Modeling Crowded Environment in Molecular Simulations

The effect of konjac glucomannan on enzyme kinetics and fluorescence spectrometry of digestive enzymes: An in vitro research from the perspective of macromolecule crowding

Konjac glucomannan (KGM) can significantly prolong gastrointestinal digestion. However, it is still worth investigating whether the macromolecular crowding (MMC) induced by KGM is correlated with digestion. In this paper, the MMC effect was quantified by fluorescence resonance energy transfer and microrheology, and the digests of starch, protein, and oil were determined. The digestive enzymes were analyzed by enzyme reaction kinetic and fluorescence quenching. The results showed that higher molecular weight (604.85 ∼ 1002.21 kDa) KGM created a larger MMC (>0.8), and influenced the digestion of macronutrients; the digests of starch, protein, and oil all decreased significantly. MMC induced by KGM decreased the Michaelis-Menten constants (Km and Vmax) of pancreatic α-amylase (PPA), pepsin (PEP), and pancreatic lipase (PPL). The larger MMC (>0.8) induced by KGM resulted in the decrease of fluorescence quenching constants (Ksv) in PPA and PPL, and the increase of Ksv in PEP. Therefore, varying degrees of MMC induced by KGM could play a role in regulating digestion and the inhibitory effect on digestion was more significant in a relatively more crowded environment induced by KGM. This study provides theoretical support for the strategies of nutrient digestion regulation from the perspective of MMC caused by dietary fiber.

Size Sensitivity of Metabolite Diffusion in Macromolecular Crowds

Metabolites play crucial roles in cellular processes, yet their diffusion in the densely packed interiors of cells remains poorly understood, compounded by conflicting reports in existing studies. Here, we employ pulsed-gradient stimulated-echo NMR and Brownian/Stokesian dynamics simulations to elucidate the behavior of nano- and subnanometer-sized tracers in crowded environments. Using Ficoll as a crowder, we observe a linear decrease in tracer diffusivity with increasing occupied volume fraction, persisting─somewhat surprisingly─up to volume fractions of 30-40%. While simulations suggest a linear correlation between diffusivity slowdown and particle size, experimental findings hint at a more intricate relationship, possibly influenced by Ficoll's porosity. Simulations and numerical calculations of tracer diffusivity in the E. coli cytoplasm show a nonlinear yet monotonic diffusion slowdown with particle size. We discuss our results in the context of nanoviscosity and discrepancies with existing studies.

Computational Approaches to Predict Protein-Protein Interactions in Crowded Cellular Environments

Investigating protein-protein interactions is crucial for understanding cellular biological processes because proteins often function within molecular complexes rather than in isolation. While experimental and computational methods have provided valuable insights into these interactions, they often overlook a critical factor: the crowded cellular environment. This environment significantly impacts protein behavior, including structural stability, diffusion, and ultimately the nature of binding. In this review, we discuss theoretical and computational approaches that allow the modeling of biological systems to guide and complement experiments and can thus significantly advance the investigation, and possibly the predictions, of protein-protein interactions in the crowded environment of cell cytoplasm. We explore topics such as statistical mechanics for lattice simulations, hydrodynamic interactions, diffusion processes in high-viscosity environments, and several methods based on molecular dynamics simulations. By synergistically leveraging methods from biophysics and computational biology, we review the state of the art of computational methods to study the impact of molecular crowding on protein-protein interactions and discuss its potential revolutionizing effects on the characterization of the human interactome.

