Understanding protein unfolding from molecular simulations

Exploring the unfolding pathways of protein families using Elastic Network Model

We explore how a protein's native structure determines its unfolding process. We examine how the local structural features, like shear, and the global structural properties, like the number of soft modes, change during unfolding. Simulations are performed using a Gaussian Network Model (GNM) with bond breaking for both thermal and force-induced unfolding scenarios. We find that unfolding starts in areas of high shear in the native structure and progressively spreads to the low shear regions. Interestingly, analysis of single domain protein families (Chymotrypsin inhibitor and Barnase) reveal that proteins with distinct unfolding pathways exhibit divergent behavior of the number of soft modes during unfolding. This suggests that the number of soft modes might be a valuable tool for understanding thermal unfolding pathways. Additionally, we found a strong link between a protein's overall structural similarity (TM-score) and its unfolding pathways, highlighting the importance of the native structure in determining how a protein unfolds.

Inhibition of Aβ16-22 Aggregation by [TEA]+[Ms]- Follows Weakening of the Hydrophobic Core and Sequestration of Peptides in Ionic Liquid Nanodomains

We developed a coarse-grained model for the protic ionic liquid, triethylammonium mesylate ([TEA]+[Ms]-), to characterize its inhibitory effects on amyloid aggregation using the K16LVFFAE22 fragment of the amyloid-β (Aβ16-22) as a model amyloidogenic peptide. In agreement with previous experiments, coarse-grained molecular dynamics simulations showed that increasing concentrations of [TEA]+[Ms]- in aqueous media led to increasingly small Aβ16-22 aggregates with low beta-sheet contents. The cause of [TEA]+[Ms]-'s inhibition of peptide aggregation was found to be a result of two interrelated effects. At a local scale, the enrichment of interactions between [TEA]+ cations and hydrophobic phenylalanine side chains weakened the hydrophobic cores of amyloid aggregates, resulting in poorly ordered structures. At a global level, peptides tended to localize at the interfaces of IL-rich nanostructures with water. At high IL concentrations, when the IL-water interface was large or fragmented, Aβ16-22 peptides were dispersed in the simulation cell, sometimes sequestered at unaggregated monomeric states. Together, these phenomena underlie [TEA]+[Ms]-'s inhibition of amyloid aggregation. This work addresses the critical lack of knowledge on the mechanisms of protein-ionic liquid interactions and may have broader implications for industrial applications.

Unraveling the Molecular Complexity of N-Terminus Huntingtin Oligomers: Insights into Polymorphic Structures

Huntington's disease (HD) is a fatal neurodegenerative disorder resulting from an abnormal expansion of polyglutamine (polyQ) repeats in the N-terminus of the huntingtin protein. When the polyQ tract surpasses 35 repeats, the mutated protein undergoes misfolding, culminating in the formation of intracellular aggregates. Research in mouse models suggests that HD pathogenesis involves the aggregation of N-terminal fragments of the huntingtin protein (htt). These early oligomeric assemblies of htt, exhibiting diverse characteristics during aggregation, are implicated as potential toxic entities in HD. However, a consensus on their specific structures remains elusive. Understanding the heterogeneous nature of htt oligomers provides crucial insights into disease mechanisms, emphasizing the need to identify various oligomeric conformations as potential therapeutic targets. Employing coarse-grained molecular dynamics, our study aims to elucidate the mechanisms governing the aggregation process and resultant aggregate architectures of htt. The polyQ tract within htt is flanked by two regions: an N-terminal domain (N17) and a short C-terminal proline-rich segment. We conducted self-assembly simulations involving five distinct N17 + polyQ systems with polyQ lengths ranging from 7 to 45, utilizing the ProMPT force field. Prolongation of the polyQ domain correlates with an increase in β-sheet-rich structures. Longer polyQ lengths favor intramolecular β-sheets over intermolecular interactions due to the folding of the elongated polyQ domain into hairpin-rich conformations. Importantly, variations in polyQ length significantly influence resulting oligomeric structures. Shorter polyQ domains lead to N17 domain aggregation, forming a hydrophobic core, while longer polyQ lengths introduce a competition between N17 hydrophobic interactions and polyQ polar interactions, resulting in densely packed polyQ cores with outwardly distributed N17 domains. Additionally, at extended polyQ lengths, we observe distinct oligomeric conformations with varying degrees of N17 bundling. These findings can help explain the toxic gain-of-function that htt with expanded polyQ acquires.

