A Hub Signaling Pathway of Antimicrobial-Antifungal-Anticancer Peptides Axis With Cationic Residue Amino Acids on N, C- Terminals Under 500 Dalton Rule Via Network Pharmacology

Background: Short cationic peptides (SCPs) with therapeutic ecacy of Antimicrobial peptides (AMPs), Antifungal peptides (AFPs), and Anticancer peptides (ACPs) are known as enhancement of host defense system. Here, we investigated the uppermost peptide(s), hub signaling pathway(s), and its associated target(s) through network pharmacology. Method: Firstly, we selected SCPs with positive amino acid residues on N-, C- terminals under 500 Dalton via RStudio. Secondly, EMBOSS pepstats, PASTA 2.0 and Aggrescan were used to remove non- AMPs, after that, ADAM, dbAMP, DBAASP v3.0 , and MLAMP were utilized for AMPs selection. AMPs-targets were identied from both SEA and STP databases. The overlapping targets between the bacteria-responsive targets (TTD and OMIM) and AMPs-targets were visualized by VENNY 2.1. Thirdly, AFPs were ltered through Antifp tool, and TTD and OMIM selected fungal responsive targets. The overlapping targets between AFPs-targets and fungal-responsive targets were visualized by VENNY 2.1. Fourthly, the overlapping targets between cancer-related targets (TTD and OMIM) and fungal-responsive targets were visualized by VENNY 2.1. Fifthly, signaling pathway analysis of overlapping targets was performed via RStudio. Finally, molecular docking study (MDS) was carried out to discover the most potent peptides on a hub signaling pathway. Results: A total of 1,833 SCPs were identied, and AMPs, AFPs, and ACPs ltration suggested that 197 peptides-30 targets, 81 peptides-6 targets, and 59 peptides-4 targets are connected, respectively. The AMPs-AFPs-ACPs axis indicated that 27 peptides-2 targets are associated. Each hub signaling pathway for enhancement of 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; nally, HPIK on STAT3, HVTK on NOS2 manifested the HIF-1 signaling pathway's highest anity. Furthermore, the two peptides have better anity scores than standard selective inhibitors (Stattic, 1400W). Conclusion: Overall, the most potent SCPs for the host 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, e cacious, and well-tolerated pharmaceutical agents 1 . Peptides are intrinsic signaling molecules, possess both biochemical and therapeutical attribution, and nearly more than 60 peptides are being used (FDA approved) worldwide as a clinical medication 2 . Peptides' critical properties as potential drug candidates are their high potency on target disease, speci city 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 pro le of short cationic peptides having signi cant antibacterial, antifungal, anticancer, and even immunomodulatory activities [5][6][7] . A report demonstrated that peptides with cation residues (Lysine, Arginine, Histidine) have more signi cant antimicrobial e cacy than peptides without cation residues 8 . Another study showed that short cationic peptides (SCPs; below six residues) expose better potency than longer peptides. Additionally, SCPs can be synthesized readily by following solid-phase peptide synthesis method 9,10 . A pivotal property of cellpenetrating peptides (CPPs) is their cationic residues, facilitating permeability into the cell membrane 11 .
Short peptides with cationic residues (Lysine, Arginine, Histidine) exist essentially in living organisms to function as antimicrobial activity 12 . In animals, antimicrobial peptides (AMPs) are often produced that act as natural innate barriers and elevate immune response to combat microbial infection [13][14][15] .
