Predicting Potential Endocrine Disrupting Chemicals Binding to Estrogen Receptor α (ERα) Using a Pipeline Combining Structure-Based and Ligand-Based in Silico Methods
Abstract
:1. Introduction
2. Results
2.1. Compounds and Database Preparation
2.1.1. Database Preparation
2.1.2. Databases Comparison
2.2. Docking
2.2.1. Docking Outcome
2.2.2. Predictiveness Curve
2.3. Pharmacophore Modeling
2.3.1. LB Pharmacophore Models
2.3.2. SB Pharmacophore Models
2.3.3. SBLB Pharmacophore Models
2.4. Combination of Docking and Pharmacophore Models
2.4.1. Consensus Protocol
2.4.2. Hierarchical Protocol
3. Discussion
3.1. Compounds and Database Preparation
3.2. Docking
3.3. Predictiveness Curve
3.4. Pharmacophores
3.5. Combination of Methods
4. Materials and Methods
4.1. Compounds, Databases Preparation, and Annotation
4.1.1. EPA Dataset
4.1.2. Validation Sets
- NR-DBIND
- EADB
4.1.3. Molecule Curation and Preparation
4.2. Structures Preparation
4.3. Docking
4.3.1. Protocol
4.3.2. Docking Performances Analyses
- Single structure docking and ensemble docking
- Predictiveness curves
4.4. Pharmacophore Modeling Protocol
4.4.1. Ligand Based Approach (LB) Models Protocol
4.4.2. Structure Based Approach (SB) Models Protocol
- Pharmacophore model optimization
4.4.3. Combination of SB and LB Pharmacophores Models
4.5. Pipelines Construction
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Abbreviations
AR | Androgen receptors |
AUC | Area under the ROC curve |
B | Binding compounds |
CAS | Chemical Abstracts Service |
DBD | DNA-binding domain |
DNA | deoxyribonucleic acid |
DSSTox | Distributed Structure-Searchable Toxicity |
EADB | Estrogenic activity database |
EDCs | Endocrine disrupting chemicals |
EF | Enrichment factor |
EPA | United states Environmental protection agency |
ER | Estrogen receptors |
FIX | Factor IX |
GR | Glucocorticoid receptors |
LB | Ligand based |
LBD | Ligand binding domain |
NB | Non-Binding compounds |
NCTR | National center for toxicological research USA |
NR | Nuclear receptor |
NR-DBIND | Nuclear Receptors Database Including Negative Data |
NTD | NH2-terminal domain |
PC | Predictiveness curve |
PDB | Protein data bank |
PPAR | Peroxisome proliferator-activated receptors |
PPV | Positive Predictive value |
PLANTS | Protein-ligand ANTSystem |
QSAR | Quantitative structure activity relationship |
RMSD | Root-mean-square deviation |
ROC | Receiver operating curve |
SB | Structure based |
SD | Standard deviation |
Se | Sensitivity |
SMILES | Simplified molecular-input line-entry system |
Sp | Specificity |
TH | scoring Threshold |
TR | Thyroid hormones receptors |
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Software | Docking Approach | Best Performances | Min AUC | Mean AUC | SD | |
---|---|---|---|---|---|---|
AUC | PDB | |||||
smina-dkoes | Single | 0.708 | [1qku] | 0.700 | 0.704 | 0.003 |
Ensemble of 2 | 0.709 | [2yja-1qku] | 0.702 | 0.703 | 0.003 | |
Ensemble of 3 | 0.710 | [2yja-1qku-1g50] | 0.704 | 0.702 | 0.003 | |
smina-vina | Single structure | 0.699 | [1a52] | 0.643 | 0.676 | 0.02 |
Ensemble of 2 | 0.696 | [1xp9-1a52] | 0.642 | 0.67 | 0.017 | |
Ensemble of 3 | 0.695 | [1xp9-1xp1-1a52] | 0.642 | 0.667 | 0.014 | |
smina-vinardo | Single structure | 0.68 | [1a52] | 0.686 | 0.704 | 0.018 |
Ensemble of 2 | 0.676 | [1xp9-1a52] | 0.619 | 0.650 | 0.019 | |
Ensemble of 3 | 0.673 | [1xp9-1xp1-1a52] | 0.618 | 0.644 | 0.018 | |
smina-ad4 | Single structure | 0.656 | [1a52] | 0.613 | 0.639 | 0.0154 |
Ensemble of 2 | 0.654 | [1x7e-1a52] | 0.618 | 0.641 | 0.009 | |
Ensemble of 3 | 0.650 | [1x7e-1qku-1a52] | 0.623 | 0.640 | 0.007 | |
PLANTS | Single structure | 0.659 | [1x7e] | 0.598 | 0.634 | 0.019 |
Ensemble of 2 | 0.660 | [1x7e-1a52] | 0.647 | 0.62 | 0 | |
Ensemble of 3 | 0.659 | [1x7e-1qku-1a52] | 0.620 | 0.642 | 0.009 | |
Surflex-dock | Single structure | 0.604 | [1a52] | 0.547 | 0.576 | 0.027 |
