# @TOME 3.0: Interfacing Protein Structure Modeling and Ligand Docking

Jean-Luc Pons, Victor Reys, François Grand, Violaine Moreau, Jerôme Gracy, Thomas Exner, Gilles Labesse

► To cite this version:

Jean-Luc Pons, Victor Reys, François Grand, Violaine Moreau, Jerôme Gracy, et al.. @TOME 3.0: Interfacing Protein Structure Modeling and Ligand Docking. Journal of Molecular Biology, 2024, 436 (17), pp.168704. 10.1016/j.jmb.2024.168704. hal-04705899

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# @TOME 3.0: Interfacing Protein Structure Modeling and Ligand Docking

Jean-Luc Pons¹, Victor Reys¹, François Grand¹, Violaine Moreau¹, Jerôme Gracy¹, Thomas E. Exner² and Gilles Labesse¹*

1 - A.B.C.I.S, CNRS UMR5048 - INSERM U1054 - Université de Montpellier 29, Rue de Navacelles, 34090 Montpellier
Cedex, France

2 - Seven Past Nine d.o.o., Hribljane 10, 1380 Cerknica, Slovenia

Correspondence to Gilles Labesse: labesse@cbs.cnrs.fr (G. Labesse)

https://doi.org/10.1016/j.jmb.2024.168704

Edited by Michael Sternberg

## Abstract

Knowledge of protein–ligand complexes is essential for efficient drug design. Virtual docking can bring important information on putative complexes but it is still far from being simultaneously fast and accurate. Receptors are flexible and adapt to the incoming small molecules while docking is highly sensitive to small conformational deviations. Conformation ensemble is providing a mean to simulate protein flexibility. However, modeling multiple protein structures for many targets is seldom connected to ligand screening in an efficient and straightforward manner. @TOME-3 is an updated version of our former pipeline @TOME-2, in which protein structure modeling is now directly interfaced with flexible ligand docking. Sequence-sequence profile comparisons identify suitable PDB templates for structure modeling and ligands from these templates are used to deduce binding sites to be screened. In addition, bound ligand can be used as pharmacophoric restraint during the virtual docking. The latter is performed by PLANTS while the docking poses are analysed through multiple chemoinformatics functions. This unique combination of tools allows rapid and efficient ligand docking on multiple receptor conformations in parallel. @TOME-3 is freely available on the web at https://atome.cbs.cnrs.fr.

© 2024 The Author(s). Published by Elsevier Ltd.

## Introduction

Drug design is still a slow and costly process despite numerous improvements thanks to various automation allowing large-scale or high-content studies. Of note, large databases of experimentally measured affinities are now available while the number of structures experimentally solved at atomic resolution is rising quickly. Nevertheless, the number of fully – both structurally and functionally – characterized pairs of protein–ligand is still low compared to the number of affinity measurements. In this context, theoretical models and virtual docking can help to shrink this gap and guide medicinal chemists.¹

Efficient comparative modeling, and now, machine-learning (e.g.: AlphaFold) led to the development of large databases of protein models.² Those databases usually give access to a single model per protein only with no binding site defined. On the ligand side, databases, such as BindingDB,³ provide access to experimentally determined affinities and molecular description files for small molecules. But there is still a lack of freely available pipelines facilitating the combination of modeling and screening. Hence, many different fragmented tools are needed and their combination is in most cases still a manual process or based on in-house workflow, with neither the workflow nor the results accessible to others. Target models are usu-
ally built, archived and potential ligand binding sites are searched before being prepared (protonation, charge computation, ...) for ligand docking. Similarly, atom types, 3D conformations or charges have also to be computed for each ligand and/or saved in dedicated formats. All this is time-consuming and quite inefficient. Evaluation of the docked poses by various scoring functions is recommended but it also implies independent tools. Distinct file formats limit the access to multiple tools for complementary analysis and consensus evaluation. These different steps represent an important burden limiting the use of virtual screening on multiple templates.

Detection and gathering of liganded templates for easier and more reliable modeling of protein–ligand complexes remain a limitation.¹ This bottleneck was alleviated in our former web-server @TOME-2, which automatically highlights the presence and nature of ligands in the templates detected by sequence similarity or fold recognition.⁴ It can also transfer ligands from one template to another. This approach has been recently extended to *ab initio* models⁵ and can provide a first liganded model for a target, but it is limited, *per se*, to known ligands. Combined with scoring functions from the chemoinformatics field, this method – we coined comparative docking⁶ – allows selection of the optimal protein models for further ligand screening. But extracting and re-formatting the model to be screened was still a mandatory step prior to moving to the docking campaign.

Here, we describe several methodological developments now implemented in one unique web-server, to provide, in a more user-friendly manner, templates for comparative modeling of protein structure, automatic binding site delimitation and easier access to focused virtual screening as well as thorough evaluation of docking poses obtained from various scoring functions. The use of this new server is illustrated with several examples after presenting the results from a validation test performed on new complexes of protein-kinases. The tool is made available through a web-server, named @TOME-3 (https://atome.cbs.cnrs.fr).

