Novel Biomimetic Human TLR2-Derived Peptides for Potential Targeting of Lipoteichoic Acid: an in Silico Assessment

biomedicines

Article Novel Biomimetic Human TLR2-Derived Peptides for Potential Targeting of Lipoteichoic Acid: An In Silico Assessment

Nikita Devnarain 1 , Ayman Y. Waddad 2,* , Beatriz G. de la Torre 3 , Fernando Albericio 2 and Thirumala Govender 1,*

1 Discipline of Pharmaceutical Sciences, College of Health Sciences, University of KwaZulu-Natal, Durban 4000, South Africa; [email protected] 2 Peptide Science Laboratory, School of Chemistry and Physics, University of KwaZulu-Natal, Durban 4001, South Africa; [email protected] 3 KRISP, School of Laboratory Medicine and Medical Sciences, College of Health Sciences, University of KwaZulu-Natal, Durban 4001, South Africa; [email protected] * Correspondence: [email protected] (A.Y.W.); [email protected] (T.G.); Tel.: +27-31-260-7367 (A.Y.W.); +27-31-260-7357 (T.G.)

Abstract: Antimicrobial resistance is one of the most significant threats to health and economy around the globe and has been compounded by the emergence of COVID-19, raising important consequences for antimicrobial resistance development. Contrary to conventional targeting approaches, the use of biomimetic application via nanoparticles for enhanced cellular targeting, cell penetration and localized antibiotic delivery has been highlighted as a superior approach to identify novel targeting ligands for combatting antimicrobial resistance. Gram-positive bacterial cell walls contain lipoteichoic acid (LTA), which binds specifically to Toll-like receptor 2 (TLR2) on human macrophages. This phenomenon has the potential to be exploited for the design of biomimetic peptides for antibacterial application. In this study, we have derived peptides from sequences present in human TLR2 that bind Citation: Devnarain, N.; Waddad, A.Y.; de la Torre, B.G.; Albericio, F.; to LTA with high affinity. In silico approaches including molecular modelling, molecular docking, Govender, T. Novel Biomimetic molecular dynamics, and thermodynamics have enabled the identification of these crucial binding Human TLR2-Derived Peptides for amino acids, the design of four novel biomimetic TLR2-derived peptides and their LTA binding Potential Targeting of Lipoteichoic potential. The outcomes of this study have revealed that one of these novel peptides binds to LTA Acid: An In Silico Assessment. more strongly and stably than the other three peptides and has the potential to enhance LTA targeting Biomedicines 2021, 9, 1063. https:// and bacterial cell penetration. doi.org/10.3390/biomedicines9081063 Keywords: TLR2-derived peptides; biomimetic; human Toll-like receptor 2; lipoteichoic acid Academic Editor: Chung Wong

Received: 5 August 2021 Accepted: 18 August 2021 1. Introduction Published: 21 August 2021 The World Health Organization has declared antimicrobial resistance as among the

Publisher’s Note: MDPI stays neutral top 10 human health threats worldwide. Bacterial infection as a result of antimicrobial with regard to jurisdictional claims in resistance has caused of millions of deaths all over the world and is predicted to cause published maps and institutional afﬁl- millions more, especially in countries where clean water, proper sanitation, and infection iations. prevention and control are lacking [1]. Current treatments of bacterial infections have suboptimal targeting of bacterial cells, which results in elevated exposure to healthy cells and probiotics, increased frequency of administration, lack of association with the bacteria, minimal concentration of treatment at the bacterial infection site and prolonged treatment period [2,3]. Lack of effective Copyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland. antimicrobials puts the success of current treatments at greater risk [1]. There is slow This article is an open access article progress in the discovery and development of new antimicrobial agents, exacerbating the distributed under the terms and burden of antimicrobial resistance on morbidity and mortality rates [4]. Hence, innovative conditions of the Creative Commons strategies for targeted treatment are necessary to curb the current resistance of bacteria Attribution (CC BY) license (https:// to antimicrobials. creativecommons.org/licenses/by/ Researchers are harnessing biomimetic strategies for enhanced targeting of therapies 4.0/). for various diseases, including cancer [5], viruses [6], fungal infections [7], central nervous

Biomedicines 2021, 9, 1063. https://doi.org/10.3390/biomedicines9081063 https://www.mdpi.com/journal/biomedicines Biomedicines 2021, 9, 1063 2 of 12

system diseases [8], and bacterial infections [9]. Biomimetic strategies in drug delivery take advantage of mechanisms of interaction between living cells and biological pathways, activities and structures and have been used to enhance the targeting, pharmacokinetics, circulation time, antibacterial activity and biosafety of nanoparticle formulations [9,10]. Biomimetics have been used to enhance the synthesis and drug delivery of nanosystems for antibacterial application in various ways [11]. Some of them include the use of biomasses from protein, fungi, and plants for the synthesis of antibacterial nanoparticles for their beneficial capping and bioreduction activities [12,13], while others involve mimicry of cell surface structures [14] and extracellular matrix structures [15] for enhanced pharma- cokinetic parameters, disruption of biofilm and bacterial toxin neutralization [16], and prevention of biofilm formation and bacterial adhesion on surfaces of biomedical device implants [17], among other benefits. In this study, we have created a novel biomimetic approach that uses nature’s binding specificity to design biomimetic peptides for targeting Gram-positive bacteria, specifically Staphylococcus aureus and methicillin-resistant Staphylococcus aureus (MRSA). When a host is infected by Gram-positive bacteria, as a host defense mechanism, the innate immune system becomes activated when membrane-bound pattern recognition receptors (PRRs) of human macrophages such as Toll-like receptor 2 (TLR2) recognize bacterial lipoteichoic acid (LTA) on Gram-positive bacterial cell walls [18]. The novelty of our strategy lies in the exploitation of the natural and unique affinity of the convex surface of TLR2 for LTA on Gram-positive bacterial cell walls [19,20]. We have taken advantage of this unique phenomenon and derived four peptides from naturally occurring amino acid sequences present in human TLR2 that bind to and interact with LTA with high affinity, which can be used to design targeted delivery systems. Due to the biomimetic components of these peptides, they may possess beneficial properties as pathogen recognition pattern, cell-penetrating peptides, which include enhanced targeting to LTA, improved antibacterial efficiency, biocompatibility, and biodegradability. This is the first report of such a biomimetic approach, which includes an in silico assessment of the binding potential of these biomimetic TLR2-derived peptides (BTp1, BTp2, BTp3, BTp4) to LTA. Based on the stability and strength of binding of these peptides to LTA, they may have the potential to be used in the surface modification of antibacterial nanoparticles for enhanced targeting of LTA on bacterial cell walls and bacterial cell penetration to deliver antibacterial drugs during bacterial infection.

