Proteins are Solitary! Pathways of Protein Folding and Aggregation in Protein Mixtures

Mechanism of Protein Aggregation Inhibition by Arginine: Blockage of Anionic Side Chains Favors Unproductive Encounter Complexes

Aggregation refers to the assembly of proteins into nonphysiological higher order structures. While amyloid has been studied extensively, much less is known about amorphous aggregation, a process that interferes with protein expression and storage. Free arginine (Arg+) is a widely used aggregation inhibitor, but its mechanism remains elusive. Focusing on myoglobin (Mb), we recently applied atomistic molecular dynamics (MD) simulations for gaining detailed insights into amorphous aggregation (Ng J. Phys. Chem. B 2021, 125, 13099). Building on that approach, the current work for the first time demonstrates that MD simulations can directly elucidate aggregation inhibition mechanisms. Comparative simulations with and without Arg+ reproduced the experimental finding that Arg+ significantly decreased the Mb aggregation propensity. Our data reveal that, without Arg+, protein-protein encounter complexes readily form salt bridges and hydrophobic contacts, culminating in firmly linked dimeric aggregation nuclei. Arg+ promotes the dissociation of encounter complexes. These "unproductive" encounter complexes are favored because Arg+ binding to D- and E- lowers the tendency of these anionic residues to form interprotein salt bridges. Side chain blockage is mediated largely by the guanidinium group of Arg+, which binds carboxylates through H-bond-reinforced ionic contacts. Our MD data revealed Arg+ self-association into a dynamic quasi-infinite network, but we found no evidence that this self-association is important for protein aggregation inhibition. Instead, aggregation inhibition by Arg+ is similar to that mediated by free guanidinium ions. The computational strategy used here should be suitable for the rational design of aggregation inhibitors with enhanced potency.

Many-Body Contributions in Water Nanoclusters

Many-body interactions in water are known to be important but difficult to treat in atomistic models and often are included only as a correction. Polarizable models treat them explicitly, with long-range many-body potentials, within their classical approximation. However, their calculation is computationally expensive. Here, we evaluate how relevant the contributions to the many-body interaction associated with different coordination shells are. We calculate the global energy minimum, and the corresponding configuration, for nanoclusters of up to 20 water molecules. We find that including the first coordination shell, i.e., the five-body term of the central molecule, is enough to approximate within 5% the global energy minimum and its structure. We show that this result is valid for three different polarizable models, the Dang-Chang, the MB-pol, and the Kozack-Jordan potentials. This result suggests a strategy to develop many-body potentials for water that are reliable and, at the same time, computationally efficient.

Bioinspired Polyethylene Glycol Coatings for Reduced Nanoparticle-Protein Interactions

Nanoparticles (NPs) and other engineered nanomaterials have great potential as nanodrugs or nanomedical devices for biomedical applications. However, the adsorption of proteins in blood circulation or similar physiological fluids can significantly alter the surface properties and therapeutic response induced by most nanomaterials. For example, interaction with proteins can change the bloodstream circulation time and availability of therapeutic NPs or hinder the accumulation in their desired target organs. Proteins can also trigger or prevent agglomeration. By combining experimental and computational approaches, we have developed NPs carrying polyethylene glycol (PEG) polymeric coatings that mimic the surface charge distribution of proteins typically found in blood, which are known to show low aggregation under normal blood conditions. Here, we show that NPs with coatings based on apoferritin or human serum albumin display better antifouling properties and weaker protein interaction compared to similar NPs carrying conventional PEG polymeric coatings.

Surfactancy in a tadpole model of proteins

We model the environment of eukaryotic nuclei by representing macromolecules by only their entropic properties, with globular molecules represented by spherical colloids and flexible molecules by polymers. We put particular focus on proteins with both globular and intrinsically disordered regions, which we represent with 'tadpole' constructed by grafting single polymers and colloids together. In Monte Carlo simulations, we find these tadpoles support phase separation via depletion flocculation, and demonstrate several surfactant behaviours, including being found preferentially at interfaces and forming micelles in single phase solution. Furthermore, the model parameters can be tuned to give a tadpole a preference for either bulk phase. However, we find entropy too weak to drive these behaviours by itself at likely biological concentrations.

Key aspects of the past 30 years of protein design

Proteins are the workhorse of life. They are the building infrastructure of living systems; they are the most efficient molecular machines known, and their enzymatic activity is still unmatched in versatility by any artificial system. Perhaps proteins' most remarkable feature is their modularity. The large amount of information required to specify each protein's function is analogically encoded with an alphabet of just ∼20 letters. The protein folding problem is how to encode all such information in a sequence of 20 letters. In this review, we go through the last 30 years of research to summarize the state of the art and highlight some applications related to fundamental problems of protein evolution.

