Cooperativity among short amyloid stretches in long amyloidogenic sequences

Insights into Network of Hot Spots of Aggregation in Nucleophosmin 1

In a protein, point mutations associated with diseases can alter the native structure and provide loss or alteration of functional levels, and an internal structural network defines the connectivity among domains, as well as aggregate/soluble states' equilibria. Nucleophosmin (NPM)1 is an abundant nucleolar protein, which becomes mutated in acute myeloid leukemia (AML) patients. NPM1-dependent leukemogenesis, which leads to its aggregation in the cytoplasm (NPMc+), is still obscure, but the investigations have outlined a direct link between AML mutations and amyloid aggregation. Protein aggregation can be due to the cooperation among several hot spots located within the aggregation-prone regions (APR), often predictable with bioinformatic tools. In the present study, we investigated potential APRs in the entire NPM1 not yet investigated. On the basis of bioinformatic predictions and experimental structures, we designed several protein fragments and analyzed them through typical aggrsegation experiments, such as Thioflavin T (ThT), fluorescence and scanning electron microscopy (SEM) experiments, carried out at different times; in addition, their biocompatibility in SHSY5 cells was also evaluated. The presented data clearly demonstrate the existence of hot spots of aggregation located in different regions, mostly in the N-terminal domain (NTD) of the entire NPM1 protein, and provide a more comprehensive view of the molecular details potentially at the basis of NPMc+-dependent AML.

The CDR1 and Other Regions of Immunoglobulin Light Chains are Hot Spots for Amyloid Aggregation

Immunoglobulin light chain-derived (AL) amyloidosis is a debilitating disease without known cure. Almost nothing is known about the structural factors driving the amyloidogenesis of the light chains. This study aimed to identify the fibrillogenic hotspots of the model protein 6aJL2 and in pursuing this goal, two complementary approaches were applied. One of them was based on several web-based computational tools optimized to predict fibrillogenic/aggregation-prone sequences based on different structural and biophysical properties of the polypeptide chain. Then, the predictions were confirmed with an ad-hoc synthetic peptide library. In the second approach, 6aJL2 protein was proteolyzed with trypsin, and the products incubated in aggregation-promoting conditions. Then, the aggregation-prone fragments were identified by combining standard proteomic methods, and the results validated with a set of synthetic peptides with the sequence of the tryptic fragments. Both strategies coincided to identify a fibrillogenic hotspot located at the CDR1 and β-strand C of the protein, which was confirmed by scanning proline mutagenesis analysis. However, only the proteolysis-based strategy revealed additional fibrillogenic hotspots in two other regions of the protein. It was shown that a fibrillogenic hotspot associated to the CDR1 is also encoded by several κ and λ germline variable domain gene segments. Some parts of this study have been included in the chapter “The Structural Determinants of the Immunoglobulin Light Chain Amyloid Aggregation”, published in Physical Biology of Proteins and Peptides, Springer 2015 (ISBN 978-3-319-21687-4).

Self-aggregation and coaggregation of the p53 core fragment with its aggregation gatekeeper variant

Recent studies suggested that p53 aggregation can lead to loss-of-function (LoF), dominant-negative (DN) and gain-of-function (GoF) effects, with adverse cancer consequences. The p53 aggregation-nucleating (251)ILTIITL(257) fragment is a key segment in wild-type p53 aggregation; however, an I254R mutation can prevent it. It was suggested that self-assembly of wild-type p53 and its cross-interaction with mutants differ from the classical amyloid nucleation-growth mechanism. Here, using replica exchange molecular dynamics (REMD) simulations, we studied the cross-interactions of this p53 core fragment and its aggregation rescue I254R mutant. We found that the core fragment displays strong aggregation propensity, whereas the gatekeeper I254R mutant tends to be disordered, consistent with experiments. Our cross-interaction results reveal that the wild-type p53 fragment promotes β-sheet formation of the I254R mutant by shifting the disordered mutant peptides into aggregating states. As a result, the system has similar oligomeric structures, inter-peptide interactions and free energy landscape as the wild type fragment does, revealing a prion-like process. We also found that in the cross-interaction system, the wild-type species has higher tendency to interact with the mutant than with itself. This phenomenon illustrates synergistic effects between the p53 (251)ILTIITL(257) fragment and the mutant resembling prion cross-species propagation, cautioning against exploiting it in drug discovery.

