Determining and differentiating protein-biopolymer interactions

Yalamanchili, Geethika

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Description

Title

Determining and differentiating protein-biopolymer interactions

Author(s)

Yalamanchili, Geethika

Issue Date

2017-12-08

Director of Research (if dissertation) or Advisor (if thesis)

Rao, Christopher

Doctoral Committee Chair(s)

Rao, Christopher

Committee Member(s)

• Sinha, Saurbah
• Shukla, Diwakar
• Sing, Charles

Department of Study

Chemical & Biomolecular Engr

Discipline

Chemical Engineering

Degree Granting Institution

University of Illinois at Urbana-Champaign

Degree Name

Ph.D.

Degree Level

Dissertation

Keyword(s)

• Biofuels
• Promoters
• Machine learning
• Random forest
• Support vector machines (SVM)
• Selex
• Bioinformatics
• Motif detection
• Expression profiling
• Readout mechanism

Abstract

The goal of my research is to understand how proteins selectively bind biopolymers. This problem is closely examined by quantitative modeling of the readout mechanism of protein binding to DNA, determining the specific DNA sequences recognized by transmembrane receptors and resolving the effect of enzymes on polysaccharides. I have developed both computational and experimental tools that identify various factors governing the protein- biopolymer interactions. Below, I list the major projects that I worked on during the course of my PhD. Promoter engineering aims at defining gene expression patterns, which in turn define the behavior of the cells. To date, promoter engineering has been done by trail and error methods experimentally, which is labor intensive and makes it difficult to identify the controlling parameters. Hence it is important to resort to more robust computational methods to make the process less probabilistic. In the past, several computational models have tried to characterize the promoter functionality and strength based on the sequence of the core binding regions (-10, -35, extended -10, discriminator region), but have not been successful. This leads us to believe that various other factors are involved in characterizing the promoter activity. In this work, we try to uncover the importance of shape features and flanking regions in differentiating the functionality of the promoter regions. We used the experimentally determined promoters regions of σ70 and σ 38 holoenzyme as our database. This work has important applications for promoter design in bacterial genome engineering. Chapters 1-4 talk about the work done on this project. Algal bioenergy systems are the future of the bio fuel industry. They promise several advantages over terrestrial biomass – faster growth rates, no land issues, avoid freshwater usage etc. However, the major obstacle in using macro-algae as an everyday fuel lies in the limited knowledge regarding the enzymatic degradation of its major sugars – Alginate and Laminarin by alginate lyases and laminarinases respectively. My work focuses on overcoming this limitation by characterizing several alginate lyases and laminarinases from 3 different bacteria (Vibrio splendidus 12B01, Vibrio splendidus 13B01 and Vibrio breoganii IC10) to develop a concoction of lyases by determining their general working conditions, effectiveness of working, mode of action and hence understanding the specific roles of each lyase in the breakdown pipeline of the sugars. Work done on this project is written in Chapters 5-9. Chemotaxis is a primitive behavioral system by which bacteria travel towards better environments. It is a well-studied mechanism in the model organism Bacillus subtilis (B.subtilis) that can perform chemotaxis towards 20 L-amino acids normally found in proteins. Recently in our lab, we found concrete evidence that B.subtilis also undergoes DNA taxis, i.e, movement in response to DNA gradient. We determined the receptor McpC to be responsible for this phenomenon. SELEX experiments are carried out to determine the McpC binding motif in B.subtilis. However, the major challenge lies in processing the millions of reads produced by SELEX, resolving the low quality reads and data integration of the filtered high quality reads. My work focuses on developing computational models to overcome this challenge and hence determining the McpC binding site. Chapters 10-11 talk about the work done on this project.

Graduation Semester

2017-12

Type of Resource

text

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Copyright and License Information

Copyright 2017 Geethika Yalamanchili

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