Designing peptide inhibitors targeting LRP6 co-receptor to restrict its association to DKK1; an Alzheimer’s disease therapeutic strategy

doi:10.21203/rs.3.rs-2453192/v1

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Designing peptide inhibitors targeting LRP6 co-receptor to restrict its association to DKK1; an Alzheimer’s disease therapeutic strategy

https://doi.org/10.21203/rs.3.rs-2453192/v1

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We aimed to design and develop short peptide inhibitors targeting Low-Density Lipoprotein receptor-related Protein 6 (LRP6) as a therapeutic for Alzheimer’s disease (AD). Considering the critical association of LRP6 co-receptor with Dickkopf-related protein 1 (DKK1), which is majorly involved in the upregulation of AD via canonical Wnt signaling. After assessing the critical amino acid residue involved in the DKK1-LRP6 complex, a 16-amino acid (16aa) length short peptide was retrieved to be used as the inhibitory peptide. Further, using alanine scanning, we generated 16 different mutated peptides and their binding potential in-silico to LRP6, which ensured the blockage of the DKK1 association to LRP6. Further, the peptides were subjected to envisage their toxicity, physicochemical properties, and ADMET properties. In the results, apart from the native inhibitory peptide (16aa), the mutated peptides 16aa8, 16aa1, 16aa7, and 16aa6 showed good binding with LRP6 which made the blockage of DKK1 association to LRP6. As a future perspective, the top-scored peptides including the native peptides will be screened for synthesizing characterizing, and validating their in vitro and in vivo therapeutic potential to treat AD through the DKK1 inhibition mechanism.

Alzheimer’s disease

DKK1

Inhibitory peptide

LRP6

Molecular docking

Protein-Protein interaction

Alzheimer's disease (AD) is an age-related, neurodegenerative disorder characterized by cognitive impairment with memory loss. AD is a neurodegenerative disorder, progressive and mainly characterized by cognitive impairments, behavioral changes, etc. (Hampel et al 2015). Each year about 4.6 million new cases of dementia are predicted with increased cases expected by 2030 (Prince M et al 2013). The formation of oligomeric aggregates of beta-amyloid peptide (Aβ 40, Aβ 42) is the main reason for AD. These aggregates interact with membrane receptors causing a neuropathological fall that ultimately results in synaptic dysfunction and death of nerve cells (Nisticò R et al 2012). These protein fragments are found in the hippocampus and other limbic brain regions which is important for learning, memory, and also in emotional regulation. Thus, identifying and treating AD is a need for the day. In the brain, the Wnt-signalling pathway plays an essential role in the nervous system from embryogenesis to higher brain function in the adult. Due to the age factor, the pathway is downregulated with the upregulation of the DKK1 protein expression.

Interaction of DKK1 with LRP6 would automatically decrease the availability of the LRP6 receptor for Wnt ligands which is essential for the formation of the Wnt-Fz-Lrp6 complex for the activation of the cascade at the cell surface (MacDonald BT et al 2009; Clevers H and Nusse R 2012; MacDonald BT and He X 2012; Nusse R and Clevers H 2017). The downstream signaling cascade from this receptor complex leads to the recruitment of the scaffold protein Dishevelled (Dvl) to the plasma membrane and inhibition of GSK3β. β-catenin can then gather in the cytoplasm and translocate to the nucleus, where it regulates the expression of Wnt target genes in association with TCF/LEF transcription factors. When the Wnt pathway is inactive, cytosolic β-catenin is degraded by a macromolecular destruction complex which is composed of the scaffold protein Axin, the tumor suppressor protein APC, and the two constitutively active serine-threonine kinases CK1 and GSK3β (Cong F et al 2003; Valenta T et al 2012; Schuijers J et al 2014). So, the association of the protein DKK1 with the LRP6 receptor deregulates the Wnt canonical pathway which is essential for synaptic development. Blocking this association could be a suitable and promising therapy for the disease. Therefore, we proposed that identifying an effective therapeutic candidate such as non-toxic peptides would be an ideal way to block this DKK1-LRP6 association.

