Not able to get an action photo – this is a headshot to use.

Author: Kallahan Minor | Major: Biology, Chemistry | Semester: Spring 2025

My research this semester, funded by the Office of Undergraduate Research, focused on computational biology methods to investigate a specific immune response mechanism in soybean plants. In particular, I analyzed the interactions between soybean Plant Elicitor Peptide Receptors (GmPEPRs) and their associated ligands (GmPEPs). Understanding the mechanisms behind these interactions helps in the development of sustainable pest management strategies by boosting natural plant immunity pathways, with the potential to reduce reliance on chemical pesticides in agriculture.

This topic was chosen due to my interest in computational biology, specifically protein modeling, synthetic biology, and drug design, fields I intend to pursue in graduate studies. Having been a part of Dr. Fiona Goggin’s lab for nearly two years, her support made this project possible, providing an introduction into computational biology despite the lab’s primary focus on traditional entomology and plant pathology research.

Throughout this semester, I analyzed structural predictions generated by AlphaFold2, examining 24 possible combinations of soybean receptors (GmPEPR1a, GmPEPR1b, GmPEPR2) and their eight ligands (GmPEP1-6 including two variants). Using the protein visualization software PyMOL, I looked at published critical interactions between the target PEPR and PEP that were previously identified as critical to binding in the Arabidopsis thaliana PEPR homolog AtPEPR1-AtPEP1.

My findings revealed distinct binding patterns among my PEPR-PEP pairs, highlighting PEPR specificity to particular PEPs. GmPEPR1b shows high interaction potential with GmPEP1, GmPEP2, and GmPEP3. GmPEPR1a interactions were scored slightly lower but showed likely binding to GmPEP1, GmPEP2, GmPEP4a, GmPEP5a, and GmPEP5b. GmPEPR2 had the lowest general likelihood scores, but showed the broadest binding potential, showing possible interactions to all PEPs except for GmPEP3.

One of the most significant challenges I faced occurred during some sequence alignments where unexpected frameshift mutations complicated interpretation. This problem was overcome by independently learning of and utilizing bioinformatics tools such as NCBI’s ORF Finder, significantly enhancing my confidence and problem-solving skills.

Throughout my research, Dr. Goggin played a critical role through weekly progress meetings where she assisted in data interpretation, next step planning, and connections to valuable collaborators. Of these collaborators, Dr. Eric Enemark (UAMS Little Rock) provided our structural predictions, Dr. Gunvant Patil (Texas Tech University) conducted large scale mutation analyses across 522 soybean accessions to expand our research scope, and Dr. Rich Adams (Dept. Entomology and Plant Pathology) provided bioinformatics insights.

Due to administrative uncertainties, my planned presentation in February at the Arkansas Bioinformatics Consortium (ArBIC) was postponed. However, I am still enrolled and am eager to present my finding at ArBIC later this year. I also plan to share my project this year at university hosting poster presentation events.

This research project has positively affected my career goals, reinforcing my interest in computational biology and preparing me well for graduate school. Upon graduation this fall, I plan to apply to graduate programs for the Fall 2026 semester, aiming to continue my exploration into computational biology.