Frequent side chain methyl carbon‐oxygen hydrogen bonding in proteins revealed by computational and stereochemical analysis of neutron structures

The propensity of backbone Cα atoms to engage in carbon‐oxygen (CH···O) hydrogen bonding is well‐appreciated in protein structure, but side chain CH···O hydrogen bonding remains largely uncharacterized. The extent to which side chain methyl groups in proteins participate in CH···O hydrogen bonding is examined through a survey of neutron crystal structures, quantum chemistry calculations, and molecular dynamics simulations. Using these approaches, methyl groups were observed to form stabilizing CH···O hydrogen bonds within protein structure that are maintained through protein dynamics and participate in correlated motion. Collectively, these findings illustrate that side chain methyl CH···O hydrogen bonding contributes to the energetics of protein structure and folding. Proteins 2015; 83:403–410. © 2014 Wiley Periodicals, Inc.     

Structure Determination of Functional Membrane Proteins using Small-Angle Neutron Scattering (SANS) with Small, Mixed-Lipid Liposomes: Native Beef Heart Mitochondrial Cytochrome c Oxidase Forms Dimers

The low-resolution three-dimensional structure of purified native beef heart mitochondrial cytochrome c oxidase (COX) in asolectin unilamellar liposomes has been measured by small-angle neutron scattering under the conditions where the protein remains fully functional. From a neutron scattering perspective, the use of mixed-lipid liposomes provided for a more homogeneous matrix than can be achieved using a single lipid. As a result, the measurements were able to be performed under conditions where the liposome scattering was essentially eliminated (contrast-matched conditions). The protein structure in the membrane was modeled as a simple parallelepiped with side lengths of (59 × 70 × 120) Å with uncertainties, respectively, (11, 12, 20 Å). The molecular mass calculated for a typical protein with this volume is estimated to be (410 ± 124) kDa, which indicates the mass of a COX dimer. The longest dimension has some uncertainty due to intermolecular scattering contributing to the data. Nevertheless, that length was estimated using an average protein density and the known dimer molecular mass. Using the same cross sectional dimensions for the structure, the length is estimated to be 120 Å. However, the measured scattering curve of the dimer in the liposome differs significantly from that calculated from the X-ray structure of the dimer in a crystal of mixed micelles (PDB 3AG1). The calculated SANS scattering from the crystal structure was fit with a parallelepiped, measuring (59 × 101 × 129) Å with fitting uncertainties, respectively, (2, 3, 3 Å). Our results suggest that COX is a functional dimer when reconstituted into mixed-lipid liposomes.     

Structures of the OmpF porin crystallized in the presence of foscholine‐12

The endogenous Escherichia coli porin OmpF was crystallized as an accidental by‐product of our efforts to express, purify, and crystallize the E. coli integral membrane protein KdpD in the presence of foscholine‐12 (FC12). FC12 is widely used in membrane protein studies, but no crystal structure of a protein that was both purified and crystallized with this detergent has been reported in the Protein Data Bank. Crystallization screening for KdpD yielded two different crystals of contaminating protein OmpF. Here, we report two OmpF structures, the first membrane protein crystal structures for which extraction, purification, and crystallization were done exclusively with FC12. The first structure was refined in space group P21 with cell parameters a = 136.7 Å, b = 210.5 Å, c = 137 Å, and β = 100.5°, and the resolution of 3.8 Å. The second structure was solved at the resolution of 4.4 Å and was refined in the P321 space group, with unit cell parameters a = 215.5 Å, b = 215.5 Å, c = 137.5 Å, and γ = 120°. Both crystal forms show novel crystal packing, in which the building block is a tetrahedral arrangement of four trimers. Additionally, we discuss the use of FC12 for membrane protein crystallization and structure determination, as well as the problem of the OmpF contamination for membrane proteins overexpressed in E. coli.     

