphology-dependent CPL can be observed under different self-assembly conditions by altering the solvents. A nanotoroid-dispersed aqueous solution with detectable CPL can be obtained by using tetrahydrofuran as a good solvent for the self-assembly, which is attributed to the involvement of the terminal group in the chiral environment formed by the homopolypeptide chains. However, such a chiral packing mode cannot be realized in nanorods self-assembled from dioxane, resulting in an inactive CPL phenomenon. Furthermore, CPL signals can be greatly amplified by co-assembly of homopolypeptides with the achiral small molecule derived from the terminal group. This work not only provides a pathway to construct aqueous CPL-active homopolypeptide nanomaterials but also reveals a potential mechanism in the self-assembly for chiral production, transfer, and amplification in polypeptide-based nanostructures.

Machine Learning-Guided Adaptive Parametrization for Coupling Terms in a Mixed United-Atom/Coarse-Grained Model for Diphenylalanine Self-Assembly in Aqueous Ionic Liquids

Precise regulation of the peptide self-assembly into ordered nanostructures with intriguing properties has attracted intense attention. However, predicting peptide assembly at atomic resolution is a challenge due to both the structural flexibility of peptides and the associated huge computational costs. A machine learning-guided adaptive parametrization method was proposed for developing a mixed atomic and coarse-grained (CG) model through a multiobjective optimization strategy. Our model incorporates the united-atom (UA) model for diphenylalanine (P) and the polarizable electrostatic-variable coarse-grained (VaCG) model for aqueous ionic liquid [BMIM]+[BF4]- solution. In this mixed model, the coupling van der Waals (vdW) interaction is addressed by introducing virtual sites (VS) in the UA model to interact with solvent CG beads. The coupling parameters, including the electrostatic parameter and vdW parameters, are automatically optimized through ML-guided adaptive parametrization. The performance of this model was tested by some microstructural properties, e.g., the average number of P-P intermolecular hydrogen bonds (HBs) and radius distribution functions (RDFs) between P and different fragments of IL, in comparison with all-atom (AA) simulations. The computational cost is significantly reduced using such a parametrization scheme, which could search tens of thousands of force-field parameter sets, while needing only a small fraction of them to be assessed with molecular dynamics (MD) simulations. We used such a mixed resolution model to investigate the self-assembly in IL-water mixtures with variants of IL concentration (X). The long-range-ordered fibril structure is formed in a pure water system (X = 0). With an increase of IL concentrations, the formation of an ordered self-assembly nanostructure is prohibited, instead forming branched fibril at X = 2 mol % or amorphous aggregates when X > 10 mol %, resulting from the interplay between π-stacking and HB interactions between P and IL. The qualitative agreement between the simulated structures and the observed morphologies in experiments indicates the applicability of ML-guided parametrization strategy in the study of complex systems, such as polymers, lipid bilayers, and polysaccharides.

The Molecular Mechanism of PSMα3 Aggregation: A New View

The emergence of a novel cross-α fibrillar structure, unlike the commonly observed sequence-independent cross-β one, of a 22-residue bacterial virulent amphipathic α-helical peptide of the phenol soluble modulin (PSM) family, PSMα3, with many deleterious effects on human life, has infused uncertainty to the paradigm of the intrinsically polymorphic, multivariate, multiphasic, and cross-sequence-cross-disease entangled protein aggregation landscape and hence on the identity of the therapeutic target. We, here, deconvolute the factors contributing to the genesis and hence the transition of lower to higher order aggregates of PSMα3 in its natural state and three noncanonical designed variants using conventional and enhanced sampling approach-based atomistic simulations. PSMα3 shows structural polymorphism with nominal α-helicity, substantial β-propensity, and dominant random-coil features, irrespective of the extent of aggregation. Moreover, the individual features of the overall amphipathicity operate alternatively depending on the extent and organization of aggregation; the dominance gradually moves from charged to hydrophobic residues with the progressive generation of higher order aggregates (dimer to oligomer to fibril) and with increasing orderedness of the self-assembled construct (oligomer vs dimer/fibril). Similarly, the contribution of interchain salt bridges decreases with increasing order of aggregation (dimer to oligomer to fibril). However, the intrachain salt bridges consistently display their role in all phases of aggregation. Such phase-independent features also include equivalent roles of electrostatic and van der Waals forces on intrachain interactions, sole contribution of van der Waals forces on interchain cross-talk, and negligible peptide-water relationship. Finally, we propose a conjugate peptide-based aggregation suppressor having a single-point proline mutation.

Multistep molecular mechanisms of Aβ16-22 fibril formation revealed by lattice Monte Carlo simulations

As a model of self-assembly from disordered monomers to fibrils, the amyloid-β fragment Aβ16-22 was subject to past numerous experimental and computational studies. Because dynamics information between milliseconds and seconds cannot be assessed by both studies, we lack a full understanding of its oligomerization. Lattice simulations are particularly well suited to capture pathways to fibrils. In this study, we explored the aggregation of 10 Aβ16-22 peptides using 65 lattice Monte Carlo simulations, each simulation consisting of 3 × 109 steps. Based on a total of 24 and 41 simulations that converge and do not converge to the fibril state, respectively, we are able to reveal the diversity of the pathways leading to fibril structure and the conformational traps slowing down the fibril formation.

GB1 hairpin kinetics: capturing the folding pathway with molecular dynamics, replica exchange and optimal dimensionality reduction

We have performed molecular dynamics (MD) and replica-exchange (REMD) simulations of folding of the GB1 hairpin peptide in aqueous solution. REMD results were consistent with a cooperative zipper folding model. 120 μ s MD trajectories at 320 K yielded relaxation times of 1.8 μ s and 100 ns, with the slower assigned to global folding. The MD folding/unfolding transitions also followed the cooperative zipper model, specifying nucleation at the central turn followed by consecutive hydrogen bond formation. Formation of hydrogen bonds and hydrophobic contacts were highly correlated. Coarse-grained kinetic models constructed with the Optimal Dimensionality Reduction (ODR) approach found a folding time of 3.3 μ s and unfolding time of 4.0 μ s . Additionally, relaxation times in the 130-170 ns range could be assigned to formation of the transition state and off-path intermediates. The unfolded state was the most highly populated and, significantly, most heterogenous, assembling the largest number of microstates, primarily composed of extended and turn structures. The folded state was also heterogenous, but a to a lesser degree, involving the fully folded and partially folded in-register hairpins at early stages of the zipper pathway. The transition state corresponded to the nucleated hairpin, with central turn and first beta-sheet hydrogen bond, while the off-path intermediates were off-register partial hairpins. Our simulation results were in excellent agreement with experimental data on folded fraction, relaxation time and folding mechanism. The new findings from this work suggest a highly cooperative zipper folding mechanism, nascent hairpin transition state and ∼100 ns relaxation related to intermediate formation.Communicated by Ramaswamy H. Sarma.

Self-Assembly of Amyloid-Beta (Aβ) Peptides from Solution to Near In Vivo Conditions

Understanding the atomistic resolution changes during the self-assembly of amyloid peptides or proteins is important to develop compounds or conditions to alter the aggregation pathways and suppress the toxicity and potentially aid in the development of drugs. However, the complexity of protein aggregation and the transient order/disorder of oligomers along the pathways to fibril are very challenging. In this Perspective, we discuss computational studies of amyloid polypeptides carried out under various conditions, including conditions closely mimicking in vivo and point out the challenges in obtaining physiologically relevant results, focusing mainly on the amyloid-beta Aβ peptides.