Protein therapeutics are vitally important clinically and commercially, with monoclonal antibody (mAb) therapeutic sales alone accounting for more than $150 billion annually. In order for these therapies to be safe and effective, their protein components must maintain their three dimensional fold and not aggregate. Nuclear magnetic resonance spectroscopy (NMR) is powerful and diverse tool to characterize this higher order structure of protein therapeutics, because NMR spectra are sensitive to molecular shape and intermolecular interactions as well as chemical structure, and NMR can reproducibly probe this information at atomic resolution. Intriguingly, since NMR can also be applied to quantify the small molecule mixtures comprising the metabolome, NMR has the potential to characterize the growth metabolomics of the cells used in large scale to manufacture mAbs, and also the cells manufactured in small scale for personalized live cell therapies such as CAR-T cancer treatment.  Exploiting NMR for these biomanufacturing needs leads to a series of computational challenges, including metrics of spectral similarity, data handling for applications of principal component analysis (PCA), spectral analysis of mixtures, and identification of spectral features by computer vision and machine learning.

Our computational methods development has three primary goals. The first goal is continued support of expert-driven biomolecular structure determination by NMR, with an emphasis on spectral reconstruction and quantification. The second goal is to develop computational alternatives to interactive analysis and assignment of spectral features, to provide practical characterization of protein therapeutics via chemometrics and machine learning that is both objective and automated. The third goal is to develop computational analytics for low-field NMR as used to monitor live cell growth in bioreactors.

References

Y. Wu, O. Sanati, M. Uchimiya, K. Krishnamurthy, A.S. Edison, and F. Delaglio: SAND: automated time-domain modeling of NMR spectra applied to metabolic quantification. Anal. Chem. 2024, 96 (5), 1843–1851. doi:10.1021/acs.analchem.3c03078 (2024).

R.G. Brinson, K.W. Elliott, L.W. Arbogast, D.A. Sheen, J.P. Giddens, J.P. Marino, and F. Delaglio: Principal Component Analysis for Automated Classification of 2D Spectra and Interferograms of Protein Therapeutics: Influence of Noise, Reconstruction Details, and Data Preparation. J. Biomol. NMR, doi:10.1007/s10858-020-00332-y (2020).

D.A. Sheen, V.K. Shen, R.G. Brinson, L.W. Arbogast, J.P. Marino, and F. Delaglio: Chemometric outlier classification of 2D-NMR spectra to enable higher order structure characterization of protein therapeutics. Chemometrics and Intelligent Laboratory Systems, 199,  103973. doi: 10.1016/j.chemolab.2020.103973 (2020).

key words

NMR; Structural biology; Spectral processing; Spectral analysis; Spectral fingerprinting; Biotherapeutics; Higher order structure; Metabolomics; Computation; Software;

Citizenship:  Open to U.S. citizens

Level:  Open to Postdoctoral applicants