# Challenges in predicting PROTAC-mediated Protein-Protein Interfaces with AlphaFold

Gilberto P Pereira, Corentin Gouzien, Paulo Cesar Telles de Souza, Juliette Martin

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Gilberto P Pereira, Corentin Gouzien, Paulo Cesar Telles de Souza, Juliette Martin. Challenges in predicting PROTAC-mediated Protein-Protein Interfaces with AlphaFold. 2024. hal-04732948

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# Challenges in predicting PROTAC-mediated Protein-Protein Interfaces with AlphaFold

Gilberto P. Pereira<sup>a,b,#</sup>, Corentin Gouzien<sup>c,#</sup>, Paulo C. T. Souza<sup>a,b</sup>, Juliette Martin<sup>*a</sup>

<sup>a</sup> Laboratoire de Biologie et Modélisation de la Cellule, CNRS, UMR 5239, Inserm, U1293, Université Claude Bernard Lyon 1, Ecole Normale Supérieure de Lyon, 46 Allée d'Italie, 69364, Lyon, France.

<sup>b</sup> Centre Blaise Pascal de Simulation et de Modélisation Numérique, Ecole Normale Supérieure de Lyon, 46 Allée d'Italie, 69364, Lyon, France.

<sup>c</sup> Laboratoire d'Océanographie Microbienne (LOMIC, UMR 7621, CNRS-SU), Observatoire Océanologique de Banyuls, Avenue Pierre Fabre, F-66650 Banyuls-sur-Mer, France

<sup>#</sup>First authorship is shared between these authors

Distributed under a Creative Commons Attribution | 4.0 International license
https://creativecommons.org/licenses/by/4.0/

<figure><img src="image_2.png" /></figure>

**Abstract:** In the last few years, predicting the structure of PROTAC-mediated complexes emerged as a fundamental step toward the design of therapeutic modalities for cancer and other conditions. The development of AlphaFold2 (AF2) caused a paradigm shift in the structural biology community. From then onwards, further developments enabled the prediction of multimeric protein structures while improving calculation efficiency, leading to the widespread usage of AF2 and to the recent release of AF3. However, AF2 does not consider ligands, suggesting that ligand-mediated protein-protein interfaces (PPIs) are challenging to predict. One of the main claims of AF3 is that this limitation has been addressed, but the currently released webserver provides only a few ligands and no PROTACs are available. In this article, we benchmark the performance of AF2 on a test set of 28 PROTAC-mediated dimers, as well as a set of 326 protein hetero-dimers orthogonal to AF2 training set, with a special attention to the interface size and presence of ligand at the interface. We then evaluated whether the newly released AF3 model is able to outperform AF2 on the prediction of PROTAC-mediated complexes, despite not being able to include PROTAC ligands in the prediction. In this letter, we aimed at identifying possible reasons why AF-based methods fail
to predict PROTAC-mediated interfaces. Our results show that AF2-multimer predictions are sensitive to the size of the interface to predict, with the majority of models being incorrect for the smallest interfaces. While it performs reasonably well for ligand-mediated interfaces in the absence of the ligand, AF2 is unable to predict PROTAC-mediated interfaces reliably. We also found that AF3 does not significantly improve upon the accuracy of AF2, as it is still unable to correctly predict PROTAC-mediated interfaces for the large majority of cases.

Introduction

In the quest to develop innovative therapies, pharmaceutical companies and research laboratories invest billions of dollars every year into the different stages of drug design pipelines¹. Recently, many of them have invested into the development of Proteolysis TAargetting Chimeras (PROTACS). PROTACs are heterobifunctional ligands composed of two small molecules connected by a linker region. One of the small-molecules binds to a protein target and the other binds to a protein called E3 ligase, which is attached to the ubiquitination machinery²,³. This machinery is responsible for ubiquitination of proteins, which tags them for proteasomal degradation. The formation of a complex between the E3 ligase and the protein target via the PROTAC thus induces the degradation of the target. PROTAC-based approaches have been explored for a variety of therapeutic targets, including proteins intimately connected to cancer⁴⁻⁶. These molecules have several advantages over small-molecule-based drugs due to their catalytic mode of action. Due to it, the concentration at which a therapeutic effect is achieved tends to be lower than standard small-molecule inhibitors, leading to a lower risk of side-effects.⁷. However, up until a few years ago, these molecules were designed serendipitously due to both a lack of structural information and limited successful applications of in silico tools.

