Determining the three-dimensional fold of a protein from approximate constraints: a simulation study

Area of Science:

• Computational Biology
• Structural Biology
• Biophysics

Background:

• Determining protein three-dimensional (3D) structure is crucial for understanding biological function.
• Traditional methods like X-ray crystallography and NMR spectroscopy have limitations for certain proteins.
• Experimental data can provide constraints for computational structure prediction.

Purpose of the Study:

• To develop and validate a new computational approach for protein 3D structure calculation.
• To utilize distance and dihedral angle constraints from diverse experimental and theoretical sources.
• To assess the accuracy and robustness of the method in predicting protein folds.

Main Methods:

• Employed distance geometry calculations using the Self Correcting Distance Geometry (SECODG) program DIAMOD.
• Generated simulated data sets with varying numbers and qualities of distance constraints from experimental data types.
• Applied the method to Staphylococcal nuclease (STN) with known coordinates and HIV-1 rev protein (REV) with unknown structure.

Main Results:

• Successfully generated correct tertiary folds for STN using both qualitative and precise distance constraints.
• Achieved root mean square deviations of 2.0 A to 8.3 A for backbone atoms, dependent on constraint quantity.
• The SECODG technique effectively detected and corrected a small fraction of incorrect distance constraints.
• Predicted a plausible helix-loop-helix model for REV consistent with experimental data.

Conclusions:

• The proposed method provides a viable alternative for protein 3D structure determination, particularly when conventional techniques fail.
• The approach is effective in generating accurate protein folds from limited or noisy experimental data.
• The SECODG technique demonstrates robustness in handling and correcting erroneous structural constraints.