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Misfolded proteins escape the cellular quality control mechanism and fail to fold properly or remain correctly folded leading to a loss in their functional specificity. Thus misfolding of proteins cause a large number of very different diseases ranging from errors in metabolism to various types of complex neurodegenerative diseases. A theoretical and computational perspective of protein misfolding is presented with a special emphasis on its salient features, mechanism and consequences. These insights quantitatively analyze different determinants of misfolding, that may be applied to design disease specific molecular targets., (Copyright © 2020 Elsevier Inc. All rights reserved.)

Plastic pollution has become a global environmental threat, which leads to an increasing concern over the consequences of plastic exposition on global health. Plastic nanoparticles have been shown to influence the folding of proteins and influence the formation of aberrant amyloid proteins, therefore potentially triggering the development of systemic and local amyloidosis. This work aims to study the interaction between nanoplastics and β-amyloid fibrils to better understand the potential role of nanoplastics in the outbreak of neurodegenerative disorders. Using microsecond-long coarse-grained molecular dynamics simulations, we investigated the interactions between neutral and charged nanoparticles made of the most common plastic materials (i.e., polyethylene, polypropylene, and polystyrene) and β-amyloid fibrils. We observe that the occurrence of contacts, region of amyloid fibril involved, and specific amino acids mediating the interaction depend on the type and charge of the nanoparticles. [ABSTRACT FROM AUTHOR]

The recent breakthrough in the field of protein structure prediction shows the relevance of using knowledge-based based scoring functions in combination with a low-resolution 3D representation of protein macromolecules. The choice of not using all atoms is barely supported by any data in the literature, and is mostly motivated by empirical and practical reasons, such as the computational cost of assessing the numerous folds of the protein conformational space. Here, we present a comprehensive study, carried on a large and balanced benchmark of predicted protein structures, to see how different types of structural representations rank in either accuracy or calculation speed, and which ones offer the best compromise between these two criteria. We tested ten representations, including low-resolution, high-resolution, and coarse-grained approaches. We also investigated the generalization of the findings to other formalisms than the widely-used “potential of mean force” (PMF) method. Thus, we observed that representing protein structures by their β carbons—combined or not with Cα—provides the best speed–accuracy trade-off, when using a “total information gain” scoring function. For statistical PMFs, using MARTINI backbone and side-chains beads is the best option. Finally, we also demonstrated the necessity of training the reference state on all atom types, and of including the Cα atoms of glycine residues, in a Cβ-based representation.

We study the nascent behavior of three model coarse-grained proteins in six rigid all-atom structures representing ribosomes that come from three domains of life. The synthesis of the proteins is implemented as a growth process. The geometry of the exit tunnel is quantified and shown to differ between the domains of life: both in volume and the size of constriction sites. This results in different characteristic times of capture within the tunnel and various probabilities of the escape. One of the proteins studied is the bacterial YibK which is knotted in its native state. A fraction of the trajectories results in knotting and the probability of doing so is largest for the bacterial ribosomes. Relaxing the condition of the rigidness of the ribosomes should result in a better avoidance of trapping and better proper folding.

The PACE force field presents an attractive model for conducting molecular dynamics simulations of membrane-protein systems. PACE is a hybrid model, in which lipids and solvents are coarse-grained consistent with the MARTINI mapping, while proteins are described by a united atom model. However, given PACE is linked to MARTINI, which is widely used to study membranes, the behavior of proteins interacting with membranes has only been limitedly examined in PACE. In this study, PACE is used to examine the behavior of several peptides in membrane environments, namely WALP peptides, melittin and influenza hemagglutinin fusion peptide (HAfp). Overall, we find PACE provides an improvement over MARTINI for modeling helical peptides, based on the membrane insertion energetics for WALP16 and more realistic melittin pore dynamics. Our studies on HAfp, which forms a helical hairpin structure, do not show the hairpin structure to be stable, which may point toward a deficiency in the model. © 2017 Wiley Periodicals, Inc. [ABSTRACT FROM AUTHOR]

