Table 1 Comparative summary of ternary complex modeling methods.

From: PRosettaC outperforms AlphaFold3 for modeling PROTAC ternary complexes

Criterion

AlphaFold-3 minimal

AlphaFold-3 core

PRosettaC ternary

Successful models (out of 36)

36

36

25

DockQ > 0.5 (high-fidelity models)

5 of 180 models (2.8%)

5 of 180 models (2.8%)

527 of 8407 models (6%)—concentrated in a few favorable systems

Use of structural cofactors

No

Yes

No

Degrader-aware modeling

No

No

Yes (warhead-guided)

Scaffold bias in DockQ

No

Yes (for AF3 Full)

No

Linker geometry enforcement

No

No

Yes

Sensitivity to protein flexibility

Low

Low

Medium–high

Improves against reference ensemble

No

No

Yes (frame-based alignment and scoring)

Sampling failure rate

0%

0%

 ~ 31% (11/36 systems failed)

  1. Overview of key performance characteristics across AF3 Minimal, AF3 Core (scaffold-stripped), and PRosettaC Ternary complex modeling strategies. Core models were derived by removing accessory proteins (e.g., Elongin B/C, DDB1) from AF3 Full predictions (Sect. “Methods”) prior to DockQ evaluation, isolating degrader-specific binding interfaces. The table highlights modeling success rate, interface constraints, flexibility handling, and dynamic compatibility. While PRosettaC excels in degrader-specific modeling and transient alignment with reference ensembles generated by MD, its sampling failures in certain geometries remain a challenge. AF3 Full models exposed substantial scoring artifacts by scaffold-derived contacts, often revealing limited degrader-target interaction fidelity after scaffolds are stripped. DockQ scores for AF3 Core reflect scaffold-stripped complexes. Success is defined as generation of parsable models suitable for scoring.
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