Uncovering a Hub Signaling Pathway of Antimicrobial-Antifungal-Anticancer Peptides’ Axis on Short Cationic Peptides via Network Pharmacology Study

Short cationic peptides (SCPs) with therapeutic efficacy of antimicrobial peptides (AMPs), antifungal peptides (AFPs), and anticancer peptides (ACPs) are known as an enhancement of the host defense system. Here, we investigated the uppermost peptide(s), hub signaling pathway(s), and their associated target(s) through network pharmacology. Firstly, we selected SCPs with positive amino acid residues on N- and C- terminals under 500 Dalton via RStudio. Secondly, the overlapping targets between the bacteria-responsive targets (TTD and OMIM) and AMPs’ targets were visualized by VENNY 2.1. Thirdly, the overlapping targets between AFPs’ targets and fungal-responsive targets were exhibited by VENNY 2.1. Fourthly, the overlapping targets between cancer-related targets (TTD and OMIM) and fungal-responsive targets were displayed by VENNY 2.1. Finally, a molecular docking study (MDS) was carried out to discover the most potent peptides on a hub signaling pathway. A total of 1833 SCPs were identified, and AMPs’, AFPs’, and ACPs’ filtration suggested that 197 peptides (30 targets), 81 peptides (6 targets), and 59 peptides (4 targets) were connected, respectively. The AMPs―AFPs―ACPs’ axis indicated that 27 peptides (2 targets) were associated. Each hub signaling pathway for the enhancement of the host defense system was “Inactivation of Rap1 signaling pathway on AMPs”, “Activation of Notch signaling pathway on AMPs―AFPs’ axis”, and “Inactivation of HIF-1 signaling pathway on AMPs―AFPs―ACPs’ axis”. The most potent peptides were assessed via MDS; finally, HPIK on STAT3 and HVTK on NOS2 and on HIF-1 signaling pathway were the most stable complexes. Furthermore, the two peptides had better affinity scores than standard inhibitors (Stattic, 1400 W). Overall, the most potent SCPs for the human defense system were HPIK on STAT3 and HVTK on NOS2, which might inactivate the HIF-1 signaling pathway.


Introduction
Since the emergence of insulin application in the 1920s, peptide therapeutics have been revealed as highly selective, safe, efficacious, and well-tolerated pharmaceutical agents [1]. Peptides are intrinsic signaling molecules, possessing both biochemical and therapeutical attribution, and nearly more than 60 peptides are being used (FDA approved) worldwide as clinical medications [2]. Peptides' critical properties as potential drug candidates are their high potency on target disease, specificity on a target protein, and minimal toxicity [3]. Certainly, peptides provide potential therapeutic intervention by binding to particular cell surface receptors, which stimulate intracellular effects. Given such unique and excellent characteristics, peptide drugs can be used as novel therapies or replacement therapies [4].
Bio-researchers have recently recognized the attractive pharmacological profile of short cationic peptides having significant antibacterial, antifungal, anticancer, and even immunomodulatory activities [5][6][7]. A report demonstrated that peptides with cation residues (Lysine, Arginine, Histidine) have more significant antimicrobial efficacy than Int. J. Mol. Sci. 2022, 23, x FOR PEER REVIEW 3 of 25 pharmacology) a hub signaling of SCPs, which might be assumed to strengthen the host defense system. The workflow diagram is depicted in Figure 1.

SCPs under 500 Dalton Rule
The number of 1833 peptides with two sufficient conditions (positive N, C-terminals' amino acid residues, under 500 Dalton rule [31]) was selected by RStudio analysis. In particular, ligands with less than 500 Dalton have a higher absorption and selectivity on targets in the drug development [32,33]. The selected peptides were enlisted (Supplementary  Table S1).

