Analyzing Phylogenetic Trees

        Phylogenetic trees are one of the most useful tools a scientist can use for understanding evolutionary patterns. At first, they look complicated and confusing, with branches clustered everywhere and seemingly random numbers on lines. However, once you learn how to read them, they become a great visual way to understand relationships between species. In my paper, I used phylogenetic trees to compare diptericin sequences across different fly species.

A phylogenetic tree reveals how closely related different sequences are. Sequences that branch close together are more similar, while sequences separated by longer branches diverged more severely. For example, if several Diptericin copies from the same species appeared in a tight cluster, that pattern suggests recent duplication. If copies from one species appear in different parts of the tree, that suggests an older and more complicated evolutionary history.

I also compared gene trees to species tree. A species tree shows the evolutionary relationships between the fly species, while a gene tree shows the relationships between the Diptericin sequences. These trees are not always identical. A gene can be duplicated, lost or evolve at different rates. This nuance is why analyzing both trees is essential for researchers to uncover patterns.

In my paper, I concluded that it is likely that Diptericin B was the most widely conserved paralog and was likely the most similar to the ancestral form of Diptericin. Diptericin A and Diptericin C appeared in more scattered groups that showed duplication in certain lineages, suggesting that the gene evolved differently in different species. 

Analyzing phylogenetic trees made me realize that evolution is much more complicated than one branch from ancestor to modern species. A tree can show conservation, duplication and divergence, all at once. In the case of Diptericin, the trees helped reveal how a small protein changed across insects and how these changes reflected different evolutionary pressures.

Studying Diptericin

        My research focused on analyzing the evolutionary history of Diptericin, an antimicrobial peptide found in insects. I studied the relationships between Diptericin A, Diptericin B, and Diptericin C to better understand how these related peptides diversified over time. One of the most interesting parts of the analysis was learning how many different techniques can be used to study a single peptide. By combining sequence analysis, phylogenetic trees, and 3D modeling, I was able to examine Diptericin from both an evolutionary and structural perspective. 

The first step to my analysis was gathering the Diptericin amino acid sequences. There were three main paralogs: Diptericin A, Diptericin B, and Diptericin C. Once collected, the sequences were aligned using MAFFT and AliView. This was the first technique I used to help organize the amino acids and match them. After the sequences were aligned, the second piece of technology I used was a program called IQ-TREE. IQ-TREE creates phylogenetic trees by analyzing how the amino acids are lined up and any inconsistencies between diptericins. The trees showed how the Diptericin sequences were related to each other. Some copies from the same species were clustered together, suggesting recent duplication. Other copies were more spread out, suggesting older divergence. I used iTOL to view the trees as it made it easier to compare patterns more clearly.

Another important part of the research was AlphaFold modeling which predicted the 3D structure of proteins. This program was especially useful as I was not only interested in the full sequence, but also specific regions like the pore. Being able to differentiate structures in the protein was essential as each had a specific purpose, for example, the pore was used to disrupt bacterial membranes. By identifying the regions, I could build more focused trees based only on that functional part.

My favorite part about learning these techniques was seeing how closely biology and technology are connected. Sequence alignments showed changes in amino acids, phylogenetic trees revealed evolutionary relationships, and AlphaFold used artificial intelligence to predict protein structure. Together, these methods made it possible to study how Diptericin diversified across insects and how gene duplication may have shaped immune defense in flies. This research also showed me that tools like AlphaFold are not limited to insect evolution. Similar technologies can be used to study many areas of biology, including human evolutionary history. Overall, studying Diptericin helped me understand how modern technology is changing biological research by making questions about evolution, structure, and function easier to explore.