bio-protac-degraders

name	bio-protac-degraders
description	Designs PROTACs, molecular glues, and bivalent degraders with explicit handling of E3 ligase choice (VHL, CRBN, IAP, MDM2, KEAP1), linker design (length, composition, rigidity), ternary complex prediction (PRosettaC, DeepTernary, AlphaFold3), cooperativity (alpha), DC50 / Dmax characterization, hook effect, and prediction-experiment reconciliation. Use when designing targeted protein degraders, planning linker SAR, predicting ternary complex stability, or building generative degrader workflows.
origin	openai4s
category	bioskills/chemoinformatics
metadata	{"tool_type":"python","primary_tool":"PRosettaC","third_party":{"name":"GPTomics/bioSkills","repository":"https://github.com/GPTomics/bioSkills","commit":"d91ed3d563019e649dc854c56ccd62551359488a","license":"MIT"}}

Version Compatibility

Reference examples tested with: PRosettaC (web service), DeepTernary research code, AlphaFold3, Boltz-1 / Boltz-2, RDKit 2024.09+, OpenMM 8.1+ (for ternary MD).

Before using code patterns, verify installed versions match. If versions differ:

• Python: pip show <package> then help(module.function) to check signatures

If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.

PROTAC and Bivalent Degrader Design

Design bifunctional molecules (PROTACs) that recruit an E3 ubiquitin ligase to a target protein, inducing target ubiquitination and proteasomal degradation. PROTACs differ from traditional drugs: a productive ternary complex (target + PROTAC + E3) is required, not just target binding. The modality has produced clinical programs, but their development and regulatory status changes rapidly and must be checked from current sources. PROTAC design balances target ligand binding, E3 ligand binding, linker geometry (length, rigidity, chemistry), cooperativity, dose-dependent ternary-complex formation, and cell permeability. Negative cooperativity and the high-concentration hook effect are distinct phenomena, although cooperativity can influence the dose-response profile.

For target ligand design, see chemoinformatics/virtual-screening and chemoinformatics/admet-prediction. For linker-only enumeration, see chemoinformatics/reaction-enumeration. For generative linker design, see chemoinformatics/generative-design.

E3 Ligase Choice

Recruited UPS component	Ligand series	Published design context	Limitations
VHL	VL-269 (Gechijian et al. 2018)	Published VHL-recruiting degraders	Expression and productive geometry are system-dependent
CRBN (cereblon)	thalidomide, pomalidomide	Extensively used recruiter series	Neosubstrate liabilities depend on recruiter and context
IAP (XIAP, cIAP1)	SMAC-mimetic-derived recruiters	Published IAP-recruiting degraders	Target scope and cellular effects require validation
MDM2	nutlin-derived recruiters	Published MDM2-recruiting degraders	Target diversity and pathway effects require validation
KEAP1	KEAP1-directed recruiters	CUL3-KEAP1 recruitment studies	Specialized use and limited comparative validation
DCAF15	Aryl sulfonamides such as E7820	DDB1-CUL4 / DCAF15 systems	Molecular-glue and degrader mechanisms require careful distinction
RNF114	Nimbolide, EN219	Covalent RNF114 recruitment	Limited tooling
RNF4	CCW16	Covalent RNF4 recruitment	Limited tooling
UBE2D (E2, not E3)	EN450	Covalent molecular-glue mechanism involving NFKB1	Do not classify as an E3-ligase recruiter

Decision: Select an E3 recruiter using evidence for ligand availability, target/E3 geometry, cellular expression, neosubstrate liabilities, and the intended biological system. CRBN and VHL are common starting points with extensive published examples, but neither is a universal first choice.

Linker Design Principles

Linkers tune ternary complex geometry and stability. The ranges below are exploratory starting points, not validated acceptance criteria:

Property	Range	Effect
Linker length	Project-defined enumerated series	Critical; geometry-dependent
Linker rigidity	Flexible (PEG) vs rigid (piperazine, pyridine)	Changes the accessible conformational ensemble
Linker chemistry	PEG, alkyl, piperazine, triazole, ether, amide	PEG common; rigid for tighter binding
Click chemistry compatibility	Triazole-forming routes are one option	Requires route- and attachment-specific synthesis review
Molecular size and polarity	Measure across the designed series	Permeability, solubility, and exposure depend on the complete molecule and its conformations

Critical: The "Goldilocks linker length" is target-specific. Too short can create a ternary clash; too long can impose an unfavorable entropic cost or permit unproductive geometries. Enumerate a series around the geometry supported by the binary structures rather than assuming a universal optimal range.

