# Exploring the interaction between a LRRK2/PP1CA interfering peptide and PP1CA

Jose Dominguez-Meijide, Samuel Murail, Rachid Boudjelloul, Pierre Tuffery, Angelita Rebollo

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Jose Dominguez-Meijide, Samuel Murail, Rachid Boudjelloul, Pierre Tuffery, Angelita Rebollo. Exploring the interaction between a LRRK2/PP1CA interfering peptide and PP1CA. Journal of Molecular Graphics and Modelling, 2025, 141, pp.109140. 10.1016/j.jmgm.2025.109140. hal-05349371

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# Exploring the interaction between a LRRK2/PP1CA interfering peptide and PP1CA

Jose Dominguez-Meijde<sup>a,1</sup>, Samuel Murail<sup>a,1</sup>, Rachid Boudjelloul<sup>b</sup>, Angelita Rebollo<sup>b,2</sup>, Pierre Tuffery<sup>a,*,2</sup> ◆

## ARTICLE INFO

**Keywords:**

Protein-protein docking

Molecular dynamics simulations

PP1

LRRK2

Parkinson disease

Interfering peptide

## ABSTRACT

Parkinson's disease (PD) is a complex neurodegenerative disorder for which there is presently only palliative treatments. Recently, the interaction between the Leucine Reach Repeat Kinase 2 (LRRK2) and the Protein Phosphatase 1 (PP1) has come to light as a priority target for PD, but the way LRRK2 and PP1 interact is currently unknown, hampering the design of modulators. We have previously identified a fragment of LRRK2 able to interfere with the interaction of LRRK2 and PP1 catalytic subunit alpha (PP1CA), and we assume it should bind PP1CA at the LRRK2/PP1CA interface. In this study, we first identify the fragments of PP1CA that are able to interfere with the LRRK2/PP1CA interaction so as to get information about the regions of PP1CA likely to face LRRK2. Next, by combining *in silico* studies with *in vitro* competition experiments, we narrow down among the various hypotheses proposed regarding its mode of binding to PP1CA. Overall, we identify one preferred binding mode between the LRRK2 fragment and PP1CA consistent with *in vitro* results obtained for mutants of the initial fragment. These results pave the way for further rational design of modulators of the LRRK2/PP1CA interaction, to further decipher the molecular mechanisms underlying PD etiology.

## 1. Introduction

Parkinson's disease (PD) is a complex neurodegenerative disorder. Its first outward visible symptoms manifest in the form of motor issues, including muscular rigidity, resting tremors, and postural instability among others. This is coupled with a variety of non-motor related features that develop progressively before and after the onset of said motor issues [1]. Despite multiple continued efforts, there is presently no clinical treatment for the root causes of the disease, with any known therapies being only palliative treatments.

The underlying mechanisms of PD are not fully understood yet. However, it has been recently suggested that the Human Leucine-Rich repeat serine/threonine-protein kinase 2, commonly referred to as LRRK2, could be a candidate target in the treatment of PD [2,3]. LRRK2 is an unusually large protein with multiple domains, including a kinase domain and structural domains. It is highly phosphorylated. Mutations in the gene associated with this protein have been linked to familial PD

and thus are considered to constitute a risk factor in developing the disease, although the specific molecular mechanisms by which this happens are still not fully understood. When these mutations are present, a cluster of residues (S910/S935/S955/S973) normally phosphorylated in the wild type become dephosphorylated, suggesting the phosphorylation level could be implied in the PD pathological mechanism [4].

Serine/Threonine Protein Phosphatase 1, or PP1, is a member of the Serine/Threonine phosphatases (PSP) family of proteins. More specifically, it is a member of the sub-family of PhosphoProtein Phosphatases (PPP), containing PP1, PP2, PP2A, PP3 (PP2B/Calcineurin), PP4, PP5, PP6 and PP7. Of this sub-family, PP1, PP2A, and PP3 are some of the most extensively studied from the family. They are involved in regulation of multiple cellular functions including proliferation and apoptosis. Compared to other phosphorylating enzymes such as kinases, the PSPs appear to have lower intrinsic specificity. Their selectivity is instead managed by the regulation subunits with which they form holoenzymes.

<footnote><sup>a</sup> Université Paris Cité, CNRS UMR8251, INSERM ERL1133, Unité de Biologie Fonctionnelle et Adaptative, F-75013 Paris, France</footnote>

<footnote><sup>b</sup> Université Paris Cité, Faculté de Pharmacie, UTCBS, Inserm U1267, CNRS UMR8258, 75006 Paris, France</footnote>

<footnote>* Corresponding author.</footnote>

<footnote>E-mail address: pierre.tuffery@u-paris.fr (P. Tuffery).</footnote>

<footnote n="1">¹ The two first authors equally contributed to this work.</footnote>

<footnote n="2">² The two last authors equally contributed to this work.</footnote>
Over 150 molecules have been validated as PP1 partners in vertebrates. Among other tasks, PP1 is involved in the dephosphorylation and subsequent inhibition of LRRK2 kinase function [5]. PP1 contains a catalytic unit (PP1CA) – in charge of the dephosphorylation of target amino acids - and regulatory units – contributing to addressing PP1 to varied cellular localization and to substrate specificity.

Given the role of PP1CA in the dephosphorylation of LRRK2, and the pathological implications of this process, efforts have focused on investigating the interaction between these two proteins, with the aim to determine potential inhibitors of their interaction and determine how such inhibition may affect the pathological behavior of LRRK2. Several groups have focused on the interaction of PP1 and LRRK2, whether by counteracting its effects by enhancing other enzymes present, or by looking into ways to prevent the dephosphorylation of LRRK2 [6,7]. Recently, we have identified a fragment of LRRK2 capable of binding PP1CA and disrupting the interaction between LRRK2 and PP1CA [8]. It is a fragment of 18 amino acids of LRRK2 located in the COR domain, at positions 1711–1728 in the h.sapiens sequence of LRRK2. It has been identified using PEPscan [9,10], an in vitro technique, scanning the complete LRRK2 sequence and testing the ability of each fragment to directly bind to PP1CA. This fragment was also shown to interfere in vitro with the interaction between full-length LRRK2 and PP1CA [8]. We call it the interfering peptide (IP) hereafter. However, so far, neither the mode of binding of this peptide to PP1CA nor the structure of the interacting LRRK2/PP1CA proteins are known. Major parts of the LRRK2 structure have been resolved through cryo-EM, both for monomer [11], dimer [12] and tetramer [13] complexes. Which of the three forms results in a functional configuration of LRRK2 remains in discussion. It remains that LRRK2 appears capable of undergoing large conformational changes (see Ref. [13]), making the use of solved structures of little use in assisting in the identification of the binding mode of IP or PP1CA to LRRK2. Interestingly, however, the IP fragment of LRRK2 is directly exposed to the solvent in the recently published trimeric structure of LRRK2 [13]. The structure of PP1CA has been solved by X-ray crystallography, and over 40 structures are available in the Protein Data Bank [14]. Although in living cells PP1 generally consists of a hetero-dimer including the catalytic unit and a regulatory unit, we consider hereafter only the catalytic unit since we have demonstrated the direct interaction of LRRK2 and PP1CA in the in vitro competition experiment and we have no information the type of the regulatory subunits that could be involved for the binding of PP1CA with LRRK2.

