Biochemistry, Biophysics, and Structural Biology Commons^™

A Pipeline To Generate Deep Learning Surrogates Of Genome-Scale Metabolic Models, Achilles Rasquinha

Department of Computer Science and Engineering: Dissertations, Theses, and Student Research

Genome-Scale Metabolic Models (GEMMs) are powerful reconstructions of biological systems that help metabolic engineers understand and predict growth conditions subjected to various environmental factors around the cellular metabolism of an organism in observation, purely in silico. Applications of metabolic engineering range from perturbation analysis and drug-target discovery to predicting growth rates of biotechnologically important metabolites and reaction objectives within dierent single-cell and multi-cellular organism types. GEMMs use mathematical frameworks for quantitative estimations of flux distributions within metabolic networks. The reasons behind why an organism activates, stuns, or fluctuates between alternative pathways for growth and survival, however, remain relatively unknown. GEMMs …

Use Of Clustering Techniques For Protein Domain Analysis, Eric Rodene

Department of Computer Science and Engineering: Dissertations, Theses, and Student Research

Next-generation sequencing has allowed many new protein sequences to be identified. However, this expansion of sequence data limits the ability to determine the structure and function of most of these newly-identified proteins. Inferring the function and relationships between proteins is possible with traditional alignment-based phylogeny. However, this requires at least one shared subsequence. Without such a subsequence, no meaningful alignments between the protein sequences are possible. The entire protein set (or proteome) of an organism contains many unrelated proteins. At this level, the necessary similarity does not occur. Therefore, an alternative method of understanding relationships within diverse sets of proteins …

Clustering And Classification Of Multi-Domain Proteins, Neethu Shah

Department of Computer Science and Engineering: Dissertations, Theses, and Student Research

Rapid development of next-generation sequencing technology has led to an unprecedented growth in protein sequence data repositories over the last decade. Majority of these proteins lack structural and functional characterization. This necessitates design and development of fast, efficient, and sensitive computational tools and algorithms that can classify these proteins into functionally coherent groups.

Domains are fundamental units of protein structure and function. Multi-domain proteins are extremely complex as opposed to proteins that have single or no domains. They exhibit network-like complex evolutionary events such as domain shuffling, domain loss, and domain gain. These events therefore, cannot be represented in the …