Decision Tree by Scenario

Goal	E3 / linker	Tools
Initial degrader series	Compare supported recruiter and linker variants	PRosettaC for ternary hypotheses
Reduce recruiter-specific liabilities	Compare alternative E3 recruiters and linker geometries	Structural hypotheses + cellular selectivity validation
Target with prior recruiter-specific evidence	Reproduce the supported recruiter context, then vary deliberately	Match the published and intended biological systems
Targeted protein degradation program	Select E3 using geometry, expression, and liabilities	Structural and experimental validation track
Novel target without an established ternary model	Multiple E3 / linker variants	Combinatorial design + PRosettaC
Molecular glue (non-PROTAC)	Use a glue-specific discovery strategy	Distinct mechanism; do not treat as linker design
Characterize cooperativity	Structural hypotheses plus experiment	ITC or SPR/BLI with matched binary and ternary measurements
Cell-active candidate	Standard development	PK + degradation cellular assays

Ternary Complex Prediction Tools

Tool	Approach	Strength	Fails when
PRosettaC	Constrained PatchDock, RosettaDock refinement, PROTAC conformer generation, repacking, and clustering	PROTAC-specific published workflow	Performance varies by complex; Rosetta/service requirements
DeepTernary	Equivariant deep learning	Fast; SE(3)	OOD chemistry
AlphaFold3	Unrestrained whole-complex prediction	Accepts proteins and ligands	No arbitrary user distance-restraint interface; benchmark PROTAC use
Boltz-1 / Boltz-2	Unrestrained whole-complex prediction	Open local models	Limited PROTAC-specific validation
HADDOCK	Information-driven, restraint-guided docking	Mature integrative docking framework	Manual restraint specification

Decision: Use a PROTAC-specific method such as PRosettaC for first-pass ternary modeling. AlphaFold3 or Boltz can provide unrestrained whole-complex predictions, but should be benchmarked on relevant ternary complexes. DeepTernary is released research code rather than a hosted API; validate it against relevant structures before prospective ranking.

Cooperativity (Alpha)

Cooperativity quantifies how the ternary complex stabilizes (or destabilizes) the binary binding:

alpha = (Kd_binary,target) / (Kd_ternary,target)

• alpha > 1: positive cooperativity (ternary stronger than binary)
• alpha = 1: no cooperativity (independent binding)
• alpha < 1: negative cooperativity (mutual destabilization)

Positive cooperativity can favor ternary-complex formation, but the preferred alpha is system- and assay-dependent and does not alone establish degradation efficacy. Alpha must be measured from binary and ternary binding experiments; a predicted structure does not directly provide it.

Measure with ITC (isothermal titration calorimetry) or SPR/BLI titrations of binary vs ternary.

DC50 / Dmax Characterization

In cellular assays:

• DC50: PROTAC concentration for 50% degradation (analogous to IC50)
• Dmax: maximum fraction degraded at any concentration

Property	What to report	Interpretation
DC50	Concentration producing 50% of the assay's fitted maximal degradation	Compare only across matched assay conditions; no universal clinical cutoff
Dmax	Maximum observed or fitted degradation and uncertainty	Required depletion is target- and phenotype-dependent
Hook effect	Full concentration-response range and concentration of any downturn	A high-concentration effect; its location is system- and assay-dependent
Cooperativity	Alpha from matched binary and ternary binding experiments	Distinct from the hook effect and insufficient by itself to predict degradation

Hook effect: at high PROTAC concentrations, binary complexes (PROTAC-target alone, PROTAC-E3 alone) dominate, and ternary complex formation drops. Dose-response curves are bell-shaped.

Ternary Complex Modeling Workflow

Goal: Predict 3D structure of target-PROTAC-E3 ternary complex.

Approach:

1. Start with binary co-crystals: target + target-ligand pose; E3 + E3-ligand pose
2. Connect via linker enumeration (combinatorial)
3. Score by geometric feasibility (linker length, no clashes)
4. Refine with energy minimization

# Pseudo-code workflow
def predict_ternary(target_pdb, target_ligand_sdf,
                    e3_pdb, e3_ligand_sdf, linker_smiles):
    # 1. Place binary complexes in same coordinate frame
    # 2. Enumerate linker connectivity from target-ligand exit vector to e3-ligand entry vector
    # 3. Score by total linker length, RMSD to expected geometry
    # 4. Apply a documented refinement protocol and test convergence
    return ternary_poses

For a production workflow, use PRosettaC or provide both proteins and the complete PROTAC as components of an unrestrained AlphaFold3 input. AlphaFold3 does not expose arbitrary chain-chain distance restraints; compare predicted interfaces and confidence with known complexes or a PROTAC-specific method.

