TY - JOUR
T1 - Minimalistic predictor of protein binding energy
T2 - Contribution of solvation factor to protein binding
AU - Choi, Jeong Mo
AU - Serohijos, Adrian W.R.
AU - Murphy, Sean
AU - Lucarelli, Dennis
AU - Lofranco, Leo L.
AU - Feldman, Andrew
AU - Shakhnovich, Eugene I.
N1 - Funding Information:
We thank Ka Yeon Kook and Juyong Lee for helpful discussions on statistical treatments and Amy I. Gilson and Nicolas Chéron for their help in the preparation of the manuscript. We appreciate Thom Vreven for his help in the ZAPP calculation. This work was supported by Defense Advanced Research Projects Agency grant No. HR0011-11-C-0093 and National Science Foundation grant No. MCB-1243837.
Publisher Copyright:
© 2015 Biophysical Society.
PY - 2015/2/17
Y1 - 2015/2/17
N2 - It has long been known that solvation plays an important role in protein-protein interactions. Here, we use a minimalistic solvation-based model for predicting protein binding energy to estimate quantitatively the contribution of the solvation factor in protein binding. The factor is described by a simple linear combination of buried surface areas according to amino-acid types. Even without structural optimization, our minimalistic model demonstrates a predictive power comparable to more complex methods, making the proposed approach the basis for high throughput applications. Application of the model to a proteomic database shows that receptor-substrate complexes involved in signaling have lower affinities than enzyme-inhibitor and antibody-antigen complexes, and they differ by chemical compositions on interfaces. Also, we found that protein complexes with components that come from the same genes generally have lower affinities than complexes formed by proteins from different genes, but in this case the difference originates from different interface areas. The model was implemented in the software PYTHON, and the source code can be found on the Shakhnovich group webpage: http://faculty.chemistry.harvard.edu/shakhnovich/software.
AB - It has long been known that solvation plays an important role in protein-protein interactions. Here, we use a minimalistic solvation-based model for predicting protein binding energy to estimate quantitatively the contribution of the solvation factor in protein binding. The factor is described by a simple linear combination of buried surface areas according to amino-acid types. Even without structural optimization, our minimalistic model demonstrates a predictive power comparable to more complex methods, making the proposed approach the basis for high throughput applications. Application of the model to a proteomic database shows that receptor-substrate complexes involved in signaling have lower affinities than enzyme-inhibitor and antibody-antigen complexes, and they differ by chemical compositions on interfaces. Also, we found that protein complexes with components that come from the same genes generally have lower affinities than complexes formed by proteins from different genes, but in this case the difference originates from different interface areas. The model was implemented in the software PYTHON, and the source code can be found on the Shakhnovich group webpage: http://faculty.chemistry.harvard.edu/shakhnovich/software.
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U2 - 10.1016/j.bpj.2015.01.001
DO - 10.1016/j.bpj.2015.01.001
M3 - Article
C2 - 25692584
AN - SCOPUS:84923253904
SN - 0006-3495
VL - 108
SP - 795
EP - 798
JO - Biophysical journal
JF - Biophysical journal
IS - 4
ER -