Hot Spot of Structural Ambivalence in Prion Protein Revealed by Secondary Structure Principal Component Analysis

Structural Variations of Prions and Prion-like Proteins Associated with Neurodegeneration

Neurodegeneration is becoming one of the leading causes of death worldwide as the population expands and grows older. There is a growing desire to understand the mechanisms behind prion proteins as well as the prion-like proteins that make up neurodegenerative diseases (NDs), including Alzheimer's disease (AD) and Parkinson's disease (PD). Both amyloid-β (Aβ) and hyperphosphorylated tau (p-tau) proteins behave in ways similar to those of the infectious form of the prion protein, PrPSc, such as aggregating, seeding, and replicating under not yet fully understood mechanisms, thus the designation of prion-like. This review aims to highlight the shared mechanisms between prion-like proteins and prion proteins in the structural variations associated with aggregation and disease development. These mechanisms largely focus on the dysregulation of protein homeostasis, self-replication, and protein aggregation, and this knowledge could contribute to diagnoses and treatments for the given NDs.

Probing the origin of prion protein misfolding via reconstruction of ancestral proteins

Prion diseases are fatal neurodegenerative diseases caused by pathogenic misfolding of the prion protein, PrP. They are transmissible between hosts, and sometimes between different species, as with transmission of bovine spongiform encephalopathy to humans. Although PrP is found in a wide range of vertebrates, prion diseases are seen only in certain mammals, suggesting that infectious misfolding was a recent evolutionary development. To explore when PrP acquired the ability to misfold infectiously, we reconstructed the sequences of ancestral versions of PrP from the last common primate, primate-rodent, artiodactyl, placental, bird, and amniote. Recombinant ancestral PrPs were then tested for their ability to form β-sheet aggregates, either spontaneously or when seeded with infectious prion strains from human, cervid, or rodent species. The ability to aggregate developed after the oldest ancestor (last common amniote), and aggregation capabilities diverged along evolutionary pathways consistent with modern-day susceptibilities. Ancestral bird PrP could not be seeded with modern-day prions, just as modern-day birds are resistant to prion disease. Computational modeling of structures suggested that differences in helix 2 could account for the resistance of ancestral bird PrP to seeding. Interestingly, ancestral primate PrP could be converted by all prion seeds, including both human and cervid prions, raising the possibility that species descended from an ancestral primate have retained the susceptibility to conversion by cervid prions. More generally, the results suggest that susceptibility to prion disease emerged prior to ~100 million years ago, with placental mammals possibly being generally susceptible to disease.

Identification of Two Early Folding Stage Prion Non-Local Contacts Suggested to Serve as Key Steps in Directing the Final Fold to Be Either Native or Pathogenic

The initial steps of the folding pathway of the C-terminal domain of the murine prion protein mPrP(90–231) are predicted based on the sequential collapse model (SCM). A non-local dominant contact is found to form between the connecting region between helix 1 and β-sheet 1 and the C-terminal region of helix 3. This non-local contact nucleates the most populated molten globule-like intermediate along the folding pathway. A less stable early non-local contact between segments 120–124 and 179–183, located in the middle of helix 2, promotes the formation of a less populated molten globule-like intermediate. The formation of the dominant non-local contact constitutes an example of the postulated Nature’s Shortcut to the prion protein collapse into the native structure. The possible role of the less populated molten globule-like intermediate is explored as the potential initiation point for the folding for three pathogenic mutants (T182A, I214V, and Q211P in mouse prion numbering) of the prion protein.

Efficient Classification of Hot Spots and Hub Protein Interfaces by Recursive Feature Elimination and Gradient Boosting

Proteins are not isolated biological molecules, which have the specific three-dimensional structures and interact with other proteins to perform functions. A small number of residues (hot spots) in protein-protein interactions (PPIs) play the vital role in bioinformatics to influence and control of biological processes. This paper uses the boosting algorithm and gradient boosting algorithm based on two feature selection strategies to classify hot spots with three common datasets and two hub protein datasets. First, the correlation-based feature selection is used to remove the highly related features for improving accuracy of prediction. Then, the recursive feature elimination based on support vector machine (SVM-RFE) is adopted to select the optimal feature subset to improve the training performance. Finally, boosting and gradient boosting (G-boosting) methods are invoked to generate classification results. Gradient boosting is capable of obtaining an excellent model by reducing the loss function in the gradient direction to avoid overfitting. Five datasets from different protein databases are used to verify our models in the experiments. Experimental results show that our proposed classification models have the competitive performance compared with existing classification methods.

Efficiently Predicting Hot Spots in PPIs by Combining Random Forest and Synthetic Minority Over-Sampling Technique

Hot spot residues bring into play the vital function in bioinformatics to find new medications such as drug design. However, current datasets are predominately composed of non-hot spots with merely a tiny percentage of hot spots. Conventional hot spots prediction methods may face great challenges towards the problem of imbalance training samples. This paper presents a classification method combining with random forest classification and oversampling strategy to improve the training performance. A strategy with an oversampling ability is used to generate hot spots data to balance the given training set. Random forest classification is then invoked to generate a set of forest trees for this oversampled training set. The final prediction performance can be computed recursively after the oversampling and training process. This proposed method is capable of randomly selecting features and constructing a robust random forest to avoid overfitting the training set. Experimental results from three data sets indicate that the performance of hot spots prediction has been significantly improved compared with existing classification methods.