In the current scenario, peptides have evolved as a safer and potential therapeutic source in curing of different diseases such as cancer (Kurrikoff K et al 2019), diabetes (Chiangjong W et al 2020), cardiovascular diseases (Perkovic V et al 2019), and remarkably this peptide-based therapeutic strategy for treating a range of diseases is increasing rapidly day by day (Thayer AM 2011). In the 1990s the peptides were about to have 15 or 20 amino acids and gradually increased with 40 to 50 and up to 70 amino acid residue length peptides were started to be used as medicinal sources in 2018 and that has also been permitted in main pharmaceutical markets (Lau JL and Dunn MK 2018; Erak M et al 2018)

Thus, we pursued the study to develop a short peptide inhibitor as a promising therapy for neurodegenerative disease. To achieve this, initially, we analyzed the critical residues involved in the DKK1-LRP6 complex to detach a short sequence which possibly will act as the inhibitory peptide to block the DKK1-LRP6 complex formation and thus the canonical Wnt signaling can be regulated (Fig. 1). We believe that this would be an ideal and effective strategy to treat AD and to establish peptide-based medications for the same. This study display and discuss different computational approaches to achieve the target inhibitory peptides, mutated inhibitory peptides, and their molecular mechanistic-based screening for further in vitro and in vivo validations.

Protein-Protein investigation:

The protein sequence of the LRP6 –DKK1 complex was retrieved from the RCSB Protein Data Bank (PDB ID: 3S8V). At first, a short sequence of 16 amino acid residues length (Asp673 to Arg688) was retrieved from the chain A, after they were recognized as critical residues involved in LRP6-DKK1 association using ‘SPPIDER’. The website was opened and chosen the options analysis of known protein-protein complexes and identification of interface. Then the pdb file was uploaded and a threshold was kept at 4% relative, job was submitted for analysis and the results have been retrieved.

Alanine Scanning And Peptide Design:

Alanine scanning was performed to identify the existing roles of the 16aa peptide in binding with LRP6. Alanine scanning was performed by mutating the first amino residue to the last amino acid of 16aa inhibitory peptide with alanine then they were saved as pdb using the ‘build structure’ module in Discovery studio visualize (BIOVIA, DS, 2019). This pdb was further optimized for geometry using the minimizing function in the UCSF Chimera software. The individual mutated peptides were modelled, energy minimized using UCSF Chimera software, and saved as the native pdb file format.

Molecular Docking Studies:

The crystal structure of LRP6 and DKK1 protein complex (PDB ID: 3S8V) was retrieved from the Protein Data Bank (https://www.rcsb.org/) and used for the docking studies. The A chain belonging to LRP6 was detached from the complex and the structure was used for the docking process with both native short peptide (16aa) and mutated peptides (16aa1 to 16aa16). Several docking platforms like Pydock, cluspro2.0, and HawkDock were implemented to authenticate the results for each program and correlate it equally. The docking results were analyzed with the help of Chimera and DS visualize programs. Further, the results were examined for LRP6 inhibitory peptide conformations with the obtained molecular mechanistic values, binding interface, and binding energies. The top-scored peptides were spotted out. Further, MD simulations of the docked complexes of LRP6-peptides were carried out to assess the stability of the same. The molecular dynamics simulation of selected peptidomimetics was carried out using the GROMACS 5.1 package with the recent GROMOS96 (53a6) force field, which plays an important role in protein dynamics (Oostenbrink C et al 2004).

Physicochemical Properties Of The Peptide:

The molecular mechanistic values electrostatics, desolvation, and Van der Waals (VdW) force of the peptides were retrieved from Pydock results and the best interaction complex (LRP6-16aa/16aa1-16aa16 peptides) was spotted and submitted to SPPIDER for confirmation of the interfaces and critical residues involved in the association. The toxicity prediction of the peptides was performed using ‘Toxinpred’ (http://crdd.osdd.net/raghava/toxinpred/) (Gupta S et al 2013) server it is an in-silico method of predicting and designing toxic/non-toxic peptides. The physicochemical properties such as Half-life, hydropathicity, and instability index were determined by Protparam (http://web.expasy.org/protparam/) tool from the Expasy database server (Gill SC and Von Hippel PH 1989).

