Prediction and molecular field view of drug resistance in HIV-1 protease mutants

Wang, Baifan; He, Yinwu; Wen, Xin; Xi, Zhen

doi:10.1038/s41598-022-07012-x

Cited by 5 publications

(3 citation statements)

References 46 publications

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“…In this study, effective chemical structures in very high and high potency groups are congruent to several previous studies. The original chemical group (0‐x‐x‐x) at the P1 moiety found in DRV analogs, the phenyl group, is commonly found in HIV‐1 PIs, such as saquinavir, ritonavir, nelfinavir, amprenavir, and lopinavir 70–72 . Similar to the P2 moiety, the original chemical group (x‐x‐0‐x), the bis‐tetrahydrofuran group, forms hydrogen bonding and vdW interactions with D29 and D30 12,73 .…”

Section: Resultsmentioning

confidence: 99%

“…the phenyl group, is commonly found in HIV-1 PIs, such as saquinavir, ritonavir, nelfinavir, amprenavir, and lopinavir. [70][71][72] Similar to the P2 moiety, the original chemical group (x-x-0-x), the bis-tetrahydrofuran group, forms hydrogen bonding and vdW interactions with D29 and D30. 12,73 Thus, these remaining chemical groups could enhance a broadly inhibitory effect for most HIV-1 PR variants.…”

Section: Evaluation Of Potent Analogs Against Hiv-1 Prsmentioning

confidence: 99%

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Machine learning‐guided design of potent darunavir analogs targeting HIV‐1 proteases: A computational approach for antiretroviral drug discovery

Chuntakaruk,

Boonpalit,

Kinchagawat

et al. 2024

J Comput Chem

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In the pursuit of novel antiretroviral therapies for human immunodeficiency virus type‐1 (HIV‐1) proteases (PRs), recent improvements in drug discovery have embraced machine learning (ML) techniques to guide the design process. This study employs ensemble learning models to identify crucial substructures as significant features for drug development. Using molecular docking techniques, a collection of 160 darunavir (DRV) analogs was designed based on these key substructures and subsequently screened using molecular docking techniques. Chemical structures with high fitness scores were selected, combined, and one‐dimensional (1D) screening based on beyond Lipinski's rule of five (bRo5) and ADME (absorption, distribution, metabolism, and excretion) prediction implemented in the Combined Analog generator Tool (CAT) program. A total of 473 screened analogs were subjected to docking analysis through convolutional neural networks scoring function against both the wild‐type (WT) and 12 major mutated PRs. DRV analogs with negative changes in binding free energy () compared to DRV could be categorized into four attractive groups based on their interactions with the majority of vital PRs. The analysis of interaction profiles revealed that potent designed analogs, targeting both WT and mutant PRs, exhibited interactions with common key amino acid residues. This observation further confirms that the ML model‐guided approach effectively identified the substructures that play a crucial role in potent analogs. It is expected to function as a powerful computational tool, offering valuable guidance in the identification of chemical substructures for synthesis and subsequent experimental testing.

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Section: Resultsmentioning

confidence: 99%

Section: Evaluation Of Potent Analogs Against Hiv-1 Prsmentioning

confidence: 99%

Machine learning‐guided design of potent darunavir analogs targeting HIV‐1 proteases: A computational approach for antiretroviral drug discovery

Chuntakaruk,

Boonpalit,

Kinchagawat

et al. 2024

J Comput Chem

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“…Despite the commercial Protease Inhibitors (PIs) have proved their role in the major advances in HIV/AIDS therapies, there are still many drawbacks, mainly in terms of toxicity and systemic 2 complications involving several organs [4]. Moreover, the emergence of multiple drug resistance mutations remains a challenge [5,6], reducing long-term viral inhibition.…”

Section: Introductionmentioning

confidence: 99%

Novel Wild Type and Mutate HIV-1 Protease Inhibitors Containing Heteroaryl Carboxamides in P2: Synthesis, Biological and ADME Evaluations

Armentano,

Lupattelli,

Bisaccia

et al. 2023

Preprint

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The Virus HIV-1 infection still represents a serious disease even if actually it is transformed in chronic pathology. Considering the crucial role of the enzyme Protease in life cycle of HIV many efforts have been made in the research of new organic compounds showing inhibitory activity. After development of several series of non peptidic inhibitors we report here the synthesis of novel simple HIV-Protease inhibitors containing heteroaryl carboxamides and their antiviral activity in vitro and in HEK293 cells. Benzofuryl- benzothienyl- and indolyl rings as well as aryl sulfonamides with different electronic properties have been introduced by efficient synthetic procedures. All compounds showed inhibitory activity similar to the commercial drug Darunavir, effective against both wild-type HIV-1 protease and that containing the V32I or V82A mutations. ADME properties were also evaluated, showing the potential of such compounds to be developed as drugs.

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Rational design, synthesis and biological evaluation of novel HIV-1 protease inhibitors containing 2-phenylacetamide derivatives as P2 ligands with potent activity against DRV-Resistant HIV-1 variants

Meng,

Gao,

Qiang

et al. 2024

Bioorganic & Medicinal Chemistry Letters

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Prediction and molecular field view of drug resistance in HIV-1 protease mutants

Cited by 5 publications

References 46 publications

Machine learning‐guided design of potent darunavir analogs targeting HIV‐1 proteases: A computational approach for antiretroviral drug discovery

Machine learning‐guided design of potent darunavir analogs targeting HIV‐1 proteases: A computational approach for antiretroviral drug discovery

Novel Wild Type and Mutate HIV-1 Protease Inhibitors Containing Heteroaryl Carboxamides in P2: Synthesis, Biological and ADME Evaluations

Rational design, synthesis and biological evaluation of novel HIV-1 protease inhibitors containing 2-phenylacetamide derivatives as P2 ligands with potent activity against DRV-Resistant HIV-1 variants

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