Thanks to the effort undertaken the last few years, there now exists a wealth of structural, biochemical and activity data regarding several different PROTAC families which can be exploited toward the construction of computational pipelines for PROTAC design. The first step in these pipelines typically pertains to the prediction of the E3-ligase/target interfaces, which can be achieved by a myriad of methods. While some groups have developed tools for this purpose⁸⁻¹², they are typically not general because they require that the PROTAC molecule be known a priori. Within our group we developed a pipeline called PROTACability¹³, which bypasses this constraint and achieves satisfactory accuracy with minimal prior information. PROTACability was based on predictions of the E3 ligase/target interfaces produced by LightDock, a classical docking software¹⁴.
The field of protein structure prediction has been deeply impacted by the AlphaFold method¹⁵, as well as protein-protein docking. Indeed, although not initially developed for protein-protein complex prediction, AlphaFold has been rapidly exploited by the community toward this goal, using hacks such as concatenating the sequence of several proteins using multi-glycine spacers ¹⁶ or modifying the input residue index ¹⁷⁻¹⁹. AlphaFold-multimer was then specifically developed for protein assemblies²⁰. These developments offer a new alternative for protein-protein docking. Recently, the new iteration of AlphaFold culminated into AlphaFold 3, the newest version which is able to take into consideration the information of ligands and model multimeric proteins accurately²⁰,²¹.

During the development of PROTACability, we also considered using AlphaFold-multimer, with little success¹³. A recent benchmark of docking tools for PROTAC-mediated complexes confirm these poor quality results [REF Rovers and Shapira]. ¹³,²². In this article, we explore further the reasons for this failure. We first use a test set of protein-protein heterodimers never seen by AlphaFold during the training phase to investigate the effect of the interface size and presence of ligand at the interfaces on the quality of the generated models. We then test the capacity of AlphaFold3 on the PROTAC-mediated interfaces on the relevant dimers, as well as the dimers in presence of the accessory proteins to better mimic the biological context of the interactions and reduce the search space. We aimed at identifying why AF-based methods struggle with PROTAC-mediated PPIs by evaluating three main points: (i) interface size, (ii) whether being ligand-mediated hampers predictive accuracy and (iii) if the problem lies with particular features of PROTACs. Our results point at an inherent limitation of AlphaFold-multimer on small interfaces in the general case, which compromises the prediction of PROTAC-mediated interfaces which are generally small. However, it appears that PROTAC-mediated interfaces are significantly harder to predict in the absence of the ligand. We hypothesized that this is a combination of the size of the interface and the fact that the interface is only stable in the presence of a PROTAC. AlphaFold3 does not remedy this situation, even when the prediction context is provided. Nonetheless, as AF3 predictions were carried out without the presence of PROTACs in the available web server, it could be the case that AF3 full capabilities have not been explored.
# Material and Methods

## Dataset

A dataset of 28 PROTAC-mediated complexes was extracted from the PDB. These complexes are composed of an E3 ligase receptor complexed with a protein substrate *via* a PROTAC.