ADMET Properties And Stability Predictions Of Peptide Inhibitors:

The bioavailability of the peptides was predicted by evaluating its ADMET using MedChem designer 5.0 (Nosratic M and Shakeran Z 2018) and pkCSM (biosig.unimelb.edu.au/pkcsm/) (Kola I and Landis J 2004). The pdb structure of the peptides was converted to SMILES format using an online SMILES translator. The SMILES were pasted into the server and automatically the structure was generated, then each structure was named accordingly. Then for pkCSM, the SMILES format of the peptide was pasted and analyzed. It helps predict the physicochemical properties of different compounds and reduces the experimental procedures to evaluate the screened compounds.

The C-terminal region of DKK1 cysteine-rich domain is crucial for interaction with LRP6 co-receptors:

The crystal structure of the LRP6 receptor with DKK1 protein (PDB ID: 3S8V) was retrieved from Protein Data Bank (https://www.rcsb.org/). The interface residues of the complex were visualized and interpreted using UCSF chimera. The FASTA sequence of LRP6 and DKK1 were retrieved and interface residues were identified from the software, after analyzing both a small stretch of the DKK1 C-terminal region amino acids (16 residues: Asp673 to Arg688) was found to be interacting majorly with LRP6 co-receptor (Table 1 and inner figure). Further, docking was proceeded with the LRP6 co-receptor with the 16 amino acid chain from the DKK1 protein using the Pydock server. A blind docking was performed without specifying the binding site of the Ligand during the docking process.

The results obtained were clustered and examined by comparing the binding energies and different docked conformations of the peptide generated at the end. Anticipatedly, the peptides were found to have interacted within the LRP6 co-receptor accurately at the exact place of the DKK1 protein binds (Fig. 2). It is well understood that the 16 amino acid sequences can hinder the binding of LRP6 with DKK1 protein independently. Then the peptide interface with the receptor was determined using SPPIDER and the interacting residues were recognized.

Table 1. Interacting residues at the interface of LRP6 and DKK1

LRP6 co-receptor	DKK1
R639 E663 D673 Y677 K684 I681 R688 Y706 E708 R751 W767 G768 G769 K770 P771 N789 V790 G791 R792 L810 D811 T812 N813 L814 D830 D831 P833 H834	Q184 E185 H204 F205 W206 S207 I209 G217 Q218 V219 T221 K222 R224 R225 S228 L231 E232 I233 F234 R236 Q248 S258 R259 L260

Computational Alanine Scanning Analysis Of The Peptide:

After analyzing the results of 16aa inhibitory peptide’s specificity and accuracy of the binding site and binding mode to the target protein, we aimed to get some combinations of the 16aa peptide and decided to select alanine scanning for this purpose. Alanine scanning of the 16aa inhibitory peptide was performed to identify the best alanine mutated inhibitory peptides, which interacted with the critical residues. These were implied in the association of LRP6 with the DKK1 protein, which thereby could allow us to shorten the non-essential portion of the peptide. The amino acids were mutated to alanine consecutively one by one using BIOVIA Discovery Studio 2020 software. Alanine substituted peptides were then modelled and minimized using the UCSF Chimera program. Subsequently, docking studies were performed for each mutated peptide with the LRP6 receptor, and the binding energies of the complex and their three-dimensional conformations of binding interfaces were obtained.

It can be observed from the values presented in Table 2, that the mutating residues Asp673 to Arg688 with alanine have no significant effect on the complex steadiness in terms of binding energy and also on structure confirmation. Moreover, we also calculated the molecular mechanistic values, such as electrostatics, desolvation, and VdW (Van der Waals) for each peptide. VdW is generally a weak force, which enhances the attraction and binding efficiency of a ligand to its receptor, and also it is concerned in the maintenance and stabilization of a drug-receptor complex.