Viscosity Prediction of High-Concentration Antibody Solutions with Atomistic Simulations

The computational prediction of the viscosity of dense protein solutions is highly desirable, for example, in the early development phase of high-concentration biopharmaceutical formulations where the material needed for experimental determination is typically limited. Here, we use large-scale atomistic molecular dynamics (MD) simulations with explicit solvation to de novo predict the dynamic viscosities of solutions of a monoclonal IgG1 antibody (mAb) from the pressure fluctuations using a Green-Kubo approach. The viscosities at simulated mAb concentrations of 200 and 250 mg/mL are compared to the experimental values, which we measured with rotational rheometry. The computational viscosity of 24 mPa·s at the mAb concentration of 250 mg/mL matches the experimental value of 23 mPa·s obtained at a concentration of 213 mg/mL, indicating slightly different effective concentrations (or activities) in the MD simulations and in the experiments. This difference is assigned to a slight underestimation of the effective mAb-mAb interactions in the simulations, leading to a too loose dynamic mAb network that governs the viscosity. Taken together, this study demonstrates the feasibility of all-atom MD simulations for predicting the properties of dense mAb solutions and provides detailed microscopic insights into the underlying molecular interactions. At the same time, it also shows that there is room for further improvements and highlights challenges, such as the massive sampling required for computing collective properties of dense biomolecular solutions in the high-viscosity regime with reasonable statistical precision.

Fibrillization Process of Human Amyloid-Beta Protein (1-40) under a Molecular Crowding Environment Mimicking the Interior of Living Cells Using Cell Debris

Molecular crowding environments play a crucial role in understanding the mechanisms of biological reactions. Inside living cells, a diverse array of molecules coexists within a volume fraction ranging from 10% to 30% v/v. However, conventional spectroscopic methods often face difficulties in selectively observing the structures of particular proteins or membranes within such molecularly crowded environments due to the presence of high background signals. Therefore, it is crucial to establish in vitro measurement conditions that closely resemble the intracellular environment. Meanwhile, the neutron scattering method offers a significant advantage in selectively observing target biological components, even within crowded environments. Recently, we have demonstrated a novel scattering method capable of selectively detecting the structures of targeted proteins or membranes in a closely mimicking intracellular milieu achieved utilizing whole-cell contents (deuterated-cell debris). This method relies on the inverse contrast matching technique in neutron scattering. By employing this method, we successfully observed the fibrillization process of human amyloid beta-protein (Aβ 1-40) under a molecular crowding environment (13.1% w/v cell debris, Aβ/cell debris = ~1/25 w/w) that closely mimics the interior of living cells. Aβ protein is well known as a major pathogenic component of Alzheimer's disease. The present results combining model simulation analyses clearly show that the intracellular environment facilitates the potential formation of even more intricate higher-order aggregates of Aβ proteins than those previously reported.

Effects of Homogeneous and Heterogeneous Crowding on Translational Diffusion of Rigid Bovine Serum Albumin and Disordered Alfa-Casein

Intracellular environment includes proteins, sugars, and nucleic acids interacting in restricted media. In the cytoplasm, the excluded volume effect takes up to 40% of the volume available for occupation by macromolecules. In this work, we tested several approaches modeling crowded solutions for protein diffusion. We experimentally showed how the protein diffusion deviates from conventional Brownian motion in artificial conditions modeling the alteration of medium viscosity and rigid spatial obstacles. The studied tracer proteins were globular bovine serum albumin and intrinsically disordered α-casein. Using the pulsed field gradient NMR, we investigated the translational diffusion of protein probes of different structures in homogeneous (glycerol) and heterogeneous (PEG 300/PEG 6000/PEG 40,000) solutions as a function of crowder concentration. Our results showed fundamentally different effects of homogeneous and heterogeneous crowded environments on protein self-diffusion. In addition, the applied "tracer on lattice" model showed that smaller crowding obstacles (PEG 300 and PEG 6000) create a dense net of restrictions noticeably hindering diffusing protein probes, whereas the large-sized PEG 40,000 creates a "less restricted" environment for the diffusive motion of protein molecules.

