New insights into template-based protein modeling techniques
Bibliographic record
Abstract
introduction: While the development of genomic sequencing methods has greatly improved the efficiency of collecting sequence data, experimental methods to obtain structure information have been lagging significantly. In order to elucidate protein structures, researchers have developed computational structural modeling techniques such as homology modeling and fold recognition (threading). The general consensus is that homology modeling is a superior approach with templates of high sequence similarity to the desired target (>30%), whereas threading is better suited for lower (<30%) sequence similarity templates. We compared recently improved threading algorithms with homology modeling to test the validity of this consensus. Methods: The most current versions of moDelleR and I-TasseR were used for model generation. We then used common assessment criteria (n-Dope, Q-mean and pRoCheCK) to verify the validity of the models. structure comparisons were also made using Chimera’s Cα root-mean-square deviation. results: Contrary to our prior expectations, the model determined by threading showed similar or even better assessment results in some criteria compared to the model generated from homology modeling. Furthermore, the structure analysis showed that homology modeling and threading protocols yield models with root-mean-square deviations of under 2 Å when used on protein sequences that share sequence identities of at least 30% to the experimentally determined protein template. discussion: We believe that recent improvements in threading algorithms will allow for broader applications of this methodology in large-scale modeling efforts. The fully automated steps could provide time efficacy. In contrast to popular belief in the modeling community, we have shown that threading could be a competitive means of modeling rather than a mere backup method.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".