SPUR 2019: Structural Bioinformatics Approaches to Gain Insight on Variant Pathogenicity


Computational modeling in biomedical sciences has become increasingly common, effective, and even necessary. The development of these techniques and the unparalleled increase of computational power has contributed to making biology a more computationally and data-centered discipline. They have also increased interest in using modelling and simulation in developing in silico methods like molecular dynamics simulations and protein structure prediction, which are essential to structural bioinformatics. Structural bioinformatics uses models to study the folding of proteins, their environments, and their interactions. This has been achieved through two major approaches: homology modelling and de novo structure prediction. Homology modeling, also known as template-based modeling, is based upon linking homologous sequences and their corresponding structure models/crystal structures for use as modeling templates. De novo structure prediction is based upon using force fields to compute the likely structures based upon energetic calculations and/or statistical probability of conformations. These developments are significant, because when compared to the number of proteins for which their amino acid sequences are known, there are comparatively very few experimentally determined protein structures. Thus, the vast majority of protein structures are unknown, which limits our mechanistic understanding of biological process. Furthermore, much less is known about the changes in protein structure upon mutations and how this relates to their pathogenicity and/or lack of thereof. We use a large collection of structural bioinformatics approaches to gain insight into the relationship between mutations and pathogenicity and its mechanisms.

This SPUR project is funded by a supplement to the National Library of Medicine Training grant T15LM007124-22; Wendy Chapman (PI), Julio Facelli (co-I).

Student Role

This project is flexible depending on the skillset of the student. He/she will learn how to run an interpret results for multiple computer tools for protein structure prediction, docking, homology, molecular dynamics simulations, etc. used in the field of structural bioinformatics. 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 & Benefits

The student will learn the basic principles of structural bioinformatics and its application to solve relevant biomedical problems. 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

Biomedical Informatics
School of Medicine

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.