## Results

@TOME 3.0 provides an interface to gather liganded structures similar to the query protein, from which theoretical 3D models of the query are built (Figures 1, S0–S4). To enrich in liganded templates, a dedicated search by PSI-BLAST is run on a subset of the PDB (a regularly updated in-house database we named CBE) containing only such complexes. This similarity search may bring also empty structures into which ligand(s) can be transferred from the liganded structures found (through protein–protein superimposition). The user can also upload (un)liganded monomeric

3D structures or models in PDB format and transfer ligands from or into these additional templates. From each template, a model of the query protein is built using SCWRL 3.0.⁷ For all the liganded models, @TOME 3.0 can delineate binding pockets and gives access to virtual ligand screening into these pockets. Then, poses docked by PLANTS are evaluated by a consensus (named *pKd*) of various affinity prediction functions (namely DSX,⁸ MedusaScore,⁹ Xscore¹⁰ and PLPChem¹¹). In addition, they are scored using a pose-quality predictor, named *LPE*, that predict a theoretical RMSD using a dedicated SVM. Users can visualize (Supplementary Figures S5, S6) or download the predicted complexes for further analysis from the same interface.

First, we evaluated the performance of @TOME 3.0 using a set of diverse protein-kinases whose liganded structures were recently solved and deposited in the PDB.¹² Protein-kinases represent a well-studied family of important and mostly monomeric therapeutic targets with multiple crystal structures of complexes. Here, we take advantage of 434 new crystal structures corresponding to 87 distinct targets (with 1 to 35 associated complexes) to evaluate docking after fully automatic modeling. @TOME computed a good pose (using the standard threshold of a 2 Å RMSD) for three-fourth of the ligands using various protocols, suggesting that efficient sampling procedures were already reached with 20 conformations screened (see Best pose selected by RMSD in Table 1). Random picking leads to a success rate of only 22.9–27.9% while using the best scoring function (DSX), as in a standard screening, the success rate rises to 30.6–36.7%. Using our consensus affinity scoring (*pKd*) and the RMSD predictor, *LPE*, the success rate increases further to 35.6–43.3% and 41.8–52.2%, respectively, for the top ranked poses. The automatic selection by @TOME (Best ATOME) reached ~50% of success for various selection schemes. This corresponds to a very good efficiency for cross-docking. We also noticed that the RMSD for two-third of the poses with a *LPE* < 1.5 fall below the 2 Å threshold.

To illustrate the use of @TOME 3.0, we modeled an estrogen-related receptor 2 (ERR2), a protein-kinase (M3K20) and a histone deacetylase (HDAC11) and, then, docked ligands from BindingDB into their respective target. These examples correspond to different levels of difficulty as comparative modeling is performed at high (80%), medium (40%) and low (<25%) sequence identity for ERR2, M3K20 and HDAC11, respectively. The combination of such a modeling-and-screening approach with AlphaFold models is also discussed for these examples.

We took advantage of the various structures of another human estrogen-related receptor 3, ERR3, to model ERR2 from human which has only one crystal structure known so far. So, we
<figure><img src="image_3.png" /><figcaption>**Figure 1.** Flowchart of @TOME 3.0. Starting from a query sequence (in one-letter code format), sequence similarity searches are performed using HHsearch and Psi-Blast on the PDB database. In parallel, the server also searches a dedicated database, named CBE, of annotated liganded protein structures extracted from the PDB. Additional templates can be uploaded through the result interface. 3D models are built using SCWRL. Some are empty protein models but ligands can be transferred from liganded templates by comparative docking. Those features were already available in @TOME 2.0. In @TOME 3.0, the best templates can be selected and are prepared automatically for multiple flexible-ligand screening using the software PLANTS.</figcaption></figure>

compare the docking of its known binders (stored in BindingDB) within its own crystal structure, within models based on ERR3 structures (78% identical) and/or within its AlphaFold models. In the first two cases, the co-crystallized ligands were used to define the binding site as well as shape restraint to guide virtual docking by PLANTS.¹³ On the contrary, the uploaded AlphaFold models are empty structures and require comparative docking to define their binding pocket. Therefore, ligands from homologous structures (mainly ERR3 crystal structures) were transferred into the AlphaFold models through protein-protein superposition and the resulting complexes were evaluated using various scoring functions. All sidechains surrounding the transferred ligands were optimized with SCWRL to limit van der Waals clashes and optimize protein-ligand interactions. This step showed that these AlphaFold models are very similar to an agonist bound conformation and, therefore, they cannot accommodate larger antagonists.

