## 3 Results and Discussion
number, the better their activity) in a dipeptide chain. Several dipeptides were composed of tryptophan (W; 11 peptides out of 23), asparagine (N), and/or glutamine (Q). Compared to other amino acids, these three residues contain \"additional\" nitrogen. Thus, the presence of one of the above-mentioned amino acids promoting the DPP IV-inhibitory activity can be explained by the descriptor defined as \"nitrogen count2\" (the higher the number, the more favorable the impact on dipeptide bioactivity). Three dipeptides (RP, VR, WR) were composed of arginine (R). Although the presence of guanidine (R) increases the number of nitrogens in a dipeptide chain, the statistical parameters (see Table 3), as well as the equation (Figure 2a), indicated that the number of guanidine groups should be reduced (or eliminated) to improve the DPP IV-inhibitory effect of dipeptide. Moreover, RP and WR peptides--numbers 22 and 40 in the plot (see Figure 3)--were located out of the confidence interval line, whereas VR (number 31) was found in the confidence interval line (dashed line in Figure 3). Some of the dipeptides were built up of proline (P), a residue containing a five-atom aliphatic ring. According to the statistics (see Table 3), the lesser the number of such rings in the peptide chain, the stronger the impact on the DPP IV-inhibitory effect. Figure 5: Backbones and structural features of peptides, summarized in Table 2: (1) peptide backbone; (2) hydrogen donors (hydrogen bond donors); (3) guanidine group; (4) nitrogen atoms; (5) methyl groups; (6) rings containing 5 atoms; (7) Csp\({}^{3}\) bonded to 3 C atoms (tertiary carbon atoms). _Appl. Sci._**2023**, _13_, 12935 \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**No.1** & **Sequence** & **ID 2** & **IC50** & **Log IC50** &
\begin{tabular}{c} **Predicted** \\ **log IC50** 3** \\ \end{tabular} \\ \hline  
P & 8521 & 3170.00 & 3.50 & 3.16 \\ \hline \hline \end{tabular} \({}^{1}\) Numbers used in Figure 3. 2 ID in the BIOPEP-UWM database of bioactive peptides (see Section 2). 3 Denoted as Log IC50 (ERM).  
\end{table} 
Table 3: Observed and predicted logIC50 values obtained for dipeptide DPP IV inhibitors. The analysis of tripeptide DPP IV inhibitors showed that the following 16 tripeptides out of the 33 sequences analyzed had comparable values of the observed and predicted logIC\({}_{50}\) (ERM variant): APG, GPA, PPG, PPL, WRF, WRG, WRH, WRI, WRL, WRP, WRQ, WRR, WRS, WRT, WRY, and WWW (see Table 4 and Figure 4). As was mentioned above, due to the minor impact of such descriptors as the number of -CH\({}_{3}\) groups squared (\"methyl count\({}^{2}\)\"), as well as the number of sp\({}^{3}\) carbon atoms bound to the other three carbon atoms (\"Csp\({}^{3}\) bonded to 3 C\"), they were not further considered as factors affecting the DPP IV-inhibitory activity of tripeptides. Finally, the bioactivity of tripeptides was found to be related to their molecular weight (MW). Moreover, based on the normalized coefficients (see Table 3), it can be concluded that the higher molecular weight of a tripeptide is more beneficial to its DPP IV-inhibitory effect. When looking first at the sequences of tripeptides with comparable values of the predicted and observed logIC\({}_{50}\), the majority of them contained tryptophans (W), prolines (P), and/or tyrosines (Y), which made them heavier than the other tripeptide sequences. Nevertheless, the next step of the research was to create the non-linear function of three descriptors related to molecular weight (see Figure 6a), with the plot (see Figure 6b) showing the relationship between the predicted logIC\({}_{50}\) and the molecular weight of a tripeptide. This function was applied for the first time to analyze the impact of specific descriptors of DPP IV inhibition by peptides. The plot enables the distinguishing of the minimum and the maximum of the function.