A Novel Statistical Potential for Protein Beta-Sheets Prediction

dc.contributor.advisorMa, Jianpeng
dc.contributor.committeeMemberNordlander, Peter J.
dc.contributor.committeeMemberRaphael, Robert M.
dc.creatorYu, Linglin
dc.date.accessioned2014-10-16T18:08:35Z
dc.date.available2014-10-16T18:08:35Z
dc.date.created2014-05
dc.date.issued2014-04-25
dc.date.submittedMay 2014
dc.date.updated2014-10-16T18:08:35Z
dc.description.abstractOne of the most long-term challenging problems in biophysics studies for both computational scientists and experimentalists is protein structure prediction, whose goal is to obtain three-dimensional native protein structure from one-dimensional sequence. In protein structure prediction problems, a fundamental problem is Beta-sheets structure prediction. Though more than 85% of experimentally solved proteins contain Beta-sheet structures, limited methods have been found to rapidly and accurately predict the folded conformations. In this study, we proposed a novel statistical potential, named NP-Beta, to predict the protein Beta-sheet structure only based on the sequence information. We included three kinds of potential terms in NP-Beta, i.e. the self-packing term, the pair interacting term and the lattice term. The number of hydrogen bonds in Beta-sheets is also considered as a potential component, corresponding to a global penalty of the potential function. Computational tests show that the new statistical potential has an outstanding performance on native structure recognition from decoys comparing to the Beta-sheet specific potentials in literature. We will apply the potential to improve the prediction of Beta-strand arrangement and registration for beta proteins.
dc.format.mimetypeapplication/pdf
dc.identifier.citationYu, Linglin. "A Novel Statistical Potential for Protein Beta-Sheets Prediction." (2014) Master’s Thesis, Rice University. <a href="https://hdl.handle.net/1911/77586">https://hdl.handle.net/1911/77586</a>.
dc.identifier.urihttps://hdl.handle.net/1911/77586
dc.language.isoeng
dc.rightsCopyright is held by the author, unless otherwise indicated. Permission to reuse, publish, or reproduce the work beyond the bounds of fair use or other exemptions to copyright law must be obtained from the copyright holder.
dc.subjectStatistical potential
dc.subjectBeta-Sheets
dc.titleA Novel Statistical Potential for Protein Beta-Sheets Prediction
dc.typeThesis
dc.type.materialText
thesis.degree.departmentApplied Physics
thesis.degree.disciplineNatural Sciences
thesis.degree.grantorRice University
thesis.degree.levelMasters
thesis.degree.nameMaster of Science
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