key: cord-0027668-iha6ifdb
authors: Oh, Ki-Kwang; Adnan, Md.; Cho, Dong-Ha
title: Uncovering a Hub Signaling Pathway of Antimicrobial-Antifungal-Anticancer Peptides’ Axis on Short Cationic Peptides via Network Pharmacology Study
date: 2022-02-12
journal: Int J Mol Sci
DOI: 10.3390/ijms23042055
sha: db292d0343b72f49ced98503b71fc3ef5053ca9c
doc_id: 27668
cord_uid: iha6ifdb

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.

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 . 

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 ).

The 1833 peptides were entered in EMBOSS Pepstats (https://www.ebi.ac.uk/Tools/seqstats/emboss_pepstats/) (Accessed on 2 May 2021) on Charge > 0 or 8 ≤ Isoelectric Point ≤ 12 [34] . Secondly, PASTA 2.0 (adjusted to "zero") (https://protein.bio.unipd.it/) (Accessed on 2 May 2021)was utilized to predict the peptide aggregation propensity [35] . Thirdly, peptide aggregation was checked by AGGRESCAN (Na4VSS ≥ −40, Na4VSS ≤ 60) (http://bioinf.uab.es/aggrescan/) (Accessed on 4 May 2021), which was based upon aggregation propensity in vitro. Among 1833 peptides, the number of 236 peptides was selected (Supplementary Table S2 ). Fourthly, the 236 peptide sequences were input to four platforms including ADAM (http://bioinformatics.cs.ntou.edu.tw/adam/svm_tool.html) (Accessed on 6 May 2021), dbAMP (http://140.138.77.240/~dbamp/) (Accessed on 8 May 2021), DBAASPv3.0 (https://dbaasp.org/prediction/general) (Accessed on 11 May 2021), and MLAMP (http://www.jci-bioinfo.cn/MLAMP) (Accessed on 13 May 2021) to discover AMPs. Finally, from the four databases, 197 out of 236 peptides were obtained as suitable for AMPs (Supplementary Table S3 ). 

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 ).

The 1833 peptides were entered in EMBOSS Pepstats (https://www.ebi.ac.uk/Tools/ seqstats/emboss_pepstats/) (Accessed on 2 May 2021) on Charge > 0 or 8 ≤ Isoelectric Point ≤ 12 [34] . Secondly, PASTA 2.0 (adjusted to "zero") (https://protein.bio.unipd.it/) (Accessed on 2 May 2021)was utilized to predict the peptide aggregation propensity [35] . Thirdly, peptide aggregation was checked by AGGRESCAN (Na4VSS ≥ −40, Na4VSS ≤ 60) (http://bioinf.uab.es/aggrescan/) (Accessed on 4 May 2021), which was based upon aggregation propensity in vitro. Among 1833 peptides, the number of 236 peptides was selected (Supplementary Table S2 Table S4 ). The number of 242 overlapping targets was also identified from the two databases (Supplementary Table S5 ). Finally, the number of 30 targets overlapped between the number of 959 AMPs' targets (extracted from the TTD and OMIM databases) ( Figure 2B ), (Table 1),  (Supplementary Table S6) , and the overlapping 242 targets were selected.

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. 

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. 

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. 

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 ). 

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 ).

The number of 91 peptides' sequences was converted to SMILE format via Dendrimer Builder (https://dendrimerbuilder.gdb.tools/) (Accessed on 21 May 2021). The SMILE format of peptide was input to SEA (http://sea.bkslab.org/) (Accessed on 24 May 2021) and STP (http://www.swisstargetprediction.ch/) (Accessed on 26 May 2021) with "Homo Sapiens" setting. The numbers of 357 and 330 targets were identified from SEA and STP, respectively (Supplementary Table S8 ). Figure 4A displays that the number of 218 overlapping targets was selected from the two databases. (Supplementary Table S9 ). The number of six overlapping targets (TPSAB1, PSEN1, PSEN2, DPP4, STAT3, and NOS2) was identified between the number of AFPs' targets (245 targets from the TTD and OMIM databases) ( Figure 4B ), (Supplementary Table S10 ) and the overlapping 218 targets.

