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Revisions №61341

branch: master 「№61341」
Commited by: Vikram K. Mulligan
GitHub commit link: 「886e40432568875a」 「№4769」
Difference from previous tested commit:  code diff
Commit date: 2020-07-15 18:55:38
linux.clang linux.gcc linux.srlz mac.clang
debug
release
unit
PyRosetta4.notebook gcc-9.gcc.python37.PyRosetta4.unit linux.clang.cxx11thread.serialization.python37.PyRosetta4.unit linux.gcc.python36.PyRosetta4.unit mac.PyRosetta.unit build.clean.debug alpine.gcc.build.debug clang-10.clang.cxx11thread.mpi.serialization.tensorflow.build.debug gcc-9.gcc.build.debug mysql postgres linux.clang.python36.build.debug linux.zeromq.debug mpi mpi.serialization linux.icc.build.debug OpenCL mac.clang.python36.build.debug build.header build.levels build.ninja_debug graphics static build.xcode beautification code_quality.clang_analysis code_quality.clang_tidy code_quality.cppcheck serialization code_quality.submodule_regression integration.mpi integration.release_debug integration.tensorflow integration.thread integration performance profile release.source scientific.ddg_ala_scan scientific.peptide_pnear_vs_ic50.debug scientific.peptide_pnear_vs_ic50 scientific.relax_fast scientific.simple_cycpep_predict.debug linux.clang.score linux.gcc.score mac.clang.score linux.scripts.pyrosetta scripts.rosetta.parse scripts.rosetta.validate scripts.rosetta.verify linux.clang.unit.release linux.gcc.unit.release gcc-9.gcc.unit util.apps

Merge pull request #4769 from RosettaCommons/vmullig/peptide_pnear_vs_ic_scitest Add a scientific test for the correlation between peptide computed PNear and experimentally-measured IC50. The rigidity of a peptide macrocycle is a determinant of binding affinity. When peptides are optimized for favourable interactions with a target, computed PNear (or DeltaG_folding) values correlate well with experimentally-measured IC50 values. This correlation has improved as the Rosetta energy function has improved, from a vague correlation with talaris2013 and talaris2014 to a pretty good correlation that actually predicts rank order with ref2015. (This is likely due to the practice of training against physics-based fluid simulations used for ref2015.) It would be good to ensure that this trend continues for future versions of the energy function, so I'm adding a scientific test.

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