The two tryptophans of β2-microglobulin have distinct roles in function and folding and might represent two independent responses to evolutionary pressure
- Sara Raimondi†1,
- Nicola Barbarini†2,
- Palma Mangione1,
- Gennaro Esposito3,
- Stefano Ricagno4,
- Martino Bolognesi4,
- Irene Zorzoli5,
- Loredana Marchese1,
- Cristina Soria1,
- Riccardo Bellazzi2,
- Maria Monti6,
- Monica Stoppini1,
- Mario Stefanelli^2,
- Paolo Magni2 and
- Vittorio Bellotti1Email author
© Raimondi et al; licensee BioMed Central Ltd. 2011
Received: 15 January 2011
Accepted: 10 June 2011
Published: 10 June 2011
We have recently discovered that the two tryptophans of human β2-microglobulin have distinctive roles within the structure and function of the protein. Deeply buried in the core, Trp95 is essential for folding stability, whereas Trp60, which is solvent-exposed, plays a crucial role in promoting the binding of β2-microglobulin to the heavy chain of the class I major histocompatibility complex (MHCI). We have previously shown that the thermodynamic disadvantage of having Trp60 exposed on the surface is counter-balanced by the perfect fit between it and a cavity within the MHCI heavy chain that contributes significantly to the functional stabilization of the MHCI. Therefore, based on the peculiar differences of the two tryptophans, we have analysed the evolution of β2-microglobulin with respect to these residues.
Having defined the β2-microglobulin protein family, we performed multiple sequence alignments and analysed the residue conservation in homologous proteins to generate a phylogenetic tree. Our results indicate that Trp60 is highly conserved, whereas some species have a Leu in position 95; the replacement of Trp95 with Leu destabilizes β2-microglobulin by 1 kcal/mol and accelerates the kinetics of unfolding. Both thermodynamic and kinetic data fit with the crystallographic structure of the Trp95Leu variant, which shows how the hydrophobic cavity of the wild-type protein is completely occupied by Trp95, but is only half filled by Leu95.
We have established that the functional Trp60 has been present within the sequence of β2-microglobulin since the evolutionary appearance of proteins responsible for acquired immunity, whereas the structural Trp95 was selected and stabilized, most likely, for its capacity to fully occupy an internal cavity of the protein thereby creating a better stabilization of its folded state.
Over the past few years, the investigation of β2-microglobulin (β2-m) amyloidogenesis has shed light on the pathogenesis of Dialysis Related Amyloidosis (DRA)  and has provided general information on the mechanism of structural transition of globular proteins into amyloid fibrils [2–4]. Single amino acid substitutions in the protein sequence enabled us to demonstrate the pivotal role of the two tryptophan (Trp) residues in the function and amyloidogenic propensity of this protein. Moreover, we have recently discovered a functional role of Trp60 in promoting the intermolecular association of β2-m with the MHCI heavy chain and in enhancing the conformational flexibility of the loop between strands D-E and the N-terminal stretch . This conformational flexibility involving Trp60 is necessary for the optimal binding of β2-m to the MHCI heavy chain, although, at the same time, this increases the intrinsic tendency of the protein to self aggregate.
In contrast, Trp95 is buried in the hydrophobic core of the protein and is apparently essential for its stability; however Trp95 does not contribute directly to the binding of the MHCI heavy chain. Tryptophan is a relatively rare amino acid within a protein sequence and its large hydrophobic surface area containing the heterocyclic ring system has a unique role in protein folding and function . These two properties drive protein remodelling during evolution wherein a trade-off exists between mutations that endow better protein function regardless of protein fitness and compensatory mutations that improve stability . The two Trp residues of β2-m represent two examples of how this amino acid can affect protein structure or function; therefore, we analysed the evolutionary tree and the conservation of the two residues in vertebrates expressing MHCI molecules. We have discovered that Trp60 is highly conserved, whereas, in some of the most basal taxa, Leu is present in position 95. To understand the possible positive effects of the replacement of Leu95 with Trp we investigated the structural impact of this mutation on human β2-m.
