Membrane Protein Structure Prediction Research Articles

Implicit model to capture electrostatic features of membrane environment.

Membrane protein structure prediction and design are challenging due to the complexity of capturing the interactions in the lipid layer, such as those arising from electrostatics. Accurately capturing electrostatic energies in the low-dielectric membrane often requires expensive Poisson-Boltzmann calculations that are not scalable for membrane protein structure prediction and design. In this work, we have developed a fast-to-compute implicit energy function that considers the realistic characteristics of different lipid bilayers, making design calculations tractable. This method captures the impact of the lipid head group using a mean-field-based approach and uses a depth-dependent dielectric constant to characterize the membrane environment. This energy function Franklin2023 (F23) is built upon Franklin2019 (F19), which is based on experimentally derived hydrophobicity scales in the membrane bilayer. We evaluated the performance of F23 on five different tests probing (1) protein orientation in the bilayer, (2) stability, and (3) sequence recovery. Relative to F19, F23 has improved the calculation of the tilt angle of membrane proteins for 90% of WALP peptides, 15% of TM-peptides, and 25% of the adsorbed peptides. The performances for stability and design tests were equivalent for F19 and F23. The speed and calibration of the implicit model will help F23 access biophysical phenomena at long time and length scales and accelerate the membrane protein design pipeline.

Sparse pseudocontact shift NMR data obtained from a non-canonical amino acid-linked lanthanide tag improves integral membrane protein structure prediction.

A single experimental method alone often fails to provide the resolution, accuracy, and coverage needed to model integral membrane proteins (IMPs). Integrating computation with experimental data is a powerful approach to supplement missing structural information with atomic detail. We combine RosettaNMR with experimentally-derived paramagnetic NMR restraints to guide membrane protein structure prediction. We demonstrate this approach using the disulfide bond formation protein B (DsbB), an α-helical IMP. Here, we attached a cyclen-based paramagnetic lanthanide tag to an engineered non-canonical amino acid (ncAA) using a copper-catalyzed azide-alkyne cycloaddition (CuAAC) click chemistry reaction. Using this tagging strategy, we collected 203 backbone HN pseudocontact shifts (PCSs) for three different labeling sites and used these as input to guide de novo membrane protein structure prediction protocols in Rosetta. We find that this sparse PCS dataset combined with 44 long-range NOEs as restraints in our calculations improves structure prediction of DsbB by enhancements in model accuracy, sampling, and scoring. The inclusion of this PCS dataset improved the Cα-RMSD transmembrane segment values of the best-scoring and best-RMSD models from 9.57Å and 3.06Å (no NMR data) to 5.73Å and 2.18Å, respectively.

Variability in linear polypeptide stabilizes proteoglycan than zinc finger protein in vascular smooth muscle cells: An In Silico approach

Background: Structural and physicochemical topologies of proteins play a considerable role in differentiating the functional properties of the biological system. We aimed to study the physicochemical similarities, structural and functional differences of versican (VCAN) and early growth response (Egr) proteins involved in vascular injuries. Methods: For the primary structure prediction, the proteomic tools Expasy's Protparam is used, likewise, for secondary structure and content prediction SOPM and SOPMA tool is used. The transmembrane regions in VCAN and EGR proteins are predicted through SOSUI (Classification and Secondary Structure Prediction of Membrane Proteins) server. The CYSREC tool is used to identify the presence of disulphide bonds in all the VCAN and EGR proteins, additionally through homology modelling the disulphide bonds are visualized and structure of the modelled proteins are validated through Rampage (Ramachandran plot), ProQ (Protein Quality Server) and ProSA (Protein Structure Analysis) server. Results: VCAN and Egr proteins resemble hydrophilic in nature, similarly negative score of the grand average of hydropathicity index confirms hydrophilic nature. The maximum molecular weight for VCAN is observed as 39265 and 61623 Dalton for EGR protein. VCAN proteins showed a higher level of basic residues except Q86W61, while all the Egr proteins were acidic residues. The extinction coefficient (EC) has unique absorbance at 280 nm wavelength. Based on the aliphatic index (AI ≥ 45) and instability index (II ≥ 40) most of the VCAN and Egr proteins were unstable. The Classification and Secondary Structure Prediction of Membrane Proteins server classifies all Egr and few VCAN and proteins are soluble nature. Secondary structure content prediction and SOPM server show most of the VCAN proteins are beta sheets and many Egr proteins are alpha-helical, while few with mixed structures. Besides these differences, the VCAN protein stability was identified by most probable disulfide (SS) bridges using CYS_REC tool and confirmed by homology modeling in tertiary structure. Whereas the probable disulfide bonds in Egr proteins were not identified. Conclusion: The findings with these functional and structural properties will add an extra room in understanding their dual role.

