One of the unique traits of membrane proteins is that a significant fraction of their hydrophobic amino acids is exposed to the hydrophobic core of lipid bilayers rather than being embedded in the protein interior, which is often not explicitly considered in the protein structure and function predictions. Here, we propose a characteristic and predictive quantity, the membrane contact probability (MCP), to describe the likelihood of the amino acids of a given sequence being in direct contact with the acyl chains of lipid molecules. We show that MCP is complementary to solvent accessibility in characterizing the outer surface of membrane proteins, and it can be predicted for any given sequence with a machine learning-based method by utilizing a training dataset extracted from MemProtMD, a database generated from molecular dynamics simulations for the membrane proteins with a known structure. As the first of many potential applications, we demonstrate that MCP can be used to systematically improve the prediction precision of the protein contact maps and structures.

Solvent accessibility has been extensively used to characterize and predict the chemical properties of the surface residues of soluble proteins. However, there is not yet a widely accepted quantity of the same dimension for the study of lipidaccessible residues of membrane proteins. In this study, we propose that lipid accessibility, defined in a similar way to solvent accessibility, can be used to characterize the lipid-accessible residues of membrane proteins. Moreover, we developed a deep learning-based method, ProtRAP (Protein Relative Accessibility Predictor), to predict the relative lipid accessibility and relative solvent accessibility of residues from a given protein sequence, which can infer which residues are likely accessible to lipids, accessible to solvent, or buried in the protein interior in one run.