PLP-Dependent Enzyme Methionine γ-Lyase: Insights into the Michaelis Complex from Molecular Dynamics and Free Energy Simulations.

Methionine γ-lyase (MGL) breaks down methionine, with the help of its cofactor pyridoxal-5'-phosphate (PLP), or vitamin B6. Methionine depletion is damaging for cancer cells but not normal cells, so MGL is of interest as a therapeutic protein. To increase our understanding and help engineer improved activity, we focused on the reactive, Michaelis complex M between MGL, covalently bound PLP, and substrate Met. M is not amenable to crystallography, as it proceeds to products. Experimental activity measurements helped exclude a mechanism that would bypass M. We then used molecular dynamics and alchemical free energy simulations to elucidate its structure and dynamics. We showed that the PLP phosphate has a pKa strongly downshifted by the protein, whether Met is present or not. Met binding affects the structure surrounding the reactive atoms. With Met, the Schiff base linkage between PLP and a nearby lysine shifts from a zwitterionic, keto form to a neutral, enol form that makes it easier for Met to approach its labile, target atom. The Met ligand also stabilizes the correct orientation of the Schiff base, more strongly than in simulations without Met, and in agreement with structures in the Protein Data Bank, where the Schiff base orientation correlates with the presence or absence of a co-bound anion or substrate analogue in the active site. Overall, the Met ligand helps organize the active site for the enzyme reaction by reducing fluctuations and shifting protonation states and conformational populations.

A Computational Model for the PLP-Dependent Enzyme Methionine γ-Lyase.

Pyridoxal-5'-phosphate (PLP) is a cofactor in the reactions of over 160 enzymes, several of which are implicated in diseases. Methionine γ-lyase (MGL) is of interest as a therapeutic protein for cancer treatment. It binds PLP covalently through a Schiff base linkage and digests methionine, whose depletion is damaging for cancer cells but not normal cells. To improve MGL activity, it is important to understand and engineer its PLP binding. We develop a simulation model for MGL, starting with force field parameters for PLP in four main states: two phosphate protonation states and two tautomeric states, keto or enol for the Schiff base moiety. We used the force field to simulate MGL complexes with each form, and showed that those with a fully-deprotonated PLP phosphate, especially keto, led to the best agreement with MGL structures in the PDB. We then confirmed this result through alchemical free energy simulations that compared the keto and enol forms, confirming a moderate keto preference, and the fully-deprotonated and singly-protonated phosphate forms. Extensive simulations were needed to adequately sample conformational space, and care was needed to extrapolate the protonation free energy to the thermodynamic limit of a macroscopic, dilute protein solution. The computed phosphate pK a was 5.7, confirming that the deprotonated, -2 form is predominant. The PLP force field and the simulation methods can be applied to all PLP enzymes and used, as here, to reveal fine details of structure and dynamics in the active site.

Knowledge-Based Unfolded State Model for Protein Design.

The design of proteins and miniproteins is an important challenge. Designed variants should be stable, meaning the folded/unfolded free energy difference should be large enough. Thus, the unfolded state plays a central role. An extended peptide model is often used, where side chains interact with solvent and nearby backbone, but not each other. The unfolded energy is then a function of sequence composition only and can be empirically parametrized. If the space of sequences is explored with a Monte Carlo procedure, protein variants will be sampled according to a well-defined Boltzmann probability distribution. We can then choose unfolded model parameters to maximize the probability of sampling native-like sequences. This leads to a well-defined maximum likelihood framework. We present an iterative algorithm that follows the likelihood gradient. The method is presented in the context of our Proteus software, as a detailed downloadable tutorial. The unfolded model is combined with a folded model that uses molecular mechanics and a Generalized Born solvent. It was optimized for three PDZ domains and then used to redesign them. The sequences sampled are native-like and similar to a recent PDZ design study that was experimentally validated.

Application of Various Molecular Modelling Methods in the Study of Estrogens and Xenoestrogens.

