CoRSeqV3-C: a novel HIV-1 subtype C specific V3 sequence based coreceptor usage prediction algorithm

Background The majority of HIV-1 subjects worldwide are infected with HIV-1 subtype C (C-HIV). Although C-HIV predominates in developing regions of the world such as Southern Africa and Central Asia, C-HIV is also spreading rapidly in countries with more developed economies and health care systems,...

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Published inRetrovirology Vol. 10; no. 1; p. 24
Main Authors Cashin, Kieran, Gray, Lachlan R, Jakobsen, Martin R, Sterjovski, Jasminka, Churchill, Melissa J, Gorry, Paul R
Format Journal Article
LanguageEnglish
Published London BioMed Central 27.02.2013
BioMed Central Ltd
Springer Nature B.V
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Online AccessGet full text
ISSN1742-4690
1742-4690
DOI10.1186/1742-4690-10-24

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Abstract Background The majority of HIV-1 subjects worldwide are infected with HIV-1 subtype C (C-HIV). Although C-HIV predominates in developing regions of the world such as Southern Africa and Central Asia, C-HIV is also spreading rapidly in countries with more developed economies and health care systems, whose populations are more likely to have access to wider treatment options, including the CCR5 antagonist maraviroc (MVC). The ability to reliably determine C-HIV coreceptor usage is therefore becoming increasingly more important. In silico V3 sequence based coreceptor usage prediction algorithms are a relatively rapid and cost effective method for determining HIV-1 coreceptor specificity. In this study, we elucidated the V3 sequence determinants of C-HIV coreceptor usage, and used this knowledge to develop and validate a novel, user friendly, and highly sensitive C-HIV specific coreceptor usage prediction algorithm. Results We characterized every phenotypically-verified C-HIV gp120 V3 sequence available in the Los Alamos HIV Database. Sequence analyses revealed that compared to R5 C-HIV V3 sequences, CXCR4-using C-HIV V3 sequences have significantly greater amino acid variability, increased net charge, increased amino acid length, increased frequency of insertions and substitutions within the GPGQ crown motif, and reduced frequency of glycosylation sites. Based on these findings, we developed a novel C-HIV specific coreceptor usage prediction algorithm (CoRSeq V3-C ), which we show has superior sensitivity for determining CXCR4 usage by C-HIV strains compared to all other available algorithms and prediction rules, including Geno2pheno [coreceptor] and WebPSSM SINSI -C, which has been designed specifically for C-HIV. Conclusions CoRSeq V3-C is now openly available for public use at http://www.burnet.edu.au/coreceptor . Our results show that CoRSeq V3-C is the most sensitive V3 sequence based algorithm presently available for predicting CXCR4 usage of C-HIV strains, without compromising specificity. CoRSeq V3-C may be potentially useful for assisting clinicians to decide the best treatment options for patients with C-HIV infection, and will be helpful for basic studies of C-HIV pathogenesis.
