Intracellular environment can change protein conformational dynamics in cells through weak interactions

Highly Optimized Simulation of Atomic Resolution Cell-Like Protein Environment

Computational approaches can provide details of molecular mechanisms in a crowded environment inside cells. Protein docking predicts stable configurations of molecular complexes, which correspond to deep energy minima. Systematic docking approaches, such as those based on fast Fourier transform (FFT), also map the entire intermolecular energy landscape by determining the position and depth of the full spectrum of the energy minima. Such mapping allows speeding up simulations by precalculating the intermolecular energy values. Our earlier study combined FFT docking with the Monte Carlo protocol, enabling simulation of cell-size, crowded protein systems with seconds, and longer trajectories at atomic resolution, several orders of magnitude longer than those achievable by alternative approaches. In this study, we present a further drastic extension of the modeling capabilities by parallelized implementation of the simulation protocol. The procedure was applied to a panel of Death Fold Domains that form nucleated polymers in human innate immune signaling, recapitulating their homooligomerization tendencies and providing insights into the molecular mechanisms of polymer nucleation. The parallelized protocol allows extension of the simulation trajectories by orders of magnitude beyond the previously reported implementation, reaching into the uncharted territory of atomic resolution simulation of cell-sized systems.

Quantifying protein-drug lifetimes in human cells by 19F NMR spectroscopy

The cellular environment is a complex and crowded space, with organelles, compartments and multitudes of molecules engaged in intricate networks of communication that modulate binary protein-ligand/protein interactions. As a result, it is becoming increasingly appreciated that evaluations of protein-drug binding should be carried out in the native cellular environment. Here, we present a proof-of-concept study where we measured the lifetime (1/koff) of a protein-drug complex in human cells by 19F NMR spectroscopy using fluorinated Cyclophilin A (CypA) bound to Cyclosporine A (CsA). Harnessing the exceptional detection sensitivity of the trifluoromethyl group attached at the para position of Phe60 in CypA, high-quality 2D 19F-19F exchange spectra were obtained in cells. Essentially identical koff values were observed in cells and in vitro, suggesting that the overall impact of the cellular environment on the lifetime of tfmF60 CypA/CsA complex is minimal. Using similar approaches for quantifying protein-drug lifetimes in the native cellular environment paves the way for efficiently screening drug libraries in human cells by 19F NMR spectroscopy.

HSSPPI: hierarchical and spatial-sequential modeling for PPIs prediction

MOTIVATION

Protein-protein interactions play a fundamental role in biological systems. Accurate detection of protein-protein interaction sites (PPIs) remains a challenge. And, the methods of PPIs prediction based on biological experiments are expensive. Recently, a lot of computation-based methods have been developed and made great progress. However, current computational methods only focus on one form of protein, using only protein spatial conformation or primary sequence. And, the protein's natural hierarchical structure is ignored.

RESULTS

In this study, we propose a novel network architecture, HSSPPI, through hierarchical and spatial-sequential modeling of protein for PPIs prediction. In this network, we represent protein as a hierarchical graph, in which a node in the protein is a residue (residue-level graph) and a node in the residue is an atom (atom-level graph). Moreover, we design a spatial-sequential block for capturing complex interaction relationships from spatial and sequential forms of protein. We evaluate HSSPPI on public benchmark datasets and the predicting results outperform the comparative models. This indicates the effectiveness of hierarchical protein modeling and also illustrates that HSSPPI has a strong feature extraction ability by considering spatial and sequential information simultaneously.

AVAILABILITY AND IMPLEMENTATION

The code of HSSPPI is available at https://github.com/biolushuai/Hierarchical-Spatial-Sequential-Modeling-of-Protein.

