Harnessing Structural Data of Insulin and Insulin Receptor for Therapeutic Designs
Bibliographic record
Abstract
To lower blood glucose concentration, insulin binds to insulin receptor (IR) that possesses two distinct insulin binding sites to trigger downstream signaling events leading to an increased uptake of glucose into muscle and fat cells. Comprehensive understandings of structural and dynamic mechanisms of insulin and its receptor are essential to design therapeutic agents for treating and delaying the onset of diabetes that affects over 347 million people worldwide. No full-length IR structure is available hitherto. Harnessing the currently available and state-of-the-art sequence and structural data, we have reviewed the insulin, IR, its extracellular domains and transmembrane domain, to derive structure-based clues to regulate aberrant insulin and its receptor. To propose testable hypotheses and future experiments, we have performed literature review, text mining, multiple structural clustering and normal mode analysis on insulin and its receptor. It appears that insulin-receptor interaction involves allostery and conformational changes (including rotation and tilting) to overcome steric clashes. To target a particular aberrant isoform of IRs, we need to identify the subtle yet distinct differences between IR isoforms. To improve the life quality of diabetics, better structure-based designs of insulin mimetics, formulation and nanotechnology-based delivery are required; efforts to bring them to patients necessitate thorough structural understandings of insulin and its receptor. J Endocrinol Metab. 2015;5(5):273-283 doi: http://dx.doi.org/10.14740/jem302w
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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) | 0.000 | 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 | 0.000 | 0.000 |
| 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".