Skip to Main Content
 

Global Search Box

 
 
 
 

Files

File List


ucin1267631564.pdf (2.44 MB)

ETD Abstract Container

Abstract Header

Lipid Accessibility Prediction and Identification of Functional Hotspots in Transmembrane Proteins

Phatak, Mukta
http://rave.ohiolink.edu/etdc/view?acc_num=ucin1267631564

Abstract Details

2010, PhD, University of Cincinnati, Engineering : Biomedical Engineering.
Membrane proteins constitute a major class of drug targets because of their key roles in signal transduction as well as transport of ions and small molecules across cell membrane. Compared to soluble proteins, a relatively small number of high resolution membrane protein structures have been resolved experimentally, underscoring the importance of computational methods for membrane protein structure and function prediction. Towards the bigger goal of predicting 3D structure of a protein, a step-by-step approach to first predict intermediate structural attributes of a protein structure from sequence is typically adopted. Here, for each residue in transmembrane domains (TMDs), the level of exposure to the lipid is captured in terms of the relative lipid accessibility (RLA) and residue depth (RD). We have developed robust sequence-based predictors for RLA in membrane proteins using low-complexity Support Vector Regression (SVR) models capable of learning from a limited number of examples in order to minimize the risk of overfitting. Our results indicate that both RLA and RD can be predicted at the level of about 0.5 CC. Further, based on the predicted RLA (RD) profiles, this work presents applications towards structural and the functional studies on the membrane proteins. We show that RLA and RD predictions developed here are sufficiently accurate and can be used to narrow down the conformational search space in the fold recognition methods towards those models which are consistent with the predicted patterns. Studies of protein-protein interactions are crucial for understanding the protein function in biological systems. In particular, the interactions between membrane proteins are increasingly becoming a focus of intensive experimental and computational research efforts because of their therapeutic potential. Here, we derive a novel signal of interaction sites in TMDs by exploiting the lipid accessibility predictions. We explored two alternative definitions of RLA (RD) that can be derived either from individual chains or complexes of TMDs in order to train two families of predictors with distinct biases. RLA (RD) predictions optimized using data from complexes are more consistent with bound states. Thus, the difference between these sequence-based predictions and the actual values observed in unbound structures, (i.e., prediction “errors” when using the unbound structure as the definition of the true state) provides a new signal of interaction sites in TMDs. While the steadily growing number of resolved structures of membrane proteins will enable further tests of such applications, based on available data we conclude that the development of robust classifiers for the prediction of interaction interfaces in membrane proteins using this approach is feasible. Future extensions and applications are discussed.
Jaroslaw Meller, PhD (Committee Chair)
Jun Ma, PhD (Committee Member)
Michael Wagner, PhD (Committee Member)
100 p.
Bioinformatics
membrane protein; Lipid Accessibility; interaction site prediction

Recommended Citations

Citations

  • Phatak, M. (2010). Lipid Accessibility Prediction and Identification of Functional Hotspots in Transmembrane Proteins [Doctoral dissertation, University of Cincinnati]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1267631564

    APA Style (7th edition)

  • Phatak, Mukta. Lipid Accessibility Prediction and Identification of Functional Hotspots in Transmembrane Proteins. 2010. University of Cincinnati, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=ucin1267631564.

    MLA Style (8th edition)

  • Phatak, Mukta. "Lipid Accessibility Prediction and Identification of Functional Hotspots in Transmembrane Proteins." Doctoral dissertation, University of Cincinnati, 2010. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1267631564

    Chicago Manual of Style (17th edition)

ucin1267631564
513
© 2010, all rights reserved.
This open access ETD is published by University of Cincinnati and OhioLINK.