In-Cell Mass Spectrometry and Ultraviolet Photodissociation Navigates the Intracellular Protein Heterogeneity

Directly probing the heterogeneous conformations of intracellular proteins within their native cellular environment remains a significant challenge in mass spectrometry (MS). Here, we establish an in-cell MS and ultraviolet photodissociation (UVPD) strategy that directly ejects proteins from living cells into a mass spectrometer, followed by 193 nm UVPD for structural analysis. Applying this approach to calmodulin (CaM), we reveal that it adopts more extended conformations within living cells compared with purified samples in vitro, highlighting the unique influence of intracellular environments on protein folding. Furthermore, UVPD analysis of calcium ion (Ca2+)-binding variants of CaM unveils not only the conformational heterogeneity induced by multiple Ca2+ modulations but also reveals distinct preferences of Ca2+ binding sites across different conformations. This strategy provides a powerful tool for interrogating the structure-function relationships of intracellular protein variants with sophisticated metal ion binding, paving the way for a deeper understanding of protein conformations within their native cellular context.

BEMM-GEN: A Toolkit for Generating a Biomolecular Environment-Mimicking Model for Molecular Dynamics Simulation

Understanding the influence of the cellular environment on protein conformations is crucial for elucidating protein functions within living cells. In studies using molecular dynamics (MD) simulation, carbon nanotubes and hydrophobic cages have been widely used to emulate the cellular environment inside specific large biomolecules such as ribosome tunnels and chaperones. However, recent studies suggest that these uniform hydrophobic models may not adequately capture the environmental effects inside each biomolecule. Based on these facts, it is necessary to generate spherical and cylindrical models with varied chemical properties corresponding to the components within target biomolecules. We developed a biomolecular environment-mimicking model generator (BEMM-GEN) that generates spherical and cylindrical models with user-specified chemical properties and allows the integration of arbitrary protein conformations into the generated models. BEMM-GEN provides model and protein complex structures, along with the corresponding parameter files for MD simulation (AMBER and GROMACS), and users immediately run their MD simulation based on the generated input files. BEMM-GEN can be freely downloaded and installed via a Python package manager (pip install BEMM-gen). The source code files and a user manual for operation are provided on GitHub (https://github.com/y4suda/BEMM-GEN).

Advancing Protein Detection and Analysis Based on Ag/Au PHCN for Enhanced SERS Sensitivity and Specificity in Biomolecular Diagnostics

In the realm of disease diagnostics, particularly for conditions such as proteinuria and hemoglobinuria, the quest for a method that combines accurate, label-free detection of protein compositions and their conformational changes remains a formidable challenge. In this study, we introduce an innovative Ag/Au plasmonic hybrid coupling nanoarray (Ag/Au PHCN) architecture marked by sub-10 nm interparticle gaps. These nanoarrays, leveraging plasmonic hybrid coupling and synergistic enhancement mechanisms, create a plethora of uniform surface-enhanced Raman spectroscopy (SERS) hotspots. The Ag/Au PHCN substrates demonstrated unparalleled sensitivity in the unmarked detection of hemoglobin (HGB), bovine serum albumin (BSA), and cytochrome C (Cyt.C) in bodily fluids, incorporating the advantages of high sensitivity, high reproducibility, durability, recyclability, and biocompatibility. Notably, the detection limits for BSA and HGB are unprecedented at 0.5 and 5 ng/mL, respectively. This achievement sets a new benchmark for label-free protein detection using two-dimensional nanostructures. Crucially, the Ag/Au PHCNs possess the novel capability to discern protein conformational changes post denaturation, underscoring their potential in probing protein functionalities. Most importantly, these nanoarrays can differentiate between normal and proteinuria-affected urine samples and monitor protein content variations over time, heralding a new era in clinical diagnostics with particular relevance to proteinuria and hemoglobinuria detection.