PP1CA is a ubiquitous phosphatase involved in numerous cellular processes [15]. It is thus possible that targeting the LRRK2/PP1CA interaction could interfere with other processes and have unintended consequences. Although these considerations are far away from the present study, they stress the need to further investigate the mode of interaction of the IP and PP1CA. They also pose the question of the way of interfering with the interaction of LRRK2 and PP1, either using a LRRK2 fragment targeting PP1CA or a PP1CA fragment targeting LRRK2.

Our main objective in this work is to discern a mode of interaction between the IP and PP1CA, with the perspective to help understanding the mechanical underpinning of LRRK2/PP1CA binding. However, here, we focus on the IP/PP1CA binding mode to get a model of the interaction to drive the further design of IPs to modulate the LRRK2/PP1CA interaction. To this aim, we use a panel of *in silico* approaches to explore the interaction between a LRRK2-derived peptide and the different sections of the catalytic domain of PP1CA with which it might potentially interact, and we confront it with *in vitro* experimental information about fragments of PP1CA likely to interact with LRRK2.

## 2. Materials and methods

### 2.1. Data

The amino-acid sequence for the PP1CA domain [16] and the LRRK2 COR domain (hereafter denoted as LRRK2-COR) [17] correspond to the UNIProt [18] accession codes P62136 (residues 1–330) and Q5S007 (residues 1512–1840) for PP1CA and LRRK2-COR, respectively. The LRRK2-based Interfering Peptide (IP) corresponds to residues 1711–1728 of the LRRK2 sequence: PMGFWSRLINRLLEISPY. The PEP-FOLD3 web server [19] was used to generate 3D models of it, using default parameters. Local geometry refinement was then achieved by energy minimization in vacuum using gromacs 5.1 for 1000 steps of steepest descent, using the charmm36-jul2017 force field, as implemented in the minimize.pdb.py script of the gromacs.py library [20].

The structure used for PP1CA corresponds to PDB identifier 3E7A [21]. It was solved using X-ray diffraction and its resolution is of 1.63 Å. For LRRK2, we considered the PDB entry 7LHT [12], solved using cryo-EM at a resolution of 3.5 Å. It corresponds to a LRRK2 dimer, but this was not considered any further in this study since we are looking for the interaction mode between PP1CA and LRRK2 ROC domain. The coordinates of some side chains are missing which we repaired using Discovery Studio [22]. Note that this structure does not contain the disordered C-terminal region (residues after position 300) hereafter denoted as the IDR.

### 2.2. Searching for PP1CA fragments binding to LRRK2 using PEPscan

PEPscan was undergone as previously described [8–10]. Overlapping dodecapeptides with two amino acid shift, spanning the complete PP1CA sequence were prepared by automatic spot synthesis (Abimed, Langerfeld, Germany) onto an amino-derived cellulose membrane, as described in Ref. [8]. The membrane was saturated using 3 % non-fat dry milk/3 % BSA (Sigma-Aldrich) (2h room temperature), incubated with purified LRRK2 protein (Thermofisher, A 15197, 4 µg/ml, 4 °C, overnight) and after several washing steps, incubated with polyclonal anti-LRRK2 antibody (Santa Cruz) 2h at room temperature, followed by HRP-conjugated secondary antibody (DAKO) for 1h at room temperature. Positive spots were visualized using the ECL system (Bio-Rad).

### 2.3. *In vitro* peptide competition of the LRRK2/PP1CA interaction

MDAMB231 cells purchased from ATCC collection were lysed and cytoplasmic extracts immunoprecipitated with anti PP1CA antibody. LRRK2/PP1CA interaction was competed *in vitro* with 1 mM of the peptides for 30 min at room temperature. Immunoprecipitates were washed and blotted with anti LRRK2 antibody and anti PP1CA, the last as internal control of protein loading. Immunoprecipitates competed with the WT peptide were used as control. Data are representative of three independent experiments. Statistical analysis was performed using the Student t-test to compare means between two samples.

### 2.4. IP/PP1CA complex modeling: deep learning based modeling

Thanks to the recent developments of deep learning, two categories of approaches can be used for complex modeling. The first one is to generate the complex structure starting from the sequences. In order to generate a high amount of potential starting configurations, we employed two different complex-building tools: Colabfold [23], Alpha-fold [24] and Rosettafold [25] starting with the full PP1CA sequence and the IP. For Rosettafold, we used the default configuration available on the web server. For Alphafold we used version 2.2.4, using multimer_v3 weights, 9 recycles and 100 seeds (resulting in 500 models). For Colabfold, we used version 1.3, using multimer_v2 weights, enabling dropout, using 9 recycles and 100 seeds (resulting in 500 models).

For the IDR C-terminus of PP1CA, for which additional sampling is
required to address its presumably higher conformational flexibility, we considered two additional strategies using Colabfold: The first approach was to create a simplified model of PP1CA consisting only of the IDR C-ter region in complex with the IP. The second one was to model LRRK2-COR which contains the IP in complex with the C-ter IDR of PP1CA.

## 2.5. HADDOCK based modeling

Besides generating the complex structures from the sequences of its components, we also generated complex structures starting from 3D models of them employing the web server version of Haddock 2.4 [26] for both blind and local docking – we define blind Docking as Docking where the entire domain is set as a passive interactor, whereas for local docking some active residues are identified. We also employed Haddock to generate complexes for the IDR interaction with the IP and the IDR region interaction with LRRK2-COR. For the case of the IDR/IP interaction, the five starting IDR structure variants were qualified as passive interactors and the IP as active interactor. For the case of the IDR/LRRK2-COR interaction, the IDR was defined as active interactor and the LRRK2-COR surface was defined as a passive interactor. Finally, we performed two sets of Domain-domain docking involving the PP1CA and LRRK2-COR. For one of these sets, the sequence of LRRK2-COR corresponding to the IP was defined as active interactor; for the other set, the three locations in PP1CA identified by PEPScan were defined as active interactors. Note that for all HADDOCK experiments, one partner is totally free to reorient itself relatively to the other one.

After each stage of docking, the predicted complexes were scored and ranked. The HADDOCK score is a weighted sum of a variety of energy terms including van der Waals, electrostatic, desolvation, and restraint violation energies (Evdw, Eelec, Edesol, and Eair, respectively). Of the 1000 structures generated during rigid body minimization, only the top 200 scoring are allowed to undergo semi-flexible annealing, and those same 200 then continue onto the final refinement stage. For each HADDOCK submission, 200 final complexes were selected, ranked and scored in the same way complexes are scored during the docking protocol, and then further organized, or ‘clustered,’ based on structural similarity, specifically by the root mean square deviation (RMSD) between the protein-ligand interface of the complexes.

## 2.6. GROMACS complex relaxation

The complexes were relaxed via a Gromacs [27] minimization and equilibration. For this purpose, we employed the AMBER03 [28] forcefield in explicit water with cubic periodic boundary conditions (PBC): the process consisted of a 5000 steps steepest descent minimization, a 50000 steps Conjugate Gradient, and an equilibration of 1ns at a temperature of 100K, 1ns at 200K, and 10ns at 300K.

