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Related Concept Videos

Predicting Products: SN1 vs. SN202:27

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Nucleophilic substitution reactions of alkyl halides can proceed via an SN1 or an SN2 mechanism. While in SN2 reactions, the nucleophile attacks the substrate simultaneously as the leaving group departs, in SN1 reactions, the substrate first dissociates to give the carbocation intermediate. Various factors such as the structure of the substrate, the strength of the nucleophile, and the nature of the solvent promote one mechanism over the other.
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Cis-regulatory sequences are short fragments of non-coding DNA that are present on the same chromosomes as the genes that they regulate. These fragments serve as binding sites for transcriptional regulators, proteins that are responsible for controlling gene transcription and differential gene expression across cell types in eukaryotes. Cis-regulatory sequences can be close to the gene of interest or thousands of bases away in the DNA sequence; however, those sequences that are further away are...
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Related Experiment Video

Updated: Jan 24, 2026

Prediction of HIV-1 Coreceptor Usage Tropism by Sequence Analysis using a Genotypic Approach
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HIVCoR: A sequence-based tool for predicting HIV-1 CRF01_AE coreceptor usage.

Sayamon Hongjaisee1, Chanin Nantasenamat2, Tanawan Samleerat Carraway3

  • 1Research Institute for Health Sciences, Chiang Mai University, Chiangmai 50200, Thailand; Faculty of Associated Medical Sciences, Chiang Mai University, Chiangmai 50200, Thailand.

Computational Biology and Chemistry
|May 31, 2019
PubMed
Summary

Predicting HIV-1 coreceptor usage is crucial for effective treatment. A new model, HIVCoR, accurately predicts usage for the CRF01_AE subtype using genetic features, outperforming existing tools.

Keywords:
CRF01_AECoreceptor usageGenotypic assaysMachine learningRandom forestSupport vector machine

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Area of Science:

  • Virology
  • Bioinformatics
  • Computational Biology

Background:

  • Accurate determination of Human Immunodeficiency Virus type 1 (HIV-1) coreceptor usage is essential for guiding treatment with coreceptor-specific inhibitors.
  • Genotypic assays offer a more feasible alternative to phenotypic assays for predicting coreceptor tropism.
  • Existing prediction models are predominantly based on HIV-1 subtypes B and C, limiting their applicability to other prevalent subtypes.

Purpose of the Study:

  • To develop and validate a robust computational model, HIVCoR, for accurate prediction of HIV-1 coreceptor usage specifically for the CRF01_AE subtype.
  • To enhance the feasibility and accessibility of genotypic tropism prediction for a globally significant HIV-1 subtype.

Main Methods:

  • HIVCoR employs machine learning algorithms, including random forest and support vector machine, for prediction.
  • Input features incorporate amino acid compositions, pseudo amino acid compositions, and relative synonymous codon usage frequencies.
  • The model was developed and validated using extensive genotypic datasets, including external validation on an objective benchmark dataset.

Main Results:

  • HIVCoR achieved a high overall success rate of 93.79% in external validation.
  • Comparative analysis demonstrated that HIVCoR significantly outperforms other existing bioinformatics tools and genotypic predictors.
  • A user-friendly webserver for HIVCoR has been established for practical use by researchers.

Conclusions:

  • HIVCoR provides a powerful and reliable tool for predicting HIV-1 coreceptor usage in the CRF01_AE subtype.
  • The model's superior performance and accessibility offer a valuable advancement for personalized HIV treatment strategies.
  • This genotypic prediction approach facilitates more informed clinical decisions, particularly in regions where CRF01_AE is prevalent.