Diverse Scientific Benchmarks for Implicit Membrane Energy Functions

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.

Automated summary

Energy functions are crucial for biomolecular modeling, with membrane protein energy functions lagging behind soluble protein ones due to sparse data and overfitting issues. To address this challenge, a suite of 12 tests on diverse independent data sets was conducted, evaluating the ability of energy functions to capture membrane protein orientation, stability, sequence, and structure. Findings point out areas for improvement in energy functions and potential integration with machine-learning-based optimization methods in the future.