## 2.7. Simulated Tempering (ST)

Simulations were undergone starting from poses obtained after relaxation. The C-ter domain (residues after position 300) corresponding to the PP1CA-IDR was removed and a manganese ion was added in the active site. On the last model, the peptide was simulated only in presence of the IDR (residues 301–330). Protein structures were prepared and protonated at pH 7.0 using the pdbfixer module of the OpenMM package [29]. Protein atoms were modeled using the Amber14SB forcefield [30]. Complexes were solvated in truncated octahedron boxes with a padding of 1.5 nm and the TIP3P water model. In addition to the active site manganese ion, counter ions were added to counter the charge of the complex, together with a 150 mM concentration of Na + Cl-ions.

Simulations were conducted with the OpenMM 7.7 Molecular Dynamics software [29]. Energy minimization was run for up to 10.000 steps, and followed by two successive equilibration steps. During production and equilibration, a 4 fs integration step was used with the help of a heavy hydrogens assigned to a 3 atomic mass units. A nonbonded

cutoff of 1.0 nm was used with the Particle Mesh Ewald (PME) algorithm for long range electrostatic interactions. A pressure of 1.0 bar was applied during equilibration and production using the Monte-Carlo algorithm and a 25 time steps interval. Temperature was equilibrated using the Langevin Middle integrator and 10 ps⁻¹ friction coefficient. During the two equilibrations the temperature was fixed to 300.0 K. During the first equilibration, position restraints of 1000 kJ mol⁻¹ nm⁻² were applied on the receptor and peptide Cα atoms, during the second equilibration the position restraints were decreased to 100 kJ mol⁻¹ nm⁻².

Simulated Tempering (ST) simulations [31] were conducted using a protocol inspired from a previous work [32] using a python script written by Peter Eastman and modified to implement on-the-fly weight calculation [33]. During ST simulations, temperature exchanges were attempted every 2 ps and 19 temperature ladders spaced exponentially between 300.0 and 400.0 K were used. Position restraints were totally removed on the peptide, as 100 kJ mol⁻¹ nm⁻² restraints were kept on the receptor Cα atoms to avoid protein unfolding at high temperature. For each two models, two ST simulation were computed. A first ST simulation of 20 µs, using only position restraints on the protein Cα atoms and a second ST simulation of 10 µs with the same position restraints on the protein Cα atoms but with an additional position distance restraint on the peptide backbone atoms of 100 kJ mol⁻¹ nm⁻² only if the peptide center of mass (c.o.m.) position moves more than 2.0 nm away from its initial position. During ST simulation of the peptide in presence of IDR, only 14 temperature ladders were used. No position restraints or distance restraints were applied neither on the peptide or on the IDR domain. The ST simulation production run had a length of 20 µs.

## 2.8. MMPBSA affinity evaluation

ST simulation trajectories were analyzed using the MDAnalysis library [34], PCA was computed on the peptide backbone atoms after trajectory alignment on receptor backbone atoms. The two first components explaining 92 % of peptide coordinate variability were used to cluster the different trajectory frames, using the HDBSCAN algorithm [22]. Concerning the simulation of the LRRK2/IDR complex, as the IDR structure is extremely flexible, Principal Component Analysis (PCA) was computed on the whole complex, the two first component explained 44 % of complex coordinate variability.

Each cluster was then submitted to classical MD simulation, consisting in an energy minimization of up to 10.000 steps, followed by and equilibration of 10 ns using position restraints on all Cα atoms (protein and peptide) of 100 KJ.mol-1.nm-2, and a 100 ns production run without restraints. This process was replicated five time with different seed.

In order to evaluate the comparative stability of the different complexes previously analyzed via simulated tempering, we employed combined molecular mechanics and Poisson-Boltzmann Surface Area analysis. We employed the open source available mmPBSA.py [35] software, based on the Ambtools software [36]. Enthalpy contribution was computed on each simulation using 2500 frames, as entropy was estimated using Normal Mode analysis on 25 frames.

A total of five simulated tempering (ST) simulations and 60 classical simulations were performed to analyze the stability of the protein-

<table><caption>**Table 1**

Summary of MD simulations.</caption><thead><tr><th>Name</th><th>Receptor Position restraints</th><th>Peptide distance restraints</th><th>Length (µs)</th><th>Simulation type</th></tr></thead><tbody><tr><td>ST_1</td><td>Yes</td><td>No</td><td>20.0</td><td>ST</td></tr><tr><td>ST_1_com</td><td>Yes</td><td>Yes</td><td>10.0</td><td>ST</td></tr><tr><td>ST_2</td><td>Yes</td><td>No</td><td>20.0</td><td>ST</td></tr><tr><td>ST_2_com</td><td>Yes</td><td>Yes</td><td>10.0</td><td>ST</td></tr><tr><td>ST_IDR</td><td>No</td><td>No</td><td>20.0</td><td>ST</td></tr><tr><td>Clust</td><td>No</td><td>No</td><td>0.1*5</td><td>Classical</td></tr></tbody></table>
peptide complexes. The details of the simulations are summarized in Table 1.

## 3. Results

### 3.1. *In vitro* identification of candidate PP1CA fragments to interact with LRRK2

To validate the binding modes of the IP, we have first searched for candidate binding regions on PP1CA. We have performed a PEPScan experiment in which PP1CA was scanned on the membrane and hybridized with LRRK2 – now commercially available at the difference of our previous studies.

As shown Fig. 1A, three fragments were considered as potential candidate binding sites. These “mirror” peptides are denoted as M1, M2 and M3. These fragments were top ranked based on the fact that based on the observation from previous experiments it is mostly series of at least three consecutive positive spots that are likely to correspond to interfering peptides. Our primary objective was here to identify a region on PP1CA surface likely to participate to the LRRK2/PP1CA interaction, as a control for the *in silico* investigations, and one such peptide was identified in our three first selected candidates (see below). Consequently, other possible regions were not investigated *in vitro*. As shown Fig. 1B, M1 is present near the N-Terminus of the PP1CA, between helices 1 and 2. M3 is located in the Intrinsically Disordered Region (IDR) part of the PP1CA. Having a lighter spot in the middle (row 4, column 24), M2 does not exhibit the correct spot pattern typical of positive

candidates with lighter spots at the edges and darker spots in the middle, typical of true positives for PEPscan experiments similar to that of the present study [9,10]. Actually, M2 includes a strand internal to the structure, which makes it a very unlikely candidate. Finally, looking at the predicted aggregation propensities of the three peptides [37], M2 is also predicted having aggregation propensity, whereas M1 and M3 do not. Thus, M2 was not considered any further as a plausible candidate, and furthermore, our simulation results (see below) never suggested a preferred stable binding of the IP close to M2.

Further characterization of M1 and M3 was undergone analyzing their ability to *in vitro* compete with the association of LRRK2 and PP1CA. Fig. 1C shows that only M1 is able to interfere with the LRRK2/PP1CA interaction. Consequently, the two structures being globular, one can expect that IP interacts with PP1CA at a location in the structure close to – but not necessarily exactly facing – that of M1 in the PP1CA structure.

