Protein Aggregation in a Nutshell: The Splendid Molecular Architecture of the Dreaded Amyloid Fibrils

Opening Amyloid-Windows to the secondary structure of proteins: The amyloidogenecity increases tenfold inside beta-sheets

Methods from artificial intelligence (AI), in general, and machine learning, in particular, have kept conquering new territories in numerous areas of science. Most of the applications of these techniques are restricted to the classification of large data sets, but new scientific knowledge can seldom be inferred from these tools. Here we show that an AI-based amyloidogenecity predictor can strongly differentiate the border- and the internal hexamers of β-pleated sheets when screening all the Protein Data Bank-deposited homology-filtered protein structures. Our main result shows that more than 30% of internal hexamers of β sheets are predicted to be amyloidogenic, while just outside the border regions, only 3% are predicted as such. This result may elucidate a general protection mechanism of proteins against turning into amyloids: if the borders of β-sheets were amyloidogenic, then the whole β sheet could turn more easily into an insoluble amyloid-structure, characterized by periodically repeated parallel β-sheets. We also present that no analogous phenomenon exists on the borders of α-helices or randomly chosen subsequences of the studied protein structures.

Succinct Amyloid and Nonamyloid Patterns in Hexapeptides

Hexapeptides are widely applied as a model system for studying the amyloid-forming properties of polypeptides, including proteins. Recently, large experimental databases have become publicly available with amyloidogenic labels. Using these data sets for training and testing purposes, one may build artificial intelligence (AI)-based classifiers for predicting the amyloid state of peptides. In our previous work (Biomolecules 2021, 11, 500), we described the Support Vector Machine (SVM)-based Budapest Amyloid Predictor (https://pitgroup.org/bap). Here, we apply the Budapest Amyloid Predictor for discovering numerous amyloidogenic and nonamyloidogenic hexapeptide patterns with accuracy between 80% and 84%, as surprising and succinct novel rules for further understanding the amyloid state of peptides. For example, we have shown that for any independently mutated residue (position marked by "x"), the patterns CxFLWx, FxFLFx, or xxIVIV are predicted to be amyloidogenic, while those of PxDxxx, xxKxEx, and xxPQxx are nonamyloidogenic. We note that each amyloidogenic pattern with two x's (e.g.,CxFLWx) describes succinctly 20² = 400 hexapeptides, while the nonamyloidogenic patterns comprising four point mutations (e.g.,PxDxxx) give 20⁴ = 160 000 hexapeptides in total. We also examine the restricted substitutions for positions "x" from subclasses of proteinogenic amino acid residues; for example, if "x" is substituted with hydrophobic amino acids, then there exist patterns containing three x's, like MxVVxx, predicted to be amyloidogenic. If we can choose for the x positions any hydrophobic amino acids, except the "structure breaker" proline, then we get amyloid patterns with five x positions, for example, xxxFxx, each corresponding to 32 768 hexapeptides. To our knowledge, no similar applications of artificial intelligence tools or succinct amyloid patterns were described before the present work.

Drosophila p38 MAPK interacts with BAG-3/starvin to regulate age-dependent protein homeostasis

As organisms age, they often accumulate protein aggregates that are thought to be toxic, potentially leading to age‐related diseases. This accumulation of protein aggregates is partially attributed to a failure to maintain protein homeostasis. A variety of genetic factors have been linked to longevity, but how these factors also contribute to protein homeostasis is not completely understood. In order to understand the relationship between aging and protein aggregation, we tested how a gene that regulates lifespan and age‐dependent locomotor behaviors, p38 MAPK (p38Kb), influences protein homeostasis as an organism ages. We find that p38Kb regulates age‐dependent protein aggregation through an interaction with starvin, a regulator of muscle protein homeostasis. Furthermore, we have identified Lamin as an age‐dependent target of p38Kb and starvin.

On the border of the amyloidogenic sequences: prefix analysis of the parallel beta sheets in the PDB_Amyloid collection

The Protein Data Bank (PDB) today contains more than 174,000 entries with the 3-dimensional structures of biological macromolecules. Using the rich resources of this repository, it is possible identifying subsets with specific, interesting properties for different applications. Our research group prepared an automatically updated list of amyloid- and probably amyloidogenic molecules, the PDB_Amyloid collection, which is freely available at the address http://pitgroup.org/amyloid. This resource applies exclusively the geometric properties of the steric structures for identifying amyloids. In the present contribution, we analyze the starting (i.e., prefix) subsequences of the characteristic, parallel beta-sheets of the structures in the PDB_Amyloid collection, and identify further appearances of these length-5 prefix subsequences in the whole PDB data set. We have identified this way numerous proteins, whose normal or irregular functions involve amyloid formation, structural misfolding, or anti-coagulant properties, simply by containing these prefixes: including the T-cell receptor (TCR), bound with the major histocompatibility complexes MHC-1 and MHC-2; the p53 tumor suppressor protein; a mycobacterial RNA polymerase transcription initialization complex; the human bridging integrator protein BIN-1; and the tick anti-coagulant peptide TAP.

The Budapest Amyloid Predictor and Its Applications

The amyloid state of proteins is widely studied with relevance to neurology, biochemistry, and biotechnology. In contrast with nearly amorphous aggregation, the amyloid state has a well-defined structure, consisting of parallel and antiparallel β-sheets in a periodically repeated formation. The understanding of the amyloid state is growing with the development of novel molecular imaging tools, like cryogenic electron microscopy. Sequence-based amyloid predictors were developed, mainly using artificial neural networks (ANNs) as the underlying computational technique. From a good neural-network-based predictor, it is a very difficult task to identify the attributes of the input amino acid sequence, which imply the decision of the network. Here, we present a linear Support Vector Machine (SVM)-based predictor for hexapeptides with correctness higher than 84%, i.e., it is at least as good as the best published ANN-based tools. Unlike artificial neural networks, the decisions of the linear SVMs are much easier to analyze and, from a good predictor, we can infer rich biochemical knowledge. In the Budapest Amyloid Predictor webserver the user needs to input a hexapeptide, and the server outputs a prediction for the input plus the 6 × 19 = 114 distance-1 neighbors of the input hexapeptide.

Configuration-Controlled Crystal and/or Gel Formation of Protected d-Glucosamines Supported by Promiscuous Interaction Surfaces and a Conformationally Heterogeneous Solution State

The configuration-dependent self-association mode of the two anomers of O-Ac,N-Fmoc-d-glucosamine, a foldamer building block, leading to gel and/or single crystal formation is described. The β-anomer of the sugar amino acid (2) forms a gel from various solvents (confirmed by SEM, rheology measurements, NMR, and ECD spectroscopy), whereas the α-anomer (1) does not form a gel with any solvent tested. Transition from the solution state to a gel is coupled to a concurrent shift of the Fmoc-groups: from a freely rotating (almost symmetrical) to a specific, asymmetric orientation. Whereas the crystal structure of the α-anomer is built as an evenly packed 3D system, the β-anomer forms a looser superstructure of well-packed 2D layers. Modeling indicates that in the lowest energy, but scarcely sampled conformer of the β-anomer, the Fmoc-group bends above the sugar moiety, stabilized by intramolecular CH↔π interactions between the aromatic rings. It is concluded that possessing an extended and promiscuous interaction surface and a conformationally heterogeneous solution state are among the basic requirements of gel formation for a candidate molecule.