A test set was extracted from the PDB as follows. All protein heterodimers (two different peptidic chains in the assembly) deposited after October 1st 2021 (training date of AlphaFold2 v3) were retrieved from the RCSB PDB website. The redundancy against the AlphaFold2 training set was removed using Foldseek<sup>23</sup> version fc40ba1afdcb20602116907fdc73a47fd4c78615 against PDB sequences with a 30% sequence identity cutoff and an 80% sequence coverage cutoff (*easy-multimersearch --min-seq-id 0.3 --cov-mode 2 -c 0.8*). Dimers with detectable sequence similarity to complexes acquired before 30 sept 2021 were removed. The intra-set redundancy was then reduced using cd-hit<sup>24,25</sup> version 4.8.1, with a 40% cutoff (step 1: redundancy reduction on a per-chain basis; step 2: addition of all chains of complexes selected in step 1, step 3: removal of remaining redundancy). Sequences shorter than 30 residues were removed. The resulting test set is composed of 340 heterodimers. Among these 340 dimers, 5 cases only were mediated by a ligand (see below).

Structures anterior to the AlphaFold training date were added to increase the number of ligand-mediated interfaces. We used the Dockground resource (https://dockground.compbio.ku.edu/bound/index.php)<sup>26</sup> to build a set of non-redundant (<30% sequence identity) hetero-dimers X-ray structures with a resolution better than 3 Å deposited earlier than 30 sep 2021. A list of 21 ligand-mediated complexes was extracted from this set. The list of the complexes used throughout this study is given in **Table S1**.

## Interface size

The interface size was defined as the accessible surface area (ASA) of separated chains minus the ASA of the complex : $\Delta ASA = ASA chain1 + ASA chain2 - ASA complex$. ASA is computed using naccess<sup>27</sup> with default parameters (probe size=1.4 Å, Z slice = 0.05 Å). Unless otherwise specified, hetero-atoms (i.e., non-standard residues, solvent and ligands) are excluded from the computation.
## Ligand-mediated interfaces

Ligand-mediated interfaces were identified by comparing the ΔASA computed with and without hetero-atoms. Cases where the difference between ΔASA with and without hetero-atoms was greater than 10% were manually verified to exclude complexes with cross-links, modified residues at the interface, or non-specific ligands at the interface (such as sulfate ions, glycerol or PEG groups).

## AlphaFold 2

AlphaFold-Multimer calculations were carried out using ColabFold version 1.5.1<sup>17,20,28</sup> using the alphafold2_multimer_v3 model with default parameters (i.e. paired+unpaired MSA, with model recycling). The best model was selected using the composite AF2 predicted quality score defined by 0.8 ipTM+0.2pTM<sup>20</sup>. We will refer to this score as the AF2 confidence score. Among the 340 cases of the test set, AF2 was able to produce a model for 326 cases, including the 5 cases with ligand-mediated interface.

## AlphaFold 3

AlphaFold 3 predictions were run on the web server (https://alphafoldserver.com/) on the 6th of June 2024, solely for PROTAC cases. Two experiments were carried out: in the first one, AF3 was provided with both sequences of the ligase and target (like AF2); in the second experiment we added the sequence of the accessory protein which is bound to the ligase in physiological context and present in the experimental structures. We refer to this experiment as 'prediction with context'. Model choice was based on the ranking score provided by AF3.

## Model evaluation

To evaluate the similarity between the experimental structures and the predicted structures from AF-Multimer and AF3, the DockQ criteria were employed<sup>29</sup>. In short, DockQ provides a continuous score from 0 to 1 (with 1 being perfect similarity) which takes into account the fraction of conserved native contacts, RMSD of the target protein and the interface RMSD between a reference structure and a predicted structure<sup>13</sup>. We used the cutoff of 0.23 for acceptable models, in agreement with CAPRI criteria<sup>30</sup>.
When predicting PROTAC-mediated interfaces with context (i. e., in presence of accessory proteins), only the DockQ score of the PROTAC-mediated interface was reported.

## Data availability

All the models analyzed in this letter are available in the Zenodo archive https://doi.org/10.5281/zenodo.13684247.