Fully automated high-quality NMR structure determination of small <Superscript>2</Superscript>H-enriched proteins

Determination of high-quality small protein structures by nuclear magnetic resonance (NMR) methods generally requires acquisition and analysis of an extensive set of structural constraints. The process generally demands extensive backbone and sidechain resonance assignments, and weeks or even months of data collection and interpretation. Here we demonstrate rapid and high-quality protein NMR structure generation using CS-Rosetta with a perdeuterated protein sample made at a significantly reduced cost using new bacterial culture condensation methods. Our strategy provides the basis for a high-throughput approach for routine, rapid, high-quality structure determination of small proteins. As an example, we demonstrate the determination of a high-quality 3D structure of a small 8 kDa protein, E. coli cold shock protein A (CspA), using <4 days of data collection and fully automated data analysis methods together with CS-Rosetta. The resulting CspA structure is highly converged and in excellent agreement with the published crystal structure, with a backbone RMSD value of 0.5 Å, an all atom RMSD value of 1.2 Å to the crystal structure for well-defined regions, and RMSD value of 1.1 Å to crystal structure for core, non-solvent exposed sidechain atoms. Cross validation of the structure with ¹⁵N- and ¹³C-edited NOESY data obtained with a perdeuterated ¹⁵N, ¹³C-enriched ¹³CH₃ methyl protonated CspA sample confirms that essentially all of these independently-interpreted NOE-based constraints are already satisfied in each of the 10 CS-Rosetta structures. By these criteria, the CS-Rosetta structure generated by fully automated analysis of data for a perdeuterated sample provides an accurate structure of CspA. This represents a general approach for rapid, automated structure determination of small proteins by NMR.     

Protein—nucleic acid interactions in bacteriophage φ29 DNA replication

Abstract: φ29 DNA replication starts at both DNA ends by a protein priming mechanism. The formation of the terminal protein-dAMP initiation complex is directed by the second nucleotide from the 3' end of the template. The transition from protein-primed initiation to normal DNA elongation has been proposed to occur by a sliding-back mechanism that is necessary for maintaining the sequences at the φ29 DNA ends. Structure—function studies have been carried out in the φ29 DNA polymerase. By site-directed mutagenesis of amino acids conserved among distantly related DNA polymerases we have shown that the N-terminal domain of φ29 DNA polymerase contains the 3'–5' exonuclease activity and the strand-displacement capacity, whereas the C-terminal domain contains the synthetic activities (protein-primed initiation and DNA polymerization). Viral protein p6 stimulates the initiation of φ29 DNA replication. The structure of the protein p6—DNA complex has been determined, as well as the main signals at the φ29 DNA ends recognized by protein p6. The DNA binding domain of protein p6 has been studied. The results indicate that an α-helical structure located in the N-terminal region of protein p6 is involved in DNA binding through the minor groove. The φ29 protein p5 is the single-stranded DNA binding (SSB) protein involved in φ29 DNA replication, by binding to the displaced single-stranded DNA (ssDNA) in the replication intermediates. In addition, protein p5 is able to unwind duplex DNA. The properties of the φ29 SSB—ssDNA complex are described. Using the four viral proteins, terminal protein, DNA polymerase, protein p6 and the SSB protein, it was possible to amplify the 19285-bp φ29 DNA molecule by a factor of 4000 after 1 h of incubation at 30°C. The infectivity of the in vitro amplified DNA was identical to that of φ29 DNA obtained from virions.     

Thermodynamic resolution: How do errors in modeled protein structures affect binding affinity predictions?

The present study addresses the effect of structural distortion, caused by protein modeling errors, on calculated binding affinities toward small molecules. The binding affinities to a total of 300 distorted structures based on five different protein–ligand complexes were evaluated to establish a broadly applicable relationship between errors in protein structure and errors in calculated binding affinities. Relatively accurate protein models (less than 2 Å RMSD within the binding site) demonstrate a 14.78 (±7.5)% deviation in binding affinity from that calculated by using the corresponding crystal structure. For structures of 2–3 Å, 3–4 Å, and >4 Å RMSD within the binding site, the error in calculated binding affinity increases to 20.8 (±5.98), 22.79 (±11.3), and 29.43 (±11.47)%, respectively. The results described here may be used in combination with other tools to evaluate the utility of modeled protein structures for drug development or other ligand‐binding studies. Proteins 2010. © 2010 Wiley‐Liss, Inc.     