Virtual docking was performed using PLANTS¹³ for binders with known affinity (IC50) as extracted from the BindingDB and the various sets of models for ERR2. Using the unique structure of ERR2, poor docking results were obtained for most compounds (see https://atome.cbs.cnrs.fr/JMB/err2_ide_a3_ic50). Indeed, very few ligands (5 out of 105) scored well ($LPE < 1.5$) and 92 had poor affinity predictions

($pK_d < 3$). Combining this crystal structure with ERR3-based models brought additional conformations (supplementary Figure S7) and shape restraints and this led to better ligand docking (https://atome.cbs.cnrs.fr/JMB/err2_def_a3_ic50). In this case, @TOME appeared to manage well with the two alternative conformations agonists/antagonist to choose optimal binding sites for most ligands. This led to docking poses matching the well-known pharmacophoric restraints for binding into the pockets of related hormone receptors. Docking poses selected automatically by @TOME (BestATOME) are predicted as good ($LPE < 1.5$) for 69 ligands (out of 105) while good affinity predictions ($pK_d > 6.0$) were predicted for 85 of them. Omitting the shape restraint affected the pose quality (27 complexes with $LPE < 1.5$) but not the affinity predictions (https://atome.cbs.cnrs.fr/JMB/err2_def_a0_ic50). For a more relevant test, we omitted the ERR2 structure and used only homology models. In that case, convincing docking for most ligands (70 with $LPE < 1.5$ and 89 with $pK_d > 6.0$) were also predicted (https://atome.cbs.cnrs.fr/JMB/err2_sim_a3_ic50). Without shape restraint (https://atome.cbs.cnrs.fr/JMB/err2_sim_a0_ic50), lower performance were observed (26 with $LPE < 1.5$ and 90 with $pK_d > 6.0$) and less accurate binding more are predicted (supplementary Figures S8 and S9). Then, we screened the AlphaFold models. As for
<table><caption>Table 1 Success rate of various selection schemes. Performances are evaluated at the standard 2 Å RMSD threshold with respect to the crystal structure. Identity: Template sequence identity to model the receptor. Poses: Number of receptors selected for the screen (1 pose per receptor generated). Template selection: Method to select receptors for docking. Shape restraints weights: Weight of the pharmacophoric restraints during the docking. Pose selection schemes: RMSD, random, DSX, pKd, Tanimoto, LPE or BestATOME.</caption><thead><tr><th>Identity threshold</th><th>Number of poses</th><th>Template selection</th><th>Shape restraints weights</th><th>Best pose</th><th>Random</th><th>DSX</th><th>pKd</th><th>Tanimoto</th><th>LPE</th><th>Best ATOME</th></tr></thead><tbody><tr><td>30%</td><td>10</td><td>Tanimoto</td><td>3</td><td>66.4</td><td>25.5</td><td>30.6</td><td>38.7</td><td>32.7</td><td>41.8</td><td>42.9</td></tr><tr><td>30%</td><td>20</td><td>Tanimoto</td><td>3</td><td>75.9</td><td>25.0</td><td>34.1</td><td>39.4</td><td>34.1</td><td>46.6</td><td>46.2</td></tr><tr><td>30%</td><td>40</td><td>Tanimoto</td><td>3</td><td><strong>82.1</strong></td><td>24.2</td><td>33.4</td><td>42.2</td><td>35.3</td><td>49.0</td><td><strong>50.8</strong></td></tr><tr><td>30%</td><td>20</td><td>Tanimoto</td><td>0</td><td>68.8</td><td>22.9</td><td>31.9</td><td>35.6</td><td>30.1</td><td>51.0</td><td>46.3</td></tr><tr><td>30%</td><td>20</td><td>Psim</td><td>3</td><td>75.3</td><td>27.7</td><td>35.6</td><td>42.5</td><td>38.6</td><td>51.4</td><td>50.1</td></tr><tr><td>30%</td><td>20</td><td>pKd</td><td>3</td><td>73.8</td><td>24.7</td><td>34.5</td><td>41.0</td><td>32.6</td><td>49.5</td><td>49.8</td></tr><tr><td>30%</td><td>20</td><td>Quality (Q)</td><td>3</td><td>77.3</td><td><strong>27.9</strong></td><td>35.4</td><td><strong>43.3</strong></td><td><strong>39.8</strong></td><td><strong>52.2</strong></td><td>49.8</td></tr></tbody></table>

comparative docking, virtual screening using flexible ligand docking, led to correct docking of small agonists and incorrect poses for most antagonists (https://atome.cbs.cnrs.fr/JMB/err2_af2_a3_ic50). Globally, few ligands led to good poses (as in the case of the ERR2 crystal structure alone) and low affinity predictions (only 25 ligands with $pKd > 3.0$). Hence, comparative modeling remains competitive in case of multiple conformations that are rarely predicted by default AlphaFold runs and might not be apparent among a tiny number of experimental structures.¹⁴

