To Bind or Not to Bind? A Comprehensive Characterization of TIR1 and Auxins Using Consensus In Silico Approaches
Abstract
:1. Introduction
2. Materials and Methods
2.1. De Novo Design of Putative Auxins and Molecular Decoys
2.2. Machine Learning
2.3. Molecular Modelling of Auxins
2.3.1. Mixed Solvent Molecular Dynamics
2.3.2. Assessment of Pocket Solvation and Its Role in Auxin Recognition
2.3.3. Molecular Dynamics
2.3.4. Coarse Metadynamics
3. Results and Discussion
3.1. De Novo Design of Putative Auxins and Decoys
3.2. Machine Learning
- Validations curves: To visualize the trend of variance and suggest the presence of bias; either underfitting or overfitting.
- Learning curves: Convey information on the learning rate as a function of n in data; i.e., if the provided examples are enough for effective classification or if the addition of data may benefit scoring.
- ROC curves and Matthews correlation coefficient (MCC): These provide a general description of the predictive power due to their relation with the confusion matrix. The inclusion of both is complementary. As MCC can take values between −1 and 1, it is possible to assess if a model is indeed providing a significant difference when applied or if its success relies more on chance, even when the area under the curve of the ROC plot would suggest otherwise.
- Detection error trade-off: Similar to the ROC curve, this metric gives information on the success of classification tasks. During classification, there is always a trade-off between the rate of falsely classified values. Therefore, this plot conveys the trend of said trade-off; an ideal classifier would keep a low rate of both false positives and false negatives, resulting in a curve downward and to the left, an inverse of sorts to an expected ROC curve. All these plots can be found in the Supplementary Information (Figures S2–S9). ROC curves and error trade-off for the models’ best models are shown in Figure 2.
3.3. Molecular Modelling of Auxins
3.3.1. Mixed Solvents Molecular Dynamics
3.3.2. Assessment of Pocket Solvation and Its Role in Auxin Recognition
3.3.3. Molecular Dynamics
3.3.4. Coarse Metadynamics
4. Conclusions
Supplementary Materials
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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PDBID | 2P1M | 2P1P | 2P1Q | 2P1O | 2P1N |
---|---|---|---|---|---|
2P1M | (RMSD 0.16 Å) H78 − 0.32 F79 − 0.18 D81 − 0.31 F82 − 0.19 C405 − 0.27 S438 − 0.19 L439 − 0.22 S440 − 0.19 S462 − 0.18 | (RMSD 0.22 Å) H78 − 0.3 F79 − 0.3 D81 − 0.58 F82 − 0.45 C405 − 0.27 R435 − 0.23 S438 − 0.23 L439 − 0.3 R489 − 0.22 | (RMSD 0.27 Å) H78 − 0.36 D81 − 0.59 F82 − 0.52 C405 − 0.29 L439 − 0.87 | (RMSD 0.33 Å) H78 − 0.41 F79 − 0.38 D81 − 0.55 F82 − 0.35 C405 − 0.54 L439 − 0.85 V463 − 0.49 A464 − 0.58 R489 − 0.33 | |
2P1P | (RMSD 0.16 Å) F79 − 0.18 D81 − 0.28 F82 − 0.52 L439 − 0.2 A464 − 0.2 R484 − 0.22 | (RMSD 0.22 Å) D81 − 0.29 F82 − 0.6 A464 − 0.22 | (RMSD 0.26 Å) F79 − 0.26 D81 − 0.29 F82 − 0.45 C405 − 0.37 R435 − 0.27 L439 − 0.66 V463 − 0.37 A464 − 0.54 | ||
2P1Q | (RMSD 0.14 Å) F49 − 0.15 L439 − 0.62 S440 − 0.16 | (RMSD 0.22 Å) H78 − 0.28 C405 − 0.29 R435 − 0.35 L439 − 0.62 V463 − 0.42 A464 − 0.4 | |||
2P1O | (RMSD 0.19 Å) H78 − 0.3 F82 − 0.22 C405 − 0.31 R435 − 0.28 V463 − 0.43 A464 − 0.42 |
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Prieto-Martínez, F.D.; Mendoza-Cañas, J.; Martínez-Mayorga, K. To Bind or Not to Bind? A Comprehensive Characterization of TIR1 and Auxins Using Consensus In Silico Approaches. Computation 2024, 12, 94. https://doi.org/10.3390/computation12050094
Prieto-Martínez FD, Mendoza-Cañas J, Martínez-Mayorga K. To Bind or Not to Bind? A Comprehensive Characterization of TIR1 and Auxins Using Consensus In Silico Approaches. Computation. 2024; 12(5):94. https://doi.org/10.3390/computation12050094
Chicago/Turabian StylePrieto-Martínez, Fernando D., Jennifer Mendoza-Cañas, and Karina Martínez-Mayorga. 2024. "To Bind or Not to Bind? A Comprehensive Characterization of TIR1 and Auxins Using Consensus In Silico Approaches" Computation 12, no. 5: 94. https://doi.org/10.3390/computation12050094