ArticlePLoS computational biology2010
Prediction of co-receptor usage of HIV-1 from genotype.
Article in PLoS computational biology, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
30 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Influenza virus genotype to phenotype predictions through machine learning: a systematic review.Emerging microbes & infections · 2021Pooled it
- Interpretable molecular encodings and representations for machine learning tasks.Computational and structural biotechnology journal · 2024Article
- Encodings and models for antimicrobial peptide classification for multi-resistant pathogens.BioData mining · 2019Review
- Factors Associated with HIV Testing Among Participants from Substance Use Disorder Treatment Programs in the US: A Machine Learning Approach.AIDS and behavior · 2017Article
- SHIVA - a web application for drug resistance and tropism testing in HIV.BMC bioinformatics · 2016Article
- Genotypic Prediction of Co-receptor Tropism of HIV-1 Subtypes A and C.Scientific reports · 2016Article
- HIV coreceptor tropism determination and mutational pattern identification.Scientific reports · 2016Article
- Exploiting HIV-1 protease and reverse transcriptase cross-resistance information for improved drug resistance prediction by means of multi-label classification.BioData mining · 2016Article
- SeqFeatR for the Discovery of Feature-Sequence Associations.PloS one · 2016Article
- Highly Accurate Structure-Based Prediction of HIV-1 Coreceptor Usage Suggests Intermolecular Interactions Driving Tropism.PloS one · 2016Article
- Effect of lysine to arginine mutagenesis in the V3 loop of HIV-1 gp120 on viral entry efficiency and neutralization.PloS one · 2015Article
- Parameters Influencing Baseline HIV-1 Genotypic Tropism Testing Related to Clinical Outcome in Patients on Maraviroc.PloS one · 2015Article
- IDEPI: rapid prediction of HIV-1 antibody epitopes and other phenotypic features from sequence data using a flexible machine learning platform.PLoS computational biology · 2014Article
- Bioinformatic analysis of HIV-1 entry and pathogenesis.Current HIV research · 2014Review
- HIV-1 tropism dynamics and phylogenetic analysis from longitudinal ultra-deep sequencing data of CCR5- and CXCR4-using variants.PloS one · 2014Article
- A simple structure-based model for the prediction of HIV-1 co-receptor tropism.BioData mining · 2014Article
- Data mining in the Life Sciences with Random Forest: a walk in the park or lost in the jungle?Briefings in bioinformatics · 2013Article
- A small set of succinct signature patterns distinguishes Chinese and non-Chinese HIV-1 genomes.PloS one · 2013Article
- Hybrid approach for predicting coreceptor used by HIV-1 from its V3 loop amino acid sequence.PloS one · 2013Article
- HIV-1 tropism testing and clinical management of CCR5 antagonists: Quebec review and recommendations.The Canadian journal of infectious diseases & medical microbiology = Journal canadien des maladies infectieuses et de la microbiologie medicale · 2013Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Human Immunodeficiency Virus 1 uses for entry into host cells a receptor (CD4) and one of two co-receptors (CCR5 or CXCR4). Recently, a new class of antiretroviral drugs has entered clinical practice that specifically bind to the co-receptor CCR5, and thus inhibit virus entry. Accurate prediction of the co-receptor used by the virus in the patient is important as it allows for personalized selection of effective drugs and prognosis of disease progression. We have investigated whether it is possible to predict co-receptor usage accurately by analyzing the amino acid sequence of the main determinant of co-receptor usage, i.e., the third variable loop V3 of the gp120 protein. We developed a two-level machine learning approach that in the first level considers two different properties important for protein-protein binding derived from structural models of V3 and V3 sequences. The second level combines the two predictions of the first level. The two-level method predicts usage of CXCR4 co-receptor for new V3 sequences within seconds, with an area under the ROC curve of 0.937+/-0.004. Moreover, it is relatively robust against insertions and deletions, which frequently occur in V3. The approach could help clinicians to find optimal personalized treatments, and it offers new insights into the molecular basis of co-receptor usage. For instance, it quantifies the importance for co-receptor usage of a pocket that probably is responsible for binding sulfated tyrosine.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.