CAPIH: A Web interface for comparative analyses and visualization of host-HIV protein-protein interactions
© Lin et al; licensee BioMed Central Ltd. 2009
Received: 30 March 2009
Accepted: 12 August 2009
Published: 12 August 2009
The Human Immunodeficiency Virus type one (HIV-1) is the major causing pathogen of the Acquired Immune Deficiency Syndrome (AIDS). A large number of HIV-1-related studies are based on three non-human model animals: chimpanzee, rhesus macaque, and mouse. However, the differences in host-HIV-1 interactions between human and these model organisms have remained unexplored.
Here we present CAPIH (C omparative A nalysis of P rotein I nteractions for H IV-1), the first web-based interface to provide comparative information between human and the three model organisms in the context of host-HIV-1 protein interactions. CAPIH identifies genetic changes that occur in HIV-1-interacting host proteins. In a total of 1,370 orthologous protein sets, CAPIH identifies ~86,000 amino acid substitutions, ~21,000 insertions/deletions, and ~33,000 potential post-translational modifications that occur only in one of the four compared species. CAPIH also provides an interactive interface to display the host-HIV-1 protein interaction networks, the presence/absence of orthologous proteins in the model organisms in the networks, the genetic changes that occur in the protein nodes, and the functional domains and potential protein interaction hot sites that may be affected by the genetic changes. The CAPIH interface is freely accessible at http://bioinfo-dbb.nhri.org.tw/capih.
CAPIH exemplifies that large divergences exist in disease-associated proteins between human and the model animals. Since all of the newly developed medications must be tested in model animals before entering clinical trials, it is advisable that comparative analyses be performed to ensure proper translations of animal-based studies. In the case of AIDS, the host-HIV-1 protein interactions apparently have differed to a great extent among the compared species. An integrated protein network comparison among the four species will probably shed new lights on AIDS studies.
Cross-species virus infections usually raise serious threats to worldwide public health. Examples include the Acquired Immunodeficiency Syndrome (AIDS) and Avian Influenza, where viruses cross species boundaries to infect humans from simians , and birds , respectively. The interactions between virus and host proteins are essential to the completion of virus life cycle, and impact directly on the pathology of infectious diseases [3–6]. Therefore, studies of host-virus interactions are critical to understanding of the virology and development of therapeutics for viral diseases. Since host switching, host specificity, and disease severity all depend on host-virus interactions, comparative studies of host-virus interactions may help unravel the host/viral factors key to these central themes in infectious disease studies.
As one of the most deadly diseases to humans, AIDS is barely life-threatening to chimpanzees , human's closest relative in the nature. Comparative studies have provided clues to the differential susceptibility to AIDS between the two species [8–10]. However, since human and chimpanzee protein sequences are almost identical in most of the cases , the amino acid substitutions that may lead to Homo-Pan divergences in protein-protein interactions (PPIs) have remained elusive. Nevertheless, it is feasible to pinpoint species-specific post-translational modifications (PTMs), which are known to affect host-virus protein interactions [12, 13] and can be altered by minor genetic changes such as single nucleotide substitutions or small insertions/deletions (indels). PTMs are pivotal to a wide range of biological processes, including signal transduction, protein targeting, receptor specificity, and PPIs . So far, comparative tools for exploring the potential influences of species-specific PTMs on host-virus interactions have not been found.
Here we develop a web-based interactive database – CAPIH (C omparative A nalysis of P rotein I nteractions for H IV-1) – for comparative studies of genetic differences between the human proteins involved in host-HIV protein interactions and their orthologues retrieved from three mammalian species: chimpanzee (Pan troglodyte), rhesus macaque (Macaca mulatta), and mouse (Mus musculus). The three latter species are all important animal models for HIV studies [15–17]. Understanding the differences in host-virus interplay between human and the model species is the basis for correct interpretation of animal-based HIV studies. Furthermore, by comparing protein interactions between species, one can potentially identify key differences that underlie chimpanzee resistance to AIDS. To facilitate inter-species comparisons of host-HIV PPIs, four main functions are provided in CAPIH. Firstly, the interface shows the presence or absence of orthologous proteins, thus enabling users to pinpoint missing protein components in the host-HIV interaction network. Secondly, the multiple sequence alignments of orthologous proteins enable users to identify species-specific amino acid substitutions, nucleotide substitutions, and indels. This information is helpful for inferring functional changes of orthologous proteins. Thirdly, predictions of 7 types of species-only PTMs (phosphorylation, methylation, sumoylation, acetylation, sulfation, N-glycosylation, and O-glycosylation) for each HIV-interacting host protein are presented for analyses of potential PTM influences on protein interactions and signal/regulatory pathway. We also collect experimentally verified PTMs in human proteins. Fourthly, CAPIH shows potential PPI hot sites on the multiple sequence alignments. Through the visualized interface, researchers can easily spot multiple host factors that directly or indirectly interact with the same HIV protein, and consider how changes in one member protein may affect the protein interaction network.