Predicting the Real-Valued Inter-Residue Distances for Proteins.

Predicting protein structure from the amino acid sequence has been a challenge with theoretical and practical significance in biophysics. Despite the recent progresses elicited by improved inter‐residue contact prediction, contact‐based structure prediction has gradually reached the performance ceiling. New methods have been proposed to predict the inter‐residue distance, but unanimously by simplifying the real‐valued distance prediction into a multiclass classification problem. Here, a lightweight regression‐based distance prediction method is shown, which adopts the generative adversarial network to capture the delicate geometric relationship between residue pairs and thus could predict the continuous, real‐valued inter‐residue distance rapidly and satisfactorily. The predicted residue distance map allows quick structure modeling by the CNS suite, and the constructed models approach the same level of quality as the other state‐of‐the‐art protein structure prediction methods when tested on CASP13 targets. Moreover, this method can be used directly for the structure prediction of membrane proteins without transfer learning.

Protein Structure Prediction and Design in a Biologically Realistic Implicit Membrane

Protein design is a powerful tool for elucidating mechanisms of function and engineering new therapeutics and nanotechnologies. Although soluble protein design has advanced, membrane protein design remains challenging because of difficulties in modeling the lipid bilayer. In this work, we developed an implicit approach that captures the anisotropic structure, shape of water-filled pores, and nanoscale dimensions of membranes with different lipid compositions. The model improves performance in computational benchmarks against experimental targets, including prediction of protein orientations in the bilayer, ΔΔG calculations, native structure discrimination, and native sequence recovery. When applied to de novo protein design, this approach designs sequences with an amino acid distribution near the native amino acid distribution in membrane proteins, overcoming a critical flaw in previous membrane models that were prone to generating leucine-rich designs. Furthermore, the proteins designed in the new membrane model exhibit native-like features including interfacial aromatic side chains, hydrophobic lengths compatible with bilayer thickness, and polar pores. Our method advances high-resolution membrane protein structure prediction and design toward tackling key biological questions and engineering challenges.

Fast Implicit Potentials for Accurate Prediction and Design of Membrane Protein Structures

Fast Implicit Potentials for Accurate Prediction and Design of Membrane Protein Structures

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.

Enhancing Structure Prediction and Design of Soluble and Membrane Proteins with Explicit Solvent-Protein Interactions

Solvent molecules interact intimately with proteins and can profoundly regulate their structure and function. However, accurately and efficiently modeling protein solvation effects at the molecular level has been challenging. Here, we present a method that improves the atomic-level modeling of soluble and membrane protein structures and binding by efficiently predicting de novo protein-solvent molecule interactions. The method predicted with unprecedented accuracy buried water molecule positions, solvated protein conformations, and challenging mutational effects on protein binding. When applied to homology modeling, solvent-bound membrane protein structures, pockets, and cavities were recapitulated with near-atomic precision even from distant homologs. Blindly refined atomic-level structures of evolutionary distant G protein-coupled receptors imply strikingly different functional roles of buried solvent between receptor classes. The method should prove useful for refining low-resolution protein structures, accurately modeling drug-binding sites in structurally uncharacterized receptors, and designing solvent-mediated protein catalysis, recognition, ligand binding, and membrane protein signaling.

Rosetta Broker for membrane protein structure prediction: concentrative nucleoside transporter 3 and corticotropin-releasing factor receptor 1 test cases

BackgroundMembrane proteins are difficult targets for structure prediction due to the limited structural data deposited in Protein Data Bank. Most computational methods for membrane protein structure prediction are based on the comparative modeling. There are only few de novo methods targeting that distinct protein family. In this work an example of such de novo method was used to structurally and functionally characterize two representatives of distinct membrane proteins families of solute carrier transporters and G protein-coupled receptors. The well-known Rosetta program and one of its protocols named Broker was used in two test cases. The first case was de novo structure prediction of three N-terminal transmembrane helices of the human concentrative nucleoside transporter 3 (hCNT3) homotrimer belonging to the solute carrier 28 family of transporters (SLC28). The second case concerned the large scale refinement of transmembrane helices of a homology model of the corticotropin-releasing factor receptor 1 (CRFR1) belonging to the G protein-coupled receptors family.ResultsThe inward-facing model of the hCNT3 homotrimer was used to propose the functional impact of its single nucleotide polymorphisms. Additionally, the 100 ns molecular dynamics simulation of the unliganded hCNT3 model confirmed its validity and revealed mobility of the selected binding site and homotrimer interface residues. The large scale refinement of transmembrane helices of the CRFR1 homology model resulted in the significant improvement of its accuracy with respect to the crystal structure of CRFR1, especially in the binding site area. Consequently, the antagonist CP-376395 could be docked with Autodock VINA to the CRFR1 model without any steric clashes.ConclusionsThe presented work demonstrated that Rosetta Broker can be a versatile tool for solving various issues referring to protein biology. Two distinct examples of de novo membrane protein structure prediction presented here provided important insights into three major areas of protein biology. Namely, the dynamics of the inward-facing hCNT3 homotrimer system, the structural changes of the CRFR1 receptor upon the antagonist binding and finally, the role of single nucleotide polymorphisms in both, hCNT3 and CRFR1 proteins, were investigated.