The Crystal Structure of Glutamine-binding Protein fromEscherichia coli

The crystal structure of the glutamine-binding protein (GlnBP) fromEscherichia coliin a ligand-free “open” conformational state has been determined by isomorphous replacement methods and refined to anR-value of 21.4% at 2.3 Å resolution. There are two molecules in the asymmetric unit, related by pseudo 4-fold screw symmetry. The refined model consists of 3587 non-hydrogen atoms from 440 residues (two monomers), and 159 water molecules. The structure has root-mean-square deviations of 0.013 Å from “deal” bond lengths and 1.5° from “ideal” bond angles.The GlnBP molecule has overall dimensions of approximately 60 Å × 40 Å × 35 Å and is made up of two domains (termed large and small), which exhibit a similar supersecondary structure, linked by two antiparallel β-strands. The small domain contains three α-helices and four parallel and one antiparallel β-strands. The large domain is similar to the small domain but contains two additional α-helices and three more short antiparallel β-strands. A comparison of the secondary structural motifs of GlnBP with those of other periplasmic binding proteins is discussed.A model of the “closed form” GlnBP-Gln complex has been proposed based on the crystal structures of the histidine-binding protein-His complex and “open form” GlnBP. This model has been successfully used as a search model in the crystal structure determination of the “closed form” GlnBP-Gln complex by molecular replacement methods. The model agrees remarkably well with the crystal structure of the Gln-GlnBP complex with root-mean-square deviation of 1.29 Å. Our study shows that, at least in our case, it is possible to predict one conformational state of a periplasmic binding protein from another conformational state of the protein. The glutamine-binding pockets of the model and the crystal structure are compared and the modeling technique is described.     

Prediction of Beta-Turn in Protein Using E-SSpred and Support Vector Machine

β-Turn is a secondary protein structure type that plays an important role in protein configuration and function. Here, we introduced an approach of β-turn prediction that used the support vector machine (SVM) algorithm combined with predicted secondary structure information. The secondary structure information was obtained by using E-SSpred, a new secondary protein structure prediction method. A 7-fold cross validation based on the benchmark dataset of 426 non-homologous protein chains was used to evaluate the performance of our method. The prediction results broke the 80% Q _total barrier and achieved Q _total = 80.9%, MCC = 0.44, and Q _predicted higher 0.9% when compared with the best method. The results in our research are coincident with the conclusion that β-turn prediction accuracy can be improved by inclusion of secondary structure information.     

Refining near‐native protein–protein docking decoys by local resampling and energy minimization

How to refine a near‐native structure to make it closer to its native conformation is an unsolved problem in protein‐structure and protein–protein complex‐structure prediction. In this article, we first test several scoring functions for selecting locally resampled near‐native protein–protein docking conformations and then propose a computationally efficient protocol for structure refinement via local resampling and energy minimization. The proposed method employs a statistical energy function based on a Distance‐scaled Ideal‐gas REference state (DFIRE) as an initial filter and an empirical energy function EMPIRE (EMpirical Protein‐InteRaction Energy) for optimization and re‐ranking. Significant improvement of final top‐1 ranked structures over initial near‐native structures is observed in the ZDOCK 2.3 decoy set for Benchmark 1.0 (74% whose global rmsd reduced by 0.5 Å or more and only 7% increased by 0.5 Å or more). Less significant improvement is observed for Benchmark 2.0 (38% versus 33%). Possible reasons are discussed. Proteins 2009. © 2008 Wiley‐Liss, Inc.     

Toward an accurate prediction of inter-residue distances in proteins using 2D recursive neural networks

Background

Protein inter-residue contact maps provide a translation and rotation invariant topological representation of a protein. They can be used as an intermediary step in protein structure predictions. However, the prediction of contact maps represents an unbalanced problem as far fewer examples of contacts than non-contacts exist in a protein structure. In this study we explore the possibility of completely eliminating the unbalanced nature of the contact map prediction problem by predicting real-value distances between residues. Predicting full inter-residue distance maps and applying them in protein structure predictions has been relatively unexplored in the past.

Results

We initially demonstrate that the use of native-like distance maps is able to reproduce 3D structures almost identical to the targets, giving an average RMSD of 0.5Å. In addition, the corrupted physical maps with an introduced random error of ±6Å are able to reconstruct the targets within an average RMSD of 2Å. After demonstrating the reconstruction potential of distance maps, we develop two classes of predictors using two-dimensional recursive neural networks: an ab initio predictor that relies only on the protein sequence and evolutionary information, and a template-based predictor in which additional structural homology information is provided. We find that the ab initio predictor is able to reproduce distances with an RMSD of 6Å, regardless of the evolutionary content provided. Furthermore, we show that the template-based predictor exploits both sequence and structure information even in cases of dubious homology and outperforms the best template hit with a clear margin of up to 3.7Å. Lastly, we demonstrate the ability of the two predictors to reconstruct the CASP9 targets shorter than 200 residues producing the results similar to the state of the machine learning art approach implemented in the Distill server.