Construction and content
CAPIH organization and implementation
The PTM prediction tools used in the study.
Since the HIV-human protein interactions encompassed a wide variety of interaction types, we classified these interactions into 7 major groups based on 65 key phrases from the HIV-1, Human Protein Interaction Database: (1) physical interaction; (2) regulatory interaction; (3) post-translational modification; (4) transportation; and (5) positive interaction (6) negative interaction (7) others. The classification of interaction key phrases can be found online at http://bioinfo-dbb.nhri.org.tw/capih/keyphrases.php?search_target=keyphrases. Note that these key phrases are retrieved from different publications. Consequently, a "biological interaction" may be represented by more than one key phrases. For instance, protein A may "bind" and "inhibit" protein B. In addition, to extend the depth of the visualized network, we also incorporated interactions between human proteins downloaded from the BIND  and HPRD databases .
Species-specific genetic changes identified by CAPIH
The numbers and distributions of species-specific substitutions and indels
The numbers of species-specific PTMs.
Description of the CAPIH Web interface
The interface also provides an alignment viewer using JalView  (The "Multiple Sequence Alignments" section; Figure 3B). JalView helps to show the alignments of orthologous protein, CDS, and UTR sequences, InterPro domains, potential protein interaction hot sites, and species-specific substitutions, indels, and PTMs. All of these features are color-shaded, and can be shown or hidden by changing the check list in the accompanying "Feature Settings" box (Figure 3B). The user can view detailed information of the predicted protein domains and species-specific genetic changes by pointing the cursor to the color-shaded boxes. Note that the features may overlap with each other. Therefore, some features may not be seen unless the overlapping features are hidden. The users are advised to take advantage of the Feature Settings box to obtain a clear view of the sequence alignment. A detailed description of JalView can be found at the JalView website http://www.jalview.org.
CAPIH also provides a JAVA-based adjustable protein interaction viewer (The "Protein Interactions" section; Figure 4). The interaction view gives the user an idea of how HIV-1 proteins interact with the proteins of interest. To extend the scope of interactions, we also include human protein interactions downloaded from the BIND and HPRD databases [30, 31], in addition to HIV-1-human protein interactions. The BIND and HPRD interactions are shown in dashed lines, whereas the HIV-1-human protein interactions in solid lines with colored triangles representing different interaction types. HIV-1 proteins, human proteins, and the protein of interest are shown in blue, green, and white circles, respectively. In this interaction graph, user adjustment is allowed. Any one of the circles can be selected by a mouse click. The selected protein then turns red and can be dragged along with the cursor. Clicking on the blank region will release it. The graph can also be dragged along by clicking and holding the left mouse click, or be zoomed in/out by using the right click in the same way. CAPIH also provides protein IDs and detailed descriptions of interactions when the users click on the corresponding part of the graph. The protein IDs and reference PubMed IDs are hyperlinked to the corresponding databases for more detailed information. An online help file can be found at http://bioinfo-dbb.nhri.org.tw/capih/help.php?search_target=help. The identified species-genetic changes are downloadable at http://bioinfo-dbb.nhri.org.tw/capih/download_table.php?search_target=download.
It has been suggested that changes in T cell surface glycans may be associated with Homo-Pan differences in CD4+ T cell-mediated immune responses against HIV infection . It is therefore of interest to investigate the differences in glycosylation between human and the other model organisms. From CAPIH, we have identified 322 and 282 human- and chimpanzee-only glycosylation events, respectively (Table 3). Many of these proteins are T cell surface antigens. For example, CAPIH shows two experimentally verified N-glycosylation sites in the CD3G molecule (NP_000064) at positions 52 and 92. However, at position 52 the glycosylation site (Asn) was substituted by Thr in mouse, whereas the one at position 92 becomes Asp and Glu in rhesus macaque and mouse, respectively. Therefore, human has one and two more N-glycosylation sites, separately, when compared with rhesus macaque and mouse. These glycosylation sites are interesting targets for experimental verification and subsequent functional analyses. If the glycosylation events are proven important for changes in immune responses, researchers can further examine CD3G-related PPIs to explore the underlying molecular mechanisms.