The number of 91 peptides' sequences was converted to SMILE format via Den drimer Builder (https://dendrimerbuilder.gdb.tools/) (Accessed on 21 May 2021). Th SMILE format of peptide was input to SEA (http://sea.bkslab.org/) (Accessed on 24 Ma 2021) and STP (http://www.swisstargetprediction.ch/) (Accessed on 26 May 2021) wit "Homo Sapiens" setting. The numbers of 357 and 330 targets were identified from SEA an STP, respectively (Supplementary Table S8 ). Figure 4A displays that the number of 21 overlapping targets was selected from the two databases. (Supplementary Table S9 ). Th number of six overlapping targets (TPSAB1, PSEN1, PSEN2, DPP4, STAT3, and NOS2 was identified between the number of AFPs' targets (245 targets from the TTD and OMIM databases) ( Figure 4B ), (Supplementary Table S10 ) and the overlapping 218 targets. 

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. 

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) 

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. 

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). 

The greatest affinity peptide on STAT3 (PDB ID: 6TLC) was "HPIK" (−7.3 kcal/mol). A representative inhibitor of STAT3 is stattic (PubChem ID: 2779853), which interrupts the tumor cell growth by inhibiting lymphoma activity [36] . Thus, MDS of stattic (PubChem ID: 2779853) was selected to compare with "HPIK". Consequently, the docking score of stattic (PubChem ID: 2779853) was −6.1 kcal/mol. The "HPIK" affinity on STAT3 (PDB ID: 6TLC) was better than stattic (PubChem ID: 2779853). The higher affinity peptide on NOS2 (PDB ID: 4NOS) was "HVTK" (−6.6 kcal/mol). A selective inhibitor of NOS2 is 1400 W (PubChem ID: 1433), which could inhibit U87MG cells (brain tumor cell) [37] . Hence, MDS of 1400 W (PubChem ID: 1433) was carried out to compare with "HVTK"; subsequently, the docking score of 1400 W (PubChem ID: 1433) was −5.2 kcal/mol. 

The greatest affinity peptide on STAT3 (PDB ID: 6TLC) was "HPIK" (−7.3 kcal/mol). A representative inhibitor of STAT3 is stattic (PubChem ID: 2779853), which interrupts the tumor cell growth by inhibiting lymphoma activity [36] . Thus, MDS of stattic (Pub-Chem ID: 2779853) was selected to compare with "HPIK". Consequently, the docking score of stattic (PubChem ID: 2779853) was −6.1 kcal/mol. The "HPIK" affinity on STAT3 (PDB ID: 6TLC) was better than stattic (PubChem ID: 2779853). The higher affinity peptide on NOS2 (PDB ID: 4NOS) was "HVTK" (−6.6 kcal/mol). A selective inhibitor of NOS2 is 1400W (PubChem ID: 1433), which could inhibit U87MG cells (brain tumor cell) [37] . Hence, MDS of 1400W (PubChem ID: 1433) was carried out to compare with "HVTK"; subsequently, the docking score of 1400W (PubChem ID: 1433) was −5.2 kcal/mol.

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

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 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 selected peptides were assessed for AMP evaluation utilizing in silico analysis. Firstly, EMBOSS Pepstats (https://www.ebi.ac.uk/Tools/seqstats/emboss_pepstats/) (Accessed on 11 June 2021) [60] were used to identify the physicochemical properties of peptides. Secondly, aggregation of peptides was filtered with rigor on both PASTA 2.0 (https://protein.bio.unipd.it/) (Accessed on 12 June 2021) [35] and AGGRESCAN (http: //bioinf.uab.es/aggrescan/) (Accessed on 12 June 2021) [34] . Subsequently, final AMPs were selected by ADAM (http://bioinformatics.cs.ntou.edu.tw/adam/svm_tool.html) (Accessed on 13 June 2021) [61] , dbAMP (http://140.138.77.240/~dbamp/) (Accessed on 13 June 2021) [62] , DBAASP v3.0 (https://dbaasp.org/prediction/general) (Accessed on 13 June 2021) [63] , and MLAMP (http://www.jci-bioinfo.cn/MLAMP) (Accessed on 13 June 2021) [64] .

