ArticleBMC biophysics2012
Clustering of HIV-1 Subtypes Based on gp120 V3 Loop electrostatic properties.
Article in BMC biophysics, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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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.
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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.
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Who cites it
11 citing papers in PubMed, 24 citations in OpenAlex.
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- Recent advances in user-friendly computational tools to engineer protein function.Briefings in bioinformatics · 2021Review
- AESOP: A Python Library for Investigating Electrostatics in Protein Interactions.Biophysical journal · 2017Article
- Comparative structural analysis of haemagglutinin proteins from type A influenza viruses: conserved and variable features.BMC bioinformatics · 2014Article
- Tenascin-C is an innate broad-spectrum, HIV-1-neutralizing protein in breast milk.Proceedings of the National Academy of Sciences of the United States of America · 2013Article
- Molecular recognition of CXCR4 by a dual tropic HIV-1 gp120 V3 loop.Biophysical journal · 2013Article
- Insights into the structure, correlated motions, and electrostatic properties of two HIV-1 gp120 V3 loops.PloS one · 2012Article
- Screening and Functional Profiling of Small-Molecule HIV-1 Entry and Fusion Inhibitors.Assay and drug development technologiesArticle
Corrections and comments
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Authors and funding
5 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe V3 loop of the glycoprotein gp120 of HIV-1 plays an important role in viral entry into cells by utilizing as coreceptor CCR5 or CXCR4, and is implicated in the phenotypic tropisms of HIV viruses. It has been hypothesized that the interaction between the V3 loop and CCR5 or CXCR4 is mediated by electrostatics. We have performed hierarchical clustering analysis of the spatial distributions of electrostatic potentials and charges of V3 loop structures containing consensus sequences of HIV-1 subtypes.
resultsAlthough the majority of consensus sequences have a net charge of +3, the spatial distribution of their electrostatic potentials and charges may be a discriminating factor for binding and infectivity. This is demonstrated by the formation of several small subclusters, within major clusters, which indicates common origin but distinct spatial details of electrostatic properties. Some of this information may be present, in a coarse manner, in clustering of sequences, but the spatial details are largely lost. We show the effect of ionic strength on clustering of electrostatic potentials, information that is not present in clustering of charges or sequences. We also make correlations between clustering of electrostatic potentials and net charge, coreceptor selectivity, global prevalence, and geographic distribution. Finally, we interpret coreceptor selectivity based on the N6X7T8|S8X9 sequence glycosylation motif, the specific positive charge location according to the 11/24/25 rule, and the overall charge and electrostatic potential distribution.
conclusionsWe propose that in addition to the sequence and the net charge of the V3 loop of each subtype, the spatial distributions of electrostatic potentials and charges may also be important factors for receptor recognition and binding and subsequent viral entry into cells. This implies that the overall electrostatic potential is responsible for long-range recognition of the V3 loop with coreceptors CCR5/CXCR4, whereas the charge distribution contributes to the specific short-range interactions responsible for the formation of the bound complex. We also propose a scheme for coreceptor selectivity based on the sequence glycosylation motif, the 11/24/25 rule, and net charge.
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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.