The genetic changes between human and chimpanzee in the cytidine deminase domains of four APOBEC proteins.
A. a substitution
Notably, the mutations in the cytidine deaminase domain are considered responsible for the host-retrovirus PPIs and the host-range specificities of retroviruses [35–37]. It is evident that the APOBEC3 members have experienced very different evolutionary paths in this domain. As shown in Table 4, APOBEC3B and 3C have obviously diverged more than 3A and 3F both in terms of the number of amino acid substitutions and the number of potential PTM changes. It is therefore speculated that APOBEC 3B/3C may have played an important role in the divergence of hominoid immune responses against retroviruses. Nevertheless, the changes in 3A and 3F, though not as drastic, can also have functional effects. Functional studies are required to unravel the biological implications of these changes. Also noteworthy is that no indels are found in the cytidine deaminase domain in all of the four proteins, suggesting strong negative selection on indels in spite of the increased substitution rate in this domain.
The interaction between human Vpr binding protein (VPRBP) with the HIV-1 Vpr accessory protein is known to be critical for HIV-1 infection (. Inspection of the multiple amino acid sequence alignment of VPRBP reveals that the mouse sequence is shorter than those of the hominoids by nearly 100 amino acid residues at the C-terminus. The C-terminal half of VPRBP has a proline-rich domain and a number of Phe-x-x-Phe repeats, which serves as the Vpr binding domain . Consequently, it is speculated that the loss of the C-terminal amino acids in mouse VPRBP may have certain effects on the Vpr-VPRBP binding affinity. This difference should be experimentally verified, and if proven true, should be taken into account in mouse-based HIV-1 studies.
Here we present the first web-based interactive tool for comparative studies of host-HIV interactions in four different model animals. The interface may provide new insights into HIV studies. Firstly, although mouse is an excellent model for HIV studies, considering the large genetic divergences that occur in protein domains between human and mouse as shown here, many of the host-HIV protein interactions are expected to differ between the two species. Therefore, the differences in genetic backgrounds must be controlled for appropriate interpretations of mouse-based HIV studies.
Secondly, human viral infections transmitted from other species have become a critical issue because humans usually lack the immunological arsenal to fight such viruses [2, 40–42]. Comparative studies of host-virus interactions provide a path to understand the possible mechanisms of how viruses break species boundary into humans, and why they cause pathological conditions in humans but rarely do so in other animals. Differences in PPIs may harbor part of the answers. Since PTMs are critical to PPIs, they should be taken into consideration when analyzing the effects of different PPIs on host pathology. Meanwhile, PTM by itself is actually critical to host-virus interactions. Glycosylation, for example, is widely known to be critical to viral recognition and entrance into target cells. Given the wide spectrum of biological functions in which PTMs are involved, variations in host protein PTM patterns should have major impacts on immune response and virus life cycle.
Thirdly, one surprising finding here is that PTMs actually differ to a great extent among the four compared species, considering that they are genetically close to one another. For example, human and chimpanzee differ from each other by an average of two amino acids per protein . In comparison, in the 1,370 proteins compared, human and chimpanzee each has more than 600 species-specific substitution-related phosphorylation sites (Table 3). In other words, on average, each HIV-interacting protein in both human and chimpanzee has an average of 0.4 species-specific phosphorlation sites. This example illustrates the importance of "PTMome". Glycome, the collective sum of all glycans and part of the PTMome (if glycolipids are not considered), is known to be remarkably larger than proteome [43, 44]. Therefore, it is easily understandable that PTMome is actually much larger than proteome. The large numbers of species-specific PTMs in HIV-interacting proteins illustrate the great potential of PTM studies in virology and AIDS studies.
The CAPIH interface is unique because it is the first web-based tool to provide comparative information of genetic changes and PTMs in host-pathogen interactions. Since cross-species viral infections have become a critical issue in public health, comparative studies of host-pathogen interactions deserve wide attention. Specifically, comparative analyses of host-HIV interactions may shed some light on the mechanisms of differences in AIDS progression between human and chimpanzee. A number of possible mechanisms have been proposed [8, 45]. However, none of them provides a systematic view in the context of host-HIV protein interactions. Furthermore, PTMs, perhaps one of the most important regulatory mechanisms of host-pathogen protein interactions, have been rarely studied in a comparative way. This interface may provide clues to the potential roles of PTMs in HIV infections, and serve as a starting point for studies on host-HIV protein interaction networks in different hosts.
