Structural characterisation of amyloid-like fibrils formed by an amyloidogenic peptide segment of β-lactoglobulin.
Vasantha GowdaMichal BilerAndrei FilippovMalisa V MantonicoEirini OrnithopoulouMathieu LinaresOleg N AntzutkinChristofer LendelPublished in: RSC advances (2021)
Protein nanofibrils (PNFs) represent a promising class of biobased nanomaterials for biomedical and materials science applications. In the design of such materials, a fundamental understanding of the structure-function relationship at both molecular and nanoscale levels is essential. Here we report investigations of the nanoscale morphology and molecular arrangement of amyloid-like PNFs of a synthetic peptide fragment consisting of residues 11-20 of the protein β-lactoglobulin (β-LG 11-20 ), an important model system for PNF materials. Nanoscale fibril morphology was analysed by atomic force microscopy (AFM) that indicates the presence of polymorphic self-assembly of protofilaments. However, observation of a single set of 13 C and 15 N resonances in the solid-state NMR spectra for the β-LG 11-20 fibrils suggests that the observed polymorphism originates from the assembly of protofilaments at the nanoscale but not from the molecular structure. The secondary structure and inter-residue proximities in the β-LG 11-20 fibrils were probed using NMR experiments of the peptide with 13 C- and 15 N-labelled amino acid residues at selected positions. We can conclude that the peptides form parallel β-sheets, but the NMR data was inconclusive regarding inter-sheet packing. Molecular dynamics simulations confirm the stability of parallel β-sheets and suggest two preferred modes of packing. Comparison of molecular dynamics models with NMR data and calculated chemical shifts indicates that both packing models are possible.
Keyphrases
- atomic force microscopy
- solid state
- single molecule
- amino acid
- molecular dynamics simulations
- high speed
- molecular dynamics
- magnetic resonance
- high resolution
- density functional theory
- electronic health record
- big data
- molecular docking
- public health
- protein protein
- artificial intelligence
- data analysis
- deep learning