Polyethylene Glycol Crowder's Effect on Enzyme Aggregation, Thermal Stability, and Residual Catalytic Activity

Protein stability and performance in various natural and artificial systems incorporating many other macromolecules for therapeutic, diagnostic, sensor, and biotechnological applications attract increasing interest with the expansion of these technologies. Here we address the catalytic activity of lysozyme protein (LYZ) in the presence of a polyethylene glycol (PEG) crowder in a broad range of concentrations and temperatures in aqueous solutions of two different molecular mass PEG samples (M_w = 3350 and 10000 g/mol). The phase behavior of PEG-protein solutions is examined by using dynamic light scattering (DLS) and small-angle X-ray scattering (SAXS), while the enzyme denaturing is monitored by using an activity assay (AS) and circular dichroism (CD) spectroscopy. Molecular dynamic (MD) simulations are used to illustrate the effect of PEG concentration on protein stability at high temperatures. The results demonstrate that LYZ residual activity after 1 h incubation at 80 °C is improved from 15% up to 55% with the addition of PEG. The improvement is attributed to two underlying mechanisms. (i) Primarily, the stabilizing effect is due to the suppression of the enzyme aggregation because of the stronger PEG-protein interactions caused by the increased hydrophobicity of PEG and lysozyme at elevated temperatures. (ii) The MD simulations showed that the addition of PEG to some degree stabilizes the secondary structures of the enzyme by delaying unfolding at elevated temperatures. The more pronounced effect is observed with an increase in PEG concentration. This trend is consistent with CD and AS experimental results, where the thermal stability is strengthened with increasing of PEG concentration and molecular mass. The results show that the highest stabilizing effect is approached at the critical overlap concentration of PEG.

An Expanded Conformation of an Antibody Fab Region by X-Ray Scattering, Molecular Dynamics, and smFRET Identifies an Aggregation Mechanism

Protein aggregation is the underlying cause of many diseases, and also limits the usefulness of many natural and engineered proteins in biotechnology. Better mechanistic understanding and characterization of aggregation-prone states is needed to guide protein engineering, formulation, and drug-targeting strategies that prevent aggregation. While several final aggregated states-notably amyloids-have been characterized structurally, very little is known about the native structural conformers that initiate aggregation. We used a novel combination of small-angle x-ray scattering (SAXS), atomistic molecular dynamic simulations, single-molecule Förster resonance energy transfer, and aggregation-prone region predictions, to characterize structural changes in a native humanized Fab A33 antibody fragment, that correlated with the experimental aggregation kinetics. SAXS revealed increases in the native state radius of gyration, R_g, of 2.2% to 4.1%, at pH 5.5 and below, concomitant with accelerated aggregation. In a cutting-edge approach, we fitted the SAXS data to full MD simulations from the same conditions and located the conformational changes in the native state to the constant domain of the light chain (C_L). This C_L displacement was independently confirmed using single-molecule Förster resonance energy transfer measurements with two dual-labeled Fabs. These conformational changes were also found to increase the solvent exposure of a predicted APR, suggesting a likely mechanism through which they promote aggregation. Our findings provide a means by which aggregation-prone conformational states can be readily determined experimentally, and thus potentially used to guide protein engineering, or ligand binding strategies, with the aim of stabilizing the protein against aggregation.