Availability and requirements
The CAPIH database is available at http://bioinfo-dbb.nhri.org.tw/hivppi/. The JAVA Runtime Environment is required to view the interactive protein networks.
acquired immunodeficiency syndrome
human immunodeficiency virus
FCC is supported by by National Health Research Institutes (NHRI) intramural funding and the National Science Council, Taiwan (under contract NSC 97-3112-B-400-015 and NSC 98-2311-B-400-002-MY3). TJC is supported by NHRI extramural funding (NHRI-EX96-9408PC) and the NSC (under contract NSC 96-2628-B-001-005-MY3 and NSC 97-2627-P-001-002. We thank three anonymous reviewers for constructive comments. We also thank Mr. Huan-Yu Lin, Mr. Meng-Bing Wong, and Mr. Ming-Hsin Tsai for programming assistance.
- Heeney JL, Dalgleish AG, Weiss RA: Origins of HIV and the evolution of resistance to AIDS. Science. 2006, 313 (5786): 462-466. 10.1126/science.1123016.PubMedView ArticleGoogle Scholar
- Lewis DB: Avian flu to human influenza. Annu Rev Med. 2006, 57: 139-154. 10.1146/annurev.med.57.121304.131333.PubMedView ArticleGoogle Scholar
- Scalzo AA, Corbett AJ, Rawlinson WD, Scott GM, Degli-Esposti MA: The interplay between host and viral factors in shaping the outcome of cytomegalovirus infection. Immunol Cell Biol. 2007, 85 (1): 46-54. 10.1038/sj.icb.7100013.PubMedView ArticleGoogle Scholar
- Visvanathan K, Lewin SR: Immunopathogenesis: role of innate and adaptive immune responses. Semin Liver Dis. 2006, 26 (2): 104-115. 10.1055/s-2006-939755.PubMedView ArticleGoogle Scholar
- Power C, Gill MJ, Johnson RT: Progress in clinical neurosciences: The neuropathogenesis of HIV infection: host-virus interaction and the impact of therapy. Can J Neurol Sci. 2002, 29 (1): 19-32.PubMedView ArticleGoogle Scholar
- Brinton MA: Host factors involved in West Nile virus replication. Ann N Y Acad Sci. 2001, 951: 207-219.PubMedView ArticleGoogle Scholar
- Varki A, Altheide TK: Comparing the human and chimpanzee genomes: searching for needles in a haystack. Genome Res. 2005, 15 (12): 1746-1758. 10.1101/gr.3737405.PubMedView ArticleGoogle Scholar
- Gonzalez E, Kulkarni H, Bolivar H, Mangano A, Sanchez R, Catano G, Nibbs RJ, Freedman BI, Quinones MP, Bamshad MJ, et al: The influence of CCL3L1 gene-containing segmental duplications on HIV-1/AIDS susceptibility. Science. 2005, 307 (5714): 1434-1440. 10.1126/science.1101160.PubMedView ArticleGoogle Scholar
- Wooding S, Stone AC, Dunn DM, Mummidi S, Jorde LB, Weiss RK, Ahuja S, Bamshad MJ: Contrasting effects of natural selection on human and chimpanzee CC chemokine receptor 5. Am J Hum Genet. 2005, 76 (2): 291-301. 10.1086/427927.PubMed CentralPubMedView ArticleGoogle Scholar
- Nguyen DH, Hurtado-Ziola N, Gagneux P, Varki A: Loss of Siglec expression on T lymphocytes during human evolution. Proceedings of the National Academy of Sciences of the United States of America. 2006, 103 (20): 7765-7770. 10.1073/pnas.0510484103.PubMed CentralPubMedView ArticleGoogle Scholar
- Chimpanzee Sequencing and Analysis Consortium.: Initial sequence of the chimpanzee genome and comparison with the human genome. Nature. 2005, 437 (7055): 69-87. 10.1038/nature04072.View ArticleGoogle Scholar
- Gao G, Luo H: The ubiquitin-proteasome pathway in viral infections. Can J Physiol Pharmacol. 2006, 84 (1): 5-14. 10.1139/y05-144.PubMedView ArticleGoogle Scholar
