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Yapay Sinir Ağları Kullanılarak Protein Katlanması Tanıma

Year 2023, Volume: 16 Issue: 2, 95 - 105, 30.04.2023
https://doi.org/10.17671/gazibtd.1141468

Abstract

Proteinler uzun aminoasit zincirlerinden oluşur ve vücut kimyasını düzenlemekle birlikte hücrelerin yapısı ve aralarındaki iletişim için öneme sahiptir. Bir proteinin hücre bazındaki görevini gerçekleştirebilmesi için, molekülü hücredeki hedefiyle etkileşime girebilecek üç boyutlu yapıya dönüştüren bir bükülme süreci olan katlanma işlemini gerçekleştirmesi gerekir. Sıcaklık, ağır metaller veya kimyasal durumlar gibi etkenler proteinlerin yanlış katlanmasına sebep olabilir. Yanlış katlanan proteinler, vücuttaki görevini yerine getiremez. Alzaymır, kistik fibrozis, deli dana hastalığı gibi hastalıklara sebep olabilir. Protein katlanması tanıma işlemi, biyologlar açısından bir problem olarak değerlendirilir. Literatürde yer alan şablon tabanlı yaklaşımlara karşın yapay sinir ağları, protein katlanması probleminin çözümüne yönelik yüksek başarım gösterir. Yapay sinir ağları, ele alınan problemin çözümü için geniş veri kümelerinde yer alan ve problemin çözümüne katkı sağlayacak bilgi kazancı yüksek özellikleri kullanan bir hesaplama tekniğidir. Bu çalışmada SCOPe 2.06, SCOPe 2.07, SCOPe 2.08 veri setleri kullanılarak şablon tabanlı yaklaşımlardan elde edilen sonuçların yapay sinir ağı yöntemi ile birleştirilerek protein katlanması tanıma işlemi gerçekleştirilmiştir. Gerçekleştirilen deneyler sonucunda yapay sinir ağı yönteminin katkısı ile literatürde yer alan sonuçların iyileştirildiği görülmüştür. Bu çalışma ile biyoinformatik alanında protein katlanması tanıma probleminin çözümüne yeni bir yaklaşım sunularak literatüre katkı sağlanması amaçlanmıştır.

Thanks

Yazarlar, TÜBİTAK ULAKBİM, Yüksek Başarım ve Grid Hesaplama Merkezine (TRUBA), Bursa Teknik Üniversitesi Yüksek Performanslı Hesaplama Laboratuvarına teşekkürlerini sunar.