A similar conclusion can be drawn for the protein-kinase M3K20. This is illustrated here with comparative modeling of a protein-kinase – M3K20 – from human at lower sequence identity: ~40%. 3D models built by @TOME, were directly used to dock known ligands of M3K20 extracted from BindingDB. As above, the ligand from each PDB template were used as shape restraints in the corresponding model. So far, 4 experimental structures have been described for this enzyme. Using only those 4 known crystal structures (https://atome.cbs.cnrs.fr/JMB/m3k20_ide_a3_ic50) led to good performance (50 ligands with LPE < 1.5 and 57 with $pKd > 6.0$) while a standard run by @TOME 3.0 combining these structures with homology models (https://atome.cbs.cnrs.fr/JMB/m3k20_def_a3_ic50) led to the best performance (52 ligands with LPE < 1.5 and 58 with $pKd > 6.0$). Then, we omitted the 4 crystal structures and used homology-based models only. The resulting docking scores (32 ligands with LPE < 1.5 and 51 with $pKd > 6.0$) as well as our visual survey of the docking poses confirmed correct docking for most of the 59 known binders (https://atome.cbs.cnrs.fr/JMB/m3k20_sim_a3_ic50). In all these three cases, we confirmed the qualitative improvement of ligand docking upon using a shape restraint in PLANTS. Indeed, without any shape restraint fewer ligand reached a LPE below 1.5 (e.g.: 6 LPE < 1.5 for https://atome.cbs.cnrs.fr/JMB/m3k20_sim_a0_ic50) while affinity predictions showed similar range and distribution as before. Therefore, such a scoring scheme may not to be a sufficient criterion to select good poses. Highlighting the protein-ligand contacts in the main result page (see supplementary Figure S10), showed two main modes of binding which correspond to the well-known DFG-in and DFG-out conformations frequently observed in protein-kinases. Here, again, @TOME managed to extract and select a diverse set of representative templates. Then, twenty AlphaFold models of M3K20 were uploaded into the @TOME webpage. Ligands from homologous crystal structures were transferred into these AlphaFold models and the resulting complexes were evaluated using scoring functions as above. Ligands
corresponding to DFG-in fitted well into some but not all AlphaFold models but always required surrounding sidechains to be optimized with SCWRL. On the contrary, most DFG-out ligands failed to fit due to numerous clashes even after sidechains optimization. The satisfactorily transferred ligands were used to define the binding pockets and virtual docking was performed as done for the homology models. As for comparative docking, only DFG-in ligands could be accommodated correctly into the AlphaFold models. All these models led to worse docking (LPE < 1.5: 16 out 59 ligands with anchor restraint and just one without; see https://atome.cbs.cnrs.fr/JMB/m3k20_af2_a3_ic50 and https://atome.cbs.cnrs.fr/JMB/m3k20_af2_a0_ic50, respectively) than standard models deduced from accurate structural alignments (see above). As a validation we successfully docked 3 out of the 4 co-crystallized ligands of M3K20 in the homology models using the @TOME selection scheme (https://atome.cbs.cnrs.fr/JMB/m3k20_sim_a3_pdb). Selecting poses by affinity prediction led to all 4 ligands correctly docked using a shape restraint of -3, while only 2 are correct without it (supplementary Figures S11–S14). Accordingly, comparative models remain superior to AlphaFold models for ligand screening when liganded crystal structures are known for similar proteins, at least, down to 40% of sequence identity.

Finally, we check the use of AlphaFold models for a more difficult case using human HDAC11. In this case, the too distantly related templates shared less than 25% of sequence identity over the 310 residue-long catalytic domain. This led to incomplete alignments or locally incorrect models due to various insertion/deletion or locally higher sequence divergence. Nevertheless, this led to reasonable docking performance with an anchoring shape restraint (see https://atome.cbs.cnrs.fr/JMB/hdac11_sim_a3_ic50) and poorer docking without those restraints (https://atome.cbs.cnrs.fr/JMB/hdac11_sim_a0_ic50). On the contrary, AlphaFold models may represent better starting points for ligand docking in this case. However, they are empty models and comparative docking is necessary to build the binding pocket for ligand docking. Hence, one still needs to gather some liganded structures for (distantly) related proteins. @TOME is providing this option but alternative tools can also transfer ligands into AlphaFold models and the resulting complexes can be uploaded into @TOME for subsequent screening. Interestingly, HDACs harbor a metal atom as a cofactor. While cofactors are not modeled in the current version of @TOME, the use of a shape restraint to guide virtual docking partially compensate for the lack of a cofactor. Indeed, it led to good poses for many ligands using AlphaFold-based models (https://atome.cbs.cnrs.fr/JMB/hdac11_af2_a3_ic50), with the metal

chelating function properly oriented toward the bottom of the active site (where the metal stands) and they also reproduce the known binding mode of related compounds in many HDACs.

Hence, @TOME-3 seems well suited to dock multiple chemical series and find an optimal binding mode for a given monomeric therapeutic target in order to guide the design of better binders. Similar conclusions were drawn on other examples.¹⁵⁻¹⁷

## Discussion

@TOME-3 is a significantly updated version of the former pipeline @TOME-2, in which protein structure modeling is now interfaced with flexible ligand docking. In @TOME-3, clustering of overlapping binders and identification of interacting residues allows automatic delimitation of putative binding-site boundaries. These binding sites can be screened multiple times with PLANTS. In addition, bound ligands can be used as shape restraints to guide docking. The resulting complexes are evaluated using the numerous scoring functions from the chemoinformatics fields. As multiple receptor conformations are used in parallel, ligand poses can be clustered to identify the most likely ones. This unique combination of tools enables rapid and efficient ligand docking on multiple conformations in parallel.

