BCL::Fold—Protein topology determination from limited NMR restraints

Weiner, Brian E.; Akin, Louesa R.; Woetzel, Nils; Karakaş, Mert; Meiler, Jens

doi:10.1002/prot.24427

Cited by 21 publications

(39 citation statements)

References 27 publications

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“…These numbers correspond to one contact for every 31, 14, and 8 residues for the respective systems. A comparable enrichment of close‐to‐native structures with sparse long‐range distance restraints from EPR or NMR was reported previously by Meiler and co‐workers. As already noted above, in terms of backbone structure, the cluster centers are usually comparably close to the native structure as the ten best‐energy models (triangles in Figs.…”

Section: Resultssupporting

confidence: 83%

“…It is interesting to not only compare the best predicted structure with the minimal RMSD100 from the native structure, which is only possible if the native structure is known of course, but also to focus on energy (or score). Without long‐range restraints, the BCL::MP‐Fold pSRII structure with the best score has an RMSD100 of 9.3 Å from the native structure, which improves to 3.4 Å with restraints . Our CS‐Rosetta calculations yield best‐energy models with RMSD100 values of 4.2 Å (without NOEs) and 1.2 Å (with NOEs), respectively.…”

Section: Resultsmentioning

confidence: 84%

“…Our RASREC CS‐Rosetta results for pSRII and DsbB can be compared to the published data obtained with BCL::MP‐Fold . BCL::MP‐Fold employs an efficient algorithm that initially assembles

α

‐helices into MP topologies at the backbone level; structures are subsequently converted to full‐atom models using the Rosetta CCD loop building and FastRelax protocols . For full‐length pSRII, using only chemical shifts in BCL::MP‐Fold, the structure with the smallest RMSD from the native structure reported by Weiner and co‐workers has an RMSD100 of 5.1 Å .…”

Section: Resultsmentioning

confidence: 99%

“…2(D)]. For this comparison, we also used the NMR structure (PDB 2KSY) as reference structure, as was done by Weiner and co‐workers . It is interesting to not only compare the best predicted structure with the minimal RMSD100 from the native structure, which is only possible if the native structure is known of course, but also to focus on energy (or score).…”

Section: Resultsmentioning

confidence: 99%

“…For DsbB, using the NMR structure (PBD 2K73) as reference, BCL::MP‐Fold generated structures with minimal RMSD100 values of 4.7 Å and 3.3 Å without and with NOE restraints, respectively . The corresponding RMSD100 values from our CS‐Rosetta simulations are 4.5 Å and 2.8 Å [data sets 8 and 2 in Fig.…”

Section: Resultsmentioning

confidence: 99%

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

et al. 2017

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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.

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Section: Resultssupporting

confidence: 83%

Section: Resultsmentioning

confidence: 84%

“…Our RASREC CS‐Rosetta results for pSRII and DsbB can be compared to the published data obtained with BCL::MP‐Fold . BCL::MP‐Fold employs an efficient algorithm that initially assembles

α

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

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

et al. 2017

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Computational modeling of membrane proteins

Leman

Ulmschneider²,

Gray³

2014

Proteins

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The determination of membrane protein (MP) structures has always trailed that of soluble proteins due to difficulties in their overexpression, reconstitution into membrane mimetics, and subsequent structure determination. The percentage of MP structures in the protein databank (PDB) has been at a constant 1-2% for the last decade. In contrast, over half of all drugs target MPs, only highlighting how little we understand about drug-specific effects in the human body. To reduce this gap, researchers have attempted to predict structural features of MPs even before the first structure was experimentally elucidated. In this review, we present current computational methods to predict MP structure, starting with secondary structure prediction, prediction of trans-membrane spans, and topology. Even though these methods generate reliable predictions, challenges such as predicting kinks or precise beginnings and ends of secondary structure elements are still waiting to be addressed. We describe recent developments in the prediction of 3D structures of both α-helical MPs as well as β-barrels using comparative modeling techniques, de novo methods, and molecular dynamics (MD) simulations. The increase of MP structures has (1) facilitated comparative modeling due to availability of more and better templates, and (2) improved the statistics for knowledge-based scoring functions. Moreover, de novo methods have benefitted from the use of correlated mutations as restraints. Finally, we outline current advances that will likely shape the field in the forthcoming decade.

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BCL::MP‐fold: Membrane protein structure prediction guided by EPR restraints

et al. 2015

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For many membrane proteins, the determination of their topology remains a challenge for methods like X-ray crystallography and nuclear magnetic resonance (NMR) spectroscopy. Electron paramagnetic resonance (EPR) spectroscopy has evolved as an alternative technique to study structure and dynamics of membrane proteins. The present study demonstrates the feasibility of membrane protein topology determination using limited EPR distance and accessibility measurements. The BCL::MP-Fold algorithm assembles secondary structure elements (SSEs) in the membrane using a Monte Carlo Metropolis (MCM) approach. Sampled models are evaluated using knowledge-based potential functions and agreement with the EPR data and a knowledge-based energy function. Twenty-nine membrane proteins of up to 696 residues are used to test the algorithm. The protein-size-normalized root-mean-square-deviation (RMSD100) value of the most accurate model is better than 8 Å for twenty-seven, better than 6 Å for twenty-two, and better than 4 Å for fifteen out of twenty-nine proteins, demonstrating the algorithm’s ability to sample the native topology. The average enrichment could be improved from 1.3 to 2.5, showing the improved discrimination power by using EPR data.

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BCL::Fold—Protein topology determination from limited NMR restraints

Cited by 21 publications

References 27 publications

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

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

Computational modeling of membrane proteins

BCL::MP‐fold: Membrane protein structure prediction guided by EPR restraints

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