Quantification of CH and NH/π-Stacking Interactions in Cells Using Nuclear Magnetic Resonance Spectroscopy

π-Stacking, a type of noncovalent interactions involving aromatic residues, plays an important role in protein folding and function. In this work, an attempt has been made to measure CH/π and NH/π stacking interactions in a protein in Escherichia coli cells using a combined double-mutant cycle and nuclear magnetic resonance spectroscopy method. The results show that the CH/π and NH/π stacking interactions are generally weaker in cells than those in the buffer. The transient intermolecular noncovalent interactions between the protein and the complex cellular environment may compete with and thus weaken the stacking interactions in the protein. The weakening of stacking interactions can enhance the local conformational opening of proteins in E. coli cells. This is evident from the faster rates of amide hydrogen/deuterium exchange observed in cells than in the buffer, for residues that undergo local conformational opening. This study highlights the influence of the cellular environment on π-stacking and the conformational dynamics of proteins.

Diffusion of proteins in crowded solutions studied by docking-based modeling

The diffusion of proteins is significantly affected by macromolecular crowding. Molecular simulations accounting for protein interactions at atomic resolution are useful for characterizing the diffusion patterns in crowded environments. We present a comprehensive analysis of protein diffusion under different crowding conditions based on our recent docking-based approach simulating an intracellular crowded environment by sampling the intermolecular energy landscape using the Markov Chain Monte Carlo protocol. The procedure was extensively benchmarked, and the results are in very good agreement with the available experimental and theoretical data. The translational and rotational diffusion rates were determined for different types of proteins under crowding conditions in a broad range of concentrations. A protein system representing most abundant protein types in the E. coli cytoplasm was simulated, as well as large systems of other proteins of varying sizes in heterogeneous and self-crowding solutions. Dynamics of individual proteins was analyzed as a function of concentration and different diffusion rates in homogeneous and heterogeneous crowding. Smaller proteins diffused faster in heterogeneous crowding of larger molecules, compared to their diffusion in the self-crowded solution. Larger proteins displayed the opposite behavior, diffusing faster in the self-crowded solution. The results show the predictive power of our structure-based simulation approach for long timescales of cell-size systems at atomic resolution.

Ribosome Tunnel Environment Drives the Formation of α-Helix during Cotranslational Folding

Protein conformations in cells are not solely determined by amino acid sequences; they also depend on cellular environments. For instance, the ribosome tunnel induces its specific α-helix formation during cotranslational folding. Owing to the link between these temporally α-helix and biological functions, the mechanism of α-helix formation inside the ribosome tunnel has been previously explored. Consequently, the conformational restrictions of the tunnel were considered one of the driving forces of α-helix formation. Conversely, the ribosomal tunnel environment, including its chemical properties, appears to influence the α-helix formation. However, a comprehensive analysis of the ribosome tunnel environment's impact on the α-helix formation has not been conducted yet due to challenges in experimentally controlling it. Therefore, as a new computational approach, we proposed a ribosome environment-mimicking model (REMM) based on the radius and components of the experimentally determined ribosome tunnel structures. Using REMM, we assessed the impact of the ribosome tunnel environment on α-helix formation. Herein, we employed carbon nanotubes (CNT) as a reference model alongside REMM because CNT reproduce conformational restrictions rather than the ribosome tunnel environment. Quantitatively, the ability to reproduce the α-helix of nascent peptides in the experimental structure was compared between the CNT and REMM using enhanced all-atom molecular dynamics simulations. Consequently, the REMM more accurately reproduced the α-helix of the nascent peptides than the CNT, highlighting the significance of the ribosome tunnel environment in α-helix formation. Additionally, we analyzed the properties of the peptide inside each model to reveal the mechanism of ribosome tunnel-specific α-helix formation. Consequently, we revealed that the chemical diversities of the tunnel are essential for the formation of backbone-to-backbone hydrogen bonds in the peptides. In conclusion, the ribosome tunnel environment, with the diverse chemical properties, drives its specific α-helix formation. By proposing REMM, we newly provide the technical basis for investigating the protein conformations in various cellular environments.

