Comprehensive and quantitative mapping of energy landscapes for protein-protein interactions by rapid combinatorial scanning.
A novel, quantitative saturation (QS) scanning strategy was developed to obtain a comprehensive data base of the structural and functional effects of all possible mutations across a large protein-protein interface. The QS scan approach was applied to the high affinity site of human growth hormone (hGH) for binding to its receptor (hGHR). Although the published structure-function data base describing this system is probably the most extensive for any large protein-protein interface, it is nonetheless too sparse to accurately describe the nature of the energetics governing the interaction. Our comprehensive data base affords a complete view of the binding site and provides important new insights into the general principles underlying protein-protein interactions. The hGH binding interface is highly adaptable to mutations, but the nature of the tolerated mutations challenges generally accepted views about the evolutionary and biophysical pressures governing protein-protein interactions. Many substitutions that would be considered chemically conservative are not tolerated, while conversely, many non-conservative substitutions can be accommodated. Furthermore, conservation across species is a poor predictor of the chemical character of tolerated substitutions across the interface. Numerous deviations from generally accepted expectations indicate that mutational tolerance is highly context dependent and, furthermore, cannot be predicted by our current knowledge base. The type of data produced by the comprehensive QS scan can fill the gaps in the structure-function matrix. The compilation of analogous data bases from studies of other protein-protein interactions should greatly aid the development of computational methods for explaining and designing molecular recognition.
Pál
Gábor
G
Department of Biochemistry and Molecular Biology and Institute for Biophysical Dynamics, Cummings Life Sciences Center, University of Chicago, Chicago, Illinois 60637.
Kouadio
Jean-Louis K
JK
Department of Biochemistry and Molecular Biology and Institute for Biophysical Dynamics, Cummings Life Sciences Center, University of Chicago, Chicago, Illinois 60637.
Artis
Dean R
DR
Department of Protein Engineering, Genentech Inc., South San Francisco, California 94080.
Kossiakoff
Anthony A
AA
Department of Biochemistry and Molecular Biology and Institute for Biophysical Dynamics, Cummings Life Sciences Center, University of Chicago, Chicago, Illinois 60637. Electronic address: koss@cummings.uchicago.
Sidhu
Sachdev S
SS
Department of Protein Engineering, Genentech Inc., South San Francisco, California 94080. Electronic address: sidhu@gene.com.
D000409:Alanine / Q000235:genetics; D019943:Amino Acid Substitution; D019295:Computational Biology; D019382:Human Growth Hormone / Q000378:metabolism; D006801:Humans; D008565:Membrane Proteins / Q000378:metabolism; D008958:Models, Molecular; D019151:Peptide Library; D011485:Protein Binding / Q000235:genetics; D011506:Proteins / Q000235:genetics / Q000378:metabolism*
D008565:Membrane Proteins; D019151:Peptide Library; D011506:Proteins; C405010:delta-hGHR; D019382:Human Growth Hormone; D000409:Alanine