In Silico Evidence That Protein Unfolding is a Precursor of Protein Aggregation

Evolution of Pyrrolysyl-tRNA Synthetase: From Methanogenesis to Genetic Code Expansion

Over 20 years ago, the pyrrolysine encoding translation system was discovered in specific archaea. Our Review provides an overview of how the once obscure pyrrolysyl-tRNA synthetase (PylRS) tRNA pair, originally responsible for accurately translating enzymes crucial in methanogenic metabolic pathways, laid the foundation for the burgeoning field of genetic code expansion. Our primary focus is the discussion of how to successfully engineer the PylRS to recognize new substrates and exhibit higher in vivo activity. We have compiled a comprehensive list of ncAAs incorporable with the PylRS system. Additionally, we also summarize recent successful applications of the PylRS system in creating innovative therapeutic solutions, such as new antibody-drug conjugates, advancements in vaccine modalities, and the potential production of new antimicrobials.

Advanced computational approaches to understand protein aggregation

Protein aggregation is a widespread phenomenon implicated in debilitating diseases like Alzheimer's, Parkinson's, and cataracts, presenting complex hurdles for the field of molecular biology. In this review, we explore the evolving realm of computational methods and bioinformatics tools that have revolutionized our comprehension of protein aggregation. Beginning with a discussion of the multifaceted challenges associated with understanding this process and emphasizing the critical need for precise predictive tools, we highlight how computational techniques have become indispensable for understanding protein aggregation. We focus on molecular simulations, notably molecular dynamics (MD) simulations, spanning from atomistic to coarse-grained levels, which have emerged as pivotal tools in unraveling the complex dynamics governing protein aggregation in diseases such as cataracts, Alzheimer's, and Parkinson's. MD simulations provide microscopic insights into protein interactions and the subtleties of aggregation pathways, with advanced techniques like replica exchange molecular dynamics, Metadynamics (MetaD), and umbrella sampling enhancing our understanding by probing intricate energy landscapes and transition states. We delve into specific applications of MD simulations, elucidating the chaperone mechanism underlying cataract formation using Markov state modeling and the intricate pathways and interactions driving the toxic aggregate formation in Alzheimer's and Parkinson's disease. Transitioning we highlight how computational techniques, including bioinformatics, sequence analysis, structural data, machine learning algorithms, and artificial intelligence have become indispensable for predicting protein aggregation propensity and locating aggregation-prone regions within protein sequences. Throughout our exploration, we underscore the symbiotic relationship between computational approaches and empirical data, which has paved the way for potential therapeutic strategies against protein aggregation-related diseases. In conclusion, this review offers a comprehensive overview of advanced computational methodologies and bioinformatics tools that have catalyzed breakthroughs in unraveling the molecular basis of protein aggregation, with significant implications for clinical interventions, standing at the intersection of computational biology and experimental research.

Genetic Algorithm for Automated Parameterization of Network Hamiltonian Models of Amyloid Fibril Formation

The time scales of long-time atomistic molecular dynamics simulations are typically reported in microseconds, while the time scales for experiments studying the kinetics of amyloid fibril formation are typically reported in minutes or hours. This time scale deficit of roughly 9 orders of magnitude presents a major challenge in the design of computer simulation methods for studying protein aggregation events. Coarse-grained molecular simulations offer a computationally tractable path forward for exploring the molecular mechanism driving the formation of these structures, which are implicated in diseases such as Alzheimer's, Parkinson's, and type-II diabetes. Network Hamiltonian models of aggregation are centered around a Hamiltonian function that returns the total energy of a system of aggregating proteins, given the graph structure of the system as an input. In the graph, or network, representation of the system, each protein molecule is represented as a node, and noncovalent bonds between proteins are represented as edges. The parameter, i.e., a set of coefficients that determine the degree to which each topological degree of freedom is favored or disfavored, must be determined for each network Hamiltonian model, and is a well-known technical challenge. The methodology is first demonstrated by beginning with an initial set of randomly parametrized models of low fibril fraction (<5% fibrillar), and evolving to subsequent generations of models, ultimately leading to high fibril fraction models (>70% fibrillar). The methodology is also demonstrated by applying it to optimizing previously published network Hamiltonian models for the 5 key amyloid fibril topologies that have been reported in the Protein Data Bank (PDB). The models generated by the AI produced fibril fractions that surpass previously published fibril fractions in 3 of 5 cases, including the most naturally abundant amyloid fibril topology, the 1,2 2-ribbon, which features a steric zipper. The authors also aim to encourage more widespread use of the network Hamiltonian methodology for fitting a wide variety of self-assembling systems by releasing a free open-source implementation of the genetic algorithm introduced here.

Hydration makes a difference! How to tune protein complexes between liquid-liquid and liquid-solid phase separation

Understanding how protein rich condensates formed upon liquid-liquid phase separation (LLPS) evolve into solid aggregates is of fundamental importance for several medical applications, since these are suspected to be hot-spots for many neurotoxic diseases. This requires developing experimental approaches to observe in real-time both LLPS and liquid-solid phase separation (LSPS), and to unravel the delicate balance of protein and water interactions dictating the free energy differences between the two. We present a vibrational THz spectroscopy approach that allows doing so from the point of view of hydration water. We focus on a cellular prion protein of high medical relevance, which we can drive to undergo either LLPS or LSPS with few mutations. We find that it is a subtle balance of hydrophobic and hydrophilic solvation contributions that allows tuning between LLPS and LSPS. Hydrophobic hydration provides an entropic driving force to phase separation, through the release of hydration water into the bulk. Water hydrating hydrophilic groups provides an enthalpic driving force to keep the condensates in a liquid state. As a result, when we modify the protein by a few mutations to be less hydrophilic, we shift from LLPS to LSPS. This molecular understanding paves the way for a rational design of proteins.