Conformational ensembles explain NMR spectra of frozen intrinsically disordered proteins

Protein regions which are intrinsically disordered, exist as an ensemble of rapidly interconverting structures. Cooling proteins to cryogenic temperatures for dynamic nuclear polarization (DNP) magic angle spinning (MAS) NMR studies suspends most of the motions, resulting in peaks that are broad but not featureless. To demonstrate that detailed conformational restraints can be retrieved from the peak shapes of frozen proteins alone, we developed and used a simulation framework to assign peak features to conformers in the ensemble. We validated our simulations by comparing them to spectra of α-synuclein acquired under different experimental conditions. Our assignments of peaks to discrete dihedral angle populations suggest that structural constraints are attainable under cryogenic conditions. The ability to infer ensemble populations from peak shapes has important implications for DNP MAS NMR studies of proteins with regions of disorder in living cells because chemical shifts are the most accessible measured parameter.

Varying molecular interactions explain aspects of crowder-dependent enzyme function of a viral protease

Biochemical processes in cells, including enzyme-catalyzed reactions, occur in crowded conditions with various background macromolecules occupying up to 40% of cytoplasm's volume. Viral enzymes in the host cell also encounter such crowded conditions as they often function at the endoplasmic reticulum membranes. We focus on an enzyme encoded by the hepatitis C virus, the NS3/4A protease, which is crucial for viral replication. We have previously found experimentally that synthetic crowders, polyethylene glycol (PEG) and branched polysucrose (Ficoll), differently affect the kinetic parameters of peptide hydrolysis catalyzed by NS3/4A. To gain understanding of the reasons for such behavior, we perform atomistic molecular dynamics simulations of NS3/4A in the presence of either PEG or Ficoll crowders and with and without the peptide substrates. We find that both crowder types make nanosecond long contacts with the protease and slow down its diffusion. However, they also affect the enzyme structural dynamics; crowders induce functionally relevant helical structures in the disordered parts of the protease cofactor, NS4A, with the PEG effect being more pronounced. Overall, PEG interactions with NS3/4A are slightly stronger but Ficoll forms more hydrogen bonds with NS3. The crowders also interact with substrates; we find that the substrate diffusion is reduced much more in the presence of PEG than Ficoll. However, contrary to NS3, the substrate interacts more strongly with Ficoll than with PEG crowders, with the substrate diffusion being similar to crowder diffusion. Importantly, crowders also affect the substrate-enzyme interactions. We observe that both PEG and Ficoll enhance the presence of substrates near the active site, especially near catalytic H57 but Ficoll crowders increase substrate binding more than PEG molecules.

GB1 Dimerization in Crowders: A Multiple Resolution Approach

In-cell protein-protein association, which is crucial in enzyme catalysis and polymerization, occurs in an environment that is highly heterogeneous and crowded. The crowder molecules exclude the reactant molecules from occupying certain regions of the cell, resulting in changes in the reaction thermodynamics and kinetics. Recent studies, both experiment and simulations, revealed that the nature of the interaction between crowder and protein species, in particular the soft interactions, plays an important role in crowder induced effects on protein association. To this end, from a simulation perspective, it is important to decipher the level of structural resolution in a protein-crowder model that can faithfully capture the influence of crowding on protein association. Here, we investigate the dimerization of model system GB1 in the presence of lysozyme crowders at two structural resolutions. The lower resolution model assumes both protein and crowder species as spherical beads, similar to the analytical scaled particle theory model, whereas the higher resolution model retains residue specific structural details for protein and crowder species. From the higher resolution model, it is found that GB1 dimer formation is destabilized in the presence of lysozyme crowders, and the destabilization is more for the side-by-side dimer compared to the domain-swapped dimer, in qualitative agreement with experimental findings. However, the low resolution CG model predicts stabilization of the dimers in the presence of the lysozyme crowder, similar to the SPT model. Our results indicate a nontrivial role of the choice of model resolution in computer simulation studies investigating crowder induced effects.