In this review, applications of various molecular modelling methods in the study of estrogens and xenoestrogens are summarized. Selected biomolecules that are the most commonly chosen as molecular modelling objects in this field are presented. In most of the reviewed works, ligand docking using solely force field methods was performed, employing various molecular targets involved in metabolism and action of estrogens. Other molecular modelling methods such as molecular dynamics and combined quantum mechanics with molecular mechanics have also been successfully used to predict the properties of estrogens and xenoestrogens. Among published works, a great number also focused on the application of different types of quantitative structure-activity relationship (QSAR) analyses to examine estrogen's structures and activities. Although the interactions between estrogens and xenoestrogens with various proteins are the most commonly studied, other aspects such as penetration of estrogens through lipid bilayers or their ability to adsorb on different materials are also explored using theoretical calculations. Apart from molecular mechanics and statistical methods, quantum mechanics calculations are also employed in the studies of estrogens and xenoestrogens. Their applications include computation of spectroscopic properties, both vibrational and Nuclear Magnetic Resonance (NMR), and also in quantum molecular dynamics simulations and crystal structure prediction. The main aim of this review is to present the great potential and versatility of various molecular modelling methods in the studies on estrogens and xenoestrogens.

Adaptive landscape flattening allows the design of both enzyme: Substrate binding and catalytic power.

Designed enzymes are of fundamental and technological interest. Experimental directed evolution still has significant limitations, and computational approaches are a complementary route. A designed enzyme should satisfy multiple criteria: stability, substrate binding, transition state binding. Such multi-objective design is computationally challenging. Two recent studies used adaptive importance sampling Monte Carlo to redesign proteins for ligand binding. By first flattening the energy landscape of the apo protein, they obtained positive design for the bound state and negative design for the unbound. We have now extended the method to design an enzyme for specific transition state binding, i.e., for its catalytic power. We considered methionyl-tRNA synthetase (MetRS), which attaches methionine (Met) to its cognate tRNA, establishing codon identity. Previously, MetRS and other synthetases have been redesigned by experimental directed evolution to accept noncanonical amino acids as substrates, leading to genetic code expansion. Here, we have redesigned MetRS computationally to bind several ligands: the Met analog azidonorleucine, methionyl-adenylate (MetAMP), and the activated ligands that form the transition state for MetAMP production. Enzyme mutants known to have azidonorleucine activity were recovered by the design calculations, and 17 mutants predicted to bind MetAMP were characterized experimentally and all found to be active. Mutants predicted to have low activation free energies for MetAMP production were found to be active and the predicted reaction rates agreed well with the experimental values. We suggest the present method should become the paradigm for computational enzyme design.

Differences in Outcomes Reported by Patients With Inflammatory Bowel Diseases vs Their Health Care Professionals.

BACKGROUND & AIMS: Inflammatory bowel disease (IBD) scoring systems combine patient-reported data with physicians' observations to determine patient outcomes, but these systems are believed to have limitations. We used real-world data from a large IBD cohort in Switzerland to compare results between patients and healthcare professionals from scoring systems for Crohn's disease (CD) and ulcerative colitis (UC). METHODS: We collected data from the Swiss IBD cohort, beginning in 2006, using 2453 reports for 1385 patients (52% female, 58% with CD). During office visits, physicians asked patients about signs and symptoms and recorded their answers (health care professional-reported outcomes). On a later date, patients received a questionnaire at home (independently of the medical visit), complete it, and sent it back to the data center. Patients also completed the short form 36 and IBD quality of life (QoL) questionnaires. We calculated Cohen's kappa (κ) statistics to assess the level of agreement in scores between patients and health care professionals (Δt between reports collected less than 2 months apart). We used Spearman correlation coefficients (ρ) to compare general well-being (GWB) and QoL scores determined by patients vs health care professionals. Our primary aim was to investigate the overall and individual level of agreement on signs and symptoms reported by health care professionals vs patients. RESULTS: The best level of agreement (although moderate) was observed for number of stools last week in patients with CD (κ = 0.47), and nocturnal diarrhea in patients with UC (κ = 0.52). Agreement was low on level of abdominal pain (κ = 0.31 for patients with CD and κ = 0.37 for patients with UC) and GWB (κ = 0.23 for patients with CD and κ = 0.26 for patients with UC). Patients reported less severe abdominal pain and worse GWB (CD) or better GWB (UC) than that determined by health care professionals. Patient self-rated GWB correlated with IBD quality of life (ρ = 0.68 for patients with CD and ρ = 0.70 for patients with UC) and SF-36 physical scores (ρ = 0.55 for patients with CD and ρ = 0.60 for patients with UC); there was no correlation between health care professional-rated GWB and QoL. CONCLUSIONS: In a comparison of patient vs health care provider-reported outcomes in a Swiss IBD cohort, we found that health care professionals seem to misinterpret patients' complaints. Patients self-rated GWB correlated with QoL scores, indicating that reporting GWB in a single question is possible and relevant, but can vary based on how the data are collected.