Results and Discussion
Conservation and phylogenetic analysis
In the gene tree reported in Figure 4, the name of each related protein is preceded by the one letter code of the two residues associated with the 96 and 137 positions corresponding to Trp60 and Trp95, respectively, in mature β2-m. The comparison between the species and the gene trees allows us to analyse the phylogenetic evolution of positions 60 and 95. Most of the sequences contain tryptophan. Only a few sequences have different amino acids in these positions. In particular, a pseudo-cluster of three proteins, derived from Chondrichthyes (cartilaginous fishes), Actinopterygii (bony fishes) and Amphibia (amphibians), contains Trp at position 60 and Leu at position 95 instead of Trp. It is worth noting that the gene tree clearly shows an evolutionary divergence of β2-m in warm-blooded vertebrates and fish . Thus, the phylogenetic analysis shows that, in some species belonging to lineages like cartilaginous fishes and amphibians, there is a different hydrophobic residue, leucine, at position 95. Most likely, Trp95 is the result of a diversification process arising during the evolution of the adaptive immune response system . The evidence that all species encoding MHCI contain Trp60 demonstrates the essential role of this residue. These results were confirmed by performing both joint and marginal reconstruction of the ancestral sequence: this sequence has a Trp at position 60 and a Leu at position 95 with good joint log likelihood values at these positions; moreover, good confidence of the reconstruction at these sites was estimated by marginal probabilities (94% for Trp and 69% for Leu).
Experimental analysis of the role of the two Trp residues in the structure and function of β2-m
Crystal structure of the Trp95Leu β2-m mutant
Data collection and refinement statistics for the β2-m Trp95Leu variant
Space group Unit cell edges (Å, °)
a = 77.41, b = 28.98, c = 54.45, β = 121.7
R merge* (%)
R work** (%)
R free (%)
Number of atoms
Most favoured region
Another unexpected structural difference observed in the Trp95Leu mutant is located in the DE loop. Such a loop was observed in several conformations in different β2-m structures and/or variants , but it always displayed high quality electron density. In the Trp95Leu isoform the electron density for the DE loop is of poor quality; such a loop was modelled in two alternative conformations, with residual electron density suggesting an even higher number of conformations. Relative to the DE loop, residue 95 is located on the opposite pole of the β2-m tertiary structure; in the crystal packing, however, the two regions, from spatially neighbouring molecules, fall close to each other, hence the observed flexibility of the C-terminal region may affect the conformational flexibility of the DE loop.
Our study indicates that Trp60 of β2-m is highly conserved, which is consistent with its essential role in the binding of β2-m to the heavy chain of MHCI. In contrast, Trp95 is buried in a hydrophobic cavity and contributes to protein stability and in some species is replaced by Leu (for example, within cartilaginous fish and amphibians). Our data suggest that the divergence between Trp or Leu at position 95 and the subsequent selection of Trp in the very large majority of species is based on a significant thermodynamic stabilization of the protein which also limits the intrinsic propensity of β2-m to make amyloid fibrils . Such an effect can be explained by the perfect fit of Trp95 in the hydrophobic cavity delimited by the side chains of Ser11, Leu23, Phe70, Pro72, Tyr78 and Arg97 (Figure 9) in the core of β2-m.
Moreover, because the tendency to self-aggregate correlates with intracellular sequestration and degradation through quality control, it is plausible that a better stability and a lower aggregation propensity have favoured a much better yield of correctly folded β2-m.
Definition of the protein family
A protein (or gene) family consists of all the sequences homologous to the protein of interest. To retrieve these sequences, one approach is a simple similarity search against current databases, performed using the Blast algorithm. A more accurate approach is based on the databases of homologous genes, which are available as HOVERGEN, HOGENOM and HOMOLENS [14–17].
A BLASTP search of our protein of interest (human β2-m) against the NCBInr database allowed us to retrieve homologous sequences only from vertebrates. As a consequence we decided to consider HOVERGEN (Homologous vertebrate gene families) as a reference database [18–20]. HOVERGEN contains all vertebrate protein sequences from the UniProt Knowledgebase (Swiss-Prot and TrEMBL) grouped according to similarity scores; the results of this clustering have been processed to avoid inconsistencies. We identified the human β2-m family using the FamFetch tool by searching HOVERGEN release 48 (May 2007) with the entry name of the protein of interest, i.e. Uniprot entry name B2MG_HUMAN.
