Computationally Driven Discovery and Characterization of SIRT3 Activating Compounds that Fully Recover Catalytic Activity under NAD <sup>+</sup> Depletion
Bibliographic record
Abstract
ABSTRACT Mammalian sirtuins (SIRT1-SIRT7) are a family of nicotinamide adenine dinucleotide (NAD + )-dependent protein deacylases that play critical roles in lifespan and age-related diseases. The physiological importance of sirtuins has stimulated intense interest in designing sirtuin activating compounds. However, except for allosteric activators of SIRT1-catalyzed reactions that are limited to specific substrates, methodologies for the rational design of sirtuin activating compounds -- including compounds that activate mitochondrial sirtuins implicated in the age-related decline of cellular metabolism -- have been lacking. Here, we use computational high-throughput screening methodologies and a biophysical model for activation of the major mitochondrial sirtuin SIRT3 to identify novel small molecule activators of the human SIRT3 enzyme from a 1.2 million compound library. Unlike previously reported SIRT3 activators like Honokiol, which only transiently upregulate SIRT3 under non-steady state conditions and reduce the steady state catalytic efficiency of the enzyme, several of the novel compounds identified here are potent SIRT3 activators in both the steady and non-steady states. Two such compounds can almost double the catalytic efficiency of the enzyme with respect to NAD + , which would be sufficient to almost entirely compensate for the loss in SIRT3 activity that occurs due to the reduction in mitochondrial coenzyme concentration associated with aging, and display AC50s (concentrations of half-maximal activation) as low as 100 nM. The current work thus reports first-in-class, non-allosteric steady state activators that activate SIRT3 through a novel, mechanism-based mode of activation and that may be developed further for therapeutic applications.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".