2. Materials and Methods 2.1. Receptor and Ligand Acquisition/Design and Preparation The 3D crystal structure of human TLR2 was acquired from RSCB Protein Data Bank [21] (PDB: 6nig), while the 3D structure of LTA was acquired from PubChem [22] (ID: 137349712). The geometry of the LTA ligand was optimized using Avogadro [23], and UCSF Chimera software [24] was used to add hydrogens and charge to the ligand. The peptides were designed and optimized using Schrödinger Maestro software [25] and prepared for molecular docking using UCSF Chimera software (Version 1.14).

2.2. Molecular Docking Molecular docking is a frequently used approach in structure-based drug design to predict the most favorable binding conformations of ligands to target molecules [26]. Herein, molecular docking was applied in two separate instances: to predict the confor- mation of LTA that binds with the highest affinity to TLR2; and to predict the preferable conformations and binding affinities of novel peptides to LTA. The software that were used to prepare molecules and perform docking include UCSF Chimera, AutoDock MGL tools and AutoDockVina [27] and Raccoon [28], using default parameters. The ligand conformations that bound with strongest affinity (kcal/mol) were thereafter subjected to molecular dynamic simulations. Biomedicines 2021, 9, 1063 3 of 12

2.3. Molecular Dynamics Simulation To provide a more in-depth understanding of the physical movements between, ﬁrstly, LTA and TLR2, and secondly, the peptides and LTA, molecular dynamic simulations were performed on the complexes for 10 and 500 ns, respectively. The PMEMD engine of the Amber 18 suite was used to perform all simulations [29]. Atomic partial charges were produced by ANTECHAMBER using General Amber Force Field commands. The Leap module solvated the systems within a water box (10 Å TIP3P). To neutralize the systems, Na+ or Cl− counter-ions were introduced. An initial 2500 step minimization was carried out, with a restraint potential of 500 kcal/mol. A 1000 step full minimization was carried out thereafter, without restraints. This was followed by gradual heating to 300 K for 50 ps, such that each system provided a ﬁxed number of atoms and volume. A potential harmonic restraint of 10 kcal/molÅ−2 was performed on the solutes of the systems. Thereafter, systems were equilibrated for 500 ps. An isobaric-isothermal ensemble (NPT) was imitated throughout the simulations (1 bar system pressure, constant number of atoms), using the SHAKE algorithm to constrain hydrogen bonds and a Langevin thermostat with collision frequency of 1 ps–1.

2.4. Post-Dynamic Analyses The root-mean-square fluctuation (RMSF), root-mean-square deviation (RMSD) and thermodynamic energy of the systems were calculated to further analyze the systems’ stability and energy inputs. The co-ordinates of each system were recorded every 1 ps and the trajectories were analyzed via the AMBER 18 CPPTRAJ module [30]. Thermodynamic Energy Calculation and Per-Residue Energy Decomposition Analysis were conducted to establish the free energy of binding (∆Gbind) of the novel peptides to LTA. Molecular mechanics integrated with the generalized Born surface area continuum solvation (MM/GBSA) method [31] were used. The molecular dynamics simulation generated a trajectory of 500,000 snapshots, which were averaged to produce ∆Gbind. The binding affinities between the peptides and LTA may be defined as Equations (1)–(5):

∆Gbind = Gcomplex − Greceptor − Gligand (1)

∆Gbind = Egas + Gsol − TS (2)

Egas = Eint + EvDW + Eele (3)

Gsol = GGB + GSA (4)

GSA = γSASA (5) where:

Eele Electrostatic potential energy from Coulomb forces Egas Gas-phase energy (based on FF14SB force ﬁeld terms) Eint Internal energy EvdW van der Waals energy Gsol Solvation free energy GGB Polar solvation energy GSA Non-polar solvation energy S Total entropy of solute SASA Solvent accessible surface area (water probe radius of 1.4 Å) T Total entropy of temperature A per-residue energy decomposition was also calculated to predict the energy contribution of each amino acid residue to the total ∆Gbind at the peptide binding site, using the AMBER14 MM/GBSA approach. Biomedicines 2021, 9, 1063 4 of 14

A per-residue energy decomposition was also calculated to predict the energy con- Biomedicines 2021, 9, 1063 tribution of each amino acid residue to the total ΔGbind at the peptide binding site, using4 of 12 the AMBER14 MM/GBSA approach.

3. Results and Discussion 3. Results and Discussion 3.1. Binding Affinity of TLR2 to LTA 3.1. Binding Affinity of TLR2 to LTA Molecular docking of LTA to the binding site on human TLR2 revealed the nine best posesMolecular of LTA bound docking to TLR2, of LTAwith totheir the respec bindingtive binding site on humanaffinities TLR2 (Figure revealed 1). The con- the nine formationbest poses of of LTA LTA that bound bound to TLR2,with highest with their affinity respective to TLR2 bindingand with affinities zero RMSD (Figure is indi-1). The catedconformation by mode number of LTA 1 that in Figure bound 1, with with highestbinding affinityenergy of to − TLR27.7 kcal/mol. and with zero RMSD is indicated by mode number 1 in Figure1, with binding energy of −7.7 kcal/mol.

