- Research article
- Open Access
Evolution of the class C GPCR Venus flytrap modules involved positive selected functional divergence
- Jianhua Cao†1,
- Siluo Huang†1,
- Ji Qian2,
- Jinlin Huang1,
- Li Jin2,
- Zhixi Su3,
- Ji Yang4 and
- Jianfeng Liu1Email author
© Cao et al; licensee BioMed Central Ltd. 2009
- Received: 04 October 2008
- Accepted: 27 March 2009
- Published: 27 March 2009
Class C G protein-coupled receptors (GPCRs) represent a distinct group of the GPCR family, which structurally possess a characteristically distinct extracellular domain inclusive of the Venus flytrap module (VFTM). The VFTMs of the class C GPCRs is responsible for ligand recognition and binding, and share sequence similarity with bacterial periplasmic amino acid binding proteins (PBPs). An extensive phylogenetic investigation of the VFTMs was conducted by analyzing for functional divergence and testing for positive selection for five typical groups of the class C GPCRs. The altered selective constraints were determined to identify the sites that had undergone functional divergence via positive selection. In order to structurally demonstrate the pattern changes during the evolutionary process, three-dimensional (3D) structures of the GPCR VFTMs were modelled and reconstructed from ancestral VFTMs.
Our results show that the altered selective constraints in the VFTMs of class C GPCRs are statistically significant. This implies that functional divergence played a key role in characterizing the functions of the VFTMs after gene duplication events. Meanwhile, positive selection is involved in the evolutionary process and drove the functional divergence of the VFTMs. Our results also reveal that three continuous duplication events occurred in order to shape the evolutionary topology of class C GPCRs. The five groups of the class C GPCRs have essentially different sites involved in functional divergence, which would have shaped the specific structures and functions of the VFTMs.
Taken together, our results show that functional divergence involved positive selection and is partially responsible for the evolutionary patterns of the class C GPCR VFTMs. The sites involved in functional divergence will provide more clues and candidates for further research on structural-function relationships of these modules as well as shedding light on the activation mechanism of the class C GPCRs.
- Functional Divergence
- Duplication Event
- Dictyostelium Discoideum
- Substitution Rate Ratio
- Altered Functional Constraint
Although ligand-binding ability are a typical character of the class C GPCR VFTMs, different receptors have different ligand-binding specificities. Moreover, the GB2 receptors have lost the ability to bind ligands but have maintained their function to can activate G proteins . The overall structural similarity between GPCRs and bacterial PBPs imply that there is a common origin among class C GPCRs via internal domain duplication. As a result, the VFTMs provide an interesting evolutionary case to investigate gene duplication and functional divergence events.
Unlike the bacterial PBPs which can bind various different ligands, most class C GPCRs expressed in the central nerve system can bind only one kind of natural ligand, implying that the VFTMs of class C GPCRs have undergone partial loss of function such as the ability to bind different ligands as well as gaining other unknown functions. Meanwhile, it would be interesting to know whether functional alterations in the VFTMs were the result of extensive changes in selective constraints (different evolutionary rate) at those sites involved.
In the present study we undertook an extensive phylogenetic analysis for the VFTMs of five typical groups of class C GPCRs. By inspecting the amino-acid sites, we report that altered selective constraints derived from positive selection resulted in the functional divergence in the VFTM domains of class C GPCRs (and this occurred after three continuous gene duplications). Our study provides a new insight into understanding the ligand-binding specificity and how the activation or modulation mechanism is refined in the class C GPCRs.
Phylogeny inference of the VFTMs
Type I functional divergence of the VFTMs
Coefficients of functional divergence (θ) for all pairwise comparisons of the VFTMs.
0.730 ± 0.076
0.761 ± 0.124
0.698 ± 0.091
0.833 ± 0.139
0.637 ± 0.100
0.338 ± 0.106
0.446 ± 0.055
0.787 ± 0.109
0.737 ± 0.101
0.001 ± 0.022
0.650 ± 0.141
0.906 ± 0.132
0.826 ± 0.147
0.915 ± 0.127
0.384 ± 0.123
Likelihood ratio test (LRT) of functional divergence for each duplicate event of the VFTMs.
