Research Mentor(s): Yang Zhang, Professor
Research Mentor School/College/Department: Department of Computational Medicine & Bioinformatics, Michigan Medicine
Presentation Date: Thursday, April 22, 2021
Session: Session 5 (3pm-3:50pm)
Breakout Room: Room 17
Proteins, a class of macromolecules essential to biological processes, are characterized by their structure which directly correlates to functionality. Residues are considered the building blocks of proteins and studying their positions has been crucial in understanding the role a protein plays in a biological system. This research project investigates different measures of residue depth through calculations of RSA, DPX, Residue Depth, HalfSpace Depth, and L1 depth. The L1 depth function surpasses other residue structure predictors in that it can obtain positions of residues buried under the protein surface, or nested in pockets. In addition, the function uses O(N) time complexity, making it much more efficient than the HalfSpace predictor. Values of depth have strong correlations with properties such as physiochemical propensities, flexibility and polarity. Data was obtained from CULLPDB, Phospho3D, and PLB datasets. After reading in necessary information into an IDE, residue depth of each protein in the datasets and the correlation coefficients between the means of depth values of amino acids and hydrophobicity index of amino acids were calculated through an algorithm written in C++. These results showed that the means of depth values were strongly relative with hydrophobicity of amino acids. In addition, the secondary structures of the residues are also associated with L1 depth values, as residues in sheets are deeper under the surface in comparison to residues in coils and helices.
Authors: Anagha Shah
Research Method: Computer Programming