FigureFigure 1. 1. BindingBinding affinities afﬁnities of of nine nine be bestst poses poses of ofLTA LTA (left) (left and) and all allnine nine conformations conformations superim- superimposed posedin the in binding the binding site of site TLR2 of TLR2 (right (right).).

3.2.3.2. MolecularMolecular Dynamics Dynamics of of TLR2/LTA TLR2/LTA Complex for Acquisition of Binding SiteSite AminoAmino AcidAcid Residues ResiduesThe docking pose of LTA with the strongest binding afﬁnity (−7.7 kcal/mol) to TLR2 wasThe selected docking to be pose simulated of LTA with with TLR2the strongest for 10 ns binding to establish affinity the ( most−7.7 kcal/mol) accurate predictionto TLR2 of wasthe interactionselected to be between simulated LTA with and TLR2 TLR2. for After 10 ns simulatingto establish thethe complex,most accurate binding prediction site amino Biomedicines 2021, 9, 1063 5 of 14 ofacid the residues interaction were between identiﬁed LTA usingand TLR2. Schrödinger After simulating Maestro andthe complex, UCSF Chimera binding softwaresite amino(Figure acid2). residues were identified using Schrödinger Maestro and UCSF Chimera software (Figure 2).

FigureFigure 2. (A)) Location Location of of LTA LTA (green) (green) binding binding on on TLR2. TLR2. (B ()B Specific) Specific binding binding site site amino amino acid acid resi- residues dues on TLR2 (blue) bound by LTA (green). (C) Sequence of TLR2 with specific binding site resi- on TLR2 (blue) bound by LTA (green). (C) Sequence of TLR2 with specific binding site residues dues highlighted in blue correlating with residues identified in (B). highlighted in blue correlating with residues identified in (B). 3.3. Design of Potential Biomimetic TLR2-Derived Targeting Peptides Using the amino acid residues of TLR2 that bind with LTA (highlighted in Figure 2), we identified four regions of significance for binding and used their amino acid sequences to design peptides with sequences as follows: CTLNGV, RRLHIPRF, YDLLYSLT and SKVFLVP. Upon drawing 3D structures of the novel biomimetic TLR2-derived peptides (BTp1, BTp2, BTp3, and BTp4, respectively) using Schrödinger Maestro software, their geometries were optimized, and structures minimized.

3.4. Binding Affinity of Biomimetic TLR2-Derived Peptides to LTA Thereafter, molecular docking of each peptide to LTA revealed the nine best poses of LTA bound to each peptide, with their respective binding affinities (Table 1). The best binding pose of BTp1 bound with −2.5 kcal/mol to LTA, BTp2 with −3.4 kcal/mol, BTp3 with −2.9 kcal/mol and BTp4 with −3.8 kcal/mol. Therefore, BTp2 and BTp4 bind with strongest affinity to LTA, compared to BTp1 and BTp3.

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3.3. Design of Potential Biomimetic TLR2-Derived Targeting Peptides Using the amino acid residues of TLR2 that bind with LTA (highlighted in Figure2), we identiﬁed four regions of signiﬁcance for binding and used their amino acid sequences to design peptides with sequences as follows: CTLNGV, RRLHIPRF, YDLLYSLT and SKVFLVP. Upon drawing 3D structures of the novel biomimetic TLR2-derived peptides (BTp1, BTp2, BTp3, and BTp4, respectively) using Schrödinger Maestro software, their geometries were optimized, and structures minimized.

3.4. Binding Afﬁnity of Biomimetic TLR2-Derived Peptides to LTA

Biomedicines 2021, 9, 1063 Thereafter, molecular docking of each peptide to6 LTAof 14 revealed the nine best poses

of LTA bound to each peptide, with their respective binding afﬁnities (Table1). The best binding pose of BTp1 bound with −2.5 kcal/mol to LTA, BTp2 with −3.4 kcal/mol, BTp3 Table 1. Binding affinity of thewith top −nine2.9 poses kcal/mol of four novel and peptides BTp4 to with lipoteichoic−3.8 acid. kcal/mol. Therefore, BTp2 and BTp4 bind with Peptide Bound to Lipoteichoic Acid Binding Affinity Biomedicines 2021, 9, 1063 strongest afﬁnity to LTA, compared to BTp1 and BTp3.6 of 14 BTp1: CTLNGV ModeAffinity (kcal/mol) Dist from rmsd l.b.Best mode rmsd u.b. Biomedicines 2021, 9, 1063 1 −2.5 0 0 6 of 14