An insight of early PrP-E200K aggregation by combined molecular dynamics/fragment molecular orbital approaches

Unveiling the events leading to the formation of prion particles is a nowadays challenge in the field of neurochemistry. Pathogenic mutants of prion protein (PrP) are characterized by both an intrinsic tendency to aggregation and scrapie conversion propensity. However, the question about a possible correlation between these two events lasts still unanswered. Here, a multilayered computational workflow was employed to investigate structure, stability, and molecular interaction properties of a dimer of PrP^C -E200K, a well-known mutant of the PrP that represents a reduced model of early aggregates of this protein. Based on the combination of molecular dynamics and quantum mechanical approaches, this study provided for an in depth insight of PrP^C -E200K dimer in terms of residue-residue interactions. Assembly hypotheses for the early aggregation of PrP^C -E200K are paved and compared with PrP^Sc models reported in the literature to find a structural link between early and late (scrapie) aggregates of this protein.

Residue-specific mobility changes in soluble oligomers of the prion protein define regions involved in aggregation

Prion (PrP) diseases are neurodegenerative diseases characterized by the formation of β-sheet rich, insoluble and protease resistant protein deposits (called PrP^Sc) that occur throughout the brain. Formation of synthetic or in vitro PrP^Sc can occur through on-pathway toxic oligomers. Similarly, toxic and infectious oligomers identified in cell and animal models of prion disease indicate that soluble oligomers are likely intermediates in the formation of insoluble PrP^Sc. Despite the critical role of prion oligomers in disease progression, little is known about their structure. In order, to obtain structural insight into prion oligomers, we generated oligomers by shaking-induced conversion of recombinant, monomeric prion protein PrP^c (spanning residues 90-231). We then obtained two-dimensional solution NMR spectra of the PrP^c monomer, a 40% converted oligomer, and a 94% converted oligomer. Heteronuclear single-quantum correlation (¹H-¹⁵N) studies revealed that, in comparison to monomeric PrP^c, the oligomer has intense amide peak signals in the N-terminal (residues 90-114) and C-terminal regions (residues 226-231). Furthermore, a core region with decreased mobility is revealed from residues ~127 to 225. Within this core oligomer region with decreased mobility, there is a pocket of increased amide peak signal corresponding to the middle of α-helix 2 and the loop between α-helices 2 and 3 in the PrP^c monomer structure. Using high-resolution solution-state NMR, this work reveals detailed and divergent residue-specific changes in soluble oligomeric models of PrP.

Comparison of force fields for Alzheimer's A β42: A case study for intrinsically disordered proteins

Intrinsically disordered proteins are essential for biological processes such as cell signalling, but are also associated to devastating diseases including Alzheimer's disease, Parkinson's disease or type II diabetes. Because of their lack of a stable three-dimensional structure, molecular dynamics simulations are often used to obtain atomistic details that cannot be observed experimentally. The applicability of molecular dynamics simulations depends on the accuracy of the force field chosen to represent the underlying free energy surface of the system. Here, we use replica exchange molecular dynamics simulations to test five modern force fields, OPLS, AMBER99SB, AMBER99SB*ILDN, AMBER99SBILDN-NMR and CHARMM22*, in their ability to model Aβ42 , an intrinsically disordered peptide associated with Alzheimer's disease, and compare our results to nuclear magnetic resonance (NMR) experimental data. We observe that all force fields except AMBER99SBILDN-NMR successfully reproduce local NMR observables, with CHARMM22* being slightly better than the other force fields.

The Effects of Ca2+ Concentration and E200K Mutation on the Aggregation Propensity of PrPC: A Computational Study

The propensity of cellular prion protein to aggregation is reputed essential for the initiation of the amyloid cascade that ultimately lead to the accumulation of neurotoxic aggregates. In this paper, we extended and applied an already reported computational workflow [Proteins 2015; 83: 1751–1765] to elucidate in details the aggregation propensity of PrP protein systems including wild type, wild type treated at different [Ca²⁺] and E200K mutant. The application of the computational procedure to two segments of PrP^C, i.e. 125–228 and 120–231, allowed to emphasize how the inclusion of complete C-terminus and last portion (120–126) of the neurotoxic segment 106–126 may be crucial to unveil significant and unexpected interaction properties. Indeed, the anchoring of N-terminus on H2 domain detected in the wild type resulted to be disrupted upon either E200K mutation or Ca²⁺ binding, and to unbury hydrophobic spots on the PrP^C surface. A peculiar dinuclear Ca²⁺ binding motif formed by the C-terminus and the S2-H2 loop was detected for [Ca²⁺] > 5 mM and showed similarities with binding motifs retraced in other protein systems, thus suggesting a possible functional meaning for its formation. Therefore, we potentiated the computational procedure by including a tool that clusterize the minima of molecular interaction fields of a proteinand delimit the regions of space with higher hydrophobic or higher hydrophilic character, hence, more likely involved in the self-assembly process. Plausible models for the self-assembly of either the E200K mutated or Ca²⁺-bound PrP^C were sketched and discussed. The present investigation provides for structure-based information and new prompts that may represent a starting point for future experimental or computational works on the PrP^C aggregation.