Following the approach used by Tourasse , the orthology of sequences was assessed by examination of the phylogenetic tree of the family provided in HOVERGEN. When multiple sequences were present for a given species, only the sequence more similar to the remaining sequences was kept, while avoiding the selection of sequences annotated as a fragment; in this way the possibility of multiple substitutions at the same site is minimized.
Multiple alignment and conservation analysis
We used HOVERGEN to provide a multiple alignment for every family obtained by MUSCLE ; however, we also performed a further analysis using CLUSTALW [22, 23], which is the most commonly used algorithm for this task [16, 17, 24]. Sequences were aligned with CLUSTALW version 2.0.12 using the default parameters (except for gap open penalty = 1).
A simple conservation analysis was performed using the tool, JALVIEW [25, 26]. Three measures were computed: (i) alignment conservation annotation measures the number of conserved physicochemical properties for each column of the alignment applying the approach used in the AMAS method ; (ii) alignment quality annotation is a measure of the likelihood of observing mutations (if any) in a particular column of the alignment. The quality score is calculated for each column in the multiple alignment by summing, for all mutations, the ratio of the two BLOSUM62 scores for a mutation pair and each residue's conserved BLOSUM62 score (which is higher); (iii) alignment consensus annotation is composed by the most frequent residue per column of the alignment, hence it represents the percentage frequency of these amino acids.
Phylogenetic (or gene) tree analysis was based on multiple alignment using four different methods for tree reconstruction: Neighbor-Joining, Maximal Parsimony, Minimum Evolution and Bayesian Inference [28, 29] The first three methods were applied using MEGA 4 [30–32]. We used the Jones-Taylor-Thornton (JTT) amino acid substitution matrix, which is based on the same counting approach as the PAM matrix but uses a much enlarged database ; it is widely used for phylogenetic analysis [16, 17] and is the most suitable choice for vertebrates . Statistical reliability of the nodes was assessed by bootstrap analysis (1000 replications) . BI was performed using MrBayes 3.1.2 [36, 37]. Four Markov chains of 30,000 generations were run (after a 5000-generation burn-in) at the default temperature (0.2) with a random starting tree and a sampling frequency of 10. A mixed substitution model was set by allowing model jumping between nine fixed-rate amino acid models.
The four phylogenetic trees (or gene trees) were re-rooted by selecting as root the gene belonging to the most basal species. The species tree of the organisms of interest was retrieved to select the best root. This species tree was also useful to analyse the evolution of the protein of interest (and specific amino acids) in comparison with species evolution. The taxonomy of the organisms is usually considered as a species tree. In particular, we used NCBI Taxonomy Browser to extract the taxonomy tree of the analysed organisms [38, 39]. It employs a database of all the organisms represented in the NCBI sequence database, and can automatically build a species tree using organisms selected by the user. When the root was selected, all the trees were re-rooted exploiting the functionalities of MEGA software. To evaluate how the protein follows species evolution, a comparison of the topology between the gene tree and the species tree was carried out using the Robinson-Foulds metric implemented in MrBayes . To evaluate the topology agreement and to highlight the importance of the amino acids under investigation, a simple reconciliation analysis was performed by GeneTree 1.3.0 . For simplifying the interpretation of the results, we considered only 20 proteins corresponding to the most representative species. Reconciliation analysis was also performed on the whole gene and species tree using the Notung 2.6 program .
Two specific amino acids of the reference protein sequence were the subjects of the evolutionary analysis (i.e. Trp60 and Trp95); therefore, we stored the positions of these amino acids. We extracted the amino acids localized in the stored positions within each sequence by considering the multiple alignments obtained as reference. In particular, we added the information related to these amino acids by concatenating the one letter code in the entry name of the proteins to generate a close-up of the amino acid evolution. To perform this procedure we used Phytreetool, contained in the Bioinformatics Toolbox 2.5 of MATLAB Version 184.108.40.2067 (R2007a). Moreover, a formal ancestral sequence reconstruction was performed using the FASTML tool, assuming a gamma distribution of rates among sites .
