Correction to Structural Adaptation of Darunavir Analogues against Primary Mutations in HIV-1 Protease

Lockbaum, G.J.; Leidner, Florian; Rusere, L.N.; Henes, M.; Kosovrasti, K.; Nachum, G.S.; Nalivaika, E.A.; Bolon, Daniel N.; Ali, Akbar; Yilmaz, Neşe Kurt; Schiffer, Celia A.

doi:10.1021/acsinfecdis.9b00098

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“…While mutations within the active site can confer resistance through steric and electrostatic alterations, how mutations outside the active site confer resistance is more difficult to ascertain. − To determine how changes remote from the active site alter drug binding, we used molecular dynamics simulations (MD) combined with machine learning to identify interactions that distinguish resistant and susceptible variants. In previous studies, a similar approach was used with HIV-1 protease variants in complex with a potent antiviral, darunavir. , This approach identified a sparse set of interactions that correlated strongly with resistance. , In this study, we extend this strategy to an antifungal drug target, the dihydrofolate reductase (DHFR) enzyme in the fungus Pneumocystis jirovecii.…”

Section: Introductionmentioning

confidence: 99%

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Deciphering Antifungal Drug Resistance in Pneumocystis jirovecii DHFR with Molecular Dynamics and Machine Learning

Leidner

Yilmaz

Schiffer

2021

J. Chem. Inf. Model.

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Drug resistance impacts the effectiveness of many new therapeutics. Mutations in the therapeutic target confer resistance; however, deciphering which mutations, often remote from the enzyme active site, drive resistance is challenging. In a series of Pneumocystis jirovecii dihydrofolate reductase variants, we elucidate which interactions are key bellwethers to confer resistance to trimethoprim using homology modeling, molecular dynamics, and machine learning. Six molecular features involving mainly residues that did not vary were the best indicators of resistance.

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Section: Introductionmentioning

confidence: 99%

“…9,10 This approach identified a sparse set of interactions that correlated strongly with resistance. 9,11 In this study, we extend this strategy to an antifungal drug target, the dihydrofolate reductase (DHFR) enzyme in the fungus Pneumocystis jirovecii.…”

Section: ■ Introductionmentioning

confidence: 99%

Deciphering Antifungal Drug Resistance in Pneumocystis jirovecii DHFR with Molecular Dynamics and Machine Learning

Leidner

Yilmaz

Schiffer

2021

J. Chem. Inf. Model.

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Design, Synthesis, and Biological Evaluation of Darunavir Analogs as HIV-1 Protease Inhibitors

Ur Rehman,

Chuntakaruk,

Amphan

et al. 2024

ACS Bio Med Chem Au

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Darunavir, a frontline treatment for HIV infection, faces limitations due to emerging multidrug resistant (MDR) HIV strains, necessitating the development of analogs with improved activity. In this study, a combinatorial in silico approach was used to initially design a series of HIV-1 PI analogs with modifications at key sites, P1′ and P2′, to enhance interactions with HIV-1 PR. Fifteen analogs with promising binding scores were selected for synthesis and evaluated for the HIV-1 PR inhibition activity. The variation of P2′ substitution was found to be effective, as seen in 5aa (1.54 nM), 5ad (0.71 nM), 5ac (0.31 nM), 5ae (0.28 nM), and 5af (1.12 nM), featuring halogen, aliphatic, and alkoxy functionalities on the phenyl sulfoxide motif exhibited superior inhibition against HIV-1 PR compared to DRV, with minimal cytotoxicity observed in Vero and 293T cell lines. Moreover, computational studies demonstrated the potential of selected analogs to inhibit various HIV-1 PR mutations, including I54M and I84V. Further structural dynamics and energetic analyses confirmed the stability and binding affinity of promising analogs, particularly 5ae, which showed strong interactions with key residues in HIV-1 PR. Overall, this study underscores the importance of flexible moieties and interaction enhancement at the S2′ subsite of HIV-1 PR in developing effective DRV analogs to combat HIV and other global health issues.

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Correction to Structural Adaptation of Darunavir Analogues against Primary Mutations in HIV-1 Protease

Abstract: The author list for this paper should appear as it does in this Addition and Correction. Daniel N. A. Bolon was inadvertently left off the author listing and should be added. The gag processing experiment (results shown in Figure S1) was performed in his laboratories by Dr. Nachum.

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Deciphering Antifungal Drug Resistance in Pneumocystis jirovecii DHFR with Molecular Dynamics and Machine Learning

Deciphering Antifungal Drug Resistance in Pneumocystis jirovecii DHFR with Molecular Dynamics and Machine Learning

Design, Synthesis, and Biological Evaluation of Darunavir Analogs as HIV-1 Protease Inhibitors

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