Background: The six-helix bundle (6-HB) is a core structure formed during the membrane fusion process of viruses with the Class I envelope proteins. Peptide inhibitors, including the marketed Enfuvirtide, blocking the membrane fusion to exert inhibitory activity were designed based on the heptads repeat interactions in 6-HB. However, the drawbacks of Enfuvirtide, such as drug resistance and short half-life , have been confirmed in clinical applications. Therefore, novel design strategies are pivotal in the development of next-generation peptide-based fusion inhibitors.
Objective: The de novo design of α-helical peptides against MERS-CoV and IAVs has successfully expedited the development of fusion inhibitors. The reported sequences were completely nonhomologous with natural peptides, which can provide some inspirations for the antiviral design against other pathogenic viruses with class I fusion proteins. Here, we design a series of artificial C-peptides based on the similar mechanism of 6-HB formation and general rules of heptads repeat interaction.
Methods: The inhibitory activity of peptides against HIV-1 was assessed by HIV-1 Env-mediated cell-cell fusion assays. Interaction between artificial C-peptides and target peptides was evaluated by circular dichroism, polyacrylamide gel electrophoresis, size-exclusion chromatography, and sedimentation velocity analysis. Molecular docking studies were performed by using Schrödinger molecular modelling software.
Results: The best-performing artificial C-peptide, 1SR, was highly active against HIV-1 env-mediated cell-cell fusion. 1SR binds to the gp41 NHR region, assembling polymer to prevent endogenous 6-HB formation.
Conclusion: We have found an artificial C-lipopeptide lead compound with inhibitory activity against HIV-1. Also, this paper enriched both N- and C-teminal heptads repeat interaction rules in 6-HB and provided an effective idea for next-generation peptide-based fusion inhibitors against HIV-1.
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http://dx.doi.org/10.2174/0109298665312274240530060233 | DOI Listing |
J Chem Inf Model
December 2024
Centre for Life Sciences, Mahindra University, Hyderabad, Telangana 500043, India.
Every year, an estimated 1.5 million people worldwide contract Hepatitis C, a significant contributor to liver problems. Although many studies have explored machine learning's potential to predict antiviral peptides, very few have addressed the problem of predicting peptides against specific viruses such as Hepatitis C.
View Article and Find Full Text PDFDiabetes Metab Syndr Obes
October 2024
Department of Endocrinology, Shanghai Pudong Hospital, Fudan University, Pudong Medical Center, Shanghai, 201399, People's Republic of China.
Objective: We aim to examine and reestablish the correlational and linear regression relationships, as well as the predictive value, between the significant facial and tongue features and the metabolic parameters in type 2 diabetes mellitus (T2DM).
Materials And Methods: From March to May 2024, we studied 269 patients with T2DM in the endocrinology department of Shanghai Pudong Hospital. The patients' facial and tongue characteristics were sampling by a tongue imaging device equipped with artificial intelligence (AI) (XiMaLife, Sinology, China) of automated and advanced machine learning algorithms.
Protein Pept Lett
September 2024
State Key Laboratory of Toxicology and Medical Countermeasures, Beijing Institute of Pharmacology and Toxicology, Beijing, P.R. China.
BMC Med
June 2024
Inserm U1016, Cochin Institute, Paris, France.
Background: IMCY-0098, a synthetic peptide developed to halt disease progression via elimination of key immune cells in the autoimmune cascade, has shown a promising safety profile for the treatment of type 1 diabetes (T1D) in a recent phase 1b trial. This exploratory analysis of data from that trial aimed to identify the patient biomarkers at baseline associated with a positive response to treatment and examined the associations between immune response parameters and clinical efficacy endpoints (as surrogates for mechanism of action endpoints) using an artificial intelligence-based approach of unsupervised explainable machine learning.
Methods: We conducted an exploratory analysis of data from a phase 1b, dose-escalation, randomized, placebo-controlled study of IMCY-0098 in patients with recent-onset T1D.
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