Equilibrium denaturation experiments
Thermodynamic stability was determined by monitoring the change in far-UV circular dichroism signals at 215 nm of protein samples equilibrated at increasing concentrations of GdnHCl at 20°C. Measurements were performed with a Jasco 710 spectropolarimeter equipped with a temperature control system, using a 1 mm path-length cell. The protein concentration was 200 μg/ml in 10 mM sodium phosphate buffer, pH 7.4 with GdnHCl concentrations ranging from 0 to 4 M. The change in ellipticities was analysed as a function of denaturant concentration according to the method described by Santoro and Bolen, to yield the free energy of unfolding in the absence of denaturant and the GdnHCl concentration at half denaturation . Experimental data were converted to the unfolded fraction using fU = (y - yN)/(yU - yN), where y is the ellipticity value at a given denaturant concentration, and yN and yU are the values of the native and unfolded protein, respectively, extrapolated from the pre- and post- transition base lines defined by the Santoro and Bolen equation .
Folding kinetics followed by far and near-UV Circular Dichroism
A Bio-Logic SFM 3 stopped-flow device coupled to a Jasco 710 spectropolarimeter was used to monitor the rapid changes of ellipticity occurring during folding and unfolding of proteins. Stopped flow traces were monitored at 215 nm with a 2 mm path-length FC-20 cell. All the experiments were performed at 30°C in 10 mM sodium phosphate buffer pH 7.4 with 0.3 mg/ml final protein concentration. The unfolding reactions were performed using a 10-fold dilution of a denaturant free solution of protein at 3 mg/ml with 8 volumes of buffer containing 6 M GdnHCl and one volume of 3 M GdnHCl to yield a final denaturant concentration of 5.1 M. The refolding experiments were carried out using a tenfold dilution of protein samples at 3mg/ml unfolded in 3 M GdnHCl into 10 mM sodium phosphate buffer, pH 7.4.
Slow changes during folding were monitored with a Jasco 710 spectropolarimeter in the near UV region using a 10 mm path-length cell at 30°C and a wavelength of 263 nm. For each protein, one volume of 3 mg/ml denatured at equilibrium in 3 M GdnHCl, was mixed with nine volumes of 10 mM sodium phosphate buffer, pH 7.4. In each case, wavelengths were chosen in regions of maximum spectral change between the native and the denatured protein forms previously recorded in the steady-state CD spectra (data not shown). All the kinetic traces acquired as a function of time from unfolding and folding experiments were fitted as previously described .
Amyloid fibril formation
Wild-type and Trp95Leu β2-m amyloid aggregation was carried out by incubating 100 μM of protein at 37°C in 50 mM phosphate buffer and 100 mM NaCl, pH 7.4, in the presence of 20% (v/v) TFE . β2-m fibril seeds (20 μg/ml) were added to the samples to prime fibrillogenesis.
Amyloid formation was evaluated by both microscopic analysis of the samples stained with Congo red as described previously  and by the thioflavin T (ThT) assay according to LeVine . ThT (Sigma-Aldrich St. Louis, MO 63103, USA) concentration was 10 μM in 50 mM glycine-NaOH buffer, pH 8.5. Measurements were recorded from three independent experiments in triplicate using a Perkin Elmer LS50 spectrofluorometer with excitation and emission wavelengths at 445 and 485 nm, respectively, with slits set at 5 nm.
