Mechanical resistance in unstructured proteins

Exploration of Protein Aggregations in Parkinson's Disease Through Computational Approaches and Big Data Analytics

Protein aggregation has been implicated in numerous neurodegenerative disorders whose etiologies are poorly understood, and for which there are no effective treatments. Here we show that the computational approaches may help us to better understand the basics of Parkinson's disease (PD). The high-resolution structural, dynamical, and mechanistic insights delivered by computational studies of protein aggregation have a unique potential to enable the rational manipulation of oligomer formation. Additionally, big data and machine learning methods may provide valuable insights to better understand the nature of proteins involved in PD and their aggregative behavior for the betterment of PD treatment.

Probing the Basis of α-Synuclein Aggregation by Comparing Simulations to Single-Molecule Experiments

Intrinsically disordered proteins often play an important role in protein aggregation. However, it is challenging to determine the structures and interactions that drive the early stages of aggregation because they are transient and obscured in a heterogeneous mixture of disordered states. Even computational methods are limited because the lack of ordered structure makes it difficult to ensure that the relevant conformations are sampled. We address these challenges by integrating atomistic simulations with high-resolution single-molecule measurements reported previously, using the measurements to help discern which parts of the disordered ensemble of structures in the simulations are most probable while using the simulations to identify residues and interactions that are important for oligomer stability. This approach was applied to α-synuclein, an intrinsically disordered protein that aggregates in the context of Parkinson's disease. We simulated single-molecule pulling experiments on dimers, the minimal oligomer, and compared them to force spectroscopy measurements. Force-extension curves were simulated starting from a set of 66 structures with substantial structured content selected from the ensemble of dimer structures generated at zero force via Monte Carlo simulations. The pattern of contour length changes as the structures unfolded through intermediate states was compared to the results from optical trapping measurements on the same dimer to discern likely structures occurring in the measurements. Simulated pulling curves were generally consistent with experimental data but with a larger number of transient intermediates. We identified an ensemble of β-rich dimer structures consistent with the experimental data from which dimer interfaces could be deduced. These results suggest specific druggable targets in the structural motifs of α-synuclein that may help prevent the earliest steps of oligomerization.

Stability and Local Unfolding of SOD1 in the Presence of Protein Crowders

Using NMR and Monte Carlo (MC) methods, we investigate the stability and dynamics of superoxide dismutase 1 (SOD1) in homogeneous crowding environments, where either bovine pancreatic trypsin inhibitor (BPTI) or the B1 domain of streptococcal protein G (PGB1) serves as a crowding agent. By NMR, we show that both crowders, and especially BPTI, cause a drastic loss in the overall stability of SOD1 in its apo monomeric form. Additionally, we determine chemical shift perturbations indicating that SOD1 interacts with the crowder proteins in a residue-specific manner that further depends on the identity of the crowding protein. Furthermore, the specificity of SOD1-crowder interactions is reciprocal: chemical shift perturbations on BPTI and PGB1 identify regions that interact preferentially with SOD1. By MC simulations, we investigate the local unfolding of SOD1 in the absence and presence of the crowders. We find that the crowders primarily interact with the long flexible loops of the folded SOD1 monomer. The basic mechanisms by which the SOD1 β-barrel core unfolds remain unchanged when adding the crowders. In particular, both with and without the crowders, the second β-sheet of the barrel is more dynamic and unfolding-prone than the first. Notably, the MC simulations (exploring the early stages of SOD1 unfolding) and the NMR experiments (under equilibrium conditions) identify largely the same set of PGB1 and BPTI residues as prone to form SOD1 contacts. Thus, contacts stabilizing the unfolded state of SOD1 in many cases appear to form early in the unfolding reaction.