## Results

### Predicting Protein-Protein Interfaces using AlphaFold-Multimer

We collected 340 protein dimers not included in and not similar to the AF2 training set to evaluate the performance of AF2 on an unbiased set. Since PROTAC-mediated interfaces are both small and mediated by a ligand, we paid special attention to the interface size and the presence of ligand at the interface. Since very few interfaces are mediated by ligands in the test set, we also considered 21 ligand-mediated interfaces included in the AF2 training set. In Figure 1, we show the model quality, assessed by the DockQ score, as a function of the size of the native interface, measured by ΔASA.

<figure><img src="image_3.png" /><figcaption>**Figure 1:** DockQ scores as a function of interface size. Here, the interface size is computed on the native complex. Black points: dimers from the test set with no ligand at the interface</figcaption></figure>
(316 cases). Filled Red triangles: dimers from the test set with ligand at the interface (5 cases). Open red triangles: dimers from the training set with ligand at the interface (21 cases). Open green squares: PROTAC-mediated dimers (28 cases). The horizontal dashed line indicates the threshold for acceptable models (DockQ=0.23).

As can be seen in Figure 1, the quality of the models predicted by AF2 is severely impacted by the interface size: interfaces smaller than 1000 Å² are very challenging to predict, with the majority of the models with a DockQ score lower than 0.23. There is a clear shift between 1000 and 2000 Å², where about 60% of the models are of acceptable quality, whereas for interfaces larger than 2000 Å², more than 75% of the models are of acceptable quality (see Table S2).

We observed a high correlation between the DockQ scores and the AF2 confidence scores, see Figure S1A, with a Pearson correlation coefficient equal to 0.79. However, some models of low quality have good AF2 confidence scores, see Figure S1B, particularly for small interfaces.

If we now focus on interfaces mediated by ligands (red triangles in Figure 1), they seem to follow the same trend as other interfaces, with a strong influence of the interface size. PROTAC-mediated interfaces (green squares in Figure 1) are all small in size, and the predicted models are of poor quality. Except for five cases, the DockQ score is lower than 0.23. Increasing the sampling by AF2 with three seeds instead of one did not improve the results (Figure S2).

To further investigate the bias related to the interface size, we monitored the size of the predicted interfaces with respect to the native ones, in the case of wrong models (DockQ<0.23). The results are shown in Figure 2. We observe a general tendency of AF2 to predict models with larger interfaces than the native complex, when the interfaces to predict are small. Of note, interfaces smaller than 1000 Å², which are frequently mispredicted (see Figure 1), systematically produce models with much larger interfaces.
<figure><img src="image_4.png" /><figcaption>**Figure 2:** Interface size in the models as a function of the native interface size, for wrong models (DockQ < 0.23). Black points: dimers from the test set with no ligand at the interface (108 cases). Filled Red triangle: dimers from the test set with ligand at the interface (1 case). Open red triangles: dimers from the training set with ligand at the interface (4 cases). Open green squares: PROTAC-mediated dimers (23 cases).</figcaption></figure>

In conclusion, the prediction of small interfaces with AF2 is still a challenge; and PROTAC-based interfaces typically belong to this category, preventing us from going further into the reasons of failure. On natural dimers, the presence of a ligand in itself does not seem to perturb the prediction.

## Attempted improvements of PROTAC-mediated interface prediction with AlphaFold 3

We tested the accuracy of AF2 and the newest AF3 on the set of challenging PROTAC-mediated dimers. The results are shown in Figure 3.
<figure><img src="image_5.png" /><figcaption>**Figure 3** : Comparison of calculated DockQ values for all PROTAC-mediated systems using either AF2 or AF3 with respect to experimental crystal structures. The black line highlights DockQ's acceptable criteria threshold at 0,23.</figcaption></figure>

As can be seen in Figure 3, the top models generated by AF3 can achieve high quality but most models are incorrect. The dataset achieves a median DockQ score of 0.04, with 5 models of acceptable or higher quality (DockQ > 0.23), compared to 5 models of at least acceptable quality with AF2.