AbstractList Doc number: 24 Abstract Background: The majority of HIV-1 subjects worldwide are infected with HIV-1 subtype C (C-HIV). Although C-HIV predominates in developing regions of the world such as Southern Africa and Central Asia, C-HIV is also spreading rapidly in countries with more developed economies and health care systems, whose populations are more likely to have access to wider treatment options, including the CCR5 antagonist maraviroc (MVC). The ability to reliably determine C-HIV coreceptor usage is therefore becoming increasingly more important. In silico V3 sequence based coreceptor usage prediction algorithms are a relatively rapid and cost effective method for determining HIV-1 coreceptor specificity. In this study, we elucidated the V3 sequence determinants of C-HIV coreceptor usage, and used this knowledge to develop and validate a novel, user friendly, and highly sensitive C-HIV specific coreceptor usage prediction algorithm. Results: We characterized every phenotypically-verified C-HIV gp120 V3 sequence available in the Los Alamos HIV Database. Sequence analyses revealed that compared to R5 C-HIV V3 sequences, CXCR4-using C-HIV V3 sequences have significantly greater amino acid variability, increased net charge, increased amino acid length, increased frequency of insertions and substitutions within the GPGQ crown motif, and reduced frequency of glycosylation sites. Based on these findings, we developed a novel C-HIV specific coreceptor usage prediction algorithm (CoRSeqV3-C ), which we show has superior sensitivity for determining CXCR4 usage by C-HIV strains compared to all other available algorithms and prediction rules, including Geno2pheno[coreceptor] and WebPSSMSINSI -C, which has been designed specifically for C-HIV. Conclusions: CoRSeqV3-C is now openly available for public use at http://www.burnet.edu.au/coreceptor . Our results show that CoRSeqV3-C is the most sensitive V3 sequence based algorithm presently available for predicting CXCR4 usage of C-HIV strains, without compromising specificity. CoRSeqV3-C may be potentially useful for assisting clinicians to decide the best treatment options for patients with C-HIV infection, and will be helpful for basic studies of C-HIV pathogenesis.
Background The majority of HIV-1 subjects worldwide are infected with HIV-1 subtype C (C-HIV). Although C-HIV predominates in developing regions of the world such as Southern Africa and Central Asia, C-HIV is also spreading rapidly in countries with more developed economies and health care systems, whose populations are more likely to have access to wider treatment options, including the CCR5 antagonist maraviroc (MVC). The ability to reliably determine C-HIV coreceptor usage is therefore becoming increasingly more important. In silico V3 sequence based coreceptor usage prediction algorithms are a relatively rapid and cost effective method for determining HIV-1 coreceptor specificity. In this study, we elucidated the V3 sequence determinants of C-HIV coreceptor usage, and used this knowledge to develop and validate a novel, user friendly, and highly sensitive C-HIV specific coreceptor usage prediction algorithm. Results We characterized every phenotypically-verified C-HIV gp120 V3 sequence available in the Los Alamos HIV Database. Sequence analyses revealed that compared to R5 C-HIV V3 sequences, CXCR4-using C-HIV V3 sequences have significantly greater amino acid variability, increased net charge, increased amino acid length, increased frequency of insertions and substitutions within the GPGQ crown motif, and reduced frequency of glycosylation sites. Based on these findings, we developed a novel C-HIV specific coreceptor usage prediction algorithm (CoRSeq.sub.V3-C), which we show has superior sensitivity for determining CXCR4 usage by C-HIV strains compared to all other available algorithms and prediction rules, including Geno2pheno.sub.[coreceptor] and WebPSSM.sub.SINSI-C, which has been designed specifically for C-HIV. Conclusions CoRSeq.sub.V3-C is now openly available for public use at Keywords: HIV-1, subtype C, gp120, V3, CCR5, CXCR4, Coreceptor, Prediction algorithm
