This project will enhance the stability, reliability and efficiency of a new computational tool, the self- correcting geometry (SECODG) method for combined assignment of NMR spectra and 3D structure calculation. The SECODG method implemented in the DIAMOD/NOAH software package dramatically reduces the time to generate protein structures from NMR data. Unlike previous distance geometry methods, which were designed for consistent data sets, the method can generate accurate structures from sets of constraints that contain errors. This project will further improve this method to be applicable for larger proteins. Several tests have demonstrated that the present method can deal with real data and is significantly faster than manual interactive spectral interpretation methods. The first test was to compare the results of automatic structure calculation from NOESY spectra that had been analyzed by earlier manual methods for 6 proteins ranging in size from 40 to 135 amino acids. The automated method assigned 70-80% of the NOESY cross peaks and the three-dimensional structures were of similar quality. In a second test, previously uninterpreted NOESY and TOCSY spectra were used by NOAH/DIAMOD to automatically calculate a 3D structure bundle for an isoform of crambin. Slight modifications and some manual assistance were required so that the program could deal with a significant number of missing chemical shifts. Based on this practical experience, the method will be improved by optimizing the error-tolerant target function, incorporating sensitivity tests for constraints, including line shape information in the peak assignment method and interfacing the program suite with programs for automated sequential assignment from other groups to develop a completely automatic computational package for direct interpretation of NMR spectra. This program package will be a powerful computational tool to speed up macromolecular structure determination from NMR da ta. Experimentally determined three-dimensional structures are the basis for designing new drugs and proteins with improved or new functions. In combination with energy minimization and Monte Carlo simulations, it will help in designing proteins with a desired structure and new functional properties.

Agency
National Science Foundation (NSF)
Institute
Division of Biological Infrastructure (DBI)
Type
Standard Grant (Standard)
Application #
9714937
Program Officer
Sylvia J. Spengler
Project Start
Project End
Budget Start
1998-09-15
Budget End
2002-08-31
Support Year
Fiscal Year
1997
Total Cost
$285,000
Indirect Cost
Name
University of Texas Medical Branch at Galveston
Department
Type
DUNS #
City
Galveston
State
TX
Country
United States
Zip Code
77555