We tested further the capability of AF3, by providing context to the prediction. Indeed, in physiological context the E3 ligase receptor is bound to an accessory protein, making a part of the surface inaccessible to an interaction with the target protein. We hypothesized that the addition of context, in the form of the accessory protein, will improve the prediction of the receptor/substrate interface, by limiting the search space available. The E3 ligase receptor/accessory protein interfaces are highly conserved and should be easy to predict. However, it appears that in this case, when context is introduced, the predictions do not significantly improve. The models predicted by AF3 with some context are marginally worse than AF3 without context. The median DockQ score is equal to 0.04 and 3 models only are of acceptable quality. By contrast, the interfaces between E3 ligase receptors and accessory proteins are generally well predicted, with all DockQ scores greater than 0.6 and a median
DockQ score equal to 0.941, see Figure S4. This means that the interfaces between accessory proteins were well predicted, but did not allow to improve the results for the E3 Ligase-Target interface. Given the current limitations of the AF3 web server in terms of available ligands, it was not possible to test whether inclusion of PROTACs would increase prediction accuracy. In conclusion, PROTAC-mediated interfaces remain very challenging to predict, even with AF3 and some context.

Discussion

Our results indicate that PROTAC-mediated interfaces remain a very challenging case of prediction for a modern DL prediction method like AlphaFold. These interfaces have several peculiarities: (1) small size, (2) presence of ligand at the interface, (3) included in larger complexes with other proteins.

In this study, we tried to deconvolve these different factors. First, we show that AlphaFold2 suffers from a significant bias against small interfaces. Within the PPIs we studied, those with larger areas were typically better predicted than small ones (Figure 1). This bias was already present in the former version of AlphaFold2 trained on monomers, as observed by Yin et al³¹ who benchmarked AlphaFold v.2.0 with the residue index hack on the 152 structures of the Protein-Protein Docking Benchmark 5.5 ³². Our results indicate that this bias persists in the version of AlphaFold2 that was specifically trained on protein-protein complexes. This certainly plays a role in the fact that PROTAC-mediated interfaces cannot be correctly predicted. Second, concerning the presence of a ligand at the interface, we have considered protein-protein interfaces that involve ligands for more than 10% of their size, to see if we could predict them in the absence of ligands - since AF2 does not model ligands. Our results indicate that these interfaces do not pose a specific challenge for the prediction, as many of them were predicted with DockQ scores between moderate (0.49) and high quality (0.80). However, both ligand-mediated and non-ligand-mediated interfaces appear to be ubiquitously affected by the interface size problem. We then tested whether AF2 could predict accurately PROTAC-mediated interfaces. It appears to perform suboptimally, only managing to produce acceptable or higher quality structures in 5 out of 28 systems. A recent benchmark conducted by others and published during the preparation of this article confirmed this limitation of AlphaFold-multimer on PROTAC-mediated complexes²². With AlphaFold3, we made the same test and also attempted to provide some context to the prediction: by adding the accessory protein, the interface accessible to the interface with the substrate would be more limited, narrowing down the solution space. However, we noted in both cases
no/marginal improvement in the predictions, even with the addition of such context. The AF3 predictions also provided 5 systems for which the predictions were passable whereas the addition of context only managed to correctly predict 3 systems.

Another peculiarity of PROTAC-mediated complexes, is that they are induced and stabilized by the ligand, and would naturally not probably interact strongly. The resulting absence of an evolutionary signal is probably another source of challenges for the accurate prediction of these complexes. A recent work of Roney and Ovchinnikov³³ provides evidence that AlphaFold2 indeed learned an energy function that encapsulates the physics governing the folded state and that the evolutionary signal serves as a guide in the global search for the optimal structure. Another recent study leads to the same conclusion, observing that AlphaFold has a remarkable capability to recover correct structures from certain perturbations without additional information provided by the multiple sequence alignment.³⁴ Along the same line, AF2Complex, proposed by Gao et al¹⁹, successfully predicted hetero-dimers starting from unpaired multiple sequence alignments, suggesting again that the AlphaFold2 network learned an implicit energy function that is enough to represent the physics of protein-protein interfaces.