The majority of HIV-1 subjects worldwide are infected with HIV-1 subtype C (C-HIV). Although C-HIV predominates in developing regions of the world such as Southern Africa and Central Asia, C-HIV is also spreading rapidly in countries with more developed economies and health care systems, whose populations are more likely to have access to wider treatment options, including the CCR5 antagonist maraviroc (MVC). The ability to reliably determine C-HIV coreceptor usage is therefore becoming increasingly more important. In silico V3 sequence based coreceptor usage prediction algorithms are a relatively rapid and cost effective method for determining HIV-1 coreceptor specificity. In this study, we elucidated the V3 sequence determinants of C-HIV coreceptor usage, and used this knowledge to develop and validate a novel, user friendly, and highly sensitive C-HIV specific coreceptor usage prediction algorithm.BACKGROUNDThe majority of HIV-1 subjects worldwide are infected with HIV-1 subtype C (C-HIV). Although C-HIV predominates in developing regions of the world such as Southern Africa and Central Asia, C-HIV is also spreading rapidly in countries with more developed economies and health care systems, whose populations are more likely to have access to wider treatment options, including the CCR5 antagonist maraviroc (MVC). The ability to reliably determine C-HIV coreceptor usage is therefore becoming increasingly more important. In silico V3 sequence based coreceptor usage prediction algorithms are a relatively rapid and cost effective method for determining HIV-1 coreceptor specificity. In this study, we elucidated the V3 sequence determinants of C-HIV coreceptor usage, and used this knowledge to develop and validate a novel, user friendly, and highly sensitive C-HIV specific coreceptor usage prediction algorithm.We characterized every phenotypically-verified C-HIV gp120 V3 sequence available in the Los Alamos HIV Database. Sequence analyses revealed that compared to R5 C-HIV V3 sequences, CXCR4-using C-HIV V3 sequences have significantly greater amino acid variability, increased net charge, increased amino acid length, increased frequency of insertions and substitutions within the GPGQ crown motif, and reduced frequency of glycosylation sites. Based on these findings, we developed a novel C-HIV specific coreceptor usage prediction algorithm (CoRSeqV3-C), which we show has superior sensitivity for determining CXCR4 usage by C-HIV strains compared to all other available algorithms and prediction rules, including Geno2pheno[coreceptor] and WebPSSMSINSI-C, which has been designed specifically for C-HIV.RESULTSWe characterized every phenotypically-verified C-HIV gp120 V3 sequence available in the Los Alamos HIV Database. Sequence analyses revealed that compared to R5 C-HIV V3 sequences, CXCR4-using C-HIV V3 sequences have significantly greater amino acid variability, increased net charge, increased amino acid length, increased frequency of insertions and substitutions within the GPGQ crown motif, and reduced frequency of glycosylation sites. Based on these findings, we developed a novel C-HIV specific coreceptor usage prediction algorithm (CoRSeqV3-C), which we show has superior sensitivity for determining CXCR4 usage by C-HIV strains compared to all other available algorithms and prediction rules, including Geno2pheno[coreceptor] and WebPSSMSINSI-C, which has been designed specifically for C-HIV.CoRSeqV3-C is now openly available for public use at http://www.burnet.edu.au/coreceptor. Our results show that CoRSeqV3-C is the most sensitive V3 sequence based algorithm presently available for predicting CXCR4 usage of C-HIV strains, without compromising specificity. CoRSeqV3-C may be potentially useful for assisting clinicians to decide the best treatment options for patients with C-HIV infection, and will be helpful for basic studies of C-HIV pathogenesis.CONCLUSIONSCoRSeqV3-C is now openly available for public use at http://www.burnet.edu.au/coreceptor. Our results show that CoRSeqV3-C is the most sensitive V3 sequence based algorithm presently available for predicting CXCR4 usage of C-HIV strains, without compromising specificity. CoRSeqV3-C may be potentially useful for assisting clinicians to decide the best treatment options for patients with C-HIV infection, and will be helpful for basic studies of C-HIV pathogenesis.
