TY - JOUR
T1 - Diverse Scientific Benchmarks for Implicit Membrane Energy Functions
AU - Alford, Rebecca F.
AU - Samanta, Rituparna
AU - Gray, Jeffrey J.
N1 - Funding Information:
This research was supported by a Hertz Foundation Fellowship (R.F.A.), a National Science Foundation Graduate Research Fellowship (R.F.A.), and NIH Grant R01-GM078221 (all authors). Computational resources were provided by the Maryland Advanced Research Computing Center (MARCC) and the Texas Advanced Computing Center (TACC). We wish to thank Karen Fleming and Pat Fleming for helpful discussions and feedback on the manuscript. We also wish to thank Julia Koehler Leman and Sergey Lyskov for development of the Rosetta benchmark server. All benchmark protocols are available within the Rosetta Software suite at https://www.rosettacommons.org to all noncommercial users for free and to commercial users for a fee. The benchmark data sets are available through the Rosetta Benchmark Server (distributed) and the Membrane Energy Function Benchmark Project GitHub Repository at https://github.com/rfalford12/Implicit-Membrane-Energy-Function-Benchmark . Current benchmark performance is available through the Rosetta Scientific Benchmark server at https://benchmark.graylab.jhu.edu/revisions?branch=scientific .
Publisher Copyright:
© 2021 American Chemical Society.
PY - 2021/8/10
Y1 - 2021/8/10
N2 - 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).
AB - 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).
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U2 - 10.1021/acs.jctc.0c00646
DO - 10.1021/acs.jctc.0c00646
M3 - Article
C2 - 34310137
AN - SCOPUS:85112530638
SN - 1549-9618
VL - 17
SP - 5248
EP - 5261
JO - Journal of Chemical Theory and Computation
JF - Journal of Chemical Theory and Computation
IS - 8
ER -