Computational study of aggregation mechanism in human lysozyme[D67H]

Aggregation of proteins is an undesired phenomena that affects both human health and bioengineered products such as therapeutic proteins. Finding preventative measures could be facilitated by a molecular-level understanding of dimer formation, which is the first step in aggregation. Here we present a molecular dynamics (MD) study of dimer formation propensity in human lysozyme and its D67H variant. Because the latter protein aggregates while the former does not, they offer an ideal system for testing the feasibility of the proposed MD approach which comprises three stages: i) partially unfolded conformers involved in dimer formation are generated via high-temperature MD simulations, ii) potential dimer structures are searched using docking and refined with MD, iii) free energy calculations are performed to find the most stable dimer structure. Our results provide a detailed explanation for how a single mutation (D67H) turns human lysozyme from non-aggregating to an aggregating protein. Conversely, the proposed method can be used to identify the residues causing aggregation in a protein, which can be mutated to prevent it.

Biobetters From an Integrated Computational/Experimental Approach

Biobetters are new drugs designed from existing peptide or protein-based therapeutics by improving their properties such as affinity and selectivity for the target epitope, and stability against degradation. Computational methods can play a key role in such design problems—by predicting the changes that are most likely to succeed, they can drastically reduce the number of experiments to be performed. Here we discuss the computational and experimental methods commonly used in drug design problems, focusing on the inverse relationship between the two, namely, the more accurate the computational predictions means the less experimental effort is needed for testing. Examples discussed include efforts to design selective analogs from toxin peptides targeting ion channels for treatment of autoimmune diseases and monoclonal antibodies which are the fastest growing class of therapeutic agents particularly for cancers and autoimmune diseases.

New Dynamic Rotamer Libraries: Data-Driven Analysis of Side-Chain Conformational Propensities

Most rotamer libraries are generated from subsets of the PDB and do not fully represent the conformational scope of protein side chains. Previous attempts to rectify this sparse coverage of conformational space have involved application of weighting and smoothing functions. We resolve these limitations by using physics-based molecular dynamics simulations to determine more accurate frequencies of rotameric states. This work forms part of our Dynameomics initiative and uses a set of 807 proteins selected to represent 97% of known autonomous protein folds, thereby eliminating the bias toward common topologies found within the PDB. Our Dynameomics derived rotamer libraries encompass 4.8 × 10(9) rotamers, sampled from at least 51,000 occurrences of each of 93,642 residues. Here, we provide a backbone-dependent rotamer library, based on secondary structure ϕ/ψ regions, and an update to our 2011 backbone-independent library that addresses the doubling of our dataset since its original publication.

Structure prediction and functional analyses of a thermostable lipase obtained from Shewanella putrefaciens

Previous experimental studies on thermostable lipase from Shewanella putrefaciens suggested the maximum activity at higher temperatures, but with little information on its conformational profile. In this study, the three-dimensional structure of lipase was predicted and a 60 ns molecular dynamics (MD) simulation was carried out at temperatures ranging from 300 to 400 K to better understand its thermostable nature at the molecular level. MD simulations were performed in order to predict the optimal activity of thermostable lipase. The results suggested strong conformational temperature dependence. The thermostable lipase maintained its bio-active conformation at 350 K during the 60 ns MD simulations.

Insights into the unfolding pathway and identification of thermally sensitive regions of phytase from Aspergillus niger by molecular dynamics simulations

Thermal stability is of great importance in the application of commercial phytases. Phytase A (PhyA) is a monomeric protein comprising twelve α-helices and ten β-sheets. Comparative molecular dynamics (MD) simulations (at 310, 350, 400, and 500 K) revealed that the thermal stability of PhyA from Aspergillus niger (A. niger) is associated with its conformational rigidity. The most thermally sensitive regions were identified as loops 8 (residues 83-106), 10 (161-174), 14 (224-230), 17 (306-331), and 24 (442-444), which are present on the surface of the protein. It was observed that solvent-exposed loops denature before or show higher flexibility than buried residues. We observed that PhyA begins to unfold at loops 8 and 14, which further extends to loop 24 at the C-terminus. The intense movement of loop 8 causes the helix H2 and beta-sheet B3 to fluctuate at high temperature. The high flexibility of the H2, H10, and H12 helices at high temperature resulted in complete denaturation. The high mobility of loop 14 easily transfers to the adjacent helices H7, H8, and H9, which fluctuate and partially unfold at high temperature (500 K). It was also observed that the salt bridges Asp110-Lys149, Asp205-Lys277, Asp335-Arg136, Asp416-Arg420, and Glu387-Arg400 are important influences on the structural stability but not the thermostability, as the lengths of these salt bridges did not increase with rising temperature. The salt bridges Glu125-Arg163, Asp299-Arg136, Asp266-Arg219, Asp339-Lys278, Asp335-Arg136, and Asp424-Arg428 are all important for thermostability, as the lengths of these bridges increased dramatically with increasing temperature. Here, for the first time, we have computationally identified the thermolabile regions of PhyA, and this information could be used to engineer novel thermostable phytases. Numerous homologous phytases of fungal as well as bacterial origin are known, and these homologs show high sequence similarity. Our findings could prove useful in attempts to increase the thermostability of homologous phytases via protein engineering.

