Integrating statistical pair potentials into protein complex prediction.
The biophysical study of protein-protein interactions and docking has important implications in our understanding of most complex cellular signaling processes. Most computational approaches to protein docking involve a tradeoff between the level of detail incorporated into the model and computational power required to properly handle that level of detail. In this work, we seek to optimize that balance by showing that we can reduce the complexity of model representation and thus make the computation tractable with minimal loss of predictive performance. We also introduce a pair-wise statistical potential suitable for docking that builds on previous work and show that this potential can be incorporated into our fast fourier transform-based docking algorithm ZDOCK. We use the Protein Docking Benchmark to illustrate the improved performance of this potential compared with less detailed other scoring functions. Furthermore, we show that the new potential performs well on antibody-antigen complexes, with most predictions clustering around the Complementarity Determining Regions of antibodies without any manual intervention.
Mintseris
Julian
J
Bioinformatics Program, Boston University, Massachusetts 02215, USA.
Pierce
Brian
B
Wiehe
Kevin
K
Anderson
Robert
R
Chen
Rong
R
Weng
Zhiping
Z
D000465:Algorithms; D000936:Antigen-Antibody Complex / Q000737:chemistry; D007140:Immunoglobulin Fab Fragments / Q000276:immunology; D008956:Models, Chemical; D008958:Models, Molecular; D015233:Models, Statistical; D009113:Muramidase / Q000276:immunology; D011485:Protein Binding; D011487:Protein Conformation; D011506:Proteins / Q000737:chemistry*
D000936:Antigen-Antibody Complex; D007140:Immunoglobulin Fab Fragments; D011506:Proteins; D009113:Muramidase