Ion mobility spectrometry-mass spectrometry (IMS-MS) methods are increasingly used to study noncovalent assemblies of peptides and proteins. This review focuses on the noncovalent self-assembly of amino acids and peptides, systems at the heart of the amyloid process that play a central role in a number of devastating diseases. Three different systems are discussed in detail: the 42-residue peptide amyloid-β42 implicated in the etiology of Alzheimer's disease, several amyloid-forming peptides with 6-11 residues, and the assembly of individual amino acids. We also discuss from a more fundamental perspective the processes that determine how quickly proteins and their assemblies denature when the analyte ion has been stripped of its solvent in an IMS-MS measurement and how to soften the measurement so that biologically meaningful data can be recorded.

Residue mutations have substantial effects on aggregation kinetics and propensities of amyloid peptides and their aggregate morphologies. Such effects are attributed to conformational transitions accessed by various types of oligomers such as steric zipper or single β-sheet. We have studied the aggregation propensities of six NNQQNY mutants: NVVVVY, NNVVNV, NNVVNY, VIQVVY, NVVQIY, and NVQVVY in water using a combination of ion-mobility mass spectrometry, transmission electron microscopy, atomic force microscopy, and all-atom molecular dynamics simulations. Our data show a strong correlation between the tendency to form early β-sheet oligomers and the subsequent aggregation propensity. Our molecular dynamics simulations indicate that the stability of a steric zipper structure can enhance the propensity for fibril formation. Such stability can be attained by either hydrophobic interactions in the mutant peptide or polar side-chain interdigitations in the wild-type peptide. The overall results display only modest agreement with the aggregation propensity prediction methods such as PASTA, Zyggregator, and RosettaProfile, suggesting the need for better parametrization and model peptides for these algorithms.