Overview
Intrinsically disordered proteins (IDPs) and intrinsically disordered regions (IDRs) do not remain in one stable three-dimensional structure. Their conformational flexibility is central to signalling, phase separation and molecular recognition, but it also makes them difficult targets for conventional structure-based design.
In a 2025 Nature paper, Liu and colleagues reported a general RFdiffusion-based strategy for designing folded protein binders from the target amino-acid sequence while allowing the target conformation and binder structure to emerge together. The study produced nanomolar binders to diverse disordered targets, validated selected complexes structurally and demonstrated functional effects in cells and in amyloid-related assays.
Why Disordered Proteins Are Hard to Target
Many drug-design methods assume that the target presents a reproducible pocket or interface. IDPs instead populate ensembles of rapidly interconverting conformations. A structural snapshot may capture only one environment-dependent state, and a pocket observed in that state may not be representative of the ensemble.
Disorder is not equivalent to lack of function. Flexible proteins frequently use short motifs, transient secondary structure and context-dependent interactions to regulate cellular processes. Their adaptability can enable one region to interact with multiple partners, but it also creates three practical design problems:
- No single receptor geometry: fixing the target in an arbitrary conformation can bias the design toward a state that is rarely populated.
- Limited interaction surface: short disordered segments may not provide enough pre-organized shape for conventional docking.
- Specificity risk: a binder that mainly recognizes a common helix or β-strand pattern may cross-react with unrelated proteins that adopt similar local structure.
The authors note that IDPs and proteins containing IDRs represent a substantial fraction of the human proteome. This makes a general binder-design method potentially valuable for research reagents, biomarker capture and future therapeutic engineering, provided specificity and biological delivery can be controlled.

Sequence-Input Diffusion and Conformational Co-Sampling
The design strategy adapts RFdiffusion so that target backbone coordinates are not required as a fixed input. The target sequence is provided while part or all of its structure is noised. During iterative denoising, both target and binder conformations are sampled. The resulting complex is then sequence-designed and filtered for structural confidence and interface quality.
Two-sided partial diffusion
Initial amylin designs bound in the 100–454 nM range. To improve affinity, the team used two-sided partial diffusion, in which both the binder and target are partially noised and jointly denoised. Allowing the target to adapt while preserving information from a parent complex improved shape complementarity. Among 174 top-ranked optimized amylin designs, 107 bound the target; the best reported binders covered distinct target conformations with dissociation constants from 3.8 to 100 nM.
Secondary-structure specification
For shorter regions with β-strand propensity, the authors modified the model so that the target sequence could be paired with a desired local secondary structure without prescribing full three-dimensional coordinates. This strategy was used for G3BP1, IL-2RG and prion protein segments and improved success for challenging short IDRs.

A Broad Panel of Disordered Targets
The study tested targets with different lengths, sequence properties and biological roles. Sequence-input diffusion generated binders for amylin, C-peptide, VP48 and a BRCA1-associated construct in diverse conformations. Secondary-structure-specified diffusion was used for G3BP1, IL-2RG and prion protein regions.
| Target | Design consideration | Reported result |
|---|---|---|
| Amylin | Short disordered hormone with a functionally important disulfide bond and amyloid-forming behaviour. | Multiple binders recognized distinct conformations; the strongest reported affinity was 3.8 nM. |
| C-peptide | Small and highly dynamic peptide with limited pre-organized structure. | Sequence-input designs were obtained and the predicted interface was supported by saturation mutagenesis. |
| G3BP1 | An IDR involved in RNA-dependent phase separation and stress-granule biology. | A strand-specified binder reached nanomolar affinity and altered phase-separation behaviour. |
| IL-2RG | Short receptor region requiring local structural specification. | Designed binder showed target engagement in a cellular colocalization assay. |
| Prion protein | A disordered region with safety considerations for cellular testing. | High-affinity designs were generated; cellular work was excluded for safety reasons. |
Structural and Specificity Validation
Computational design becomes compelling only when the intended binding mode is experimentally supported. The team solved crystal structures for selected amylin and G3BP1 complexes. The G3BP1-11 complex showed a Cα RMSD of approximately 0.8 Å between the designed and experimental complex, while the amylin-22 complex reproduced the designed groove and interface geometry at high resolution.
When a C-peptide complex could not be crystallized, site-saturation mutagenesis provided a lower-resolution footprint: substitutions at the predicted interface and binder core were less tolerated, supporting the model. An all-by-all binding panel showed strong target specificity overall, with weak cross-reactivity observed only at high concentrations in two tested cases.
From Binding to Functional Control
Cellular target engagement
Fluorescence colocalization experiments showed that multiple designed proteins could fold and engage their corresponding full-length targets in mammalian cells. Interface mutations disrupted colocalization for selected short targets, providing additional evidence that recognition depended on the designed contact surface.
Regulating G3BP1 phase separation
G3BP1 is a central regulator of stress-granule assembly. The G3BP1-11 binder shifted the G3BP1–RNA phase boundary in vitro and reduced arsenite-induced puncta formation in cells. This result illustrates how an IDR binder can serve as a mechanistic tool, not only a capture reagent.
Inhibiting and reversing amylin fibrils
At a 1:4 binder-to-amylin molar ratio, the tested binders strongly inhibited fibril formation. Amylin-36 also dissociated pre-formed fibrils in a concentration-dependent manner. When fused to receptor-targeting EndoTags, the binder promoted cellular uptake and lysosomal trafficking of monomeric and fibrillar amylin. These experiments demonstrate functional potential, but they do not establish therapeutic efficacy in animals or patients.