Table 1. Binding affinity2 of the top−2.4 nine poses of9.222 four novel peptides12.669 to lipoteichoic acid. Table 1. Binding affinity of the top nine poses of four novel peptides to lipoteichoic acid. Biomedicines 2021, 9, 1063 3 −2.3 5.016 6 7.706of 14 Peptide Bound to Lipoteichoic Acid Binding Affinity PeptideTable Bound 1. Binding to Lipoteichoic affinity of theAcid top nine poses of4 four novel− 2.2peptides Binding to lipoteichoic Affinity8.403 acid. 13.391 5 −2.2 8.094 12.23 BTp1:BTp1: CTLNGVPeptide CTLNGV Bound to Lipoteichoic Acid ModeAffinityMode (kcal/mol) Binding Affinity Dist Affinity from (kcal/mol) rmsd l.b.Best mode Dist rmsd from u.b. rmsd l.b. Best mode rmsd u.b. Table 1. Binding affinity of the top nine poses of four novel peptides to lipoteichoic acid. BTp1: CTLNGV ModeAffinity16 1 (kcal/mol)−−2.52.2 Dist from− rmsd2.59.0720 l.b.Best mode rmsd14.517 0u.b. 0 0 Peptide Bound to Lipoteichoic Acid 1 27 2−2.5 −−2.42.2 Binding Affinity−0 2.4 9.2229.339 0 12.66913.51 9.222 12.669 BTp1: CTLNGV ModeAffinity2 38 (kcal/mol)3−2.4 −−2.32.2Dist from rmsd9.222− 2.3l.b.Best 5.0168.196 mode rmsd12.669 u.b. 7.70611.353 5.016 7.706 1 −2.5 0 0 3 49 −2.3 −−2.22.1 5.016− 8.4032.631 7.706 13.3918.354 2 −2.44 9.222 2.212.669 8.403 13.391 4 5 −2.2 −2.2 8.403 8.094 13.391 12.23 BTp2: RRLHIPRF 3 ModeAffinity−2.35 (Kcal/mol)5.016 Dist − from2.2 rmsd7.706 l.b.Best mode rmsd 8.094 u.b. 12.23 5 6 −2.2 −2.2 8.094 9.072 12.23 14.517 4 1 −2.26 −3.4 8.403 −2.20 13.391 0 9.072 14.517 6 −2.2 9.072 14.517 5 72 −2.2 −−2.23.4 8.094 − 9.3393.659 12.23 13.5110.855 7 7−2.2 9.3392.2 13.51 9.339 13.51 6 83 −2.2 −−2.23.4 9.072 8.1962.62 14.517 11.353 5.43 8 8−2.2 8.196−2.2 11.353 8.196 11.353 7 94 −2.29 −−2.13.3 9.339 −2.12.6311.369 13.51 8.3542.413 2.631 8.354 8 9 5 −2.2−2.1 −3.3 8.1962.631 5.024 11.3538.354 9.196 BTp2: RRLHIPRF ModeAffinity (Kcal/mol) Dist from rmsd l.b.Best mode rmsd u.b. BTp2: RRLHIPRF ModeAffinity9 6 Mode−2.1 (Kcal/mol) −3.2 Affinity Dist2.631 from (Kcal/mol)rmsd3.682 l.b.Best 8.354 mode rmsd Dist11.033 u.b. from rmsd l.b. Best mode rmsd u.b. BTp2: RRLHIPRF 1 −3.4 0 0 1 7 −3.4 −3.2 0 4.461 0 10.666 BTp2: RRLHIPRF ModeAffinity2 (Kcal/mol)1 −3.4 Dist from rmsd− 3.4l.b.Best3.659 mode rmsd u.b.10.855 0 0 1 2 8 −3.4−3.4 −3.2 0 3.659 1.816 0 10.855 3.079 3 2 −3.4 −3.42.62 5.43 3.659 10.855 2 3 −3.4−3.4 3.6592.62 10.855 5.43 4 3 −3.3 −3.41.369 2.413 2.62 5.43 3 4 9 −3.4−3.3 −3.2 2.621.369 5.128 5.43 2.413 8.731 4 5 5 −3.34−3.3 −3.3 1.3695.024 −3.3 5.024 2.413 9.196 9.196 1.369 2.413

5 6 6 −3.3−3.2 −3.2 5.0243.682 3.682 9.19611.033 11.033 BTp3: YDLLYSLT ModeAffinity5 (Kcal/mol) Dist− from3.3 rmsd l.b.Best mode rmsd 5.024 u.b. 9.196 6 7 7 −3.2−3.2 −3.2 3.6824.461 4.461 11.03310.666 10.666 1 6 −2.9 −3.20 0 3.682 11.033 7 8 8 −3.2−3.2 −3.2 4.4611.816 1.816 10.6663.079 3.079 8 2 −3.27 −2.8 1.816 −3.22.658 3.079 5.748 4.461 10.666 3 −2.8 − 1.639 4.576 9 9 8−3.2 −3.2 5.1283.2 5.128 8.731 8.731 1.816 3.079 9 4 −3.29 −2.7 5.128 −3.24.626 8.731 9.394 5.128 8.731

BTp3: YDLLYSLT ModeAffinity5 (Kcal/mol)−2.7 Dist from rmsd8.39 l.b.Best mode rmsd12.838 u.b. BTp3:BTp3:BTp3: YDLLYSLT YDLLYSLT YDLLYSLT ModeAffinity ModeAffinityMode (Kcal/mol) (Kcal/mol) Dist Afﬁnity from Dist rmsd (Kcal/mol)from l.b.Best rmsd mode l.b.Best rmsd u.b.mode Dist rmsd from u.b. rmsd l.b. Best mode rmsd u.b. 1 6 −2.9 −2.7 0 5.11 0 9.703 1 1 −2.9 −2.9 0 0 0 0 2 7 1−2.8 −2.7 2.658−2.9 8.117 5.748 12.937 0 0 2 2 −2.8 −2.8 2.658 2.658 5.748 5.748 3 8 2−2.8 −2.6 1.639−2.8 3.682 4.576 8.654 2.658 5.748 3 3 −2.8 −2.8 1.639 1.639 4.576 4.576 4 −2.7 4.626− 9.394 4 4 −2.73 −2.7 4.626 2.84.626 9.394 9.394 1.639 4.576 5 5 −2.7−2.7 8.39 8.39 12.83812.838 5 4 −2.7 −2.78.39 12.838 4.626 9.394 6 6 −2.7−2.7 5.11 5.11− 9.703 9.703 7 7 6 −2.75−2.7 −2.7 8.1178.117 2.7 5.11 12.93712.937 9.703 8.39 12.838 8 8 79 −2.66−2.6 −−2.72.6 3.6823.682 −2.7 8.1174.175 8.654 8.654 12.937 8.33 5.11 9.703 8 7 −2.6 −2.73.682 8.654 8.117 12.937 8 −2.6 3.682 8.654 9 −2.6 4.175 8.33 9 −2.6 4.175 8.33 9 −2.6 4.175 8.33 BTp4:BTp4: SKVFLVP SKVFLVP ModeAffinityMode (Kcal/mol) Afﬁnity Dist (Kcal/mol) from rmsd l.b.Best mode Dist rmsd from u.b. rmsd l.b. Best mode rmsd u.b. 9 −2.6 4.175 8.33 1 1 −3.8 −3.80 0 0 0 2 2 −3.7 −3.73.574 6.868 3.574 6.868 BTp4: SKVFLVP ModeAffinity3 (Kcal/mol)3 −3.7 Dist from rmsd− 3.7l.b.Best1.862 mode rmsd u.b. 2.555 1.862 2.555 BTp4: SKVFLVP ModeAffinity1 4 −3.84 (Kcal/mol) −3.6 Dist from0 − rmsd3.62.005 l.b.Best 0mode rmsd3.056 u.b. 2.005 3.056 BTp4: SKVFLVP 2 1ModeAffinity 5 −3.75−3.8 −(Kcal/mol)3.6 3.574 Dist −0 from 3.62.577 rmsd 6.868l.b.Best 0 mode 3.93 rmsd 2.577u.b. 3.93 2 −3.7 3.574 6.868 3 16 −3.76 −−3.83.6 1.862 −3.63.5010 2.555 7.01 0 3.501 7.01 4 3 −3.6−3.7 2.0051.862 3.056 2.555 27 7 −−3.73.5 −3.53.5742.83 6.8686.586 2.83 6.586 5 4 −3.6−3.6 2.5772.005 3.93 3.056 38 −−3.73.5 − 1.8627.209 2.55512.029 6 5 −3.68−3.6 3.5012.577 3.5 7.01 3.93 7.209 12.029 49 −−3.63.4 2.0051.885 3.0563.139 7 6 −3.59−3.6 2.833.501 −3.4 6.586 7.01 1.885 3.139 8 7 5 −3.5−3.5 −3.6 7.2092.83 2.577 12.0296.586 3.93 9 6 −3.4 −3.6 1.885 3.501 3.139 7.01 8 −3.5 7.209 12.029 7 −3.5 2.83 6.586 9 −3.4 1.885 3.139 8 −3.5 7.209 12.029 9 −3.4 1.885 3.139