θ ± S.E.(ML)
0.787 ± 0.109
0.737 ± 0.101
0.650 ± 0.141
0.906 ± 0.132
0.826 ± 0.147
0.915 ± 0.127
0.338 ± 0.106
0.446 ± 0.055
0.001 ± 0.022
0.384 ± 0.123
These results suggest that these sites probably played an important role in defining ligand-binding specificity for the VFTMs in class C GPCRs. In contrast, radical amino acid substitutions with very different chemical properties were found at the same positions in bacterial PBPs, thus indicating that altered selective constraints is related to the functional divergence between bacterial PBPs and the VFTMs in class C GPCRs.
Positive selection in the VFTM-coding DNA sequences
LRT statistics 2Δℓ = 2(ℓ1 – ℓ0) for model comparisons.
Alternative model vs. Null model
M2 (positive selection) vs. M0 (one-ratio)
M2 (positive selection) vs. M1 (nearly neutral)
Molecular time scale estimation of the VFTMs
Reconstruction of the ancestral VFTMs
Gene or domain duplications have long been thought to be the primary driving events for producing evolutionary novel genes. In our study we have shown that gene duplication plays an important role in the evolutionary process of class C GPCRs. After undergoing three continuous duplications, the GB2 group retained their original structure and functions and are similarly related to bacterial PBPs except for their ligand-binding abilities. In contrast, the other class C GPCR groups under relaxed evolutionary constraints and functional divergence, lead to the diversification and formation of the mGluR, CaSR, T1R and GB1 groups. Because of freely accumulating amino acid replacements, type I functional divergence events resulted in shaping the unique characteristics of each group within class C GPCRs. When sites at the periphery of the ligand-binding pockets were replaced by different amino-acids there was no serious deleterious effects on the survival, the rudiments of each group were fundamentally shaped. In contrast, the well-conserved sites in the ligand-binding pockets are highly variable, which may result in diversification and different ligand specification. In particular, the VFTMs of GB1 and CaSR groups appear to have acquired novel functions for binding new types of ligands, thus explaining the dramatic difference in amino acid composition. Meanwhile, the GB2 group may have functionally diverged through gene duplication events, which would lead to the acquisition of unknown or loss of intrinsic functions.
Although the VFTMs of class C GPCRs were distant originated from bacterial PBPs, their basic ligand-binding ability was intrinsically identical. However, the role was changed from transporting nutrient substances in bacteria to initiating signal transduction in class C GPCRs. The functional alterations under the positive selected functional divergence evolved the complexity of the VFTMs of class C GPCRs.
According to Darwinian theory, complexity derived by a stepwise process of elaboration and optimization under natural selection [25, 26]. The VFTMs of the class C GPCRs provides an illustration of this theory. Our results indicate that the functions of VFTMs were generated by molecular exploitation, which recruit of older molecules (PBPs), previously constrained for a different role, into a new functional complex (class C GPCRs). The complexity in the class C GPCR VFTMs consequently arose the biological complexity by a stepwise Darwinian process. In addition, further evidence indicate that positive Darwinian selection played an indispensable role in the origin and evolution of the genes involved in brain development and perception . Our results provide an important insight on how the role of positive selection has a strong effect on the development on the evolutionary process of the class C GPCR VFTM domains.
Finally, the functional divergence rates shift for the class C GPCR HD domains show a similar pattern of stepwise change in amino acid replacement resulting in changes in G-protein coupling ability and different ligand recognition. Despite the lack of experimental evidence for the origination of the HD domains, we observed a group of sites that was highly conserved in class C GPCRs, implying their common origination of seven transmembrane domains. According to the evolutionary pattern of class C GPCRs and the related duplicate events, we propose that an ancestor composed of the bacterial PBP-like domain and a rhodopsin-like transmembrane domain acted as the precursor for the class C GPCRs. The GABABR group arose through a series of point mutations after the first duplicate event. Consequently, two possibilities can result from the selective constraint differences for gene duplication: one being that it may become more conserved in groups such as mGluR, CaSR, T1R or GB1, which induced new functions; the other one may become more variable in groups such as GB2, which resulted in functional relaxation or loss of functions.
Our results demonstrate that type I functional divergence involved positive selection is partially responsible for driving the evolution of class C GPCRs. Moreover, three internal duplication events had occurred within the class C GPCR VFTMs at the early stage of vertebrates, resulting in the present class C GPCRs. The sites involved in functional divergence may provide extra clues and widen our search for more candidates for further research on the relationship between structures and function, as well as shedding light on the activation mechanism of class C GPCRs.