Why This Work Matters
The key advance is methodological. Instead of choosing one uncertain structure for a disordered target, the model searches target and binder conformations together. This reframes disorder from an obstacle that must be removed into a design variable that can be sampled.
The approach could support several research directions:
- conformation-selective probes for dissecting IDP biology;
- reagents for biomarker enrichment and detection;
- binders that modulate phase separation or transient protein interactions;
- starting points for targeted localization or degradation strategies; and
- new ways to interrogate proteins that lack persistent small-molecule pockets.
These opportunities should be separated from therapeutic readiness. Designed binders must still be optimized for specificity, stability, expression, delivery, immunogenicity and performance in disease-relevant models.
Limitations and Practical Design Risks
- Large computational funnels: approximately 10,000–50,000 diffused designs were generated for each target before filtering to experimentally manageable sets.
- Optimization is often required: initial hits may need partial diffusion or additional sequence refinement to reach strong affinity.
- Conformation-dependent specificity: binders may recognize a local secondary-structure pattern shared by off-target proteins.
- Structural validation is not always available: crystallization can fail, requiring mutational or biophysical alternatives.
- Biological context matters: intracellular localization, post-translational modification, partner competition and target concentration can change performance.
- Therapeutic translation remains unproven: the reported results are research-stage demonstrations, not evidence of clinical safety or efficacy.
How CD ComputaBio Can Support Flexible-Target and Binder Programs
Projects involving IDPs and IDRs require more than a single structure prediction. Target-region selection, ensemble interpretation, binder generation, interface analysis and mutation prioritization should be designed as one connected workflow.
| Research need | Related support | Connection to this study |
|---|---|---|
| Characterize a disordered target sequence | Protein Sequence Analysis Service | Supports region selection, motif review and identification of sequence features relevant to design. |
| Model target and binder structures | Protein Structure Modeling Service | Provides structural hypotheses and confidence assessment for downstream design decisions. |
| Evaluate designed interfaces | Protein-Protein Interactions Analysis Service | Examines contacts, hot spots and interaction patterns across candidate complexes. |
| Assess folding plausibility | Protein Foldability Verification | Helps prioritize binder sequences that are more likely to adopt the intended monomeric structure. |
| Prioritize affinity-improving substitutions | Protein Affinity Maturation Mutation Design | Supports computational mutation selection for affinity and interface optimization. |
| Evaluate mutation effects | Protein Mutation Effect Modeling | Assesses how substitutions may affect structure, stability or binding performance. |
Contact Us
If your target is intrinsically disordered, structurally heterogeneous or difficult to address with conventional pocket-based methods, CD ComputaBio can help define a computational strategy covering target-region analysis, binder modeling, interface assessment and mutation prioritization. Contact our scientific team to discuss your target sequence, biological objective and validation plan.
References
- Liu C, Wu K, Choi H, et al. Diffusing protein binders to intrinsically disordered proteins. Nature. 2025;644:809–817. https://doi.org/10.1038/s41586-025-09248-9
- Watson JL, Juergens D, Bennett NR, et al. De novo design of protein structure and function with RFdiffusion. Nature. 2023;620:1089–1100. https://doi.org/10.1038/s41586-023-06415-8
- Dauparas J, Anishchenko I, Bennett N, et al. Robust deep learning–based protein sequence design using ProteinMPNN. Science. 2022;378:49–56. https://doi.org/10.1126/science.add2187
- Jumper J, Evans R, Pritzel A, et al. Highly accurate protein structure prediction with AlphaFold. Nature. 2021;596:583–589. https://doi.org/10.1038/s41586-021-03819-2
For Research Use Only. This page summarizes published research and describes computational research services. It does not constitute medical advice, a clinical claim or a guarantee of experimental or therapeutic success.