Development of pseudosymmetry analysis to identify key residues in transport protein mechanisms

Transport proteins, despite making up approximately 10% of the human genome, are understudied. Multiple computational tools exist to analyse them, but no tool exists to predict which residues are likely to be involved in substrate binding or in the mechanism of the transporter. In this thesis, I present GAPS-Pro, a tool to analyse transport proteins based on their pseudosymmetrical properties and to predict the function of individual amino acid residues based on sequence information alone. The mechanisms and structures of transport proteins are symmetrical, however their substrates and coupling ions are not. Therefore, asymmetry has had to evolve within the substrate binding site of the transporter for it to adapt to different functions. However, the symmetric mechanism and structure needs to be maintained such that the transporter remains functional. By identifying strongly symmetric or strongly asymmetric residues, it is possible to predict the function of a residue from sequence information alone. Parameters for GAPS-Pro are developed for three major superfamilies of transporter: the mitochondrial carrier family, the major facilitator superfamily, and the amino acid polyamine organocation superfamily. The software is then tested on transporters for which there is a large amount of experimental data to test the effectiveness of the procedure. GAPS-Pro is then used to select residues for analysis of the human phosphate carrier and the human citrate carrier by mutagenesis, and its versatility is shown by using it to predict the sequence of the ancestral mitochondrial transporter. Work is then presented which uses other computational techniques, such as phylogenetic analysis, to study transporters in a variety of protists. Finally, a discussion about the future directions of GAPS-Pro and the current limitations is presented.

Name dc:type.qualificationname

Doctor of Philosophy (PhD)

Level dc:type.qualificationlevel

Doctoral

Grantor dc:publisher.institution

University of Cambridge

Year dc:date.issued

2023

Author dc:creator

• King, Alannah

Advisor dc:contributor.advisor

• Kunji, Edmund

• Computational Biology
• Evoution
• Membrane Proteins
• Novel Method
• Symmetry
• Transport Proteins

Licence

• https://apollo8-f-pro.lib.cam.ac.uk/bitstreams/a0cc4b24-fece-42d3-b975-2ca460364c4d/download
• https://www.rioxx.net/licenses/all-rights-reserved/

Language dc:language

eng

DOI dc:identifier.doi

https://doi.org/10.17863/CAM.104390

OAI identifier oai:identifier

oai:www.repository.cam.ac.uk:1810/361645