Adaptive landscape flattening in amino acid sequence space for the computational design of protein:peptide binding.

For the high throughput design of protein:peptide binding, one must explore a vast space of amino acid sequences in search of low binding free energies. This complex problem is usually addressed with either simple heuristic scoring or expensive sequence enumeration schemes. Far more efficient than enumeration is a recent Monte Carlo approach that adaptively flattens the energy landscape in sequence space of the unbound peptide and provides formally exact binding free energy differences. The method allows the binding free energy to be used directly as the design criterion. We propose several improvements that allow still more efficient sampling and can address larger design problems. They include the use of Replica Exchange Monte Carlo and landscape flattening for both the unbound and bound peptides. We used the method to design peptides that bind to the PDZ domain of the Tiam1 signaling protein and could serve as inhibitors of its activity. Four peptide positions were allowed to mutate freely. Almost 75 000 peptide variants were processed in two simulations of 109 steps each that used 1 CPU hour on a desktop machine. 96% of the theoretical sequence space was sampled. The relative binding free energies agreed qualitatively with values from experiment. The sampled sequences agreed qualitatively with an experimental library of Tiam1-binding peptides. The main assumption limiting accuracy is the fixed backbone approximation, which could be alleviated in future work by using increased computational resources and multi-backbone designs.

Accurate PDZ/Peptide Binding Specificity with Additive and Polarizable Free Energy Simulations.

PDZ domains contain 80-100 amino acids and bind short C-terminal sequences of target proteins. Their specificity is essential for cellular signaling pathways. We studied the binding of the Tiam1 PDZ domain to peptides derived from the C-termini of its Syndecan-1 and Caspr4 targets. We used free energy perturbation (FEP) to characterize the binding energetics of one wild-type and 17 mutant complexes by simulating 21 alchemical transformations between pairs of complexes. Thirteen complexes had known experimental affinities. FEP is a powerful tool to understand protein/ligand binding. It depends, however, on the accuracy of molecular dynamics force fields and conformational sampling. Both aspects require continued testing, especially for ionic mutations. For six mutations that did not modify the net charge, we obtained excellent agreement with experiment using the additive, AMBER ff99SB force field, with a root mean square deviation (RMSD) of 0.37 kcal/mol. For six ionic mutations that modified the net charge, agreement was also good, with one large error (3 kcal/mol) and an RMSD of 0.9 kcal/mol for the other five. The large error arose from the overstabilization of a protein/peptide salt bridge by the additive force field. Four of the ionic mutations were also simulated with the polarizable Drude force field, which represents the first test of this force field for protein/ligand binding free energy changes. The large error was eliminated and the RMS error for the four mutations was reduced from 1.8 to 1.2 kcal/mol. The overall accuracy of FEP indicates it can be used to understand PDZ/peptide binding. Importantly, our results show that for ionic mutations in buried regions, electronic polarization plays a significant role.

Computational Design of the Tiam1 PDZ Domain and Its Ligand Binding.

PDZ domains direct protein-protein interactions and serve as models for protein design. Here, we optimized a protein design energy function for the Tiam1 and Cask PDZ domains that combines a molecular mechanics energy, Generalized Born solvent, and an empirical unfolded state model. Designed sequences were recognized as PDZ domains by the Superfamily fold recognition tool and had similarity scores comparable to natural PDZ sequences. The optimized model was used to redesign the two PDZ domains, by gradually varying the chemical potential of hydrophobic amino acids; the tendency of each position to lose or gain a hydrophobic character represents a novel hydrophobicity index. We also redesigned four positions in the Tiam1 PDZ domain involved in peptide binding specificity. The calculated affinity differences between designed variants reproduced experimental data and suggest substitutions with altered specificities.

Probing the stereospecificity of tyrosyl- and glutaminyl-tRNA synthetase with molecular dynamics.

