An Exhaustive Shape-Based Approach for Proteins' Secondary, Tertiary and Quaternary Structures Indexing, Retrieval and Docking
Bibliographic record
Abstract
Over the past ten years, the number of three-dimensional protein structures has grown exponentially This is due, mainly, to the advent of high throughput systems. Consequently, molecular biologists need systems to enable them to effectively store, manage and explore these vast repositories of three-dimensional structures. They want to determine if an unknown structure is in fact a new one, if it has been subjected to a mutation, and/or to which family it possibly belongs. Furthermore, they require the ability to find similar proteins in terms of functionalities. Importantly, they aim to find docking sites. That is, they aim to determine the possible sites for the binding of two proteins, namely the ligand and the receptor, in order to form a stable complex. This similarity in functionality, and specifically the task to find docking sites, are related to outer the shape of the protein The outer shape (or envelope), in part, determines whether two proteins may have similar functionalities and may thus aid us to determine the location of such protein binding sites. The previously introduced docking problem may be better understood from the perspective of drug design. Most diseases and drugs work on the same basic principle. When we become ill, a foreign protein docks itself on a healthy protein and modified its functionality. Such a docking is possible if the two proteins have two subregions that are compatible in terms of three-dimensional shape, a bit like two pieces of a puzzle. Drugs are designed to act in a similar way. Namely, a drug docks on the same active site and prevents the docking of foreign proteins which can potentially cause illness (Paquet and Viktor, 2010).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".