The sequences investigated in this study were obtained from GenBank http://www.ncbi.nlm.nih.gov and Swiss-Prot http://www.expasy.org non-redundant databases by manually using gapped BLAST and PSI-BLAST search tools [28, 29]. The protein tertiary structures were collected from the Protein Data Bank http://www.rcsb.org/pdb by using accession number searches and the family pattern data were retrieved from HOVERGEN http://pbil.univ-lyon1.fr/databases/hovergen.php and Pfam database http://pfam.sanger.ac.uk. After removing partial and redundant sequences, the final dataset produced 70 complete sequences involving 24 species that included Drosophila melanogaster, Caenorhabditis elegans and Dictyostelium discoideum.
Multiple alignment and phylogenetic reconstruction
Multiple alignments were conducted by ClustalW program  with default parameters, followed by manual editing using BioEdit . The phylogenetic trees were produced by the neighbor-joining (NJ) method  with the Jones-Taylor-Thornton (JTT) probability model and gamma-distributed (a = 1.0) rates among the sites were inferred by using MEGA4 software . Bootstrap with 1000 repetitions was carried out to assess the confidence degree of nodes in the phylogenetic trees. The maximum likelihood (ML) method was used for the phylogenetic reconstruction to validate the tree topology. By using Proml program in PHYLIP package  with hidden Markov model (HMM) rates, gamma-distributed rates approximated by 5 rate categories, with coefficient of variation of rates = 1.0. For estimation of divergence time, a linearized NJ tree was used to convert the average distance of protein sequences to the molecular time scale under the global clock model [24, 35, 36]. In this study, a GABAB-like receptor from Dictyostelium discoideum (slime mold), GrlJ, was used as the root (1085 million years ago, Mya) to calibrate the time scale [37, 38].
Analysis of type I functional divergence
Type I functional divergence analysis was carried out as previously described [39–42] by DIVERGE software . Coefficients of functional divergence (θ), an indicator for the level of type I functional divergence among two homologous gene clusters, were calculated by DIVERGE with null hypothesis θ = 0. The sites (k) with critical contribution to overall functional divergence were predicted according to their posterior probabilities (Q k ), an indicator for the level of functional constraints . The sites with Q k > 0.67 were only meaningful for type I functional divergence in the present study. A matrix of type I functional distance (d F ), defined as d F = -ln(1 - θ), was created by using all θ values of all pairwise clusters. As the independence assumption, d F (A, B) = b F (A) + b F (B), the functional branch length of a given cluster, b F , was estimated by non-negative least-square method implemented by MATLAB software.
Homologous molecular modelling
The homologous models of the VFTMs of class C GPCRs were generated using x-ray crystal structures of rat mGluR1 [PDB: 1EWK] and two PBPs from Escherichia coli [PDB: 2LIV and 2LBP] as templates. Models were manually refined with ViTO  using the sequence alignment of the rat mGluR1 VFTM. Final models were built using Modeler9v3  and evaluated using dynamic evolutionary trace as implemented in ViTO. The figures were prepared using UCSF Chimera software .
Test of positive selection on the VFTMs
In order to estimate positive selection of the VFTMs, three models, M0 (one ratio), M1 (near neutral) and M2 (positive selection), were conducted by the CODEML program implemented in PAML4b package  based on the codon of VFTMs-coding sequences. The nonsynonymous/synonymous substitution rate ratio (ω = d N /d S ) indicating the difference of selective constraints was also calculated. Assuming that the synonymous substitution is virtually neutral, ω > 1 indicates positive selection, ω < 1 indicates negative selection, and ω ≈ 1 indicates neutral evolution . If the alternative model indicates that an estimated ω > 1 and the likelihood ratio test (LRT) statistic, 2Δℓ = 2(ℓ1-ℓ0), is greater than the corresponding critical values of the χ2 distribution, then positive selection can be inferred .
Reconstruction of ancestral sequences
The ancestral amino acid sequences were inferred by distance-based Bayesian method implemented by the Ancestor program . The alignment of present-day sequences and the NJ tree topologies were used to estimate each ancestral node based on the branch length and the JTT model of amino acid substitution. The result was evaluated by the average accuracy.
We would like to thank Dr Barry Wong (Wuhan University, P. R. China) for advice and reading of the manuscript. This work was supported by Program of Introducing Talents of Discipline to Universities Grant (B08029), National Basic Research Program of China (Grant 2007CB914202), National Natural Science Foundation of China (Grants 30530820 and 30470368) and Hi-Tech Research and Development Program of China (863 project) (Grant 2006AA02Z326 and 2007AA02Z322). All of the above grants were awarded to JFL.
