Comput. Biol. Med. - Improving protein secondary structure prediction using a multi-modal BP method.

Tópicos

{ sequenc(1873) structur(1644) protein(1328) }
{ method(2212) result(1239) propos(1039) }
{ model(2656) set(1616) predict(1553) }
{ data(1714) softwar(1251) tool(1186) }
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{ imag(1947) propos(1133) code(1026) }
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{ take(945) account(800) differ(722) }
{ framework(1458) process(801) describ(734) }
{ clinic(1479) use(1117) guidelin(835) }
{ extract(1171) text(1153) clinic(932) }
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Resumo

Methods for predicting protein secondary structures provide information that is useful both in ab initio structure prediction and as additional restraints for fold recognition algorithms. Secondary structure predictions may also be used to guide the design of site directed mutagenesis studies, and to locate potential functionally important residues. In this article, we propose a multi-modal back propagation neural network (MMBP) method for predicting protein secondary structures. Using a Knowledge Discovery Theory based on Inner Cognitive Mechanism (KDTICM) method, we have constructed a compound pyramid model (CPM), which is composed of three layers of intelligent interface that integrate multi-modal back propagation neural network (MMBP), mixed-modal SVM (MMS), modified Knowledge Discovery in Databases (KDD()) process and so on. The CPM method is both an integrated web server and a standalone application that exploits recent advancements in knowledge discovery and machine learning to perform very accurate protein secondary structure predictions. Using a non-redundant test dataset of 256 proteins from RCASP256, the CPM method achieves an average Q(3) score of 86.13% (SOV99=84.66%). Extensive testing indicates that this is significantly better than any other method currently available. Assessments using RS126 and CB513 datasets indicate that the CPM method can achieve average Q(3) score approaching 83.99% (SOV99=80.25%) and 85.58% (SOV99=81.15%). By using both sequence and structure databases and by exploiting the latest techniques in machine learning it is possible to routinely predict protein secondary structure with an accuracy well above 80%. A program and web server, called CPM, which performs these secondary structure predictions, is accessible at http://kdd.ustb.edu.cn/protein_Web/.

Resumo Limpo

method predict protein secondari structur provid inform use ab initio structur predict addit restraint fold recognit algorithm secondari structur predict may also use guid design site direct mutagenesi studi locat potenti function import residu articl propos multimod back propag neural network mmbp method predict protein secondari structur use knowledg discoveri theori base inner cognit mechan kdticm method construct compound pyramid model cpm compos three layer intellig interfac integr multimod back propag neural network mmbp mixedmod svm mms modifi knowledg discoveri databas kdd process cpm method integr web server standalon applic exploit recent advanc knowledg discoveri machin learn perform accur protein secondari structur predict use nonredund test dataset protein rcasp cpm method achiev averag q score sov extens test indic signific better method current avail assess use rs cb dataset indic cpm method can achiev averag q score approach sov sov use sequenc structur databas exploit latest techniqu machin learn possibl routin predict protein secondari structur accuraci well program web server call cpm perform secondari structur predict access httpkddustbeducnproteinweb

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