Community-wide evaluation of methods for predicting the effect of mutations on protein-protein interactions
Fleishman, Sarel J.
Bates, Paul A.
Kastritis, Panagiotis L.
Rodrigues, Joao P.G.L.M
Bonvin, Alexandre M.J.J.
Pierce, Brian G.
UMass Chan AffiliationsProgram in Bioinformatics and Integrative Biology
Document TypeJournal Article
*Protein Interaction Mapping
Biochemistry, Biophysics, and Structural Biology
MetadataShow full item record
AbstractCommunity-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, 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 both existing and new prediction methodologies.
SourceProteins. 2013 Nov;81(11):1980-7. doi: 10.1002/prot.24356. Epub 2013 Aug 23. Link to article on publisher's site
Permanent Link to this Itemhttp://hdl.handle.net/20.500.14038/25922
Full author list omitted for brevity. For the full list of authors, see article.
Related ResourcesLink to Article in PubMed
Showing items related by title, author, creator and subject.
Protein-protein docking benchmark version 4.0Hwang, Howook; Vreven, Thom; Janin, Joel; Weng, Zhiping (2010-11-15)We updated our protein-protein docking benchmark to include complexes that became available since our previous release. As before, we only considered high-resolution complex structures that are nonredundant at the family-family pair level, for which the X-ray or NMR unbound structures of the constituent proteins are also available. Benchmark 4.0 adds 52 new complexes to the 124 cases of Benchmark 3.0, representing an increase of 42%. Thus, benchmark 4.0 provides 176 unbound-unbound cases that can be used for protein-protein docking method development and assessment. Seventeen of the newly added cases are enzyme-inhibitor complexes, and we found no new antigen-antibody complexes. Classifying the new cases according to expected difficulty for protein-protein docking algorithms gives 33 rigid body cases, 11 cases of medium difficulty, and 8 cases that are difficult. Benchmark 4.0 listings and processed structure files are publicly accessible at http://zlab.umassmed.edu/benchmark/.
Updates to the Integrated Protein-Protein Interaction Benchmarks: Docking Benchmark Version 5 and Affinity Benchmark Version 2Vreven, Thom; Moal, Iain H.; Vangone, Anna; Pierce, Brian G.; Kastritis, Panagiotis L.; Torchala, Mieczyslaw; Chaleil, Raphael; Jimenez-Garcia, Brian; Bates, Paul A.; Fernandez-Recio, Juan; et al. (2015-09-25)We present an updated and integrated version of our widely used protein-protein docking and binding affinity benchmarks. The benchmarks consist of non-redundant, high-quality structures of protein-protein complexes along with the unbound structures of their components. Fifty-five new complexes were added to the docking benchmark, 35 of which have experimentally measured binding affinities. These updated docking and affinity benchmarks now contain 230 and 179 entries, respectively. In particular, the number of antibody-antigen complexes has increased significantly, by 67% and 74% in the docking and affinity benchmarks, respectively. We tested previously developed docking and affinity prediction algorithms on the new cases. Considering only the top 10 docking predictions per benchmark case, a prediction accuracy of 38% is achieved on all 55 cases and up to 50% for the 32 rigid-body cases only. Predicted affinity scores are found to correlate with experimental binding energies up to r=0.52 overall and r=0.72 for the rigid complexes.
Evaluating template-based and template-free protein-protein complex structure predictionVreven, Thom; Hwang, Howook; Pierce, Brian G.; Weng, Zhiping (2014-03-01)We compared the performance of template-free (docking) and template-based methods for the prediction of protein-protein complex structures. We found similar performance for a template-based method based on threading (COTH) and another template-based method based on structural alignment (PRISM). The template-based methods showed similar performance to a docking method (ZDOCK) when the latter was allowed one prediction for each complex, but when the same number of predictions was allowed for each method, the docking approach outperformed template-based approaches. We identified strengths and weaknesses in each method. Template-based approaches were better able to handle complexes that involved conformational changes upon binding. Furthermore, the threading-based and docking methods were better than the structural-alignment-based method for enzyme-inhibitor complex prediction. Finally, we show that the near-native (correct) predictions were generally not shared by the various approaches, suggesting that integrating their results could be the superior strategy.