- Huang WH, Chen CW, Wu HL, Chen PJ: Post-translational modification of delta antigen of hepatitis D virus. Curr Top Microbiol Immunol. 2006, 307: 91-112. full_text.PubMedGoogle Scholar
- Seet BT, Dikic I, Zhou MM, Pawson T: Reading protein modifications with interaction domains. Nat Rev Mol Cell Biol. 2006, 7 (7): 473-483. 10.1038/nrm1960.PubMedView ArticleGoogle Scholar
- Braun SE, Johnson RP: Setting the stage for bench-to-bedside movement of anti-HIV RNA inhibitors-gene therapy for AIDS in macaques. Front Biosci. 2006, 11: 838-851. 10.2741/1841.PubMedView ArticleGoogle Scholar
- Zink MC, Laast VA, Helke KL, Brice AK, Barber SA, Clements JE, Mankowski JL: From mice to macaques – animal models of HIV nervous system disease. Curr HIV Res. 2006, 4 (3): 293-305. 10.2174/157016206777709410.PubMedView ArticleGoogle Scholar
- McCune JM: AIDS RESEARCH: Animal Models of HIV-1 Disease. Science. 1997, 278 (5346): 2141-2142. 10.1126/science.278.5346.2141.PubMedView ArticleGoogle Scholar
- Fu W, Sanders-Beer BE, Katz KS, Maglott DR, Pruitt KD, Ptak RG: Human immunodeficiency virus type 1, human protein interaction database at NCBI. Nucleic Acids Res. 2009, D417-422. 10.1093/nar/gkn708. 37 Database
- Flicek P, Aken BL, Beal K, Ballester B, Caccamo M, Chen Y, Clarke L, Coates G, Cunningham F, Cutts T, et al: Ensembl 2008. Nucleic acids research. 2008, D707-714. 36 Database
- 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.PubMed CentralPubMedView ArticleGoogle Scholar
- Chen FC, Chen CJ, Li WH, Chuang TJ: Human-specific insertions and deletions inferred from mammalian genome sequences. Genome Res. 2007, 17 (1): 16-22. 10.1101/gr.5429606.PubMed CentralPubMedView ArticleGoogle Scholar
- Chen FC, Chen CJ, Chuang TJ: INDELSCAN: a web server for comparative identification of species-specific and non-species-specific insertion/deletion events. Nucleic Acids Res. 2007, W633-638. 10.1093/nar/gkm350. 35 Web Server
- Chen H, Xue Y, Huang N, Yao X, Sun Z: MeMo: a web tool for prediction of protein methylation modifications. Nucleic Acids Res. 2006, W249-253. 10.1093/nar/gkl233. 34 Web Server
- Wong YH, Lee TY, Liang HK, Huang CM, Wang TY, Yang YH, Chu CH, Huang HD, Ko MT, Hwang JK: KinasePhos 2.0: a web server for identifying protein kinase-specific phosphorylation sites based on sequences and coupling patterns. Nucleic Acids Res. 2007, W588-594. 10.1093/nar/gkm322. 35 Web Server
- Xue Y, Zhou F, Fu C, Xu Y, Yao X: SUMOsp: a web server for sumoylation site prediction. Nucleic Acids Res. 2006, W254-257. 10.1093/nar/gkl207. 34 Web Server
- Lee TY, Huang HD, Hung JH, Huang HY, Yang YS, Wang TH: dbPTM: an information repository of protein post-translational modification. Nucleic Acids Res. 2006, D622-627. 10.1093/nar/gkj083. 34 Database
- Kiemer L, Bendtsen JD, Blom N: NetAcet: prediction of N-terminal acetylation sites. Bioinformatics (Oxford, England). 2005, 21 (7): 1269-1270. 10.1093/bioinformatics/bti130.View ArticleGoogle Scholar
- UCSC genome browser. [http://hgdownload.cse.ucsc.edu/downloads.html]
- Chen YC, Lo YS, Hsu WC, Yang JM: 3D-partner: a web server to infer interacting partners and binding models. Nucleic Acids Res. 2007, W561-567. 10.1093/nar/gkm346. 35 Web Server
- Bader GD, Betel D, Hogue CW: BIND: the Biomolecular Interaction Network Database. Nucleic Acids Res. 2003, 31 (1): 248-250. 10.1093/nar/gkg056.PubMed CentralPubMedView ArticleGoogle Scholar
