Bibliographic record
Abstract
Protein modeling, which consists of a broad range of computational techniques to understand the properties of proteins, has become an integral part of structural biology and drug design. Modeling can be used to predict the secondary structure or folding of a protein based on its sequence alone, to predict the three-dimensional (3-D) structure of a protein based on knowledge of the structure of a related protein, to design new proteins, and also to predict properties that depend on the experimentally determined 3-D structure of a protein. Examples of such properties include drug binding, protein–protein interactions, and interactions with elements in a protein's environment, including ions, lipids, carbohydrates, and nucleic acids. Conformational changes in proteins can be investigated by using molecular dynamics simulations to provide detailed insights into the dynamics of proteins, a crucial aspect of protein function. In recent developments, quantum mechanical calculations have been used much more often to study reactions in proteins. With the ever-rising power of computers, increasingly detailed aspects of protein function can now be investigated by using modeling methods, at a scale and level of detail that is often very difficult or impossible to achieve by an experimental approach. In this chapter, the main principles and techniques involved in protein modeling are introduced. Some reported examples will also be provided to highlight how protein modeling can be used in complementary fashion with other methods.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.107 | 0.081 |
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".