Science Inventory

Cross-species molecular docking method to support predictions of species susceptibility to chemical effects

Citation:

Schumann, P., D. Chang, S. Mayasich, S. Vliet, T. Brown, AND C. LaLone. Cross-species molecular docking method to support predictions of species susceptibility to chemical effects. Computational Toxicology. Elsevier B.V., Amsterdam, Netherlands, 30(4):100319, (2024). https://doi.org/10.1016/j.comtox.2024.100319

Impact/Purpose:

The advancement of protein structural prediction tools, exemplified by AlphaFold and Iterative Threading ASSEmbly Refinement, has led to an exponential increase in available protein structures for hundreds of thousands of species. 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.

Description:

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.