How native proteins aggregate in solution: A dynamic Monte Carlo simulation

Prediction of Protein Aggregation Propensity via Data-Driven Approaches

Protein aggregation occurs when misfolded or unfolded proteins physically bind together and can promote the development of various amyloid diseases. This study aimed to construct surrogate models for predicting protein aggregation via data-driven methods using two types of databases. First, an aggregation propensity score database was constructed by calculating the scores for protein structures in the Protein Data Bank using Aggrescan3D 2.0. Moreover, feature- and graph-based models for predicting protein aggregation have been developed by using this database. The graph-based model outperformed the feature-based model, resulting in an R2 of 0.95, although it intrinsically required protein structures. Second, for the experimental data, a feature-based model was built using the Curated Protein Aggregation Database 2.0 to predict the aggregated intensity curves. In summary, this study suggests approaches that are more effective in predicting protein aggregation, depending on the type of descriptor and the database.

A comprehensive review of protein misfolding disorders, underlying mechanism, clinical diagnosis, and therapeutic strategies

INTRODUCTION

Proteins are the most common biological macromolecules in living system and are building blocks of life. They are extremely dynamic in structure and functions. Due to several modifications, proteins undergo misfolding, leading to aggregation and thereby developing neurodegenerative and systemic diseases. Understanding the pathology of these diseases and the techniques used to diagnose them is therefore crucial for their effective management . There are several techniques, currently being in use to diagnose them and those will be discussed in this review.

AIM/OBJECTIVES

Current review aims to discuss an overview of protein aggregation and the underlying mechanisms linked to neurodegeneration and systemic diseases. Also, the review highlights protein misfolding disorders, their clinical diagnosis, and treatment strategies.

METHODOLOGY

Literature related to neurodegenerative and systemic diseases was explored through PubMed, Google Scholar, Scopus, and Medline databases. The keywords used for literature survey and analysis are protein aggregation, neurodegenerative disorders, Alzheimer's disease, Parkinson's disease, systemic diseases, protein aggregation mechanisms, etc. DISCUSSION /CONCLUSION: This review summarises the pathogenesis of neurodegenerative and systemic disorders caused by protein misfolding and aggregation. The clinical diagnosis and therapeutic strategies adopted for the management of these diseases are also discussed to aid in a better understanding of protein misfolding disorders. Many significant concerns about the role, characteristics, and consequences of protein aggregates in neurodegenerative and systemic diseases are not clearly understood to date. Regardless of technological advancements, there are still great difficulties in the management and cure of these diseases. Therefore, for better understanding, diagnosis, and treatment of neurodegenerative and systemic diseases, more studies to identify novel drugs that may aid in their treatment and management are required.

Predicting non-equilibrium folding behavior of polymer chains using the steepest-entropy-ascent quantum thermodynamic framework

The steepest-entropy-ascent quantum thermodynamic (SEAQT) framework is used to explore the influence of heating and cooling on polymer chain folding kinetics. The framework predicts how a chain moves from an initial non-equilibrium state to stable equilibrium along a unique thermodynamic path. The thermodynamic state is expressed by occupation probabilities corresponding to the levels of a discrete energy landscape. The landscape is generated using the Replica Exchange Wang-Landau method applied to a polymer chain represented by a sequence of hydrophobic and polar monomers with a simple hydrophobic-polar amino acid model. The chain conformation evolves as energy shifts among the levels of the energy landscape according to the principle of steepest entropy ascent. This principle is implemented via the SEAQT equation of motion. The SEAQT framework has the benefit of providing insight into structural properties under non-equilibrium conditions. Chain conformations during heating and cooling change continuously without sharp transitions in morphology. The changes are more drastic along non-equilibrium paths than along quasi-equilibrium paths. The SEAQT-predicted kinetics are fitted to rates associated with the experimental intensity profiles of cytochrome c protein folding with Rouse dynamics.