Biomedicines 2021, 9, 1063 7 of 14 Biomedicines 2021, 9, 1063 6 of 12

3.5. Molecular Dynamics, Stability, Thermodynamics and Per-Residue Binding Free Energy of BTp3.5. /LTA Molecular Complexes Dynamics, Stability, Thermodynamics and Per-Residue Binding Free Energy of BTp /LTA Complexes The conformations of each peptide that bound with strongest affinity to LTA (high- The conformations of each peptide that bound with strongest afﬁnity to LTA (highlighted in bold and cyan in Table 1) from molecular docking studies were then subjected lighted in bold and cyan in Table1) from molecular docking studies were then subjected to molecular dynamics simulations for 500 ns each, to validate the docking scores and to to molecular dynamics simulations for 500 ns each, to validate the docking scores and provide a more in depth understanding of the atomic interactions between LTA and the to provide a more in depth understanding of the atomic interactions between LTA and peptides. Figure 3A illustrates snapshots at 100 ns intervals of each of the four novel pep- the peptides. Figure3A illustrates snapshots at 100 ns intervals of each of the four novel tides simulated with LTA for 500 ns each. The graphs in Figure 3B depict RMSD calcula- peptides simulated with LTA for 500 ns each. The graphs in Figure3B depict RMSD tionscalculations of simulations of simulations of each of novel each peptide novel peptide bound boundto LTA, to and LTA, finally and ﬁnally superimposed superimposed onto oneonto graph one graph for comparison for comparison purposes. purposes. In addition, In addition, MM/GBSA MM/GBSA thermodynamic thermodynamic energy energy cal- culationscalculations and and per-residue per-residue energy energy decomposition decomposition of of every every complex complex are are provided provided in in detail. detail.

FigureFigure 3. 3. TrajectoryTrajectory analyses of four novelnovel peptidespeptides boundbound toto lipoteichoic lipoteichoic acid. acid. ( A(A)) Snapshots Snapshots during dur- ing500 500 nssimulations ns simulations of each of each peptide peptide bound bound to lipoteichoic to lipoteichoic acid atacid 100 at ns 100 intervals. ns intervals. (B) Graphs (B) Graphs depicting depictingRMSD calculations RMSD calculations of each of of the each four of simulations the four simulations and superimposed and superimposed in the last in graph. the last graph. Biomedicines 2021, 9, 1063 8 of 14

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From Figure 3, information regarding the stability of the four simulations can be deduced.From Figure Figure 3A3 ,confirms information that BTp1 regarding and LT theA stabilitybound unstably, of the four as evident simulations in the can 100 be ns deduced.snapshot Figurewhen they3A confirms detached that from BTp1 each and other LTA and bound in the unstably, 400 ns snapshot as evident (Figure in the S1) 100 when ns snapshotthey were when only bound they detached via a hydrogen from each bond other between and THR2 in the and 400 O12, ns snapshot and a carbon (Figure hydro- S1) whengen bond they between were only CYS1 bound and viaO12, a of hydrogen BTp1 and bond LTA, between respectively. THR2 RMSD and O12, calculations and a carbon (Fig- hydrogenure 3B) confirmed bond between instability CYS1 andof the O12, complex of BTp1 as and the LTA, system respectively. did not reach RMSD convergence calculations (Figurethroughout3B) confirmed the simulation instability and the of theenergy complex fluctuated as the beyond system dida 2 Å not range reach (~4 convergence Å). The en- throughoutergy fluctuation the simulationof each residue and throughout the energy the fluctuated simulation beyond was established a 2 Å range via (~4 RMSF Å). Thecal- energyculations, fluctuation which showed of each that residue during throughout the 500 ns the simulation simulation of wasthe BTp1 established and LTA via bound RMSF calculations,complex, GLY5 which of BTp1 showed was that the during most stably the 500 bound ns simulation residue ofas theits energy BTp1 and fluctuated LTA bound the complex,least, while GLY5 the ofenergy BTp1 of was VAL6 the fluctuated most stably the bound most (Figure residue S5). as its energy fluctuated the least,Figure while the4A,C energy present of VAL6specific fluctuated energy contribu the mosttions (Figure of each S5). amino acid residue of BTp1 that Figurecontributes4A,C present to binding. specific energyVan der contributions Waals (vdW) of each contribution amino acid residueto binding of BTp1 is thatpredominantly contributes tofrom binding. LEU3 Vanand derASN4, Waals while (vdW) electrostatic contribution (EEL) to energy binding contribution is predominantly stems fromfrom LEU3ASN4. and These ASN4, two whileresidues electrostatic are significant (EEL) to energy the binding contribution of BTp1 stems to LTA. from Finally, ASN4. TheseFigure two 4B residuesalso provides are significant the different to the ener bindinggy contributions of BTp1 to LTA.of each Finally, component Figure4 Bof also the providessystem, thewhich different shows energy the total contributions binding offree each energy component of the ofbound the system, complex which is shows−11.77 thekcal/mol. total binding free energy of the bound complex is −11.77 kcal/mol.

