TMP-SSurface: A Deep Learning-Based Predictor for Surface Accessibility of Transmembrane Protein Residues
Abstract
:1. Introduction
2. Results and Discussion
2.1. Feature Analysis
2.2. Effect of Window Size
2.3. Hyper-Parameter Tuning
2.4. Ablation Study
2.5. Comparison with Previous Predictors
2.6. Short Sequence Test
2.7. TMP Type Test
2.8. Case Study
3. Materials and Methods
3.1. Benchmark Datasets
3.2. Calculation of rASA
3.3. Encoding of Protein Fragments
3.4. Model Design
3.5. From Capsule Length to rASA
3.6. Performance Evaluation
4. Conclusions
Supplementary Materials
Author Contributions
Funding
Conflicts of Interest
References
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Method | Year | Samples | Algorithm | TMP Type | Seq Region | Measure |
---|---|---|---|---|---|---|
ProperTM [14] | 2004 | 59 | knowledge | α-TMP | TM region | Burial state |
ASAP [18] | 2006 | 73 | SVR | all TMP | TM region | ASA |
TMX [15] | 2007 | 43 | SVC | α-TMP | TM region | Burial state |
MPRAP [19] | 2010 | 80 | SVR | α-TMP | full sequence | rASA |
Yao et al. (2011) [16] | 2011 | 53 | SVM | α-TMP | TM region | Burial state |
Yao et al. (2012) [20] | 2012 | 122 | RF | all TMP | TM region | ASA |
TMexpoSVR [17] | 2013 | 110 | SVR | α-TMP | TM region | rASA |
TMexpoSVC [17] | 2013 | 110 | SVC | α-TMP | TM region | Burial state |
MenBrain-Rasa [21,22] | 2015 | 80 | SVR | α-TMP | full sequence | rASA |
Feature | CC | MAE |
---|---|---|
One-hot | 0.417 | 0.203 |
PSSM | 0.387 | 0.206 |
One-hot + PSSM | 0.577 | 0.158 |
Window Size | CC |
---|---|
13 | 0.534 |
15 | 0.551 |
17 | 0.576 |
19 | 0.581 |
21 | 0.578 |
23 | 0.565 |
Num of Inception Blocks | No. of Parameters | CC | MAE |
---|---|---|---|
1 | 3,790,671 | 0.506 | 0.203 |
2 | 6,617,295 | 0.537 | 0.170 |
3 | 12,614,607 | 0.579 | 0.157 |
4 | 25,798,927 | 0.568 | 0.164 |
5 | 58,045,711 | 0.577 | 0.158 |
Num of Dynamic Routings | CC | MAE |
---|---|---|
1 | 0.558 | 0.167 |
2 | 0.568 | 0.164 |
3 | 0.577 | 0.158 |
4 | 0.573 | 0.160 |
5 | 0.575 | 0.160 |
6 | 0.569 | 0.164 |
Model | CC | MAE |
---|---|---|
CNN | 0.163 | 0.191 |
Inception | 0.415 | 0.167 |
CapsuleNet | 0.503 | 0.151 |
Without inception | 0.504 | 0.150 |
Without CapsuleNet | 0.422 | 0.166 |
TMP-SSurface | 0.584 | 0.144 |
Predictor | CC | MAE | Failure | Time Cost (min) |
---|---|---|---|---|
MPRAP | 0.397 | 0.176 | 9 | 6.5 |
MemBrain-Rasa | 0.545 | 0.153 | 7 | 23.7 |
TMP-Ssurface | 0.584 | 0.144 | 0 | 4.7 |
Sequenc Length | Sequence Number | CC | MAE |
---|---|---|---|
Less than 30 | 89 | 0.533 | 0.224 |
Testing dataset (30–5000) | 50 | 0.584 | 0.144 |
TMP Types | Protein Number | CC | MAE |
---|---|---|---|
α-helical TMPs | 45 | 0.597 | 0.139 |
β-barrel TMPs | 5 | 0.511 | 0.151 |
all-TMP | 50 | 0.584 | 0.144 |
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Lu, C.; Liu, Z.; Kan, B.; Gong, Y.; Ma, Z.; Wang, H. TMP-SSurface: A Deep Learning-Based Predictor for Surface Accessibility of Transmembrane Protein Residues. Crystals 2019, 9, 640. https://doi.org/10.3390/cryst9120640
Lu C, Liu Z, Kan B, Gong Y, Ma Z, Wang H. TMP-SSurface: A Deep Learning-Based Predictor for Surface Accessibility of Transmembrane Protein Residues. Crystals. 2019; 9(12):640. https://doi.org/10.3390/cryst9120640
Chicago/Turabian StyleLu, Chang, Zhe Liu, Bowen Kan, Yingli Gong, Zhiqiang Ma, and Han Wang. 2019. "TMP-SSurface: A Deep Learning-Based Predictor for Surface Accessibility of Transmembrane Protein Residues" Crystals 9, no. 12: 640. https://doi.org/10.3390/cryst9120640
APA StyleLu, C., Liu, Z., Kan, B., Gong, Y., Ma, Z., & Wang, H. (2019). TMP-SSurface: A Deep Learning-Based Predictor for Surface Accessibility of Transmembrane Protein Residues. Crystals, 9(12), 640. https://doi.org/10.3390/cryst9120640