Bio-templated synthesis of porous silica nano adsorbents to wastewater treatment inspired by a circular economy

Water is an indispensable substance in human life activities. However, due to industrial discharge problems, water resources are polluted, so there is an urgent need for material and technology for wastewater treatment. This paper presents an innovative synthesis of porous silica microspheres (PSM) from a biomass template material (fish skin collagen) to treat protein from wastewater. The collagen from the biomass template was rich in amino, carboxyl, and hydroxyl groups that effectively controlled the hydrolysis rate of tetraethyl orthosilicate (TEOS) and promoted the synthesis of structured PSM. X-ray photoelectron spectroscopy (XPS), X-ray powder diffraction (XRD), nitrogen sorption isotherms measurements, scanning electron microscopy (SEM) and transmission electron microscopy (TEM) were utilized to characterize the chemical composition, crystalline structure, and pore architecture of the synthesized PSM. The characterization results confirmed that the PSM were spherical with the microporous and mesoporous structure in shape and approximately 400 nm in size. Moreover, the pore size could be tuned by addition of mesitylene (TMB). The large number of silicon hydroxyl groups on the PSM surface effectively bound proteins in wastewater and greatly improved the overall absorption rate. The PSM adsorption capacity for lysozyme (LZ) was 49.5 mg/g, and the adsorption behavior was well described by a pseudo-second-order kinetic model and the Langmuir model. Most importantly, the PSM effectively removed protein from actual industrial wastewater, thereby realizing a high value-added utilization of wastewater pollutants.

Protein folding in vitro and in the cell: From a solitary journey to a team effort

Correct protein folding is essential for the health and function of living organisms. Yet, it is not well understood how unfolded proteins reach their native state and avoid aggregation, especially within the cellular milieu. Some proteins, especially small, single-domain and apparent two-state folders, successfully attain their native state upon dilution from denaturant. Yet, many more proteins undergo misfolding and aggregation during this process, in a concentration-dependent fashion. Once formed, native and aggregated states are often kinetically trapped relative to each other. Hence, the early stages of protein life are absolutely critical for proper kinetic channeling to the folded state and for long-term solubility and function. This review summarizes current knowledge on protein folding/aggregation mechanisms in buffered solution and within the bacterial cell, highlighting early stages. Remarkably, teamwork between nascent chain, ribosome, trigger factor and Hsp70 molecular chaperones enables all proteins to overcome aggregation propensities and reach a long-lived bioactive state.

Mechanism of Thermal Protein Aggregation: Experiments and Molecular Dynamics Simulations on the High-Temperature Behavior of Myoglobin

Proteins that encounter unfavorable solvent conditions are prone to aggregation, a phenomenon that remains poorly understood. This work focuses on myoglobin (Mb) as a model protein. Upon heating, Mb produces amorphous aggregates. Thermal unfolding experiments at low concentration (where aggregation is negligible), along with centrifugation assays, imply that Mb aggregation proceeds via globally unfolded conformers. This contrasts studies on other proteins that emphasized the role of partially folded structures as aggregate precursors. Molecular dynamics (MD) simulations were performed to gain insights into the mechanism by which heat-unfolded Mb molecules associate with one another. A prerequisite for these simulations was the development of a method for generating monomeric starting structures. Periodic boundary condition artifacts necessitated the implementation of a partially immobilized water layer lining the walls of the simulation box. Aggregation simulations were performed at 370 K to track the assembly of monomeric Mb into pentameric species. Binding events were preceded by multiple unsuccessful encounters. Even after association, protein-protein contacts remained in flux. Binding was mediated by hydrophobic contacts, along with salt bridges that involved hydrophobically embedded Lys residues. Overall, this work illustrates that atomistic MD simulations are well suited for garnering insights into protein aggregation mechanisms.

