title: A Rat α-Fetoprotein Binding Activity Prediction Model to Facilitate Assessment of the Endocrine Disruption Potential of Environmental Chemicals.
abstract: Endocrine disruptors such as polychlorinated biphenyls (PCBs), diethylstilbestrol (DES) and dichlorodiphenyltrichloroethane (DDT) are agents that interfere with the endocrine system and cause adverse health effects. Huge public health concern about endocrine disruptors has arisen. One of the mechanisms of endocrine disruption is through binding of endocrine disruptors with the hormone receptors in the target cells. Entrance of endocrine disruptors into target cells is the precondition of endocrine disruption. The binding capability of a chemical with proteins in the blood affects its entrance into the target cells and, thus, is very informative for the assessment of potential endocrine disruption of chemicals. α-fetoprotein is one of the major serum proteins that binds to a variety of chemicals such as estrogens. To better facilitate assessment of endocrine disruption of environmental chemicals, we developed a model for α-fetoprotein binding activity prediction using the novel pattern recognition method (Decision Forest) and the molecular descriptors calculated from two-dimensional structures by Mold² software. The predictive capability of the model has been evaluated through internal validation using 125 training chemicals (average balanced accuracy of 69%) and external validations using 22 chemicals (balanced accuracy of 71%). Prediction confidence analysis revealed the model performed much better at high prediction confidence. Our results indicate that the model is useful (when predictions are in high confidence) in endocrine disruption risk assessment of environmental chemicals though improvement by increasing number of training chemicals is needed.
authors.affiliation: Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR 72079, USA. Huixiao.Hong@fda.hhs.gov.
authors.forename: Huixiao
authors.initials: H
authors.lastname: Hong
authors.affiliation: Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR 72079, USA. jieeshen@gmail.com.
authors.forename: Jie
authors.initials: J
authors.lastname: Shen
authors.affiliation: Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR 72079, USA. Huiwen.Ng@fda.hhs.gov.
authors.forename: Hui Wen
authors.initials: HW
authors.lastname: Ng
authors.affiliation: Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR 72079, USA. Suguna.Sakkiah@fda.hhs.gov.
authors.forename: Sugunadevi
authors.initials: S
authors.lastname: Sakkiah
authors.affiliation: Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR 72079, USA. haoye.ecust@gmail.com.
authors.forename: Hao
authors.initials: H
authors.lastname: Ye
authors.affiliation: Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR 72079, USA. weigong.ge@fda.hhs.gov.
authors.forename: Weigong
authors.initials: W
authors.lastname: Ge
authors.affiliation: Environmental Laboratory, U.S. Army Engineer Research and Development Center, 3909 Halls Ferry Road, Vicksburg, MS 39180, USA. Ping.Gong@usace.army.mil.
authors.forename: Ping
authors.initials: P
authors.lastname: Gong
authors.affiliation: Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR 72079, USA. Wenming.Xiao@fda.hhs.gov.
authors.forename: Wenming
authors.initials: W
authors.lastname: Xiao
authors.affiliation: Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR 72079, USA. Weida.Tong@fda.hhs.gov.
authors.forename: Weida
authors.initials: W
authors.lastname: Tong
mesh_terms: D000818:Animals; D003198:Computer Simulation; D052244:Endocrine Disruptors / Q000633:toxicity*; D004781:Environmental Exposure; D004785:Environmental Pollutants / Q000633:toxicity*; D008954:Models, Biological; D051381:Rats; D000509:alpha-Fetoproteins / Q000378:metabolism*
chemical_list: C483800:Afp protein, rat; D052244:Endocrine Disruptors; D004785:Environmental Pollutants; D000509:alpha-Fetoproteins
keywords: assessment; binding; disruption; endocrine; model; prediction; α-fetoprotein