The stereospecificity of aminoacyl-tRNA synthetases helps exclude d-amino acids from protein synthesis and could perhaps be engineered to allow controlled d-amino acylation of tRNA. We use molecular dynamics simulations to probe the stereospecificity of the class I tyrosyl- and glutaminyl-tRNA synthetases (TyrRS, GlnRS), including wildtype enzymes and three point mutants suggested by three different protein design methods. l/d binding free energy differences are obtained by alchemically and reversibly transforming the ligand from L to D in simulations of the protein-ligand complex. The D81Q mutation in Escherichia coli TyrRS is homologous to the D81R mutant shown earlier to have inverted stereospecificity. D81Q is predicted to lead to a rotated ligand backbone and an increased, not a decreased l-Tyr preference. The E36Q mutation in Methanococcus jannaschii TyrRS has a predicted l/d binding free energy difference ΔΔG of just 0.5±0.9kcal/mol, compared to 3.1±0.8kcal/mol for the wildtype enzyme (favoring l-Tyr). The ligand ammonium position is preserved in the d-Tyr complex, while the carboxylate is shifted. Wildtype GlnRS has a similar preference for l-glutaminyl adenylate; the R260Q mutant has an increased preference, even though Arg260 makes a large contribution to the wildtype ΔΔG value.

Computational protein design: the Proteus software and selected applications.

We describe an automated procedure for protein design, implemented in a flexible software package, called Proteus. System setup and calculation of an energy matrix are done with the XPLOR modeling program and its sophisticated command language, supporting several force fields and solvent models. A second program provides algorithms to search sequence space. It allows a decomposition of the system into groups, which can be combined in different ways in the energy function, for both positive and negative design. The whole procedure can be controlled by editing 2-4 scripts. Two applications consider the tyrosyl-tRNA synthetase enzyme and its successful redesign to bind both O-methyl-tyrosine and D-tyrosine. For the latter, we present Monte Carlo simulations where the D-tyrosine concentration is gradually increased, displacing L-tyrosine from the binding pocket and yielding the binding free energy difference, in good agreement with experiment. Complete redesign of the Crk SH3 domain is presented. The top 10000 sequences are all assigned to the correct fold by the SUPERFAMILY library of Hidden Markov Models. Finally, we report the acid/base behavior of the SNase protein. Sidechain protonation is treated as a form of mutation; it is then straightforward to perform constant-pH Monte Carlo simulations, which yield good agreement with experiment. Overall, the software can be used for a wide range of application, producing not only native-like sequences but also thermodynamic properties with errors that appear comparable to other current software packages.

Molecular dynamics simulations show that conformational selection governs the binding preferences of imatinib for several tyrosine kinases.

Tyrosine kinases transmit cellular signals through a complex mechanism, involving their phosphorylation and switching between inactive and active conformations. The cancer drug imatinib binds tightly to several homologous kinases, including Abl, but weakly to others, including Src. Imatinib specifically targets the inactive, so-called "DFG-out" conformation of Abl, which differs from the preferred, "DFG-in" conformation of Src in the orientation of a conserved Asp-Phe-Gly (DFG) activation loop. However, recent x-ray structures showed that Src can also adopt the DFG-out conformation and uses it to bind imatinib. The Src/Abl-binding free energy difference can thus be decomposed into two contributions. Contribution i measures the different protein-imatinib interactions when either kinase is in its DFG-out conformation. Contribution ii depends on the ability of imatinib to select or induce this conformation, i.e. on the relative stabilities of the DFG-out and DFG-in conformations of each kinase. Neither contribution has been measured experimentally. We use molecular dynamics simulations to show that contribution i is very small, 0.2 +/- 0.6 kcal/mol; imatinib interactions are very similar in the two kinases, including long range electrostatic interactions with the imatinib positive charge. Contribution ii, deduced using the experimental binding free energy difference, is much larger, 4.4 +/- 0.9 kcal/mol. Thus, conformational selection, easy in Abl, difficult in Src, underpins imatinib specificity. Contribution ii has a simple interpretation; it closely approximates the stability difference between the DFG-out and DFG-in conformations of apo-Src. Additional calculations show that conformational selection also governs the relative binding of imatinib to the kinases c-Kit and Lck. These results should help clarify the current framework for engineering kinase signaling.

A molecular mechanics model for imatinib and imatinib:kinase binding.

