From structural biology to designing therapy for inborn errors of metabolism
Bibliographic record
Abstract
At the SSIEM Symposium in Istanbul 2010, I presented an overview of protein structural approaches in the study of inborn errors of metabolism (Yue and Oppermann 2011). Five years on, the field is going strong with new protein structures, uncovered catalytic functions and novel chemical matters for metabolic enzymes, setting the stage for the next generation of drug discovery. This article aims to update on recent advances and lessons learnt on inborn errors of metabolism via the protein-centric approach, citing examples of work from my group, collaborators and co-workers that cover diverse pathways of transsulfuration, cobalamin and glycogen metabolism. Taking into consideration that many inborn errors of metabolism result in the loss of enzyme function, this presentation aims to outline three key principles that guide the design of small molecule therapy in this technically challenging field: (1) integrating structural, biochemical and cell-based data to evaluate the wide spectrum of mutation-driven enzyme defects in stability, catalysis and protein-protein interaction; (2) studying multi-domain proteins and multi-protein complexes as examples from nature, to learn how enzymes are activated by small molecules; (3) surveying different regions of the enzyme, away from its active site, that can be targeted for the design of allosteric activators and inhibitors.
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 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".