Membrane protein contact and structure prediction using co-evolution in conjunction with machine learning.

De novo membrane protein structure prediction is limited to small proteins due to the conformational search space quickly expanding with length. Long-range contacts (24+ amino acid separation)–residue positions distant in sequence, but in close proximity in the structure, are arguably the most effective way to restrict this conformational space. Inverse methods for co-evolutionary analysis predict a global set of position-pair couplings that best explain the observed amino acid co-occurrences, thus distinguishing between evolutionarily explained co-variances and these arising from spurious transitive effects. Here, we show that applying machine learning approaches and custom descriptors improves evolutionary contact prediction accuracy, resulting in improvement of average precision by 6 percentage points for the top 1L non-local contacts. Further, we demonstrate that predicted contacts improve protein folding with BCL::Fold. The mean RMSD100 metric for the top 10 models folded was reduced by an average of 2 Å for a benchmark of 25 membrane proteins.

Discrimination of Native-like States of Membrane Proteins with Implicit Membrane-based Scoring Functions.

A scoring protocol based on implicit membrane-based scoring functions and a new protocol for optimizing the positioning of proteins inside the membrane was evaluated for its capacity to discriminate native-like states from misfolded decoys. A decoy set previously established by the Baker lab (Proteins: Struct., Funct., Genet. 2006, 62, 1010-1025) was used along with a second set that was generated to cover higher resolution models. The Implicit Membrane Model 1 (IMM1), IMM1 model with CHARMM 36 parameters (IMM1-p36), generalized Born with simple switching (GBSW), and heterogeneous dielectric generalized Born versions 2 (HDGBv2) and 3 (HDGBv3) were tested along with the new HDGB van der Waals (HDGBvdW) model that adds implicit van der Waals contributions to the solvation free energy. For comparison, scores were also calculated with the distance-scaled finite ideal-gas reference (DFIRE) scoring function. Z-scores for native state discrimination, energy vs root-mean-square deviation (RMSD) correlations, and the ability to select the most native-like structures as top-scoring decoys were evaluated to assess the performance of the scoring functions. Ranking of the decoys in the Baker set that were relatively far from the native state was challenging and dominated largely by packing interactions that were captured best by DFIRE with less benefit of the implicit membrane-based models. Accounting for the membrane environment was much more important in the second decoy set where especially the HDGB-based scoring functions performed very well in ranking decoys and providing significant correlations between scores and RMSD, which shows promise for improving membrane protein structure prediction and refinement applications. The new membrane structure scoring protocol was implemented in the MEMScore web server ( http://feiglab.org/memscore ).

Improving prediction of helix-helix packing in membrane proteins using predicted contact numbers as restraints.

One of the challenging problems in tertiary structure prediction of helical membrane proteins (HMPs) is the determination of rotation of α-helices around the helix normal. Incorrect prediction of helix rotations substantially disrupts native residue-residue contacts while inducing only a relatively small effect on the overall fold. We previously developed a method for predicting residue contact numbers (CNs), which measure the local packing density of residues within the protein tertiary structure. In this study, we tested the idea of incorporating predicted CNs as restraints to guide the sampling of helix rotation. For a benchmark set of 15 HMPs with simple to rather complicated folds, the average contact recovery (CR) of best-sampled models was improved for all targets, the likelihood of sampling models with CR greater than 20% was increased for 13 targets, and the average RMSD100 of best-sampled models was improved for 12 targets. This study demonstrated that explicit incorporation of CNs as restraints improves the prediction of helix-helix packing. Proteins 2017; 85:1212-1221. © 2017 Wiley Periodicals, Inc.

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

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.