@TOME-3 is mainly dedicated to ligand docking for monomeric proteins with structures solved experimentally or modelled at a sequence identity above 35% with their templates. Users can dock up to 100 compounds, at a time, rendering this server mainly dedicated to detection of optimal poses in order to help biologists and medicinal chemists to annotate binding sites and design new probes and drug candidates rather than screening huge chemical libraries.

## Materials and Methods

### Building a database of receptor models

Sequence similarity searches relying on Psi-BLAST¹⁸ and HHSEARCH¹⁹ are run to extract structures from the PDB but also an in-house database (CBE) containing only annotated protein-ligand complexes extracted from the PDB. Partial models are automatically built by modeling side-chain substitutions using SCWRL⁷ while strictly conserved side-chains are fixed. Then the resulting models are clustered by MaxCluster²⁰ and ranked using QMEAN.²¹ Complete models can be built using MODELLER²² in an interactive manner, as described previously.⁴ Of note, @TOME currently provides models only for monomers. These comparative modeling steps have been described earlier in more details.⁴ Finally, a colour scheme has been implemented to ease selection of valid align-
ments and/or models in the sequence alignment window (red/orange/green dots for bad, medium and good models on the main page, respectively).

Sequence alignment and the corresponding partial models can be visualized and edited on-the-fly using a dedicated JavaScript applet. The edited alignment can be saved back into the front page. Using this applet, one can check the position of conserved residues and modify the position of insertions/deletions (hereafter indels) for better subsequent modeling, as previously described in the software ViTO.²³

This initial database of protein models can be supplemented by uploading structures in PDB format using the server interface (main menu).

## Automatic binding site detection and selection

In a second step, @TOME extracts ligands from each PDB/CBE template and categorizes them as small molecules, ions, peptides, sugars and nucleic acids. The number of selected ligands (depending on the criteria chosen upon submission) is shown on the main output page. The nature, name and molecular weight of each compound are tabulated, and this table can be visualized by clicking onto the "numbering" button (column entitled Complx) for each template. Residues in close contacts (with 4/6/8 Å distance thresholds) are highlighted in the structural alignments to show the position and the local conservation of the binding site. Ligands are automatically clustered according to their contacts with the corresponding model. Meta-binding sites are deduced from overlapping binding sites and the largest and/or most frequently detected pocket is prioritized for subsequent virtual screening. This step is usually sufficient to remove spurious binders coming from purification or crystallization buffers.

For each complex, a binding pocket is defined by all the residues having an atom within a close distance (up to 8 Å) from any atom of the ligand. An interaction profile between selected protein models and associated ligand(s), is computed to estimate a percent of similarity (Psim) between complexes. A consensus affinity prediction (pKd) is deduced from 4 scoring functions: DSX,⁸ MedusaScore,⁹ XScore¹⁰ and chemPLP.¹¹ A quality score, Q, is computed for each model-ligand complex, based on a ligand score (using the AMMP force field), the LPC score²⁴ and the Qmean score for the model.

By default, only 'small molecules' are extracted but users can include other type of ligands (ions, peptides, oligosaccharides and/or nucleic acids) at the submission step. In the main result page, a new column is created (named comparative docking) with a pKd score shown with a link toward a dedicated page in which the corresponding complex(es) can be visualized with the JSmol applet (https://jmol.sourceforge.net/).

## Building new complexes and new binding sites

Alternative templates and/or ligands can be selected afterward either directly on the front page or through the main menu (Modify ligand selection) which leads to a dedicated page. This page displays the list of ligands and various schemes for their semi-automatic selection (e.g. nature of compounds, meta-binding sites and/or the sequence conservation within the binding sites). A JSmol applet can display each selected ligand in its original binding site. Once selected, the corresponding complexes can be evaluated to delineate their binding sites within a list of selected alignments (using the procedure: binding site analysis). In addition, ligands can also be transferred from one structure to another (procedure: comparative docking). This procedure resembles the one above but, in that case, ligands are brought into non-native binding sites (including apo-forms). Orientation of sidechains in the binding sites, is optimized using SCWRL (with strictly conserved sidechains kept fixed or not) to better accommodate the incoming ligand. Finally, users can upload new templates (liganded or not) and the binding sites of the corresponding models must be delineated and evaluated (after ligand transfer if necessary) using the above procedures. In all cases, affinity predictions are estimated as above and the score for the best binder (pKd) is highlighted on the front page, with a link to a table listing all the evaluated complexes. Again, all these complexes (and the corresponding protein-protein superimposition) can be displayed with JSmol or downloaded for further evaluation.

These three steps can be useful when not enough templates are found or liganded after the similarity search step. The interaction profile must be recomputed to take into account the newly built complexes in the Psim score (procedure: compute profile of contacts).

