Prediction of peptide and protein propensity for amyloid formation

Understanding which peptides and proteins have the potential to undergo amyloid formation and what driving forces are responsible for amyloid-like fiber formation and stabilization remains limited. This is mainly because proteins that can undergo structural changes, which lead to amyloid formation, are quite diverse and share no obvious sequence or structural homology, despite the structural similarity found in the fibrils. To address these issues, a novel approach based on recursive feature selection and feed-forward neural networks was undertaken to identify key features highly correlated with the self-assembly problem. This approach allowed the identification of seven physicochemical and biochemical properties of the amino acids highly associated with the self-assembly of peptides and proteins into amyloid-like fibrils (normalized frequency of β-sheet, normalized frequency of β-sheet from LG, weights for β-sheet at the window position of 1, isoelectric point, atom-based hydrophobic moment, helix termination parameter at position j+1 and ΔGº values for peptides extrapolated in 0 M urea). Moreover, these features enabled the development of a new predictor (available at http://cran.r-project.org/web/packages/appnn/index.html) capable of accurately and reliably predicting the amyloidogenic propensity from the polypeptide sequence alone with a prediction accuracy of 84.9 % against an external validation dataset of sequences with experimental in vitro, evidence of amyloid formation

The Contrasting Effect of Macromolecular Crowding on Amyloid Fibril Formation

Amyloid fibrils associated with neurodegenerative diseases can be considered biologically relevant failures of cellular quality control mechanisms. It is known that in vivo human Tau protein, human prion protein, and human copper, zinc superoxide dismutase (SOD1) have the tendency to form fibril deposits in a variety of tissues and they are associated with different neurodegenerative diseases, while rabbit prion protein and hen egg white lysozyme do not readily form fibrils and are unlikely to cause neurodegenerative diseases. In this study, we have investigated the contrasting effect of macromolecular crowding on fibril formation of different proteins.As revealed by assays based on thioflavin T binding and turbidity, human Tau fragments, when phosphorylated by glycogen synthase kinase-3β, do not form filaments in the absence of a crowding agent but do form fibrils in the presence of a crowding agent, and the presence of a strong crowding agent dramatically promotes amyloid fibril formation of human prion protein and its two pathogenic mutants E196K and D178N. Such an enhancing effect of macromolecular crowding on fibril formation is also observed for a pathological human SOD1 mutant A4V. On the other hand, rabbit prion protein and hen lysozyme do not form amyloid fibrils when a crowding agent at 300 g/l is used but do form fibrils in the absence of a crowding agent. Furthermore, aggregation of these two proteins is remarkably inhibited by Ficoll 70 and dextran 70 at 200 g/l.We suggest that proteins associated with neurodegenerative diseases are more likely to form amyloid fibrils under crowded conditions than in dilute solutions. By contrast, some of the proteins that are not neurodegenerative disease-associated are unlikely to misfold in crowded physiological environments. A possible explanation for the contrasting effect of macromolecular crowding on these two sets of proteins (amyloidogenic proteins and non-amyloidogenic proteins) has been proposed

Selenomethionine Incorporation into Amyloid Sequences Regulates Fibrillogenesis and Toxicity

The capacity of a polypeptide chain to engage in an amyloid formation process and cause a conformational disease is contained in its sequence. Some of the sequences undergoing fibrillation contain critical methionine (Met) residues which in vivo can be synthetically substituted by selenomethionine (SeM) and alter their properties

A Deep Neural Network for Simultaneous Estimation of b Jet Energy and Resolution

We describe a method to obtain point and dispersion estimates for the energies of jets arising from b quarks produced in proton-proton collisions at an energy of s = 13 TeV at the CERN LHC. The algorithm is trained on a large sample of simulated b jets and validated on data recorded by the CMS detector in 2017 corresponding to an integrated luminosity of 41 fb - 1 . A multivariate regression algorithm based on a deep feed-forward neural network employs jet composition and shape information, and the properties of reconstructed secondary vertices associated with the jet. The results of the algorithm are used to improve the sensitivity of analyses that make use of b jets in the final state, such as the observation of Higgs boson decay to b b ¯