Peptit bağ açılarını kullanarak yapay sinir ağı tabanlı proteinlerin sekonder yapı tahmini
Estimation of protein secondary structure based on artificial neural networks by using peptide bond angles
- Tez No: 185072
- Danışmanlar: YRD. DOÇ. DR. ALİ KARCI
- Tez Türü: Yüksek Lisans
- Konular: Bilgisayar Mühendisliği Bilimleri-Bilgisayar ve Kontrol, Computer Engineering and Computer Science and Control
- Anahtar Kelimeler: 3URWHLQOHU3HSWLWOHU<DSD\6LQLU$÷ODUÃVII, Proteins, Peptides, Artificial Neural Networks.VIII
- Yıl: 2006
- Dil: Türkçe
- Üniversite: Fırat Üniversitesi
- Enstitü: Fen Bilimleri Enstitüsü
- Ana Bilim Dalı: Bilgisayar Mühendisliği Ana Bilim Dalı
- Bilim Dalı: Belirtilmemiş.
- Sayfa Sayısı: Belirtilmemiş.
Özet
ÖZETYüksek Lisans Tezi<808ù$.+(6$3/$0$7(.1ø./(5øø/(3527(ø1<$3,7$+0ø1øMurat '(0ø5)ÃUDWhQLYHUVLWHVLFen Bilimleri EnstitüsüBilgisayar 0ÂKHQGLVOL÷L$QDELOLP'DOÃ2006, Sayfa : 82bu bedenleøQVDQ EHGHQL \HU\Â]ÂQGHNL HQ NDUPDúÃN PDNLQHGLU +D\DWÃPÃ] ERyuncaJ|UÂU LúLWLU QHIHV DOÃU \ÂUÂU NRúDU YH ]HYN DOÃUÃ] 9ÂFXGXPX]GDNL KHU úH\ PLOLPHWUHQLQ ELQGHELUL EÂ\ÂNOÂ÷ÂQGHNL KÂFUHOHUGHQ ROXúXU +ÂFUHOHU YÂFXGXQ KDQJL SDUoDVÃQà ROXúWXUX\RUODUVD EXE|OJHGHLKWL\DoGX\XODQER\XWDYHúHNOHVDKLSROXrlar. %X\DSÃLoHULVLQGH|QHPOLELU\HUHVDKLSRODQ SURWHLQOHU LVH HúVL] WDVDUÃPODUÃ\OD LQVDQÃQ DNOÃQà KD\UHWH GÂúÂUHFHN NDGDU NDUPDúÃN ELU RNDGDUGDNXVXUVX]ELU\DSÃ\DVDKLSWLU8QXWPDPDN JHUHNLU NL EXJÂQ GÂQ\DGD \Â]ELQOHUFH ELOLP DGDPà SURWHLQ NRQXVXndaoDOÃúPDNWDGÃU %XQD UD÷PHQ EX NXVXUVX] PDNURPROHNÂOOHU KDOD DNÃOODUD GXUJXQOXN YHUHFHNNDGDUPXKWHúHP\DSÃVÃ\ODNHúIHGLOHPH\HQ\|QOHUL\OHDUDúWÃUPDFÃODUÃQLOJLOHULQLoHNPHNWHGLUOHU<DSÃODUà LWLEDUL\OH SURWHLQOHU ELULQFLO LNLQFLO ÂoÂQFÂO YH G|UGÂQFÂO \DSà ROPDN Â]HUHdört düzeye sahiptirler. %LULQFLO\DSÃDPLQRDVLWDUGÃúÃOODUÃQGDQROXúDQGÂ]]LQFLU\DSÃGÃUøNLQFLO\DSÃE|OJHVHO úHNLOOHQPHOHUGHQ PH\GDQDJHOHQ\DSÃGÃU hoÂQFÂO\DSÃLNLQFLO\DSÃODUÃQELUDUD\Dveya daha fazla polipeptit zincirinin bir araya gelmesiJHOPHVLLOHROXúXU'|UGÂQFÂO\DSÃLVHEirLOHDUDGDROXúDQHWNLOHúLPOHUVRQXFXQGDPH\GDQDJHOHQ\DSÃGÃU%X WH]GH SURWHLQOHULQ LNLQFLO \DSÃODUÃQÃQ Â]HULQGH oDOÃúÃOPÃú YH LNLQFLO \DSÃ\à RUWD\Dkoyabilmek için peptitlerdeki merkezil karbon DWRPODUà DUDVÃQGDNL ED÷ DoÃODUÃQGDQ \ROD oÃNDUDNyapay sinirLNLQFLO \DSÃQÃQ WDKPLQL \DSÃOPD\D oDOÃúÃOPÃúWÃU %XQX JHUoHNOHúWLUHELOPHN LoLQD÷ODUÃQGDQ ID\GDODQÃOPÃúWÃU øOHUL EHVOHPHOL \DSD\ VLQLU Dֈ PRGHOL Â]HULQGH /HYHQEHUJ-0DUJTXDUGW |÷UHQPH DOJRULWPDVà LOH VRQXoODU HOGH HGLOPLúWLU 6RQXoWD Âo IDUNOà WHVW GDWDVÃüzerinde %YHRUDQODUÃQGDEDúDUÃHOGHHGLOPLúWLU
Özet (Çeviri)
ABSTRACTMaster ThesisESTIMATION OF PROTEIN SECONDARY STRUCTURE BASED ONARTIFICAL NEURAL NETWORKS BY USING PEPTIDE BOND ANGLESMurat '(0ø5)ÃUDWhQLYHUVLW\Institute of Natural and Applied ScienceComputer Engineering Division2006, Pages : 82Human body is the most complex machine on the earth. Thaht?s why, we can see, hear,breathe, run and enjoy along our life. All the parts of our body constitute from cells. Cells havedifferent sizes and shapes with respect to organs they constitute. There are complex andamazing important structures in the cells and they are called proteins.They are a lot of researchers study on the proteins on the earth. Although theseresearchers, these perfect macromolecules have a lot of undiscovered aspects, and they attractresearchers.Proteins have four different structures such as primary, secondary, tertiary andquaternary. The primary structure consist of amino acids sequences and chain. Proteinsecondary structure refers to certain common repeating structures found in proteins.There are two types of secondary structures: alpha-helix and beta-pleated sheet. Tertiarystructure is the full 3-dimensional folded structure of polypeptide chain. Quaternarystructure is only present if there is more than one polypeptide chain. With multiplepolypeptide chains, quaternary structure is their interconnecitons and organization.In tihs thesis, we studied on the secondary structures of proteins and in order to depictthe secondary structure, we tried to estimate the angle between polypeptide bonds betweenalpha-carbons. At this aim, we used artifical neural networks. The results obatined by usingfeedforward neural network with Levenberg-Marquardt learning algorithm. At theconsequence, we have obtained success rates for three different data set as 70%, 68.42% and49.12%.
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