Crystallization and structure determination
The β2-m Trp95Leu mutant was crystallized under the same conditions used for the β2-m Trp60Val mutant . X-ray diffraction data were collected using the crystallization mother liquor (19-20% PEG 4000, 20% glycerol, 0.2 M ammonium acetate, 0.1 M MES pH 6.0) as cryoprotectant on an ID14-1 crystallography beamline, at 100 K (ESRF, Grenoble, Switzerland). Diffraction data were processed using MOSFLM and SCALA [49, 50]. β2-m Trp95Leu structure solution was achieved by molecular replacement, using MOLREP  and the wild-type β2-m atomic coordinates (PDB entry 1LDS) as search model. The structure was then refined with REFMAC5, at 1.57 Å resolution, applying the maximum likelihood residual, anisotropic B-factor refinement, riding hydrogen atoms, and atomic displacement parameter refinement using the 'tls' method . Model building and structure analysis was performed with COOT . Figure 8 was prepared using Pymol (http://www.pymol.org). Atomic coordinates and structure factors for β2-m Trp95Leu have been deposited with the Protein Data Bank, with accession code 3QDA.
This work was supported by MIUR (PRIN project 20083ERXWS), Fondazione Cariplo (projects 2007-5151 and 2009-2543) and Regione Lombardia. We thank Gian Gaetano Tartaglia for calculating the prediction of the aggregation propensity of β2-m and Winston Hutchinson for critical reading of the manuscript. Mario Stefanelli passed away on October 18th, 2010 and we will never forget his unique bright mind.
- Gejyo F, Yamada T, Odani S, Nakagawa Y, Arakawa M, Kunitomo T, Kataoka H, Suzuki M, Hirasawa Y, Shirahama T, Cohen AS, Schmid K: A new form of amyloid protein associated with chronic hemodialysis was identified as beta 2-microglobulin. Biochem Biophys Res Commun. 1985, 129: 701-706. 10.1016/0006-291X(85)91948-5.View ArticlePubMedGoogle Scholar
- Ozawa D, Yagi H, Ban T, Kameda A, Kawakami T, Naiki H, Goto Y: Destruction of amyloid fibrils of a beta2-microglobulin fragment by laser beam irradiation. J Biol Chem. 2009, 284: 1009-1017.View ArticlePubMedGoogle Scholar
- Bellotti V, Chiti F: Amyloidogenesis in its biological environment: challenging a fundamental issue in protein misfolding diseases. Curr Opin Struct Biol. 2008, 18: 771-779. 10.1016/j.sbi.2008.10.001.View ArticlePubMedGoogle Scholar
- Esposito G, Ricagno S, Corazza A, Rennella E, Gümral D, Mimmi MC, Betto E, Pucillo CE, Fogolari F, Viglino P, Raimondi S, Giorgetti S, Bolognesi B, Merlini G, Stoppini M, Bolognesi M, Bellotti V: The controlling roles of Trp60 and Trp95 in beta2-microglobulin function, folding and amyloid aggregation properties. J Mol Biol. 2008, 378: 887-897. 10.1016/j.jmb.2008.03.002.View ArticlePubMedGoogle Scholar
- Eichner T, Kalverda AP, Thompson GS, Homans SW, Radford SE: Conformational conversion during amyloid formation at atomic resolution. Mol Cell. 2011, 41: 161-172. 10.1016/j.molcel.2010.11.028.View ArticlePubMedPubMed CentralGoogle Scholar
- Samanta U, Pal D, Chakrabarti P: Environment of tryptophan side chains in proteins. Proteins. 2000, 38: 288-300. 10.1002/(SICI)1097-0134(20000215)38:3<288::AID-PROT5>3.0.CO;2-7.View ArticlePubMedGoogle Scholar
- Tokuriki N, Tawfik DS: Protein dynamism and evolvability. Science. 2009, 324: 203-207. 10.1126/science.1169375.View ArticlePubMedGoogle Scholar
