Project Background
Molecular mimicry is one mechanism by which an infectious agent may trigger an autoimmune disease in a human subject and occurs when foreign- and self-peptides contain similar epitopes that activate an autoimmune response in a susceptible individual (https://www.sciencedirect.com/science/article/pii/S0896841123001245). In previous studies we utilized in silico methods to determine structural properties between pairs of infectious epitopes (EINF) and type 1 diabetes epitopes (ET1D) such as the RMSD, electrostatics, and hydrophobicity of each pair (https://www.computer.org/csdl/proceedings-article/bibm/2023/10385653/1TObwfoLaxO). These studies demonstrated that the Mistry et al.' sequence homology pipeline proves successful for finding epitope pairs that also exhibit structural homology and in identifying epitope pairs that are good candidates for high likelihood of triggering molecular mimicry and deserving further investigation. It also provided evidence that sequence and structural homology might not be the whole picture, and that electrostatic potential and folding must be considered. Still full docking calculations may be necessary to advance the in-silico molecular mimicry predictions. I this summer project we will focus on structural and docking approaches to further understand the molecular mimicry mechanisms for exacerbation and onset of autoimmune diseases beyond type 1 diabetes.
Student Role
The selected student will learn how to run an interpret results for multiple computer tools used in the field of structural bioinformatics for protein structure prediction, docking, homology, molecular dynamics simulations, etc. The student will be able to work as a collaborator in some of the disease-oriented problems in the lab or apply structural bioinformatics techniques to biomedical problem of his/her own interest.
Student Learning Outcomes and Benefits
The student will learn the basic principles and tool used in structural bioinformatics and its application to solve relevant biomedical problems, with special emphasis in the molecular mimicry mechanisms for triggering autoimmune diseases. We expect that the results of the student investigations will contribute to publications and/or conference presentations from our lab in which the student will be listed as a co-author as per usual publication guidelines.
Julio Facelli
Facelli’s research interests are centered in the application of advance computing techniques to solve important problems in the biomedical domain. The projects in his research group use similar computational infrastructure and tools to maximize the synergy among projects, benefiting the students, post docs and faculty in the group who are exposed to a variety of biomedical problems that are addressed by a common set of computational approaches. The principal research projects currently underway in his group are:
Environmental Health Informatics
Big Data Applications to Biomedical Informatics
Protein Structure Prediction to Understand Variant Pathogenicity
Crystal Structure Prediction (CSP) of Pharmaceutical Drugs
Distributed Information Systems for Clinical and Translational Research
"I use a combination of lab meetings and one on one meetings. I do not micro-manage projects, but expect sincere engagement and participation. The student will work within a team of post docs and graduate students with a great deal of experience in bimolecular modeling and in close collaboration with the staff of the Center for High Performance Computing."