Dynamics of Structural Elements of GB1 β-Hairpin Revealed by Tryptophan-Cysteine Contact Formation Experiments

A molecular dynamics simulation study of glycine/serine octapeptides labeled with 2,3-diazabicyclo[2.2.2]oct-2-ene fluorophore

The compound 2,3-diazabicyclo[2.2.2]oct-2-ene (DBO) is a versatile fluorophore widely used in Förster resonance energy transfer (FRET) spectroscopy studies due to its remarkable sensitivity, enabling precise donor-acceptor distance measurements, even for short peptides. Integrating time-resolved and FRET spectroscopies with molecular dynamics simulations provides a robust approach to unravel the structure and dynamics of biopolymers in a solution. This study investigates the structural behavior of three octapeptide variants: Trp-(Gly-Ser)3-Dbo, Trp-(GlyGly)3-Dbo, and Trp-(SerSer)3-Dbo, where Dbo represents the DBO-containing modified aspartic acid, using molecular dynamics simulations. Glycine- and serine-rich amino acid fragments, common in flexible protein regions, play essential roles in functional properties. Results show excellent agreement between end-to-end distances, orientational factors from simulations, and the available experimental and theoretical data, validating the reliability of the GROMOS force field model. The end-to-end distribution, modeled using three Gaussian distributions, reveals a complex shape, confirmed by cluster analysis highlighting a limited number of significant conformations dominating the peptide landscape. All peptides predominantly adopt a disordered state in the solvent, yet exhibit a compact shape, aligning with the model of disordered polypeptide chains in poor solvents. Conformations show marginal dependence on chain composition, with Ser-only chains exhibiting slightly more elongation. This study enhances our understanding of peptide behavior, providing valuable insights into their structural dynamics in solution.

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.

A deep autoencoder framework for discovery of metastable ensembles in biomacromolecules

Biomacromolecules manifest dynamic conformational fluctuation and involve mutual interconversion among metastable states. A robust mapping of their conformational landscape often requires the low-dimensional projection of the conformational ensemble along optimized collective variables (CVs). However, the traditional choice for the CV is often limited by user-intuition and prior knowledge about the system, and this lacks a rigorous assessment of their optimality over other candidate CVs. To address this issue, we propose an approach in which we first choose the possible combinations of inter-residue Cα-distances within a given macromolecule as a set of input CVs. Subsequently, we derive a non-linear combination of latent space embedded CVs via auto-encoding the unbiased molecular dynamics simulation trajectories within the framework of the feed-forward neural network. We demonstrate the ability of the derived latent space variables in elucidating the conformational landscape in four hierarchically complex systems. The latent space CVs identify key metastable states of a bead-in-a-spring polymer. The combination of the adopted dimensional reduction technique with a Markov state model, built on the derived latent space, reveals multiple spatially and kinetically well-resolved metastable conformations for GB1 β-hairpin. A quantitative comparison based on the variational approach-based scoring of the auto-encoder-derived latent space CVs with the ones obtained via independent component analysis (principal component analysis or time-structured independent component analysis) confirms the optimality of the former. As a practical application, the auto-encoder-derived CVs were found to predict the reinforced folding of a Trp-cage mini-protein in aqueous osmolyte solution. Finally, the protocol was able to decipher the conformational heterogeneities involved in a complex metalloenzyme, namely, cytochrome P450.

The SARS-CoV-2 nucleocapsid protein is dynamic, disordered, and phase separates with RNA

The SARS-CoV-2 nucleocapsid (N) protein is an abundant RNA-binding protein critical for viral genome packaging, yet the molecular details that underlie this process are poorly understood. Here we combine single-molecule spectroscopy with all-atom simulations to uncover the molecular details that contribute to N protein function. N protein contains three dynamic disordered regions that house putative transiently-helical binding motifs. The two folded domains interact minimally such that full-length N protein is a flexible and multivalent RNA-binding protein. N protein also undergoes liquid-liquid phase separation when mixed with RNA, and polymer theory predicts that the same multivalent interactions that drive phase separation also engender RNA compaction. We offer a simple symmetry-breaking model that provides a plausible route through which single-genome condensation preferentially occurs over phase separation, suggesting that phase separation offers a convenient macroscopic readout of a key nanoscopic interaction.

The SARS-CoV-2 nucleocapsid protein is dynamic, disordered, and phase separates with RNA

The SARS-CoV-2 nucleocapsid (N) protein is an abundant RNA binding protein critical for viral genome packaging, yet the molecular details that underlie this process are poorly understood. Here we combine single-molecule spectroscopy with all-atom simulations to uncover the molecular details that contribute to N protein function. N protein contains three dynamic disordered regions that house putative transiently-helical binding motifs. The two folded domains interact minimally such that full-length N protein is a flexible and multivalent RNA binding protein. N protein also undergoes liquid-liquid phase separation when mixed with RNA, and polymer theory predicts that the same multivalent interactions that drive phase separation also engender RNA compaction. We offer a simple symmetry-breaking model that provides a plausible route through which single-genome condensation preferentially occurs over phase separation, suggesting that phase separation offers a convenient macroscopic readout of a key nanoscopic interaction.