SCPs under 500 Dalton Rule
The number of 1833 peptides with two sufficient conditions (positive N, C-terminals' amino acid residues, under 500 Dalton rule [31]) was selected by RStudio analysis. In particular, ligands with less than 500 Dalton have a higher absorption and selectivity on targets in the drug development [32,33]. The selected peptides were enlisted (Supplementary Table S1).

AMPs' Targets' Identification
The number of 197 peptides was converted into SMILE format via Dendrimer Builde (https://dendrimerbuilder.gdb.tools/) (Accessed on 16 May 2021). The SMILE format o peptide was input to the SEA (http://sea.bkslab.org/) (Accessed on 28 October 2021) an STP (http://www.swisstargetprediction.ch/) (Accessed on 18 May 2021) databases wit "Homo Sapiens" setting. The number of 375 and 355 targets associated with the 197 pep tides were identified by SEA and STP, respectively (Figure 2A), (Supplementary Table S4 The number of 242 overlapping targets was also identified from the two databases (Sup  plementary Table S5). Finally, the number of 30 targets overlapped between the numbe of 959 AMPs' targets (extracted from the TTD and OMIM databases) ( Figure 2B), (Tabl 1), (Supplementary Table S6), and the overlapping 242 targets were selected.

Signaling Pathways Responsive to Bacterial Infection on Human
The 13 out of the overlapping 30 targets were notably enriched in 11 signaling pathways via KEGG pathway enrichment analysis ( Figure 3A). The targets of the 11 signaling pathways were enlisted ( Table 2). The 13 targets were associated with the number of 197 peptides, and the constructed peptide-targets' networks identified 210 nodes and 1011 edges ( Figure 3B). The peptide-targets' network analysis via the overlapping 30 targets was constructed by STRING, which indicated 30 nodes and 68 edges ( Figure 3C). Among 11 signaling pathways, inactivation of Rap1 signaling pathway was identified as a hub signaling pathway through a bubble chart. Among 11 signaling pathways, the Rap1 signaling pathway's targets were SRC, FPR1, and ITGB1, which were constructed with 158 nodes (3 targets, 155 peptides) and 216 edges on a size map ( Figure 3D). Among the three targets (SRC, FPR1, and ITGB1), ITGB1 connected to 117 peptides was the highest degree of value. It implies that ITGB1 plays a vital role in Rap1 signaling pathways in human defense systems against bacterial infection.

Signaling Pathways Responsive to Bacterial Infection on Human
The 13 out of the overlapping 30 targets were notably enriched in 11 signaling pathways via KEGG pathway enrichment analysis ( Figure 3A). The targets of the 11 signaling pathways were enlisted ( Table 2). The 13 targets were associated with the number of 197 peptides, and the constructed peptide-targets' networks identified 210 nodes and 1011 edges ( Figure 3B). The peptide-targets' network analysis via the overlapping 30 targets was constructed by STRING, which indicated 30 nodes and 68 edges ( Figure 3C). Among 11 signaling pathways, inactivation of Rap1 signaling pathway was identified as a hub signaling pathway through a bubble chart. Among 11 signaling pathways, the Rap1 signaling pathway's targets were SRC, FPR1, and ITGB1, which were constructed with 158 nodes (3 targets, 155 peptides) and 216 edges on a size map ( Figure 3D). Among the three targets (SRC, FPR1, and ITGB1), ITGB1 connected to 117 peptides was the highest degree of value. It implies that ITGB1 plays a vital role in Rap1 signaling pathways in human defense systems against bacterial infection.

Physicochemical Refinement for AFPs
The number of 197 peptides (AMPs) was input into AntipDS1_binary_model1, An-tipDS1_binary_model2, and AntipDS1_binary_model3 in an antifungal peptide screening platform. Thereby, the number of 91 peptides was accepted by AFPs, which were defined as AMPs and AFPs with dual efficacy for enhancement of human defense system (Supplementary Table S7).