Critical Nucleus Structure and Aggregation Mechanism of the C-terminal Fragment of Copper-Zinc Superoxide Dismutase Protein

The aggregation of the copper-zinc superoxide dismutase (SOD1) protein is linked to familial amyotrophic lateral sclerosis, a progressive neurodegenerative disease. A recent experimental study has shown that the (147)GVIGIAQ(153) SOD1 C-terminal segment not only forms amyloid fibrils in isolation but also accelerates the aggregation of full-length SOD1, while substitution of isoleucine at site 149 by proline blocks its fibril formation. Amyloid formation is a nucleation-polymerization process. In this study, we investigated the oligomerization and the nucleus structure of this heptapeptide. By performing extensive replica-exchange molecular dynamics (REMD) simulations and conventional MD simulations, we found that the GVIGIAQ hexamers can adopt highly ordered bilayer β-sheets and β-barrels. In contrast, substitution of I149 by proline significantly reduces the β-sheet probability and results in the disappearance of bilayer β-sheet structures and the increase of disordered hexamers. We identified mixed parallel-antiparallel bilayer β-sheets in both REMD and conventional MD simulations and provided the conformational transition from the experimentally observed parallel bilayer sheets to the mixed parallel-antiparallel bilayer β-sheets. Our simulations suggest that the critical nucleus consists of six peptide chains and two additional peptide chains strongly stabilize this critical nucleus. The stabilized octamer is able to recruit additional random peptides into the β-sheet. Therefore, our simulations provide insights into the critical nucleus formation and the smallest stable nucleus of the (147)GVIGIAQ(153) peptide.

Structural Insight into Tau Protein's Paradox of Intrinsically Disordered Behavior, Self-Acetylation Activity, and Aggregation

Tau is an intrinsically disordered protein (IDP) implicated in Alzheimer's disease. Recently, tau proteins were discovered to be able to catalyze self-acetylation, which may promote its pathological aggregation. Understanding the paradox of tau's random-like conformations, aggregation propensity, and enzymatic activity are challenging questions. We characterized the atomic structures of two truncated tau constructs, K18 and K19, consisting of, respectively, only the four- and three-repeats of tau protein, providing structural insights into tau's paradox. Extensive 4.8 μs replica-exchange molecular dynamics simulations of the tau proteins achieved quantitative correlation with experimental Cα chemical shifts. Our results revealed (1) dynamically ordered conformations with close lysine-cysteine distances essential for tau self-acetylation and (2) high β-sheet content and large hydrophobic surface exposure for the two critical hexapeptides (275VQIINK280 and 306VQIVYK311), crucial for tau aggregation. Together, they illuminate tau's perplexing behavior of how its disordered state can accomplish both roles.

PredHS: a web server for predicting protein-protein interaction hot spots by using structural neighborhood properties

Identifying specific hot spot residues that contribute significantly to the affinity and specificity of protein interactions is a problem of the utmost importance. We present an interactive web server, PredHS, which is based on an effective structure-based hot spot prediction method. The PredHS prediction method integrates many novel structural and energetic features with two types of structural neighborhoods (Euclidian and Voronoi), and combines random forest and sequential backward elimination algorithms to select an optimal subset of features. PredHS achieved the highest performance identifying hot spots compared with other state-of-the-art methods, as benchmarked by using an independent experimentally verified dataset. The input to PredHS is protein structures in the PDB format with at least two chains that form interfaces. Users can visualize their predictions in an interactive 3D viewer and download the results as text files. PredHS is available at http://www.predhs.org.

Prediction of gene phenotypes based on GO and KEGG pathway enrichment scores

Observing what phenotype the overexpression or knockdown of gene can cause is the basic method of investigating gene functions. Many advanced biotechnologies, such as RNAi, were developed to study the gene phenotype. But there are still many limitations. Besides the time and cost, the knockdown of some gene may be lethal which makes the observation of other phenotypes impossible. Due to ethical and technological reasons, the knockdown of genes in complex species, such as mammal, is extremely difficult. Thus, we proposed a new sequence-based computational method called kNNA-based method for gene phenotypes prediction. Different to the traditional sequence-based computational method, our method regards the multiphenotype as a whole network which can rank the possible phenotypes associated with the query protein and shows a more comprehensive view of the protein's biological effects. According to the prediction result of yeast, we also find some more related features, including GO and KEGG information, which are making more contributions in identifying protein phenotypes. This method can be applied in gene phenotype prediction in other species.