- Peri S, Navarro JD, Kristiansen TZ, Amanchy R, Surendranath V, Muthusamy B, Gandhi TK, Chandrika KN, Deshpande N, Suresh S, et al: Human protein reference database as a discovery resource for proteomics. Nucleic Acids Res. 2004, D497-501. 10.1093/nar/gkh070. 32 Database
- Clamp M, Cuff J, Searle SM, Barton GJ: The Jalview Java alignment editor. Bioinformatics. 2004, 20 (3): 426-427. 10.1093/bioinformatics/btg430.PubMedView ArticleGoogle Scholar
- Sawyer SL, Emerman M, Malik HS: Ancient adaptive evolution of the primate antiviral DNA-editing enzyme APOBEC3G. PLoS biology. 2004, 2 (9): E275-10.1371/journal.pbio.0020275.PubMed CentralPubMedView ArticleGoogle Scholar
- Zhang J, Webb DM: Rapid evolution of primate antiviral enzyme APOBEC3G. Human molecular genetics. 2004, 13 (16): 1785-1791. 10.1093/hmg/ddh183.PubMedView ArticleGoogle Scholar
- Bogerd HP, Doehle BP, Wiegand HL, Cullen BR: A single amino acid difference in the host APOBEC3G protein controls the primate species specificity of HIV type 1 virion infectivity factor. Proc Natl Acad Sci USA. 2004, 101 (11): 3770-3774. 10.1073/pnas.0307713101.PubMed CentralPubMedView ArticleGoogle Scholar
- Schrofelbauer B, Chen D, Landau NR: A single amino acid of APOBEC3G controls its species-specific interaction with virion infectivity factor (Vif). Proc Natl Acad Sci USA. 2004, 101 (11): 3927-3932. 10.1073/pnas.0307132101.PubMed CentralPubMedView ArticleGoogle Scholar
- Takeuchi H, Matano T: Host factors involved in resistance to retroviral infection. Microbiology and immunology. 2008, 52 (6): 318-325. 10.1111/j.1348-0421.2008.00040.x.PubMedView ArticleGoogle Scholar
- Brass AL, Dykxhoorn DM, Benita Y, Yan N, Engelman A, Xavier RJ, Lieberman J, Elledge SJ: Identification of host proteins required for HIV infection through a functional genomic screen. Science. 2008, 319 (5865): 921-926. 10.1126/science.1152725.PubMedView ArticleGoogle Scholar
- Zhang S, Feng Y, Narayan O, Zhao LJ: Cytoplasmic retention of HIV-1 regulatory protein Vpr by protein-protein interaction with a novel human cytoplasmic protein VprBP. Gene. 2001, 263 (1–2): 131-140. 10.1016/S0378-1119(00)00583-7.PubMedView ArticleGoogle Scholar
- Sims AC, Burkett SE, Yount B, Pickles RJ: SARS-CoV replication and pathogenesis in an in vitro model of the human conducting airway epithelium. Virus Res. 2008, 133 (1): 33-44. 10.1016/j.virusres.2007.03.013.PubMed CentralPubMedView ArticleGoogle Scholar
- Frieman M, Heise M, Baric R: SARS coronavirus and innate immunity. Virus Res. 2008, 133 (1): 101-112. 10.1016/j.virusres.2007.03.015.PubMed CentralPubMedView ArticleGoogle Scholar
- Peiris M: Pathogenesis of avian flu H5N1 and SARS. Novartis Found Symp. 2006, 279: 56-60. full_text. discussion 60-55, 216–219.PubMedView ArticleGoogle Scholar
- Freeze HH: Genetic defects in the human glycome. Nat Rev Genet. 2006, 7 (7): 537-551. 10.1038/nrg1894.PubMedView ArticleGoogle Scholar
- Walsh CT, Garneau-Tsodikova S, Gatto GJ: Protein posttranslational modifications: the chemistry of proteome diversifications. Angew Chem Int Ed Engl. 2005, 44 (45): 7342-7372. 10.1002/anie.200501023.PubMedView ArticleGoogle Scholar
- Kim A, Pettoello-Mantovani M, Goldstein H: Decreased susceptibility of peripheral blood mononuclear cells from individuals heterozygous for a mutant CCR5 allele to HIV infection. J Acquir Immune Defic Syndr Hum Retrovirol. 1998, 19 (2): 145-149.PubMedView ArticleGoogle Scholar