The Use of Internal and External Functional Domains to Improve Transmembrane Protein Topology Prediction
Bibliographic record
Abstract
I hereby declare that I am the sole author of this thesis. This is a true copy of the thesis, including any required final revisions, as accepted by my examiners. I understand that my thesis may be made electronically available to the public. ii Membrane proteins are involved in vital cellular functions and have important implications in disease processes, drug design and therapy. However, it is difficult to obtain diffraction quality crystals to study transmembrane protein structure. Transmembrane protein topology prediction tools try to fill in the gap between abundant number of transmembrane proteins and scarce number of known membrane protein structures (3D structure and biochemically characterized topology). However, at present, the prediction accuracy is still far from perfect. TMHMM is the current state-of-the-art method for membrane protein topology prediction. In order to improve the prediction accuracy of TMHMM, based upon the method of GenomeScan, the author implemented AHMM (augmented HMM) by incorporating functional domain information externally to TMHMM. Results show that AHMM is better than TMHMM on both helix and sidedness prediction. This improvement is verified by both statistical tests as well as sensitivity and specificity studies. It is expected that when more and more functional domain predictors are available, the prediction accuracy will be further improved. iii Acknowledgements The research is supervised by Dr. Paul Kearney along with help from Dr. Daniel G. Brown in an early course project. My readers are Dr. Daniel G. Brown and Dr. Ming Li. I would also like to thank my colleagues Ms. Brona Brejova, Mr. John Tsang and Mr. Mike Hu from the University of Waterloo, Dr. Peter Ehlers and Dr. Andrei Turinsky from the University of Calgary for their helpful discussions and Dr. Michel Dominguez from Caprion for his opinion on functional domain sidedness. This thesis is dedicated to God for His love, mercy and grace. iv
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".