Computational Prediction of HIV‑1 Resistance to Protease Inhibitors

The development of mutations in HIV-1 protease (PR) hinders the activity of antiretroviral drugs, forcing changes in drug prescription. Most resistance assessments used to date rely on expert-based rules on predefined sets of stereotypical mutations; such an information-driven approach cannot capture new polymorphisms or be applied for new drugs. Computational modeling could provide a more general assessment of drug resistance and could be made available to clinicians through the Internet. We have created a protocol involving sequence comparison and all-atom protein–ligand induced fit simulations to predict resistance at the molecular level. We first compared our predictions with the experimentally determined IC₅₀ values of darunavir, amprenavir, ritonavir, and indinavir from reference PR mutants displaying different resistance levels. We then performed analyses on a large set of variants harboring more than 10 mutations. Finally, several sequences from real patients were analyzed for amprenavir and darunavir. Our computational approach detected all of the genotype changes triggering high-level resistance, even those involving a large number of mutations

Characteristics of 20 HIV-1 infected patients included in the study.

<p>*Patients were selected due to a failing ART with the exception of patients no 2, 13, 16, 19 who were infected with an NNRTI-resistant strain and a treatment naïve patient no 4 who had the K103R mutation; a and b indicate a first and a second sample; m: male; f: female; age: years;</p><p>**Figure within brackets indicate the number of weeks from the start of the last ongoing NNRTI-treatment to the sampling date.</p><p>***Figure within brackets indicates the number of weeks from cessation of the prior NNRTI-containing treatment to the sampling date.</p><p>****ABC: abacavir; 3TC: lamivudine; FTC: emitricitabine; TDF: tenofovir; ZDV: zidovudine: LPV/r: lopinavir/ritonavir; ATV/r: atazanavir/ritonavir: DRV/r: darunavir/ritonavir; RAL: raltegravir; T20: enfuvirtid.</p

Predicted drug susceptibility in women at enrolment and at delivery.

<p>Predicted drug susceptibility in women at enrolment and at delivery.</p

Piqueria trinervia

原著和名: ステビア科名: キク科 = Compositae採集地: 千葉県 船橋市三山2-2-1 東邦大学 (下総 東邦大学)採集日: 1974/9/26採集者: 萩庭丈壽整理番号: JH025454国立科学博物館整理番号: TNS-VS-97545

Characteristics of women at enrolment and at delivery.

<p>Characteristics of women at enrolment and at delivery.</p

HIV resistant mutations in HIV-infected pregnant women at enrolment, at delivery and acquired during pregnancy.

<p>HIV resistant mutations in HIV-infected pregnant women at enrolment, at delivery and acquired during pregnancy.</p

MOESM1 of Lack of concordance between residual viremia and viral variants driving de novo infection of CD4+ T cells on ART

Additional file 1: Figure S1. Sorting strategy used to purify the different CD4+ T-cell subsets

Study profile.

<p>Study profile.</p

Frequency of resistance mutations by drug category at enrolment, at delivery and acquired during pregnancy.

<p>Frequency of resistance mutations by drug category at enrolment, at delivery and acquired during pregnancy.</p

Baseline characteristics at enrolment of study participants (N = 113).

<p>Baseline characteristics at enrolment of study participants (N = 113).</p