Frank Delaglio

Delaglio Group



Call: (240) 314-6122


  • Ph.D., Graduate Department of Pharmaceutical Sciences, Osaka University, 2001
  • B.A., Syracuse University Department of Chemistry, 2001


Dr. Frank Delaglio develops 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. His current focus is 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. In the NMR community, Dr. Delaglio is best known as the developer of the NMRPipe software system (, which has been cited over 11,000 times in peer-reviewed literature and has been used to help generate over 40% of the protein structures ever measured by NMR.

Dr. Delaglio’s areas of interest include multidimensional spectral processing and analysis, chemometrics and machine learning, data visualization, and NMR homology approaches to biomolecular structure determination. His work always includes the goal of producing practical, working software, and he is a regular presenter at advanced courses and workshops for the European Molecular Biology Organization (EMBO) and the UCONN Center for Biomolecular NMR Data Processing and Analysis (


Characterization of Biologics by NMR Spectroscopy, Chemometrics, and Machine Learning

With annual sales exceeding $150 billion, growth of biologic therapeutics is outpacing that of small-molecule drugs, and the development, manufacture, and delivery of biologics present very different challenges.  Among biologics, monoclonal antibodies (mAbs) have become particularly attractive for drug development, since it is possible to select and duplicate antibodies that bind with high affinity and specificity to most any target, and since biomanufacturing platforms for mAbs can be reused for more than one therapeutic.

Classification of mAb Variants by Chemometrics and Machine Learning.  Left: overlay of 34 1H/13C NMR spectra of three types of NISTmAb samples, native, G0F glycoform, and partially deglycosylated.  Right: Principal Component Analysis (PCA) applied directly to spectral intensities, with complete linkage cluster analysis applied to the PCA scatterplot to classify the spectra into three groups automatically.  The three clusters determined in this way correctly classify the spectra according to the three sample types.

Characterization of biologics and their formulations requires monitoring high order structure (HOS), since misfolding or aggregation can lead to loss of efficacy or harmful immune responses.  Nuclear Magnetic Resonance (NMR) is a powerful multi-attribute method that can be applied directly to intact biologics and their formulations to meet this important measurement need.  However, typical biomolecular NMR applications use isotopically enriched samples, require long measurement times, and need extensive and often subjective interactive analysis by an expert in order to extract structural information.  Previous work has shown that fast NMR measurement techniques combined with Non-Uniform Sampling (NUS) can generate practical two-dimensional 1H/13C spectra at natural isotopic abundance for molecules as large as intact monoclonal antibodies.  Leveraging the IgG1k NIST reference mAb (NISTmAb), Dr. Delaglio’s current research aims to reveal and classify small variations of structure and interaction by direct computational analysis of the shapes of NMR spectra as an alternative to interactive analysis and assignment of spectral features.  This paves the way for NMR characterization of biologics via chemometrics and machine learning that is both objective and automated.

Principal Component Analysis of 1D 1H Diffusion Edited NMR Spectra of Protein Therapeutics.
A simple approach for reconstruction of non-uniformly sampled pseudo-3D NMR data for accurate measurement of spin relaxation parameters.
Reducing the measurement time of exact NOEs by non-uniform sampling.
Principal component analysis for automated classification of 2D spectra and interferograms of protein therapeutics: influence of noise, reconstruction details, and data preparation.
Assessment of the Higher-Order Structure of Formulated Monoclonal Antibody Therapeutics by 2D Methyl Correlated NMR and Principal Component Analysis.
Comparative Analysis of One-Dimensional Protein Fingerprint by Line Shape Enhancement and Two-Dimensional 1H,13C Methyl NMR Methods for Characterization of the Higher Order Structure of IgG1 Monoclonal Antibodies.
Best Practices in Utilization of 2D-NMR Spectral Data as the Input for Chemometric Analysis in Biopharmaceutical Applications.
Chemometric Outlier Classification of 2D-NMR Spectra to Enable Higher Order Structure Characterization of Protein Therapeutics.
Practical Guidelines for 13C-Based NMR Metabolomics.
Enabling adoption of 2D-NMR for the higher order structure assessment of monoclonal antibody therapeutics.
Selective suppression of excipient signals in 2D 1H-13C methyl spectra of biopharmaceutical products.
Non-Uniform Sampling for All: More NMR Spectral Quality, Less Measurement Time.
Nonuniform sampling in multidimensional NMR for improving spectral sensitivity.
Multivariate Analysis of Two-Dimensional 1H, 13C Methyl NMR Spectra of Monoclonal Antibody Therapeutics To Facilitate Assessment of Higher Order Structure.
NMRbox: A Resource for Biomolecular NMR Computation.
Sparse multidimensional iterative lineshape-enhanced (SMILE) reconstruction of both non-uniformly sampled and conventional NMR data.
Probing Invisible, Excited Protein States by Non-Uniformly Sampled Pseudo-4D CEST Spectroscopy.
A practical implementation of de-Pake-ing via weighted Fourier transformation.
A practical implementation of cross-spectrum in protein backbone resonance assignment.
TALOS+: a hybrid method for predicting protein backbone torsion angles from NMR chemical shifts.