NMR in the Cloud

Autorentext
Jeffrey C. Hoch, Ph.D.
Director, Gregory P. Mullen NMR Structural Biology Facility
Professor, Department of Molecular Biology and Biophysics
Molecular, Microbial and Structural Biology
UConn Health
Farmington, CT 06030

Dr. Hoch received his Masters and PhD degrees in Physical Chemistry from Harvard University. His laboratory works on problems related to the physical basis for the biological activity of proteins. Broadly, this includes their structure, dynamics, stability, and interactions with other molecules. He is a leader in NMR data processing and analysis for biomolecular applications: he chaired a 1990 NATO Advanced Research Workshop on Computational Aspects of Biomolecular NMR, chaired the inaugural Gordon Research Conference on the same topic in 1999, co-authored the book NMR Data Processing” with Dr. Alan Stern (1996), and currently directs two international online NMR resources: the National Center for Biomolecular NMR Data Processing and Analysis (nmrbox.org) and the Biological Magnetic Resonance Data Bank (bmrb.io).

He and his group have extensive experience in operating UConn Health's Gregory P. Mullen NMR Structural Biology Facility (which Dr. Hoch directs) that includes spectrometers operating at 400, 500, 600, and 800 MHz. The latter three instruments are equipped with state-of-the-art cryogenically cooled probes. Computational resources include a power 1000-node cluster.

Klappentext

NMR in the Cloud: Resources for Moving Biomolecular NMR Research to the cloud is the ideal reference for Biomolecular researchers to explore and incorporate cloud-based tools to increase productivity of their projects. The COVID-19 pandemic, by forcing social distancing and work-from-home, has accelerated these trends and increased the demand for online resources for data processing and analysis, data storage, and remote collaboration and training tools. The trend toward online research will continue well after the pandemic subsides, because online resources remain valuable even in the absence of restrictions on in-person work. They increase the efficiency of the research enterprise and make it more robust in the face of disruptions of all sorts, environmental as well as public health.

The book is structured around four parts. The first introduces reproducible computing for Bio-NMR, and collaborative computing tools. Part two presents online NMR platforms, including WeNMR, SB Grid, BMBR, Scientific Cloud Computing tools, and data Stewardship. The third part covers NMR specific online tools, including NMRfx, NMRPipe, NMRFAM-SPARKY, MNova, Chemical Shift Prediction, Peak Assignment, SPINACH, GISSMO, CSRosetta, NMR Metabolomics, Protein Structure Prediction, and reproducibility in the cloud environment. Part four presents case studies that help researchers envision how to select and deploy these tools to make their biological quest more productive and impactful.

Biomolecular researchers will benefit from this book by gaining insights into selecting and using these analytical tools in the most productive way. Core Bio-NMR Managers may as well benefit from offering the book as reference for their customers.

Inhalt

Part I Introduction1. Reproducible Computing for Bio-NMR: NMRbox2. Collaborative Computing Project for NMR

Part II Online Platforms3. WeNMR4. SB-Grid5. Biological Magnetic Resonance Data Bank6. Resources for Online Training7. Resources for Remote Collaboration8. Scientific Cloud Computing9. Data Stewardship

Part III NMR Online Tools10. NMRfx11. NMRPipe12. NMRFAM-SPARKY13. MNova14. Chemical Shift Prediction (possible contributors)15. Peak Assignment16. Spin Simulation Using SPINACH17. Spin Modeling with GISSMO18. Deconvolving 2D Spectra for Complex Mixtures19. Relaxation and Rate Analysis (possible contributors)20. CS-Rosetta21. NMR Metabolomics22. Protein Structure Prediction23. Reproducibility in the Cloud

Part IV Case Studies24. Case Study: Covid-19 NMR Consortium25. Case Study: NUScon26. Case Study: NMRFAM Workshop27. Case Study: ICMRBS virtual seminars