Interestingly, AMPs have tremendous therapeutic potential to function as antifungal peptides (AFPs) by suppressing the fungal growth such as Candida conidia and hyphae 16,17 . It implies that AMPs play essential roles in boosting the immune system against fungal attack and hence, they are considered new biopharmaceuticals to ght or treat fungal infections. Recent studies have supported that cationic peptides act as immune modulators, recognizing signal molecules like lipopolysaccharide secreted by bacterial or fungal molecules 18,19 .
Evidence also suggests that AMPs demonstrate the antitumor activity by stimulating human cancer cells 20 . The constructed AMPs have positive amino acid residues that can bind effectively with negatively charged cancer cells components 21 . A study proves that AMPs can potentially disrupt the cancer cell membrane due to the strong electrostatic attraction present between positively charged AMPs and negatively charged molecule "phosphatidylserine" on cancer cells' plasma membranes 22 . Another report supports that AMPs activate the host immune defense system, working as anticancer peptides (ACPs) 23 . Despite these advantages, peptides have some intrinsic weaknesses, such as high molecular weight, degradability, and low permeability 24 . However, these limitations can be resolved through traditional design of biotherapeutic peptides that are more suitable for use as convenient therapeutics.
Multifunctional and useful cell-penetrating peptides offer more therapeutics and diagnostic merit, leading to the development of future medicines with improved target delivery, e cacy, and pharmacokinetic properties. From these points of view, we used diverse multiple putative AMPs (or) AFPs prediction tools to identify potential therapeutic of SCPs. The nal peptides of ACPs were selected via public databases and thus completed AMPs-AFPs-ACPs axis on SCPs.
In this study, we performed network pharmacology (NP) concept to achieve the AMPs-AFPs-ACPs axis.
NP is a collective, systemic, and holistic approach to investigate the relation of molecule(s) and target (s), nd the optimal molecule(s) on target protein(s), and provide a crucial hint for identifying the mechanism of a potential lead molecule(s) [25][26][27] . Moreover, Zhang B. et al. described that NP accelerates the decoding TCM (Traditional Chinese Medicine) from an empirical-based therapy to an evidence-based therapy system, which improves modern drug discovery strategies 28 .
In our study, network pharmacology-based analysis was utilized to investigate triple therapeutic feasibility (AMPs-AFPs-ACPs axis) of SCPs. Firstly, SCPs (N, and C-terminal cationic groups; ≤ 500 Dalton) were selected via RStudio analysis. Secondly, the physicochemical propensity of selected SCPs was identi ed via AMPs screening platform, and a hub signaling pathway of AMPs between AMPsrelated targets and host-responsive targets were analyzed. Thirdly, the AFPs screening platform was used to nd AFPs from selected AMPs, and a hub signaling pathway of AMPs-AFPs axis was identi ed between AFPs-related targets and host-responsive targets. Fourthly, AMPs-AFPs-ACPs axis was constructed by retrieving cancer-related targets from public databases. Fifthly, SCPs accepted by AMPs-AFPs-ACPs axis and targets on a hub signaling pathway were subjected to perform MDS. Finally, we found (via network pharmacology) a hub signaling of SCPs which might assume to strengthen the host defense system. Figure 1 shows the overall work ow.

