<?xml version="1.0" encoding="UTF-8"?><?xml-stylesheet type="text/xsl" href="static/style.xsl"?><OAI-PMH xmlns="http://www.openarchives.org/OAI/2.0/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/ http://www.openarchives.org/OAI/2.0/OAI-PMH.xsd"><responseDate>2026-09-18T19:03:36Z</responseDate><request verb="GetRecord" identifier="oai:repository.rice.edu:1911/77586" metadataPrefix="dim">https://repository.rice.edu/server/oai/request</request><GetRecord><record><header><identifier>oai:repository.rice.edu:1911/77586</identifier><datestamp>2024-01-11T20:57:42Z</datestamp><setSpec>com_1911_8299</setSpec><setSpec>col_1911_13110</setSpec></header><metadata><dim:dim xmlns:dim="http://www.dspace.org/xmlns/dspace/dim" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns:doc="http://www.lyncode.com/xoai" xsi:schemaLocation="http://www.dspace.org/xmlns/dspace/dim http://www.dspace.org/schema/dim.xsd">
   <dim:field mdschema="dc" element="contributor" qualifier="advisor">Ma, Jianpeng</dim:field>
   <dim:field mdschema="dc" element="contributor" qualifier="committeeMember">Nordlander, Peter J.</dim:field>
   <dim:field mdschema="dc" element="contributor" qualifier="committeeMember">Raphael, Robert M.</dim:field>
   <dim:field mdschema="dc" element="creator">Yu, Linglin</dim:field>
   <dim:field mdschema="dc" element="date" qualifier="accessioned">2014-10-16T18:08:35Z</dim:field>
   <dim:field mdschema="dc" element="date" qualifier="available">2014-10-16T18:08:35Z</dim:field>
   <dim:field mdschema="dc" element="date" qualifier="created">2014-05</dim:field>
   <dim:field mdschema="dc" element="date" qualifier="issued">2014-04-25</dim:field>
   <dim:field mdschema="dc" element="date" qualifier="submitted">May 2014</dim:field>
   <dim:field mdschema="dc" element="date" qualifier="updated">2014-10-16T18:08:35Z</dim:field>
   <dim:field mdschema="dc" element="identifier" qualifier="citation">Yu, Linglin. &amp;quot;A Novel Statistical Potential for Protein Beta-Sheets Prediction.&amp;quot; (2014) Master’s Thesis,  Rice University.  &amp;lt;a href=&amp;quot;https://hdl.handle.net/1911/77586&amp;quot;&amp;gt;https://hdl.handle.net/1911/77586&amp;lt;/a&amp;gt;.</dim:field>
   <dim:field mdschema="dc" element="identifier" qualifier="uri">https://hdl.handle.net/1911/77586</dim:field>
   <dim:field mdschema="dc" element="description" qualifier="abstract">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.</dim:field>
   <dim:field mdschema="dc" element="format" qualifier="mimetype">application/pdf</dim:field>
   <dim:field mdschema="dc" element="language" qualifier="iso">eng</dim:field>
   <dim:field mdschema="dc" element="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.</dim:field>
   <dim:field mdschema="dc" element="subject">Statistical potential</dim:field>
   <dim:field mdschema="dc" element="subject">Beta-Sheets</dim:field>
   <dim:field mdschema="dc" element="title">A Novel Statistical Potential for Protein Beta-Sheets Prediction</dim:field>
   <dim:field mdschema="dc" element="type">Thesis</dim:field>
   <dim:field mdschema="dc" element="type" qualifier="material">Text</dim:field>
   <dim:field mdschema="thesis" element="degree" qualifier="department">Applied Physics</dim:field>
   <dim:field mdschema="thesis" element="degree" qualifier="discipline">Natural Sciences</dim:field>
   <dim:field mdschema="thesis" element="degree" qualifier="grantor">Rice University</dim:field>
   <dim:field mdschema="thesis" element="degree" qualifier="level">Masters</dim:field>
   <dim:field mdschema="thesis" element="degree" qualifier="name">Master of Science</dim:field>
   <dim:field mdschema="others" element="access-status">open.access</dim:field>
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