So, in theory, it should be possible for AlphaFold2, and by extension AlphaFold3, to predict a non-natural interface in the absence of an evolutionary signal. However, the absence of the PROTAC molecule poses a significant challenge, as the physics governing this interaction is not adequately represented. As the PROTAC molecule itself appears to have a key role in stabilizing the protein-protein interface of these complexes, its absence further complicates predicting biologically meaningful PPIs target-E3 ligase systems.

An additional point is that many systems within our dataset only differ in the PROTAC molecule that composes the ternary complex and the resulting protein-protein interface. These systems are highly plastic and mobile, which introduces additional challenges in the correct prediction of these interfaces³⁵. One possible avenue for improvement would be to utilize ensemble-generation methods to maximize the likelihood of finding physiologically relevant structures³⁵⁻³⁸. To summarize, because of their small size and peculiar physics, PROTAC-mediated complex prediction remains challenging even with cutting-edge deep-learning methods. The release of RoseTTAFold-All-Atom³⁵,³⁹ in 2024, an alternative to the AF family of methods, as it is described as being able to consider the effects of ligands within protein-protein interfaces. It remains to be seen whether this modeling suite achieves better performance than AlphaFold3 in predicting highly plastic, “artificial” and shallow protein-protein interfaces such as those in PROTAC-mediated systems.

## Supporting Information
Table S1: PDB Ids used in the study; Table S2: Repartition of cases by interface size range and model quality; Figure S1: Model quality, AF2 confidence score and interface size plots for the full dataset; Figure S2: Model quality and AF2 confidence score in the PROTAC set, with 1 and 3 seeds; Figure S3: AlphaFold3 results for the 28 PROTAC-mediated complexes.

## AUTHOR INFORMATION

### Corresponding Author

Juliette Martin – Laboratoire de Biologie et Modélisation de la Cellule, CNRS, UMR 5239, Inserm, U1293, Université Claude Bernard Lyon 1, Ecole Normale Superieure de Lyon, 46 Allée d'Italie, 69364, Lyon, France. ORCID: 0000-0002-4787-0885; E-mail: juliette.martin@ens-lyon.fr

## AUTHOR CONTRIBUTIONS

The manuscript was written and revised by all authors. Approval for publication was obtained from all authors.

## CONFLICT OF INTEREST

The authors declare no conflict of interest

## ACKNOWLEDGMENTS

G.P, C.G, P.C.T.S and J.M would like to thank the support of the French National Center for Scientific Research (CNRS). G.P. and P.C.T.S would also like to thank the funding from research collaboration agreements with PharmCADD. We also acknowledge the support of the Centre Blaise Pascal's IT test platform at ENS de Lyon (Lyon, France) for the computer facilities. The platform operates the SIDUS solution developed by Emmanuel Quemener⁴⁰. For the purpose of open access, the authors have applied a Creative Commons Attribution (CC BY) license to any Author Accepted Manuscript version arising from this submission.

## ABBREVIATIONS

AF - AlphaFold; PPI - Protein-Protein Interfaces; PROTAC - Proteolysis Targeting Chimera.
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Supplementary Information