- Bockaert J, Pin JP: Molecular tinkering of G protein-coupled receptors: an evolutionary success. EMBO J. 1999, 18: 1723-1729. 10.1093/emboj/18.7.1723.PubMed CentralView ArticlePubMedGoogle Scholar
- Pin J-P, Galvez T, Prezeau L: Evolution, structure, and activation mechanism of family 3/C G-protein-coupled receptors. Pharmacology & Therapeutics. 2003, 98: 325-354. 10.1016/S0163-7258(03)00038-X.View ArticleGoogle Scholar
- Fredriksson R, Lagerstrom MC, Lundin LG, Schioth HB: The G-protein-coupled receptors in the human genome form five main families. Phylogenetic analysis, paralogon groups, and fingerprints. Molecular pharmacology. 2003, 63: 1256-1272. 10.1124/mol.63.6.1256.View ArticlePubMedGoogle Scholar
- Kolakowski LF: GCRDb: a G-protein-coupled receptor database. Receptors Channels. 1994, 2: 1-7.PubMedGoogle Scholar
- Bowery NG: GABAB receptor: a site of therapeutic benefit. Curr Opin Pharmacol. 2006, 6: 37-43. 10.1016/j.coph.2005.10.002.View ArticlePubMedGoogle Scholar
- Hammerland LG, Krapcho KJ, Garrett JE, Alasti N, Hung BC, Simin RT, Levinthal C, Nemeth EF, Fuller FH: Domains determining ligand specificity for Ca2+ receptors. Molecular pharmacology. 1999, 55: 642-648.PubMedGoogle Scholar
- O'Hara PJ, Sheppard PO, Thogersen H, Venezia D, Haldeman BA, McGrane V, Houamed KM, Thomsen C, Gilbert TL, Mulvihill ER: The ligand-binding domain in metabotropic glutamate receptors is related to bacterial periplasmic binding proteins. Neuron. 1993, 11: 41-52. 10.1016/0896-6273(93)90269-W.View ArticlePubMedGoogle Scholar
- Okamoto T, Sekiyama N, Otsu M, Shimada Y, Sato A, Nakanishi S, Jingami H: Expression and purification of the extracellular ligand binding region of metabotropic glutamate receptor subtype 1. J Biol Chem. 1998, 273: 13089-13096. 10.1074/jbc.273.21.13089.View ArticlePubMedGoogle Scholar
- Parmentier ML, Joly C, Restituito S, Bockaert J, Grau Y, Pin JP: The G protein-coupling profile of metabotropic glutamate receptors, as determined with exogenous G proteins, is independent of their ligand recognition domain. Molecular pharmacology. 1998, 53: 778-786.PubMedGoogle Scholar
- Pin JP, Joly C, Heinemann SF, Bockaert J: Domains involved in the specificity of G protein activation in phospholipase C-coupled metabotropic glutamate receptors. Embo J. 1994, 13: 342-348.PubMed CentralPubMedGoogle Scholar
- Takahashi K, Tsuchida K, Tanabe Y, Masu M, Nakanishi S: Role of the large extracellular domain of metabotropic glutamate receptors in agonist selectivity determination. J Biol Chem. 1993, 268: 19341-19345.PubMedGoogle Scholar
- Galvez T, Parmentier ML, Joly C, Malitschek B, Kaupmann K, Kuhn R, Bittiger H, Froestl W, Bettler B, Pin JP: Mutagenesis and modeling of the GABAB receptor extracellular domain support a venus flytrap mechanism for ligand binding. J Biol Chem. 1999, 274: 13362-13369. 10.1074/jbc.274.19.13362.View ArticlePubMedGoogle Scholar
- Han G, Hampson DR: Ligand binding to the amino-terminal domain of the mGluR4 subtype of metabotropic glutamate receptor. J Biol Chem. 1999, 274: 10008-10013. 10.1074/jbc.274.15.10008.View ArticlePubMedGoogle Scholar
- Felder CB, Graul RC, Lee AY, Merkle HP, Sadee W: The Venus flytrap of periplasmic binding proteins: an ancient protein module present in multiple drug receptors. AAPS PharmSci. 1999, 1: E2-10.1208/ps010202.View ArticlePubMedGoogle Scholar
- Liu M, Sun T, Hu J, Chen W, Wang C: Study on the mechanism of the BtuF periplasmic-binding protein for vitamin B12. Biophys Chem. 2008, 135: 19-24. 10.1016/j.bpc.2008.02.017.View ArticlePubMedGoogle Scholar