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.

Conversion of (peptide) sequences into SMILES format
The sequences of the nal selected AMPs and AFPs were converted to SMILES format through Dendrimer Builder (https://dendrimerbuilder.gdb.tools/) 62 .
Identi cation of peptide-target networks and microbial related targets on database Based on SMILES (format), target targets related to selected peptides were extracted from both SEA (http://sea.bkslab.org/) 63 and STP (http://www.swisstargetprediction.ch/) 64 with "Homo Sapiens" setting. The overlapping targets in the peptide(s)-target(s) networks between SEA and STP were identi ed by VENNY 2.1 (https://bioinfogp.cnb.csic.es/tools/venny/). The bacterial responsive targets on human were obtained with "bacterial/germ/bacilli" from both TTD (http://db.idrblab.net/ttd/) 65 and OMIM (https://www.omim.org/) 66 databases. After that, the overlapping targets between peptide(s)-target (s) and bacterial responsive targets were identi ed by VENNY 2.1 (https://bioinfogp.cnb.csic.es/tools/venny/). Bubble plot of signaling pathway analysis of overlapping targets between peptide-targets and bacterial responsive targets network The nal overlapping targets (bacterial responsive targets on the human) networks ware visualized by STRING (https://string-db.org/) 67 . A bubble plot of Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway based on the nal overlapping targets was constructed by RStudio.
Identi cation of peptide-targets network and fungal related targets on database Based on SMILES, targets associated with selected peptides were identi ed via both SEA (http://sea.bkslab.org/) and STP (http://www.swisstargetprediction.ch/) with "Homo Sapiens" setting. The overlapping targets in peptide-target network between SEA and STP were identi ed by VENNY 2.1 (https://bioinfogp.cnb.csic.es/tools/venny/). The fungal targets associated with a human were obtained from both TTD (http://db.idrblab.net/ttd/) and OMIM (https://www.omim.org/), entering as "fungal". The overlapping targets between peptide-target targets and fungal related targets were identi ed by VENNY 2.1 (https://bioinfogp.cnb.csic.es/tools/venny/). Bubble plot of signaling pathway analysis of overlapping targets between peptide-targets and fungal responsive targets network The nal overlapping targets (fungal responsive targets on the human) construction was visualized by STRING (https://string-db.org/). A bubble plot of Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway based on the nal overlapping targets was constructed by RStudio.
Identi cation of peptide-target targets network and cancer-related targets on database Based on SMILES, targets associated with selected peptides were identi ed via both SEA (http://sea.bkslab.org/) and STP (http://www.swisstargetprediction.ch/) with "Homo Sapiens" setting. The cancer-related targets on human were obtained with "cancer/tumor/neoplasia/carcinoma" from TTD (http://db.idrblab.net/ttd/) and OMIM (https://www.omim.org/). The overlapping targets between peptidetargets and cancer-related targets were identi ed by VENNY 2.1 (https://bioinfogp.cnb.csic.es/tools/venny/). Bubble plot of signaling pathway analysis of overlapping targets between peptide-targets and cancerrelated targets The nal overlapping targets (cancer-related targets on the human) construction was visualized by STRING (https://string-db.org/). RStudio constructed a bubble plot of KEGG pathway based on the nal 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) 68 . Finally, the converted .pdb peptide was converted into .pdbqt format through Autodock.
Preparation for docking of target proteins and positive controls to compare with nal peptides Two target proteins of cancer i.e., STAT3 (.pdb ID: 6TLC), NOS2 (.pdb ID: 4NOS) identi ed from STRING were converted into .pdbqt format (https://www.rcsb.org/) from .pdb format in order to test the a nity of ligands via Autodock (http://autodock.scripps.edu/) 69 . Subsequently, two positive controls i.e., stattic (Pubchem ID: 2779853) for STAT3 and 1400W (Pubchem ID: 1433) for NOS2, were converted into.pdb format from .sdf format to upload on Pymol, and each of two positive controls was converted again into .pdbqt format to measure a nity through Autodock.

SCPs under 500 Dalton rule
The number of 1,833 peptides with two su cient conditions (positive N, C-terminals amino acid residues, under 500 Dalton) was selected by RStudio analysis. Table 1 displayed the amount (Da) of each amino acid. The selected peptides were enlisted. (Supplementary Table S1).

