Integral Membrane Proteins (IMPs) include many biomedically-important gate keepers, receptors, transporters, homeostasis regulators, and potential drug discovery targets. Three-dimensional (3D) structure determination of IMPs by X-ray crystallography, cryo-electron microscopy (cryo-EM), or Nuclear Magnetic Resonance (NMR) methods remains a major challenge for structural biology. While NMR can generally provide accurate 3D structures of small soluble proteins, structure determination by solution NMR of IMPs, prepared in stabilizing membrane-mimicking environments which generally require 2H,13C,15N-enrichment of the IMP, can be quite challenging. Evolutionary couplings (ECs), evolutionarily-correlated mutations derived from multiple sequence alignments, can also be used to provide information about native residue pair contacts, and to model the 3D structures of IMPs. Combining EC and NMR data provides a powerful approach for overcoming incompleteness of NMR NOESY data obtained for perdeuterated IMP samples, and the challenges in identifying true native protein structure contacts from the phylogenetic EC analysis. In particular, inter-helical contact information that is difficult to obtain for perdeuterated IMPs by NMR is well represented in the sequence co-variance EC data. Our goals are to develop a robust, reproducible, and fully automated EC-NMR platform suitable for accurate and reliable structure determination of IMPs, particularly ?-helical IMPs, and apply these methods for 3D structure analysis of biomedically- important IMPs. EC-NMR will be further developed using ?-barrel and ?-helical IMPs of known structure, and then applied to studies of IMPs of unknown structure selected from designated NIH NIAID priority pathogenic bacteria. We will (i) further develop and apply the Single Protein Production (SPP) method for producing isotope-enriched IMPs in E. coli, (ii) implement a micro-scale NMR screening pipeline for IMP sample optimization, (iiii) rigorously and comprehensively address the question of how EC and NOESY data quality and quantity correlate with the accuracy of EC-NMR structures, (iv) design improved algorithms for structure determination of IMPs combining ECs and NMR data, and (v) develop tools for validation of IMP structures determined by both conventional NMR and EC-NMR methods. Advanced molecular modeling methods will be implemented to improve accuracy of EC-NMR structures. ECs will also be combined with NMR data to identify and determine structures of multiple ?native states? of proteins. This study will expand the range of proteins that can be studied by NMR, provide more accurate structural and dynamic information than can be obtained with existing methods, and provide fundamental structural information needed for future antibiotic drug development targeted to high-priority pathogens.

Public Health Relevance

Integral Membrane Proteins (IMPs) include many biomedically-important gate keepers, receptors, transporters, homeostasis regulators, and potential drug discovery targets. Structure determination of IMPs by X-ray crystallography, cryo-Electron Microscopy (cryo-EM), or Nuclear Magnetic Resonance (NMR) methods remains a major challenge for structural biology. We proposed to develop a robust and reproducible platform (called EC-NMR), for accurate, reliable, validated, and reproducible structure determination of IMPs, and apply this platform for structure determination of membrane proteins selected from the genomes of designated NIH NIAID priority pathogenic bacteria.

Agency
National Institute of Health (NIH)
Institute
National Institute of General Medical Sciences (NIGMS)
Type
Research Project (R01)
Project #
1R01GM120574-01A1
Application #
9383967
Study Section
Biochemistry and Biophysics of Membranes Study Section (BBM)
Program Officer
Wehrle, Janna P
Project Start
2017-09-01
Project End
2021-06-30
Budget Start
2017-09-01
Budget End
2018-06-30
Support Year
1
Fiscal Year
2017
Total Cost
Indirect Cost
Name
Rutgers University
Department
Type
Organized Research Units
DUNS #
001912864
City
Piscataway
State
NJ
Country
United States
Zip Code
08854