- Cuneo MJ, Changela A, Miklos AE, Beese LS, Krueger JK, Hellinga HW: Structural analysis of a periplasmic binding protein in the tripartite ATP-independent transporter family reveals a tetrameric assembly that may have a role in ligand transport. J Biol Chem. 2008, 283: 32812-32820. 10.1074/jbc.M803595200.PubMed CentralView ArticlePubMedGoogle Scholar
- Kuner R, Kohr G, Grunewald S, Eisenhardt G, Bach A, Kornau HC: Role of heteromer formation in GABAB receptor function. Science. 1999, 283: 74-77. 10.1126/science.283.5398.74.View ArticlePubMedGoogle Scholar
- White JH, Wise A, Main MJ, Green A, Fraser NJ, Disney GH, Barnes AA, Emson P, Foord SM, Marshall FH: Heterodimerization is required for the formation of a functional GABA(B) receptor. Nature. 1998, 396: 679-682. 10.1038/25354.View ArticlePubMedGoogle Scholar
- Kaupmann K, Malitschek B, Schuler V, Heid J, Froestl W, Beck P, Mosbacher J, Bischoff S, Kulik A, Shigemoto R, et al: GABA(B)-receptor subtypes assemble into functional heteromeric complexes. Nature. 1998, 396: 683-687. 10.1038/25360.View ArticlePubMedGoogle Scholar
- Jones KA, Borowsky B, Tamm JA, Craig DA, Durkin MM, Dai M, Yao WJ, Johnson M, Gunwaldsen C, Huang LY, et al: GABA(B) receptors function as a heteromeric assembly of the subunits GABA(B)R1 and GABA(B)R2. Nature. 1998, 396: 674-679. 10.1038/25348.View ArticlePubMedGoogle Scholar
- Pin JP, Kniazeff J, Goudet C, Bessis AS, Liu J, Galvez T, Acher F, Rondard P, Prezeau L: The activation mechanism of class-C G-protein coupled receptors. Biol Cell. 2004, 96: 335-342. 10.1016/j.biolcel.2004.03.005.View ArticlePubMedGoogle Scholar
- Kniazeff J, Galvez T, Labesse G, Pin JP: No ligand binding in the GB2 subunit of the GABA(B) receptor is required for activation and allosteric interaction between the subunits. J Neurosci. 2002, 22: 7352-7361.PubMedGoogle Scholar
- McLysaght A, Hokamp K, Wolfe KH: Extensive genomic duplication during early chordate evolution. Nature genetics. 2002, 31: 200-204. 10.1038/ng884.View ArticlePubMedGoogle Scholar
- Wang Y, Gu X: Evolutionary patterns of gene families generated in the early stage of vertebrates. J Mol Evol. 2000, 51: 88-96.PubMedGoogle Scholar
- Bridgham JT, Carroll SM, Thornton JW: Evolution of hormone-receptor complexity by molecular exploitation. Science. 2006, 312 (5770): 97-101. 10.1126/science.1123348.View ArticlePubMedGoogle Scholar
- Adami C: Evolution. Reducible complexity. Science. 2006, 312 (5770): 61-63. 10.1126/science.1126559.View ArticlePubMedGoogle Scholar
- Wu DD, Zhang YP: Positive Darwinian selection in human population: A review. Chinese Science Bulletin. 2008, 53: 1457-1467. 10.1007/s11434-008-0202-z.View ArticleGoogle Scholar
- Altschul SF, Madden TL, Schaffer AA, Zhang J, Zhang Z, Miller W, Lipman DJ: Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Res. 1997, 25: 3389-3402. 10.1093/nar/25.17.3389.PubMed CentralView ArticlePubMedGoogle Scholar
- Schaffer AA, Aravind L, Madden TL, Shavirin S, Spouge JL, Wolf YI, Koonin EV, Altschul SF: Improving the accuracy of PSI-BLAST protein database searches with composition-based statistics and other refinements. Nucleic Acids Res. 2001, 29: 2994-3005. 10.1093/nar/29.14.2994.PubMed CentralView ArticlePubMedGoogle Scholar
- Thompson JD, Gibson TJ, Plewniak F, Jeanmougin F, Higgins DG: The CLUSTAL_X windows interface: flexible strategies for multiple sequence alignment aided by quality analysis tools. Nucleic Acids Res. 1997, 25: 4876-4882. 10.1093/nar/25.24.4876.PubMed CentralView ArticlePubMedGoogle Scholar