Table 2. Results of alanine scanning and molecular docking of inhibitory and mutated peptides with LRP6

Code	Conf	Electrostatics	Desolvation	VdW	Total	Rank
16aa	DNRIYWTDISLKTISR	-11.990	-14.493	21.969	-24.286	6
16aa1	ANRIYWTDISLKTISR	-28.715	-2.747	41.939	-27.268	2
16aa2	DARIYWTDISLKTISR	-15.812	-7.565	-0.283	-23.405	11
16aa3	DNAIYWTDISLKTISR	-13.553	-14.045	48.960	-22.703	15
16aa4	DNRAYWTDISLKTISR	-17.432	-7.877	22.974	-23.011	14
16aa5	DNRIAWTDISLKTISR	-9.744	-18.103	57.904	-22.056	17
16aa6	DNRIYATDISLKTISR	-19.637	-6.813	39.339	-22.516	16
16aa7	DNRIYWADISLKTISR	-13.829	-12.985	17.198	-25.093	4
16aa8	DNRIYWTAISLKTISR	-12.543	-17.823	30.949	-27.271	1
16aa9	DNRIYWTDASLKTISR	-15.562	-13.317	30.765	-25.803	3
16aa10	DNRIYWTDIALKTISR	-8.805	-21.140	65.568	-23.388	12
16aa11	DNRIYWTDISAKTISR	-11.126	-15.316	31.547	-23.288	13
16aa12	DNRIYWTDISLATISR	-19.478	-10.312	60.557	-23.734	10
16aa13	DNRIYWTDISLKAISR	-16.639	-7.245	-2.822	-24.166	7
16aa14	DNRIYWTDISLKTASR	-22.078	-1.807	-0.394	-23.924	9
16aa15	DNRIYWTDISLKTIAR	-12.001	-14.568	21.417	-24.427	5
16aa16	DNRIYWTDISLKTISA	-15.186	-9.681	8.077	-24.060	8

Different 16 amino acid length short-peptides (Asp673 to Arg688) derived from alanine scanning was further docked and analyzed to validate its binding efficiency and binding site interactions to LRP6. We also ensured whether all these peptides, including the unmutated 16aa inhibitory peptide, were also binding to the target site of DKK1, which was highly involved in LRP6 contact. We utilized three docking platforms pyDock, Hawkdock, and Cluspro 2.0 to validate extensively and to confirm the identical output results of our docking studies. The results presented in Fig. 3 show that the best poses retrieved from each software resulted in conformations very close to the original 16 amino acid peptide, thereby validating its effectiveness as a prevailing DKK1 inhibitor. Figure 4 illustrates the possible mechanism of action of the designed and screened peptides on the pre-associated LRP6 and DKK1. Figure 4A displays the complex of 16aa8-LRP6 (Cyan & Orange) and 16aa8-DKK1 (Pink & Green). Figure 4B is the sandwich view of the same complex. This view gives a clear idea about how the peptide is blocking the association of DKK1 to LRP6 and also gives the vision to recognize the dual effect and binding efficiency of the 16aa8 peptide to inhibit both Lrp6 and DKK1 at their target site. The same tactic was further verified also to all the screened mutated peptides and unmutated inhibitory peptides and interestingly, all peptides were shown the same kind of effect. Figure 4C is the docking result of 16aa8 shows the details of interaction with the key residues of DKK1 identified earlier.

Further, the initially docked complexes of 16aa and screened mutated peptides (16aa1, 16aa6-8) with target protein LRP6 (Chain A from PDB ID: 3S8V) were subjected to MD Simulation studies for analyzing the stability in terms of RMSD and molecular interactions during the 50ns period. The stability of the protein-peptide complexes was analyzed using the RMSD of backbone atoms. The calculated average RMSD of these complexes were in between the 0.2 to 0.6 nm range (Fig. 5). At the end of the 50 ns simulation, all the protein-peptide complexes attained their stable conformations. The calculated RMSF revealed the occurrence of slight fluctuations in the loop region. The active site residues were stable at 0.3 nm deviation.

Physiochemical Properties Of The Peptide:

Several tools such as ExPASy (ProtParam) and ToxinPred were used to identify the half-life and toxicity of the peptide. Half-life is defined as calculating the time taken for half of the quantity of a particular drug or any compounds in a cell to get vanished after its process. ProtParam relies on the "N-end rule", which relates to the half-life of a protein to the individuality of its N-terminal residue. Considering the N-terminal of the sequence considered is F (Phe), the estimated half-life is 1.1 to 4.4 hours (mammalian reticulocytes, in vitro) for all peptides (Table 3). The instability index (II) is computed to be -0.832 to 0.23 this classifies the peptide 16aa7 as unstable and remaining are stable. The physical stability of peptides influenced by both intrinsic and external factors. Prediction of peptide stability suggests the precautionary steps be taken to make the therapeutically potent unstable peptides to stable one through certain biochemical analysis. ToxinPred was used to predict the toxicity of peptides with desired toxicity by mutating the minimum number of amino acids. ‘ToxinPred’ tool allows submitting query peptide in FASTA format and to optimize the peptide sequence to get maximum/minimum/desired toxicity based upon the Quantitative Matrix-based position-specific scores by comparing with the toxic/non-toxic data set. The predicted ‘ToxinPred’ toxicity results of the peptides indicated that it is non-toxic compared to the mutated peptides in silico.

Table 3. Results of physicochemical, toxicity, and ADMET properties of the 16aa inhibitory and 16aa1, 16aa7-9 peptides

Name	MlogP	S + logD	Molecular weight	Instability index*	Half-life*	Toxicity **
16aa	-10.251	-5.818	1967.268	0.23	1.1 hr	Non-toxin
16aa1	-9.484	-5.544	1923.258	0.23	4.4 hrs	Non-toxin
16aa7	-9.757	-5.877	1937.242	-5.08	1.1hr	Non-toxin
16aa8	-9.484	-5.64	1923.258	0.23	1.1 hr	Non-toxin
16aa9	10.627	-6.41	1925.187	0.23	1.1hr	Non-toxin

MlogP. Moriguchi estimation of LogP; S + logD – Octanol water partition co-efficient; *results from Protparam; **Results from Toxinpred.

Further, we predicted the fraction unbound and total clearance of the unmutated and mutated peptides screened using pkCSM. In drug development, the fraction unbound in plasma offers a better perceptive of the pharmacokinetic properties of the peptides and helps in choosing the best one. The unbound fractions are active and bind to proteins to make drug-protein complex. The resulted unbound fraction ranges from 0.371 to 0.373 for the top five compounds. The total clearance is examined as the proportionally constant (CLtot) and it is the mixture of renal and hepatic clearance. The values range from − 0.42 to -0.898 Log(ml/min/kg) indicates the adaptive nature of these compounds' physiological environment while carrying out therapeutic validations. Then the maximum tolerated dose (MRTD) was calculated which provides the toxic dose threshold in humans. The values obtained were 0.438 for all the five peptides and this indicates that the compounds are non-toxic to humans.

AD is a neurogenerative disorder that accounts for two-thirds of the population which is progressed by cognitive impairment and memory loss a common cause of dementia. Synaptic loss is the major evidence in the AD patients due to the aggregation of the tau protein leads to neurofibrillary tangles and neuronal death (Apostolova LG 2016; Terry RD et al 1911). In recent studies, the synaptic dysfunction is initiated by short oligomers of Aß that regulate the pathways which are critical for brain function and stability, and also it is responsible for the deregulation of Wnt signaling, essential for the neural development (Forner S et al 2017). Wnt signaling pathway is also known as the ß-catenin pathway involved in the development of organs, cells, and other metabolic processes of cells. In the presence of Wnt proteins, it binds with frizzled and LRP6 receptors to form a wnt-frz-LRP6 complex to activate the ß-catenin pathway. Once the complex is formed, it provides a signal to Dishevelled proteins (Dvl), which prevent the hydrolysis of the ß-catenin by the destruction complex (Axin, GSK3ß, APC, and CK1) and it remains stable (MacDonald BT et al 2009; Angers S and Moon RT 2009; Rosso SB and Inestrosa NC 2013). Then the ß-catenin will gather in the cytoplasm and translocated into the nucleus regulates the expression of Wnt target genes in association with TCF/LEF transcription factors (Gordon MD and Nusse R 2006). Whereas in the absence of Wnt proteins, Dvl proteins are inactive due to the absence of wnt-frz-LRP6 complex, thus ß-catenin is phosphorylated and degraded by the destruction complex (Salic A et al 2000; Price MA 2006; Wu D and Pan W 2010).

