<aside> 💵 5500 VITA ➕ 3500 USDC | Longevity Hackathon 1st Prize 1000 USDT | PrivateAI

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Presentation

https://youtu.be/Gt-XDLevySw?feature=shared

Introduction

Lifelong accumulation of advanced glycation end products (AGEs) contributes to the development of age-related pathologies. Non-enzymatic modification of long-turnover proteins – like those in the extracellular matrix (ECM) – is proposed as a new hallmark of aging (1). Consequently, the removal of AGEs and/or prevention of their formation is a promising therapeutic modality aimed at mitigating physiological deterioration associated with advanced age.

Goal

Our goal is creation of such therapies using ML/AI, in silico screens, and modeling tools, as well as the subsequent (pre-)clinical product development.

Scope

• Enzymatic or chemical breakage of covalent crosslinks formed as a result of non-enzymatic glycosylation (glycation) of the extracellular matrix proteins, such as collagens (including means of delivery, ADME-Tox, etc.)
• Engineered biologics that remove noxious metabolic precursors of AGEs.
• Small molecule inhibitors/attenuators of glycation: dicarbonyl scavengers, transition metal chelators, transglycators, etc.
• Stimulators of cellular defense mechanisms against electrophilic stress.

Product development tracks

Tentatively, our approach to alleviating the glycation burden is conceptually organized into four parallel product development tracks:

• [HACK-01] De novo active site modeling based on substrate and catalyzed reaction inputs. Essentially, we’ll build an active site around the substrate of choice so that the desired reaction is catalyzed. From the practical standpoint, the most interesting substrate would be glucosepane, which is the most abundant crosslink in the ECM (2).
• [HACK-02] Predicted protein conformations screening for suitable active sites. Recently, Meta.AI reported on predicting conformations of 600M proteins from metagenomic sequence data (3). Our task would be screening these proteins for a suitable active site against modeled catalytic determinants from the previous step.
• [HACK-03] In silico simulation of directed evolution of deglycating enzymes. AGEs are irreversible and get released from proteins only when the latter are broken down during normal turnover. Long-turnover proteins, however, continuously accrue glycation adducts/crosslinks. Several deglycase enzymes that remove advanced glycation adducts have been described: MnmC restores lysines from CELs in free amino acid and peptidomimetic contexts (4); glyoxalase Glo2 acts as a deglycase for hemithioacetal in (methyl)glyoxalated glutathione (5). These two enzymes could be starting points for directed evolution of a therapeutic enzyme with broad antiglycation specificities.
• [HACK-04] Small molecule agonists of Glo1. Excessive production of (methyl)glyoxal dicarbonyl compounds can potentially be dampened by enhancing cellular detoxification machinery represented by the Glo1/2 glyoxalase duo. Small-molecule agonists of Glo1 can be utilized to expedite the detoxification process gaining a therapeutic effect in diabetes, neurodegeneration, and aging (6, 7).

References

1. Fedintsev, A., and Moskalev, A. (2020) Stochastic non-enzymatic modification of long-lived macromolecules - A missing hallmark of aging. Ageing Res Rev. 62, 101097