Structure from function: screening structural models with functional data

Abstract

Structural constraints derived from different antibody epitopes on human growth hormone (hGH) were used to screen three-dimensional models of hGH that were generated by computer algorithms. Previously, alanine-scanning mutagenesis defined the residues that modulate binding to 21 different monoclonal antibodies to hGH. These functional epitopes were composed of 4-14 side chains whose alpha-carbons clustered within 4-23 A. Distance and topographic constraints for these functional epitopes were virtually the same as constraints derived from known x-ray structures of protein-antigen complexes. The constraints were used to evaluate about 1400 models of hGH that were computer-generated by a secondary-structure prediction and packing algorithm. On average each functional epitope reduced the number of models in the pool by a factor of 2, so that 8 monoclonal antibodies could reduce the number of possible models to < 10. The average root-mean-square deviation of alpha-carbon coordinates between the x-ray structure and either the pool of starting models or final models ranged from 13 to 16 A or 4 to 7 A, respectively, depending on the pool of starting models and the level of constraints imposed. All of the final models had the correct folding topography, and the best model was within 3.8 A root-mean-square deviation of the x-ray coordinates. This model was as close as it could have been because the models were built by using ideal helices and those in the x-ray structure are not. Our studies suggest that epitope mapping data can effectively screen structural models and, when coupled to predictive algorithms, can help to generate low-resolution models of a protein.