## 3 Results and Discussion
### Representing the ABFPs with a Reduced Subset  
#### 3.3.1 The Selection of the Best Representative Subset  
The original space of 174 ABFPs, illustrated by the HSPN model at the optimal cutoff of 0.65 (Figure 4B), can be further simplified by applying the scaffold extraction algorithm implemented in the StarPep toolbox. This algorithm allows the topological simplification of the network by removing nodes with equal or similar values of centrality measures but retaining those that still share a local similarity below a certain cutoff. As the reduction of ABFPs intends to keep the HSPN topology and properties, it is applied to all type of nodes, from the most central to the atypical ones (singletons). The reduction was performed by ranking the harmonic and hub-bridge centrality values of the nodes and by applying similarity cutoffs from relaxed criteria (retaining all peptides sharing < 0.9) to more restrictive similarities (<0.45 of sequence identity). This step produces several subsets for each centrality metric (Table 3 and Supplementary File S3). Figure 5: METNs’ visualization using the Fruchterman–Reingold layout. Red nodes represent the metadata, while the ABFPs are in gray color. (**A**) METN built with the source database information. (**B**) METN built with the origin information. As mentioned before, the best subset produced by the HSPN model and representing the original space should be composed of a minimum number of ABPFs with a coverage of the original space of <50%. However, the coverage is not the unique criterion for selecting a representative subset of the original space; the distribution of the HSPN model in the bi-dimensional (2D) space should also be considered. An effective subset should have topological representativeness over all the network, representing connected communities, isolated communities and even singletons. In this sense, a subset extracted under the criterion of only one centrality measure might not fulfil the expected 2D coverage. Thus, we decided to fuse the information of the HC and HB centralities, since they rank the network nodes according to their position using different definitions. Thus, the union of the subsets 6, 7 and 8, which are highlighted in bold in Table 3, was evaluated. Subsets 6, 7 and 8 display a coverage of <50% with a low of number of ABFPs, but are promising for their union (HC \(\cup\) HB) at the same cutoff value. It is notable that the union between two subsets includes the common peptides (intersection) and the singular peptides from each subset. The union of subsets 6, 7 and 8 resulted in fasta files (Supplementary File S3) containing 85, 66 and 52 ABFPs, representing 49%, 38% and 30% of the HSPN model, respectively. The Supplementary File S3 also contains the 221 ABFPs registered in StarPepDB, and the 174 used to generate the HSPNs. Finally, the subsets 6, 7 and 8 from each centrality metric as well as their resulting fusion (HC \(\cup\) HB) were overlapped on the HSPN model to evaluate their 2D coverage/spatial distribution (Figure S1). In the three subsets, the union of the centrality measures (HC \(\cup\) HB) at the evaluated similarity cutoffs displayed a better 2D spatial distribution on the HSPN model. Particularly, the HC \(\cup\) HB from subset 7 showed the best trade-off, considering the lowest number of peptides with the best 2D coverage (Figure S1). Figure 6 summarizes the overlapping of the HC \(\cup\) HB from subset 6, 7 and 8 with the HSPN model. The overlapped subsets are represented by small black nodes over the colored nodes that represent the communities in the HSPN model. Additionally were among the ten most relevant ABFPs, as ranked by the HC and HB centralities (Table 1). Its noteworthy that other ABFPs not identified among the top-ranked in the HSPN model were brought up significantly when constructing the HSPN with 66 representatives, e.g., starPep_00522 and starPep_00514 (Figure 7A,B and Supplementary File S4). On the other hand, 5 out of the 20 singletons appeared in the representative subset.