The Relative Distance Prediction of Transmembrane Protein Surface Residue Based on Improved Residual Networks
Abstract
:1. Introduction
2. Materials and Methods
2.1. Benchmark Dataset
2.2. Protein Sequence Descriptors
2.2.1. Sequence Encoding
2.2.2. Evolutionary Conservation
2.2.3. Co-Evolutionary Information
2.2.4. Relative Solution Accessibility
2.3. The Representation of Residue Distance
2.3.1. Feature Combination
2.3.2. Label Matrix Generation
2.4. Deep Learning Model Details
2.4.1. Model Design
2.4.2. Model Training
2.5. Performance Metrics
3. Results
3.1. Characteristic Validity Analysis
3.2. Network Structure Analysis
3.3. Model Performance Analysis
3.4. The Setting of Residue-Residue Distances Threshold
3.5. Comparison with Residue Contact Prediction Models
4. Discussion
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Sample Availability
Abbreviations
TMP | transmembrane protein |
RB | the residual block |
SEB | the Squeeze-and-Excitation block |
PCC | Pearson correlation coefficient |
MCC | Matthews correlation coefficient |
SurTrain | the training set |
SurValid | the validation set |
SurTest | the test set |
OH | the one-hot encoding |
EC | evolutionary conservation |
MSA | multiple sequence alignment |
rASA | the relative solvent accessible surface area |
PSSM | position-specific scoring matrix |
CCM | CCMpred |
FCNN | full convolutional neural network |
MAE | mean absolute error |
MAE | mean square error |
ReLU | rectified linear activation function |
ELU | exponential linear unit |
CNN | convolutional neural network |
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Features | TraMAE | TraMSE | TraPCC | ValMAE | ValMSE | ValPCC |
---|---|---|---|---|---|---|
OH | 0.0358 | 0.1254 | 0.7920 | 0.0759 | 0.0102 | 0.2016 |
HHM | 0.0637 | 0.0079 | 0.3378 | 0.0645 | 0.0081 | 0.3321 |
CCM | 0.0387 | 0.0036 | 0.7706 | 0.0447 | 0.0049 | 0.7315 |
OH+HHM | 0.0602 | 0.0067 | 0.3483 | 0.0698 | 0.0089 | 0.3256 |
OH+CCM | 0.0306 | 0.0018 | 0.8586 | 0.0566 | 0.0067 | 0.5888 |
HHM+CCM | 0.0472 | 0.0048 | 0.6983 | 0.0506 | 0.0062 | 0.6825 |
OH+HHM+CCM | 0.0422 | 0.0038 | 0.7253 | 0.0504 | 0.0059 | 0.6864 |
SEB-RB Blocks | TraMAE | TraMSE | TraPCC | ValMAE | ValMSE | ValPCC |
---|---|---|---|---|---|---|
5 | 0.0925 | 0.0163 | 0.1757 | 0.0921 | 0.0137 | 0.2166 |
6 | 0.0470 | 0.0048 | 0.6965 | 0.0492 | 0.0055 | 0.6877 |
7 | 0.0482 | 0.0051 | 0.6932 | 0.0490 | 0.0054 | 0.6837 |
8 | 0.0472 | 0.0048 | 0.6983 | 0.0506 | 0.0062 | 0.6825 |
9 | 0.0471 | 0.0047 | 0.6972 | 0.0493 | 0.0058 | 0.6980 |
10 | 0.0483 | 0.0051 | 0.6932 | 0.0484 | 0.0054 | 0.6884 |
11 | 0.0463 | 0.0047 | 0.7091 | 0.0474 | 0.0052 | 0.7013 |
12 | 0.0447 | 0.0045 | 0.7222 | 0.0483 | 0.0052 | 0.7105 |
13 | 0.0458 | 0.0046 | 0.7173 | 0.0480 | 0.0055 | 0.6973 |
14 | 0.0433 | 0.0041 | 0.7243 | 0.0481 | 0.0055 | 0.6991 |
15 | 0.0470 | 0.0048 | 0.7030 | 0.0481 | 0.0054 | 0.6942 |