Unfolding of the SARS-CoV-2 spike protein through infrared and ultraviolet-C radiation based disinfection

The spreading of coronavirus from contacting surfaces and aerosols created a pandemic around the world. To prevent the transmission of SARS-CoV-2 virus and other contagious microbes, disinfection of contacting surfaces is necessary. In this study, a disinfection box equipped with infrared (IR) radiation heating and ultraviolet-C (UV-C) radiation is designed and tested for its disinfection ability against pathogenic bacteria and SARS-CoV-2 spike protein. The killing of a Gram-positive, namely, S. aureus and a Gram-negative namely, S. typhi bacteria was studied followed by the inactivation of the spike protein. The experimental parameters were optimized using a statistical tool. For the broad-spectrum antibacterial activity, the optimum condition was holding at 65.61 °C for 13.54 min. The killing of the bacterial pathogen occurred via rupturing the cell walls as depicted by electron microscopy. Further, the unfolding of SARS-CoV-2 spike protein and RNase A was studied under IR and UV-C irradiations at the aforesaid optimized condition. The unfolding of both the proteins was confirmed by changes in the secondary structure, particularly an increase in β-sheets and a decrease in α-helixes. Remarkably, the higher penetration depth of IR waves up to subcutaneous tissue resulted in lower optimum disinfection temperature, <70 °C in vogue. Thus, the combined UV-C and IR radiation is effective in killing the pathogenic bacteria and denaturing the glycoproteins.

Biomimetic design of inhibitors of immune checkpoint LILRB4

Immune checkpoint inhibitors have become a hot spot in the treatment of acute myeloid leukemia (AML), the most common acute leukemia (blood cancer) in adults. In the present study, molecular insights into the molecular interactions between an immune checkpoint leukocyte immunoglobulin-like receptor b4 (LILRB4) and its mAb h128-3 was explored using molecular dynamics (MD) simulation for the biomimetic design of peptide inhibitor of LILRB4. Both hydrophobic interaction and electrostatic interaction were found favorable for the binding of the mAb h128-3 on LILRB4, and hydrophobic interaction was identified as the main driving force. The key amino acid residues for the binding of mAb h128-3 on LILRB4 were identified as Y93, D94, D106, Y34, S103, W107, Y61, N30, E27, Y33, Y59, W95, S92 through MM-PBSA (molecular mechanics-Poisson-Boltzmann surface area) method. Based on this, an inhibitor library with the sequence of SXDXYXSY (Where X is an arbitrary amino acid residue) were designed. Two peptide inhibitors, SADHYHSY and SVDWYHSY were obtained through screening using molecular docking and MD simulations, and then validated by successful blocking of LILRB4 through the covering of LILRB4 surface by these inhibitors. These results would be helpful for the research and development of therapies for AML.

Direct Simulations of Phase Behavior of Mixtures of Oppositely Charged Proteins/Nanoparticles and Polyelectrolytes

We use direct simulations of particle-polyelectrolyte mixtures using the single chain in mean field framework to extract the phase diagram for such systems. At high charges of the particles and low concentration of polymers, we observe the formation of a coacervate phase involving the particles and polyelectrolytes. At low particle charges and/or high concentration of polymers, the mixture undergoes a segregative phase separation into particle-rich and polymer-rich phases, respectively. We also present results for the influence of particle charge heterogeneity on the phase diagram.

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.

Aggregation of biologically important peptides and proteins: inhibition or acceleration depending on protein and metal ion concentrations

The process of aggregation of proteins and peptides is dependent on the concentration of proteins, and the rate of aggregation can be altered by the presence of metal ions, but this dependence is not always a straightforward relationship. In general, aggregation does not occur under normal physiological conditions, yet it can be induced in the presence of certain metal ions. However, the extent of the influence of metal ion interactions on protein aggregation has not yet been fully comprehended. A consensus has thus been difficult to reach because the acceleration/inhibition of the aggregation of proteins in the presence of metal ions depends on several factors such as pH and the concentration of the aggregated proteins involved as well as metal concentration level of metal ions. Metal ions, like Cu2+, Zn2+, Pb2+ etc. may either accelerate or inhibit aggregation simply because the experimental conditions affect the behavior of biomolecules. It is clear that understanding the relationship between metal ion concentration and protein aggregation will prove useful for future scientific applications. This review focuses on the dependence of the aggregation of selected important biomolecules (peptides and proteins) on metal ion concentrations. We review proteins that are prone to aggregation, the result of which can cause serious neurodegenerative disorders. Furthering our understanding of the relationship between metal ion concentration and protein aggregation will prove useful for future scientific applications, such as finding therapies for neurodegenerative diseases.