Conclusions

The methodology presented here, if complemented by more complex reconstruction protocols, can represent a possible path to improve machine learning algorithms for 3D protein structure prediction. Moreover, it can be used as an intermediary step in protein structure predictions either on its own or complemented by NMR restraints.     

Characterization of a modified nitrogenase Fe protein from Klebsiella pneumoniae in which the 4Fe4S cluster has been replaced by a 4Fe4Se cluster

The Azotobacter vinelandii nifS gene product has been used with selenocysteine to reconstitute Klebsiella pneumoniae nitrogenase Fe protein. Chemical analysis and extended X-ray absorption fine structure (EXAFS) spectroscopy show that the 4Fe4S cluster present in the native protein is replaced by a 4Fe4Se cluster. As well, EXAFS spectroscopy shows that the bond lengths to the cysteine thiolate ligands shrink by 0.05 Å (from 2.28 to 2.23 Å) upon reduction, whereas the Fe–Fe distance is essentially unchanged. Thus, the core of the 4Fe4Se cluster remains essentially static on reduction, whilst the external cysteine thiolate ligands are pulled in towards the cluster. Compared with native (S)–Fe protein, the (Se)–Fe protein has a 20-fold increased rate of MgATP-induced Fe chelation, a sixfold decreased specific activity for acetylene reduction, a fivefold decreased rate of MgATP-dependent electron transfer from (Se)–Fe protein to MoFe protein, and a fourfold increase in the ATP to 2e ^? ratio. The high ATP to 2e ^? ratio and decreased specific activity are consistent with a lower rate of dissociation of oxidized (Se)–Fe protein from reduced MoFe protein. Thus, the relatively small adjustments in the Fe protein structure necessary to accommodate the 4Fe4Se cluster are transmitted both to adjacent residues that dock at the surface of the MoFe protein and to the ATP hydrolysis sites located approximately 19 Å away.     

Prediction of Protein Secondary Structure Using Feature Selection and Analysis Approach

The prediction of the secondary structure of a protein from its amino acid sequence is an important step towards the prediction of its three-dimensional structure. However, the accuracy of ab initio secondary structure prediction from sequence is about 80 % currently, which is still far from satisfactory. In this study, we proposed a novel method that uses binomial distribution to optimize tetrapeptide structural words and increment of diversity with quadratic discriminant to perform prediction for protein three-state secondary structure. A benchmark dataset including 2,640 proteins with sequence identity of less than 25 % was used to train and test the proposed method. The results indicate that overall accuracy of 87.8 % was achieved in secondary structure prediction by using ten-fold cross-validation. Moreover, the accuracy of predicted secondary structures ranges from 84 to 89 % at the level of residue. These results suggest that the feature selection technique can detect the optimized tetrapeptide structural words which affect the accuracy of predicted secondary structures.     

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

When experimental protein NMR data are too sparse to apply traditional structure determination techniques, de novo protein structure prediction methods can be leveraged. Here, we describe the incorporation of NMR restraints into the protein structure prediction algorithm BCL::Fold. The method assembles discreet secondary structure elements using a Monte Carlo sampling algorithm with a consensus knowledge‐based energy function. New components were introduced into the energy function to accommodate chemical shift, nuclear Overhauser effect, and residual dipolar coupling data. In particular, since side chains are not explicitly modeled during the minimization process, a knowledge based potential was created to relate experimental side chain proton–proton distances to C_β–C_β distances. In a benchmark test of 67 proteins of known structure with the incorporation of sparse NMR restraints, the correct topology was sampled in 65 cases, with an average best model RMSD100 of 3.4 ± 1.3 Å versus 6.0 ± 2.0 Å produced with the de novo method. Additionally, the correct topology is present in the best scoring 1% of models in 61 cases. The benchmark set includes both soluble and membrane proteins with up to 565 residues, indicating the method is robust and applicable to large and membrane proteins that are less likely to produce rich NMR datasets. Proteins 2014; 82:587–595. © 2013 Wiley Periodicals, Inc.     