## Building a database of binding sites

By default, a database of binding sites suitable for ligand docking is automatically built from the liganded templates detected by sequence-structure similarity search. If enough liganded templates of good quality are automatically detected, the user can move directly to virtual screening. Conversely, new templates or complexes can be uploaded or built on-the-fly as described just above. At any time, users can modulate the pocket selection and the corresponding ligands. A menu "Modify selections of template" gives access to a page showing a table of pockets, with some selection menus for semi-automatic selection and switch button for manual selection. The default selection relies on the Psim and Q scores, but their thresholds can be modified. Similarly, three levels of ligand and pocket redundancy can be selected. Alternatively,
the user can manually select the complexes they want to use as template for subsequent ligand screening. Then, the edited database must be saved.

All models are prepared for virtual screening using SPORES,²⁵ a companion software of the docker PLANTS.¹³

## Submitting ligand virtual screening

A dedicated page is accessible to upload a set of ligands (in the SYBIL mol2 file format), to name the run, to select some parameters and finally submit a docking using the software PLANTS.¹³ First, the number of pockets can vary from 1 to 20. These pockets can be selected by the Tanimoto similarity of the ligand to dock with the “anchor” (see below), the *Qmean* score of the protein model, the *PSim* score or *Q* score of the complex. The ligands originally present (or transferred) in the selected pockets, named here “anchors”, have been used to delimit the binding site boundaries in the previous steps. Diverse anchors define distinct binding pockets, an important parameter that is not so often varied in docking. These “anchors” can also be used as shape restraints to guide subsequent docking by PLANTS with a weighting scheme that can be modulated from 0 to -5 (default value in @TOME: -3). In addition, side-chains can be made flexible during the docking by PLANTS or their conformation optimized afterward using SCWRL. The docking speed is set by default to 2, but it can be adjusted, from 1 (slower but more accurate) to 4 (quicker but less accurate) as in the original PLANTS.

## Evaluating docking poses

As in the comparative docking module (see above), a table displays for each pose its predicted affinity (pKd score deduced from 4 scoring functions: *chemPLP* as implemented in PLANTS, *DSX*, *MedusaScore* and *X-Score*), its similarity to the protein–ligand contact profile (*PSim*), its complex quality (*Q*) as well as the *LPC* score and the similarity to the anchor as a Tanimoto score computed by *OpenBabel* using ECFP2 descriptors.²⁶ An additional score (*LPE*) was set up using a SVM trained to provide the user with a predicted theoretical RMSD in docking position. It was computed using the SVMTorch software²⁷ and a non-redundant database of 95 crystallographic structures complexes (listed in *supplementary Table S1*) derived from the PDBBind²⁸ and a representative set of different families in CATH classification. For each structure, models were built with various templates (at sequence identity above 30%, 60% or 90%) and different anchors, leading to 7235 complexes. RMSD were calculated between the modeled and the crystallographic position of ligand. 49 PDB entries (3850 vectors) were used to train the SVM in a multi-variable linear regression mode and 46 PDB entries (3385 vec-

tors) were used for validation. Learning relied on five descriptors: the similarity score (*Tanimoto*), the *AnchorFit* score from PLANTS, the *PSim* score, the complementarity function score (*LPC*) and the number of atoms. The model was trained with a Gaussian kernel in regression mode, a high precision required to stop learning (−e = 0.001) and a sparse data mode. The resulting theoretical RMSD, called “Ligand Position Error” (*LPE*), reached a Pearson correlation coefficient of 0.81 on the test set and an average RMSE of 1.26 Å.

Finally, poses are clustered after structural superimposition of the complexes using *fconv*.²⁹ The cluster with the best average *LPE* determines the best orientation.

When a screening is completed, a new column (named virtual screening) is created on the main page and the best score is highlighted. This score is linked to a table listing all the computed complexes and various chemoinformatic scores. The corresponding complexes can be displayed through the *JSmol* viewer, enabling evaluation of protein–ligand contacts (and measures of distances and angles). The anchor defining the shape restraint can also be displayed in orange sticks.

In this interface, the complexes can be ranked according to various features (*LPE*, *pKd*, *LPC*, *PSim* or their *Tanimoto* score with respect to the template anchor). The computed complexes can be viewed superimposed or download to allow further investigations.

## Benchmarking performances on protein-kinases inhibitors

We benchmarked the novel features implemented in @TOME-3 using a set of recent protein-kinases complexes. They represent a large family of well-studied therapeutic targets with numerous structures solved but also numbers of ligands awaiting co-crystallization. In addition, they show significant structural rearrangements depending on the ligand nature, which is challenging for automatic modeling and screening. To this end, in September 2020, a database of 486 human receptors was built using a sequence identity threshold of 30% (average *QMEAN* of 0.28). A validation dataset (*Table S2*) of ligands was later extracted from the PDB by gathering recent structures (released between October 2020 and August 2022). Structures containing multiple chains, multiple ligands, post-translational modifications, a title containing the word ‘Allosteric’ or ‘Covalent’ were discarded. Biochemical and/or crystallization agents were also discarded as well as a few targets with highly biased set of ligands (e.g.: ACVR1). This extraction yielded an unbiased validation set of 434 complexes corresponding to a representative set of 87 different protein-kinases (out of ~500 human protein-kinases).
Several protocols were tested, mainly by
modifying either the number of templates (10, 20
or 40) or the template selection method
(Tanimoto, Q, pKd or Psim score). We evaluated
their success rate by computing RMSD of the
poses with respect to the original crystal structure
at 2-Å threshold.