Conformational Fluctuations and Induced Orientation of a Protein, Its Solvation Shell, and Bulk Water in Weak Non-Unfolding External Electric Fields

Extreme external electric fields have been reported to disrupt the tertiary structure of stably folded proteins; however, the effects of weaker electric fields on many biomolecules, especially net-uncharged proteins, and on the surrounding aqueous environment have been rarely discussed. To explore these effects at the atomic level, here, we have used molecular dynamics simulations to estimate rotational motion and induced structural fluctuations in the model protein ubiquitin and its hydration layer due to applied non-unfolding electrostatic fields. When exposed to weak electric fields of up to 0.2 V nm-1, ubiquitin displayed competition between internal structure-maintaining molecular interactions and the external orienting force, which disrupted the local structure in certain regions of the protein. Moreover, relative to hydration water, bulk water showed a greater tendency to align with the electric field, indicating that the presence of protein caused hydration water to acquire rotational mobility different from that in a pure-water system. The differential influence of the applied electric field on the hydration and bulk water surrounding ubiquitin will be common to almost all (nonmembrane) biomacromolecules. Our findings highlight the importance of local dipoles and their electric polarizability even in net-uncharged biomolecules.

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.

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.

Expanding the toolbox for predictive parameters describing antibody stability considering thermodynamic and kinetic determinants

PURPOSE

Introduction of the activation energy (E_a) as a kinetic parameter to describe and discriminate monoclonal antibody (mAb) stability.

METHODS

E_a is derived from intrinsic fluorescence (IF) unfolding thermograms. An apparent irreversible three-state fit model based on the Arrhenius integral is developed to determine E_a of respective unfolding transitions. These activation energies are compared to the thermodynamic parameter of van´t Hoff enthalpies (∆H_vh). Using a set of 34 mAbs formulated in four different formulations, both the apparent thermodynamic and kinetic parameters together with apparent melting temperatures are correlated collectively with each other to storage stabilities to evaluate its predictive power with respect to long-term effects potentially reflected in shelf-life.

RESULTS

E_a allows for the discrimination of (i) different parent mAbs, (ii) different variants that originate from parent mAbs, and (iii) different formulations. Interestingly, we observed that the E_a of the CH₂ unfolding transition shows strongest correlations with monomer and aggregate content after storage at accelerated and stress conditions when collectively compared to ∆H_vh and T_m of the CH₂ transition. Moreover, the predictive parameters determined for the CH₂ domain show generally stronger correlations with monomer and aggregate content than those derived for the Fab. Qualitative assessment by ranking E_a of the Fab domain showed good agreement with monomer content in storage stabilities of individual mAb sub-sets.

CONCLUSION

E_a from IF unfolding transitions can be used in addition to other commonly used thermodynamic predictive parameters to discriminate and characterize thermal stability of different mAbs in different formulations. Hence, it shows great potential for antibody engineering and formulation scientists.

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.

Hydration Layer of Only a Few Molecules Controls Lipid Mobility in Biomimetic Membranes

Self-assembly of biomembranes results from the intricate interactions between water and the lipids’ hydrophilic head groups. Therefore, the lipid–water interplay strongly contributes to modulating membrane architecture, lipid diffusion, and chemical activity. Here, we introduce a new method of obtaining dehydrated, phase-separated, supported lipid bilayers (SLBs) solely by controlling the decrease of their environment’s relative humidity. This facilitates the study of the structure and dynamics of SLBs over a wide range of hydration states. We show that the lipid domain structure of phase-separated SLBs is largely insensitive to the presence of the hydration layer. In stark contrast, lipid mobility is drastically affected by dehydration, showing a 6-fold decrease in lateral diffusion. At the same time, the diffusion activation energy increases approximately 2-fold for the dehydrated membrane. The obtained results, correlated with the hydration structure of a lipid molecule, revealed that about six to seven water molecules directly hydrating the phosphocholine moiety play a pivotal role in modulating lipid diffusion. These findings could provide deeper insights into the fundamental reactions where local dehydration occurs, for instance during cell–cell fusion, and help us better understand the survivability of anhydrobiotic organisms. Finally, the strong dependence of lipid mobility on the number of hydrating water molecules opens up an application potential for SLBs as very precise, nanoscale hydration sensors.

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.

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

We present a computational and experimental study on the folding and aggregation in solutions of multiple protein mixtures at different concentrations. We show how in protein mixtures each component is capable of maintaining its folded state at densities greater than the one at which they would precipitate in single-species solutions. We demonstrate the generality of our observation over many different proteins using computer simulations capable of fully characterizing the cross-aggregation phase diagram of all the mixtures. Dynamic light scattering experiments were performed to evaluate the aggregation of two proteins, bovine serum albumin (BSA) and consensus tetratricopeptide repeat (CTPR), in solutions of one or both proteins. The experiments confirm our hypothesis and the simulations. These findings elucidate critical aspects of the cross-regulation of expression and aggregation of proteins exerted by the cell and on the evolutionary selection of folding and non-aggregating protein sequences, paving the way for new experimental tests.