Structural basis for potent and broad inhibition of HIV-1 RT by thiophene[3,2-d]pyrimidine non-nucleoside inhibitors
- Yale University, United States
- Shandong University, China
- KU Leuven, Belgium
Abstract
Rapid generation of drug-resistant mutations in HIV-1 reverse transcriptase (RT), a prime target for anti-HIV therapy, poses a major impediment to effective anti-HIV treatment. Our previous efforts have led to the development of two novel non-nucleoside reverse transcriptase inhibitors (NNRTIs) with piperidine-substituted thiophene[3,2-d]pyrimidine scaffolds, compounds K-5a2 and 25a, which demonstrate highly potent anti-HIV-1 activities and improved resistance profiles compared with etravirine and rilpivirine, respectively. Here, we have determined the crystal structures of HIV-1 wild-type (WT) RT and seven RT variants bearing prevalent drug-resistant mutations in complex with K-5a2 or 25a at ~2 Å resolution. These high-resolution structures illustrate the molecular details of the extensive hydrophobic interactions and the network of main chain hydrogen bonds formed between the NNRTIs and the RT inhibitor binding pocket, and provide valuable insights into the favorable structural features that can be employed for designing NNRTIs that are broadly active against drug-resistant HIV-1 variants.
Data availability
Diffraction data and atomic coordinates have been deposited in the Protein Data Bank under the accession codes 6C0J, 6C0K, 6C0L, 6CGF, 6C0N, 6C0O, 6C0P, 6C0R, 6DUF, 6DUG, and 6DUH.
-
Crystal structure of HIV-1 reverse transcriptase in complex with nonnucleoside inhibitor K-5a2Publicly available at the RCSB Protein Data Bank (accession no. 6C0J).
-
Crystal structure of HIV-1 K103N mutant reverse transcriptase in complex with non-nucleoside inhibitor K-5a2Publicly available at the RCSB Protein Data Bank (accession no. 6C0K).
-
Crystal structure of HIV-1 E138K mutant reverse transcriptase in complex with non-nucleoside inhibitor K-5a2Publicly available at the RCSB Protein Data Bank (accession no. 6C0L).
-
Crystal structure of HIV-1 Y188L mutant reverse transcriptase in complex with non-nucleoside inhibitor K-5a2Publicly available at the RCSB Protein Data Bank (accession no. 6CGF).
-
Crystal structure of HIV-1 reverse transcriptase in complex with non-nucleoside inhibitor 25aPublicly available at the RCSB Protein Data Bank (accession no. 6C0N).
-
Crystal structure of HIV-1 K103N mutant reverse transcriptase in complex with non-nucleoside inhibitor 25aPublicly available at the RCSB Protein Data Bank (accession no. 6C0O).
-
Crystal structure of HIV-1 E138K mutant reverse transcriptase in complex with non-nucleoside inhibitor 25aPublicly available at the RCSB Protein Data Bank (accession no. 6C0P).
-
Crystal structure of HIV-1 K103N/Y181C mutant reverse transcriptase in complex with non-nucleoside inhibitor 25aPublicly available at the RCSB Protein Data Bank (accession no. 6C0R).
-
Crystal structure of HIV-1 reverse transcriptase V106A/F227L mutant in complex with non-nucleoside inhibitor 25aPublicly available at the RCSB Protein Data Bank (accession no. 6DUF).
-
Crystal structure of HIV-1 reverse transcriptase K101P mutant in complex with non-nucleoside inhibitor 25aPublicly available at the RCSB Protein Data Bank (accession no. 6DUG).
-
Crystal structure of HIV-1 reverse transcriptase Y181I mutant in complex with non-nucleoside inhibitor 25aPublicly available at the RCSB Protein Data Bank (accession no. 6DUH).
-
Crystal structure of HIV-1 reverse transcriptase (RT) in complex with Rilpivirine (TMC278, Edurant), a non-nucleoside rt-inhibiting drugPublicly available at the RCSB Protein Data Bank (accession no. 4G1Q).
-
HIV-1 Reverse Transcriptase in Complex with TMC125Publicly available at the RCSB Protein Data Bank (accession no. 3MEC).