Examples of screening of these set of protein-
kinases are made available: https://atome.cbs.
cnrs.fr/JMB/validation_@TOME3_ProteinKinases_
2020_2022.

Real-case studies on three therapeutic targets

We evaluated the performances of our server
using three proteins belonging to distinct folds and
corresponding to different levels of sequence
similarity with known crystal structures.
Sequences were submitted to @TOME 3.0 and
fully or semi-automatic procedures were followed
to select various sets of templates (see Results
section). Ligands with known IC50 were extracted
from BindingDB and their 3D conformations and
charges were computed using OpenBabel.
Docking by PLANTS used models selectedd
based on the Tanimoto scores estimating the
similarity between the ligand to dock and the
shape restraint.

As an ultimate test, we built ‘unbiased’ models of
these three targets using AlphaFold 2.2 (https://
github.com/google-deepmind/alphafold) installed
in-house, by removing their known crystal
structures from the selected templates. Those
empty models were uploaded into @TOME and
filled in by comparative docking using liganded
templates as sources for anchors.

Server configuration

@TOME-3 runs on a dedicated cluster hosted in
the lab and composed of 48 CPU, 64 Go RAM, 16
To storage, running under Linux Ubuntu Operating
System. The webserver was developed within the
Apache2 environment using scripts written in perl
and bash languages.

CRediT authorship contribution
statement

**Jean-Luc Pons:** Writing – review & editing, Software, Data curation, Conceptualization. **Victor Reys:** Writing – review & editing, Validation, Software, Data curation. **François Grand:** Software. **Violaine Moreau:** Investigation. **Jerôme Gracy:** Software. **Thomas E. Exner:** Software. **Gilles Labesse:** Writing – review & editing, Writing – original draft, Validation, Methodology, Funding acquisition, Data curation, Conceptualization.

DATA AVAILABILITY

Data will be made available on request.

DECLARATION OF COMPETING INTEREST

The authors declare that they have no known
competing financial interests or personal
relationships that could have appeared to
influence the work reported in this paper.

Acknowledgements

We thank Muriel Gelin and Melanie Schneider for
helpful discussion. We wish also to acknowledge
the useful comments from the referees.

Funding

This research was supported by CNRS, INSERM
and "Investissement d'avenir en Bioinformatics"
(ANR-10-BINF-0003 Project BIP:BIP) as well as
the FRM (DBI201312285579) and the G.I.S.
ChemBioFrance, La Ligue contre le Cancer (PhD
thesis VR).

**Appendix A. Supplementary material**

Supplementary material to this article can be
found online at https://doi.org/10.1016/j.jmb.2024.
168704.

Received 31 January 2024;
Accepted 9 July 2024;
Available online 22 July 2024

**Keywords:**
ligand docking;
chemoinformatics;
homology modelling;
comparative docking;
drug design

**References**

1. Zhou, H., Caon, H., Skolnick, J., (2018). FINDSITE<sup>comb2.0</sup>: A new approach for virtual ligand screening of proteins and virtual target screening of biomolecules. *J. Chem. Inf. Model.* **58**, 2343–2354.

2. Varadi, M., Anyango, S., Deshpande, M., Nair, S., Natassia, C., Yordanova, G., Yuan, D., Stroe, O., Wood, G., Laydon, A., Žídek, A., Green, T., Tunyasuvunakool, K., Petersen, S., Jumper, J., Clancy, E., Green, R., Vora, A., Lutfi, M., Figurnov, M., Cowie, A., Hobbs, N., Kohli, P., Kleywegt, G., Birney, E., Hassabis, D., Velankar, S., (2022). AlphaFold Protein Structure Database: massively expanding the structural coverage of protein-sequence space with high-accuracy models. *Nucleic Acids Res.* **50**, D439–D444.
3. Gilson, M.K., Liu, T., Baitaluk, M., Nicola, G., Hwang, L., Chong, J., (2016). BindingDB in 2015: A public database for medicinal chemistry, computational chemistry and systems pharmacology. *Nucleic Acids Res.* **44**, D1045–D1053.

4. Pons, J.-L., Labesse, G., (2009). @TOME-2: a new pipeline for comparative modeling of protein-ligand complexes. *Nucleic Acids Res.* **37**, W485–W491.

5. Hekkelman, M.L., de Vries, I., Joosten, R.P., Perrakis, A., (2023). AlphaFill: enriching AlphaFold models with ligands and cofactors. *Nat. Methods* **20**, 205–213.

6. Martin, L., Catherinot, V., Labesse, G., (2006). kinDOCK: a tool for comparative docking of protein kinase ligands. *Nucleic Acids Res.* **34**, W325–W329.