Information flow and protein dynamics: the interplay between nuclear magnetic resonance spectroscopy and molecular dynamics simulations

Proteins participate in information pathways in cells, both as links in the chain of signals, and as the ultimate effectors. Upon ligand binding, proteins undergo conformation and motion changes, which can be sensed by the following link in the chain of information. Nuclear magnetic resonance (NMR) spectroscopy and molecular dynamics (MD) simulations represent powerful tools for examining the time-dependent function of biological molecules. The recent advances in NMR and the availability of faster computers have opened the door to more detailed analyses of structure, dynamics, and interactions. Here we briefly describe the recent applications that allow NMR spectroscopy and MD simulations to offer unique insight into the basic motions that underlie information transfer within and between cells.

Protein unfolding from free-energy calculations: integration of the Gaussian network model with bond binding energies

Motivated by single molecule experiments, we study thermal unfolding pathways of four proteins, chymotrypsin inhibitor, barnase, ubiquitin, and adenylate kinase, using bond network models that combine bond energies and elasticity. The protein elasticity is described by the Gaussian network model (GNM), to which we add prescribed bond binding energies that are assigned to all (nonbackbone) connecting bonds in the GNM of native state and assumed identical for simplicity. Using exact calculation of the Helmholtz free energy for this model, we consider bond rupture single events. The bond designated for rupture is chosen by minimizing the free-energy difference for the process, over all (nonbackbone) bonds in the network. Plotting the free-energy profile along this pathway at different temperatures, we observe a few major partial unfolding, metastable or stable, states, that are separated by free-energy barriers and change role as the temperature is raised. In particular, for adenylate kinase we find three major partial unfolding states, which is consistent with single molecule FRET experiments [Pirchi et al., Nat. Commun. 2, 493 (2011)] for which hidden Markov analysis reveals between three and five such states. Such states can play a major role in enzymatic activity.

Enhanced sampling techniques in biomolecular simulations

Biomolecular simulations are routinely used in biochemistry and molecular biology research; however, they often fail to match expectations of their impact on pharmaceutical and biotech industry. This is caused by the fact that a vast amount of computer time is required to simulate short episodes from the life of biomolecules. Several approaches have been developed to overcome this obstacle, including application of massively parallel and special purpose computers or non-conventional hardware. Methodological approaches are represented by coarse-grained models and enhanced sampling techniques. These techniques can show how the studied system behaves in long time-scales on the basis of relatively short simulations. This review presents an overview of new simulation approaches, the theory behind enhanced sampling methods and success stories of their applications with a direct impact on biotechnology or drug design.

First passage analysis of the folding of a β-sheet miniprotein: is it more realistic than the standard equilibrium approach?

