SCPs under 500 Dalton rule
The number of 1,833 peptides with two sufficient 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).
Physicochemical refinement for AMPs
The 1,833 peptides were input in EMBOSS Pepstats (https://www.ebi.ac.uk/Tools/seqstats/emboss_pepstats/) on Charge > 0 or 8 ≤ Isoelectric Point ≤ 12 29. Secondly, PASTA 2.0 (adjusted to "zero") (https://protein.bio.unipd.it/) was utilized to predict the peptide aggregation propensity 30. Thirdly, peptide aggregation was checked by AGGRESCAN (Na4VSS ≥ -40, Na4VSS ≤ 60) (http://bioinf.uab.es/aggrescan/) which was based upon aggregation propensity in vitro. Among 1,833 peptides, the number of 236 peptides were 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), dbAMP (http://140.138.77.240/~dbamp/), DBAASPv3.0 (https://dbaasp.org/prediction/general), and MLAMP (http://www.jci-bioinfo.cn/MLAMP) to discover AMPs. Finally, from the four databases, 197 out of 236 peptides were obtained as suitable for AMPs (Supplementary Table S3).
AMPs-targets identification
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 identified by SEA and STP, respectively (Supplementary Table S4). The number of 242 overlapping targets was also identified 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 AMPs-targets (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 identified 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 refinement 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 defined as AMPs and AFPs with dual-efficacy for enhancement of host defense system (Supplementary Table S7).
AFPs-targets identification
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 identified 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 identified 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 identified 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 efficacy 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.
Cancer-related targets and ACPs-targets identification
TTD and OMIM selected the number of 4,247 cancer-related targets (Supplementary Table S12). Figure 6A exhibited that the number of 4 out of 6 AFP-responsive targets was overlapped with the 4,247 cancer-related targets. Figure 6B showed that 2 targets (STAT3 and NOS2) were targeted to only HIF-1 signaling pathway via KEGG pathway enrichment analysis. Table 3C showed the detailed description of the signaling. Figure 6C exhibited that the 2 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 6D showed that peptide- targets networking analysis via overlapping 4 targets (PSEN1, DPP4, STAT3, NOS2) was constructed by STRING (6 nodes and 2 edges). Figure 6E exhibited that only two targets (STAT3 and NOS2) are related directly to HIF-1 signaling pathway. 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 axis.
MDS on HIF-1 signaling pathway for host defense system
The ultimate signaling pathway, HIF-1 signaling pathway, was connected to STAT3 (.pdb ID: 6TLC) and NOS2 (.pdb ID: 4NOS): The number of 8 peptides (KPIK, KPVK, KVPK, HPIK, KAFK, KFGK, KSFK, and KFSK) were targeted to STAT3 target, additionally, the number of 19 peptides (RVVK, HMCK, KMCH, HVTK, KCMH, KIIK, KVIK, KILK, KVLK, KALK, KIVK, KIGK, KAIGK, KIAGK, KAGVK, KAGIK, KAGLK, KIGGK, and KVGGK) were targeted to NOS2 target. Table 4 displayed the physicochemical properties of the 27 peptides. The number of 8 peptides was targeted to STAT3 (.pdb ID: 6TLC) and their priorities are as follows: HPIK (-7.3 kcal/mol); KAFK (-7.1 kcal/mol); KPIK (-7.0 kcal/mol); KPVK (-6.8 kcal/mol); KVPK (-6.8 kcal/mol); KFGK (-6.8 kcal/mol); KSFK (-6.7 kcal/mol); and KFSK (-6.4 kcal/mol). Figure 7A showed that “HPIK” peptide was the strongest affinity on STAT3 (.pdb ID: 6TLC) in HIF-1 signaling pathway among 8 peptides. Table 5 displayed its detailed information. Likewise, 19 peptides was targeted to NOS2 (.pdb ID: 4NOS), their priorities are as follows: HVTK (-6.6 kcal/mol); KILK (-6.4 kcal/mol); KAGVK (-6.1 kcal/mol); KIGGK (-6.0 kcal/mol); KAGLK (-5.8 kcal/mol); KAIGK (-5.6 kcal/mol); HMCK (-5.5 kcal/mol); KIAGK (-5.5 kcal/mol); KVIK (-5.5 kcal/mol); KALK (-5.5 kcal/mol); RVVK (-5.4 kcal/mol); KIIK (-5.4 kcal/mol); KIVK (-5.4 kcal/mol); KMCH (-5.3 kcal/mol); KVGGK (-5.3 kcal/mol); KCMH (-5.1 kcal/mol); KVLK (-5.1 kcal/mol); and KAIGK (-5.0 kcal/mol). Figure 7B showed that “HVTK” peptide was the strongest affinity on NOS2 (.pdb ID: 4NOS) in HIF-1 signaling pathway among 19 peptides. Table 6 showed its detailed information. This result showed that the uppermost promising peptides to strengthen immune system against cancer were “HPIK” on STAT3 (.pdb ID: 6TLC) and “HVTK” on NOS2 (.pdb ID: 4NOS).
MDS of positive controls on HIF-1 signaling pathway
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 31. 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 1400W (PubChem ID: 1433), which could inhibit U87MG cells (brain tumor cell) 32. 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.