The majority of HIV-1 subjects worldwide are infected with HIV-1 subtype C (C-HIV). Although C-HIV predominates in developing regions of the world such as Southern Africa and Central Asia, C-HIV is also spreading rapidly in countries with more developed economies and health care systems, whose populations are more likely to have access to wider treatment options, including the CCR5 antagonist maraviroc (MVC). The ability to reliably determine C-HIV coreceptor usage is therefore becoming increasingly more important. In silico V3 sequence based coreceptor usage prediction algorithms are a relatively rapid and cost effective method for determining HIV-1 coreceptor specificity. In this study, we elucidated the V3 sequence determinants of C-HIV coreceptor usage, and used this knowledge to develop and validate a novel, user friendly, and highly sensitive C-HIV specific coreceptor usage prediction algorithm. We characterized every phenotypically-verified C-HIV gp120 V3 sequence available in the Los Alamos HIV Database. Sequence analyses revealed that compared to R5 C-HIV V3 sequences, CXCR4-using C-HIV V3 sequences have significantly greater amino acid variability, increased net charge, increased amino acid length, increased frequency of insertions and substitutions within the GPGQ crown motif, and reduced frequency of glycosylation sites. Based on these findings, we developed a novel C-HIV specific coreceptor usage prediction algorithm (CoRSeqV3-C), which we show has superior sensitivity for determining CXCR4 usage by C-HIV strains compared to all other available algorithms and prediction rules, including Geno2pheno[coreceptor] and WebPSSMSINSI-C, which has been designed specifically for C-HIV. CoRSeqV3-C is now openly available for public use at http://www.burnet.edu.au/coreceptor. Our results show that CoRSeqV3-C is the most sensitive V3 sequence based algorithm presently available for predicting CXCR4 usage of C-HIV strains, without compromising specificity. CoRSeqV3-C may be potentially useful for assisting clinicians to decide the best treatment options for patients with C-HIV infection, and will be helpful for basic studies of C-HIV pathogenesis.
Background The majority of HIV-1 subjects worldwide are infected with HIV-1 subtype C (C-HIV). Although C-HIV predominates in developing regions of the world such as Southern Africa and Central Asia, C-HIV is also spreading rapidly in countries with more developed economies and health care systems, whose populations are more likely to have access to wider treatment options, including the CCR5 antagonist maraviroc (MVC). The ability to reliably determine C-HIV coreceptor usage is therefore becoming increasingly more important. In silico V3 sequence based coreceptor usage prediction algorithms are a relatively rapid and cost effective method for determining HIV-1 coreceptor specificity. In this study, we elucidated the V3 sequence determinants of C-HIV coreceptor usage, and used this knowledge to develop and validate a novel, user friendly, and highly sensitive C-HIV specific coreceptor usage prediction algorithm. Results We characterized every phenotypically-verified C-HIV gp120 V3 sequence available in the Los Alamos HIV Database. Sequence analyses revealed that compared to R5 C-HIV V3 sequences, CXCR4-using C-HIV V3 sequences have significantly greater amino acid variability, increased net charge, increased amino acid length, increased frequency of insertions and substitutions within the GPGQ crown motif, and reduced frequency of glycosylation sites. Based on these findings, we developed a novel C-HIV specific coreceptor usage prediction algorithm (CoRSeq V3-C ), which we show has superior sensitivity for determining CXCR4 usage by C-HIV strains compared to all other available algorithms and prediction rules, including Geno2pheno [coreceptor] and WebPSSM SINSI -C, which has been designed specifically for C-HIV. Conclusions CoRSeq V3-C is now openly available for public use at http://www.burnet.edu.au/coreceptor . Our results show that CoRSeq V3-C is the most sensitive V3 sequence based algorithm presently available for predicting CXCR4 usage of C-HIV strains, without compromising specificity. CoRSeq V3-C may be potentially useful for assisting clinicians to decide the best treatment options for patients with C-HIV infection, and will be helpful for basic studies of C-HIV pathogenesis.