In contrast to proteins, much less attention has been focused on the development of computational models for describing RNA molecules, which are being recognized as playing key roles in many cellular functions. Current atomically detailed force fields are not accurate enough to capture the properties of even simple nucleic acid constructs. In this article, we review our efforts to develop coarse-grained (CG) models that capture the underlying physics for the particular length scale of interest. Two models are discussed. One of them is the three interaction site (TIS) model, in which each nucleotide is represented by three beads corresponding to sugar, phosphate, and base. The other is the self-organized polymer (SOP) model, in which each nucleotide is represented as a single interaction center. Applications of the TIS model to study the complexity of hairpin formation and the effects of crowding in a shifting equilibrium between two conformations in human telomerase pseudoknot are described. The work on crowding illustrates a direct link to the activity of telomerase. We use the SOP model to describe the response of the Tetrahymena ribozyme to force. The simulated unfolding pathways agree well with single molecule pulling experiments. We also review predictions for the unfolding pathways for the Azoarcus ribozyme. The success of the CG applications to describe dynamics in RNA gives hope that more complex processes involving RNA-protein interactions can be tackled using variants of the proposed models. [ABSTRACT FROM AUTHOR]

This review is a tutorial for scientists interested in the problem of protein structure prediction, particularly those interested in using coarse-grained molecular dynamics models which are optimized using lessons learned from the energy landscape theory of protein folding. We also present a review of the results of the AMH/AMC/AMW/AWSEM family of coarse-grained molecular dynamics protein folding models to illustrate the points covered in the first part of the article. Accurate coarse-grained structure prediction models can be used to investigate a wide range of conceptual and mechanistic issues outside of protein structure prediction; specifically, the paper concludes by reviewing how AWSEM has, in recent years, been able to elucidate questions related to the unusual kinetic behavior of artificially designed proteins, multidomain protein misfolding, and the initial stages of protein aggregation. [ABSTRACT FROM AUTHOR]

We explored the energy-parameter space of our coarse-grained UNRES force field for large-scale ab initio simulations of protein folding, to obtain good initial approximations for hierarchical optimization of the force field with new virtual-bond-angle bending and side-chain-rotamer potentials which we recently introduced to replace the statistical potentials. 100 sets of energy-term weights were generated randomly, and good sets were selected by carrying out replica-exchange molecular dynamics simulations of two peptides with a minimal α-helical and a minimal β-hairpin fold, respectively: the tryptophan cage (PDB code: 1L2Y) and tryptophan zipper (PDB code: 1LE1). Eight sets of parameters produced native-like structures of these two peptides. These eight sets were tested on two larger proteins: the engrailed homeodomain (PDB code: 1ENH) and FBP WW domain (PDB code: 1E0L); two sets were found to produce native-like conformations of these proteins. These two sets were tested further on a larger set of nine proteins with α or α + β structure and found to locate native-like structures of most of them. These results demonstrate that, in addition to finding reasonable initial starting points for optimization, an extensive search of parameter space is a powerful method to produce a transferable force field. © 2009 Wiley Periodicals, Inc. J Comput Chem, 2009 [ABSTRACT FROM AUTHOR]

New force fields that are both simple and accurate are needed for computationally efficient molecular simulation studies to give insight into the actual features of the protein folding process. In this work, we assess a force field based on a new combination of two coarse-grained potentials taken from the bibliography. These potentials have already been proved efficient in representing different types of interactions, namely the side-chain interactions and the backbone hydrogen bonds. Now we combine them weighing their contribution to the global energy with a very simplified parameterization. To assess this combination of potentials, we use our evolutionary method to carry out energy minimization experiments for a set of all-α, all-β, and (α + β) protein structures. Our results, based on the assembly of short rigid native fragments, suggest that this combination of potentials can be successfully employed in coarse-grained folding simulations. © 2008 Wiley Periodicals, Inc. J Comput Chem, 2008 [ABSTRACT FROM AUTHOR]

