About

Rafael Brüschweiler received his Ph.D. in Physical Chemistry from ETH Zurich, Switzerland, and completed a postdoctoral fellowship at the Department of Molecular Biology at the Scripps Research Institute, La Jolla. He has held positions at Florida State University, Tallahassee, including full professor and Associate Director for Biophysics at the National High Magnetic Field Laboratory. In 2013, he joined Ohio State University with joint appointments at the Department of Chemistry and Biochemistry and the Department of Biological Chemistry and Pharmacology. He is a full Professor, an Ohio Research Scholar (endowed), and serves as the NMR Executive Director for the Ohio State Campus Chemical Instrument Center and the NSF-funded National Gateway Ultrahigh Field NMR Center, which features the first 1.2 GHz NMR Spectrometer in the U.S. His research is primarily funded by the National Science Foundation and the National Institutes of Health. Professor Brüschweiler's research is highly cross-disciplinary, focusing on biophysical chemistry and analytical chemistry. His work emphasizes understanding the role of dynamics and interactions of proteins for their function and analyzing complex biological mixtures in metabolomics. His main research tools include high-field nuclear magnetic resonance (NMR) and high-performance computation, with efforts directed toward developing new techniques to enhance resolution, sensitivity, and speed. His biological research includes studying proteins such as GTPase K-Ras and its mutants, p53/MDM2, sodium-calcium exchanger NCX, Cu2+-ATPase, ubiquitin, and enzymes like arginine kinase and glucokinase. He also conducts metabolic studies on microbes, drosophila, E. coli, yeast, and cancer cell lines to identify biomarkers, biochemical pathways, and novel metabolites related to health and disease. Additionally, his work combines biophysical chemistry with nano-science, including nanoparticle-assisted NMR spin relaxation, and involves developing machine-learning tools for automated NMR data analysis and interpretation. He is actively accepting new graduate students interested in protein dynamics, computational biomolecular studies, metabolomics, and NMR method development.

Research topics

• Computer Science
• Biological system
• Data Mining
• Physics
• Biochemistry
• Nuclear magnetic resonance
• Chemistry
• Artificial Intelligence
• Biology
• Algorithm
• Statistics
• Materials science
• Chromatography
• Mathematics
• Nanotechnology
• Stereochemistry

Selected publications

• Predicting protein flexibility with <scp>AlphaFold</scp>

  Proteins Structure Function and Bioinformatics · 2023 · 68 citations

  Senior authorCorresponding
  • Computer Science
  • Biological system
  • Chemistry

  order parameter profiles. The method is demonstrated for a set nine proteins of different sizes and variable amounts of dynamics and disorder.

  DOI

• DEEP picker is a deep neural network for accurate deconvolution of complex two-dimensional NMR spectra

  Nature Communications · 2021 · 169 citations

  Senior authorCorresponding
  • Computer Science
  • Artificial Intelligence
  • Computer Science

  The analysis of nuclear magnetic resonance (NMR) spectra for the comprehensive and unambiguous identification and characterization of peaks is a difficult, but critically important step in all NMR analyses of complex biological molecular systems. Here, we introduce DEEP Picker, a deep neural network (DNN)-based approach for peak picking and spectral deconvolution which semi-automates the analysis of two-dimensional NMR spectra. DEEP Picker includes 8 hidden convolutional layers and was trained on a large number of synthetic spectra of known composition with variable degrees of crowdedness. We show that our method is able to correctly identify overlapping peaks, including ones that are challenging for expert spectroscopists and existing computational methods alike. We demonstrate the utility of DEEP Picker on NMR spectra of folded and intrinsically disordered proteins as well as a complex metabolomics mixture, and show how it provides access to valuable NMR information. DEEP Picker should facilitate the semi-automation and standardization of protocols for better consistency and sharing of results within the scientific community.

  DOI

• 2D NMR-Based Metabolomics with HSQC/TOCSY NOAH Supersequences

  Analytical Chemistry · 2021 · 42 citations

  Senior authorCorresponding
  • Chemistry
  • Biological system
  • Nuclear magnetic resonance

  H detection) for the compact acquisition of multiple 2D NMR data sets with significant gains in sensitivity, resolution, and/or time. The new pulse sequences, which are demonstrated for both metabolite model mixtures and mouse urine, offer an attractive approach for the efficient measurement of multiple 2D NMR spectra (HSQCsi and/or HSQCsi-TOCSY and TOCSY) of metabolomics samples in a single experiment for the accurate and comprehensive identification and quantitation of metabolites. These new methods bring to bear the advantages of 2D NMR to metabolomics studies with larger cohorts of samples.

  DOI

Frequent coauthors

• Dawei Li

  The Ohio State University

  156 shared

• Martin Blackledge

  CEA Grenoble

  149 shared

• Guillaume Bouvignies

  École Normale Supérieure - PSL

  144 shared

• Lei Brüschweiler-Li

  The Ohio State University

  123 shared

• Fengli Zhang

  University of Jinan

  80 shared

• Phineus R. L. Markwick

  University of California, San Diego

  70 shared

• Stephan Grzesiek

  University of Basel

  49 shared

• Kerem Bingol

  Environmental Molecular Sciences Laboratory

  49 shared

Awards & honors

• John S. Swenton Award for Outstanding Teaching
• William Lloyd Evans Lecture
• Devon W. Meek Lecture
• Edward Mack, Jr. Lecture
• David J. Hart Symposium

Similar researchers at Ohio State University

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