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1.  Community-wide Evaluation of Methods for Predicting the Effect of Mutations on Protein-Protein Interactions 
Proteins  2013;81(11):1980-1987.
Community-wide blind prediction experiments such as CAPRI and CASP provide an objective measure of the current state of predictive methodology. Here we describe a community-wide assessment of methods to predict the effects of mutations on protein-protein interactions. Twenty-two groups predicted the effects of comprehensive saturation mutagenesis for two designed influenza hemagglutinin binders and the results were compared with experimental yeast display enrichment data obtained using deep sequencing. The most successful methods explicitly considered the effects of mutation on monomer stability in addition to binding affinity, carried out explicit side chain sampling and backbone relaxation, and evaluated packing, electrostatic and solvation effects, and correctly identified around a third of the beneficial mutations. Much room for improvement remains for even the best techniques, and large-scale fitness landscapes should continue to provide an excellent test bed for continued evaluation of methodological improvement.
doi:10.1002/prot.24356
PMCID: PMC4143140  PMID: 23843247
CAPRI; hemagglutinin; binding; deep mutational scanning; yeast display
2.  Community-wide assessment of protein-interface modeling suggests improvements to design methodology 
Fleishman, Sarel J | Whitehead, Timothy A | Strauch, Eva-Maria | Corn, Jacob E | Qin, Sanbo | Zhou, Huan-Xiang | Mitchell, Julie C. | Demerdash, Omar N.A | Takeda-Shitaka, Mayuko | Terashi, Genki | Moal, Iain H. | Li, Xiaofan | Bates, Paul A. | Zacharias, Martin | Park, Hahnbeom | Ko, Jun-su | Lee, Hasup | Seok, Chaok | Bourquard, Thomas | Bernauer, Julie | Poupon, Anne | Azé, Jérôme | Soner, Seren | Ovali, Şefik Kerem | Ozbek, Pemra | Ben Tal, Nir | Haliloglu, Türkan | Hwang, Howook | Vreven, Thom | Pierce, Brian G. | Weng, Zhiping | Pérez-Cano, Laura | Pons, Carles | Fernández-Recio, Juan | Jiang, Fan | Yang, Feng | Gong, Xinqi | Cao, Libin | Xu, Xianjin | Liu, Bin | Wang, Panwen | Li, Chunhua | Wang, Cunxin | Robert, Charles H. | Guharoy, Mainak | Liu, Shiyong | Huang, Yangyu | Li, Lin | Guo, Dachuan | Chen, Ying | Xiao, Yi | London, Nir | Itzhaki, Zohar | Schueler-Furman, Ora | Inbar, Yuval | Patapov, Vladimir | Cohen, Mati | Schreiber, Gideon | Tsuchiya, Yuko | Kanamori, Eiji | Standley, Daron M. | Nakamura, Haruki | Kinoshita, Kengo | Driggers, Camden M. | Hall, Robert G. | Morgan, Jessica L. | Hsu, Victor L. | Zhan, Jian | Yang, Yuedong | Zhou, Yaoqi | Kastritis, Panagiotis L. | Bonvin, Alexandre M.J.J. | Zhang, Weiyi | Camacho, Carlos J. | Kilambi, Krishna P. | Sircar, Aroop | Gray, Jeffrey J. | Ohue, Masahito | Uchikoga, Nobuyuki | Matsuzaki, Yuri | Ishida, Takashi | Akiyama, Yutaka | Khashan, Raed | Bush, Stephen | Fouches, Denis | Tropsha, Alexander | Esquivel-Rodríguez, Juan | Kihara, Daisuke | Stranges, P Benjamin | Jacak, Ron | Kuhlman, Brian | Huang, Sheng-You | Zou, Xiaoqin | Wodak, Shoshana J | Janin, Joel | Baker, David
Journal of molecular biology  2011;414(2):10.1016/j.jmb.2011.09.031.
The CAPRI and CASP prediction experiments have demonstrated the power of community wide tests of methodology in assessing the current state of the art and spurring progress in the very challenging areas of protein docking and structure prediction. We sought to bring the power of community wide experiments to bear on a very challenging protein design problem that provides a complementary but equally fundamental test of current understanding of protein-binding thermodynamics. We have generated a number of designed protein-protein interfaces with very favorable computed binding energies but which do not appear to be formed in experiments, suggesting there may be important physical chemistry missing in the energy calculations. 28 research groups took up the challenge of determining what is missing: we provided structures of 87 designed complexes and 120 naturally occurring complexes and asked participants to identify energetic contributions and/or structural features that distinguish between the two sets. The community found that electrostatics and solvation terms partially distinguish the designs from the natural complexes, largely due to the non-polar character of the designed interactions. Beyond this polarity difference, the community found that the designed binding surfaces were on average structurally less embedded in the designed monomers, suggesting that backbone conformational rigidity at the designed surface is important for realization of the designed function. These results can be used to improve computational design strategies, but there is still much to be learned; for example, one designed complex, which does form in experiments, was classified by all metrics as a non-binder.
doi:10.1016/j.jmb.2011.09.031
PMCID: PMC3839241  PMID: 22001016
3.  Classification of heterodimer interfaces using docking models and construction of scoring functions for the complex structure prediction 
Protein–protein docking simulations can provide the predicted complex structural models. In a docking simulation, several putative structural models are selected by scoring functions from an ensemble of many complex models. Scoring functions based on statistical analyses of heterodimers are usually designed to select the complex model with the most abundant interaction mode found among the known complexes, as the correct model. However, because the formation schemes of heterodimers are extremely diverse, a single scoring function does not seem to be sufficient to describe the fitness of the predicted models other than the most abundant interaction mode. Thus, it is necessary to classify the heterodimers in terms of their individual interaction modes, and then to construct multiple scoring functions for each heterodimer type. In this study, we constructed the classification method of heterodimers based on the discriminative characters between near-native and decoy models, which were found in the comparison of the interfaces in terms of the complementarities for the hydrophobicity, the electrostatic potential and the shape. Consequently, we found four heterodimer clusters, and then constructed the multiple scoring functions, each of which was optimized for each cluster. Our multiple scoring functions were applied to the predictions in the unbound docking.
PMCID: PMC3169947  PMID: 21918618
classification of heterodimers; prediction of complex structures; scoring functions; protein-protein docking; CAPRI

Results 1-3 (3)