Protein Allostery Study in Cells Using NMR Spectroscopy

Protein allostery is commonly observed in vitro. But how protein allostery behaves in cells is unknown. In this work, a protein monomer-dimer equilibrium system was built with the allosteric effect on the binding characterized using NMR spectroscopy through mutations away from the dimer interface. A chemical shift linear fitting method was developed that enabled us to accurately determine the dissociation constant. A total of 28 allosteric mutations were prepared and grouped to negative allosteric, nonallosteric, and positive allosteric modulators. ∼ 50% of mutations displayed the allosteric-state changes when moving from a buffered solution into cells. For example, there were no positive allosteric modulators in the buffered solution but eight in cells. The change in protein allostery is correlated with the interactions between the protein and the cellular environment. These interactions presumably drive the surrounding macromolecules in cells to transiently bind to the monomer and dimer mutational sites and change the free energies of the two species differently which generate new allosteric effects. These surrounding macromolecules create a new protein allostery pathway that is only present in cells.

Studying protein stability in crowded environments by NMR

Most proteins perform their functions in crowded and complex cellular environments where weak interactions are ubiquitous between biomolecules. These complex environments can modulate the protein folding energy landscape and hence affect protein stability. NMR is a nondestructive and effective method to quantify the kinetics and equilibrium thermodynamic stability of proteins at an atomic level within crowded environments and living cells. Here, we review NMR methods that can be used to measure protein stability, as well as findings of studies on protein stability in crowded environments mimicked by polymer and protein crowders and in living cells. The important effects of chemical interactions on protein stability are highlighted and compared to spatial excluded volume effects.

Exploring the dynamics and structure of PpiB in living Escherichia coli cells using electron paramagnetic resonance spectroscopy

The combined effects of the cellular environment on proteins led to the definition of a fifth level of protein structural organization termed quinary structure. To explore the implication of potential quinary structure for globular proteins, we studied the dynamics and conformations of Escherichia coli (E. coli) peptidyl-prolyl cis/trans isomerase B (PpiB) in E. coli cells. PpiB plays a major role in maturation and regulation of folded proteins by catalyzing the cis/trans isomerization of the proline imidic peptide bond. We applied electron paramagnetic resonance (EPR) techniques, utilizing both Gadolinium (Gd(III)) and nitroxide spin labels. In addition to using standard spin labeling approaches with genetically engineered cysteines, we incorporated an unnatural amino acid to achieve Gd(III)-nitroxide orthogonal labeling. We probed PpiB's residue-specific dynamics by X-band continuous wave EPR at ambient temperatures and its structure by double electron-electron resonance (DEER) on frozen samples. PpiB was delivered to E. coli cells by electroporation. We report a significant decrease in the dynamics induced by the cellular environment for two chosen labeling positions. These changes could not be reproduced by adding crowding agents and cell extracts. Concomitantly, we report a broadening of the distance distribution in E. coli, determined by Gd(III)-Gd(III) DEER measurements, as compared with solution and human HeLa cells. This suggests an increase in the number of PpiB conformations present in E. coli cells, possibly due to interactions with other cell components, which also contributes to the reduction in mobility and suggests the presence of a quinary structure.

Progress, Challenges and Opportunities of NMR and XL-MS for Cellular Structural Biology

The validity of protein structures and interactions, whether determined under ideal laboratory conditions or predicted by AI tools such as Alphafold2, to precisely reflect those found in living cells remains to be examined. Moreover, understanding the changes in protein structures and interactions in response to stimuli within living cells, under both normal and disease conditions, is key to grasping proteins' functionality and cellular processes. Nevertheless, achieving high-resolution identification of these protein structures and interactions within living cells presents a technical challenge. In this Perspective, we summarize the recent advancements in in-cell nuclear magnetic resonance (NMR) and in vivo cross-linking mass spectrometry (XL-MS) for studying protein structures and interactions within a cellular context. Additionally, we discuss the challenges, opportunities, and potential benefits of integrating in-cell NMR and in vivo XL-MS in future research to offer an exhaustive approach to studying proteins in their natural habitat.