Linker Geometry Assessment

from rdkit import Chem
from rdkit.Chem import AllChem

def attachment_distance(target_ligand, e3_ligand,
                        target_attachment_idx, e3_attachment_idx):
    """
    Measure an attachment-point distance after both ligands have been placed in
    the same ternary-complex coordinate frame.
    """
    p1 = target_ligand.GetConformer().GetAtomPosition(target_attachment_idx)
    p2 = e3_ligand.GetConformer().GetAtomPosition(e3_attachment_idx)
    return p1.Distance(p2)

An attachment-point distance does not map uniquely to a linker atom count: bond geometry, rigidity, branching, solvation, and the relative protein orientation all matter. Enumerate chemically synthesizable linker candidates, sample their conformers in the ternary geometry, and retain candidates that can connect without severe strain or clashes.

Generative Linker Design

REINVENT 4 can generate linkers, but it does not provide the ternary_score / deepternary interface shown in some informal examples. Export generated candidates, run an installed and validated ternary-prediction workflow separately, and then join the structural scores back to the candidates. Do not assume DeepTernary is a web API or a built-in REINVENT scoring component.

Per-Tool Failure Modes

PRosettaC -- inaccessible E3 in selected ligase

Trigger: Target's known binding mode incompatible with E3 ligase orientation.

Mechanism: The selected binary poses, exit vectors, linker conformations, or protein orientation may not support a compatible ternary geometry.

Symptom: Low ternary complex scores; high RMSD across replicates.

Fix: Try a different E3 such as CRBN or VHL and compare against experimentally resolved ternary complexes with compatible exit-vector geometry.

DeepTernary -- novel chemotype

Trigger: Target ligand or E3 ligand outside training distribution.

Mechanism: A ligand, linker, target, or E3 outside the method's validated domain may require extrapolation.

Symptom: Predicted ternary complex unrealistic.

Fix: Compare with an independently configured structural method and relevant known complexes; validate prospective ranking experimentally.

Hook effect at high PROTAC concentration

Trigger: PROTAC concentration becomes high enough that separate target-PROTAC and E3-PROTAC binary complexes compete with productive ternary-complex formation.

Mechanism: Saturation by binary complexes reduces the population of productive ternary complex. Negative cooperativity can worsen ternary formation but is not the definition of the hook effect.

Symptom: Degradation increases and then decreases across a sufficiently broad concentration-response experiment.

Fix: Confirm the downturn experimentally over a broad dose range, then optimize ternary-complex geometry, cooperativity, exposure, and dosing without assuming that linker shortening alone will solve it.

Insufficient cell permeability

Trigger: Measured permeability or cellular exposure is poor relative to biochemical activity.

Mechanism: Size, exposed polarity, conformation, ionization, or efflux may limit intracellular exposure.

Symptom: Cellular degradation potency is substantially worse than biochemical ternary-complex or binding measurements.

Fix: Optimize linker and exposed polarity using measured permeability, solubility, and intracellular exposure across the series. Do not impose a universal MW or TPSA cutoff.

E3-target distance miscalculation

Trigger: Computing linker length from binary models without ternary refinement.

Mechanism: A distance from separately positioned binary structures does not determine the accessible ternary geometry or linker conformational ensemble.

Symptom: PROTACs synthesized at wrong linker length; no degradation.

Fix: Use a ternary structural hypothesis to define a chemically diverse linker series, then compare conformational feasibility and experimental degradation across that series.

Molecular glue vs PROTAC confusion

Trigger: Designing as PROTAC when target lacks defined ligand.

Mechanism: A molecular glue stabilizes or induces a protein-protein interaction without the two-ligand-plus-linker architecture assumed by a PROTAC workflow.

Symptom: Design too rigid; no degradation despite ternary prediction.

Fix: For targets without known ligand, consider molecular glue discovery instead.

Reconciliation: PRosettaC vs AlphaFold3

Aspect	PRosettaC	AlphaFold3
Approach	PROTAC-specific Rosetta sampling	Unrestrained foundation-model prediction
Accuracy	Higher average DockQ in one 36-structure comparison, but only 25 complexes were modeled and most predictions were low quality	Limited PROTAC-specific validation
Speed	Measure for the installed workflow and hardware	Measure for the selected service or local hardware
Access	Web service	AlphaFold Server or local installation, subject to their terms and limits
Restraints	Method-specific setup	No arbitrary user distance restraints
Decision	Use as a PROTAC-specific structural hypothesis	Use as an independently benchmarked structural hypothesis

Use PRosettaC or another benchmarked structural method to generate hypotheses, then measure ternary binding/cooperativity and cellular degradation experimentally. Do not treat any one modeling method as a validated universal ranker.