Assembly of protein structure from sparse experimental data: An efficient Monte Carlo model

A new, efficient method for the assembly of protein tertiary structure from known, loosely encoded secondary structure restraints and sparse information about exact side chain contacts is proposed and evaluated. The method is based on a new, very simple method for the reduced modeling of protein structure and dynamics, where the protein is described as a lattice chain connecting side chain centers of mass rather than Cαs. The model has implicit built-in multibody correlations that simulate short- and long-range packing preferences, hydrogen bonding cooperativity and a mean force potential describing hydrophobic interactions. Due to the simplicity of the protein representation and definition of the model force field, the Monte Carlo algorithm is at least an order of magnitude faster than previously published Monte Carlo algorithms for structure assembly. In contrast to existing algorithms, the new method requires a smaller number of tertiary restraints for successful fold assembly; on average, one for every seven residues as compared to one for every four residues. For example, for smaller proteins such as the B domain of protein G, the resulting structures have a coordinate root mean square deviation (cRMSD), which is about 3 Å from the experimental structure; for myoglobin, structures whose backbone cRMSD is 4.3 Å are produced, and for a 247-residue TIM barrel, the cRMSD of the resulting folds is about 6 Å. As would be expected, increasing the number of tertiary restraints improves the accuracy of the assembled structures. The reliability and robustness of the new method should enable its routine application in model building protocols based on various (very sparse) experimentally derived structural restraints. Proteins 32:475–494, 1998. © 1998 Wiley-Liss, Inc.     

An NMR strategy for fragment-based ligand screening utilizing a paramagnetic lanthanide probe

A nuclear magnetic resonance-based ligand screening strategy utilizing a paramagnetic lanthanide probe is presented. By fixing a paramagnetic lanthanide ion to a target protein, a pseudo-contact shift (PCS) and a paramagnetic relaxation enhancement (PRE) can be observed for both the target protein and its bound ligand. Based on PRE and PCS information, the bound ligand is then screened from the compound library and the structure of the ligand–protein complex is determined. PRE is an isotropic paramagnetic effect observed within 30 Å from the lanthanide ion, and is utilized for the ligand screening in the present study. PCS is an anisotropic paramagnetic effect providing long-range (~40 Å) distance and angular information on the observed nuclei relative to the paramagnetic lanthanide ion, and utilized for the structure determination of the ligand–protein complex. Since a two-point anchored lanthanide-binding peptide tag is utilized for fixing the lanthanide ion to the target protein, this screening method can be generally applied to non-metal-binding proteins. The usefulness of this strategy was demonstrated in the case of the growth factor receptor-bound protein 2 (Grb2) Src homology 2 (SH2) domain and its low- and high-affinity ligands.     

Predictions of protein backbone bond distances and angles from first principles

A procedure is described, based on a spline-function representation of ab initio peptide conformational geometry maps, that allows one to predict backbone bond distances and angles of proteins as functions of the peptide ?(N-C^α)/Ψ(C^α-C′) torsions with an accuracy comparable to that of high-resolution protein crystallography. For example, for the more than 40 residues of crambin, the rms deviation between predicted and crystallographic values of N-C^α-C′ is 1.9° for the 1.5 Å resolution structure and 1.9° for the 0.83 Å resolution structure, compared with angle variations of < 10°. Accurate information on protein backbone geometries is important for establishing dictionaries of flexible geometry functions for use in empirical peptide and protein modeling. © 1995 John Wiley & Sons, Inc.     

Contact prediction for beta and alpha‐beta proteins using integer linear optimization and its impact on the first principles 3D structure prediction method ASTRO‐FOLD

An integer linear optimization model is presented to predict residue contacts in β, α + β, and α/β proteins. The total energy of a protein is expressed as sum of a C^α? C^α distance dependent contact energy contribution and a hydrophobic contribution. The model selects contact that assign lowest energy to the protein structure as satisfying a set of constraints that are included to enforce certain physically observed topological information. A new method based on hydrophobicity is proposed to find the β‐sheet alignments. These β‐sheet alignments are used as constraints for contacts between residues of β‐sheets. This model was tested on three independent protein test sets and CASP8 test proteins consisting of β, α + β, α/β proteins and it was found to perform very well. The average accuracy of the predictions (separated by at least six residues) was ～61%. The average true positive and false positive distances were also calculated for each of the test sets and they are 7.58 Å and 15.88 Å, respectively. Residue contact prediction can be directly used to facilitate the protein tertiary structure prediction. This proposed residue contact prediction model is incorporated into the first principles protein tertiary structure prediction approach, ASTRO‐FOLD. The effectiveness of the contact prediction model was further demonstrated by the improvement in the quality of the protein structure ensemble generated using the predicted residue contacts for a test set of 10 proteins. Proteins 2010. © 2010 Wiley‐Liss, Inc.     

