Elucidating protein secondary structure with circular dichroism and a neural network

Atomistic Molecular Dynamics Simulations of ABA-Type Polymer Peptide Conjugates: Insights into Supramolecular Structures and their Circular Dichroism Spectra

A combination of atomistic molecular dynamics (aMD) simulations and circular dichroism (CD) analysis is used to explore supramolecular structures of amphiphilic ABA-type triblock polymer peptide conjugates (PPC). Using the example of a recently introduced PPC with pH- and temperature responsive self-assembling behavior [Otter et al., Macromolecular Rapid Communications 2018, 39, 1800459], this study shows how molecular dynamics simulations of simplified fragment molecules can add crucial information to CD data, which helps to correctly identify the self-assembled structures and monitor the folding/unfolding pathways of the molecules. The findings offer insights into the nature of structural transitions induced by external stimuli, thus contributing to the understanding of the connection of microscopic structures with macroscopic properties.

Docosahexaenoic acid promotes vesicle clustering mediated by alpha-Synuclein via electrostatic interaction

α-Synuclein (α-Syn) is an intrinsically disordered protein whose aggregation is associated with Parkinson's disease, dementia, and other neurodegenerative diseases known as synucleinopathies. However, the functional role of α-Syn is still unclear, although it has been shown to be involved in the regulation of neurotransmitter release via the interaction with synaptic vesicles (SVs), vesicle clustering, and SNARE complex assembly. Fatty acids have significant occupancy in synaptic vesicles; and recent studies suggest the interaction of fatty acids with α-Syn affect the formation of (pathological) aggregates, but it is less clear how fatty acids affects the functional role of α-Syn including α-Syn-membrane interactions, in particular with (SV-like) vesicles. Here, we report the concentration dependent effect of docosahexaenoic acid (DHA) in synaptic-like vesicle clustering via α-Syn interaction. Through molecular dynamics simulation, we revealed that DHA promoted vesicle clustering is due to the electrostatic interaction between DHA in the membrane and the N-terminal region of α-Syn. Moreover, this increased electrostatic interaction arises from a change in the macroscopic properties of the protein-membrane interface induced by (preferential solvation of) DHA. Our results provide insight as to how DHA regulates vesicle clustering mediated by α-Syn and may further be useful to understand its physiological as well as pathological role. Description: In physiological environments, α-Synuclein (α-Syn) localizes at nerve termini and synaptic vesicles and interacts with anionic phospholipid membranes to promote vesicle clustering. Docosahexaenoic acid (DHA) increases binding affinity between α-Syn and lipid membranes by increasing electrostatic interaction energy through modulating the local and global membrane environment and conformational properties of α-Syn.

SOMSpec as a General Purpose Validated Self-Organising Map Tool for Rapid Protein Secondary Structure Prediction From Infrared Absorbance Data

A protein's structure is the key to its function. As protein structure can vary with environment, it is important to be able to determine it over a wide range of concentrations, temperatures, formulation vehicles, and states. Robust reproducible validated methods are required for applications including batch-batch comparisons of biopharmaceutical products. Circular dichroism is widely used for this purpose, but an alternative is required for concentrations above 10 mg/mL or for solutions with chiral buffer components that absorb far UV light. Infrared (IR) protein absorbance spectra of the Amide I region (1,600-1700 cm-1) contain information about secondary structure and require higher concentrations than circular dichroism often with complementary spectral windows. In this paper, we consider a number of approaches to extract structural information from a protein infrared spectrum and determine their reliability for regulatory and research purpose. In particular, we compare direct and second derivative band-fitting with a self-organising map (SOM) approach applied to a number of different reference sets. The self-organising map (SOM) approach proved significantly more accurate than the band-fitting approaches for solution spectra. As there is no validated benchmark method available for infrared structure fitting, SOMSpec was implemented in a leave-one-out validation (LOOV) approach for solid-state transmission and thin-film attenuated total reflectance (ATR) reference sets. We then tested SOMSpec and the thin-film ATR reference set against 68 solution spectra and found the average prediction error for helix (α + 310) and β-sheet was less than 6% for proteins with less than 40% helix. This is quantitatively better than other available approaches. The visual output format of SOMSpec aids identification of poor predictions. We also demonstrated how to convert aqueous ATR spectra to and from transmission spectra for structure fitting. Fourier self-deconvolution did not improve the average structure predictions.

Current Approaches in Supersecondary Structures Investigation

Proteins expressed during the cell cycle determine cell function, topology, and responses to environmental influences. The development and improvement of experimental methods in the field of structural biology provide valuable information about the structure and functions of individual proteins. This work is devoted to the study of supersecondary structures of proteins and determination of their structural motifs, description of experimental methods for their detection, databases, and repositories for storage, as well as methods of molecular dynamics research. The interest in the study of supersecondary structures in proteins is due to their autonomous stability outside the protein globule, which makes it possible to study folding processes, conformational changes in protein isoforms, and aberrant proteins with high productivity.

Circular dichroism for secondary structure determination of proteins with unfolded domains using a self-organising map algorithm SOMSpec

Many proteins and peptides are increasingly being recognised to contain unfolded domains or populations that are key to their function, whether it is in ligand binding or material assembly. We report an approach to determine the secondary structure for proteins with suspected significant unfolded domains or populations using our neural network approach SOMSpec. We proceed by derandomizing spectra by removing fractions of random coil (RC) spectra prior to secondary structure fitting and then regenerating α-helical and β-sheet contents for the experimental proteins. Application to bovine serum albumin spectra as a function of temperature proved to be straightforward, whereas lysozyme and insulin have hidden challenges. The importance of being able to interrogate the SOMSpec output to understand the best matching units used in the predictions is illustrated with lysozyme and insulin whose partially melted proteins proved to have significant βII content and their CD spectrum looks the same as that for a random coil.

Bayesian inference assessment of protein secondary structure analysis using circular dichroism data - how much structural information is contained in protein circular dichroism spectra?

Circular dichroism spectroscopy is an important tool for determining the structural characteristics of biomolecules, particularly the secondary structure of proteins. In this paper we propose a Bayesian model that estimates the covariance structure within a measured spectrum and quantifies the uncertainty associated with the inferred secondary structures and characteristic spectra associated with each secondary structure type. Furthermore, we used tools from Bayesian model selection to determine the best secondary structure classification scheme and illustrate a technique for comparing whether or not two or more measured protein spectra share the same secondary structure. Our findings suggest that it is not possible to identify more than 3 distinct secondary structure classes from CD spectra above 175 nm. The inclusion of data from wavelengths between 175 and 200 nm did not substantially affect the ability to determine secondary structure fractions.