Common Errors

Symptom	Cause	Fix
PRosettaC fails to converge	Input geometry, sampling, or service/configuration problem	Inspect inputs and logs; compare justified recruiter/linker hypotheses
DeepTernary returns clashing pose	Prediction outside a validated domain or incorrect interface	Inspect confidence and clashes; compare an independent method and known structures
AlphaFold3 ternary unrealistic	Unrestrained prediction has a low-confidence or incorrect interface	Inspect confidence and compare with PRosettaC or known ternary structures
Cellular phenotype disagrees with target-degradation assays	Exposure, off-target degradation, assay timing, or pathway effects	Measure target engagement/degradation and use proteome-wide selectivity assays where appropriate
Degradation decreases at high PROTAC concentration	Hook effect from competing binary complexes	Confirm with a broad dose range; optimize ternary geometry and exposure
Synthesis is impractical	Proposed connectivity lacks a credible route	Obtain medicinal-chemistry review and redesign attachment chemistry or linker
Poor permeability or intracellular exposure	Size, exposed polarity, conformation, or efflux	Measure the bottleneck and optimize the series; avoid a universal size cutoff

References

• Békés M, Langley DR, Crews CM. "PROTAC targeted protein degraders: the past is prologue." Nat. Rev. Drug Discov. 21:181–200 (2022). DOI: 10.1038/s41573-021-00371-6.
• Drummond ML, Williams CI. "In Silico Modeling of PROTAC-Mediated Ternary Complexes: Validation and Application." J. Chem. Inf. Model. 59:1634–1644 (2019). DOI: 10.1021/acs.jcim.8b00992.
• Schapira M, Calabrese MF, Bullock AN, Crews CM. "Targeted protein degradation: expanding the toolbox." Nat. Rev. Drug Discov. 18:949–963 (2019). DOI: 10.1038/s41573-019-0047-y.
• Gechijian LN et al. "Functional TRIM24 degrader via conjugation of ineffectual bromodomain and VHL ligands." Nat. Chem. Biol. 14:405–412 (2018). DOI: 10.1038/s41589-018-0010-y.
• Zaidman D, Prilusky J, London N. "PRosettaC: Rosetta Based Modeling of PROTAC Mediated Ternary Complexes." J. Chem. Inf. Model. 60:4894–4903 (2020). DOI: 10.1021/acs.jcim.0c00589.
• Xue F, Zhang M, Li S et al. "SE(3)-equivariant ternary complex prediction towards target protein degradation." Nat. Commun. 16:5514 (2025). DOI: 10.1038/s41467-025-61272-5.
• Schulz JM, Schürer SI, Reynolds RC, Schürer SC. "PRosettaC outperforms AlphaFold3 for modeling PROTAC ternary complexes." Sci. Rep. 15:37620 (2025). DOI: 10.1038/s41598-025-21502-8.
• Bondeson DP et al. "Catalytic in vivo protein knockdown by small-molecule PROTACs." Nat. Chem. Biol. 11:611–617 (2015). DOI: 10.1038/nchembio.1858.
• Ward CC et al. "Covalent Ligand Screening Uncovers a RNF4 E3 Ligase Recruiter for Targeted Protein Degradation Applications." ACS Chem. Biol. 14:2430–2440 (2019). DOI: 10.1021/acschembio.8b01083.
• Luo M et al. "Chemoproteomics-enabled discovery of covalent RNF114-based degraders that mimic natural product function." Cell Chem. Biol. 28:559–566.e15 (2021). DOI: 10.1016/j.chembiol.2021.01.005.
• King EA et al. "Chemoproteomics-enabled discovery of a covalent molecular glue degrader targeting NF-kappaB." Cell Chem. Biol. 30:394–402.e9 (2023). DOI: 10.1016/j.chembiol.2023.02.008.
• Abramson J et al. "Accurate structure prediction of biomolecular interactions with AlphaFold 3." Nature 630:493–500 (2024). DOI: 10.1038/s41586-024-07487-w.
• REINVENT 4 official repository and installation documentation: https://github.com/MolecularAI/REINVENT4.
• HADDOCK3 official documentation: https://www.bonvinlab.org/haddock3/.
• Boltz official repository and documentation: https://github.com/jwohlwend/boltz.

Related Skills

• chemoinformatics/molecular-io - Parse linker and ligand SMILES
• chemoinformatics/reaction-enumeration - Linker enumeration combinatorial
• chemoinformatics/generative-design - REINVENT linker mode
• chemoinformatics/conformer-generation - Ternary conformer sampling
• chemoinformatics/virtual-screening - Validate target ligand binding
• chemoinformatics/free-energy-calculations - Ternary ABFE / cooperativity
• chemoinformatics/admet-prediction - PROTAC ADMET specific challenges
• structural-biology/structure-io - PDB / mmCIF for ternary complex