AMPs-targets identi cation
The number of 197 peptides sequences were converted into SMILE format via Dendrimer Builder (https://dendrimerbuilder.gdb.tools/). The SMILE format of peptide sequences was input to SEA (http://sea.bkslab.org/) and STP (http://www.swisstargetprediction.ch/) databases with "Homo Sapiens" setting. Figure 2A showed that the number of 375 and 355 targets associated with the 197 peptides were identi ed by SEA and STP, respectively (Supplementary Table S4). The number of 242 overlapping targets was also identi ed from the two databases (Supplementary Table S5). Finally, Figure  2B and Table 2 displayed that the number of 30 targets overlapped between the number of 959 AMPstargets (extracted from TTD and OMIM databases) (Supplementary Table S6) and overlapping 242 targets were selected.
Signaling pathways responsive to bacterial infection on human Figure 3A exhibited that 13 out of overlapping 30 targets were notably enriched in 11 signaling pathways via KEGG pathway enrichment analysis. Table 3A showed that the detailed description of the 11 signaling was enlisted. Figure 3B displayed that the 13 targets were associated with the number of 197 peptides, and the constructed peptide-targets networks manifested 210 nodes and 1,011 edges. Figure 3C showed that peptide-targets network analysis via overlapping 30 targets was constructed by STRING, which indicated 30 nodes and 68 edges. Among 11 signaling pathways, inactivation of Rap1 signaling pathway was identi ed as a hub signaling pathway through bubble plot. Figure 3D exhibited that among 11 signaling pathways, the Rap1 signaling pathway's targets were SRC, FPR1, and ITGB1, which was constructed with 158 nodes (3 targets, 155 peptides) and 216 edges on a size map. Among the 3 targets (SRC, FPR1, and ITGB1), ITGB1 connected to 117 peptides was on the highest degree of value. It implies that ITGB1 played a vital role in Rap1 signaling pathways in host defense systems against bacterial infection.
Physicochemical re nement for AFPs The number of 197 peptides (AMPs) were input into AntipDS1_binary_model1, AntipDS1_binary_model2, and AntipDS1_binary_model3 in antifungal peptide screening platform. Thereby, the number of 91 peptides was accepted as AFPs which were de ned as AMPs and AFPs with dual-e cacy for enhancement of host defense system (Supplementary Table S7).

AFPs-targets identi cation
The number of 91 peptides sequences was converted to SMILE format via Dendrimer Builder (https://dendrimerbuilder.gdb.tools/). The SMILE format of peptide sequences was input to SEA (http://sea.bkslab.org/) and STP (http://www.swisstargetprediction.ch/) with "Homo Sapiens" setting. The number of 357 and 330 targets were identi ed from SEA and STP, respectively (Supplementary Table  S8). Figure 4A displayed that the number of 218 overlapping targets was selected from the two databases. (Supplementary Table S9). Figure 4B showed that the number of 6 overlapping targets (TPSAB1, PSEN1, PSEN2, DPP4, STAT3, and NOS2) was identi ed between the number of AFPs-targets (245 targets from TTD and OMIM databases) (Supplementary Table S10) and overlapping 218 targets.
Signaling pathways responsive to fungal infection on human Figure 5A showed that 6 targets (TPSAB1, PSEN1, PSEN2, DPP4, STAT3, and NOS2) were connected to 3 signaling pathways via KEGG pathway enrichment analysis. Table 3B showed the detailed description of the 3 signaling. The 6 targets (TPSAB1, PSEN1, PSEN2, DPP4, STAT3, and NOS2) were related to the number of 81 peptides (Supplementary Table S11). Figure 5B exhibited the constructed network exposed 87 nodes (81 peptides, 6 targets) and 1,011 edges. Figure 5C displayed that peptide-targets networking analysis via overlapping 6 targets (TPSAB1, PSEN1, PSEN2, DPP4, STAT3, and NOS2) was constructed by STRING, which indicated 6 nodes and 2 edges. Among 3 signaling pathways, activation of Notch signaling pathway was identi ed as a hub signaling pathway through bubble plot. Figure 5D showed that 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). Among the 4 targets, PSEN1 and PSEN2 were connected to 9 peptides (KLCK, KCLK, KALK, KVLK, KLGGK, KAFK, KFGK, KFSK, and KSFK) which might have more e cacy than any other AFPs. Besides, it implies that both PSEN1 and PSEN2 played an essential role in the Notch signaling pathway, in aspects of the host defense system against fungal infection on AMPs-AFPs axis.