- Kumar S, Hedges SB: A molecular timescale for vertebrate evolution. Nature. 1998, 392: 917-920. 10.1038/31927.View ArticlePubMedGoogle Scholar
- Choi W, Lee EY, Choi TJ: Cloning and sequence analysis of the beta2-microglobulin transcript from flounder, Paralichthys olivaceous. Mol Immunol. 2006, 43: 1565-1572. 10.1016/j.molimm.2005.09.021.View ArticlePubMedGoogle Scholar
- Batuwangala T, Shepherd D, Gadola SD, Gibson KJ, Zaccai NR, Fersht AR, Besra GS, Cerundolo V, Jones EY: The crystal structure of human CD1b with a bound bacterial glycolipid. J Immunol. 2004, 172: 2382-2388.View ArticlePubMedGoogle Scholar
- Tartaglia GG, Pawar AP, Campioni S, Dobson CM, Chiti F, Vendruscolo M: Prediction of aggregation-prone regions in structured proteins. J Mol Biol. 2008, 380: 425-436. 10.1016/j.jmb.2008.05.013.View ArticlePubMedGoogle Scholar
- Ricagno S, Raimondi S, Giorgetti S, Bellotti V, Bolognesi M: Human beta-2 microglobulin W60V mutant structure: Implications for stability and amyloid aggregation. Biochem Biophys Res Commun. 2009, 380: 543-547. 10.1016/j.bbrc.2009.01.116.View ArticlePubMedGoogle Scholar
- Ricagno S, Colombo M, de Rosa M, Sangiovanni E, Giorgetti S, Raimondi S, Bellotti V, Bolognesi M: DE loop mutations affect beta-2 microglobulin stability and amyloid aggregation. Biochem Biophys Res Commun. 2008, 377: 146-150. 10.1016/j.bbrc.2008.09.108.View ArticlePubMedGoogle Scholar
- Tourasse NJ, Li WH: Selective constraints, amino acid composition, and the rate of protein evolution. Mol Biol Evol. 2000, 17: 656-664.View ArticlePubMedGoogle Scholar
- Robinson-Rechavi M, Boussau B, Laudet V: Phylogenetic dating and characterization of gene duplications in vertebrates: the cartilaginous fish reference. Mol Biol Evol. 2004, 21: 580-586.View ArticlePubMedGoogle Scholar
- Keane TM, Creevey CJ, Pentony MM, Naughton TJ, Mclnerney JO: Assessment of methods for amino acid matrix selection and their use on empirical data shows that ad hoc assumptions for choice of matrix are not justified. BMC Evol Biol. 2006, 6: 29-10.1186/1471-2148-6-29.View ArticlePubMedPubMed CentralGoogle Scholar
- Cao J, Huang S, Qian J, Huang J, Jin L, Su Z, Yang J, Liu J: Evolution of the class C GPCR Venus flytrap modules involved positive selected functional divergence. BMC Evol Biol. 2009, 9: 67-10.1186/1471-2148-9-67.View ArticlePubMedPubMed CentralGoogle Scholar
- Duret L, Mouchiroud D, Gouy M: HOVERGEN: a database of homologous vertebrate genes. Nucleic Acids Res. 1994, 22: 2360-2365. 10.1093/nar/22.12.2360.View ArticlePubMedPubMed CentralGoogle Scholar
- Duret L, Perrière G, Gouy M: HOVERGEN: database and software for comparative analysis of homologous vertebrate genes. Bioinformatics Databases and Systems. Edited by: Letovsky S. 1999, Boston: MA Kluwer Academic Publishers, 13-29. [Bioinformatics Databases and Systems]Google Scholar
- Penel S, Arigon AM, Dufayard JF, Sertier AS, Daubin V, Duret L, Gouy M, Perrière G: Databases of homologous gene families for comparative genomics. BMC Bioinformatics. 2009, 6 (Suppl 10): S3-View ArticleGoogle Scholar
- Edgar RC: MUSCLE: a multiple sequence alignment method with reduced time and space complexity. BMC Bioinformatics. 2004, 5: 113-10.1186/1471-2105-5-113.View ArticlePubMedPubMed CentralGoogle Scholar
- Thompson JD, Higgins DG, Gibson TJ: CLUSTAL W: improving the sensitivity of progressive multiple sequence alignment through sequence weighting, position-specific gap penalties and weight matrix choice. Nucleic Acids Res. 1994, 22: 4673-4680. 10.1093/nar/22.22.4673.View ArticlePubMedPubMed CentralGoogle Scholar