Physiological models to study the effect of molecular crowding on multi-drug bound proteins: insights from SARS-CoV-2 main protease

Molecular Dynamics simulations are often used in drug design. However, such simulations do not account for the physiological environment of the receptor; hence overlook its impact on biomolecular interactions. To address this lacuna, we identified three objectives to pursue - develop models of physiological environment, study a drug-receptor complex in such environments, and identify methods to analyze these complicated simulations. Two novel physiological models were developed and studied. The first, called 'm10', comprises of 10 of the most abundant cytoplasmic metabolites at physiological concentrations. The second, called 'phy', supplements m10 with an additional crowder protein to elicit macromolecular crowding effect. The main protease (M^pro) of SARS-CoV-2, being essential for viral replication, is an attractive drug target for COVID-19. Hence, we chose M^pro docked with multiple drugs as our model drug-receptor system. With a plethora of compounds, physiological systems can be exceedingly large and complex. A novel Spark-based software (SparkTraj) was developed to rapidly analyze non-specific contacts and water interactions. Our study shows that crowding enhances the difference in the dynamics of apo- vs drug-bound complexes. Metabolites, at times as a cluster, were seen interacting with the protease, drugs, and binding sites in drug-free receptor. Except one that crawled to an adjacent pocket in phy, the drugs remained in their respective pockets in all simulations. Given these observations, we hope that the models and approach presented here would help the optimization, evaluation, and selection of potential drugs. Generic biomolecular dynamics could also benefit from such models and tools.Communicated by Ramaswamy H. Sarma.

Monitoring In Vitro Extracellular Matrix Protein Conformations in the Presence of Biomimetic Bone-Regeneration Scaffolds Using Functionalized Gold-Edge-Coated Triangular Silver Nanoparticles

In the cellular environment, high noise levels, such as fluctuations in biochemical reactions, protein variability, molecular diffusion, cell-to-cell contact, and pH, can both mediate and interfere with cellular functions. In this work, gold edge-coated triangular silver nanoparticles (AuTSNP) were validated as a promising new tool to indicate protein conformational transitions in cultured cells and to monitor essential protein activity in the presence of an optimized bone biomimetic chitosan-based scaffold whose rational design mimics the ECM as a natural scaffold. A chitosan-based scaffold formulation with hydroxyapatite (CS/HAp) was selected due to its promising features for orthopedic applications, including combined high mechanical strength biocompatibility and biodegradability. Functionalized AuTSNP-based tests with the model ECM protein, fibronectin (Fn), illustrate that the protein interactions can be clearly sensed over time through the local surface plasmon resonance (LSPR) technique. This demonstrates that AuTNSP are a powerful tool to detect protein conformational activity in the presence of biomimetic bone tissue regeneration scaffolds within a cellular environment that comprises a diversity of molecular cues.

Effects of External Perturbations on Protein Systems: A Microscopic View

Protein folding can be viewed as the origami engineering of biology resulting from the long process of evolution. Even decades after its recognition, research efforts worldwide focus on demystifying molecular factors that underlie protein structure-function relationships; this is particularly relevant in the era of proteopathic disease. A complex co-occurrence of different physicochemical factors such as temperature, pressure, solvent, cosolvent, macromolecular crowding, confinement, and mutations that represent realistic biological environments are known to modulate the folding process and protein stability in unique ways. In the current review, we have contextually summarized the substantial efforts in unveiling individual effects of these perturbative factors, with major attention toward bottom-up approaches. Moreover, we briefly present some of the biotechnological applications of the insights derived from these studies over various applications including pharmaceuticals, biofuels, cryopreservation, and novel materials. Finally, we conclude by summarizing the challenges in studying the combined effects of multifactorial perturbations in protein folding and refer to complementary advances in experiment and computational techniques that lend insights to the emergent challenges.

Modeling of the thermal properties of SARS-CoV-2 S-protein

We calculate the thermal and conformational states of the spike glycoprotein (S-protein) of SARS-CoV-2 at seven temperatures ranging from 3°C to 95°C by all-atom molecular dynamics (MD) µs-scale simulations with the objectives to understand the structural variations on the temperatures and to determine the potential phase transition while trying to correlate such findings of the S-protein with the observed properties of the SARS-CoV2. Our simulations revealed the following thermal properties of the S-protein: 1) It is structurally stable at 3°C, agreeing with observations that the virus stays active for more than two weeks in the cold supply chain; 2) Its structure varies more significantly at temperature values of 60°C-80°C; 3) The sharpest structural variations occur near 60°C, signaling a plausible critical temperature nearby; 4) The maximum deviation of the receptor-binding domain at 37°C, corroborating the anecdotal observations that the virus is most infective at 37°C; 5) The in silico data agree with reported experiments of the SARS-CoV-2 survival times from weeks to seconds by our clustering approach analysis. Our MD simulations at µs scales demonstrated the S-protein's thermodynamics of the critical states at around 60°C, and the stable and denatured states for temperatures below and above this value, respectively.