### 3.2. Modeling the LRRK2/PP1CA interaction in the light of the identified interfering peptides

We first considered generating a model of LRRK2-COR in interaction with PP1CA to get indications about the candidate interfaces between the two partners, keeping in mind that the IP might interact with PP1CA in a different manner than LRRK2-COR. It appeared rapidly that the exact conformation of the complete LRRK2 is still in question [11–13]. Firstly, it seems that LRRK2 can undergo rather large conformational changes and secondly, its oligomeric state is still debated. We thus

<figure><img src="image_3.png" /><figcaption>**Fig. 1.** A: PEPScan results for the PP1CA Membrane hybridized with LRRK2. Lines are numbered using the L1, L3 labels (y-axis), and columns using numbers (x-axis). Spots surrounded by rectangles correspond to the peptides M1, M2, M3 discussed in the text. B: Location of M1, M2 and M3 in the PP1CA structure. The experimentally solved domain structure is represented in gray, and the modeled IDR C-terminus in light green. The catalytic histidine and the manganese ions are represented in purple. Red, pink and dark blue depict M1, M2 and M3, respectively. C: *in vitro* competition of LRRK2/PP1CA interaction by M1 (50 % of inhibition compared to the control) and M3 (20 % of inhibition compared to the control). top: Western blot bottom: The densitometry of 3 independent experiments is also shown. *, p 0.015; **, p 0.005. Error bars correspond to one standard deviation. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)</figcaption></figure>
decided to limit the search to LRRK2-COR that contains the IP. Fig. 2 shows the results for the best scoring complexes generated using Colabfold [23] (A), Haddock [26] using mirror IP residues as active partners to drive the docking of LRRK2-COR, (B) and Haddock using the IP residues of LRRK2-COR as active partner to drive the docking of PP1CA (C).

The results are varied. Colabfold generated a conformation in which LRRK2-COR is blocking the access to the PP1CA active site, but the IP is not located at the interface, but opposite to it (Fig. 2A). Using Haddock, when the active partner is PP1CA, for all five top scoring poses, LRRK2-COR approaches the location of M1, and for two out of the five best poses the IP contacts it (Fig. 2B). Conversely, when the active partner corresponds to the IP, two distinct orientations are observed among the top scoring poses. One involves a contact between the IP sequence and the M1 region. The other involves a contact between the IP and M3 (Fig. 2C). In total, it remains that those interactions of LRRK2-COR with PP1CA mediated by the IP target both the M1 and M3 regions.

### 3.3. Identification of candidate binding site of the IP

We then turned to the interaction of the IP with PP1CA. Results are summarized Fig. 3. Similarly to what was observed using the complete LRRK2-COR domain, the results are varied. Using Colabfold [24] and Colabfold (Fig. 3A) the preferential IP binding modes target locations close to the active site entry, and the estimated model quality are low (best pdockQ [38] of 0.26 and best peptide pdockq2 [39] 0.01). We also attempted to generate models specific of the PP1CA-IDR alone in interaction with the IP, but we did observe interaction between the IP and the IDR in only one model out of five with very low confidence scores (pdockQ ~ 0.02, see Supporting Information). Using RosettaFold [25] (Fig. 3B), poses tend to target a region close enough to M1. Finally using HADDOCK (Fig. 3C), poses tend to target both the IDR and the M1 site. Strikingly, over all these poses, only two poses interacting with M1 and one interacting with the IDR remained stable after a simple relaxation procedure (Fig. 3D).

### 3.4. Simulated Tempering

The diversity of the results of the docking approaches, made it necessary to further explore the binding of the peptide with the protein using enhanced conformational sampling approaches. We conducted a comprehensive investigation into the stability of three distinct poses using simulated tempering (ST) simulations. The rationale behind selecting ST over classical molecular dynamics (MD) simulations lies in its superior sampling efficiency. ST simulations enable temperature fluctuations between 300 K and 400 K throughout the simulation. This dynamic temperature range enhances the likelihood of the peptide escaping non-favorable conformations, thereby facilitating a more thorough exploration of the conformational space.

As illustrated in Figure S1, ST simulations provide a less-than-ideal temperature sampling. However, even with imperfect temperature

regulation, ST simulations significantly enhance the conformational sampling of peptide binding around PP1CA. This improvement is critical for accurately capturing the dynamic interactions and potential binding poses of the peptide.

Figure S2 presents the root-mean-square deviation (RMSD) of the PP1CA backbone atoms over the course of the simulations. The RMSD values predominantly remain within the 2 to 3 Å range, suggesting a stable backbone PP1CA structure. However, in rare cases the RMSD can reach 4 to 5 Å range, these cases are associated to temperature peaks during the ST simulation. As for the following analysis, we will focus on 300K frames, these deviations are not relevant and we observed that the receptor PP1CA structure is stable at ambient temperature.

To characterize the conformational sampling of IP peptide around PP1CA we computed principal component analysis (PCA) of the peptide coordinates, following the alignment of the simulation trajectory to the backbone atoms of PP1CA (Fig. 4). The PCA helps in visualizing the primary modes of structural variation in the peptide. The HDBSCAN [40] clustering identified nine distinct clusters within the conformational space. These clusters are depicted in the same figure, providing insights into the diverse structural conformations the peptide adopts when interacting with PP1CA. Indeed, the clusters correspond to peptide poses covering a large part of PP1CA surface, some being on opposite faces of PP1CA structure, proving the ability of ST to largely sample the conformational space. The nine clusters can be distributed in two classes, the first one composed of the largest clusters 2, and the smaller 1, 4, 8, 9 are located next to M1, as small clusters 3, 5, 6, 7 are located on the opposite side, facing positions 178, 198 and 213–218, next to M2.

A single ST simulation lasting 20 µs was performed to investigate the interaction between the intrinsically disordered region (IDR) of PP1CA and the IP. As illustrated in Figure S3, the temperature sampling during this simulation is significantly improved compared to previous simulations. This enhanced temperature control facilitates a more accurate and comprehensive exploration of the conformational landscape of the peptide-receptor complex.

Figure S4 displays the root-mean-square deviation (RMSD) of the PP1CA-IDR, which ranges from 3 to 8 Å from the initial model. The notably high RMSD values are expected, given the intrinsically disordered nature of the receptor domain. This intrinsic disorder contributes to substantial structural flexibility and variability, which is accurately captured by the elevated RMSD observed throughout the simulation.

During the clustering analysis of the IDR-IP conformations using HDBSCAN, three primary clusters were identified and retained, as shown in Figure S5.

To characterize each cluster conformation, we performed a classical molecular dynamics (MD) simulation, starting from the pose with the lowest root mean square deviation (RMSD) from the average cluster peptide structure. Each simulation was run with 5 replicas and a simulation time of 100 ns. Based on these, we now turn to the analysis of the binding affinities of the IP for PP1CA. Fig. 5 depicts the comparative binding affinities of the various clusters, using molecular mechanics Poisson-Boltzmann surface area (MMPBSA - see methods). The highest

<figure><img src="image_4.png" /><figcaption>**Fig. 2.** Models of LRRK2-COR in interaction with PP1CA. PP1CA is depicted in gray, with the M1 region in red, M2 in violet, M3 in blue, IDR in yellow. LRRK2-COR in depicted in light brown, with the IP in green. A: Model obtained using Colabfold. B: Model obtained using Haddock with PP1CA as active region. C: Model obtained using Haddock with IP as active region. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)</figcaption></figure>
<figure><img src="image_5.png" /><figcaption>**Fig. 3.** Illustration of the complexes between PP1CA domain and IP obtained by the different complex generation methods. PP1CA in gray, with the catalytic HIS in purple, M1 in red, M2 in violet, M3 in blue, and the IDR in yellow, IP in green. A: Complexes generated from the sequences using Colabfold. B: Complexes generated from the sequences using Rosettafold. C: Complexes generated from the structures using HADDOCK. D: Complexes that remain in contact after relaxation and equilibration via GROMACs. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)</figcaption></figure>

<figure><img src="image_6.png" /><figcaption>**Fig. 4.** Distribution and cluster of explored conformations of IP around PP1CA. The clusters are displayed as a function of the two principal components of the simulations. PC 1 accouts for 85.4 % variance, and PC2 for 7.1 %. Each cluster is assigned a number and a different color. Their representative conformations are depicted around (IP: red, M1: blue, M2: purple). Alphafold3 [41] models are indicated as cyan dots. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)</figcaption></figure>

binding affinity are observed for clusters 1 and 5. Additionally, Figure S6 depicts the stability of these clusters during the simulations, and show that clusters 1, 5 also appear among the most stable based on RMSD measurements. Lastly, the binding affinities obtained for the simulations involving only the IDR are weaker, but one must also keep in mind that for an IDR, it is possible that the sampling was insufficient to obtain reliable results. However, those observations are consistent with our *in vitro* competition results, not showing any interfering ability of M3, and we do not consider this interaction as likely.

