Rosenhouse-Dantsker Unlocking Protein Structure-Function Relationships

Computational and Experimental Approaches and Applications
Erscheinungsjahr 2026
ISBN: 978-3-032-07511-6
Verlag: Springer International Publishing
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, 398 Seiten

Reihe: Biomedical and Life Sciences (R0)

Unraveling the complex mechanisms governing the relationship between protein structure and function requires a multifaceted approach. In particular, the integration of experimental and computational approaches is exceptionally powerful in this context. However, while experimental techniques, such as X-ray crystallography and nuclear magnetic resonance (NMR), have been used for protein structure determination for over half a century, only in the last decade have computational tools gained sufficient power to assume a prominent role in the study of protein structure and function alongside experimental techniques.

This volume offers a comprehensive overview of contemporary computational and experimental methods employed in protein structure-function studies, organized into two sections. The computational methods section delves into protein structure prediction, advanced simulation techniques, artificial intelligence and machine learning, ligand binding site identification, and protein-protein interaction prediction. The experimental methods section focuses on state-of-the-art structural methods, fluorescence microscopy, imaging techniques, and model membrane systems.

This book is an invaluable resource for a wide audience of research scientists with an interest in the molecular and cellular facets of biological processes, especially those who seek to investigate the intricate world of proteins and their functions through cutting-edge methodologies.

Zielgruppe

Research

Autoren/Hrsg.

Rosenhouse-Dantsker, Avia

Inhaltsverzeichnis

 Advanced computational methods in protein simulations.- Enhanced sampling and free energy calculations in protein simulations.- Design of protein structure and function.- Artificial intelligence in protein structure prediction.- Optimized methods for site identification in proteins.- Machine Learning tools for Peptide Protein Interaction Prediction.- Simulating Protein Ligand Binding with Neural Network Potentials.- Prediction of protein complex structures using protein protein interaction data.- Advances in X ray crystallography and cryo EM structural studies of protein structure and function.- Fluorescence microscopy methods for studying dynamics of fluorescent proteins in vivo.- Nanoscale fluorescence imaging of proteins.- Multiplexed protein imaging methods.- Determining protein structure by combining protein chemistry with mass spectrometry.- NMR spectroscopy techniques as a tool for characterizing the interactions in protein-protein complexes.- Electrophysiology tools in the study of ion channel proteins.

Über Autor(innen)

Dr. Rosenhouse-Dantsker’s M.Sc. and D.Sc. theses in chemistry focused on quantum theory at the interface of chemistry, physics, and mathematics. After continuing in this direction for several years as a postdoctoral fellow, she became interested in research at the interface of chemistry, biology, and medicine. Pursuing postdoctoral training at the Mount Sinai School of Medicine in New-York, she first performed computational biology research on G protein-coupled receptors and then delved into experimental research in the ion channel field. Since 2002, Dr. Rosenhouse-Dantsker’s research has focused on the modulation of potassium channels by ions, proteins, and lipids using a combination of experimental and computational approaches. Her research has been published in Nature Chemical Biology, PNAS, J Neuroscience, J Lipid Research, and JBC, among other leading journals. In 2008, Dr. Rosenhouse-Dantsker joined the University of Illinois Chicago where she is now a Clinical Associate Professor. Dr. Rosenhouse-Dantsker has co-edited two volumes on the modulation of protein function by cholesterol (Springer), and served as the editor of   (Springer).