Ensemble of 2 | 0.616 | [1xp1-1x7e] | 0.556 | 0.594 | 0.020 | |
Ensemble of 3 | 0.623 | [1xp1-1x7e-1a52] | 0.562 | 0.605 | 0.015 |
Docking Approach | Performances | Se = 0.25 | Se = 0.5 | Se = 0.75 | |
---|---|---|---|---|---|
smina_dkoes | Single | P(active) | 0.137 | 0.094 | 0.094 |
(1qku) | TH | −7 | −6 | −6 | |
Sp | 0.918 | 0.766 | 0.601 | ||
EF | 1.9 | 1.65 | 1.65 | ||
PPV | 56/237 | 111/631 | 167/1052 | ||
Ensemble de 2 | P(active) | 0.134 | 0.094 | 0.094 | |
(2yja-1qku) | TH | −7 | −6 | −6 | |
Sp | 0.916 | 0.759 | 0.597 | ||
EF | 1.89 | 1.63 | 1.63 | ||
PPV | 56/242 | 111/645 | 167/1061 | ||
Ensemble de 3 | P(active) | 0.137 | 0.13 | 0.091 | |
(2yja-1qku-1g50) | TH | −8 | −7 | −6 | |
Sp | 0.915 | 0.777 | 0.599 | ||
EF | 2.37 | 1.9 | 1.59 | ||
PPV | 56/244 | 111/605 | 167/1057 | ||
PLANTS | Single | P(active) | 0.127 | 0.103 | 0.081 |
(1x7e) | TH | −79 | −72 | −64 | |
Sp | 0.876 | 0.723 | 0.501 | ||
EF | 1.9 | 1.69 | 1.42 | ||
PPV | 55/328 | 110/719 | 165/1261 | ||
Ensemble of 2 | P(active) | 0.123 | 0.097 | 0.08 | |
(1x7e-1a52) | TH | −82 | −73 | −66 | |
Sp | 0.86 | 0.707 | 0.49 | ||
EF | 1.69 | 1.58 | 1.42 | ||
PPV | 55/362 | 110/753 | 165/1287 | ||
Ensemble of 3 | P(active) | 0.122 | 0.096 | 0.079 | |
(1x7e-1a52-1qku) | TH | −82 | −73 | −66 | |
Sp | 0.857 | 0.701 | 0.493 | ||
EF | 1.65 | 1.6 | 1.41 | ||
PPV | 55/369 | 110/767 | 165/1279 |
Scoring Threshold (TH) | Performances | EPA | Estrogenic Activity DataBase (EADB) | Nuclear Receptors DataBase Including Negative Data (NR-DBIND) |
---|---|---|---|---|
TH = −7 | Se | 0.79 | 0.48 | 0.93 |
Sp | 0.55 | 0.58 | 0.03 | |
PPV | 176/2442 | 63/232 | 513/732 | |
TH = −6 | Se | 0.46 | 0.77 | 0.99 |
Sp | 0.78 | 0.198 | 0.001 | |
PPV | 103/2442 | 101/232 | 553/732 |
EPA Database | EADB | NR-DBIND | |||
---|---|---|---|---|---|
Performances | Train Set | Test Set | Validation Set | Validation Set | |
LB pharmacophores | Se (B/total_B) | 0.305 (51/167) | 0.232 (13/56) | ||
Sp (NB/total_NB) | 0.973 (45/1664) | 0.960 (22/555) | |||
SB pharmacophores | Se (B/total_B) | 0.251 (42/167) | 0.232 (13/56) | ||
Sp (NB/total_NB) | 0.990 (16/1664) | 0.987 (7/555) | |||
SBLB pharmacophores | Se (B/total_B) | 0.371 (62/1664) | 0.321 (18/56) | 0.557 (73/131) | 0.819 (458/554) |
Sp (NB/total_NB) | 0.968 (53/167) | 0.595 (25/555) | 0.871 (13/101) | 0.629 (66/178) |
TH | −7 | −6 | |||||
---|---|---|---|---|---|---|---|
Se | Sp | PPV | Se | Sp | PPV | ||
Consensus protocol | EPA database | 0.56 | 0.76 | 124/652 | 0.81 | 0.54 | 180/1205 |
EADB | 0.832 | 0.495 | 109/160 | 0.931 | 0.158 | 122/207 | |
NR-DBIND | 0.986 | 0.029 | 546/719 | 1.0 | 0.005 | 554/731 | |
Hierarchical protocol | EPA database | 0.25 | 0.99 | 55/84 | 0.32 | 0.98 | 72/117 |
EADB | 0.206 | 0.960 | 27/31 | 0.370 | 0.911 | 52/61 | |
NR-DBIND | 0.756 | 0.635 | 419/484 | 0.814 | 0.635 | 451/516 |
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Sellami, A.; Montes, M.; Lagarde, N. Predicting Potential Endocrine Disrupting Chemicals Binding to Estrogen Receptor α (ERα) Using a Pipeline Combining Structure-Based and Ligand-Based in Silico Methods. Int. J. Mol. Sci. 2021, 22, 2846. https://doi.org/10.3390/ijms22062846
Sellami A, Montes M, Lagarde N. Predicting Potential Endocrine Disrupting Chemicals Binding to Estrogen Receptor α (ERα) Using a Pipeline Combining Structure-Based and Ligand-Based in Silico Methods. International Journal of Molecular Sciences. 2021; 22(6):2846. https://doi.org/10.3390/ijms22062846
Chicago/Turabian StyleSellami, Asma, Matthieu Montes, and Nathalie Lagarde. 2021. "Predicting Potential Endocrine Disrupting Chemicals Binding to Estrogen Receptor α (ERα) Using a Pipeline Combining Structure-Based and Ligand-Based in Silico Methods" International Journal of Molecular Sciences 22, no. 6: 2846. https://doi.org/10.3390/ijms22062846