7. Canutescu, A.A., Shelenkov, A.A., Dunbrack Jr., R.L., (2003). A graph theory algorithm for protein side-chain prediction. *Protein Sci.* **12**, 2001–2014.

8. Neudert, G., Klebe, G., (2011). DSX: A knowledge-based scoring function for the assessment of protein-ligand complexes. *J. Chem. Inf. Model.* **51**, 2731–2745.

9. Yin, S., Biedermannova, L., Vondrasek, J., Dokholyan, N. V., (2008). MedusaScore: An accurate force-field based scoring function for virtual drug screening. *J. Chem. Inf. Model.* **48**, 1656–1662.

10. Wang, R., Lai, L., Wang, S., (2002). Further development and validation of empirical scoring functions for structure-based binding affinity prediction. *J. Comput.-Aided Mol. Des.* **16**, 11–26.

11. Korb, O., Stützle, T., Exner, T.E., (2009). Empirical scoring functions for advanced protein-ligand docking with PLANTS. *J. Chem. Inf. Model.* **49**, 84–96.

12. Berman, H.M., Westbrook, J., Feng, Z., Gilliland, G., Bhat, T.N., Weissig, H., Shindyalov, I.N., Bourne, P.E., (2000). The Protein Data Bank. *Nucleic Acids Res.* **28**, 235–242.

13. Korb, O., Stützle, T., Exner, T.E., (2006). PLANTS: Application of ant colony optimization to structure-based drug design. *Lect. Notes Comput. Sci* **4150**, 247–258.

14. Sala, D., Engelberger, F., Mchaourab, H.S., Meiler, J., (2023). Modeling conformational states of proteins with AlphaFold. *Curr. Opin. Struct. Biol.* **81**, 102645

15. Schneider, M., Pons, J.L., Bourguet, W., Labesse, G., (2020). Towards accurate high-throughput ligand affinity prediction by exploiting structural ensembles, docking metrics and ligand similarity. *Bioinformatics* **36**, 160–168.

16. Ferreira, R., Pons, J.L., Labesse, G., (2019). Insights into substrate and inhibitor selectivity among human GLUT

transporters through comparative modeling and molecular docking. *ACS Omega* **4**, 4748–4760.

17. Alexandre, T., Lupan, A., Helynck, O., Vichier-Guerre, S., Dugué, L., Gelin, M., Haouz, A., Labesse, G., Munier-Lehmann, H., (2019). First-in-class allosteric inhibitors of bacterial IMPDHs. *Eur. J. Med. Chem.* **167**, 124–132.

18. Altschul, S.F., Madden, T.L., Schäffer, A.A., Zhang, J., Zhang, Z., Miller, W., Lipman, D.J., (1997). Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. *Nucleic Acids Res.* **25**, 33100–33402.

19. Soding, J., (2005). Protein homology detection by HMM-HMM comparison. *Bioinformatics* **21**, 951–960.

20. Ortiz, A.R., Strauss, C.E., Olmea, O., (2002). MAMMOTH (matching molecular models obtained from theory): an automated method for model comparison. *Protein Sci.* **11**, 2606–2621.

21. Benkert, P., Tosatto, S.C., Schomburg, D.A., (2008). comprehensive scoring function for model quality assessment. *Proteins* **7**, 1261–1277.

22. Eswar, N., Webb, B., Marti-Renom, M.A., Madhusudhan, M.S., Eramian, D., Shen, M.Y., Pieper, U., Sali, A., (2006). Protein Structure Modeling With MODELLER. *Curr. Protoc. Bioinformatics* **5**, 5–6.

23. Catherinot, V., Labesse, G., (2004). VITO: tool for refinement of protein sequence structure alignments. *Bioinformatics* **20**, 3694–3696.

24. Sobolev, V., Sorokine, A., Prilusky, J., Abola, E.E., Edelman, M., (1999). Automated analysis of interatomic contacts in proteins. *Bioinformatics* **15**, 327–332.

25. ten Brink, T., Exner, T.E., (2009). Influence of protonation, tautomeric, and stereoisomeric states on protein-ligand docking results. *J. Chem. Inf. Model.* **49**, 1535–1546.

26. O'Boyle, N.M., Banck, M., James, C.A., Morley, C., Vandermeersch, T., Hutchison, G.R., (2011). Open Babel: An open chemical toolbox. *J. Cheminf.* **3**, 33.

27. Collobert, R., Bengio, S., (2001). SVMTorch: Support vector machines for large-scale regression problems. *J. Mach. Learn. Res.* **1**, 143–160.

28. Liu, Z., Li, Y., Han, L., Li, J., Liu, J., Zhao, Z., Nie, W., Liu, Y., Wang, R., (2015). PDB-wide collection of binding data: current status of the PDBbind database. *Bioinformatics* **31**, 405–412.

29. Neudert, G., Klebe, G., (2011). fconv: Format conversion, manipulation and feature computation of molecular data. *Bioinformatics* **27**, 1021–1022.