Short-Time Transport Properties of Bidisperse Suspensions of Immunoglobulins and Serum Albumins Consistent with a Colloid Physics Picture

The crowded environment of biological systems such as the interior of living cells is occupied by macromolecules with a broad size distribution. This situation of polydispersity might influence the dependence of the diffusive dynamics of a given tracer macromolecule in a monodisperse solution on its hydrodynamic size and on the volume fraction. The resulting size dependence of diffusive transport crucially influences the function of a living cell. Here, we investigate a simplified model system consisting of two constituents in aqueous solution, namely, of the proteins bovine serum albumin (BSA) and bovine polyclonal gamma-globulin (Ig), systematically depending on the total volume fraction and ratio of these constituents. From high-resolution quasi-elastic neutron spectroscopy, the separate apparent short-time diffusion coefficients for BSA and Ig in the mixture are extracted, which show substantial deviations from the diffusion coefficients measured in monodisperse solutions at the same total volume fraction. These deviations can be modeled quantitatively using results from the short-time rotational and translational diffusion in a two-component hard sphere system with two distinct, effective hydrodynamic radii. Thus, we find that a simple colloid picture well describes short-time diffusion in binary mixtures as a function of the mixing ratio and the total volume fraction. Notably, the self-diffusion of the smaller protein BSA in the mixture is faster than the diffusion in a pure BSA solution, whereas the self-diffusion of Ig in the mixture is slower than in the pure Ig solution.

J-aggregation of 5, 10, 15, 20-tetraphenyl-21H, 23H-porphinetetrasulfonic acid in a molecular crowding environment simulated using dextran

In a molecular crowding environment, different thermodynamics is often observed in a dilute solution. One such example is the promotion of the formation of amyloids, which are causal agents of Alzheimer's disease. Although a considerable number of molecular crowding studies have been reported, its effect remains unclear. In this study, we investigated a J-aggregation of a porphyrin derivative, 5, 10, 15, 20-tetraphenyl-21H,23H-porphinetetrasulfonic acid (TPPS), in a molecular crowding environment simulated by dextran (Dex) in HClO₄, HCl, and NaCl solutions. The changes in the number of monomers in the J-aggregate (n) with the concentration of Dex (C_Dex) depended on the type of solution. No change in n was observed in the NaCl solution, which indicated that the Dex solution did not affect the J-aggregation because of the ionic strength effect. In the HCl solution, the aggregation behavior changed with the pH. Further, at a low pH, the electrostatic interactions promoted J-aggregation by the volume exclusion of Dex, while the aggregation was suppressed at a high pH owing to steric hindrance. A different aggregation mechanism, involving the hydrogen bonding between NH in the center of the TPPS macrocyclic frame and the SO₃H and ClO₄^- functional groups, was responsible for the J-aggregation in the HClO₄ solution. Moreover, the n value increased owing to the volume exclusion effect. We expect that this study will be useful for further elucidation of the molecular crowding effect.

