Protein structure dynamics by crosslinking mass spectrometry

Epitope mapping of SARS-CoV-2 RBDs by hydroxyl radical protein footprinting reveals the importance of including negative antibody controls

Understanding protein-protein interactions is crucial for drug design and investigating biological processes. Various techniques, such as CryoEM, X-ray spectroscopy, linear epitope mapping, and mass spectrometry-based methods, can be employed to map binding regions on proteins. Commonly used mass spectrometry-based techniques are cross-linking and hydrogen‑deuterium exchange (HDX). Another approach, hydroxyl radical protein footprinting (HRPF), identifies binding residues on proteins but faces challenges due to high initial costs and complex setups. This study introduces a generally applicable method using Fenton chemistry for epitope mapping in a standard mass spectrometry laboratory. It emphasizes the importance of controls, particularly the inclusion of a negative antibody control, not widely utilized in HRPF epitope mapping. Quantification by TMT labelling is introduced to reduce false positives, enabling direct comparison between sample conditions and biological triplicates. Additionally, six technical replicates were incorporated to enhance the depth of analysis. Observations on the receptor-binding domain (RBD) of SARS-CoV-2 Spike Protein, Alpha and Delta variants, revealed both binding and opening regions. Significantly changed peptides upon mixing with a negative control antibody suggested structural alterations or nonspecific binding induced by the antibody alone. Integration of negative control antibody experiments and high overlap between biological triplicates led to the exclusion of 40% of significantly changed regions. The final identified binding region correlated with existing literature on neutralizing antibodies against RBD. The presented method offers a straightforward implementation for HRPF analysis in a generic mass spectrometry-based laboratory. Enhanced data reliability was achieved through increased technical and biological replicates alongside negative antibody controls.

Trauma diagnostic-related target proteins and their detection techniques

Trauma is a significant health issue that not only leads to immediate death in many cases but also causes severe complications, such as sepsis, thrombosis, haemorrhage, acute respiratory distress syndrome and traumatic brain injury, among trauma patients. Target protein identification technology is a vital technique in the field of biomedical research, enabling the study of biomolecular interactions, drug discovery and disease treatment. It plays a crucial role in identifying key protein targets associated with specific diseases or biological processes, facilitating further research, drug design and the development of treatment strategies. The application of target protein technology in biomarker detection enables the timely identification of newly emerging infections and complications in trauma patients, facilitating expeditious medical interventions and leading to reduced post-trauma mortality rates and improved patient prognoses. This review provides an overview of the current applications of target protein identification technology in trauma-related complications and provides a brief overview of the current target protein identification technology, with the aim of reducing post-trauma mortality, improving diagnostic efficiency and prognostic outcomes for patients.

Understanding the cell: Future views of structural biology

Determining the structure and mechanisms of all individual functional modules of cells at high molecular detail has often been seen as equal to understanding how cells work. Recent technical advances have led to a flush of high-resolution structures of various macromolecular machines, but despite this wealth of detailed information, our understanding of cellular function remains incomplete. Here, we discuss present-day limitations of structural biology and highlight novel technologies that may enable us to analyze molecular functions directly inside cells. We predict that the progression toward structural cell biology will involve a shift toward conceptualizing a 4D virtual reality of cells using digital twins. These will capture cellular segments in a highly enriched molecular detail, include dynamic changes, and facilitate simulations of molecular processes, leading to novel and experimentally testable predictions. Transferring biological questions into algorithms that learn from the existing wealth of data and explore novel solutions may ultimately unveil how cells work.