Deciphering Membrane Protein Energetics using Deep Sequencing; Towards Robust Design and Structure Prediction of Membrane Proteins

The energetics of membrane-protein interactions dictates protein topology and structure, which in turn determines the function and expression levels of all plasma membrane proteins. However, systematic and reliable quantification of membrane-protein energetics has been challenging. I recently developed a deep mutational scanning method, dsTβL(Elazar et al., 2016a) (deep-sequencing TOXCAT-β-lactamase), to monitor the effects of hundreds of point mutations on insertion and association within the bacterial inner membrane. The assay quantifies insertion-energy profiles for each amino acid residue across the membrane, revealing that the hydrophobicity of biological membranes is significantly higher than appreciated. In addition, a key feature of the dsTβL profiles is that they show asymmetries for Arg, Lys, and His, in agreement with the ‘positive-inside’ rule. The three profiles, however, are not identical: whereas Lys and Arg are favored by 2 kcal/mol near the cytoplasm compared to near the periplasm, the titratable amino acid His shows a more modest asymmetry of 1 kcal/mol; moreover, only Arg stabilizes the segment near the cytosol, whereas Lys and His are nearly neutral at the cytosol- membrane interface. Based on these findings, together with Jonathan Weinstein, we developed a novel graphical topology-prediction algorithm named TopGraph(Elazar et al., 2016b), based on a sequence search for minimum energy of insertion using the dsTβL experimental insertion scale rather than statistics derived from known structures. Unlike many existing predictors, TopGraph exhibits high accuracy even on large transporters with no structural homologues. Furthermore, results suggest that the ‘positive-inside’ rule, which is known to orient segments with respect to the membrane, can also drive insertion of marginally hydrophobic segments in large membrane domains. These insights may aid structure prediction, engineering, and design of membrane proteins.

Folding Membrane Proteins by Contacts Inferred from Non-Membrane Proteins and Near-Atomic Level Refinement

Computational prediction of membrane protein structure is very challenging due to lack of sufficient solved structures as templates or training data. Recently direct evolutionary coupling (EC) analysis has shed some light on protein contact prediction and accordingly contact-assisted folding. Limited by information within a single protein family, EC analysis is effective only on some very large-sized families. We have developed a deep transfer learning method to predict contacts for membrane proteins not in a large family, by making use of information in thousands of non-membrane protein families. Deep learning can learn complex patterns from large datasets and has recently revolutionized object and speech recognition and the GO game. Our deep learning method learns contact patterns and the complex relationship between contacts and protein features (amino acid property, residue conservation level and residue co-evolution strength) from non-membrane proteins, but can predict membrane protein contacts very well. Tested on 398 non-redundant membrane proteins, the average top L/10 long-range contact prediction accuracy of our method is 0.78, much better than the representative EC method CCMpred (0.52), the CASP11 winner MetaPSICOV (0.61) and the methods trained by only membrane proteins (0.60). When some membrane proteins are added into the training data, we can further improve accuracy by ∼4%. Using only our predicted contacts, we can correctly fold 160 test membrane proteins with RMSD <8A while homology modeling, CCMpred and MetaPSICOV contacts can fold only 10, 60 and 87 proteins, respectively. We further refine the 3D models built from predicted contacts using Upside, a near-atom-level and ultra-fast molecular dynamics method. In addition to membrane-, residue-specific and depth-dependent energy potential, Upside improves model quality by also refining non-membrane portions and refolding loop regions. Initial results indicate that Upside can indeed improve our contact-assisted models.

Interplay between hydrophobicity and the positive-inside rule in determining membrane-protein topology

The energetics of membrane-protein interactions determine protein topology and structure: hydrophobicity drives the insertion of helical segments into the membrane, and positive charges orient the protein with respect to the membrane plane according to the positive-inside rule. Until recently, however, quantifying these contributions met with difficulty, precluding systematic analysis of the energetic basis for membrane-protein topology. We recently developed the dsTβL method, which uses deep sequencing and in vitro selection of segments inserted into the bacterial plasma membrane to infer insertion-energy profiles for each amino acid residue across the membrane, and quantified the insertion contribution from hydrophobicity and the positive-inside rule. Here, we present a topology-prediction algorithm called TopGraph, which is based on a sequence search for minimum dsTβL insertion energy. Whereas the average insertion energy assigned by previous experimental scales was positive (unfavorable), the average assigned by TopGraph in a nonredundant set is -6.9 kcal/mol. By quantifying contributions from both hydrophobicity and the positive-inside rule we further find that in about half of large membrane proteins polar segments are inserted into the membrane to position more positive charges in the cytoplasm, suggesting an interplay between these two energy contributions. Because membrane-embedded polar residues are crucial for substrate binding and conformational change, the results implicate the positive-inside rule in determining the architectures of membrane-protein functional sites. This insight may aid structure prediction, engineering, and design of membrane proteins. TopGraph is available online (topgraph.weizmann.ac.il).

