# Deep mutational scanning of the HIV-1 Env protein and HIV-targeted host chemokine receptors > **NIH NIH R01** · UNIVERSITY OF ILLINOIS AT URBANA-CHAMPAIGN · 2020 · $377,748 ## Abstract The HIV-1 surface glycoprotein Env engages host cell receptors, including either the CCR5 or CXCR4 chemokine receptors, to drive the necessary protein rearrangements that mediate virus entry into the cell. Therapies blocking HIV-1 Env interactions with chemokine receptors are clinically effective, underscoring their importance. This proposal uses the new technology of deep mutational scanning to comprehensively determine sequence-function relationships in CCR5, CXCR4 and Env. Deep mutational scanning combines unbiased, diverse libraries of mutations with in vitro evolution and deep sequencing, making it possible to determine the relative phenotypes of many thousands of mutations in a single experiment. From this unprecedented mutational data, a protein's sequence-fitness landscape can be experimentally mapped, from which functional sites and important residues for stabilizing discrete conformations can be inferred. The sequence-fitness landscape also reveals mutations that can be combined to engineer variants with new or enhanced properties. Deep mutational scanning has primarily been limited to proteins that are expressed in phage, bacteria or yeast, but in this proposal, libraries encompassing all single amino acid substitutions of CCR5, CXCR4 and Env expressed in human cells will be evolved. The specific aims of this proposal are Aim 1: To determine the oligomeric organization of CCR5 and CXCR4 by deep mutational scanning. When libraries of CCR5 and CXCR4 are sorted for high affinity to antibodies recognizing resting conformations, conserved residues in the sequence-fitness landscapes map to transmembrane surfaces of the receptors. We hypothesize that these conserved surfaces are dimerization sites, which will be validated using biochemical methods. Residue conservation scores from the mutational scans will guide computational modeling of the dimeric states. Aim 2: To comprehensively map the sequence-fitness landscapes of CCR5 and CXCR4 during signaling responses to agonists. A cell sorter will be adapted for continuous mixing and sorting of Ca2+- indicator stained libraries with chemokines. Critical residues for chemokine interactions, G protein coupling, and adopting an active conformation will be conserved in the sequence-fitness landscapes. Aim 3: To characterize the interaction between chemokine receptors and HIV-1 gp120-CD4 by deep mutational scanning. CCR5 and CXCR4 sequence-fitness landscapes for tight affinity to gp120-CD4 will reveal similarities and differences in how these chemokine receptors are engaged by R5 and X4 HIV-1 strains, and how maraviroc- resistant Env clones have altered CCR5 interaction footprints. Aim 4: To comprehensively determine the sequence-fitness landscape of HIV-1 Env interacting with soluble CD4 and broadly neutralizing antibodies VRC01 and PG16. These protein ligands recognize distinct Env quaternary structures, despite CD4 and VRC01 sharing a common binding site. Deep mutational scanning, covering over 17,... ## Key facts - **NIH application ID:** 9864044 - **Project number:** 5R01AI129719-04 - **Recipient organization:** UNIVERSITY OF ILLINOIS AT URBANA-CHAMPAIGN - **Principal Investigator:** Erik Procko - **Activity code:** R01 (R01, R21, SBIR, etc.) - **Funding institute:** NIH - **Fiscal year:** 2020 - **Award amount:** $377,748 - **Award type:** 5 - **Project period:** 2017-03-01 → 2021-02-28 ## Primary source NIH RePORTER: https://reporter.nih.gov/project-details/9864044 ## Citation > US National Institutes of Health, RePORTER application 9864044, Deep mutational scanning of the HIV-1 Env protein and HIV-targeted host chemokine receptors (5R01AI129719-04). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/9864044. Licensed CC0. --- *[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*