Imatinib is an important anticancer drug, which binds specifically to the Abl kinase and blocks its signalling activity. To model imatinib:protein interactions, we have developed a molecular mechanics force field for imatinib and four close analogues, which is consistent with the CHARMM force field for proteins and nucleic acids. Atomic charges and Lennard-Jones parameters were derived from a supermolecule ab initio approach. We considered the ab initio energies and geometries of a probe water molecule interacting with imatinib fragments at 32 different positions. We considered both a neutral and a protonated imatinib. The final RMS deviation between the ab initio and force field energies, averaged over both forms, was 0.2 kcal/mol. The model also reproduces the ab initio geometry and flexibility of imatinib. To apply the force field to imatinib:Abl simulations, it is also necessary to determine the most likely imatinib protonation state when it binds to Abl. This was done using molecular dynamics free energy simulations, where imatinib is reversibly protonated during a series of MD simulations, both in solution and in complex with Abl. The simulations indicate that imatinib binds to Abl in its protonated, positively-charged form. To help test the force field and the protonation prediction, we did MD free energy simulations that compare the Abl binding affinities of two imatinib analogs, obtaining good agreement with experiment. Finally, two new imatinib variants were considered, one of which is predicted to have improved Abl binding. This variant could be of interest as a potential drug.

Computational protein design: software implementation, parameter optimization, and performance of a simple model.

Computational protein design will continue to improve as new implementations and parameterizations are explored. An automated protein design procedure is implemented and applied to the full redesign of 16 globular proteins. We combine established but simple ingredients: a molecular mechanics description of the protein where nonpolar hydrogens are implicit, a simple solvent model, a folded state where the backbone is fixed, and a tripeptide model of the unfolded state. Sequences are selected to optimize the folding free energy, using a simple heuristic algorithm to explore sequence and conformational space. We show that a balanced parametrization, obtained here and in our previous work, makes this procedure effective, despite the simplicity of the ingredients. Calculations were done using our Proteins @ Home distributed computing platform, with the help of several thousand volunteers. We describe the software implementation, the optimization of selected terms in the energy function, and the performance of the method. We allowed all amino acids to mutate except glycines, prolines, and cysteines. For 15 of the 16 test proteins, the scores of the computed sequences were comparable to those of natural homologues. Using the low energy computed sequences in a BLAST search of the SWISSPROT database, we could retrieve natural sequences for all protein families considered, with no high-ranking false-positives. The good stability of the designed sequences was supported by molecular dynamics simulations of selected sequences, which gave structures close to the experimental native structure.

Molecular dynamics simulations show that bound Mg2+ contributes to amino acid and aminoacyl adenylate binding specificity in aspartyl-tRNA synthetase through long range electrostatic interactions.

Molecular recognition between the aminoacyl-tRNA synthetase enzymes and their cognate amino acid ligands is essential for the faithful translation of the genetic code. In aspartyl-tRNA synthetase (AspRS), the co-substrate ATP binds preferentially with three associated Mg2+ cations in an unusual, bent geometry. The Mg2+ cations play a structural role and are thought to also participate catalytically in the enzyme reaction. Co-binding of the ATP x Mg3(2+) complex was shown recently to increase the Asp/Asn binding free energy difference, indicating that amino acid discrimination is substrate-assisted. Here, we used molecular dynamics free energy simulations and continuum electrostatic calculations to resolve two related questions. First, we showed that if one of the Mg2+ cations is removed, the Asp/Asn binding specificity is strongly reduced. Second, we computed the relative stabilities of the three-cation complex and the 2-cation complexes. We found that the 3-cation complex is overwhelmingly favored at ordinary magnesium concentrations, so that the protein is protected against the 2-cation state. In the homologous LysRS, the 3-cation complex was also strongly favored, but the third cation did not affect Lys binding. In tRNA-bound AspRS, the single remaining Mg2+ cation strongly favored the Asp-adenylate substrate relative to Asn-adenylate. Thus, in addition to their structural and catalytic roles, the Mg2+ cations contribute to specificity in AspRS through long range electrostatic interactions with the Asp side chain in both the pre- and post-adenylation states.

Free-energy simulations and experiments reveal long-range electrostatic interactions and substrate-assisted specificity in an aminoacyl-tRNA synthetase.