Considering both the MMPBSA affinity calculations and RMSD stability data, we decided to focus our subsequent analyses on clusters 1 and 5. These clusters demonstrated the best combination of high affinity and structural stability, making them the most promising candidates for further investigation into the binding interactions and potential therapeutic targeting of the PP1CA and LRRK2.

We subsequently decided to decompose the energy contributions of individual IP residues to the overall affinity for PP1CA in clusters 1 and 5, to identify the key residues responsible for the binding affinity. As illustrated in Fig. 6, the decomposition of energy contributions revealed that for cluster 1, residues W5, R7, and Y18 were the primary

<figure><img src="image_7.png" /><figcaption>**Fig. 5.** MMPBSA affinity per cluster. A: clusters identified for simulations involving the full PP1CA domain. B: clusters identified for simulations involving the IDR only. The nine clusters identified around the PP1CA domain and the three clusters identified around the PP1CA-IDR domain were further examined using classical molecular dynamics (MD) simulations. For each cluster, a single representative structure - chosen as the one closest to the average cluster structure - was selected and subjected to MD simulations with five different seeds, each running for 100 ns. Error bars represent a 95 % confidence interval computed over the 5 replicas. All p-values (MannWhitney U Test) in between 0.03 and 0.008 except between clusters (2,3,4), (6,7) and (8,9) (A) and clusters (2,3) (B).</figcaption></figure>

contributors to the binding affinity with PP1CA. These residues exhibited the most significant interaction energies, indicating their critical role in stabilizing the PP1CA-LRRK2 complex. In contrast, for cluster 5, the analysis identified residues R7, R11, and to a lesser extent S16 as the most important contributors to the binding affinity with PP1CA.

Turning to a more detailed analysis within clusters, the complete details of the interactions is given in Figures S10-S13 using LigPlot [42] and ProLif [43]. From the ProLif analysis, it seems that interactions observed in cluster 1 tend to involve more hydrophobic contributions than in cluster 5. However, we focus here on the salt-bridge interactions for which some differences appear, as detailed in Table 2, and illustrated in Fig. 6C and D, S9 and S10. The two clusters target different regions of PP1CA. For cluster 1, we observed several key salt-bridge interactions. The terminal residue Y18, COO- group, forms a salt-bridge with receptor residues R36 and K147, with occurrence frequencies of 93.1 % and 48.9 %, respectively. The peptide residue R7 forms a salt-bridge with the receptor residue E5, occurring 92.2 % of the time. Additionally, within the peptide itself, residue R11 forms a salt-bridge with residue E14, which occurs for only 35.1 % of the time.

In cluster 5, the terminal residue Y18, COO- group, forms a salt-bridge with receptor residue R188 with a frequency of 84.9 %, and with peptide residue E14 at a frequency of 15.5 %. Peptide residue R7 forms highly frequent salt-bridges with receptor residues D179 and D203, with occurrence rates of 99.9 % and 99.6 %, respectively. The contribution of E14 is minor. Furthermore, residue R11 within the peptide forms a very stable salt-bridge with receptor residue D179,
<figure><img src="image_8.png" /><figcaption>**Fig. 6.** A: positions of IP poses corresponding to cluster 1 and 5 on PP1CA. Color codes are similar to that of Fig. 3. B: Energy contribution of each IP residue for cluster 1 and 5. C,D: details if the interactions for cluster 5(C) and 1 (D). Error bars represent a 95 % confidence interval computed over the 5 replicas. More details about the interactions involved in cluster 1 and 5 are available in Figures S9 and S10, generated using LigPlot [42] and ProLIF [43]. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)</figcaption></figure>

**Table 2**

Summary of the most frequent IP/PP1CA salt bridges observed for cluster 1 and 5 (% occurrence in cluster). Only interactions observed with a frequency of more than 10 % are reported. Interactions were detected using a cut-off distance of 5.0 Å between representative atoms of amino acids (CZ, NZ, CG, CD for R, K, D and E, respectively). For Y18, the C-terminal C atom was also considered).

<table><thead><tr><th>Cluster 1</th><th></th><th>Cluster 5</th><th></th></tr><tr><th>Cluster1</th><th></th><th>Cluster5</th><th></th></tr></thead><tbody><tr><td>R7 - E5</td><td>92.2</td><td>R7 - D179</td><td>99.9</td></tr><tr><td>R11 - E14</td><td>35.1</td><td>R7 - D203</td><td>99.6</td></tr><tr><td>Y18 - R36</td><td>93.1</td><td>R11 - D179</td><td>98.9</td></tr><tr><td>Y18 - K147</td><td>48.9</td><td>E14 - R188</td><td>15.5</td></tr><tr><td></td><td></td><td>Y18 - R188</td><td>84.9</td></tr><tr><td></td><td></td><td>Y18 - E14</td><td>15.5</td></tr></tbody></table>

occurring 98.87 % of the time. Looking back to the PEPscan results (Fig. 1A), it is interesting to note that this binding mode involves residues of the fragment line 3, spots 29–31.

### 3.5. *In vitro* competition of LRRK2/PP1CA interaction

Our analyses show that the salt bridges associated with the two best clusters involve the same IP residues, but with different frequencies. Particularly, R7 has strong interactions in both clusters, which is not the case for R11 which strongly interacts with PP1CA D179 in cluster 5 but much less in cluster 1. Based on this, one can expect that the impact of substituting these residues could be different hypothesizing the IP binding site corresponding to either cluster 1 or cluster 5 poses.

To get deeper insights in the interaction of the peptide with PP1CA, we considered to substitute these amino acids and analyze their effect on peptide inhibitory potency on the PP1CA/LRRK2 interaction, targeting R7, R11 and E14 (as a control).