- Hall TA: BioEdit: a user-friendly biological sequence alignment editor and analysis program for Windows 95/98/NT. Nucl Acids Symp Ser. 1999, 41: 95-98.Google 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
- 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
- Felsenstein J: PHYLIP (Phylogeny Inference Package) version 3.6. 2005, Department of Genome Sciences, University of Washington, Seattle: Distributed by the authorGoogle Scholar
- Gu X: Early metazoan divergence was about 830 million years ago. J Mol Evol. 1998, 47: 369-371. 10.1007/PL00013150.View ArticlePubMedGoogle Scholar
- Takezaki N, Rzhetsky A, Nei M: Phylogenetic test of the molecular clock and linearized trees. Mol Biol Evol. 1995, 12: 823-833.PubMedGoogle Scholar
- Douzery EJ, Snell EA, Bapteste E, Delsuc F, Philippe H: The timing of eukaryotic evolution: does a relaxed molecular clock reconcile proteins and fossils?. Proc Natl Acad Sci USA. 2004, 101: 15386-15391. 10.1073/pnas.0403984101.PubMed CentralView ArticlePubMedGoogle Scholar
- Prabhu Y, Muller R, Anjard C, Noegel AA: GrlJ, a Dictyostelium GABAB-like receptor with roles in post-aggregation development. BMC Dev Biol. 2007, 7: 44-10.1186/1471-213X-7-44.PubMed CentralView ArticlePubMedGoogle Scholar
- Gu J, Wang Y, Gu X: Evolutionary analysis for functional divergence of Jak protein kinase domains and tissue-specific genes. J Mol Evol. 2002, 54: 725-733. 10.1007/s00239-001-0072-3.View ArticlePubMedGoogle Scholar
- Gu X: Statistical methods for testing functional divergence after gene duplication. Mol Biol Evol. 1999, 16: 1664-1674.View ArticlePubMedGoogle Scholar
- Zhou H, Gu J, Lamont SJ, Gu X: Evolutionary analysis for functional divergence of the toll-like receptor gene family and altered functional constraints. J Mol Evol. 2007, 65: 119-123. 10.1007/s00239-005-0008-4.View ArticlePubMedGoogle Scholar
- Zheng Y, Xu D, Gu X: Functional divergence after gene duplication and sequence-structure relationship: a case study of G-protein alpha subunits. J Exp Zoolog B Mol Dev Evol. 2007, 308: 85-96. 10.1002/jez.b.21140.View ArticleGoogle Scholar
- Gu X, Velden Vander K: DIVERGE: phylogeny-based analysis for functional-structural divergence of a protein family. Bioinformatics. 2002, 18: 500-501. 10.1093/bioinformatics/18.3.500.View ArticlePubMedGoogle Scholar
- Catherinot V, Labesse G: ViTO: tool for refinement of protein sequence-structure alignments. Bioinformatics. 2004, 20: 3694-3696. 10.1093/bioinformatics/bth429.View ArticlePubMedGoogle Scholar
- Sali A, Blundell TL: Comparative protein modelling by satisfaction of spatial restraints. J Mol Biol. 1993, 234: 779-815. 10.1006/jmbi.1993.1626.View ArticlePubMedGoogle Scholar
- Pettersen EF, Goddard TD, Huang CC, Couch GS, Greenblatt DM, Meng EC, Ferrin TE: UCSF Chimera–a visualization system for exploratory research and analysis. J Comput Chem. 2004, 25: 1605-1612. 10.1002/jcc.20084.View ArticlePubMedGoogle Scholar
- Yang Z: PAML 4: phylogenetic analysis by maximum likelihood. Mol Biol Evol. 2007, 24: 1586-1591. 10.1093/molbev/msm088.View ArticlePubMedGoogle Scholar
- Zhang ZD, Weinstock G, Gerstein M: Rapid evolution by positive Darwinian selection in T-cell antigen CD4 in primates. J Mol Evol. 2008, 66: 446-456. 10.1007/s00239-008-9097-1.View ArticlePubMedGoogle Scholar
- Zhang J, Nei M: Accuracies of ancestral amino acid sequences inferred by the parsimony, likelihood, and distance methods. J Mol Evol. 1997, 44 (Suppl 1): S139-146. 10.1007/PL00000067.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.