- Larkin MA, Blackshields G, Brown NP, Chenna R, McGettigan PA, McWilliam H, Valentin F, Wallace IM, Wilm A, Lopez R, Thompson JD, Gibson TJ, Higgins DG: Clustal W and Clustal × version 2.0. Bioinformatics. 2007, 23: 2947-2948. 10.1093/bioinformatics/btm404.View ArticlePubMedGoogle Scholar
- Quental R, Azevedo L, Matthiesen R, Amorim A: Comparative analyses of the Conserved Oligomeric Golgi (COG) complex in vertebrates. BMC Evol Biol. 2010, 10: 212-10.1186/1471-2148-10-212.View ArticlePubMedPubMed CentralGoogle Scholar
- Clamp M, Cuff J, Searle SM, Barton GJ: The Jalview Java alignment editor. Bioinformatics. 2004, 20: 426-427. 10.1093/bioinformatics/btg430.View ArticlePubMedGoogle Scholar
- Waterhouse AM, Procter JB, Martin DM, Clamp M, Barton GJ: Jalview Version 2--a multiple sequence alignment editor and analysis workbench. Bioinformatics. 2009, 25: 1189-1191. 10.1093/bioinformatics/btp033.View ArticlePubMedPubMed CentralGoogle Scholar
- Livingstone CD, Barton GJ: Protein Sequence Alignments: A Strategy for the Hierarchical Analysis of Residue Conservation. Comput Appl Biosci. 1993, 9: 745-756.PubMedGoogle Scholar
- Saitou N, Nei M: The neighbor-joining method: a new method for reconstructing phylogenetic trees. Mol Biol Evol. 1987, 4: 406-425.PubMedGoogle Scholar
- Fitch WM: Towards defining the course of evolution: minimum change for a specific tree topology. Syst Zool. 1971, 20: 406-416. 10.2307/2412116.View ArticleGoogle Scholar
- Kumar S, Tamura K, Nei M: MEGA: Molecular Evolutionary Genetics Analysis software for microcomputers. Comput Appl Biosci. 1994, 10: 189-191.PubMedGoogle Scholar
- Kumar S, Dudley J, Nei M, Tamura K: MEGA: A biologist-centric software for evolutionary analysis of DNA and protein sequences. Briefings in Bioinformatics. 2008, 9: 299-306. 10.1093/bib/bbn017.View ArticlePubMedPubMed CentralGoogle Scholar
- Tamura K, Dudley J, Nei M, Kumar S, MEGA4: Molecular Evolutionary Genetics Analysis (MEGA) software version 4.0. Mol Biol Evol. 2007, 24: 1596-1599. 10.1093/molbev/msm092.View ArticlePubMedGoogle Scholar
- Jones DT, Taylor WR, Thornton JM: The rapid generation of mutation data matrices from protein sequences. Comput Appl Biosci. 1992, 8: 275-282.PubMedGoogle Scholar
- Loughran NB, O'Connor B, O'Fágáin C, O'Connell MJ: The phylogeny of the mammalian heme peroxidases and the evolution of their diverse functions. BMC Evol Biol. 2008, 8: 101-10.1186/1471-2148-8-101.View ArticlePubMedPubMed CentralGoogle Scholar
- Felsenstein J: Confidence limits on phylogenies: an approach using the bootstrap. Evolution. 1985, 39: 783-791. 10.2307/2408678.View ArticleGoogle Scholar
- Huelsenbeck JP, Ronquist F: MRBAYES: Bayesian inference of phylogenetic trees. Bioinformatics. 2001, 17: 754-755. 10.1093/bioinformatics/17.8.754.View ArticlePubMedGoogle Scholar
- Ronquist F, Huelsenbeck JP: MrBayes 3: Bayesian phylogenetic inference under mixed models. Bioinformatics. 2003, 19: 1572-1574. 10.1093/bioinformatics/btg180.View ArticlePubMedGoogle Scholar
- Benson DA, Karsch-Mizrachi I, Lipman DJ, Ostell J, Rapp BA, Wheeler DL, GenBank: Nucleic Acids Res. 2000, 28: 15-18. 10.1093/nar/28.1.15.View ArticlePubMedPubMed CentralGoogle Scholar