An *in vitro* competition assay was performed in order to show whether the mutant peptides R7S, R7A or E14Q (Fig. 7A) are able to compete the LRRK2/PP1CA interaction similarly to the wild type (WT) IP identified previously [8]. The choice of using a serine to replace the arginine and a glutamine to replace the glutamate instead of the classical

alanine replacement came from the wish to challenge the role of salt bridges in the binding, suppressing amino acid charge while preserving at best side chain size, polarity, and beyond, peptide solubility. Indeed, glutamine differs from the glutamate by only one polar atom, and has no charge. Likewise, serine has no charge, and using it to replace an arginine is more likely to have less impact on the peptide structure than replacing it with an alanine which is more hydrophobic. Serine was preferred to threonine that is branched. Lysates of the MDA-MB231 cell line were immunoprecipitated with anti-PP1CA antibody and the interaction with LRRK2 was competed using peptides WT, R7S, R7A and E14Q mutants. LRRK2 was detected only in immunoprecipitates competed with 1 mM of R7A mutant peptide. As expected, LRRK2 was not detected in immunoprecipitates competes with the WT peptide, as previously described [8]. LRRK2 was neither detected in immunoprecipitates competed with peptides R7S and E14Q, suggesting that these mutant peptides behave mostly as the WT peptide. Amounts of precipitated PP1CA were used as internal control and showed similar levels in all conditions. The percentage of competition is also shown in the figure, confirming that the mutant peptide R7A is not able to compete the LRRK2/PP1CA interaction. Fig. 7B shows results about R11. Strikingly, R11S abolishes the interfering properties of the peptide, suggesting a key role or R11 in the peptide binding to PP1CA. From Figures S11 and S13, one notes that R7 is more interacting with polar residues in cluster 5 than in cluster 1, which could explain why the substitution R7S could be better stabilized in cluster 5. Results observed for the double mutant R7SR11S are more difficult to interpret since the interfering ability of the peptide is reduced but not as drastically as for the single mutants. However, this makes sense since from Fig. 7A, R7S seems to have more potent interfering ability than the wild type, which could compensate the loss coming from the R11S substitution. Overall, these highlight the critical role of R11, and are more compatible with cluster 5 binding mode than that of cluster1.

### 4. Discussion

In this study, we aimed to obtain better information on the binding mode of a fragment of LRRK2 (IP) able to interfere with the LRRK2/
<figure><img src="image_9.png" /><figcaption>**Fig. 7.** R7S, E14Q and R7SR11S peptides in vitro competition of PP1CA/LRRK2 interaction. A) MDA MB231 cells were lysed and cytoplasmic extracts immunoprecipitated with anti-PP1CA antibody. The interaction PP1CA/LRRK2 was competed in vitro with 1 mM of R7S (45 % of inhibition compared to control peptide - WT), R7A, E14Q (A) or R11S (15 % of inhibition compared to non-competition control), R7SR11S (40 % of inhibition compared to non-competition control). B) peptides for 30 min at room temperature. Immunoprecipitates were washed and blotted with anti LRRK2 or PP1CA antibodies, the former as internal control of protein loading. Immunoprecipitates competed with the original WT peptide were used as control. An example of Western blot is shown. The densitometry of 3 independent experiments is also shown. \*p 0.07, \*\*p 0.005. The densitometric analyses of the Western blot is shown with the percentage of competition. Error bars correspond to one standard deviation.</figcaption></figure>

PP1CA interaction, in the context of PD. The IP was formerly shown able to bind to PP1CA [8]. The structure of the LRRK2/PP1CA complex being not available, we have combined *in silico* and *in vitro* approaches to get information about the binding mode of the IP with PP1CA. PEPscan was first used to identify candidate regions of PP1CA that could be located at LRRK2/PP1CA interface. The results obtained were not straightforward since several candidate fragments were identified. Competition revealed however that PP1CA fragment M1 is able to interfere with the LRRK2/PP1CA interaction, suggesting that the LRRK2/PP1CA interface should be in its vicinity. Note that due to the large area associated in protein-protein complexes, this does not imply that the IP should face M1.

In *silico* analyses to further characterize the binding mode of the LRRK2/PP1CA interactions also provided varied results. First, considering the complete LRRK2-COR and PP1CA domains, three different approaches (Colabfold, Rosettafold, HADDOCK) that start from different information (sequence for Colabfold and Rosettafold, structure for HADDOCK), gave results that are rather different. Colabfold models are not compatible with the interaction of M1 and the IP, whereas RosettaFold and HADDOCK models are better to this respect. Considering the interaction of only the IP with PP1CA, the same heterogeneity was observed. Colabfold best models position the IP facing a region that is far from that of M1 and M2, close to the entry of the catalytic pocket, whereas Rosettafold and HADDOCK proposed binding poses compatible with an interaction with M1 and M3. Among all these poses, only three revealed stable after a relaxation, two facing M1 and one facing M3 - but M3 does not seem associated with any interfering ability, as a result of *in vitro* competition. Further investigations were conducted using ST simulations to get a better sampling of the conformational space. After careful analysis, only two clusters seemed relevant, one being in the vicinity of M1 and the other rather far away, but still at the entry of the catalytic site pocket – a different position than that proposed by Colabfold. This is fully compatible with an interfering ability for the LRRK2/PP1CA interaction. Of note, some clusters that have poorer estimated free energies of binding are rather far away from the starting positions of the molecular dynamics simulations which highlights the effectiveness of the ST protocol used for sampling. Moreover, even if the

preferred pose is close to the position of M1 identified on PP1CA, the small difference in free energy of binding observed for clusters 1 and 5, would also suggest that the peptide probably does not bind to PP1CA at only one stable position but more probably, it is able to navigate through different positions over the PP1CA surface, including those of clusters 1 and 5. Interestingly as well, as seen on Fig. 4, only very few poses correspond to the PP1CA active site, a region that is preferred for Alphafold3 [41] but that was not really emerging from our analyses. Another observation of interest is that the peptide itself keeps its helical structure over all the simulations, a positive point in terms of affinity of binding. This conformation is also that of the IP in the LRRK2-COR structure, and it is interesting to note that the two arginines facing the solvent in LRRK2-COR structure, one can suppose that LRRK2-COR could interact with PP1CA in a similar manner, an hypothesis that we have however not further investigated here.

Finally, to test our observations, we have studied the effect of several mutations of the IP. The main outcome of this study is the role of IP's R11. It seems involved in rather stable salt-bridge interactions that are probably critical for the binding of IP and PP1CA. The high frequencies of these interactions indicate their importance in maintaining the binding conformation and contributing to the overall binding affinity. Another site of interest is R7, which seems to contribute importantly to both of the two best poses we have identified. But the role of R7, is less clear. Despite its replacement by an alanine has drastic effect on the IP binding to PP1CA, its replacement by a serine shows a slightly enhanced interfering ability. It is possible that the replacement by an alanine has consequences on the stability of the peptide structure, which has not been further investigated here. Overall, our results tend however to indicate that the pose associated with cluster 5 is the most likely, which calls into question the idea that interfering peptides extracted from LRRK2 and PP1CA should face each other. However, let us recall on the one hand that protein-protein interfaces are sufficiently broad to explain the different binding sites of interfering peptides, and on the other hand that binding dynamics could also contribute to escaping a static view of the interference mechanism.

Finally, we would like to emphasize that there is still room for confirmation of the current results. Firstly, it could be welcome to
challenge the PP1CA sites identified as interacting with IP's R11. Secondly, we have focused on the PP1CA unit only, whereas the presence of the other regulatory PP1 units could be considered as well. It would be interesting to explore how the interfering capacity of the IP is conditioned by the presence of such additional units. But currently, very few is known about which regulatory units are involved in the LRRK2/PP1 interaction. These questions lead however far beyond the scope of the present study. Currently, our results provide only preliminary and indirect information on how PP1CA and LRRK2 interact in 3D. It could be exploited for the design of more effective modulators of the LRRK2-PP1CA interaction.

