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Related Concept Videos

Diabetic Retinopathy01:27

Diabetic Retinopathy

DefinitionDiabetic retinopathy is a microvascular complication of diabetes affecting the retinal blood vessels.Risk FactorsDiabetic retinopathy is present in almost all individuals with type 1 diabetes and more than 60% of those with type 2 diabetes after two decades of disease.The risk increases with poor glycemic control, hypertension, dyslipidemia, smoking, pregnancy, and puberty.Although cataracts and glaucoma are also more frequent in people with diabetes, retinopathy remains the leading...

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Related Experiment Video

Updated: Jul 10, 2026

Quantification of Vascular Parameters in Whole Mount Retinas of Mice with Non-Proliferative and Proliferative Retinopathies
12:28

Quantification of Vascular Parameters in Whole Mount Retinas of Mice with Non-Proliferative and Proliferative Retinopathies

Published on: March 12, 2022

A Disorder-Aware Computational Framework to Identify Structurally Tractable Targets in Proliferative

Mak B Djulbegovic1, Nedym Hadzijahic2, David J Taylor Gonzalez3

  • 1Wills Eye Hospital, Thomas Jefferson University Hospital, Philadelphia, Pennsylvania.

Ophthalmology Science
|July 9, 2026
PubMed
Summary

This study developed an AI framework to identify drug targets for proliferative vitreoretinopathy (PVR). The approach prioritized SNAIL1, a key protein in epithelial-mesenchymal transition (EMT), and designed a potential therapeutic binder.

Keywords:
Artificial intelligenceEpithelial–mesenchymal transitionIntrinsic disorderProliferative vitreoretinopathySNAIL1

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Application of Optical Coherence Tomography to a Mouse Model of Retinopathy
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Area of Science:

  • Ophthalmology
  • Computational Biology
  • Drug Discovery

Background:

  • Proliferative vitreoretinopathy (PVR) is a leading cause of vision loss after retinal detachment surgery.
  • Current treatments for PVR lack efficacy, and pharmacologic options are limited.
  • Epithelial-mesenchymal transition (EMT) is a key driver of PVR, but its regulators are poorly understood.

Purpose of the Study:

  • To develop a computational framework to identify and prioritize drug targets for PVR.
  • To evaluate EMT-associated proteins for structural tractability using artificial intelligence (AI).
  • To enable structure-based drug design for PVR treatment.

Main Methods:

  • A computational pipeline was used to screen 25 EMT-associated proteins implicated in PVR.
  • Methods included intrinsic disorder profiling, redox-sensitive disorder-to-order transition (DOT) analysis, and protein-protein interaction network assessment.
  • Structure-based modeling and AI-driven binder design (RFdiffusion, AlphaFold2) were applied to prioritize candidates.

Main Results:

  • The framework identified several proteins with intrinsic disorder and DOT potential.
  • Snail Family Transcriptional Repressor 1 (SNAIL1) was prioritized due to its disorder profile, DOT potential, and network connectivity.
  • A de novo binder targeting SNAIL1's disordered region was designed with high structural confidence.

Conclusions:

  • A novel AI-driven computational framework can prioritize structurally tractable EMT regulators for PVR.
  • SNAIL1 is a promising therapeutic target for PVR.
  • The study provides a foundation for developing disorder-based therapies for fibrotic retinal diseases.