Efficient computation of transfer free energies of amino acids in beta-barrel membrane proteins

GeTFEP: A general transfer free energy profile of transmembrane proteins

Free energy of transferring amino acid side-chains from aqueous environment into lipid bilayers, known as transfer free energy (TFE), provides important information on the thermodynamic stability of membrane proteins. In this study, we derived a TFE profile named General Transfer Free Energy Profile (GeTFEP) based on computation of the TFEs of 58 β-barrel membrane proteins (βMPs). The GeTFEP agrees well with experimentally measured and computationally derived TFEs. Analysis based on the GeTFEP shows that residues in different regions of the transmembrane (TM) segments of βMPs have different roles during the membrane insertion process. Results further reveal the importance of the sequence pattern of TM strands in stabilizing βMPs in the membrane environment. In addition, we show that GeTFEP can be used to predict the positioning and the orientation of βMPs in the membrane. We also show that GeTFEP can be used to identify structurally or functionally important amino acid residue sites of βMPs. Furthermore, the TM segments of α-helical membrane proteins can be accurately predicted with GeTFEP, suggesting that the GeTFEP is of general applicability in studying membrane protein.

High-resolution structure prediction of β-barrel membrane proteins

[Formula: see text]-Barrel membrane proteins ([Formula: see text]MPs) play important roles, but knowledge of their structures is limited. We have developed a method to predict their 3D structures. We predict strand registers and construct transmembrane (TM) domains of [Formula: see text]MPs accurately, including proteins for which no prediction has been attempted before. Our method also accurately predicts structures from protein families with a limited number of sequences and proteins with novel folds. An average main-chain rmsd of 3.48 Å is achieved between predicted and experimentally resolved structures of TM domains, which is a significant improvement ([Formula: see text]3 Å) over a recent study. For [Formula: see text]MPs with NMR structures, the deviation between predictions and experimentally solved structures is similar to the difference among the NMR structures, indicating excellent prediction accuracy. Moreover, we can now accurately model the extended [Formula: see text]-barrels and loops in non-TM domains, increasing the overall coverage of structure prediction by [Formula: see text]%. Our method is general and can be applied to genome-wide structural prediction of [Formula: see text]MPs.