TY - JOUR
T1 - A lipophilicity-based energy function for membrane-protein modelling and design
AU - Weinstein, Jonathan Yaacov
AU - Elazar, Assaf
AU - Fleishman, Sarel Jacob
N1 - We thank Rebecca Alford for help with the membrane protein framework in Rosetta and for suggesting the use of splines for fitting the dsTβL profiles and Shlomo-Yakir Hoch, Rotem Barzilay and Assaf Glick for developing the TMHOP web server. We also thank Adi Goldenzweig, Olga Khersonsky and Saar Shoer for helpful comments. The research was supported by charitable donations from Sam Switzer and family and from Anne Christopoulos and Carolyn Hewitt. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
PY - 2019/8/28
Y1 - 2019/8/28
N2 - Membrane-protein design is an exciting and increasingly successful research area which has led to landmarks including the design of stable and accurate membrane-integral proteins based on coiled-coil motifs. Design of topologically more complex proteins, such as most receptors, channels, and transporters, however, demands an energy function that balances contributions from intra-protein contacts and protein-membrane interactions. Recent advances in water-soluble all-atom energy functions have increased the accuracy in structure-prediction benchmarks. The plasma membrane, however, imposes different physical constraints on protein solvation. To understand these constraints, we recently developed a high-throughput experimental screen, called dsTβL, and inferred apparent insertion energies for each amino acid at dozens of positions across the bacterial plasma membrane. Here, we express these profiles as lipophilicity energy terms in Rosetta and demonstrate that the new energy function outperforms previous ones in modelling and design benchmarks. Rosetta ab initio simulations starting from an extended chain recapitulate two-thirds of the experimentally determined structures of membrane-spanning homo-oligomers with <2.5Å root-mean-square deviation within the top-predicted five models (available online: http://tmhop.weizmann.ac.il). Furthermore, in two sequence-design benchmarks, the energy function improves discrimination of stabilizing point mutations and recapitulates natural membrane-protein sequences of known structure, thereby recommending this new energy function for membrane-protein modelling and design.
AB - Membrane-protein design is an exciting and increasingly successful research area which has led to landmarks including the design of stable and accurate membrane-integral proteins based on coiled-coil motifs. Design of topologically more complex proteins, such as most receptors, channels, and transporters, however, demands an energy function that balances contributions from intra-protein contacts and protein-membrane interactions. Recent advances in water-soluble all-atom energy functions have increased the accuracy in structure-prediction benchmarks. The plasma membrane, however, imposes different physical constraints on protein solvation. To understand these constraints, we recently developed a high-throughput experimental screen, called dsTβL, and inferred apparent insertion energies for each amino acid at dozens of positions across the bacterial plasma membrane. Here, we express these profiles as lipophilicity energy terms in Rosetta and demonstrate that the new energy function outperforms previous ones in modelling and design benchmarks. Rosetta ab initio simulations starting from an extended chain recapitulate two-thirds of the experimentally determined structures of membrane-spanning homo-oligomers with <2.5Å root-mean-square deviation within the top-predicted five models (available online: http://tmhop.weizmann.ac.il). Furthermore, in two sequence-design benchmarks, the energy function improves discrimination of stabilizing point mutations and recapitulates natural membrane-protein sequences of known structure, thereby recommending this new energy function for membrane-protein modelling and design.
UR - http://www.scopus.com/inward/record.url?scp=85072059023&partnerID=8YFLogxK
U2 - 10.1371/journal.pcbi.1007318
DO - 10.1371/journal.pcbi.1007318
M3 - مقالة
C2 - 31461441
SN - 1553-734X
VL - 15
JO - PLoS Computational Biology
JF - PLoS Computational Biology
IS - 8
M1 - e1007318
ER -