Article and author information
Author details
Funding
Howard Hughes Medical Institute (Investigator Program)
- Thomas A Steitz
National Institute of General Medical Sciences (GM022778)
- Thomas A Steitz
National Natural Science Foundation of China (81273354)
- Xinyong Liu
Key research and development project of Shandong Province (2017CXGC1401)
- Xinyong Liu
Major Project of Science and Technology of Shandong Province (2015ZDJS04001)
- Xinyong Liu
Young Scholars Program of Shandong University (2016WLJH32)
- Peng Zhan
Key Project of National Natural Science Foundation of China for International Cooperation (81420108027)
- Xinyong Liu
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Reviewing Editor
- Axel T Brunger, Stanford University, United States
Version history
- Received: March 2, 2018
- Accepted: July 18, 2018
- Accepted Manuscript published: July 25, 2018 (version 1)
- Version of Record published: August 7, 2018 (version 2)
Copyright
© 2018, Yang et al.
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
Metrics
-
- 2,236
- views
-
- 379
- downloads
-
- 55
- citations
Views, downloads and citations are aggregated across all versions of this paper published by eLife.
Download links
A two-part list of links to download the article, or parts of the article, in various formats.
Downloads (link to download the article as PDF)
Open citations (links to open the citations from this article in various online reference manager services)
Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)
Structural basis for potent and broad inhibition of HIV-1 RT by thiophene[3,2-d]pyrimidine non-nucleoside inhibitors
eLife 7:e36340.
https://doi.org/10.7554/eLife.36340
Further reading
-
- Microbiology and Infectious Disease
Mechanisms by which Mycobacterium tuberculosis (Mtb) evades pathogen recognition receptor activation during infection may offer insights for the development of improved tuberculosis (TB) vaccines. Whilst Mtb elicits NOD-2 activation through host recognition of its peptidoglycan-derived muramyl dipeptide (MDP), it masks the endogenous NOD-1 ligand through amidation of glutamate at the second position in peptidoglycan side-chains. As the current BCG vaccine is derived from pathogenic mycobacteria, a similar situation prevails. To alleviate this masking ability and to potentially improve efficacy of the BCG vaccine, we used CRISPRi to inhibit expression of the essential enzyme pair, MurT-GatD, implicated in amidation of peptidoglycan side-chains. We demonstrate that depletion of these enzymes results in reduced growth, cell wall defects, increased susceptibility to antibiotics, altered spatial localization of new peptidoglycan and increased NOD-1 expression in macrophages. In cell culture experiments, training of a human monocyte cell line with this recombinant BCG yielded improved control of Mtb growth. In the murine model of TB infection, we demonstrate that depletion of MurT-GatD in BCG, which is expected to unmask the D-glutamate diaminopimelate (iE-DAP) NOD-1 ligand, yields superior prevention of TB disease compared to the standard BCG vaccine. In vitro and in vivo experiments in this study demonstrate the feasibility of gene regulation platforms such as CRISPRi to alter antigen presentation in BCG in a bespoke manner that tunes immunity towards more effective protection against TB disease.
-
- Microbiology and Infectious Disease
Despite much progress, image processing remains a significant bottleneck for high-throughput analysis of microscopy data. One popular platform for single-cell time-lapse imaging is the mother machine, which enables long-term tracking of microbial cells under precisely controlled growth conditions. While several mother machine image analysis pipelines have been developed in the past several years, adoption by a non-expert audience remains a challenge. To fill this gap, we implemented our own software, MM3, as a plugin for the multidimensional image viewer napari. napari-MM3 is a complete and modular image analysis pipeline for mother machine data, which takes advantage of the high-level interactivity of napari. Here, we give an overview of napari-MM3 and test it against several well-designed and widely used image analysis pipelines, including BACMMAN and DeLTA. Researchers often analyze mother machine data with custom scripts using varied image analysis methods, but a quantitative comparison of the output of different pipelines has been lacking. To this end, we show that key single-cell physiological parameter correlations and distributions are robust to the choice of analysis method. However, we also find that small changes in thresholding parameters can systematically alter parameters extracted from single-cell imaging experiments. Moreover, we explicitly show that in deep learning-based segmentation, ‘what you put is what you get’ (WYPIWYG) – that is, pixel-level variation in training data for cell segmentation can propagate to the model output and bias spatial and temporal measurements. Finally, while the primary purpose of this work is to introduce the image analysis software that we have developed over the last decade in our lab, we also provide information for those who want to implement mother machine-based high-throughput imaging and analysis methods in their research.