Importance of Hydrogen Bonding in Crowded Environments: A Physical Chemistry Perspective

Cells are heterogeneous on every length and time scale; cytosol contains thousands of proteins, lipids, nucleic acids, and small molecules, and molecular interactions within this crowded environment determine the structure, dynamics, and stability of biomolecules. For decades, the effects of crowding at the atomistic scale have been overlooked in favor of more tractable models largely based on thermodynamics. Crowding can affect the conformations and stability of biomolecules by modulating water structure and dynamics within the cell, and these effects are nonlocal and environment dependent. Thus, characterizing water's hydrogen-bond (H-bond) networks is a critical step toward a complete microscopic crowding model. This perspective provides an overview of molecular crowding and describes recent time-resolved spectroscopy approaches investigating H-bond networks and dynamics in crowded or otherwise complex aqueous environments. Ultrafast spectroscopy combined with atomistic simulations has emerged as a powerful combination for studying H-bond structure and dynamics in heterogeneous multicomponent systems. We discuss the ongoing challenges toward developing a complete atomistic description of macromolecular crowding from an experimental as well as a theoretical perspective.

Atomistic Simulation of Lysozyme in Solutions Crowded by Tetraethylene Glycol: Force Field Dependence

The behavior of biomolecules in crowded environments remains largely unknown due to the accuracy of simulation models and the limited experimental data for comparison. Here we chose a small crowder of tetraethylene glycol (PEG-4) to investigate the self-crowding of PEG-4 solutions and molecular crowding effects on the structure and diffusion of lysozyme at varied concentrations from dilute water to pure PEG-4 liquid. Two Amber-like force fields of Amber14SB and a99SB-disp were examined with TIP3P (fast diffusivity and low viscosity) and a99SB-disp (slow diffusivity and high viscosity) water models, respectively. Compared to the Amber14SB protein simulations, the a99SB-disp model yields more coordinated water and less PEG-4 molecules, less intramolecular hydrogen bonds (HBs), more protein-water HBs, and less protein-PEG HBs as well as stronger interactions and more hydrophilic and less hydrophobic contacts with solvent molecules. The a99SB-disp model offers comparable protein-solvent interactions in concentrated PEG-4 solutions to that in pure water. The PEG-4 crowding leads to a slow-down in the diffusivity of water, PEG-4, and protein, and the decline in the diffusion from atomistic simulations is close to or faster than the hard sphere model that neglects attractive interactions. Despite these differences, the overall structure of lysozyme appears to be maintained well at different PEG-4 concentrations for both force fields, except a slightly large deviation at 370 K at low concentrations with the a99SB-disp model. This is mainly attributed to the strong intramolecular interactions of the protein in the Amber14SB force field and to the large viscosity of the a99SB-disp water model. The results indicate that the protein force fields and the viscosity of crowder solutions affect the simulation of biomolecules under crowding conditions.

Improving coarse-grained models of protein folding through weighting of polar-polar/hydrophobic-hydrophobic interactions into crowded spaces

Herein were tested 7 hydrophobic-polar sequences in two types of 2D-square space lattices, homogeneous and correlated, the latter simulating molecular crowding included as a geometric boundary restriction. Optimization of 2D structures was carried out using a variant of Dill's model, inspired by convex function, taking into account both hydrophobic (Dill's model) and polar interactions, including more structural information to reach better folding solutions. While using correlated networks, degrees of freedom in the folding of sequences were limited; as a result in all cases, more successful structural trials were found in comparison to a homogeneous lattice. The majority of employed sequences were designed by our workgroup, two of them were folded with other approaches, and another is a modified version of a previous sequence, initial forms of the other two have been employed but without taking into account polar-polar contributions. Three of them are newly proposed, intended to test the conjoint hydrophobic-hydrophobic and polar-polar contributions in crowded spaces. One sequence turned out to be the most difficult of the seven folded, this perhaps due to intrinsic (i) degrees of freedom and (ii) motifs of the expected 2D HP structure. Meanwhile two-sequence, although optimal folding was not achieved for neither of the two approaches, folding with correlated network approach not only produced better results than homogeneous space, but for them the best values found with crowding were very close to the expected optimal fitness. In general, five sequences were better folded with medium lattice units for correlated media; instead, another two sequences were better folded with a bit larger degree of lattice unit, revealing that depending on the degrees of freedom and particular folding, motifs in each sequence would require tuned crowding to achieve better folding. Therefore, the main goal herein was to obtain a modified 2D HP lattice model to mimic folding of proteins or secondary structures, like β-sheets, taking into account both hydrophobic-hydrophobic and polar-polar interactions, and fold them in a crowded environment. This simple but enough construction would be conducted to determine the needed information to fold sequences in a sort of a minimal but complete heuristic model. Finally, we claim that all folded sequences into crowded spaces achieve better results than homogeneous ones.