Systematic Evaluation of the CS-Rosetta De Novo Structure Prediction Method for Membrane Proteins

In silico structure prediction of proteins on the basis of their sequence is a major challenge in computational structural biology, even though the ca. 100.000 entries stored in the Protein Data Bank now give a broad sampling of folds. Membrane proteins are particularly challenging cases due to their size and complexity, and high-resolution structural data is often lacking due to experimental challenges. We have investigated how the recently developed RASREC CS-Rosetta methodology benefits from integrating sparse NMR data for de novo structure prediction of membrane proteins. In particular, we tested the 4-TM disulfide binding protein B and sensory rhodopsin (full-length and 4-TM subdomain) for which both NMR and high-resolution X-ray crystal structures are available. We systematically investigated the effect of varying the type of data (chemical shifts, NOE distance restraints) and the amount of data (number of long-range NOEs) on the accuracy of the structure prediction. Our results show that RASREC CS-Rosetta can reliably predict membrane protein structures even with very sparse NMR data, and determine the minimal amount of NMR data needed.

Improved 3D Structure Prediction of Beta-Barrel Membrane Proteins by using Evolutionary Coupling Constraints, Reduced State Space and an Empirical Potential Function

Beta-barrel membrane proteins are found in the outer membrane of gram-negative bacteria, mitochondria, and chloroplasts. They carry out diverse biological functions, including pore formation, membrane anchoring, enzyme activity, and bacterial virulence. In addition, beta-barrel membrane proteins increasingly serve as scaffolds for bacterial surface display and nanopore-based DNA sequencing. Due to difficulties in experimental structure determination, they are sparsely represented in the protein structure databank and computational methods can help to understand their biophysical principles. We have developed a novel computational method to predict the 3D structure of beta-barrel membrane proteins using evolutionary coupling (EC) constraints and a reduced state space. Combined with an empirical potential function, we can successfully predict strand register at >80% accuracy for a set of 49 non-homologous proteins with known structures. This is a significant improvement from previous results using EC alone (44%) [1] and using empirical potential function alone (73%) [2]. Our method is general and can be applied to genome-wide structural prediction.[1] Sikander Hayat, Chris Sander, Debora S. Marks, and Arne Elofsson. All-atom 3D structure prediction of transmembrane β-barrel proteins from sequences. PNAS 2015 112 (17) 5413-5418.[2] Hammad Naveed, Yun Xu, Ronald Jackups, Jr., and Jie Liang. Predicting Three-Dimensional Structures of Transmembrane Domains of β-Barrel Membrane Proteins. Journal of the American Chemical Society 2012 134 (3), 1775-1781.

Design Principles of Membrane Protein Structures

Membrane proteins play key role in cellular signaling and ion transport. Statistical analysis of expanding database of high-resolution membrane protein structures in Protein Data Bank (PDB) provides useful information about membrane protein structure and function. We used RosettaMembrane software (Yarov-Yarovoy V et al (2006) Proteins) to analyze ∼300 unique alpha helical membrane protein structures in PDB and derive knowledge based energy function for membrane protein structure prediction, membrane protein-protein docking, and membrane protein design. The RosettaMembrane residue environment energy term is based on amino acid propensities in hydrophobic, interface, and water layers of the membrane and depends on the residue burial state - from being completely buried within a protein environment to being completely exposed either to the lipid or water environments. Residue buried state is determined from the number of residue neighbors within 6 and 10 A spheres. The RosettaMembrane residue-residue interaction term is based on the propensities of amino acid pairs to be in close proximity to each other within hydrophobic, interface, and water layers. Results of our statistical analysis reveal fine details of favorable and unfavorable environments for all amino acids types in all membrane layers and residue burial states. We find that large hydrophobic amino acids are favorable facing the hydrophobic core of the lipid bilayer. Small amino acids are favorable facing the protein core within the hydrophobic layer of the membrane. Aromatic or positively charged amino acids and favorable facing the lipid head groups. Residue-residue interactions are often favored between polar and charged amino acids and also between some of small and large hydrophobic amino acids inside of the protein core within the hydrophobic layer of the membrane. These data will be useful for rational design of novel membrane protein structures and functions.

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

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 (BioChemical Library membrane protein 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 RMSD100 value of the most accurate model is better than 8 Å for 27, better than 6 Å for 22, and better than 4 Å for 15 of the 29 proteins, demonstrating the algorithms' 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.