Markov modeling of peptide folding in the presence of protein crowders

We use Markov state models (MSMs) to analyze the dynamics of a β-hairpin-forming peptide in Monte Carlo (MC) simulations with interacting protein crowders, for two different types of crowder proteins [bovine pancreatic trypsin inhibitor (BPTI) and GB1]. In these systems, at the temperature used, the peptide can be folded or unfolded and bound or unbound to crowder molecules. Four or five major free-energy minima can be identified. To estimate the dominant MC relaxation times of the peptide, we build MSMs using a range of different time resolutions or lag times. We show that stable relaxation-time estimates can be obtained from the MSM eigenfunctions through fits to autocorrelation data. The eigenfunctions remain sufficiently accurate to permit stable relaxation-time estimation down to small lag times, at which point simple estimates based on the corresponding eigenvalues have large systematic uncertainties. The presence of the crowders has a stabilizing effect on the peptide, especially with BPTI crowders, which can be attributed to a reduced unfolding rate ku, while the folding rate kf is left largely unchanged.

Self-assembly of the full-length amyloid Aβ42 protein in dimers

The self-assembly of amyloid (Aβ) proteins into nano-aggregates is a hallmark of Alzheimer's disease (AD) development, yet the mechanism of how disordered monomers assemble into aggregates remains elusive. Here, we applied long-time molecular dynamics simulations to fully characterize the assembly of Aβ42 monomers into dimers. Monomers undergo conformational changes during their interaction, but the resulting dimer structures do not resemble those found in fibril structures. To identify natural conformations of dimers among a set of simulated ones, validation approaches were developed and applied, and a subset of dimer conformations were characterized. These dimers do not contain long β-strands that are usually found in fibrils. The dimers are stabilized primarily by interactions within the central hydrophobic regions and the C-terminal regions, with a contribution from local hydrogen bonding. The dimers are dynamic, as evidenced by the existence of a set of conformations and by the quantitative analyses of the dimer dissociation process.

The structure of misfolded amyloidogenic dimers: computational analysis of force spectroscopy data

Progress in understanding the molecular mechanism of self-assembly of amyloidogenic proteins and peptides requires knowledge about their structure in misfolded states. Structural studies of amyloid aggregates formed during the early aggregation stage are very limited. Atomic force microscopy (AFM) spectroscopy is widely used to analyze misfolded proteins and peptides, but the structural characterization of transiently formed misfolded dimers is limited by the lack of computational approaches that allow direct comparison with AFM experiments. Steered molecular dynamics (SMD) simulation is capable of modeling force spectroscopy experiments, but the modeling requires pulling rates 10(7) times higher than those used in AFM experiments. In this study, we describe a computational all-atom Monte Carlo pulling (MCP) approach that enables us to model results at pulling rates comparable to those used in AFM pulling experiments. We tested the approach by modeling pulling experimental data for I91 from titin I-band (PDB ID: 1TIT) and ubiquitin (PDB ID: 1UBQ). We then used MCP to analyze AFM spectroscopy experiments that probed the interaction of the peptides [Q6C] Sup35 (6-13) and [H13C] Aβ (13-23). A comparison of experimental results with the computational data for the Sup35 dimer with out-of-register and in-register arrangements of β-sheets suggests that Sup35 monomers adopt an out-of-register arrangement in the dimer. A similar analysis performed for Aβ peptide demonstrates that the out-of-register antiparallel β-sheet arrangement of monomers also occurs in this peptide. Although the rupture of hydrogen bonds is the major contributor to dimer dissociation, the aromatic-aromatic interaction also contributes to the dimer rupture process.

Peptide folding in the presence of interacting protein crowders

Using Monte Carlo methods, we explore and compare the effects of two protein crowders, BPTI and GB1, on the folding thermodynamics of two peptides, the compact helical trp-cage and the β-hairpin-forming GB1m3. The thermally highly stable crowder proteins are modeled using a fixed backbone and rotatable side-chains, whereas the peptides are free to fold and unfold. In the simulations, the crowder proteins tend to distort the trp-cage fold, while having a stabilizing effect on GB1m3. The extent of the effects on a given peptide depends on the crowder type. Due to a sticky patch on its surface, BPTI causes larger changes than GB1 in the melting properties of the peptides. The observed effects on the peptides stem largely from attractive and specific interactions with the crowder surfaces, and differ from those seen in reference simulations with purely steric crowder particles.