The final AMPs' sequences with FASTA format were input to the Antifp database (https://webs.iiitd.edu.in/raghava/antifp/predict3.php) (Accessed on 15 June 2021) [65] . The final AFPs were selected by the classifier of AntipDS1_binary_model1, AntipDS1_ binary_model2, and AntipDS1_binary_model3.

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] .

Based on SMILES (format), targets related to selected peptides were extracted from both SEA (http://sea.bkslab.org/) (Accessed on 17 June 2021) [67] and STP (http://www. swisstargetprediction.ch/) (Accessed on 17 June 2021) [68] with "Homo Sapiens" setting. The overlapping targets in the peptide(s)-target(s) networks between SEA and STP were identified by VENNY 2.1 (https://bioinfogp.cnb.csic.es/tools/venny/) (Accessed on 19 June 2021) [69] . The bacterial-responsive targets on human were obtained with "bacterial/germ/bacilli" from both the TTD (http://db.idrblab.net/ttd/) (Accessed on 19 June 2021) [70] and OMIM (https://www.omim.org/) (Accessed on 20 June 2021) [71] databases. After that, the overlapping targets between peptide(s)-target(s) and bacterial-responsive targets were identified by VENNY 2.1 (https://bioinfogp.cnb.csic.es/tools/venny/) (Accessed on 21 June 2021).

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.

Based on SMILES, targets associated with selected peptides were identified via both SEA (http://sea.bkslab.org/) (Accessed on 22 June 2021) and STP (http://www. swisstargetprediction.ch/) (Accessed on 22 June 2021) with "Homo Sapiens" setting. The overlapping targets in peptide-target network between SEA and STP were identified by VENNY 2.1 (https://bioinfogp.cnb.csic.es/tools/venny/) (Accessed on 23 June 2021). The fungal targets associated with human were obtained from both TTD (http://db.idrblab.net/ttd/) (Accessed on 23 June 2021) and OMIM (https://www.omim.org/) (Accessed on 23 June 2021), entering as "fungal". The overlapping targets between peptide-targets and fungalrelated targets were identified by VENNY 2.1 (https://bioinfogp.cnb.csic.es/tools/venny/) (Accessed on 24 June 2021).

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.

Based on SMILES, targets associated with selected peptides were identified via both SEA (http://sea.bkslab.org/) (Accessed on 24 June 2021) and STP (http://www. swisstargetprediction.ch/) (Accessed on 24 June 2021) with "Homo Sapiens" setting. The cancer-related targets on human were obtained with "cancer/tumor/neoplasia/carcinoma" from TTD (http://db.idrblab.net/ttd/) (Accessed on 25 June 2021) and OMIM (https: //www.omim.org/) (Accessed on 25 June 2021). The overlapping targets between peptidetargets and cancer-related targets were identified by VENNY 2.1 (https://bioinfogp.cnb. csic.es/tools/venny/) (Accessed on 25 June 2021).

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.

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.

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.

The final peptides were docked on target proteins, processing autodock4 by setting up four energy ranges and eight exhaustiveness ranges as defaults to obtain 10 different poses of ligand molecules [75] . The 2D binding interactions were constructed through LigPlot+ v.2.2 (https://www.ebi.ac.uk/thornton-srv/software/LigPlus/) (Accessed on 30 June 2021) [76] .

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. 

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Antibacterial, antifungal, anticancer activities and structural bioinformatics analysis of six naturally occurring temporins

Peptide design for antimicrobial and immunomodulatory applications

A short artificial antimicrobial peptide shows potential to prevent or treat bone infections

Short cationic peptidomimetic antimicrobials

Small cationic antimicrobial peptides delocalize peripheral membrane proteins

The cationic cell-penetrating peptide CPPTAT derived from the HIV-1 protein TAT is rapidly transported into living fibroblasts: Optical, biophysical, and metabolic evidence

Cationic peptides: A new source of antibiotics

Antimicrobial peptides in animals and their role in host defences

Alarmins: Chemotactic activators of immune responses

A Re-evaluation of the Role of Host Defence Peptides in Mammalian Immunity

Cathelicidin-inspired antimicrobial peptides as novel antifungal compounds

Immune defence against Candida fungal infections

Antimicrobial and host-defense peptides as new anti-infective therapeutic strategies

Can innate immunity be enhanced to treat microbial infections?