- Wheeler DL, Chappey C, Lash AE, Leipe DD, Madden TL, Schuler GD, Tatusova TA, Rapp BA: Database resources of the National Center for Biotechnology Information. Nucleic Acids Res. 2000, 28: 10-14. 10.1093/nar/28.1.10.View ArticlePubMedPubMed CentralGoogle Scholar
- Robinson DR, Foulds LR: Comparison of phylogenetic trees. Mathematical Biosciences. 1981, 53: 131-147. 10.1016/0025-5564(81)90043-2.View ArticleGoogle Scholar
- Page RD: GeneTree: comparing gene and species phylogenies using reconciled trees. Bioinformatics. 1998, 14: 819-820. 10.1093/bioinformatics/14.9.819.View ArticlePubMedGoogle Scholar
- Chen K, Durand D, Farach-Colton M: Notung: A program for dating gene duplications and optimizing gene family trees. J Comput Biol. 2000, 7: 429-447. 10.1089/106652700750050871.View ArticlePubMedGoogle Scholar
- Pupko T, Pe'er I, Graur D, Hasegawa M, Friedman N: A branch-and-bound algorithm for the inference of ancestral amino-acid sequences when the replacement rate varies among sites: Application to the evolution of five gene families. Bioinformatics. 2002, 18: 1116-1123. 10.1093/bioinformatics/18.8.1116.View ArticlePubMedGoogle Scholar
- Santoro MM, Bolen DW: Unfolding free-energy changes determined by the linear extrapolation method. 1. Unfolding of phenyl-methanesulfonyl alpha-chymotrypsin using different denaturants. Biochemistry. 1988, 27: 8063-8068. 10.1021/bi00421a014.View ArticlePubMedGoogle Scholar
- Chiti F, Mangione P, Andreola A, Giorgetti S, Stefani M, Dobson CM, Bellotti V, Taddei N: Detection of two partially structured species in the folding process of the amyloidogenic protein β2-microglobulin. J Mol Biol. 2001, 307: 379-391. 10.1006/jmbi.2000.4478.View ArticlePubMedGoogle Scholar
- Yamamoto S, Yamaguchi I, Hasegawa K, Tsutsumi S, Goto Y, Gejyo F, Naiki H: Glycosaminoglycans enhance the trifluoroethanol-induced extension of β2-microglobulin-related amyloid fibrils at a neutral pH. J Am Soc Nephrol. 2004, 15: 126-133. 10.1097/01.ASN.0000103228.81623.C7.View ArticlePubMedGoogle Scholar
- Puchtler H, Sweat F, Levine M: On the binding of Congo Red by Amyloid. J Histochem Cytochem. 1962, 10: 355-364. 10.1177/10.3.355.View ArticleGoogle Scholar
- LeVine H: Thioflavine T interaction with synthetic Alzheimer's disease β-amyloid peptides: detection of amyloid aggregation in solution. Protein Sci. 1993, 2: 404-410.View ArticlePubMedPubMed CentralGoogle Scholar
- CCP4: The CCP4 suite: programs for protein crystallography. Acta Crystallogr D Biol Crystallogr. 1994, 50: 760-763. 10.1107/S0907444994003112.View ArticleGoogle Scholar
- Leslie AGW: Recent changes to the MOSFLM package for processing film and image plate data. Joint CCP4 + ESF-EACMB. Newsletter on Protein Crystallography. 1992, 26:Google Scholar
- Vagin AA, Teplyakov A: MOLREP: an automated program for molecular replacement. J Appl Crystallogr. 1997, 30: 1022-1025. 10.1107/S0021889897006766.View ArticleGoogle Scholar
- Murshudov GN, Vagin AA, Dodson EJ: Refinement of macromolecular structures by the maximum-likelihood method. Acta Crystallogr D Biol Crystallogr. 1997, 53: 240-255. 10.1107/S0907444996012255.View ArticlePubMedGoogle Scholar
- Emsley P, Cowtan K: Coot: model-building tools for molecular graphics. Acta Crystallogr D Biol Crystallogr. 2004, 60: 2126-2132. 10.1107/S0907444904019158.View ArticlePubMedGoogle Scholar
This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.