Observed steric crowding at modest coverage requires a particular membrane-binding scheme or a complementary mechanism

Membrane shape transitions, including fusion and fission, play an important role in many biological processes. It is therefore essential to understand mechanisms of "curvature generation," the mathematical quantification of membrane shape. Among the different mechanisms is the effect of steric pressure between proteins crowded on a surface. At a higher curvature, there is more space for the crowders and less steric pressure. Currently, the physical model of curvature induction by crowding views the proteins as being bound to the surface as a whole rather than to the underlying lipids. Here, we split the previously understood model into two pieces: first, the reduction in steric pressure due to reduced collisions between proteins, and second, the increased area available to the protein that is independent of other crowders. The cases are distinguished by how the crowder is attached to the membrane. When a protein is attached to a specific lipid, as is the case in a typical crowding experiment, one should not model its lateral entropy; this has already been accounted for by the underlying lipid. The Carnahan-Starling pressure includes this lateral entropy. The revised theory predicts that a purely entropic crowding mechanism is inconsistent with observations of reshaping at the lower range of surface coverage, suggesting that an additional mechanism is at play.

Effect of Molecular Crowding on Complexation of Metal Ions and 8-Quinolinol-5-Sulfonic Acid

The effect of molecular crowding on macromolecular reactions has been revealed by many researchers. In this study, we investigate the complexation of metal ions (Zn, Co, and Cd) with 8-quinolinol-5-sulfonic acid as a model of small-molecular reactions in molecular crowding. The complexation constants for 1:1, 1:2, and total complexation in the presence of polyethylene glycol (PEG, a molecular crowding reagent) are evaluated based on the increase in the reactant activity by volume exclusion and the decrease in the water activity due to the change in osmotic pressure. All complexation constants are enhanced by increasing the concentration of PEG. Its mechanisms differ for 1:1, 1:2, and total complexation. The 1:1 complexation is promoted only by the influence of the water activity, while the reactant and water activities influence the increase in the 1:2 complexation constant. Increasing the molecular weight of PEG further increases the complexation constants, as dehydration of the complex is promoted by a higher hydration number of PEG. Because this study gives the fundamental knowledge for the protein-metal interaction, in which solvation is an important factor, in molecular crowding, it provides new insights into molecular crowding studies and should attract the attention of a broad spectrum of biochemistry researchers.

Computational methods for exploring protein conformations

Proteins are dynamic molecules that can transition between a potentially wide range of structures comprising their conformational ensemble. The nature of these conformations and their relative probabilities are described by a high-dimensional free energy landscape. While computer simulation techniques such as molecular dynamics simulations allow characterisation of the metastable conformational states and the transitions between them, and thus free energy landscapes, to be characterised, the barriers between states can be high, precluding efficient sampling without substantial computational resources. Over the past decades, a dizzying array of methods have emerged for enhancing conformational sampling, and for projecting the free energy landscape onto a reduced set of dimensions that allow conformational states to be distinguished, known as collective variables (CVs), along which sampling may be directed. Here, a brief description of what biomolecular simulation entails is followed by a more detailed exposition of the nature of CVs and methods for determining these, and, lastly, an overview of the myriad different approaches for enhancing conformational sampling, most of which rely upon CVs, including new advances in both CV determination and conformational sampling due to machine learning.

Protein-complex stability in cells and in vitro under crowded conditions

Biology is beginning to appreciate the effects of the crowded and complex intracellular environment on the equilibrium thermodynamics and kinetics of protein folding. The next logical step involves the interactions between proteins. We review quantitative, wet-experiment based efforts aimed at understanding how and why high concentrations of small molecules, synthetic polymers, biologically relevant cosolutes and the interior of living cells affect the energetics of protein-protein interactions. We then address popular theories used to explain the effects and suggest expeditious paths for a more methodical integration of experiment and simulation.