Antimicrobial peptides with selective antitumor mechanisms: Prospect for anticancer applications

From antimicrobial to anticancer peptides. A review

Bavituximab plus paclitaxel and carboplatin for the treatment of advanced non-small-cell lung cancer

Plant antimicrobial peptides as potential anticancer agents

A comprehensive review on current advances in peptide drug development and design

Cell-Penetrating Peptides in Diagnosis and Treatment of Human Diseases: From Preclinical Research to Clinical Application

Active ingredients and mechanisms of Phellinus linteus (grown on Rosa multiflora) for alleviation of Type 2 diabetes mellitus through network pharmacology

Network pharmacology approach to bioactive chemical compounds identified from Lespedeza bicolor lignum methanol extract by GC-MS for amelioration of hepatitis

Network pharmacology of bioactives from Sorghum bicolor with targets related to diabetes mellitus

Traditional Chinese medicine network pharmacology: Theory, methodology and application. Chin

Cell penetrating peptides in ocular drug delivery: State of the art

Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings

Ligand efficiency as a guide in fragment hit selection and optimization

The influence of drug-like concepts on decision-making in medicinal chemistry

Transcriptome analysis of psacothea hilaris: De novo assembly and antimicrobial peptide prediction

PASTA 2.0: An improved server for protein aggregation prediction

STAT3 inhibition is a therapeutic strategy for ABC-like diffuse large B-cell lymphoma

NOS2 inhibitor 1400 W induces autophagic flux and influences extracellular vesicle profile in human glioblastoma U87MG cell line

Cationic host defense (antimicrobial) peptides

Analysis of differentially expressed genes in bacterial and fungal keratitis

Relaxin modulates proinflammatory cytokine secretion from human decidual macrophages

Glucagon reduces airway hyperreactivity, inflammation, and remodeling induced by ovalbumin

A Player and Coordinator: The Versatile Roles of Eosinophils in the Immune System

Prolactin-induced activation of nuclear factor κB in bovine mammary epithelial cells: Role in chronic mastitis

Prolactin modulation of immune and inflammatory responses

Estrogen and recurrent UTI: What are the facts?

Estrogen supports urothelial defense mechanisms

Role of tumour necrosis factor (TNF) in host defence against tuberculosis: Implications for immunotherapies targeting TNF

IL-17 signaling in host defense and inflammatory diseases

AMP-Activated protein kinase and host defense against infection

Mucosal Immunity and the FOXO1 Transcription Factors

Inhibition of contact sensitivity by farnesylthiosalicylic acid-amide, a potential rap1 inhibitor

Inflammatory signals induce neurotrophin expression in human microglial cells

Neurotrophins: Peripherally and centrally acting modulators of tactile stimulus-induced inflammatory pain hypersensitivity

Hypoxia and fungal pathogenesis: To air or not to air

Notch system in the linkage of innate and adaptive immunity

Hypoxia inducible factor pathway inhibitors as anticancer therapeutics

Targeting HIF-1 for cancer therapy

Role of hypoxia in cancer therapy by regulating the tumor microenvironment

EMBOSS: The European Molecular Biology Open Software Suite

A large-scale structural classification of Antimicrobial peptides

dbAMP: An integrated resource for exploring antimicrobial peptides with functional activities and physicochemical properties on transcriptome and proteome data

Database of antimicrobial/cytotoxic activity and structure of peptides as a resource for development of new therapeutics

Imbalanced multi-label learning for identifying antimicrobial peptides and their functional types

Silico Approach for Prediction of Antifungal Peptides

Dendrimer building toolkit: Model building and characterization of various dendrimer architectures

Relating protein pharmacology by ligand chemistry

SwissTargetPrediction: Updated data and new features for efficient prediction of protein targets of small molecules

A Tool for the Comparison and Identification of Candidate Genes Based on Venn Diagrams

Therapeutic target database 2020: Enriched resource for facilitating research and early development of targeted therapeutics

Leveraging knowledge across phenotype-gene relationships

STRING v11: Protein-protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets

Open Babel: An Open chemical toolbox

Software news and updates AutoDock4 and AutoDockTools4: Automated docking with selective receptor flexibility

Anthraquinone Derivatives as an Immune Booster and their Therapeutic Option Against COVID-19

Multiple ligand-protein interaction diagrams for drug discovery