A Novel Statistical Potential for Protein Beta-Sheets Prediction

One 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.

Name thesis:degree_name

Master of Science

Level thesis:degree_level

Masters

Discipline thesis:degree_discipline

Natural Sciences

Grantor

Rice University

Year dc:date.issued

2014

Author dc:creator

• Yu, Linglin

Advisor dc:contributor.advisor

• Ma, Jianpeng

Committee members dc:contributor.committeemember

• Nordlander, Peter J.
• Raphael, Robert M.

• Statistical potential
• Beta-Sheets

Statement dc:rights

• Copyright 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.

Language dc:language.iso

eng

Handle dc:identifier.uri

https://hdl.handle.net/1911/77586

OAI identifier oai:identifier

oai:repository.rice.edu:1911/77586