Advances in the study of supercooled water

In this review, we report recent progress in the field of supercooled water. Due to its uniqueness, water presents numerous anomalies with respect to most simple liquids, showing polyamorphism both in the liquid and in the glassy state. We first describe the thermodynamic scenarios hypothesized for the supercooled region and in particular among them the liquid-liquid critical point scenario that has so far received more experimental evidence. We then review the most recent structural indicators, the two-state model picture of water, and the importance of cooperative effects related to the fact that water is a hydrogen-bonded network liquid. We show throughout the review that water's peculiar properties come into play also when water is in solution, confined, and close to biological molecules. Concerning dynamics, upon mild supercooling water behaves as a fragile glass former following the mode coupling theory, and it turns into a strong glass former upon further cooling. Connections between the slow dynamics and the thermodynamics are discussed. The translational relaxation times of density fluctuations show in fact the fragile-to-strong crossover connected to the thermodynamics arising from the existence of two liquids. When considering also rotations, additional crossovers come to play. Mobility-viscosity decoupling is also discussed in supercooled water and aqueous solutions. Finally, the polyamorphism of glassy water is considered through experimental and simulation results both in bulk and in salty aqueous solutions. Grains and grain boundaries are also discussed.

Lattice protein design using Bayesian learning

Protein design is the inverse approach of the three-dimensional (3D) structure prediction for elucidating the relationship between the 3D structures and amino acid sequences. In general, the computation of the protein design involves a double loop: A loop for amino acid sequence changes and a loop for an exhaustive conformational search for each amino acid sequence. Herein, we propose a novel statistical mechanical design method using Bayesian learning, which can design lattice proteins without the exhaustive conformational search. We consider a thermodynamic hypothesis of the evolution of proteins and apply it to the prior distribution of amino acid sequences. Furthermore, we take the water effect into account in view of the grand canonical picture. As a result, on applying the 2D lattice hydrophobic-polar (HP) model, our design method successfully finds an amino acid sequence for which the target conformation has a unique ground state. However, the performance was not as good for the 3D lattice HP models compared to the 2D models. The performance of the 3D model improves on using a 20-letter lattice proteins. Furthermore, we find a strong linearity between the chemical potential of water and the number of surface residues, thereby revealing the relationship between protein structure and the effect of water molecules. The advantage of our method is that it greatly reduces computation time, because it does not require long calculations for the partition function corresponding to an exhaustive conformational search. As our method uses a general form of Bayesian learning and statistical mechanics and is not limited to lattice proteins, the results presented here elucidate some heuristics used successfully in previous protein design methods.

Nonrefoldability is Pervasive Across the E. coli Proteome

Decades of research on protein folding have primarily focused on a subset of small proteins that can reversibly refold from a denatured state. However, these studies have generally not been representative of the complexity of natural proteomes, which consist of many proteins with complex architectures and domain organizations. Here, we introduce an experimental approach to probe protein refolding kinetics for whole proteomes using mass spectrometry-based proteomics. Our study covers the majority of the soluble E. coli proteome expressed during log-phase growth, and among this group, we find that one-third of the E. coli proteome is not intrinsically refoldable on physiological time scales, a cohort that is enriched with certain fold-types, domain organizations, and other biophysical features. We also identify several properties and fold-types that are correlated with slow refolding on the minute time scale. Hence, these results illuminate when exogenous factors and processes, such as chaperones or cotranslational folding, might be required for efficient protein folding.

Protein Unfolding and Aggregation near a Hydrophobic Interface

The behavior of proteins near interfaces is relevant for biological and medical purposes. Previous results in bulk show that, when the protein concentration increases, the proteins unfold and, at higher concentrations, aggregate. Here, we study how the presence of a hydrophobic surface affects this course of events. To this goal, we use a coarse-grained model of proteins and study by simulations their folding and aggregation near an ideal hydrophobic surface in an aqueous environment by changing parameters such as temperature and hydrophobic strength, related, e.g., to ions concentration. We show that the hydrophobic surface, as well as the other parameters, affect both the protein unfolding and aggregation. We discuss the interpretation of these results and define future lines for further analysis, with their possible implications in neurodegenerative diseases.

Identification of Protein Functional Regions

Protein sequence stores the information relative to both functionality and stability, thus making it difficult to disentangle the two contributions. However, the identification of critical residues for function and stability has important implications for the mapping of the proteome interactions, as well as for many pharmaceutical applications, e. g. the identification of ligand binding regions for targeted pharmaceutical protein design. In this work, we propose a computational method to identify critical residues for protein functionality and stability and to further categorise them in strictly functional, structural and intermediate. We evaluate single site conservation and use Direct Coupling Analysis (DCA) to identify co-evolved residues both in natural and artificial evolution processes. We reproduce artificial evolution using protein design and base our approach on the hypothesis that artificial evolution in the absence of any functional constraint would exclusively lead to site conservation and co-evolution events of the structural type. Conversely, natural evolution intrinsically embeds both functional and structural information. By comparing the lists of conserved and co-evolved residues, outcomes of the analysis on natural and artificial evolution, we identify the functional residues without the need of any a priori knowledge of the biological role of the analysed protein.