The Kinetics of Amyloid Fibril Formation by de Novo Protein Albebetin and Its Mutant Variants

Key charged residues influence the amyloidogenic propensity of the helix-1 region of serum amyloid A

Increased plasma levels of serum amyloid A (SAA), an acute-phase protein that is secreted in response to inflammation, may lead to the accumulation of amyloid in various organs thereby obstructing their functions. Severe cases can lead to a systemic disorder called AA amyloidosis. Previous studies suggest that the N-terminal helix is the most amyloidogenic region of SAA. Moreover, computational studies implicated a significant role for Arg-1 and the residue-specific interactions formed during the fibrillization process. With a focus on the N-terminal region of helix-1, SAA1-13, mutational analysis was employed to interrogate the roles of the amino acid residues, Arg-1, Ser-5, Glu-9, and Asp-12. The truncated SAA1-13 fragment was systematically modified by substituting the key residues with alanine or uncharged but structurally similar amino acids. We monitored the changes in the amyloidogenic propensities, associated conformational markers, and morphology of the amyloids resulting from the mutation of SAA1-13. Mutating out Arg-1 resulted in much reduced aggregation propensity and a lack of detectable β-structures alluding to the importance of salt-bridge interactions involving Arg-1. Our data revealed that by systematically mutating the key amino acid residues, we can modulate the amyloidogenic propensity and alter the time-dependent conformational variation of the peptide. When the behaviors of each mutant peptide were analyzed, they provided evidence consistent with the aggregation pathway predicted by MD simulation studies. Here, we detail the important temporal molecular interactions formed by Arg-1 with Ser-5, Glu-9, and Asp-12 and discuss its mechanistic implications on the self-assembly of the helix-1 region of SAA.

Influence of HIV-1 Genomic RNA on the Formation of Gag Biomolecular Condensates

Biomolecular condensates (BMCs) play an important role in the replication of a growing number of viruses, but many important mechanistic details remain to be elucidated. Previously, we demonstrated that the pan-retroviral nucleocapsid (NC) and HIV-1 pr55Gag (Gag) proteins phase separate into condensates, and that HIV-1 protease (PR)-mediated maturation of Gag and Gag-Pol precursor proteins yields self-assembling BMCs that have HIV-1 core architecture. Using biochemical and imaging techniques, we aimed to further characterize the phase separation of HIV-1 Gag by determining which of its intrinsically disordered regions (IDRs) influence the formation of BMCs, and how the HIV-1 viral genomic RNA (gRNA) could influence BMC abundance and size. We found that mutations in the Gag matrix (MA) domain or the NC zinc finger motifs altered condensate number and size in a salt-dependent manner. Gag BMCs were also bimodally influenced by the gRNA, with a condensate-promoting regime at lower protein concentrations and a gel dissolution at higher protein concentrations. Interestingly, incubation of Gag with CD4+ T cell nuclear lysates led to the formation of larger BMCs compared to much smaller ones observed in the presence of cytoplasmic lysates. These findings suggest that the composition and properties of Gag-containing BMCs may be altered by differential association of host factors in nuclear and cytosolic compartments during virus assembly. This study significantly advances our understanding of HIV-1 Gag BMC formation and provides a foundation for future therapeutic targeting of virion assembly.

A guide to studying protein aggregation

Disrupted protein folding or decreased protein stability can lead to the accumulation of (partially) un- or misfolded proteins, which ultimately cause the formation of protein aggregates. Much of the interest in protein aggregation is associated with its involvement in a wide range of human diseases and the challenges it poses for large-scale biopharmaceutical manufacturing and formulation of therapeutic proteins and peptides. On the other hand, protein aggregates can also be functional, as observed in nature, which triggered its use in the development of biomaterials or therapeutics as well as for the improvement of food characteristics. Thus, unmasking the various steps involved in protein aggregation is critical to obtain a better understanding of the underlying mechanism of amyloid formation. This knowledge will allow a more tailored development of diagnostic methods and treatments for amyloid-associated diseases, as well as applications in the fields of new (bio)materials, food technology and therapeutics. However, the complex and dynamic nature of the aggregation process makes the study of protein aggregation challenging. To provide guidance on how to analyse protein aggregation, in this review we summarize the most commonly investigated aspects of protein aggregation with some popular corresponding methods.

Identification of Amyloidogenic Regions in Pseudomonas aeruginosa Ribosomal S1 Protein

Bacterial S1 protein is a functionally important ribosomal protein. It is a part of the 30S ribosomal subunit and is also able to interact with mRNA and tmRNA. An important feature of the S1 protein family is a strong tendency towards aggregation. To study the amyloidogenic properties of S1, we isolated and purified the recombinant ribosomal S1 protein of Pseudomonas aeruginosa. Using the FoldAmyloid, Waltz, Pasta 2.0, and AGGRESCAN programs, amyloidogenic regions of the protein were predicted, which play a key role in its aggregation. The method of limited proteolysis in combination with high performance liquid chromatography and mass spectrometric analysis of the products, made it possible to identify regions of the S1 protein from P. aeruginosa that are protected from the action of proteinase K, trypsin, and chymotrypsin. Sequences of theoretically predicted and experimentally identified amyloidogenic regions were used to synthesize four peptides, three of which demonstrated the ability to form amyloid-like fibrils, as shown by electron microscopy and fluorescence spectroscopy. The identified amyloidogenic sites can further serve as a basis for the development of new antibacterial peptides against the pathogenic microorganism P. aeruginosa.

AbsoluRATE: An in-silico method to predict the aggregation kinetics of native proteins

Protein aggregation has two aspects, namely, mechanistic and kinetics. Understanding protein aggregation kinetics is critical for prediction of progression of diseases caused by amyloidosis, accumulation of aggregates in biotherapeutics during storage and engineering commercial nano-biomaterials. In this work, we have collected experimentally determined absolute protein aggregation rates and developed an SVM based regression model to predict absolute rates of protein and peptide aggregation near-physiological conditions. The regression model achieved a correlation coefficient of 0.72 with MAE of 0.91 (natural log of k_app, where k_app is in hour^-1) using leave-one-out cross-validation on a dataset of 82 non-redundant proteins/peptides. The model accounts for the experimental conditions (such as temperature, pH, ionic and protein concentration) and sequence-based properties. The amino acid sequence features revealed by this model as being important for aggregation kinetics, are also associated with the aggregation mechanism. In particular, inherent aggregation propensity of the protein/peptide sequence and number of aggregation prone regions (APRs) unpunctuated by the gatekeeping residues, were found to play important roles in the prediction of the absolute aggregation rates. This analysis shows that mechanism and kinetics of protein aggregation are coupled via common sequence attributes. The aggregation kinetic prediction method developed in this work is available at https://web.iitm.ac.in/bioinfo2/absolurate-pred/index.html.