## Declaration of generative AI and AI-assisted technologies in the writing or data analysis of the manuscript

During the preparation of the work the authors did not use any AI or AI-assisted technology and take full responsibility for the content of the published article.

## CRediT authorship contribution statement

Jose Domínguez-Méijde: Writing – review & editing, Writing – original draft, Investigation. Samuel Murail: Writing – review & editing, Conceptualization. Rachid Boudjelloul: Investigation. Angelita Rebollo: Writing – review & editing, Writing – original draft, Supervision, Funding acquisition, Formal analysis, Conceptualization. Pierre Tuffery: Writing – review & editing, Writing – original draft, Supervision, Project administration, Funding acquisition, Conceptualization.

## Data and code availability

Models of the complexes used to start the MD simulations, as well as scripts, trajectories, and cluster representative conformations are freely available from zenodo: https://doi.org/10.5281/zenodo.15690505.

## Funding

This work was supported by the Agence Nationale de la Recherche (ANR) [grant number ANR-21-CE16-0003-02].

## 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.

## Appendix A. Supplementary data

Supplementary data to this article can be found online at https://doi.org/10.1016/j.jmgm.2025.109140.

## References

[1] J. Jankovic, E.K. Tan, Parkinson's disease: etiopathogenesis and treatment, J. Neurol. Neurosurg. Psychiatry 91 (2020) 795-808, https://doi.org/10.1136/jnnp-2019-322338.

[2] J.-Q. Li, L. Tan, J.-T. Yu, The role of the LRRK2 gene in parkinsonism, Mol. Neurodegener. 9 (2014) 47, https://doi.org/10.1186/1750-1326-9-47.

[3] D. Jennings, S. Huntwork-Rodriguez, A.G. Henry, J.C. Sasaki, R. Meisner, D. Diaz, et al., Preclinical and clinical evaluation of the LRRK2 inhibitor DNL201 for Parkinson's disease, Sci. Transl. Med. 14 (648) (2022) eabj2658, https://doi.org/10.1126/scitranslmed.abj2658.

[4] T. De Wit, V. Baekelandt, E. Lobbestael, LRRK2 phosphorylation: behind the scenes, Neuroscientist 24 (5) (2018) 486-500, https://doi.org/10.1177/1073858418756309.

[5] A. Marchand, M. Drouyer, A. Sarchione, M.-C. Chartier-Harlin, J.-M. Taymans, LRRK2 phosphorylation, more than an epiphenomenon, Front. Neurosci. 14 (2020) 527, https://doi.org/10.3389/fnins.2020.00527.

[6] E. Lobbestael, J. Zhao, I.N. Rudenko, A. Beylina, F. Gao, J. Wetter, M. Beullens, M. Bollen, M.R. Cookson, V. Baekelandt, R.J. Nichols, J.-M. Taymans, Identification of protein phosphatase 1 as a regulator of the LRRK2 phosphorylation cycle, Biochem. J. 456 (2013) 119-128, https://doi.org/10.1042/BJ20121772.

[7] K. Berndsen, P. Lis, W.M. Yeshaw, P.S. Wawro, R.S. Nirujogi, M. Wightman, T. Macartney, M. Dorward, A. Knebel, F. Tonelli, S.R. Pfeffer, D.R. Alessi, PPM1H phosphatase counteracts LRRK2 signaling by selectively dephosphorylating rab proteins, eLife 8 (2019) e50416, https://doi.org/10.7554/eLife.50416.

[8] C.Z. Dong, H. Bruzzoni-Giovanelli, Y. Yu, K. Dorgham, C. Parizot, J.M. Zini, J. Y. Brossas, P. Tuffery, A. Rebollo, Identification of peptides interfering with the LRRK2/PP1 interaction, PLoS One 15 (2020) e0237110, https://doi.org/10.1371/journal.pone.0237110.

[9] A. Rebollo, E. Savier, P. Tuffery, Pepscan approach for the identification of protein-protein interfaces: lessons from experiment, Biomolecules 11 (2021) 772, https://doi.org/10.3390/biom11060772.

[10] A. Rebollo, L. Fliedel, P. PEPscan Tuffery, A broad spectrum approach for the characterization of protein-binder interactions? Biomolecules 12 (2022) 178, https://doi.org/10.3390/biom12020178.

[11] C.K. Deniston, J. Salogiannis, S. Mathea, D.M. Snead, I. Lahiri, M. Matyszewski, O. Donosa, R. Watanabe, J. Böhning, A.K. Shiau, S. Knapp, E. Villa, S.L. Reck-Peterson, A.E. Leschziner, Structure of LRRK2 in Parkinson's disease and model for microtubule interaction, Nature 588 (2020) 344-349, https://doi.org/10.1038/s41586-020-2673-2.

[12] A. Myasnikov, H. Zhu, P. Hixson, B. Xie, K. Yu, A. Pitre, J. Peng, J. Sun, Structural analysis of the full-length human LRRK2, Cell 184 (2021) 3519-3527.e10, https://doi.org/10.1016/j.cell.2021.05.004.

[13] H. Zhu, F. Tonelli, A. Prescott, D.R. Alessi, J. Sun, Rab29-dependent asymmetrical activation of leucine-rich repeat kinase 2, Science 382 (2024) 1404-1411. https://www.scienc.org/doi/10.1126/science.adi9926.

[14] H.M. Berman, The protein data bank, Nucleic Acids Res. 28 (2000) 235-242, https://doi.org/10.1093/nar/28.1.235.

[15] H. Ceulemans, M. Bollen, Functional diversity of protein Phosphatase-1, a cellular economizer and reset button, Phys. Rev 84 (2004) 1-39, https://doi.org/10.1152/physrev.00013.2003.

[16] M.S. Choy, M. Hieke, G.S. Kumar, G.R. Lewis, K.R. Gonzalez-DeWhitt, R.P. Kessler, B.J. Stein, M. Hessenberger, A.C. Nairn, W. Peti, R. Page, Understanding the antagonism of retinoblastoma protein dephosphorylation by PNUTS provides insights into the PP1 regulatory code, Proc. Natl. Acad. Sci. 111 (2014) 4097-4102, https://doi.org/10.1073/pnas.1317395111.

[17] E. McGrath, D. Waschbüsch, B.M. Baker, A.R. Khan, in: LRRK2 Binds to the Rab32 Subfamily in a GTP-dependent Manner via its Armadillo domain, 12, 2021, pp. 133-146, https://doi.org/10.1080/21541248.2019.1666623.

[18] The UniProt Consortium, A. Bateman, M.-J. Martin, S. Orchard, M. Grane, S. Ahmad, E. Alpi, E.H. Bowler-Barnett, R. Britto, H. Bye-A-Jee, A. Cukura, P. Denny, et al., UniProt: the universal protein knowledgebase in 2023, Nucleic Acids Res. 51 (2023) D523-D531, https://doi.org/10.1093/nar/gkac1052.

[19] A. Lamiable, P. Thévenet, J. Rey, M. Vavrusa, P. Derreumaux, P. Tuffery, PEP-FOLD3: faster of novo structure prediction for linear peptides in solution and in complex, Nucleic Acids Res. 44 (2016) W449-W454, https://doi.org/10.1093/nar/gkw329.

[20] S. Murail, Gromacs.py [software], Zenodo (October 10, 2018), https://zenodo.org/records/1455734.