Engineering crowding sensitivity into protein linkers

The intracellular environment contains a high concentration of biomacromolecules that present steric barriers and ample surface area for weak chemical interactions. Consequently, these forces influence protein conformations and protein self-assembly, with an outcome that depends on the sum of the effects resulting from crowding. Linkers are disordered domains that lack tertiary structure, and this flexible nature would render them susceptible to compression or extension under crowded conditions, compared to the equilibrium conformation in a dilute buffer. The change in distance between the linked proteins can become essential where it attenuates protein activity. In this chapter, we first discuss the experimental findings in vitro and in the cell on how linkers and other relevant macromolecules are affected by crowding. We focus on the dependence on the linker's size, flexibility, and the intra- and intermolecular interactions. Although the experimental data on the systematic variation of proteins in a buffer and cells is limited, extrapolating the available insights allows us to propose a protocol on how to engineer the directionality of crowding effects in the linker. Finally, we describe a straightforward experimental protocol on the determination of crowding sensitivity in a buffer and cell.

Duplex-tetraplex equilibria in guanine- and cytosine-rich DNA

Noncanonical four-stranded DNA structures, including G-quadruplexes and i-motifs, have been discovered in the cell and are implicated in a variety of genomic regulatory functions. The tendency of a specific guanine- and cytosine-rich region of genomic DNA to adopt a four-stranded conformation depends on its ability to overcome the constraints of duplex base-pairing by undergoing consecutive duplex-to-coil and coil-to-tetraplex transitions. The latter ability is determined by the balance between the free energies of participating ordered and disordered structures. In this review, we present an overview of the literature on the stability of G-quadruplex and i-motif structures and discuss the extent of duplex-tetraplex competition as a function of the sequence context of the DNA and environmental conditions including temperature, pH, salt, molecular crowding, and the presence of G-quadruplex-binding ligands. We outline how the results of in vitro studies can be expanded to understanding duplex-tetraplex equilibria in vivo.

Ultrafast Spectroscopy of Lipid-Water Interfaces: Transmembrane Crowding Drives H-Bond Dynamics

Biology takes place in crowded, heterogeneous environments, and it is therefore essential to account for crowding effects in our understanding of biophysical processes at the molecular level. Comparable to the cytosol, proteins occupy approximately 30% of the plasma membrane surface; thus, crowding should have an effect on the local structure and dynamics at the lipid-water interface. Using a combination of ultrafast two-dimensional infrared spectroscopy and molecular dynamics simulations, we quantify the effects of membrane peptide concentration on the picosecond interfacial H-bond dynamics. The measurements reveal a nonmonotonic dependence of water orientation and dynamics as a function of transmembrane peptide:lipid ratio. We observe three dynamical regimes: a "pure lipid-like" regime at low peptide concentrations, a bulk-like region at intermediate peptide concentrations where dynamics are faster by ∼20% compared to those of the pure lipid bilayer, and a crowded regime where high peptide concentrations slow dynamics by ∼50%.

Protein thermal stability

Proteins, in general, fold to a well-organized three-dimensional structure in order to function. The stability of this functional shape can be perturbed by external environmental conditions, such as temperature. Understanding the molecular factors underlying the resistance of proteins to the thermal stress has important consequences. First of all, it can aid the design of thermostable enzymes able to perform efficient catalysis in the high-temperature regime. Second, it is an essential brick of knowledge required to decipher the evolutionary pathways of life adaptation on Earth. Thanks to the development of atomistic simulations and ad hoc enhanced sampling techniques, it is now possible to investigate this problem in silico, and therefore provide support to experiments. After having described the methodological aspects, the chapter proposes an extended discussion on two problems. First, we focus on thermophilic proteins, a perfect model to address the issue of thermal stability and molecular evolution. Second, we discuss the issue of how protein thermal stability is affected by crowded in vivo-like conditions.