Simulations of first-passage folding of the antiparallel β-sheet miniprotein beta3s, which has been intensively studied under equilibrium conditions by A. Caflisch and co-workers, show that the kinetics and dynamics are significantly different from those for equilibrium folding. Because the folding of a protein in a living system generally corresponds to the former (i.e., the folded protein is stable and unfolding is a rare event), the difference is of interest. In contrast to equilibrium folding, the Ch-curl conformations become very rare because they contain unfavorable parallel β-strand arrangements, which are difficult to form dynamically due to the distant N- and C-terminal strands. At the same time, the formation of helical conformations becomes much easier (particularly in the early stage of folding) due to short-range contacts. The hydrodynamic descriptions of the folding reaction have also revealed that while the equilibrium flow field presented a collection of local vortices with closed ”streamlines”, the first-passage folding is characterized by a pronounced overall flow from the unfolded states to the native state. The flows through the locally stable structures Cs-or and Ns-or, which are conformationally close to the native state, are negligible due to detailed balance established between these structures and the native state. Although there are significant differences in the general picture of the folding process from the equilibrium and first-passage folding simulations, some aspects of the two are in agreement. The rate of transitions between the clusters of characteristic protein conformations in both cases decreases approximately exponentially with the distance between the clusters in the hydrogen bond distance space of collective variables, and the folding time distribution in the first-passage segments of the equilibrium trajectory is in good agreement with that for the first-passage folding simulations.

Interaction of Nucleic Acid Bases with the Au(111) Surface

The fate of an individual DNA molecule when it is deposited on a hard inorganic surface in a "dry" environment is unknown, while it is a crucial determinant for nanotechnology applications of nucleic acids. In the absence of experimental approaches that are able to unravel the three-dimensional atomic structure of the target system, here we tackle the first step toward a computational solution of the problem. By using first-principles quantum mechanical calculations of the four nucleobases on the Au(111) surface, we present results for the geometries, energetics, and electronic structure, in view of developing a force field that will enable classical simulations of DNA on Au(111) to investigate the structural modifications of the duplex in these non-native conditions. We fully characterize each system at the individual level. We find that van der Waals interactions are crucial for a correct description of the geometry and energetics. However, the mechanism of adsorption is well beyond pure dispersion interactions. Indeed, we find charge sharing between the substrate and the adsorbate, the formation of hybrid orbitals, and even bonding orbitals. Yet, this molecule-surface association is qualitatively distinct from the thiol adsorption mechanism: we discuss such differences and also the relation to the adsorption mechanism of pure aromatic molecules.

Insights into the Binding of Intrinsically Disordered Proteins from Molecular Dynamics Simulation

Intrinsically disordered proteins (IDPs) are a class of protein that, in the native state, possess no well-defined secondary or tertiary structure, existing instead as dynamic ensembles of conformations. They are biologically important, with approximately 20% of all eukaryotic proteins disordered, and found at the heart of many biochemical networks. To fulfil their biological roles, many IDPs need to bind to proteins and/or nucleic acids. And while unstructured in solution, IDPs typically fold into a well-defined three-dimensional structure upon interaction with a binding partner. The flexibility and structural diversity inherent to IDPs makes this coupled folding and binding difficult to study at atomic resolution by experiment alone, and computer simulation currently offers perhaps the best opportunity to understand this process. But simulation of coupled folding and binding is itself extremely challenging; these molecules are large and highly flexible, and their binding partners, such as DNA or cyclins, are also often large. Therefore, their study requires either or both simplified representations and advanced enhanced sampling schemes. It is not always clear that existing simulation techniques, optimized for studying folded proteins, are well-suited to IDPs. In this article, we examine the progress that has been made in the study of coupled folding and binding using molecular dynamics simulation. We summarise what has been learnt, and examine the state of the art in terms of both methodologies and models. We also consider the lessons to be learnt from advances in other areas of simulation and highlight the issues that remain of be addressed.

Cooperativity in thermal and force-induced protein unfolding: integration of crack propagation and network elasticity models

We investigate force-induced and temperature-induced unfolding of proteins using the combination of a gaussian network model and a crack propagation model based on "bond"-breaking independent events. We assume the existence of threshold values for the mean strain and strain fluctuations that dictate bond rupture. Surprisingly, we find that this stepwise process usually leads to a few cooperative, first-order-like, transitions in which several bonds break simultaneously, reminiscent of the "avalanches" seen in disordered networks.