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Updated: Jul 10, 2026
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.
Objective:
Proliferative vitreoretinopathy (PVR) remains a major cause of failure after rhegmatogenous retinal detachment repair and lacks effective pharmacologic therapies. Although epithelial-mesenchymal transition (EMT) is central to PVR pathogenesis, the structural determinants governing the tractability of EMT regulators, particularly those involving intrinsic disorder, remain poorly defined. We developed a disorder-aware, artificial intelligence-enabled computational framework to evaluate EMT-associated proteins in PVR and prioritize structurally tractable regulators for structure-based targeting.
Design:
A computational, hypothesis-generating study employing an in silico screening and structural modeling pipeline.
Subjects:
No human subjects or biological specimens were included. The dataset comprised 25 EMT-associated proteins implicated in PVR, curated through a narrative review of peer-reviewed literature.
Methods:
Candidate proteins were evaluated using a multistage pipeline integrating intrinsic disorder profiling (Rapid Intrinsic Disorder Analysis Online), redox-sensitive disorder-to-order transition (DOT) analysis (AIUPred), and protein-protein interaction network assessment (Search Tool for the Retrieval of Interacting Genes/Proteins [STRING]). Structure-based modeling and generative binder design were then applied to the top-ranked candidate using RFdiffusion for de novo backbone generation, protein message passing neural network for sequence design, and AlphaFold2 for structural validation.
Main Outcome Measures:
Primary measures were the proportion of intrinsically disordered residues, redox-sensitive disorder change, STRING network coherence within EMT-related pathways, and the structural consistency of the designed binder-target complex, assessed by root mean square deviation (RMSD) and mean per-residue confidence (predicted local distance difference test [pLDDT]).
Results:
Of the 25 EMT-associated proteins screened, several exhibited intermediate intrinsic disorder profiles and measurable DOT potential. Snail Family Transcriptional Repressor 1 (SNAIL1) emerged as the highest-priority candidate, demonstrating an intermediate intrinsic disorder profile (∼35%), a pronounced redox-sensitive DOT region, and selective connectivity within EMT-related signaling networks. Functional mapping of the SNAIL1 C-terminal DOT segment identified 6 basic residues with literature-supported or motif-based regulatory significance (K187, R191, R224, K234, K253, and R264). Following sequence design and structural validation, the top-ranked binder exhibited the lowest structural deviation within the generated ensemble (RMSD 18.5 Å) and high per-residue confidence (mean pLDDT 0.84).
Conclusions:
Our study introduces a disorder-informed computational framework for prioritizing structurally tractable EMT regulators in PVR. As a proof-of-concept, the pipeline nominates SNAIL1 and generates a structure-aware de novo binder targeting its C-terminal DOT region, providing a foundation for disorder-based therapeutic discovery in fibrotic retinal disease.
Financial Disclosures:
Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.
Insights
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.
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.