[21] M.S. Kelker, R. Page, W. Peti, Crystal structures of protein Phosphatase-1 bound to Nodularin-R and tautomycin: a novel scaffold for structure-based drug design of serine/threonine phosphatase inhibitors, J. Mol. Biol. 385 (2009) 11-21, https://doi.org/10.1016/j.jmb.2008.10.053.

[22] Biovia, Dassault Systèmes, Discovery Studio Visualizer, V 21.1.0.20298, Dassault Systèmes, San Diego, 2020. https://www.3ds.com/products/biovia/discovery-studio/visualization.

[23] M. Mirdita, K. Schütze, Y. Moriwaki, L. Heo, S. Ovchinnikov, M. Steinegger, ColabFold: making protein folding accessible to all, Nat. Methods 19 (2022) 679-682, https://doi.org/10.1038/s41592-022-01488-1.

[24] J. Jumper, R. Evans, A. Pritzel, T. Green, M. Figurnov, O. Ronneberger, K. Tunyasunvunakool, R. Bates, A. Zídek, A. Potapenko, et al., Highly accurate protein structure prediction with AlphaFold, Nature 596 (2021) 583-589, https://doi.org/10.1038/s41586-021-03819-2.

[25] M. Baek, F. DiMaio, I. Anishchenko, J. Dauparas, S. Ovchinnikov, G.R. Lee, J. Wang, Q. Cong, L.N. Kinch, R.D. Schaeffer, et al., Accurate prediction of protein structures and interactions using a three-track neural network, Science 373 (2021) 871-876, https://doi.org/10.1126/science.abj8754.

[26] G.C.P. Van Zundert, J.P.G.L.M. Rodrigues, M. Trellet, C. Schmitz, P.L. Kastritis, E. Karaca, A.S.J. Melquiond, M. Van Dijk, S.J. De Vries, A.M.J.J. Bonvin, The HADDOCK2.2 web server: user-friendly integrative modeling of biomolecular complexes, J. Mol. Biol. 428 (2016) 720-725, https://doi.org/10.1016/j.jmb.2015.09.014.

[27] M.J. Abraham, T. Murtola, R. Schulz, S. Páll, J.C. Smith, B. Hess, E. Lindahl, GROMACS: high performance molecular simulations through multi-level parallelism from laptops to supercomputers, SoftwareX 1-2 (2015) 19-25, https://doi.org/10.1016/j.softx.2015.06.001.

[28] J.W.D. Ponder, D.A. Case, Force fields for protein simulations, in: Advances in Protein Chemistry, 66, Elsevier, 2003, pp. 27-85, https://doi.org/10.1016/S0065-3233(03)66002-X, 27-85.

[29] P. Eastman, J. Swails, J.D. Chodera, R.T. McGibbon, Y. Zhao, K.A. Beauchamp, L.-P. Wang, A.C. Simmonett, M.P. Harrigan, C.D. Stern, R.P. Wiewiora, B.R. Brooks, V.S. Pande, OpenMM 7: rapid development of high performance algorithms for molecular dynamics, PLoS Comput. Biol. 13 (2017) e1005659, https://doi.org/10.1371/journal.pcbi.1005659.
[30] D.A. Case, V. Babin, J. Berryman, R.M. Betz, Q. Cai, D.S. Cerutti, I.I.I.T. E. Cheatham, T.A. Darden, R.E. Duke, H. Gohlke, et al., Amber 14. https://orbilu.uni.lu/handle/10993/16614, 2014. (Accessed 19 April 2023).

[31] E. Marinari, G. Parisi, Simulated tempering: a new monte carlo scheme, Europhys. Lett. EPL 19 (1992) 451-458, https://doi.org/10.1209/0295-5075/19/6/002.

[32] K. Dorgham, S. Murail, P. Tuffery, E. Savier, J. Bravo, A. Rebollo, Binding and kinetic analysis of human protein phosphatase PP2A interactions with caspase 9 protein and the interfering peptide C9h, Pharmaceutics 14 (2022) 2055, https://doi.org/10.3390/pharmaceutics14102055.

[33] P.H. Nguyen, Y. Okamoto, P. Derreumaux, Communication: simulated tempering with fast on-the-fly weight determination, J. Chem. Phys. 138 (2013) 061102, https://doi.org/10.1063/1.4792046.

[34] N. Michaud-Agrawal, E.J. Denning, T.B. Woolf, O. Beckstein, MDAnalysis: a toolkit for the analysis of molecular dynamics simulations, J. Comput. Chem. 32 (2011) 2319-2327, https://doi.org/10.1002/jcc.21787.

[35] B.R.I. Miller, T.D. McGee Jr., J.M. Swails, N. Homeyer, H. Gohlke, A.E. Roitberg, MMPBSA.py: an efficient program for end-state free energy calculations, J. Chem. Theor. Comput. 8 (2012) 3314-3321, https://doi.org/10.1021/ct300418h.

[36] D.A. Case, H.M. Aktulga, K. Belfon, D.S. Cerutti, G.A. Cisneros, V.W.D. Cruzeiro, N. Forouzesh, T.J. Giese, A.W. Götz, H. Gohlke, et al., AmberTools, J. Chem. Inf. Model. 63 (2023) 6183-6191, https://doi.org/10.1021/acs.jcim.3c01153.

[37] J. Planas-Iglesias, S. Borko, J. Swiatkowski, M. Elias, M. Havlasek, O. Salamon, E. Grakova, A. Kunka, T. Martinovic, J. Damborsky, J. Martinovic, AggreProt: a web server for predicting and engineering aggregation prone regions in proteins, Nucleic Acids Res. 52 (2024) W159-W169, https://doi.org/10.1093/nar/gkae420.

[38] P. Bryant, G. Pozzati, A. Elofsson, Improved prediction of protein-protein interactions using AlphaFold2, Nat. Commun. 13 (2022) 1265, https://doi.org/10.1038/s41467-022-28865-w.

[39] W. Zhu, A. Shenoy, P. Kundrotas, A. Elofsson, Evaluation of AlphaFold-Multimer prediction on multi-chain protein complexes, Bioinformatics 39 (7) (2023) btad424, https://doi.org/10.1093/bioinformatics/btad424.

[40] R.J.G.B. Campello, D. Moulavi, J. Sander, Density-based clustering based on hierarchical density estimates, in: J. Pei, V.S. Tseng, L. Cao, H. Motoda, G. Xu (Eds.), Advances in Knowledge Discovery and Data Mining, Springer, Berlin, Heidelberg, 2013, pp. 160-172, https://doi.org/10.1007/978-3-642-37456-2_14. Lecture Notes in Computer Science.

[41] J. Abramson, J. Adler, J. Dunger, R. Evans, T. Green, A. Pritzel, O. Ronneberger, L. Willmore, A.J. Ballard, J. Bambrick, et al., Accurate structure prediction of biomolecular interactions with AlphaFold 3, Nature 630 (2024) 493-500, https://doi.org/10.1038/s41586-024-07487-w.

[42] R.A. Laskowski, M.B. Swindells, LigPlot+: multiple ligand-protein interaction diagrams for drug discovery, J. Chem. Inf. Model. 51 (2011) 2778-2786. https://pubs.acs.org/doi/10.1021/ci200227u.

[43] C. Bouysset, S. Fiorucci, ProLIF: a library to encode molecular interactions as fingerprints, J Cheminform 13 (2021) 72, https://doi.org/10.1186/s13321-021-00548-6.