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Pregled bibliografske jedinice broj: 72089

Prediction of secondary protein structure by means of the electron-ion interaction pseudopotential model


Štambuk, Nikola; Konjevoda, Paško
Prediction of secondary protein structure by means of the electron-ion interaction pseudopotential model // Book of Abstracts 3rd Research Workshop and Graduate School on Physics and Chemistry at Quantum System / Nagy, Agnes (ur.).
Deberecen: Department of Theoretical Physics, University of Debrecen, 2001. (predavanje, međunarodna recenzija, sažetak, znanstveni)


CROSBI ID: 72089 Za ispravke kontaktirajte CROSBI podršku putem web obrasca

Naslov
Prediction of secondary protein structure by means of the electron-ion interaction pseudopotential model

Autori
Štambuk, Nikola ; Konjevoda, Paško

Vrsta, podvrsta i kategorija rada
Sažeci sa skupova, sažetak, znanstveni

Izvornik
Book of Abstracts 3rd Research Workshop and Graduate School on Physics and Chemistry at Quantum System / Nagy, Agnes - Deberecen : Department of Theoretical Physics, University of Debrecen, 2001

Skup
3rd Research Workshop and Graduate School on Physics and Chemistry at Quantum System

Mjesto i datum
Debrecen, Mađarska, 14.05.2001. - 18.05.2001

Vrsta sudjelovanja
Predavanje

Vrsta recenzije
Međunarodna recenzija

Ključne riječi
ionisation potential; machine learning; mRNA; nucleotide; neural network; protein folding

Sažetak
The information necessary for the protein biosynthesis is contained in its messenger RNA nucleotide sequence. The aim of the paper is to provide a model of secondary protein folding based on the nucleotide string analysis, i.e. purine and pyrimidine base recombinations. It is shown that ionisation potential of purine and pyrimidine bases, with respect to their electron donor-acceptor relationships enables the extraction of fast computational algorithms that define secondary protein structure with > 90% precision. The method has been verified by means of the artificial neural network and machine learning procedures, on a test set of 160 alpha- and beta-protein folds from standard protein databases. The algorithms presented in this study enable simple, fast and accurate prediction of the secondary protein folding types on a personal computer.

Izvorni jezik
Engleski

Znanstvena područja
Temeljne medicinske znanosti



POVEZANOST RADA


Projekti:
00981108

Ustanove:
Institut "Ruđer Bošković", Zagreb

Profili:

Avatar Url Nikola Štambuk (autor)


Citiraj ovu publikaciju:

Štambuk, Nikola; Konjevoda, Paško
Prediction of secondary protein structure by means of the electron-ion interaction pseudopotential model // Book of Abstracts 3rd Research Workshop and Graduate School on Physics and Chemistry at Quantum System / Nagy, Agnes (ur.).
Deberecen: Department of Theoretical Physics, University of Debrecen, 2001. (predavanje, međunarodna recenzija, sažetak, znanstveni)
Štambuk, N. & Konjevoda, P. (2001) Prediction of secondary protein structure by means of the electron-ion interaction pseudopotential model. U: Nagy, A. (ur.)Book of Abstracts 3rd Research Workshop and Graduate School on Physics and Chemistry at Quantum System.
@article{article, author = {\v{S}tambuk, Nikola and Konjevoda, Pa\v{s}ko}, editor = {Nagy, A.}, year = {2001}, pages = {101}, keywords = {ionisation potential, machine learning, mRNA, nucleotide, neural network, protein folding}, title = {Prediction of secondary protein structure by means of the electron-ion interaction pseudopotential model}, keyword = {ionisation potential, machine learning, mRNA, nucleotide, neural network, protein folding}, publisher = {Department of Theoretical Physics, University of Debrecen}, publisherplace = {Debrecen, Ma\djarska} }
@article{article, author = {\v{S}tambuk, Nikola and Konjevoda, Pa\v{s}ko}, editor = {Nagy, A.}, year = {2001}, pages = {101}, keywords = {ionisation potential, machine learning, mRNA, nucleotide, neural network, protein folding}, title = {Prediction of secondary protein structure by means of the electron-ion interaction pseudopotential model}, keyword = {ionisation potential, machine learning, mRNA, nucleotide, neural network, protein folding}, publisher = {Department of Theoretical Physics, University of Debrecen}, publisherplace = {Debrecen, Ma\djarska} }




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