<table><caption>Table S1. PDB Ids used in this study. Numbers in parentheses refer to the number of cases.</caption><thead><tr><th>Dataset</th><th>Ids</th></tr></thead><tbody><tr><td>PROTACS (28)</td><td>5HXB 5T35 6BN7 6BOY 6HAX 6HAY 6HR2 6W7O 6W8I 6XK9 6ZHC 7JTO 7JTP 7KHH 7LPS 7PI4 7Q2J 7S4E 7Z6L 7ZNT 8BB2 8BB3 8BB4 8BB5 8BDS 8BDT 8BDX 8BEB</td></tr><tr><td>Test set, no ligand (321)</td><td>7G80 7PYV 7Q1U 7Q4H 7Q72 7QBM 7QE5 7QE8 7QGS 7QIH 7QLA 7QUU 7R2E 7R49 7SH3 7SHQ 7SJ4 7SJQ 7SL9 7SO0 7SR4 7SVT 7SZA 7T0Y 7T5P 7T7I 7TA3 7TCR 7TJ4 7TL7 7TPB 7TVJ 7TYD 7TZH 7U20 7U5O 7U5P 7UBZ 7UCC 7UFW 7UJ3 7UL2 7ULA 7UM4 7UPM 7URV 7VLJ 7VRC 7VUO 7VVB 7VW6 7W3U 7W47 7WA4 7WDK 7WFC 7WGE 7WKI 7WKO 7WMV 7WN0 7WQU 7WQZ 7WRS 7X3K 7X9S 7XAD 7XAE 7XDD 7XEY 7XGE 7XKY 7XLD 7XPC 7XQK 7XQW 7XVG 7XVK 7XYQ 7Y1Q 7Y4A 7Y6B 7Y8U 7Y9N 7YAG 7YCJ 7YCO 7YGG 7YK3 7YKA 7YLD 7YLS 7YN3 7Y03 7YR5 7YTT 7YUI 7YZ9 7Z3Q 7Z71 7Z72 7Z7E 7ZAU 7ZCG 7ZCL 7ZCM 7ZD5 7ZEY 7ZH3 7ZHR 7ZJM 7ZJU 7ZSC 7ZYU 8A1G 8A4A 8A82 8A8C 8A8F 8A8M 8ADB 8AIM 8AIN 8AIW 8AJ8 8AJN 8AJY 8AKO 8ALK 8ALO 8ALS 8ALZ 8APY 8AV2 8AW4 8B05 8B08 8B0U 8B2N 8B2Q 8B3S 8B5S 8B8V 8BA1 8BAN 8BB7 8BCT 8BE2 8BFJ 8BGM 8BJH 8BMX 8BOS 8BOZ 8BUZ 8BV0 8BVR 8BW8 8BWF 8BYP 8BZR 8C0J 8C26 8C3K 8C44 8C47 8C5H 8CD0 8CJB 8CQI 8CQZ 8CT8 8CUS 8CUU 8CWS 8CXJ 8CXR 8D4S 8D5V 8DC0 8DD7 8DLE 8DML 8DMQ 8DRN 8DSO 8DY1 8DYE 8DYO 8DYP 8E0E 8E1D 8EFP 8EFW 8EFX 8EHE 8EHO 8ENB 8EXE 8EY4 8EZL 8EZR 8F0M 8F19 8F6D 8F7A 8FFU 8FHK 8FJ2 8FN8 8FN9 8FZZ 8G0P 8G4Y 8GBQ 8GJA 8GKO 8GP6 8GQE 8GRJ 8GS1 8GTG 8GUO 8GXE 8GZV 8H1I 8H3U 8H3X 8H62 8H6S 8H8A 8HBN 8HBV 8HCI 8HF3 8HFC 8HFP 8HGJ 8HHJ 8HKH 8HL7 8HLM 8HM3 8HSB 8HTC 8I2E 8I3G 8I4E 8I6J 8I8Y 8I9M 8I9Q 8IF0 8IJ9 8IN9 8IPQ 8IPS 8ITP 8IZN 8J09 8J1R 8J64 8JCA 8JMQ 8JO3 8JTK 8JTW 8JWJ 8K4R 8ODO 8OI2 8OI3 8ONT 8OOF 8ORN 8OYP 8P98 8PFC 8PIJ 8PKC 8PNL 8PQ7 8Q00 8Q5R 8Q66 8QF4 8QVW 8R4C 8R4O 8R7A 8R8Q 8RCQ 8RGI 8SBB 8SJJ 8SM0 8SNL 8SO9 8SSM 8ST7 8ST8 8ST9 8SYM 8T12 8T1C 8TJ3 8TWD 8U45 8UH1 8VOH 8VOI 8VX9 8WNS 8WQR 8WTB 8WWY 8X6B 8X77 8X8T 8XLD 8XY7</td></tr><tr><td>Test set with ligand (5)</td><td>7WR6 7XJP 7ZHM 8FDB 8OW6</td></tr><tr><td>Training set with ligand (21)</td><td>1ZHH 2EJF 2FYD 2WDT 3EJD 3N7R 3NY7 3P71 3ZDF 4A69 4ETW 4F48 4I6N 4IG7 4JQU 4MRT 4PWV 5CZD 5EJD 5GPG 5KP8</td></tr></tbody></table>
