Skip to content
#protein folding Review Open access

Investigating protein conformational ensembles by native mass spectrometry and complementary biophysical techniques—the case of α-synuclein

Oct 2026 · Frontiers in Biophysics · 0 citations · 229 references
Mass Spectrometry Techniques and Applications

Abstract

One of the challenges of modern structural biology is to account for protein structural dynamics. Intrinsically disordered proteins (IDPs), which do not fold spontaneously into a defined conformation, existing, instead, as highly heterogeneous conformational ensembles, provide an extreme and challenging example of protein dynamics. Native mass spectrometry (nMS) allows dissecting the molecular population in its main conformational components of different compactness, assessing structural parameters, covalent modifications, and binding states for each resolved species. This review summarizes the work performed by our research group in investigating the conformational states of human α-synuclein (α-syn), an IDP involved in Parkinson’s disease (PD) and other neurodegenerative conditions, by applying nMS. This case study is taken as an example, highlighting the complementarity of nMS to other biophysical techniques, such as circular dichroism (CD), Fourier-transform infrared (FTIR) spectroscopy, nuclear magnetic resonance (NMR), atomic force microscopy (AFM), and molecular-dynamics (MD) simulations. This multi-technological approach is useful in analyzing the response of α-syn conformational ensembles to covalent modifications and noncovalent interactions with metal ions, small ligands, and other proteins.

Read PDF

Similar papers

#computer vision Review Open access May 2015

A survey study on major technical barriers affecting the decision to adopt cloud services

The comparison of adopter and non-adopter sample reveals three potential adoption inhibitor, security, data privacy, and portability, which underlines the importance of the technical and security perspectives for research investigating the adoption of technology.

Nattakarn Phaphoom, Xiaofeng Wang, S. Samuel et al. · 111 citations · ⚡8
#computer vision Open access Feb 2018

Lean Internal Startups for Software Product Innovation in Large Companies: Enablers and Inhibitors

This study investigates how Lean internal startup facilitates software product innovation in large companies and identifies its enablers and inhibitors, and shows the potential of the method-in-action framework to investigate the Lean startup approach in non-startup context.

Henry Edison, Nina M. Smørsgård, Xiaofeng Wang et al. · 78 citations · ⚡6
#computer vision Book Open access Jul 2015

Understanding the affect of developers: theoretical background and guidelines for psychoempirical software engineering

This paper highlights the challenges to conduct proper affect-related studies with psychology, provides a comprehensive literature review in affect theory, and proposes guidelines for conducting psychoempirical software engineering.

D. Graziotin, Xiaofeng Wang, P. Abrahamsson · 56 citations · ⚡4
#machine learning Open access May 2017

What Influences the Speed of Prototyping? An Empirical Investigation of Twenty Software Startups

This study conducts a multiple case study on twenty European software startups and proposes a prototype-centric learning model in early stage software startups, and identifies factors that occur as barriers but also facilitators for prototyping in earlystage software startups.

Anh Nguyen-Duc, Xiaofeng Wang, P. Abrahamsson · 44 citations · ⚡5
#protein folding Open access Sep 2026

Programmable design of functional proteins from natural language

Pinal, a 16-billion-parameter foundation model that produces protein candidates from natural-language functional descriptions, supports natural language as a high-level interface for candidate generation in protein design, enabling programmable exploration with reduced reliance on manually specified structural or seque...

Fengyuan Dai, Shiyang You, Yudian Zhu et al. · 31 citations · ⚡3

Related blog posts

Google DeepMind Blog Sep 30, 2026

Introducing SynthID Bio

Proof of concept for watermarking AI-generated proteins while preserving biological function.

MIT News · Artificial Intelligence Aug 27, 2026

Looking beyond natural sequences

A new machine-learning framework aims to improve the success rate of computational protein design while moving away from results that reproduce sequences found in nature.

We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.