Specific recognition of their cognate amino acid substrates by the aminoacyl-tRNA synthetase enzymes is essential for the correct translation of the genetic code. For aspartyl-tRNA synthetase (AspRS), electrostatic interactions are expected to play an important role, since its three substrates (aspartate, ATP, tRNA) are all electrically charged. We used molecular-dynamics free-energy simulations and experiments to compare the binding of the substrate Asp and its electrically neutral analogue Asn to AspRS. The preference for Asp is found to be very strong, with good agreement between simulations and experiment. The simulations reveal long-range interactions that electrostatically couple the amino acid ligand, ATP, and its associated Mg2+ cations, a histidine side chain (His448) next to the amino acid ligand and a flexible loop that closes over the active site in response to amino acid binding. Closing this loop brings a negatively charged glutamate into the active site; this causes His448 to recruit a labile proton, which interacts favorably with Asp and accounts for most of the Asp/Asn discrimination. Cobinding of the second substrate, ATP, increases specificity for Asp further and makes the system robust towards removal of His448, which is mutated to a neutral amino acid in many organisms. Thus, AspRS specificity is assisted by a labile proton and a cosubstrate, and ATP acts as a mobile discriminator for specific Asp binding to AspRS. In asparaginyl-tRNA synthetase, a close homologue of AspRS, a few binding-pocket differences modify the charge balance so that asparagine binding predominates.

Cys(x)His(y)-Zn2+ interactions: possibilities and limitations of a simple pairwise force field.

In zinc proteins, the Zn2+ cation frequently binds with a tetrahedral coordination to cysteine and histidine side chains. We examine the possibilities and limitations of a classical, pairwise force field for molecular dynamics of such systems. Hartree Fock and density functional calculations are used to obtain geometries, charge distributions, and association energies of side chain analogues bound to Zn2+. Both ionized and neutral cysteines are considered. Two parameterizations are obtained, then tested and compared through molecular dynamics simulations of two small, homologous proteins in explicit solvent: Protein Kinase C and the Cysteine Rich Domain (CRD) of Raf, which have two Cys3His-Zn2+ groups each. The lack of explicit polarizability and charge transfer in the force field leads to poor accuracy for the association energies, and to parameters--including the zinc charge, that depend on the number of bound cysteines and their protonation state. Nevertheless, the structures sampled with the best parameterization are in good overall agreement with experiment, and have zinc coordination geometries compatible with related structures in the Cambridge Structural Database and the Protein Data Bank. Non-optimized parameters lead to poorer structures. This suggests that while a simple force field is not appropriate for processes involving exchange between water and amino acids in the zinc coordination sphere (e.g. protein unfolding), it can be useful for equilibrium simulations of stable Cys3His zinc fingers.

Cys(x)His(y)-Zn2+ interactions: thiol vs. thiolate coordination.

In zinc proteins, the Zn2+ cation frequently binds with a tetrahedral coordination to cysteine and histidine side chains, for example, in many DNA-binding proteins, where it plays primarily a structural role. We examine the possibility of thiolate protonation in Cys(x)His(y)-Zn2+ groups, both in proteins and in solution, through a combination of theoretical calculations and database analysis. Seventy-five percent of the thiolate-coordinated zincs in the Cambridge Structural Database are tetrahedral, while di-alkanethiol coordination always involves five or more ligands. Ab initio quantum calculations are performed on (ethanethiol/thiolate)(3)imidazole-Zn2+ complexes in vacuum, yielding geometries and gas phase basicities. Protonating one (respectively two) thiolates increases the Zn-S(thiol) distance by 0.4 A (respectively 0.3 A), providing a structural marker for protonation. The stabilities of the complexes in solution are compared by combining the gas phase basicities with continuum dielectric solvation calculations. In a continuum solvent with permittivity epsilon = 4, 20, or 80, one of three thiolates is predicted to be protonated at neutral pH. By extension, Cys4-Zn2+ groups are expected to be protonated in the same conditions. In contrast, most Cys3His and Cys4 geometries in the Protein Data Bank (PDB) appear consistent with all-thiolate Zn2+ coordination. This apparent discrepancy is resolved by two recent surveys of zinc protein structures, which suggest that these all-thiolate sites are stabilized by charged and polar groups nearby in the protein, thus overcoming their intrinsic instability. However, the experimental resolution is not sufficient in all the PDB structures to rule out a thiol/thiolate mixture, and protonated thiolates may occur in some proteins not solved at high resolution or not represented in the PDB, as suggested by recent mass spectrometry experiments; this possibility should be allowed for in X-ray structure refinement.