Diverse Scientific Benchmarks for Implicit Membrane Energy Functions

Rebecca F. Alford, Rituparna Samanta, Jeffrey J. Gray

Research output: Contribution to journalArticlepeer-review


Energy functions are fundamental to biomolecular modeling. Their success depends on robust physical formalisms, efficient optimization, and high-resolution data for training and validation. Over the past 20 years, progress in each area has advanced soluble protein energy functions. Yet, energy functions for membrane proteins lag behind due to sparse and low-quality data, leading to overfit tools. To overcome this challenge, we assembled a suite of 12 tests on independent data sets varying in size, diversity, and resolution. The tests probe an energy function's ability to capture membrane protein orientation, stability, sequence, and structure. Here, we present the tests and use the franklin2019 energy function to demonstrate them. We then identify areas for energy function improvement and discuss potential future integration with machine-learning-based optimization methods. The tests are available through the Rosetta Benchmark Server (https://benchmark.graylab.jhu.edu/) and GitHub (https://github.com/rfalford12/Implicit-Membrane-Energy-Function-Benchmark).

Original languageEnglish (US)
Pages (from-to)5248-5261
Number of pages14
JournalJournal of Chemical Theory and Computation
Issue number8
StatePublished - Aug 10 2021

ASJC Scopus subject areas

  • Computer Science Applications
  • Physical and Theoretical Chemistry


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