Physicochemical Refinement for AFPs
The number of 197 peptides (AMPs) was input into AntipDS1_binary_model1, An-tipDS1_binary_model2, and AntipDS1_binary_model3 in an antifungal peptide screening platform. Thereby, the number of 91 peptides was accepted by AFPs, which were defined as AMPs and AFPs with dual efficacy for enhancement of human defense system (Supplementary Table S7).

Signaling Pathways Responsive to Fungal Infection on Human
The six targets (TPSAB1, PSEN1, PSEN2, DPP4, STAT3, and NOS2) were connected to three signaling pathways via KEGG pathway enrichment analysis ( Figure 5A). Table 3 shows the targets of the three signaling pathways. The six targets (TPSAB1, PSEN1, PSEN2, DPP4, STAT3, and NOS2) were related to the number of 81 peptides (Supplementary Table S11). The constructed network exposed 87 nodes (81 peptides, 6 targets) and 1011 edges ( Figure 5B). The peptide-targets' networking analysis via overlapping six targets (TPSAB1, PSEN1, PSEN2, DPP4, STAT3, and NOS2) was constructed by STRING, indicating six nodes and two edges ( Figure 5C). Among three signaling pathways, activation of Notch signaling pathway was identified as a hub signaling pathway through a bubble chart. Notch signaling pathway's targets were both PSEN1 and PSEN2, and their peptides-targets' network was constructed on a size map (34 nodes and 45 edges) ( Figure 5D). Among the four targets, PSEN1 and PSEN2 were connected to nine peptides (KLCK, KCLK, KALK, KVLK, KLGGK, KAFK, KFGK, KFSK, and KSFK), which might have more efficacy than any other AFPs. Additionally, it implies that both PSEN1 and PSEN2 play a pivotal role in the Notch signaling pathway of the human defense system against fungal infection on the AMPs-AFPs' axis.

Signaling Pathways Responsive to Fungal Infection on Human
The six targets (TPSAB1, PSEN1, PSEN2, DPP4, STAT3, and NOS2) were co to three signaling pathways via KEGG pathway enrichment analysis ( Figure 5A). shows the targets of the three signaling pathways. The six targets (TPSAB1, PSEN2, DPP4, STAT3, and NOS2) were related to the number of 81 peptides (Sup tary Table S11). The constructed network exposed 87 nodes (81 peptides, 6 targ 1011 edges ( Figure 5B). The peptide-targets' networking analysis via overlapping gets (TPSAB1, PSEN1, PSEN2, DPP4, STAT3, and NOS2) was constructed by S indicating six nodes and two edges ( Figure 5C). Among three signaling pathways tion of Notch signaling pathway was identified as a hub signaling pathway th bubble chart. Notch signaling pathway's targets were both PSEN1 and PSEN2, a peptides-targets' network was constructed on a size map (34 nodes and 45 edges) 5D). Among the four targets, PSEN1 and PSEN2 were connected to nine peptides KCLK, KALK, KVLK, KLGGK, KAFK, KFGK, KFSK, and KSFK), which might ha efficacy than any other AFPs. Additionally, it implies that both PSEN1 and PSEN pivotal role in the Notch signaling pathway of the human defense system agains infection on the AMPs-AFPs' axis. (A)

Cancer-Related Targets and ACPs' Targets' Identification
TTD and OMIM selected the number of 4247 cancer-related targets (Supplementary Table S12). The number of four out of six AFP-responsive targets was overlapped with the 4247 cancer-related targets ( Figure 6A). The two targets (STAT3 and NOS2) were targeted to only HIF-1 signaling pathway via KEGG pathway enrichment analysis ( Figure 6B), (Table 4). The two targets (STAT3 and NOS2) were related to the number of 27 peptides, and the constructed networks revealed 29 nodes (27 peptides, 2 targets) and 27 edges ( Figure  6C). The peptide-targets' networking analysis via overlapping four targets (PSEN1, DPP4, STAT3, NOS2) was constructed by STRING (six nodes and two edges) ( Figure 6D). Only two targets (STAT3 and NOS2) were related directly to HIF-1 signaling pathway ( Figure  6E). Both STAT3 and NOS2 targets were directly associated with HIF-1 signaling pathway, which played a crucial role in defending the cancer attack. The HIF-1 signaling pathway was connected particularly to all AMPs-AFPs-ACPs' axes.

