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Frank Delaglio

Principal Investigator

I develop computational methods for extracting useful information from Nuclear Magnetic Resonance (NMR) data, to support development and manufacturing of drugs and vaccines, and to support basic research in structural biology.  At NIST I focus on development of approaches for characterization of the high order structure of biologics, so that these important therapeutics can continue to be safe and effective, and become more available and affordable.  My areas of interest include multidimensional spectral processing and analysis, spectral fingerprinting, data visualization, and NMR homology approaches to biomolecular structure determination, and my work always includes the goal of producing practical, working software.  

In the NMR community, I'm best known as the developer of the NMRPipe software system (http://www.ibbr.umd.edu/nmrpipe/install.html), which has been cited over 10,000 times in peer-reviewed literature, and has been used to help generate over 40% of protein structures ever measured by NMR.

A full publication list is available on Google Scholar.

Publications

Practical Guidelines to 13C-based NMR Metabolomics

Author(s)
Arthur S. Edison, Adrien Le Guennec, Frank Delaglio, Eriks Kupce
We present an overview of 13C-based NMR metabolomics. At first glance, the low sensitivity of 13C relative to 1H NMR might seem like too great an obstacle to

Enabling adoption of 2D-NMR for the higher order structure assessment of monoclonal antibody therapeutics

Author(s)
Robert G. Brinson, John P. Marino, Frank Delaglio, Luke W. Arbogast, Ryan M. Evans, Anthony J. Kearsley, Yves Aubin, Gregory Pierens, Xinying Jia, David Keizer, Jonas Stahle, Goran Widmalm, Chad Lawrence, Patrick Reardon, John Cort, Koichi Kato, Stuart Parnham, Andreas Blomgren, Torgny Rundlof, Kang Chen, David Keire, Thea Suter-Stahel, Gerhard Wider, Donna Baldisseri, Julie Wu, Mats Wikstrom, Medhi Mobli
Of the top ten drugs on the global market in 2016, seven of them are biologics with a combined market value of over US$60 billion. Moreover, around 2,800

Letter in response to V. Stodden et al., Science 354, 1240 (2016).

Author(s)
Mark W. Maciejewski, Adam D. Schuyler, Michael R. Gryk, Ion I. Moraru, Pedro R. Romero, Eldon L. Ulrich, Hamid R. Eghbalnia, Miron Livny, Frank Delaglio, Jeffrey C. Hoch
Stodden et al. (1) bring welcome attention to the problem of reproducibility of computational analyses, and recommend guidelines for addressing the barriers to
Created October 23, 2018, Updated February 8, 2019