Systematic evaluation of CS-Rosetta for membrane protein structure prediction with sparse NOE restraints

Abstract

We critically test and validate the CS-Rosetta methodology for de novo structure prediction of α-helical membrane proteins (MPs) from NMR data, such as chemical shifts and NOE distance restraints. By systematically reducing the number and types of NOE restraints, we focus on determining the regime in which MP structures can be reliably predicted and pinpoint the boundaries of the approach. Five MPs of known structure were used as test systems, phototaxis sensory rhodopsin II (pSRII), a subdomain of pSRII, disulfide binding protein B (DsbB), microsomal prostaglandin E2 synthase-1 (mPGES-1), and translocator protein (TSPO). For pSRII and DsbB, where NMR and X-ray structures are available, resolution-adapted structural recombination (RASREC) CS-Rosetta yields structures that are as close to the X-ray structure as the published NMR structures if all available NMR data are used to guide structure prediction. For mPGES-1 and Bacillus cereus TSPO, where only X-ray crystal structures are available, highly accurate structures are obtained using simulated NMR data. One main advantage of RASREC CS-Rosetta is its robustness with respect to even a drastic reduction of the number of NOEs. Close-to-native structures were obtained with one randomly picked long-range NOEs for every 14, 31, 38, and 8 residues for full-length pSRII, the pSRII subdomain, TSPO, and DsbB, respectively, in addition to using chemical shifts. For mPGES-1, atomically accurate structures could be predicted even from chemical shifts alone. Our results show that atomic level accuracy for helical membrane proteins is achievable with CS-Rosetta using very sparse NOE restraint sets to guide structure prediction. Proteins 2017; 85:812-826. © 2016 Wiley Periodicals, Inc.

Macromolecular modeling and design in Rosetta: recent methods and frameworks

“…Chemical shifts were used for fragment picking using CS-Rosetta 113 , which could be used in conjunction with NOE, RDC 114 , PCS [115][116][117] and PRE data. Improvements, for instance through RASREC resampling 118 allowed the use of sparse 119 or unassigned data 120 , easier obtainable data (backbone-only 121 ), modeling larger and more complex proteins 122 , membrane proteins 123 , symmetric systems 124 , and combination with data from SAXS 125 , cryoEM 126 , distance restraints from homologous proteins 127 and evolutionary couplings 128 . CS-Rosetta also has the AutoNOE 129,130 module for automatic assignment of NOESY data for use in structure calculations.…”

Macromolecular Modeling and Design in Rosetta: New Methods and Frameworks

“…Chemical shifts were used for fragment picking using CS-Rosetta 113 , which could be used in conjunction with NOE, RDC 114 , PCS [115][116][117] and PRE data. Improvements, for instance through RASREC resampling 118 allowed the use of sparse 119 or unassigned data 120 , easier obtainable data (backbone-only 121 ), modeling larger and more complex proteins 122 , membrane proteins 123 , symmetric systems 124 , and combination with data from SAXS 125 , cryoEM 126 , distance restraints from homologous proteins 127 and evolutionary couplings 128 . CS-Rosetta also has the AutoNOE 129,130 module for automatic assignment of NOESY data for use in structure calculations.…”

The emerging role of physical modeling in the future of structure determination

“…Also, paramagnetic relaxation enhancement (PRE) [65], pseudo-contact shift (PCS) [66], and residual dipolar coupling (RDC) [67] restraints have been used to similar effect. Recently, the RASREC algorithm was developed, which yields better models with narrower sampling [17,68] and has been applied to NMR on deuterated samples up to 40 kDa [69,70].…”