Automated Procedure for Contact-Map-Based Protein Structure Reconstruction

Knowledge of the three-dimensional structures of ion channels allows for modeling their conductivity characteristics using biophysical models and can lead to discovering their cellular functionality. Recent studies show that quality of structure predictions can be significantly improved using protein contact site information. Therefore, a number of procedures for protein structure prediction based on their contact-map have been proposed. Their comparison is difficult due to different methodologies used for validation. In this work, a Contact Map-to-Structure pipeline (C2S_pipeline) for contact-based protein structure reconstruction is designed and validated. The C2S_pipeline can be used to reconstruct monomeric and multimeric proteins. The median RMSD of structures obtained during validation on a representative set of protein structures, equaled 5.27 Å, and the best structure was reconstructed with RMSD of 1.59 Å. The validation is followed by a detailed case study on the KcsA ion channel. Models of KcsA are reconstructed based on different portions of contact site information. Structural feature analysis of acquired KcsA models is supported by a thorough analysis of electrostatic potential distributions inside the channels. The study shows that electrostatic parameters are correlated with structural quality of models. Therefore, they can be used to discriminate between high and low quality structures. We show that 30 % of contact information is needed to obtain accurate structures of KcsA, if contacts are selected randomly. This number increases to 70 % in case of erroneous maps in which the remaining contacts or non-contacts are changed to the opposite. Furthermore, the study reveals that local reconstruction accuracy is correlated with the number of contacts in which amino acid are involved. This results in higher reconstruction accuracy in the structure core than peripheral regions.     

The effects of codon usage on the formation of secondary structures of nucleocapsid protein of peste des petits ruminants virus

The nucleocapsid (N) protein of peste des petits ruminants virus (PPRV) with a conserved amino acid usage pattern plays an important role in viral replication. The primary objective of this study was to estimate roles of synonymous codon usages of PPRV N gene and tRNA abundances of host in the formation of secondary structure of N protein. The potential effects of synonymous codon usages of N gene and tRNA abundances of host on shaping different folding units (α-helix, β-strand and the coil) in N protein were estimated, based on the information about the modeling secondary structure of PPRV N protein. The synonymous codon usage bias was found in different folding units in PPRV N protein. To better understand the role of translation speed caused by variant tRNA abundances in shaping the specific folding unit in N protein, we modeled the changing trends of tRNA abundance at the transition boundaries from one folding unit to another folding unit (β-strand → coil, coil → β-strand, α-helix → coil, coil → α-helix). The obvious fluctuations of tRNA abundance were identified at the two transition boundaries (β-strand → coil and coil → β-strand) in PPRV N protein. Our findings suggested that viral synonymous codon usage bias and cellular tRNA abundance variation might have potential effects on the formation of secondary structure of PPRV N protein.     

The three-dimensional structure of the FMO protein from <Emphasis Type="Italic">Pelodictyon phaeum</Emphasis> and the implications for energy transfer

The Fenna–Matthews–Olson (FMO) antenna protein from the green bacterium Pelodictyon phaeum mediates the transfer of energy from the peripheral chlorosome antenna complex to the membrane-bound reaction center. The three-dimensional structure of this protein has been solved using protein crystallography to a resolution limit of 2.0 Å, with R _work and R _free values of 16.6 and 19.9%, respectively. The structure is a trimer of three identical subunits related by a threefold symmetry axis. Each subunit has two beta sheets that surround 8 bacteriochlorophylls. The bacteriochlorophylls are all five-coordinated, with the axial ligand being a histidine, serine, backbone carbonyl, or bound water molecule. The arrangement of the bacteriochlorophylls is generally well conserved in comparison to other FMO structures, but differences are apparent in the interactions with the surrounding protein. In this structure the position and orientation of the eighth bacteriochlorophyll is well defined and shows differences in its location and the coordination of the central Mg compared to previous models. The implications of this structure on the ability of the FMO protein to perform energy transfer are discussed in terms of the experimental optical measurements.