Cancer-Related Targets and ACPs' Targets' Identification
TTD and OMIM selected the number of 4247 cancer-related targets (Supplementary Table S12). The number of four out of six AFP-responsive targets was overlapped with the 4247 cancer-related targets ( Figure 6A). The two targets (STAT3 and NOS2) were targeted to only HIF-1 signaling pathway via KEGG pathway enrichment analysis ( Figure 6B), ( Table 4). The two targets (STAT3 and NOS2) were related to the number of 27 peptides, and the constructed networks revealed 29 nodes (27 peptides, 2 targets) and 27 edges ( Figure 6C). The peptide-targets' networking analysis via overlapping four targets (PSEN1, DPP4, STAT3, NOS2) was constructed by STRING (six nodes and two edges) ( Figure 6D). Only two targets (STAT3 and NOS2) were related directly to HIF-1 signaling pathway ( Figure 6E). Both STAT3 and NOS2 targets were directly associated with HIF-1 signaling pathway, which played a crucial role in defending the cancer attack. The HIF-1 signaling pathway was connected particularly to all AMPs-AFPs-ACPs' axes.  Table 7). This result showed that the uppermost promising peptides to strengthen the immune system against cancer were "HPIK" on STAT3 (PDB ID: 6TLC) and "HVTK" on NOS2 (PDB ID: 4NOS).

Discussion
The SCPs were selected by two rigorous criteria: ≤ 500 Dalton and N-, C-terminal

