Poster #65 - Lindsey Riggs

A toolkit for the analysis of contiguous regions of hydrophobicity in a protein sequence

Lindsey Riggs[1], Connor Pitman[1], Ezry Santiago-McRae[1], Ruchi Lohia[2], Ryan Lamb[1], Kaitlin Bassi[1], Thomas T. Joseph[3], Matthew E.B. Hansen[4], and Grace Brannigan[1,5]* [1]Center for Computational and Integrative Biology, Rutgers University-Camden, 201 Broadway, 08103, NJ, USA [2]Department of Cancer Biology, Perelman School of Medicine, University of Pennsylvania, 3400 Civic Center Blvd, 19104, PA, USA [3]Department of Anesthesiology and Critical Care, Perelman School of Medicine, University of Pennsylvania, JMB 305, 3620 Hamilton Walk, 19104, PA, USA [4]Department of Genetics, Perelman School of Medicine, University of Pennsylvania, 3400 Civic Center Blvd, 19104, PA, USA [5]Department of Physics, Rutgers University-Camden, 201 Broadway, 08103, NJ, USA

Current bioinformatics tools can characterize sequences using features such as charge, sequence conservation, or sequence patterns, but they do not identify hydrophobic modules that may be relevant for functional annotation or mutation analysis. Therefore, we developed the algorithm, blobulation, to identify contiguous regions of hydrophobicity in a protein sequence, which can be used for large-scale data analysis. Applying blobulation to human genetic variation data, we discovered that disease-associated mutations are enriched in stretches of hydrophobic regions compared to non-hydrophobic regions [1]. These results highlighted the significance of hydrophobic stretches in human proteins. To make blobulation an easily accessible tool for different user types, we implemented this algorithm into three tools: a command-line interface, a Visual Molecular Dynamics plug-in, and a web tool. These tools are used for detecting, visualizing, and characterizing hydrophobic modularity and allow us to define the local context around each residue in a sequence. Here, we present examples of how a user could use the toolkit and the information revealed by using the blobulation algorithm. The blobulator web tool can be found at www.blobulator.branniganlab.org, and the source code with a pip installable command line tool, as well as the VMD plugin with installation instructions, can be found on GitHub at www.GitHub.com/BranniganLab/blobulator. [1] R Lohia, M.E.B. Hansen, and G. Brannigan. PNAS, 2022.