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Pharmacokinetic Models: Comparison and Selection Criterion

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Updated: May 10, 2026

Prediction of HIV-1 Coreceptor Usage (Tropism) by Sequence Analysis using a Genotypic Approach
07:06

Prediction of HIV-1 Coreceptor Usage (Tropism) by Sequence Analysis using a Genotypic Approach

Published on: December 1, 2011

A predictive model for HIV type 1 coreceptor selectivity.

Chris A Kieslich1, David Shin, Aliana López de Victoria

  • 11 Department of Bioengineering, University of California , Riverside, California.

AIDS Research and Human Retroviruses
|July 2, 2013
PubMed
Summary
This summary is machine-generated.

A new model predicts HIV-1 coreceptor usage (CCR5/CXCR4) from V3 loop sequences. This tool aids in understanding disease stage and personalizing coreceptor-specific therapies for HIV infection.

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Last Updated: May 10, 2026

Prediction of HIV-1 Coreceptor Usage (Tropism) by Sequence Analysis using a Genotypic Approach
07:06

Prediction of HIV-1 Coreceptor Usage (Tropism) by Sequence Analysis using a Genotypic Approach

Published on: December 1, 2011

Genotypic Inference of HIV-1 Tropism Using Population-based Sequencing of V3
11:10

Genotypic Inference of HIV-1 Tropism Using Population-based Sequencing of V3

Published on: December 27, 2010

A Restriction Enzyme Based Cloning Method to Assess the In vitro Replication Capacity of HIV-1 Subtype C Gag-MJ4 Chimeric Viruses
14:23

A Restriction Enzyme Based Cloning Method to Assess the In vitro Replication Capacity of HIV-1 Subtype C Gag-MJ4 Chimeric Viruses

Published on: August 31, 2014

Area of Science:

  • Virology
  • Immunology
  • Computational Biology

Background:

  • The V3 loop of HIV-1 gp120 is crucial for coreceptor binding (CCR5/CXCR4) and cell entry.
  • CCR5 tropism is linked to early HIV infection, while CXCR4 tropism indicates disease progression.

Purpose of the Study:

  • To develop a predictive statistical model for HIV-1 coreceptor selectivity.
  • To infer disease state based on V3 loop sequence characteristics.

Main Methods:

  • Analysis of 2,054 V3 loop sequences from patient data.
  • Utilizing net charge and specific sequence markers (N(6)X(7)[T/S](8)X(9) motif, 11/24/25 rule) for prediction.

Main Results:

  • A predictive model for coreceptor selectivity was established.
  • Model performance is comparable to existing sequence-based methods.
  • The model infers disease state from V3 loop physicochemical properties.

Conclusions:

  • The model offers a supportive diagnostic tool for coreceptor selection in HIV-1.
  • Physicochemical characteristics of V3 loop sequences can predict coreceptor usage and disease stage.
  • This approach may facilitate personalized medical decisions for coreceptor-specific HIV therapies.