Discussion
The SCPs were selected by two rigorous criteria: ≤500 Dalton and N-, C-terminal cationic amino acid residues. The number of 1833 SCPs was identified, and, consequently, 197 peptides (AMPs), 91 peptides (AMPs-AFPs' axis), and 59 peptides (AMPs-AFPs-ACPs' axis) were selected. The SCPs associated with signaling pathways were as follows: 197 peptides, 13 targets (AMPs); 81 peptides, 6 targets (AMPs-AFPs' axis); and 27 peptides, 4 targets (AMPs-AFPs-ACPs' axis). It was reported that SCPs have functioned as antimicrobial agents and host defense adjuvants [38]. A study suggested that TLR4 is an upregulated representative target in keratitis of bacterial infection, whereas SOD2 is an upregulated representative target in keratitis of fungal infection from Differentially Expressed Genes (DEGs) [39]. It implies that host responses against bacterial and fungal attack might induce significant differences in the immune system. Hence, we regarded it as an independent perturbation of the bacterial and fungal infection. A study indicated that AMPs could bind with negatively charged ions (phosphatidylserine) on the cancer cell membrane and trigger the host defense system [20]. Thus, we performed the analysis of AMPs-AFPs-ACPs' axis to investigate potential SCPs for the host immune system.
AMPs-targets' network showed that the therapeutic efficacy of the host defense system was directly associated with 30 targets. The result of the KEGG pathway analysis of 30 targets indicated that 11 signaling pathways were connected to 13 out of 30 targets, suggesting that these signaling pathways were directly related to bacterial infection responses in the human immune system.
The description of the 11 signaling pathways with bacterial infection were briefly discussed as follows. Relaxin signaling pathway: Relaxin prevents inflammatory cytokine induced by endotoxin in THP-1 (human monocytic cell line), which specializes the immune cells in the period of preterm birth [40]. Glucagon signaling pathway: Glucagon alleviates inflammatory responses of the airway due to association with the reduction of eosinophils and T lymphocytes by inhibiting TCD4+ cell proliferation [41,42]. Prolactin signaling pathway: Prolactin accelerates secretion of proinflammatory cytokines in peripheral immune cells, modulating the level of responses against pathogens [43,44]. Estrogen signaling pathway: Estrogen increases in the level of expression of AMPs in the host, thereby interrupting bacterial proliferation [45]. Additionally, estrogen stimulated the expression level of cell-cell junction proteins, thereby intensifying the epithelial rigidity and prohibiting unnecessary loss of outer cells during infection [46]. TNF signaling pathway: Tumor Necrosis Factor (TNF) can induce the recruitment of inflammatory cells and control the mechanism of antimicrobial activities [47]. It implies that TNF can work as a buffer element for immunopotentiation. IL-17 signaling pathway: The knockout groups of IL-17 are more highly susceptible to K. pneumonia infection than are the IL-17 expression groups [48]. AMPK signaling pathway: Activation of AMPK improves the host defense system against bacterial infection. Moreover, AMPK is associated with the innate and adaptive immune system [49]. FoxO signaling pathway: FoxO1 protein is expressed by a bacterial infection, strengthening the epithelial barrier of host cells and inducing the recruitment of Tregs (Regulatory T Cells) to activate the antibacterial defense [50]. HIF-1 signaling pathway: HIF-1α activation in the hypoxic condition recruits inflammatory-associated cells such as macrophages, neutrophils, and dendritic cells as well as inducing offensive cytokine production under bacterial infection [51]. HIF-1 inhibition can be a good strategy to relieve the inflammation level induced by the bacterial attack in aspects of the host immune system. Rap1 signaling pathway: The inactivation of Rap1 in lymphocytes is a representative treatment against inflammatory disorders [52]. On AMPs' signaling pathways, the key mechanism might inhibit the Rap1 signaling pathway selected based on the rich factor.
AMPs-AFPs' axis-target networks showed that the therapeutic efficacy of the host defense system was directly associated with six targets. The result of the KEGG pathway analysis of six targets was connected to three signaling pathways. Neurotrophin signaling pathway: Inflammation signals in microglial cells induce the secretion of neurotrophins that function as mediators of pain [53,54]. It implies that the neurotrophin signaling pathway's inactivation might modulate inflammatory-related proteins' expression level, thereby resolving host defense-induced inflammation. HIF-1 signaling pathway: The deletion of hypoxia-regulated targets are resistant to fungal infection; more importantly, the low-oxygen condition makes fungal virulence attenuate in murine models [55]. Thus, inactivation of HIF-1 might interrupt the fungal penetration and host immune system. Notch signaling pathway: the Notch system plays important roles in Th1 and Th2 cell differentiation, and Notch-mediated immune responses are related to T cell development [56]. It supports the idea that the activation of Notch signaling pathway contributes to enhancing the host defense system. On AMPs-AFPs' axis signaling pathways, a key signaling pathway is to activate the Notch signaling pathway, which was identified based on the rich factor AMPs-AFPs-ACPs' axis-target networks exhibited that the therapeutic efficacy of the host defense system was directly associated with four targets. The result of the KEGG pathway analysis on four targets was connected to one signaling pathway. HIF-1 signaling pathway: HIF-1 overexpression contributes to tumor growth, angiogenesis, and metastasis. However, the overexpression is caused by an oxygen-depleted condition in tumor cells [57,58]. Furthermore, hypoxia creates severe conditions under resistance to cancer therapy such as radiation and medication, increasing tumor survival [59]. It suggests that inactivation of the HIF-1 signaling pathway is an optimal strategy for cancer therapy. This work focused on immunomodulatory activities of SCPs, which may improve immune defenses and provide key therapeutic agents from large-scale peptides. We performed the MDT to select promising peptide candidate(s) on the HIF-1 signaling pathway, and, hence, the standard molecules (stattic and 1400 W) were compared with them. Moreover, we suggested a hub signaling pathway (HIF-1 signaling pathway), two key SCPs (HPIK and HVTK), and two key targets (STAT3 and NOS2). This analysis collectively suggested an overlapping signaling pathway "HIF-1 signaling pathway" on AMPs, AMPs-AFPs' axis, and AMPs-AFPs-ACPs' axis. Therefore, the inactivation of the HIF-1 signaling pathway using two selected peptides is a feasible treatment strategy for enhancing the host defense system.

