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Record W4403638937 · doi:10.1103/physrevx.14.041019

Computationally Driven Discovery and Characterization of SIRT3-Activating Compounds that Fully Recover Catalytic Activity under <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:mrow> <mml:msup> <mml:mrow> <mml:mi>NAD</mml:mi> </mml:mrow> <mml:mo>+</mml:mo> </mml:msup> </mml:mrow> </mml:math> Depletion

2024· article· en· W4403638937 on OpenAlexaff
Xiangying Guan, Rama Krishna Dumpati, Sudipto Munshi, Santu Chall, Rahul Bose, Ali Rahnamoun, Célina Reverdy, Gauthier Errasti, Thomas Delacroix, Anisha Ghosh, Raj Chakrabarti

Bibliographic record

VenuePhysical Review X · 2024
Typearticle
Languageen
FieldMedicine
TopicSirtuins and Resveratrol in Medicine
Canadian institutionsMcGill University
Fundersnot available
KeywordsComputer scienceCharacterization (materials science)Combinatorial chemistryChemistryMaterials scienceNanotechnology

Abstract

fetched live from OpenAlex

Many chronic, age-related disorders could be mitigated by enhancing the activity of enzymes that regulate biochemical signaling pathways, but all known modes of enzyme activation rely on allosteric binding sites, which have only been identified in less than 10% of proteins. Sirtuins (SIRT1-7) are nicotinamide adenine dinucleotide ( <a:math xmlns:a="http://www.w3.org/1998/Math/MathML" display="inline"> <a:mrow> <a:msup> <a:mrow> <a:mi>NAD</a:mi> </a:mrow> <a:mo>+</a:mo> </a:msup> </a:mrow> </a:math> )-dependent deacylases playing critical roles in lifespan and age-related diseases. The physiological importance of sirtuins and the reduction in their catalytic activity with the age-related decline in <c:math xmlns:c="http://www.w3.org/1998/Math/MathML" display="inline"> <c:mrow> <c:msup> <c:mrow> <c:mi>NAD</c:mi> </c:mrow> <c:mo>+</c:mo> </c:msup> </c:mrow> </c:math> levels have stimulated intense interest in designing sirtuin-activating compounds; however, except for substrate-specific allosteric SIRT1 activators, methodologies for rational design of sirtuin-activating compounds are lacking. Here, we introduce methods for the activation of such enzymes that do not rely on allosteric binding sites, and we demonstrate their successful application to the discovery of first-in-class activators of sirtuin enzymes. We establish how all-atom simulations of an enzyme’s active site under the potential of a small molecule modulator can be used to identify molecular properties that achieve desired changes in local enzyme conformational degrees of freedom conducive to the enhancement of catalytic activity. We apply computational high-throughput screening based on this biophysical model for activation of sirtuin enzymes to the major mitochondrial sirtuin SIRT3, which plays a critical role in age-related disorders but does not have a known allosteric site; we thereby identify first-in-class, nonallosteric activators of this enzyme. These compounds are the first reported steady-state activators of the major mitochondrial sirtuin enzyme, and they operate according to a mode of action not shared by any existing drug. Two such compounds can almost double the catalytic efficiency of SIRT3 with respect to <e:math xmlns:e="http://www.w3.org/1998/Math/MathML" display="inline"> <e:mrow> <e:msup> <e:mrow> <e:mi>NAD</e:mi> </e:mrow> <e:mo>+</e:mo> </e:msup> </e:mrow> </e:math> , thus compensating for the loss in SIRT3 activity that occurs due to the age-related decline in <g:math xmlns:g="http://www.w3.org/1998/Math/MathML" display="inline"> <g:mrow> <g:msup> <g:mrow> <g:mi>NAD</g:mi> </g:mrow> <g:mo>+</g:mo> </g:msup> </g:mrow> </g:math> , and they may be developed for therapeutic applications to combat multiple types of age-related disorders. These discoveries establish a foundation for the development of a new class of drugs that function through the activation of enzymes by modulation of local conformational ensembles. Published by the American Physical Society 2024

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.938
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.022
GPT teacher head0.275
Teacher spread0.252 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

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