Abstract: In the field of evolutionary structural genomics, methods are needed to evaluate why genomes evolved to contain the fold distributions that are observed. In order to study the effects of population dynamics in the evolved genomes we need fast and accurate evolutionary models which can analyze the effects of selection, drift and fixation of a protein sequence in a population that are grounded by physical parameters governing the folding and binding properties of the sequence. In this study, various knowledge-based, force field, and statistical methods for protein folding have been evaluated with four different folds: SH2 domains, SH3 domains, Globin-like, and Flavodoxin-like, to evaluate the speed and accuracy of the energy functions. Similarly, knowledge-based and force field methods have been used to predict ligand binding specificity in SH2 domain. To demonstrate the applicability of these methods, the dynamics of evolution of new binding capabilities by an SH2 domain is demonstrated. [Copyright &y& Elsevier]

Abstract: We use Monte Carlo simulations to analyze the behavior of a family of previously reported, statistically derived, mean field potentials for protein folding (the DFIRE potentials from the Zhou laboratory). The potentials may consider different interaction centers (alpha carbons, beta carbons, or side-chain centroids), depending on the details of the coarse-grained model simulated. The type of association of the helices in different simulated coiled-coil proteins provides interesting information on the real capabilities of these interaction schemes along the sampling of the topological space available to the protein model. [Copyright &y& Elsevier]

Abstract: A variety of small cageless chaperones have been discovered that can assist protein folding without the consumption of ATP. These include mini-chaperones (catalytically active fragments of larger chaperones), as well as small proteins such as α-casein and detergents acting as “artificial chaperones.” These chaperones all possess exposed hydrophobic patches on their surface that act as recognition sites for misfolded proteins. They lack the complexity of chaperonins (that encapsulate proteins in their inner rings) and their study can offer insight into the minimal requirements for chaperone function. We use molecular dynamics simulations to investigate how a cageless chaperone, modeled as a sphere of tunable hydrophobicity, can assist folding of a substrate protein. We find that under steady-state (non-stress) conditions, cageless chaperones that bind to a single substrate protein increase folding yields by reducing the time the substrate spends in an aggregation-prone state in a dual manner: (a) by competing for aggregation-prone hydrophobic sites on the surface of a protein, hence reducing the time the protein spends unprotected in the bulk and (b) by accelerating folding rates of the protein. In both cases, the chaperone must bind to and hold the protein loosely enough to allow the protein to change its conformation and fold while bound. Loose binding may enable small cageless chaperones to help proteins fold and avoid aggregation under steady-state conditions, even at low concentrations, without the consumption of ATP. [Copyright &y& Elsevier]

Single-molecule experimental techniques such as optical tweezers or atomic force microscopy are a direct probe of the mechanical unfolding/folding of individual proteins. They are also a means to investigate free energy landscapes. Protein force spectroscopy alone provides limited information; theoretical models relate measurements to thermodynamic and kinetic properties of the protein, but do not reveal atomic level information. By building a molecular model of the protein and probing its properties through numerical simulation, one can gauge the response to an external force for individual interatomic interactions and determine structures along the unfolding pathway. In combination, single-molecule force probes and molecular simulations contribute to uncover the rich behavior of proteins when subjected to mechanical force. We focus on how simplified protein models have been instrumental in showing how general properties of the free energy landscape of a protein relate to its response to mechanical perturbations. We discuss the role of simple protein models to explore the complexity of free energy landscapes and highlight important conceptual issues that more chemically accurate models with all-atom representations of proteins and solvent cannot easily address. Native-centric, coarse-grained models, despite simplifications in chemical detail compared to all-atom models, can reproduce and interpret experimental results. They also highlight instances where the theoretical framework used to interpret single-molecule data is too simple. However, these simple models are not able to reproduce experimental findings where non-native contacts are involved. Mechanical forces are ubiquitous in the cell and it is increasingly clear that the way a protein responds to mechanical perturbation is important. Unlabelled Image • Mechanical forces are ubiquitous in the cell; studying how proteins respond to mechanical perturbation is important. • Simulations provide an atomistic description of the process of protein unfolding. • Simple native-centric protein models can qualitatively reproduce and provide an interpretation of experimental protein unfolding results. • Reduced chemical detail can be offset by the ability to perform longer and multiple replicates of unfolding simulations. • They can help to evaluate the theoretical framework used to analyze mechanical unfolding experiments. [ABSTRACT FROM AUTHOR]