The majority of HIV-1 subjects worldwide are infected with HIV-1 subtype C (C-HIV). Although C-HIV predominates in developing regions of the world such as Southern Africa and Central Asia, C-HIV is also spreading rapidly in countries with more developed economies and health care systems, whose populations are more likely to have access to wider treatment options, including the CCR5 antagonist maraviroc (MVC). The ability to reliably determine C-HIV coreceptor usage is therefore becoming increasingly more important. In silico V3 sequence based coreceptor usage prediction algorithms are a relatively rapid and cost effective method for determining HIV-1 coreceptor specificity. In this study, we elucidated the V3 sequence determinants of C-HIV coreceptor usage, and used this knowledge to develop and validate a novel, user friendly, and highly sensitive C-HIV specific coreceptor usage prediction algorithm. We characterized every phenotypically-verified C-HIV gp120 V3 sequence available in the Los Alamos HIV Database. Sequence analyses revealed that compared to R5 C-HIV V3 sequences, CXCR4-using C-HIV V3 sequences have significantly greater amino acid variability, increased net charge, increased amino acid length, increased frequency of insertions and substitutions within the GPGQ crown motif, and reduced frequency of glycosylation sites. Based on these findings, we developed a novel C-HIV specific coreceptor usage prediction algorithm (CoRSeq.sub.V3-C), which we show has superior sensitivity for determining CXCR4 usage by C-HIV strains compared to all other available algorithms and prediction rules, including Geno2pheno.sub.[coreceptor] and WebPSSM.sub.SINSI-C, which has been designed specifically for C-HIV. CoRSeq.sub.V3-C is now openly available for public use at http://www.burnet.edu.au/coreceptor. Our results show that CoRSeq.sub.V3-C is the most sensitive V3 sequence based algorithm presently available for predicting CXCR4 usage of C-HIV strains, without compromising specificity. CoRSeq.sub.V3-C may be potentially useful for assisting clinicians to decide the best treatment options for patients with C-HIV infection, and will be helpful for basic studies of C-HIV pathogenesis.
ArticleNumber 24
Audience Academic
Author Cashin, Kieran
Gray, Lachlan R
Churchill, Melissa J
Gorry, Paul R
Sterjovski, Jasminka
Jakobsen, Martin R
AuthorAffiliation 7 Present address: Department of Biomedicine, Aarhus University, Aarhus, Denmark
3 Departments of Biochemistry and Molecular Biology, Monash University, Melbourne, VIC, Australia
6 Departments of Infectious Diseases, Monash University, Melbourne, VIC, Australia
5 Departments of Medicine, Monash University, Melbourne, VIC, Australia
1 Center for Virology, Burnet Institute, 85 Commercial Rd, Melbourne 3004VIC, Australia
2 Department of Microbiology and Immunology, University of Melbourne, Parkville, VIC, Australia
4 Departments of Microbiology, Monash University, Melbourne, VIC, Australia
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BackLink https://www.ncbi.nlm.nih.gov/pubmed/23446039$$D View this record in MEDLINE/PubMed
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COPYRIGHT 2013 BioMed Central Ltd.
2013 Cashin et al; licensee 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.
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ISSN 1742-4690
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Issue 1
Keywords HIV-1
subtype C
gp120
CXCR4
Coreceptor
CCR5
V3
Prediction algorithm
Language English
License 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.
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Snippet Background The majority of HIV-1 subjects worldwide are infected with HIV-1 subtype C (C-HIV). Although C-HIV predominates in developing regions of the world...
The majority of HIV-1 subjects worldwide are infected with HIV-1 subtype C (C-HIV). Although C-HIV predominates in developing regions of the world such as...
Background The majority of HIV-1 subjects worldwide are infected with HIV-1 subtype C (C-HIV). Although C-HIV predominates in developing regions of the world...
Doc number: 24 Abstract Background: The majority of HIV-1 subjects worldwide are infected with HIV-1 subtype C (C-HIV). Although C-HIV predominates in...
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StartPage 24
SubjectTerms Algorithms
Amino acids
Analysis
Antibodies
Antiviral agents
Biomedical and Life Sciences
Biomedicine
Cancer Research
Care and treatment
Computational Biology - methods
Departments
Development and progression
Entropy
Genotype
Health aspects
HIV (Viruses)
HIV Envelope Protein gp120 - genetics
HIV infection
HIV-1 - genetics
HIV-1 - physiology
Humans
Infections
Infectious Diseases
Molecular Biology - methods
Protein Structure
Receptors, HIV - analysis
Vaccine
Viral Tropism
Virology
Virology - methods
Viruses
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Title CoRSeqV3-C: a novel HIV-1 subtype C specific V3 sequence based coreceptor usage prediction algorithm
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