Figure 4. (A) Per-residue energy decomposition analysis showing specific energy contributions of Figure 4. (A) Per-residue energy decomposition analysis showing specific energy contributions of each amino acid residue of BTp1 that contributes to binding. (B) Different energy contributions of each amino acid residue of BTp1 that contributes to binding. (B) Different energy contributions of each component of the system via MM/GBSA thermodynamics. (C) Binding interaction diagram of eachBTp1 component to residues of of the TLR2. system via MM/GBSA thermodynamics. (C) Binding interaction diagram of BTp1 to residues of TLR2. On the other hand, Figure 3A also shows that BTp2 and LTA bound tightly and con- On the other hand, Figure3A also shows that BTp2 and LTA bound tightly and sistently throughout the simulation, compared to BTp1. The second RMSD graph in Fig- consistently throughout the simulation, compared to BTp1. The second RMSD graph ure 3B indicates that after 454 ns, the system reached convergence and energy fluctuations in Figure3B indicates that after 454 ns, the system reached convergence and energy remained within a 2 Å range. Particularly, at 400 ns (Figure S2), just before the system fluctuations remained within a 2 Å range. Particularly, at 400 ns (Figure S2), just before the reached convergence, multiple interactions between BTp2 and LTA were observed. Hy- system reached convergence, multiple interactions between BTp2 and LTA were observed. drogens bonds were formed between ARG1 and O12, ARG2 with O12, H4 and H54, HIS4 Hydrogens bonds were formed between ARG1 and O12, ARG2 with O12, H4 and H54, with O10, ILE5 with H5, H42 and H43, ARG7 with O8, and PHE8 with O8, of BTp2 and HIS4 with O10, ILE5 with H5, H42 and H43, ARG7 with O8, and PHE8 with O8, of BTp2 LTA, respectively. In addition, HIS4 of BTp2 interacts with H67 of LTA via a Pi-Sigma and LTA, respectively. In addition, HIS4 of BTp2 interacts with H67 of LTA via a Pi-Sigma bond. These data support the results from molecular docking studies, which demon- bond. These data support the results from molecular docking studies, which demonstrated strated stronger binding of BTp2 to LTA, than BTp1. From Figure S5, we can see that the stronger binding of BTp2 to LTA, than BTp1. From Figure S5, we can see that the ARG7 of ARG7 of BTp2 fluctuated the most in the system, along with ARG1 and ARG2, with the BTp2 fluctuated the most in the system, along with ARG1 and ARG2, with the highest RMSF highest RMSF values, while PRO6 fluctuated the least. Further analyses show in Figure values, while PRO6 fluctuated the least. Further analyses show in Figure5A,C specific 5A,C specific energy contributions of each amino acid residue of BTp2 that contributes to energy contributions of each amino acid residue of BTp2 that contributes to binding. From this, most of the vdW and EEL energy contribution to binding is predominantly from ARG7, Biomedicines 2021, 9, 1063 9 of 14

binding. From this, most of the vdW and EEL energy contribution to binding is predominantly from ARG7, which is in accordance with results of RMSF calculations. Furthermore, Figure 5B also provides the different energy contributions of each component of the system, which shows the total binding free energy of the bound complex is −24.07 kcal/mol. This is stronger than binding of BTp1 to LTA, which supports binding affinity results from molecular docking studies that showed BTp2 binds better to LTA than BTp1. The trajectory of the BTp3/LTA system in Figure 3A showed that BTp3 remained bound to LTA throughout the 500 ns simulation, more strongly than BTp1 but less than BTp2. However, the third RMSD analysis in Figure 3B showed that the system did not Biomedicines 2021, 9, 1063 reach convergence, as evident by the large spike in energy after 421 ns. Moreover, energy8 of 12 deviations were greater than 2 Å (~6 Å), indicating the instability of the complex. In addition, Figure S3 clearly shows that at 400 ns, the complex is stable with several bonds and whichinteractions, is in accordance while at 500 with ns, resultsmany of of the RMSF interactions calculations. are lost. Furthermore, In more detail,Figure the5 B400also ns providessnapshot the shows different a carbon energy hydrogen contributions bond betw of eacheen component TYR1 and H54, of the an system,d alkyl which interactions shows thebetween total bindingLEU3 with free C23 energy and C35, of the and bound LEU7 complex with C23 is and−24.07 C31, kcal/mol. of BTp3 and This LTA, is stronger respec- thantively, binding while the of BTp1500 ns tosnapshot LTA, which only shows supports carbon binding hydrogen afﬁnity bond results interactions from molecular between dockingSER6 and studies H56, and that SER6 showed and BTp2 H57, bindsof BTp3 better and toLTA, LTA respectively. than BTp1.

