Finding Hidden Structure in Protein Sequences
Not all proteins reveal their function through visible structure alone. Some must be understood directly from their sequence. This project introduces a computational framework for identifying meaningful modular regions within protein sequences using residue-level properties such as hydrophobicity and charge. By detecting local environments without relying on known secondary structures, the work provides new tools for interpreting mutations and understanding evolutionary relationships.
This research was presented at the Graduate Poster Exhibition during the 2025 SPARK! (Showcase of Projects, Art, Research, and Knowledge). Developed within the Ph.D. program in Computational and Integrative Biology, the project was completed by Connor Pitman. His work builds on prior computational methods to refine how scientists define local sequence context and analyze coevolution across proteins.
Abstract: Playing with Power – Investigating the Link Between Intra-protein Interactions and Contiguous Hydrophobocity
Though protein structure is the conventional link between protein sequence and function, structure is not directly accessible from the genome for all proteins. Predicting or interpreting the effects of mutations often requires identifying the local sequence context. Since protein structures are modular, the “local sequence” is frequently determined by the local secondary structure element.
Some proteins, such as intrinsically disordered proteins (IDPs), can only be analyzed using their sequences. In cases like these, detecting innate modularity independent of secondary structure elements using only residue-level properties such as hydrophobicity and charge is extremely valuable. We have previously used contiguous hydrophobicity (“blobulation”) to detect local sequence context for analysis of disease-associated mutations.
Here, we detail the blobulation algorithm and demonstrate its ability to detect subsequences associated with hydrophobic environments, such as the core of a globular protein or transmembrane helices. We also present results from a coevolution analysis showing that these hydrophobic subsequences (“blobs”) have evolutionary significance.
We find that pairs of similarly hydrophobic blobs are enriched for coevolving residues. Additionally, within these blobs, the types of coevolving amino acid pairs change depending on the blob containing them. These findings suggest that blobulation provides a meaningful framework for defining the context around coevolving pairs and could be useful in further coevolution studies.
Graduate Poster Exhibition at SPARK!
The Graduate Poster Exhibition celebrates the research and creative work of the graduate community, showcasing everything from prose and code to original research and artistic expression. As part of SPARK! (Showcase of Projects, Art, Research, and Knowledge), a reimagining of Research Week, the exhibition highlights the depth, range, and impact of graduate scholarship and invites the campus community to engage with ideas taking shape across disciplines.
Bridging Disciplines: The Center for Computational and Integrative Biology
The Center for Computational and Integrative Biology (CCIB) at Rutgers–Camden combines experimental and computational methods to address complex biological questions. CCIB offers graduate programs leading to M.S. and Ph.D. degrees, emphasizing a holistic understanding of biological systems from molecular to population levels. The curriculum equips students like Basirat with the skills to conduct innovative research at the intersection of biology, chemistry, computer science, mathematics, and physics.
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