## 3 Results and Discussion
### Motif Discovery Assisted by Complex Networks
found that the following motifs \"RIRV,\" \"RIVQRIK,\" and \"IGKEFKR\" appeared with more frequency in 242 ABFPs collected from APD and BaAMP databases with respect to a curated negative set [31], when using the \"MERCI\" software [72]. In this sense, we agree with them in the detection of the \"RIRV\" which was fully integrated in the RIRVR motif detected in cluster 14 by the MAFFT algorithm, and also enriched in the BaAMP dataset. Although \"RIVQRIK\" and \"IGKEFKR\" were not detected as such, we could identify in clusters 15 and among the singletons by the MSA methods, the \"RIV\" and \"FIK\" patterns, which are part of them. These two last three-amino acid motifs were also enriched in the extended dataset. In a previous report, the authors also found that the dipeptides \"IR/RI\", \"WR/RW\", and \"KK\" were the most common among the selected ABFPs [31]. Certainly, these dipeptides are present in the motifs discovered with the intervention of complex networks, at a relatively high frequency. For example, the \"IR/RI\", \"WR/RW\" and \"KK\" dipeptides appear in 9/7, 7/8 and 12 of the total motifs, respectively. In addition to them, the \"RR\" and \"KL\" dipeptides also displayed a similar representation among the motifs. In a recent report, arginine-rich motifs of the antibiofilm activity of peptides designed for sequestering the nucleotide second messenger c-di-GMP, involved in the formation of _P. aeruginosa_ and _K. pneumoniae_ biofilms, were revisited [73]. The key role of the DRR and [RK] RxxD motifs in these sequestering peptides (SP) to specifically bind to c-di-GMP was demonstrated by nuclear magnetic resonance (NMR)-based experiments [18]. These motifs associated with CSPs are difficult to discover using bioinformatics methods, since their sourcing peptides are probably still not registered or underrepresented in databases. However, the peptide R4F4 (RRRRFFFF), with a proven antibiofilm activity on _P. aeruginosa_ through c-di-GMP sequestration [73; 74], bears a more frequent motif (RRRR) among the arginine-rich ABFPs. In fact, \"RRRR\" was detected in our complex network-assisted motif search (Tables 5 and 6). On the other hand, the role of the WWW motif in disrupting preformed biofilms of methicillin-resistant _Staphylococcus aureus_ was elucidated by NMR and arginine scan \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**No** & **Motif** & **Cluster** & **Method** & **Enrichment Ratio** \\ \hline
1 & FWKWA & 4 & MAFFT & (3.0)/(2.83) \\
2 & KNKRK & 7 & MUSCLE & (3.0)/(2.22) \\
3 & [GR]GG & 7 & MAFFT/STREME & (3.0)/(1.90) \\
4 & KVLK & 11 & STREME & (3.0)/(4.0) \\
5 & KRFL & 11 & STREME & (3.0)/(1.8) \\
6 & WWVI & 15 & STREME & (3.0)/(3.0) \\
7 & KR[AKL]JKK & 17 & MUSCLE & (6.0)/(3.0) \\
8 & LRK & 17 & STREME & (3.0)/(1.27) \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Summary of the discovered ABFP motifs per network community (cluster), enriched in two datasets. experiments in 2017 [75]; the WWW motif hardly appears in ABFP databases, being only represented by the designed peptide TetraF2W-RR [76]. As both the motif discovery and the distinction of ABFP hallmarks are highly influenced by peptide databases' composition and by the searching algorithm, here, we provide new ABFP motifs discovered from combining network science with AB- and AF-based computational tools for motif detection. Unlike the study reported in [31], our approach hardly depends on the selection of a negative set to retrieve relevant information from the reported ABFPs; this may represent an advantage, since final outcomes/conclusions could be biased by the selection of a negative set [77]. The motifs listed in 5, 6 and especially in Table 7 are useful for the \"in silico\" generation of peptide libraries addressing antibiofilm and antimicrobial activities, as well as for the optimization of antibiofilm candidates for clinical purposes.