Figure 5. (A) Per-residue energy decomposition analysis showing specific energy contributions of Figure 5. (A) Per-residue energy decomposition analysis showing specific energy contributions of each amino acid residue of BTp2 that contributes to binding. (B) Different energy contributions of each amino acid residue of BTp2 that contributes to binding. (B) Different energy contributions of each component of the system via MM/GBSA thermodynamics. (C) Binding interaction diagram of eachBTp2 component to residues of of the TLR2. system via MM/GBSA thermodynamics. (C) Binding interaction diagram of BTp2 to residues of TLR2. Furthermore, Figure 6A,C show specific energy contributions of each amino acid The trajectory of the BTp3/LTA system in Figure3A showed that BTp3 remained residue of BTp3 that contributes to binding. Most of the EEL energy contribution to bound to LTA throughout the 500 ns simulation, more strongly than BTp1 but less than binding is from THR8, while vdW contribution is mainly from LEU4. Furthermore, Figure BTp2. However, the third RMSD analysis in Figure3B showed that the system did not 6B also shows the total binding free energy of the bound complex is −17.81 kcal/mol, which reach convergence, as evident by the large spike in energy after 421 ns. Moreover, energy is stronger than binding of BTp1 to LTA, but less than that of BTp2. deviations were greater than 2 Å (~6 Å), indicating the instability of the complex. In addition, Figure S3 clearly shows that at 400 ns, the complex is stable with several bonds and interactions, while at 500 ns, many of the interactions are lost. In more detail, the 400 ns snapshot shows a carbon hydrogen bond between TYR1 and H54, and alkyl interactions between LEU3 with C23 and C35, and LEU7 with C23 and C31, of BTp3 and LTA, respectively, while the 500 ns snapshot only shows carbon hydrogen bond interactions between SER6 and H56, and SER6 and H57, of BTp3 and LTA, respectively. Furthermore, Figure6A,C show specific energy contributions of each amino acid residue of BTp3 that contributes to binding. Most of the EEL energy contribution to binding is from THR8, while vdW contribution is mainly from LEU4. Furthermore, Figure6B also shows the total binding free energy of the bound complex is −17.81 kcal/mol, which is stronger than binding of BTp1 to LTA, but less than that of BTp2. From molecular docking studies, BTp4 bound the strongest to LTA, compared to the other peptides. This was supported by the snapshots of the 500 ns trajectory of BTp4 bound to LTA in Figure3A, which showed that the complex remained bound throughout the entire simulation. In addition, the fourth RMSD graph in Figure3B indicates that the system reached convergence after 480 ns as fluctuations remained within a 2 Å range. After reaching convergence, at 500 ns (Figure S4), an alkyl interaction exists between VAL3 and C31, and hydrogen bonds are formed between VAL3 with H5 and H10, PHE4 and H55, LEU5 with H4, H54 and H57, and 2 hydrogen bonds between PRO7 and O12, of BTp4 and LTA, respectively. In addition, Figure7A,C show specific energy contributions of each amino acid residue of BTp4 that contributes to binding. Most of the EEL energy Biomedicines 2021, 9, 1063 10 of 14

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contribution to binding is from SER1 and LYS2, while vdW and total energy contribution is Biomedicines 2021, 9, 1063 10 of 14 mainly from PHE4. Furthermore, Figure7B also shows the total binding free energy of the bound complex is −12.71 kcal/mol, which is still less than binding of BTp2 to LTA.

Figure 6. (A) Per-residue energy decomposition analysis showing specific energy contributions of each amino acid residue of BTp3 that contributes to binding. (B) Different energy contributions of each component of the system via MM/GBSA thermodynamics. (C) Binding interaction diagram of BTp3 to residues of TLR2.

From molecular docking studies, BTp4 bound the strongest to LTA, compared to the other peptides. This was supported by the snapshots of the 500 ns trajectory of BTp4 bound to LTA in Figure 3A, which showed that the complex remained bound throughout the entire simulation. In addition, the fourth RMSD graph in Figure 3B indicates that the system reached convergence after 480 ns as fluctuations remained within a 2 Å range. After reaching convergence, at 500 ns (Figure S4), an alkyl interaction exists between VAL3 and C31, and hydrogen bonds are formed between VAL3 with H5 and H10, PHE4 and H55, LEU5 with H4, H54 and H57, and 2 hydrogen bonds between PRO7 and O12, of BTp4 and LTA, respectively. In addition, Figure 7A,C show specific energy contributions Figure 6. (A) Per-residue energy decomposition analysis showing specific energy contributions of Figureof each 6. amino(A) Per-residue acid residue energy of decomposition BTp4 that cont analysisributes showing to binding. speciﬁc Most energy of the contributions EEL energy of each amino acid residue of BTp3 that contributes to binding. (B) Different energy contributions of each amino acid residue of BTp3 that contributes to binding. (B) Different energy contributions of eachcontribution component to bindingof the system is from via SER1MM/GBSA and LY thermodynamics.S2, while vdW ( Cand) Binding total energy interaction contribution diagram of each component of the system via MM/GBSA thermodynamics. (C) Binding interaction diagram of BTp3is mainly to residues from PHE4. of TLR2. Furthermore, Figure 7B also shows the total binding free energy of BTp3the bound to residues complex of TLR2. is −12.71 kcal/mol, which is still less than binding of BTp2 to LTA. From molecular docking studies, BTp4 bound the strongest to LTA, compared to the other peptides. This was supported by the snapshots of the 500 ns trajectory of BTp4 bound to LTA in Figure 3A, which showed that the complex remained bound throughout the entire simulation. In addition, the fourth RMSD graph in Figure 3B indicates that the system reached convergence after 480 ns as fluctuations remained within a 2 Å range. After reaching convergence, at 500 ns (Figure S4), an alkyl interaction exists between VAL3 and C31, and hydrogen bonds are formed between VAL3 with H5 and H10, PHE4 and H55, LEU5 with H4, H54 and H57, and 2 hydrogen bonds between PRO7 and O12, of BTp4 and LTA, respectively. In addition, Figure 7A,C show specific energy contributions of each amino acid residue of BTp4 that contributes to binding. Most of the EEL energy contribution to binding is from SER1 and LYS2, while vdW and total energy contribution is mainly from PHE4. Furthermore, Figure 7B also shows the total binding free energy of the bound complex is −12.71 kcal/mol, which is still less than binding of BTp2 to LTA.