Structure-Based Drug Discovery for Prion Disease Using a Novel Binding Simulation

The accumulation of abnormal prion protein (PrP(Sc)) converted from the normal cellular isoform of PrP (PrP(C)) is assumed to induce pathogenesis in prion diseases. Therefore, drug discovery studies for these diseases have focused on the protein conversion process. We used a structure-based drug discovery algorithm (termed Nagasaki University Docking Engine: NUDE) that ran on an intensive supercomputer with a graphic-processing unit to identify several compounds with anti-prion effects. Among the candidates showing a high-binding score, the compounds exhibited direct interaction with recombinant PrP in vitro, and drastically reduced PrP(Sc) and protein-aggresomes in the prion-infected cells. The fragment molecular orbital calculation showed that the van der Waals interaction played a key role in PrP(C) binding as the intermolecular interaction mode. Furthermore, PrP(Sc) accumulation and microgliosis were significantly reduced in the brains of treated mice, suggesting that the drug candidates provided protection from prion disease, although further in vivo tests are needed to confirm these findings. This NUDE-based structure-based drug discovery for normal protein structures is likely useful for the development of drugs to treat other conformational disorders, such as Alzheimer's disease.

Chameleon sequences in neurodegenerative diseases

Chameleon sequences can adopt either alpha helix sheet or a coil conformation. Defining chameleon sequences in PDB (Protein Data Bank) may yield to an insight on defining peptides and proteins responsible in neurodegeneration. In this research, we benefitted from the large PDB and performed a sequence analysis on Chameleons, where we developed an algorithm to extract peptide segments with identical sequences, but different structures. In order to find new chameleon sequences, we extracted a set of 8315 non-redundant protein sequences from the PDB with an identity less than 25%. Our data was classified to "helix to strand (HE)", "helix to coil (HC)" and "strand to coil (CE)" alterations. We also analyzed the occurrence of singlet and doublet amino acids and the solvent accessibility in the chameleon sequences; we then sorted out the proteins with the most number of chameleon sequences and named them Chameleon Flexible Proteins (CFPs) in our dataset. Our data revealed that Gly, Val, Ile, Tyr and Phe, are the major amino acids in Chameleons. We also found that there are proteins such as Insulin Degrading Enzyme IDE and GTP-binding nuclear protein Ran (RAN) with the most number of chameleons (640 and 405 respectively). These proteins have known roles in neurodegenerative diseases. Therefore it can be inferred that other CFP's can serve as key proteins in neurodegeneration, and a study on them can shed light on curing and preventing neurodegenerative diseases.

Factor Analysis of Conformations and NMR Signals of Rotaxanes: AIMD and Polarizable MD Simulations

The interlocked ⟨rod | ring⟩ structures of pseudorotaxanes and [2]rotaxanes are usually maintained by the complex hydrogen-bonding (H-bonding) network between the rod and ring. Ab initio molecular dynamics (AIMD) using generalized energy-based fragmentation approach and polarizable force field (polar FF)-based molecular dynamics (MD) simulations were performed to investigate the conformational changes of mechanically interlocked systems and to obtain the ensemble-averaged NMR chemical shifts. Factor analysis (FA) demonstrates that the ring H-donor (2,6 pyridinedicarboxamide group) plays an important role in the ring-rod recognition. In comparison to the conventional fixed-charge force field, the polarization effect is crucial to account for the H-bonding interactions in supramolecular systems. In the hybrid scheme, the polar FF-based MD simulations are used to generate different initial states for the AIMD simulations, which are able to give better prediction of ensemble-averaged NMR signals for chemically equivalent amide protons. The magnitude of the deshielding shift of NMR signal is correlated with the length of hydrogen bond. The polar FF model with variable charges shows that the dipole-dipole interactions between the flexible diethylene glycol chain of ring and polar solvents induce the upfield shifts of NMR signals of rod H-donors and the directional distribution of the neighboring CH3CN solvents.

Intrinsically disordered proteins: emerging interaction specialists

Intrinsically disordered proteins or regions of proteins (IDPs/IDRs) most often function through protein-protein interactions, when they permanently or transiently bind partner molecules with diverse functional consequences. There is a rapid advance in our understanding of the ensuing functional modes, obtained from describing atomic details of individual complexes, proteome-wide studies of interactomes and characterizing loosely assembled hydrogels and tightly packed amyloids. Here we briefly survey the most important recent methodological developments and structural-functional observations, with the aim of increasing the general appreciation of IDPs/IDRs as 'interaction specialists'.