16 | 0.0449 | 0.0044 | 0.7225 | 0.0489 | 0.0056 | 0.6997 |
17 | 0.0425 | 0.0040 | 0.7335 | 0.0464 | 0.0052 | 0.7104 |
Layers | TraMAE | TraMSE | TraPCC | ValMAE | ValMSE | ValPCC |
---|---|---|---|---|---|---|
1 | 0.0438 | 0.0042 | 0.7170 | 0.0478 | 0.0054 | 0.6911 |
2 | 0.0474 | 0.0048 | 0.6952 | 0.0509 | 0.0062 | 0.6726 |
3 | 0.0447 | 0.0045 | 0.7222 | 0.0483 | 0.0052 | 0.7105 |
4 | 0.0456 | 0.0046 | 0.7161 | 0.0470 | 0.0054 | 0.7061 |
Function | TraMAE | TraMSE | TraPCC | ValMAE | ValMSE | ValPCC |
---|---|---|---|---|---|---|
ELU | 0.0502 | 0.0054 | 0.6628 | 0.0504 | 0.0056 | 0.6602 |
ReLU | 0.0447 | 0.0045 | 0.7222 | 0.0483 | 0.0052 | 0.7105 |
Features | MAE | MSE | PCC |
---|---|---|---|
CCM | 0.0473 | 0.0054 | 0.7238 |
HHM+CCM | 0.0504 | 0.0055 | 0.6999 |
OH+HHM+CCM | 0.0522 | 0.0063 | 0.6878 |
Threshold | ACC | Precision | Recall | F1 | MCC |
---|---|---|---|---|---|
5.5 | 0.9825 | 0.9055 | 0.1725 | 0.2755 | 0.3667 |
6 | 0.9777 | 0.9434 | 0.1837 | 0.2950 | 0.3887 |
6.5 | 0.9737 | 0.9607 | 0.1951 | 0.3128 | 0.4066 |
7 | 0.9725 | 0.9666 | 0.2219 | 0.3483 | 0.4372 |
7.5 | 0.9728 | 0.9682 | 0.2658 | 0.4021 | 0.4814 |
8 | 0.9736 | 0.9656 | 0.3221 | 0.4654 | 0.5313 |
8.5 | 0.9728 | 0.9619 | 0.3631 | 0.5089 | 0.5651 |
9 | 0.9697 | 0.9620 | 0.3794 | 0.5274 | 0.5788 |
9.5 | 0.9684 | 0.9655 | 0.4039 | 0.5534 | 0.5997 |
10 | 0.9645 | 0.9676 | 0.4065 | 0.5576 | 0.6020 |
10.5 | 0.9594 | 0.9674 | 0.3973 | 0.5498 | 0.5940 |
11 | 0.9546 | 0.9647 | 0.3895 | 0.5422 | 0.5859 |
Model | ProNum | ACC | Precision | Recall | F1 | MCC |
---|---|---|---|---|---|---|
PSICOV | 128 | 0.0011 | 0.9062 | 0.0011 | 0.0022 | 0.0000 |
Freecontact | 178 | 0.0612 | 0.9831 | 0.0612 | 0.0959 | 0.0000 |
DEEPCON | 178 | 0.0024 | 0.9944 | 0.0024 | 0.0047 | 0.0000 |
TMP-SurResD | 178 | 0.9736 | 0.9656 | 0.3221 | 0.4654 | 0.5313 |
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Chen, Q.; Guo, Y.; Jiang, J.; Qu, J.; Zhang, L.; Wang, H. The Relative Distance Prediction of Transmembrane Protein Surface Residue Based on Improved Residual Networks. Mathematics 2023, 11, 642. https://doi.org/10.3390/math11030642
Chen Q, Guo Y, Jiang J, Qu J, Zhang L, Wang H. The Relative Distance Prediction of Transmembrane Protein Surface Residue Based on Improved Residual Networks. Mathematics. 2023; 11(3):642. https://doi.org/10.3390/math11030642
Chicago/Turabian StyleChen, Qiufen, Yuanzhao Guo, Jiuhong Jiang, Jing Qu, Li Zhang, and Han Wang. 2023. "The Relative Distance Prediction of Transmembrane Protein Surface Residue Based on Improved Residual Networks" Mathematics 11, no. 3: 642. https://doi.org/10.3390/math11030642
APA StyleChen, Q., Guo, Y., Jiang, J., Qu, J., Zhang, L., & Wang, H. (2023). The Relative Distance Prediction of Transmembrane Protein Surface Residue Based on Improved Residual Networks. Mathematics, 11(3), 642. https://doi.org/10.3390/math11030642