Figure 7. (A) Per-residue energy decomposition analysis showing speciﬁc energy contributions of each amino acid residue of BTp4 that contributes to binding. (B) Different energy contributions of each component of the system via MM/GBSA thermodynamics. (C) Binding interaction diagram of BTp4 to residues of TLR2.

Taken together, from the initial molecular docking studies, BTp2 and BTp4 showed the highest potential to bind to LTA, with stronger binding afﬁnities than BTp1 and BTp3. After molecular dynamics simulations and post-analyses, only complexes of BTp2 and BTp4 bound to LTA reached convergence, which supported data from molecular docking studies. From this, the most stable peptide that bound to LTA during the 500 ns simulation was BTp2,

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in terms of RMSD calculations (Figure3B), as the energy fluctuations were within a ~2 Å range throughout the 500 ns simulation. All other peptides bound to LTA with large energy fluctuations and overall higher energy averages during the simulations. Furthermore, BTp2 binds to LTA with the most negative total binding free energy of −24.07 kcal/mol, compared to all other peptides (BTp2 > BTp3 > BTp4 > BTp1), which suggests that this is the most favorable peptide/LTA bound state. A similar study in rohu fish was conducted to understand the binding between LTA and rohu TLR2 where the binding was found to be −1.92 kcal/mol with only one hydrogen bond present in the interaction plot [32]. Fish TLRs are structurally homologus to human TLRs with a structural sequence identity of ~30–70% [33]. In regard to this, our four novel proposed biomimetic TLR2-derived peptides have stronger binding affinity and more hydrogen bonds interacting between peptides residues and LTA suggesting their competitive binding to LTA.

3.6. Lipophilicity of BTp2 Lipophilicity influences the absorption, distribution, metabolism and excretion (ADME) properties of a compound. The logp value gives an idea as to whether or not a compound will penetrate living tissue—a positive logp value suggests that the compound is more lipophilic and will be able to penetrate cell membranes [34,35]. Using the online tool called SwissADME [36], BTp2 was found to have a positive logp value of 1.44 and can therefore pass through cell membranes [35]. Hence, BTp2 not only has the potential to target LTA, but also the potential to be a cell-penetrating peptide. This property will enhance the cell penetration capability of the peptide if used for nanoparticle surface modifaction or coating a drug delivery system.

4. Conclusions Biomimicry has been applied in drug delivery to benefit from the advantages that come with natural biological pathways [10]. In this study, we aimed to design four biomimetic peptides derived from human TLR2 to evaluate their mimicry resmblelance to human TLR2 and target bacterial LTA as well as their possible cell-penetrating peptide properties. These targeting peptides can be used to surface modify nanodrug delivery systems for enhanced targeting of LTA on bacterial cell walls and improved delivery of antibacterial treatments. This study was a preliminary phase of this evaluation, to determine the potential of these novel peptides to bind to LTA and remain stably bound. However, previous literature reporting compounds identified from in silico approaches has demonstrated successful progression in which the experimental findings concured with the data from molecular modeling [37–40]. Molecular docking studies, molecular dynamic simulations, post-analyses and thermodynamic energy calculations have shown that all four peptides bind to LTA, but BTp2 and BTp4 bind more strongly than the others. From our results, we have established that BTp2 (RRLHIPRF) bound to LTA more strongly and stably than the other three peptides, and possesses favourable lipophilic properties to enable its cell- penetrating functions. This peptide, therefore, has the potential to target and bind to LTA on bacterial cell walls and permeate the cell membrane, and can be used to surface modify nanodrug delivery systems for enhanced bacterial cell targeting, penetration and improved delivery of antibacterial agents. This serves as a platform for further formulation work into enhanced, targeted nano-antibiotic delivery systems for the treatment of Gram-positive bacterial infection.

Supplementary Materials: The following are available online at https://www.mdpi.com/article/ 10.3390/biomedicines9081063/s1, Figures S1–S4: Interaction bonds between all biomimetic TLR2- derived peptides with LTA; Figure S5: Mean energy ﬂuctuations (RMSF calculations) of each peptide over 500 ns. Author Contributions: Conceptualization, A.Y.W.; methodology, A.Y.W. and N.D.; software, N.D.; validation, B.G.d.l.T., F.A. and T.G.; formal analysis, N.D.; resources, F.A. and T.G.; writing—original draft preparation, N.D.; writing—review and editing, A.Y.W., B.G.d.l.T., F.A. and T.G.; supervision, Biomedicines 2021, 9, 1063 11 of 12

B.G.d.l.T., F.A. and T.G.; project administration, T.G. and F.A.; funding acquisition, T.G. and F.A. All authors have read and agreed to the published version of the manuscript. Funding: The authors acknowledge the College of Health Sciences, the University of KwaZulu-Natal (UKZN), and the UKZN Nanotechnology Platform, the National Research Foundation of South Africa (NRF Grant No. 116652, 106040), NRF Competititve Programme for Rated Researcher No.129326, NRF Blue Sky’s Research Programme No. 120386, the Medical Research Council (MRC) of South Africa for financial support and Centre for High-Performance Computing (CHPC), Cape Town, for supercomputing resources. Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable. Data Availability Statement: All data produced from this study may be requested from Prof Thiru- mala Govender ([email protected]). UCSF Chimera software may be downloaded from https://www.cgl.ucsf.edu/chimera/download.html (Accessed Date 16 August 2021), Avogadro software from https://avogadro.cc/ (Accessed Date 16 August 2021), Schrödinger Maestro software from https://www.schrodinger.com/products/maestro (Accessed Date 16 August 2021), AutoDock MGL Tools from http://mgltools.scripps.edu/downloads (Accessed Date 16 August 2021)„ AutoDock Vina from http://vina.scripps.edu/download.html (Accessed Date 16 August 2021), and Raccoon from http://autodock.scripps.edu/resources/raccoon (Accessed Date 16 August 2021). Conflicts of Interest: The authors declare no conflict of interest.

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