Chapter 16
Molecular Modeling Method
Applications: Probing the Mechanism
of Endocrine Disruptor Action
Xianhai Yang, Huihui Liu and Rebecca Kusko
Abstract The potential endocrine-related detrimental effects of endocrinedisrupting chemicals (EDCs) on humans and wildlife are a growing worldwide concern. The mechanism of action (MOA) of EDCs induced endocrine-related diseases
and endocrine dysfunction can be summarized as the interactions between EDCs and
biomacromolecules in endocrine system. Thus, insights into the endocrine-linked
MOA of EDCs with corresponding targets will pave the way for developing screening methods of EDCs, prioritizing, and constructing endocrine-related adverse outcome pathways. To date, batteries of laboratory bioassays have been developed and
employed to distinguish whether EDCs activate/inhibit/bind to a target or not. However, such test methods poorly assess the underlying molecular mechanisms. Molecular modeling methods are an essential and powerful tool in deciphering the mechanism of endocrine disruptor action. In this chapter, several critical processes related
to performing the molecular modeling are described. Topics include preparing 3D
biomacromolecules and EDCs structures, obtaining and refining the EDC–biomacromolecule complex, and probing the underlying interaction mechanism. Among these
topics, we have emphasized revealing the underlying mechanism by analyzing binding patterns and noncovalent interactions and calculating binding energy. Lastly,
future directions in molecular modeling are also proposed.
Keywords Endocrine-disrupting chemicals (EDCs) · Mechanism of endocrine
disruptor action · Molecular modeling · Homology modeling · Molecular
X. Yang (B) · H. Liu
Jiangsu Key Laboratory of Chemical Pollution Control and Resources Reuse,
School of Environmental and Biological Engineering, Nanjing University
of Science and Technology, Nanjing 210094, China
e-mail: xhyang@njust.edu.cn
H. Liu
e-mail: hhliu@njust.edu.cn
R. Kusko
Immuneering Corporation, Cambridge, MA, USA
e-mail: bkusko@immuneering.com
© Springer Nature Switzerland AG 2019
H. Hong (ed.), Advances in Computational Toxicology, Challenges and Advances
in Computational Chemistry and Physics 30,
https://doi.org/10.1007/978-3-030-16443-0_16
315
Molecular Modeling Method
Applications: Probing the Mechanism
of Endocrine Disruptor Action
Xianhai Yang, Huihui Liu and Rebecca Kusko
Abstract The potential endocrine-related detrimental effects of endocrinedisrupting chemicals (EDCs) on humans and wildlife are a growing worldwide concern. The mechanism of action (MOA) of EDCs induced endocrine-related diseases
and endocrine dysfunction can be summarized as the interactions between EDCs and
biomacromolecules in endocrine system. Thus, insights into the endocrine-linked
MOA of EDCs with corresponding targets will pave the way for developing screening methods of EDCs, prioritizing, and constructing endocrine-related adverse outcome pathways. To date, batteries of laboratory bioassays have been developed and
employed to distinguish whether EDCs activate/inhibit/bind to a target or not. However, such test methods poorly assess the underlying molecular mechanisms. Molecular modeling methods are an essential and powerful tool in deciphering the mechanism of endocrine disruptor action. In this chapter, several critical processes related
to performing the molecular modeling are described. Topics include preparing 3D
biomacromolecules and EDCs structures, obtaining and refining the EDC–biomacromolecule complex, and probing the underlying interaction mechanism. Among these
topics, we have emphasized revealing the underlying mechanism by analyzing binding patterns and noncovalent interactions and calculating binding energy. Lastly,
future directions in molecular modeling are also proposed.
Keywords Endocrine-disrupting chemicals (EDCs) · Mechanism of endocrine
disruptor action · Molecular modeling · Homology modeling · Molecular
X. Yang (B) · H. Liu
Jiangsu Key Laboratory of Chemical Pollution Control and Resources Reuse,
School of Environmental and Biological Engineering, Nanjing University
of Science and Technology, Nanjing 210094, China
e-mail: xhyang@njust.edu.cn
H. Liu
e-mail: hhliu@njust.edu.cn
R. Kusko
Immuneering Corporation, Cambridge, MA, USA
e-mail: bkusko@immuneering.com
© Springer Nature Switzerland AG 2019
H. Hong (ed.), Advances in Computational Toxicology, Challenges and Advances
in Computational Chemistry and Physics 30,
https://doi.org/10.1007/978-3-030-16443-0_16
315