Big Data from Sparse Data: Diverse Scientific Benchmarks Reveal Optimization Imperatives for Implicit Membrane Energy Functions

Abstract

In computational protein structure prediction and design, energy functions discriminate non-native from near-native biomolecule conformations. These functions approximate the balance of enthalpic and entropic contributions to protein stability through mathematical models derived from thermodynamic and structural data. Over the past decade, an influx of high-resolution data paired with machine learning techniques has significantly improved the accuracy of soluble protein energy functions. However, membrane protein energy functions remain low-resolution because the experimental data are sparse, leading to overfitting. To overcome this challenge, we assembled a suite of 12 computational benchmark tests against experimental targets. The tests probe various membrane protein energy function capabilities ranging from reproducing protein stabilities and orientations to accurately predicting the three-dimensional structures of monomeric proteins and protein complexes. To evaluate current performance, we ran the benchmark on Franklin19, the current state-of-the-art implicit membrane protein energy function in Rosetta. The benchmark revealed areas for improvement including treatment of electrostatic interactions, representation of the interfacial head-group region, and accounting for entropic effects on protein orientation. Ultimately, these benchmarks will enable big-data-style optimization of membrane energy functions, leading to improved membrane protein design capabilities.