α-Synuclein dimer structures found from computational simulations

Dimer formation is likely the first step in the oligomerization of α-synuclein in Lewy bodies. In order to prevent α-synuclein aggregation, knowledge of the atomistic structures of possible α-synuclein dimers and the interaction affinity between the dimer domains is a necessary prerequisite in the process of rational design of dimerization inhibitors. Using computational methodology, we have investigated several possible α-synuclein dimer structures, focusing on dimers formed from α-helical forms of the protein found when it is membrane-bound, and dimers formed from β-sheet conformations predicted by simulations. Structures and corresponding binding affinities for the interacting monomers in possible α-synuclein dimers, along with properties including the contributions from different interaction energies and the radii of gyration, were found through molecular docking followed by MD simulations and binding-energy calculations. We found that even though α-synuclein is highly charged, hydrophobic contributions play a significant role in stabilizing dimers.

Conformational and aggregation properties of the 1-93 fragment of apolipoprotein A-I

Several disease-linked mutations of apolipoprotein A-I, the major protein in high-density lipoprotein (HDL), are known to be amyloidogenic, and the fibrils often contain N-terminal fragments of the protein. Here, we present a combined computational and experimental study of the fibril-associated disordered 1-93 fragment of this protein, in wild-type and mutated (G26R, S36A, K40L, W50R) forms. In atomic-level Monte Carlo simulations of the free monomer, validated by circular dichroism spectroscopy, we observe changes in the position-dependent β-strand probability induced by mutations. We find that these conformational shifts match well with the effects of these mutations in thioflavin T fluorescence and transmission electron microscopy experiments. Together, our results point to molecular mechanisms that may have a key role in disease-linked aggregation of apolipoprotein A-I.

Computational approaches to understanding protein aggregation in neurodegeneration

The generation of toxic non-native protein conformers has emerged as a unifying thread among disorders such as Alzheimer's disease, Parkinson's disease, and amyotrophic lateral sclerosis. Atomic-level detail regarding dynamical changes that facilitate protein aggregation, as well as the structural features of large-scale ordered aggregates and soluble non-native oligomers, would contribute significantly to current understanding of these complex phenomena and offer potential strategies for inhibiting formation of cytotoxic species. However, experimental limitations often preclude the acquisition of high-resolution structural and mechanistic information for aggregating systems. Computational methods, particularly those combine both all-atom and coarse-grained simulations to cover a wide range of time and length scales, have thus emerged as crucial tools for investigating protein aggregation. Here we review the current state of computational methodology for the study of protein self-assembly, with a focus on the application of these methods toward understanding of protein aggregates in human neurodegenerative disorders.

Diverse metastable structures formed by small oligomers of α-synuclein probed by force spectroscopy

Oligomeric aggregates are widely suspected as toxic agents in diseases caused by protein aggregation, yet they remain poorly characterized, partly because they are challenging to isolate from a heterogeneous mixture of species. We developed an assay for characterizing structure, stability, and kinetics of individual oligomers at high resolution and sensitivity using single-molecule force spectroscopy, and applied it to observe the formation of transient structured aggregates within single oligomers of α-synuclein, an intrinsically-disordered protein linked to Parkinson's disease. Measurements of the molecular extension as the proteins unfolded under tension in optical tweezers revealed that even small oligomers could form numerous metastable structures, with a surprisingly broad range of sizes. Comparing the structures formed in monomers, dimers and tetramers, we found that the average mechanical stability increased with oligomer size. Most structures formed within a minute, with size-dependent rates. These results provide a new window onto the complex α-synuclein aggregation landscape, characterizing the microscopic structural heterogeneity and kinetics of different pathways.