<table><caption>Table S2. Repartition of cases by interface size range (expressed in Å²) and model quality (assessed by the DockQ score). Numbers in parentheses correspond to numbers of cases.</caption><thead><tr><th></th><th>DockQ score &lt; 0.23</th><th>DockQ score &gt;=0.23</th></tr></thead><tbody><tr><td>ΔASA&lt;500</td><td>92% (12)</td><td>8% (1)</td></tr><tr><td>500≤ΔASA&lt;1000</td><td>76% (25)</td><td>24% (8)</td></tr><tr><td>1000≤ΔASA&lt;1500</td><td>43% (31)</td><td>57% (41)</td></tr><tr><td>1500≤ΔASA&lt;2000</td><td>42% (26)</td><td>58% (36)</td></tr><tr><td>2000≤ΔASA&lt;2500</td><td>25% (17)</td><td>75% (51)</td></tr><tr><td>2500≤ΔASA&lt;3000</td><td>18% (7)</td><td>82% (32)</td></tr><tr><td>3000≤ΔASA&lt;3500</td><td>11% (2)</td><td>89% (16)</td></tr><tr><td>3500≤ΔASA&lt;4000</td><td>6% (1)</td><td>94% (17)</td></tr><tr><td>4000≤ΔASA&lt;6000</td><td>22% (8)</td><td>78% (28)</td></tr><tr><td>6000≤ΔASA</td><td>6% (1)</td><td>94% (15)</td></tr></tbody></table>
<figure><img src="image_6.png" /><figcaption>Figure S1. Model quality, AF2 confidence score and interface size. Points are colored according to the AF2 confidence score (0.8 ipTM+0.2pTM).</figcaption></figure>
<figure><img src="image_7.png" /><figcaption>Figure S2. Model quality and AF2 confidence score in the PROTAC set, with 1 and 3 seeds. Points are colored according to the AF2 confidence score (0.8 ipTM+0.2pTM), and the shape indicates the number of seeds used for AF2 calculation.</figcaption></figure>
<figure><img src="image_8.png" /><figcaption>**Figure S3** - AlphaFold3 results for the 28 PROTAC-mediated complexes. In this experiment, all proteins within the PDB entry were provided to AlphaFold server and all 5 top-predicted models were queried against their corresponding crystal structure. We extracted the best ranked solution to AF3.</figcaption></figure>

The data in Figure S3 show that AF3 is apparently performing at an acceptable level for the full systems, with an average DockQ score of 0.75. However, once these predictions are separated into two categories, the Ligase-Target interface DockQ and the PPIs involving the ligase and accessory proteins, we see the same pattern as in AF2-Multimer. In general, Ligase-Target interfaces remain difficult to predict with AF3, with only 4 systems out of 28 achieving a DockQ score above 0.23 for at least one predicted structure. This result is only marginally better than the result obtained with AF2, where three out of 28 systems were correctly predicted. In contrast, all interfaces not including the target protein were well predicted (all above Dock > 0.6) and the average DockQ score was around 0.9. This data paint a concerning picture: While on the surface these multi-protein complexes appear to be correctly predicted by AF3, it turns out that the most important interface is still missed in the large majority of the cases.