The Selection of Peptides via RStudio
The standard peptides were selected with positive amino acids (Lysine, Arginine, Histidine) on both terminals (N-terminal, C-terminal) or less than 500 Dalton. The selection method of these species was based on RStudio.

The Conversion of SMILES Format
The sequences of the final selected AMPs and AFPs were converted to SMILES format through Dendrimer Builder (https://dendrimerbuilder.gdb.tools/) (Accessed on 16 June 2021) [66].

Bubble Chart of Signaling Pathway Analysis of Overlapping Targets between Peptide-Targets and Bacterial-Responsive Targets' Network
The final overlapping targets' (bacterial-responsive targets on humans) networks were visualized by STRING (https://string-db.org/) (Accessed on 21 June 2021) [72]. A bubble chart of the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway based on the final overlapping targets was constructed by RStudio.

Bubble Chart of Signaling Pathway Analysis of Overlapping Targets between Peptide-Targets and Fungal-Responsive Targets' Network
The final overlapping targets' (fungal-responsive targets on the human) construction was visualized by STRING (https://string-db.org/) (Accessed on 24 June 2021). A bubble chart of the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway based on the final overlapping targets was constructed by RStudio.

Bubble Chart of Signaling Pathway Analysis of Overlapping Targets between Peptide-Targets and Cancer-Related Targets
The final overlapping targets (cancer-related targets on the human) construction was visualized by STRING (https://string-db.org/) (Accessed on 26 June 2021). RStudio constructed a bubble chart of the KEGG pathway based on the final overlapping targets.

Preparation for Docking of Peptide Molecules
The peptide molecules were converted into SMILES format from Dendrimer builder. The converted SMILES were again converted into .pdb format using Open Babel (http: //www.cheminfo.org/Chemistry/Cheminformatics/FormatConverter/index.html) (Ac-cessed on 27 June 2021) [73]. Finally, the converted .pdb peptide was converted into .pdbqt format through Autodock.

Preparation for Docking of Target Proteins and Positive Controls to Compare with Final Peptides
Two target proteins of cancer, i.e., STAT3 (PDB ID: 6TLC) and NOS2 (PDB ID: 4NOS), identified from STRING were converted into .pdbqt format (https://www.rcsb.org/) (Accessed on 28 June 2021) from .pdb format in order to test the affinity of ligands via Autodock (http://autodock.scripps.edu/) (Accessed on 28 June 2021) [74]. Subsequently, two positive controls, i.e., stattic (PubChem ID: 2779853) for STAT3 and 1400 W (PubChem ID: 1433) for NOS2, were converted into.pdb format from .sdf format to upload to Pymol, and each of the two positive controls was converted again into .pdbqt format to measure affinity through Autodock.

Conclusions
The uppermost SCPs of AMPs-AFPs-ACPs' axis for immunopotentiation were firstly investigated through network pharmacology. The number of 1833 SCPs was funneled sequentially through a peptide screening platform; thereby, the numbers of 197 SCPs (AMPs) and 91 SCPs (AMPs-AFPs axis) were obtained. The number of 27 SCPs (AMPs-AFPs-ACPs' axis) was obtained as the final promising peptides through cancerrelated targets' analysis. The 27 SCPs (AMPs-AFPs-ACPs' axis) were connected to only the HIF-1 signaling pathway with HPIK-STAT3 and HVTK-NOS2. This analysis provided the network of two SCPs, two targets, and one signaling pathway for the host defense system. Consequently, the key findings on the AMPs-AFPs-ACPs' axis could be a promising therapeutic strategy for cellular protection against immune disorders.