bioRxivBioinformaticsPreprint
EditorForge: An Active-Site-Aware Framework for Inverse-Folding-Based Protein Redesign
Anthony Chen, Jibraan Siddiqui, William Taucar, Leo Tiralongo, Maxim Tkachenko, Andy Xu, Sehaj Bawa, Sihan Guo, Owen Pinska, Jonathan Rim, Joshua Shi, Michael Wang, Eason Zhao
Abstract
Inverse-folding models can rapidly generate protein sequences compatible with a supplied backbone, but unconstrained redesign is poorly suited to enzyme and genome-editor-associated domains, where catalytic, substrate-proximal, and conserved structural regions must remain protected. In this paper, we present EditorForge, a modular constraint-and-audit suite for editor-domain protein redesign that wraps fixed-backbone inverse folding with explicit design masks, fixed-position enforcement, active-site-proximity auditing, active-site-shielded regeneration, and downstream structural quality control. Using full-length Moloney murine leukemia virus reverse transcriptase structure 4MH8 (MMLV RT 4MH8) as a demonstration target, EditorForge first restricted redesign to a bounded 25-position envelope while fixing 428 residues. An initial audit detected active-site-proximal failure modes despite fixed-position integrity. Later, the Active Site Shield module then removed five unsafe design positions, replaced them with lower-contact alternatives, and regenerated candidates under stricter constraints. Post Shield Audit evaluated 24 regenerated candidates, all of which satisfied the hard sequence/mask and active-site-shield constraints. For the eight candidates that were selected or returned for structure-prediction/refolding quality control, Enhanced RefoldQC found that all 8 evaluated predicted structures passed the computational structure-QC screen. That said, the selected 8 candidates passed the computational structure-QC screen, with global C RMSD values of 1.2061-1.5555 {degrees}A, active-site C RMSD values of 0.4098-1.8397 {degrees}A, mutation-neighborhood C RMSD values of 1.3155-1.6848 {degrees}A, and average pLDDT-like confidence values of 94.87-95.11. In short, EditorForge provides a reproducible triage layer that converts general inverse-folding output into constrained and editor-specific candidate sets for downstream structural and biological review on top of existing structural prediction tools.
- assay: protein structure
- research focus: protein design
- approach: deep learning
- approach: statistical model
- Citations
- 0
Reproducibility ledger
Nobody has reported trying to reproduce this paper yet. Did you replicate an experiment or run the code? Your report saves the next lab a week.
Sign in to file a reproducibility report.
Related papers
- Inverse FoldDir: Structure-conditioned Protein Sequence Design by Dirichlet Flow Matching
Alp Tartici, Mihajlo Stojković, … Bruce J. Wittmann·bioRxiv·Sep 11, 2026
- CatIF-RL: Activity-Oriented Enzyme Sequence Design by Steered Inverse Protein Folding
Yanheng Li, Jialong Xiong, … Feng Wang·Journal of Chemical Information and ModelingPeer-reviewed·Jul 17, 2026
- Computational enzyme design by catalytic motif scaffolding
Markus Braun, Adrian Tripp, … Gustav Oberdorfer·NaturePeer-reviewed·Dec 3, 2025
- A Deep SE(3)-Equivariant Model for Learning Inverse Protein Folding
Matt McPartlon, Ben Lai, Jinbo Xu·bioRxiv·Apr 15, 2022
- De Novo Protein Fold Design Through Sequence-Independent Fragment Assembly Simulations
Robin Pearce, Xiaoqiang Huang, … Yang Zhang·Proceedings of the National Academy of SciencesPeer-reviewed·Jan 19, 2023
- Proteus software for physics-based protein design
D. Mignon, K. Druart, … T. Simonson·bioRxiv·Jun 30, 2020