Discussion
The SCPs were selected with two rigor criteria: ≤ 500 Dalton and N-, C-terminal cationic amino acid residues. The number of 1,833 SCPs was identi ed, and consecutively, 197 peptides (AMPs), 91 peptides (AMPs-AFPs axis), and 59 peptides (AMPs-AFPs-ACPs axis) were selected. The associated SCPs with signaling pathways are as followed: 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 33 . 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 Differential Expressed Genes (DEGs) 34 . It entails that host responses against bacterial and fungal attack might induce signi cant 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 35 . 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 e cacy of host defense system was directly associated with 30 s. 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.  43 . It implies that TNF can work as a buffer element for immunopotentiation. IL-17 signaling pathway: The knock-out groups of IL-17 are highly susceptible to K. pneumonia infection than IL-17 expression groups 44 . AMPK signaling pathway: Activation of AMPK improves host defense system against bacterial infection. Moreover, AMPK is associated with the innate and adaptive immune system 45 . FoxO signaling pathway: FoxO1 protein is expressed by a bacterial infection, strengthening the epithelial barrier of host cells and induces the recruitment of Tregs (Regulatory T Cells) to activate antibacterial defense 46 . HIF-1 signaling pathway: HIF-1α activation in the hypoxic condition recruits in ammatory-associated cells such as macrophages, neutrophils, and dendritic cells as well as induces offensive cytokine production under bacterial infection 47 . HIF-1 inhibition can be a good strategy to relieve in ammation 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 in ammatory disorders 48 . 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 e cacy of host defense system was directly associated with 6 targets. The result of the KEGG pathway analysis of 6 targets were connected to 3 signaling pathways. Neurotrophin signaling pathway: In ammation signals in microglial cells induce the secretion of neurotrophin that function as mediators of pain 49,50 . It implies that the neurotrophin signaling pathway's inactivation might modulate in ammatory-related proteins' expression level, thereby resolving host defense-induced in ammation. HIF-1 signaling pathway: The deletion of hypoxiaregulated targets are resistant to fungal infection; more importantly, the low-oxygen condition makes fungal virulence attenuate in murine models 51 . Thus, inactivation of HIF-1 might interrupt the fungal penetration and host immune system. Notch signaling pathway: Notch system plays important roles in Th1 and Th2 cell differentiation, and Notch-mediated immune responses are related to T cell development 52 . It supports 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 identi ed based on the rich factor AMPs-AFPs-ACPs axis-target networks exhibited that the therapeutic e cacy of host defense system was directly associated with 4 targets. The result of the KEGG pathway analysis on 4 targets was connected to 1 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 53,54 . Furthermore, hypoxia makes severe conditions under resistance to cancer therapy such as radiation and medication, increasing tumor survival 55 . It suggests that inactivation of a HIF-1 signaling pathway is an optimal strategy for cancer therapy. This work has been focused on immunomodulatory activities of SCPs, which may improve immune defenses and provide key therapeutic agents from large-scale peptides. We have performed the MDT to select promising peptide candidate(s) on the HIF-1 signaling pathway, and hence the standard molecules (static and 1400w) were compared with them. Moreover, we have 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 HIF-1 signaling pathway using two selected peptides is a feasible treatment strategy for enhancing the host defense system.

Conclusion
The uppermost SCPs of AMPs-AFPs-ACPs axis for immunopotentiation were rstly investigated through network pharmacology. The number of 1,833 SCPs was funneled sequentially through peptide screening platform, thereby, the number of 197 SCPs (AMPs), and 91 SCPs (AMPs-AFPs axis) were obtained. The number of 27 SCPs (AMPs-AFPs-ACPs axis) was obtained as nal 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 provides the network of two SCPs, two targets, and one signaling pathway for the host defense system. Consequently, the key ndings on AMPs-AFPs-ACPs axis could be a promising therapeutic strategy for cellular protection against immune disorders.      Figure 1 Work ow of AMPs-AFPs-ACPs axis analysis on network pharmacology.

Supplementary Files
This is a list of supplementary les associated with this preprint. Click to download.