İçeriğe geç
akaturk Akademik ölçüm

Makale detayı · 2017

Systematic errors in detecting biased agonism: Analysis of current methods and development of a new model-free approach

Scientific Reports

YÖKSİS OpenAlex Açık erişim · gold SJR Q1 JCR Q1 Atıf 76 Üst %10 Yüzdelik 92.8% FWCI 3.18
Yıl
2017
ISSN
2045-2322
Tür
article

Veri kaynağı ayrımı

  • YÖKSİS YÖKSİS makale kaydı
  • OpenAlex OpenAlex zenginleştirmesi (özet, atıf, konular)

Özet

İngilizce (OpenAlex)

Discovering biased agonists requires a method that can reliably distinguish the bias in signalling due to unbalanced activation of diverse transduction proteins from that of differential amplification inherent to the system being studied, which invariably results from the non-linear nature of biological signalling networks and their measurement. We have systematically compared the performance of seven methods of bias diagnostics, all of which are based on the analysis of concentration-response curves of ligands according to classical receptor theory. We computed bias factors for a number of β-adrenergic agonists by comparing BRET assays of receptor-transducer interactions with Gs, Gi and arrestin. Using the same ligands, we also compared responses at signalling steps originated from the same receptor-transducer interaction, among which no biased efficacy is theoretically possible. In either case, we found a high level of false positive results and a general lack of correlation among methods. Altogether this analysis shows that all tested methods, including some of the most widely used in the literature, fail to distinguish true ligand bias from "system bias" with confidence. We also propose two novel semi quantitative methods of bias diagnostics that appear to be more robust and reliable than currently available strategies.

Konular

  • Receptor Mechanisms and Signaling
  • Computational Drug Discovery Methods
  • Gene Regulatory Network Analysis

Birincil konu Receptor Mechanisms and Signaling

Yazarlar

  1. HASAN ONGUN ONARAN ANKARA ÜNİVERSİTESİ
  2. Caterina Ambrosio
  3. ÖZLEM UĞUR
  4. Erzsebet Madaras Koncz
  5. Maria Cristina Gro
  6. Vanessa Vezzi
  7. Sudarshan Rajagopal
  8. Tommaso Costa