支持预测物种对化学效应敏感性的跨物种分子对接方法

IF 3.1 Q2 TOXICOLOGY
Peter G. Schumann , Daniel T. Chang , Sally A. Mayasich , Sara M.F. Vliet , Terry N. Brown , Carlie A. LaLone
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引用次数: 0

摘要

以 AlphaFold 和 Iterative Threading ASSEmbly Refinement 为代表的蛋白质结构预测工具的发展,使得基于现有蛋白质序列和结构数据的跨物种蛋白质结构预测成为可能。在本研究中,我们介绍了一种创新的分子对接方法,该方法利用丰富的结构数据加强了对不同物种化学敏感性的预测。我们将雄激素受体作为内分泌功能的相关调节剂来演示这种方法。通过使用蛋白质结构,该方法将物种易感性与功能框架联系起来,并通过对各种开源工具的新颖整合,帮助将分子对接纳入支持下一代风险评估(NGRA)范例的新方法(NAM)库中。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Cross-species molecular docking method to support predictions of species susceptibility to chemical effects

The advancement of protein structural prediction tools, exemplified by AlphaFold and Iterative Threading ASSEmbly Refinement, has enabled the prediction of protein structures across species based on available protein sequence and structural data. In this study, we introduce an innovative molecular docking method that capitalizes on this wealth of structural data to enhance predictions of chemical susceptibility across species. We demonstrated this method using the androgen receptor as a pertinent modulator of endocrine function. By using protein structures, this method contextualizes species susceptibility within a functional framework and helps to integrate molecular docking into the repertoire of New Approach Methodologies (NAMs) that support the Next-Generation Risk Assessment (NGRA) paradigm through the novel integration of various open-source tools.

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来源期刊
Computational Toxicology
Computational Toxicology Computer Science-Computer Science Applications
CiteScore
5.50
自引率
0.00%
发文量
53
审稿时长
56 days
期刊介绍: Computational Toxicology is an international journal publishing computational approaches that assist in the toxicological evaluation of new and existing chemical substances assisting in their safety assessment. -All effects relating to human health and environmental toxicity and fate -Prediction of toxicity, metabolism, fate and physico-chemical properties -The development of models from read-across, (Q)SARs, PBPK, QIVIVE, Multi-Scale Models -Big Data in toxicology: integration, management, analysis -Implementation of models through AOPs, IATA, TTC -Regulatory acceptance of models: evaluation, verification and validation -From metals, to small organic molecules to nanoparticles -Pharmaceuticals, pesticides, foods, cosmetics, fine chemicals -Bringing together the views of industry, regulators, academia, NGOs
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