The Wnt signaling pathway is regulated by a range of secreted endogenous antagonists which alters Wnt-receptor interaction at the plasma membrane (Cruciate CM and Niehrs C 2013). The best categorized Wnt antagonists are Dickkopf (Dkk) proteins which are a family of glycoproteins with four members in vertebrates from Dkk1-4 (Cruciate CM and Niehrs C 2013; Niehrs C 2006). Among the four, Dkk1 is a chief Wnt antagonist inhibits the pathway by binding to the LRP6 receptor to prevent the formation of the complex Wnt-Fz-LRP6. Thus, improved levels of Dkk1 would decline the ease of use of the LRP6 receptor for Wnt ligands (Bafico A et al 2001; Mao B et al 2001; Semenov MV et al 2001). Therefore, the association of the protein DKK1 with LRP6 could deregulate the pathway and leads to synaptic dysfunction especially in the aged people. Blocking of the complex could be the promising and best therapy for Alzheimer’s disease. In our study, we designed 16 short peptides through alanine scanning of DKK1. Protein –peptide docking results showed that 16aa1, 16aa7, 16aa8 and 16aa9 had better binding affinity to LRP6 in comparison to native DKK1. To analyse the stability of the complex, RMSD was calculated for 50ns from which we found all the 4 peptide complex had better stability than the native DKK1 throughout the simulation. In addition, all the four peptides are predicted to be non-toxic in pkcsm and Expasy. In our study, thus we found this strategy to develop novel and a straightforward, non-toxic peptide-based therapeutics to treat Alzheimer’s disease.

In this study, we aimed to design and develop a peptide-based therapeutic source for Alzheimer’s disease. After assessing the critical role of the DKK1-LRP6 complex affecting non-canonical Wnt signaling which is critical for Alzheimer’s disease, we identified the critical residues involved in that complex. Further, we designed an inhibitory peptide by mutating the C-terminal cysteine rich short sequence of DKK1 through alanine scanning mutagenesis thereby preventing the binding of antagonist DKK1 to LRP6. From the 16 peptides designed, 16aa1, 16aa6, 16aa7, 16aa8 was highly stable and had shown high binding affinity to LRP6. All the above mentioned peptide complexes had key residue interaction similar to that of LRP6-DKK1 (native) complex and had better binding affinity than the native complex. Four peptides obtained through the peptide mimic strategy might serve as a promising therapeutic option for blocking the LRP6 –DKK1 complex formation. However, in-vitro studies such as binding assay and kinetic analysis should be done to further evaluate it as an inhibitory peptide for LRP6.

DKK1- Dickkopf-related protein 1

LRP6-Low-Density Lipoprotein receptor-related Protein 6

AD- Alzheimer’s disease

SPPIDER- Solvent accessibility based Protein-Protein Interface Identification and Recognition

TCF/LEF- T-cell factor/lymphoid enhancer factor

SMILES- Simplified Molecular-Input Line-Entry System

ADMET- Absorption, Distribution, Metabolism, Excretion and Toxicity

MRTD- maximum tolerated dose

RMSF- Root mean-square-fluctuations

RMSD- Root Mean Square Deviation

Funding

The authors declare that no funds, grants, or other support were received during the preparation of this manuscript.

Data availability

The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.

Competing Interests

The authors have no relevant financial or non-financial interests to disclose

Author Contributions

All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by [Dhivya Bakthavachalam] and [Sivakumar Arumugam]. The first draft of the manuscript was written by [Dhivya Bakthavachalam] and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

Acknowledgement

The authors would like to thank the Vellore Institute of Technology for extending their support for this study.

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No competing interests reported.

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Designing peptide inhibitors targeting LRP6 co-receptor to restrict its association to DKK1; an Alzheimer’s disease therapeutic strategy

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Abstract

Figures

Introduction

Materials And Methods

Protein-Protein investigation:

Alanine Scanning And Peptide Design:

Molecular Docking Studies:

Physicochemical Properties Of The Peptide:

ADMET Properties And Stability Predictions Of Peptide Inhibitors:

Results And Discussion

The C-terminal region of DKK1 cysteine-rich domain is crucial for interaction with LRP6 co-receptors:

Computational Alanine Scanning Analysis Of The Peptide:

Physiochemical Properties Of The Peptide:

Discussion

Conclusion

Abbreviations

Declarations

Data availability

References

Additional Declarations

Status:

Version 1