## 3 Results and Discussion
In the case of hairpins, IGLOO found a more favorable minimum than the ones obtained by BH (+17.52 kcal/mol for HPN1 and +1.41 kcal/mol for HPN2) and the HYBRID algorithm (+7.18 kcal/mol for HPN1 and +3.5 kcal/mol for HPN2). The performance with respect to the low-energy minima exhibiting helices diverges from that of the hairpins, with the BH method finding the best minimum; the best structures found by IGLOO and the HYBRID algorithm having energies 1.14 kcal/mol and 11.89 kcal/mol higher than the best one of the BH structure, respectively. We note that while the BH method finds low-energy helices, it encounters difficulties in the case of hairpins. The HYBRID algorithm explores the different regions of the PES but seems to perform poorly when it comes to refining the explorations locally. Finally, IGLOO manages to quickly identify diverse regions and perform refined local exploration of these regions, where the best minima in each region were found at similar CPU times during the simultaneous multi-basin exploration of the IGLOO runs. The two-dimensional projection of the minimized conformations issued from df-c-Myb PES explorations is depicted in Figure 4 for three evenly distributed CPU times (\(5\times 10^{4}\), \(10\times 10^{4}\) and \(15\times 10^{4}\) s). The two dimensions of the projection, the \({}^{4}\)C\(\alpha-^{7}\)C\(\alpha\) and \({}^{2}\)C\(\alpha-^{5}\)C\(\alpha\) interatomic distances, were chosen because they allow a clear spatial separation of the structural motifs of interest. Regions corresponding to HPN1, HPN2 and HLX structures can thus be approximately delineated on the 2D projection (ovals). The dark dots present on the \(10\times 10^{4}\) and \(15\times 10^{4}\) s snapshots correspond to minima already located at the CPU time of the previous snapshot, i.e., \(5\times 10^{4}\) and \(10\times 10^{4}\) s respectively. The BH method struggles to explore the PES of df-c-Myb correctly, with some areas visited very infrequently (HPN2) or not at all (HLX) after \(15\times 10^{4}\) s of exploration, while the others are heavily oversampled. Indeed, very long times are needed to explore the conformational space corresponding to the helices, as shown by the \(\sim\)\(3\times 10^{5}\) s needed to locate a low-energy helix (Figure 3). The BH local exploration also sometimes appears to be of poor quality, as evidenced by the high energy of the most stable HPN1 hairpin found by this algorithm, even though the algorithm has sampled a large part of the corresponding PES region. The initial space visited by the HYBRID algorithm is more extended than the one visited by the BH method. After \(5\times 10^{4}\) s of exploration, minima are found in the three zones corresponding to the structural motifs. However, during the subsequent exploration, the algorithm concentrates on areas that have already been explored or those that are close to them, and struggles to extend the exploration to previously unvisited regions. Some areas therefore remain unexplored after \(15\times 10^{4}\) s while others appear to be oversampled. A major weakness of the HYBRID algorithm lies in its inability to properly explore locally: although the zones corresponding to the HPN1, HPN2, and HLX motifs are visited, the energies of the corresponding low-energy isomers found by the algorithm remain relatively high (Figure 3). Finally, the IGLOO algorithm extends its initial exploration to the whole space and then concentrates on certain zones, including those corresponding to hairpins and helices. Its local refinement is also highly efficient, with the energy of the most stable isomer found by this method being the lowest one in the case of HPN1 and HPN2 and competitive for HLX (Figure 3). In summary, the BH method and the HYBRID algorithm struggle to cover the space to be explored, oversample certain areas, and show poor performance relative to local refinement. In contrast, the IGLOO algorithm explores the PES in a comprehensive fashion, focusing iteratively on the relevant basins.