Influence of model resolution on geometric simulations of antibody aggregation

It is estimated that allergies afflict up to 40% of the world's population. A primary mediator for allergies is the aggregation of antigens and IgE antibodies bound to cell-surface receptors, FcεRI. Antibody/antigen aggregate formation causes stimulation of mast cells and basophils, initiating cellular degranulation and releasing immune mediators which produce an allergic or anaphylactic response. Understanding the shape and structure of these aggregates can provide critical insights into the allergic response. We have previously developed methods to geometrically model, simulate and analyze antibody aggregation inspired by rigid body robotic motion simulations. Our technique handles the large size and number of molecules involved in aggregation, providing an advantage over traditional simulations such as molecular dynamics (MD) and coarse-grained energetic models. In this paper, we study the impact of model resolution on simulations of geometric structures using both our previously developed Monte Carlo simulation and a novel application of rule-based modeling. These methods complement each other, the former providing explicit geometric detail and the latter providing a generic representation where multiple resolutions can be captured. Our exploration is focused on two antigens, a man-made antigen with three binding sites, DF3, and a common shrimp allergen (antigen), Pen a 1. We find that impact of resolution is minimal for DF3, a small globular antigen, but has a larger impact on Pen a 1, a rod-shaped molecule. The volume reduction caused by the loss in resolution allows more binding site accessibility, which can be quantified using a rule-based model with implicit geometric input. Clustering analysis of our simulation shows good correlation when compared with available experimental results. Moreover, collisions in all-atom reconstructions are negligible, at around 0.2% at 90% reduction.

Aggregation and network formation in self-assembly of protein (H3.1) by a coarse-grained Monte Carlo simulation

Multi-scale aggregation to network formation of interacting proteins (H3.1) are examined by a knowledge-based coarse-grained Monte Carlo simulation as a function of temperature and the number of protein chains, i.e., the concentration of the protein. Self-assembly of corresponding homo-polymers of constitutive residues (Cys, Thr, and Glu) with extreme residue-residue interactions, i.e., attractive (Cys-Cys), neutral (Thr-Thr), and repulsive (Glu-Glu), are also studied for comparison with the native protein. Visual inspections show contrast and similarity in morphological evolutions of protein assembly, aggregation of small aggregates to a ramified network from low to high temperature with the aggregation of a Cys-polymer, and an entangled network of Glu and Thr polymers. Variations in mobility profiles of residues with the concentration of the protein suggest that the segmental characteristic of proteins is altered considerably by the self-assembly from that in its isolated state. The global motion of proteins and Cys polymer chains is enhanced by their interacting network at the low temperature where isolated chains remain quasi-static. Transition from globular to random coil transition, evidenced by the sharp variation in the radius of gyration, of an isolated protein is smeared due to self-assembly of interacting networks of many proteins. Scaling of the structure factor S(q) with the wave vector q provides estimates of effective dimension D of the mass distribution at multiple length scales in self-assembly. Crossover from solid aggregates (D ∼ 3) at low temperature to a ramified fibrous network (D ∼ 2) at high temperature is observed for the protein H3.1 and Cys polymers in contrast to little changes in mass distribution (D ∼ 1.6) of fibrous Glu- and Thr-chain configurations.

Construction of an implicit membrane environment for the lattice Monte Carlo simulation of transmembrane protein

Due to the complexity of biological membrane, computer simulation of transmembrane protein's folding is challenging. In this paper, an implicit biological membrane environment has been constructed in lattice space, in which the lipid chains and water molecules were represented by the unoccupied lattice sites. The biological membrane was characterized with three features: stronger hydrogen bonding interaction, membrane lateral pressure, and lipophobicity index for the amino acid residues. In addition to the hydrocarbon core spanning region and the water solution, the lipid interface has also been represented in this implicit membrane environment, which was proved to be effective for the transmembrane protein's folding. The associated Monte Carlo simulations have been performed for SARS-CoV E protein and M2 protein segment (residues 18–60) of influenza A virus. It was found that the coil–helix transition of the transmembrane segment occurred earlier than the coil–globule transition of the two terminal domains. The folding process and final orientation of the amphipathic helical block in water solution are obviously influenced by its corresponding hydrophobicity/lipophobicity. Therefore, this implicit membrane environment, though in lattice space, can make an elaborate balance between different driving forces for the membrane protein's folding, thus offering a potential means for the simulation of transmembrane protein oligomers in feasible time.

Versatile approach to access the low temperature thermodynamics of lattice polymers and proteins

We show that Wang-Landau sampling, combined with suitable Monte Carlo trial moves, provides a powerful method for both the ground state search and the determination of the density of states for the hydrophobic-polar (HP) protein model and the interacting self-avoiding walk (ISAW) model for homopolymers. We obtain accurate estimates of thermodynamic quantities for HP sequences with >100 monomers and for ISAWs up to >500 monomers. Our procedure possesses an intrinsic simplicity and overcomes the limitations inherent in more tailored approaches making it interesting for a broad range of protein and polymer models.