References

  • M. Levitt, C. Chothia, “Structural patterns in globular proteins.” Nature, 261(5561), 552-558, 1976.
  • P. Sudha, D. Ramyachitra, P. Manikandan, “Enhanced artificial neural network for protein fold recognition and structural class prediction”, Gene Reports, 12, 261-275, 2018.
  • J. S. Butler, S. N. Loh, “Folding and misfolding mechanisms of the p53 DNA binding domain at physiological temperature”, Protein science, 15(11), 2457-2465, 2006.
  • Y. Kaya, R. Tekin, “Epileptik nöbetlerin tespiti için aşırı öğrenme makinesi tabanlı uzman bir sistem”, Bilişim Teknolojileri Dergisi, 5(2), 33-40, 2012.
  • A. Haltaş, A. Alkan, “Medline veritabanı üzerinde bulunan tıbbi dokümanların kanser türlerine göre otomatik sınıflandırılması”, Bilişim Teknolojileri Dergisi, 9(2), 181, 2016.
  • G. Akgül, A.A. Çelik, Z.E. Aydın, Z. K. Öztürk, “Hipotiroidi Hastalığı Teşhisinde Sınıflandırma Algoritmalarının Kullanımı”, Bilişim Teknolojileri Dergisi, 13(3), 255-268, 2020.
  • A. Şenol, Y. Canbay, M. Kaya, “Makine Öğrenmesi Yaklaşımlarını Kullanarak Salgınları Erken Evrede Tespit Etme Alanındaki Eğilimler”, Bilişim Teknolojileri Dergisi, 14(4), 2021.
  • M. AlQuraishi, “Machine learning in protein structure prediction”, Current opinion in chemical biology, 65, 1-8, 2021.
  • C. Ekenna, S. Thomas, N.M. Amato, “Adaptive local learning in sampling based motion planning for protein Folding”, BMC systems biology, 10(2), 165-179, 2016.
  • J. Zhu, H. Zhang, S.C. Li, C. Wang, L. Kong, S. Sun, D. Bu, ”Improving protein fold recognition by extracting fold-specific features from predicted residue–residue contacts” , Bioinformatics, 33(23), 3749-3757, 2017.
  • K. Yan, J. Wen, J. X. Liu, Y. Xu, B. Liu, “Protein fold recognition by combining support vector machines and pairwise sequence similarity scores”, IEEE/ACM Transactions on Computational Biology and Bioinformatics, 18(5), 2008-2016, 2020.
  • Y. Yang, E. Faraggi, H. Zhao, Y. Zhou, “Improving protein fold recognition and template-based modeling by employing probabilistic-based matching between predicted one-dimensional structural properties of query and corresponding native properties of templates”, Bioinformatics, 27(15), 2076-2082, 2011.
  • S. Makigaki, T. Ishida, “Improvement of template-based protein structure prediction by using chimera alignment”, In Proceedings of the 2018 8th International Conference on Bioscience, Biochemistry and Bioinformatics, Tokyo, Japonya, 32-37, Ocak 2018.
  • J. Söding, A. Biegert, A.N. Lupas, “The HHpred interactive server for protein homology detection and structure prediction” , Nucleic acids research, 33(2), 244-248,2005.
  • B. Liu,Y. Zhu, “ProtDec-LTR3.0: protein remote homology detection by incorporating profile-based features into learning to rank”, Ieee Access, 7, 102499-102507, 2019.
  • L. Wei, Q. Zou, “Recent progress in machine learning-based methods for protein fold Recognition”, International journal of molecular sciences, 17(12), 2118, 2016.
  • M. Corrales, P. Cusco, D. R. Usmanova, H.C. Chen, N.S. Bogatyreva, G.J. Filion, D.N. Ivankov, “Machine learning: how much does it tell about protein folding rates?”, PloS one, 10(11), 2015.
  • F. Noé, G. De Fabritiis, C. Clementi, “Machine learning for protein folding and Dynamics”, Current opinion in structural biology, 60, 77-84, 2020.
  • Internet: SCOPe: Structural Classification of Proteins — extended, https://scop.berkeley.edu, 15.05.2022.
  • D. M. Halaby, A. Poupon, J. P. Mornon, “The immunoglobulin fold family: sequence analysis and 3D structure comparisons”, Protein engineering, 12(7), 563-571,1999.
  • T. J. Richmond, F. M. Richards, “Packing of α-helices: Geometrical constraints and contact area”, Journal of molecular biology, 119(4), 537-555, 1978.
  • T. J. P. Hubbard, T. L. Blundell, “Comparison of solvent-inaccessible cores of homologous proteins: definitions useful for protein modelling. Protein Engineering”, Design and Selection, 1(3), 159-171, 1987.
  • J. Rozewicki,, S. Li,, K. M. Amada,D. M. Standley, K. Katoh, “MAFFT-DASH: integrated protein sequence and structural alignment”, Nucleic acids research, 47(W1), W5-W10, 2019.
  • V. Adar, Protein-ligand etkileşimleri, http://www.magum.hacettepe.edu.tr/MMKurs/KURS1Proteinligand.pdf, 24.06.2022.
  • Y. N. Imai, Y. Inoue, L. Nakanishi, K. Kitaura, “Cl–π interactions in protein–ligand complexes”, Protein Science, 17(7), 1129-1137, 2008.
  • R. Rojas, Neural Network A Systematic Introduction, Springer, Heidelberg, Almanya, 1996.
  • M. M. Yılmaz, Periferik sinir defekt onarımında biyolojik kondüit modeli: de-epitelize insan amniyotik membranı ve adipoz kökenli mezenkimal kök hücre tabakası içeren sinir kondüit modelinin sinir iyileşmesine etkisinin değerlendirilmesi, Uzmanlık Tezi, Hacettepe Üniversitesi, Tıp Fakültesi, 2020.
  • J. Ma, J. Tang, “A review for dynamics in neuron and neuronal network”, Nonlinear Dynamics, 89(3), 1569-1578, 2017.
  • A. Eliasy, J. Przychodzen, “The role of AI in capital structure to enhance corporate funding strategies”. Array, 6, 2020.
  • I. H. Sarker, “Deep cybersecurity: a comprehensive overview from neural network and deep learning perspective”, SN Computer Science, 2(3), 1-16, 2021.
  • V. Nair, G. E.Hinton, “Rectified linear units improve restricted boltzmann machines”, Icml, 2010.
  • X. Glorot, A. Bordes, Y. Bengio,” Deep sparse rectifier neural networks”, In Proceedings of the fourteenth international conference on artificial intelligence and statistics, Fort Lauderdale, A.B.D, 315-323, Nisan 2011.
  • I. Goodfellow, Y. Bengio, A. Courville, “Deep learning (adaptive computation and machine learning series”, Cambridge Massachusetts, 321-359, 2011.
  • M. Bağ, Derin öğrenme kullanarak IP üzerinden ses hizmeti veren şebekelerde sahtekarlığa yönelik çağrıların tespiti, Yüksek Lisans Tezi, Ankara Üniversitesi, Fen Bilimleri Enstitüsü, 2019.
  • Y.N.Fu’adah, N.K.C. Pratiwi, M.A. Pramudito, N. İbrahim,”Convolutional neural network (CNN) for automatic skin cancer classification system”, IOP Conf. Ser. Mater. Sci. Eng., 982, 12005, 2020.
  • G. Korkmaz, E. Eroğlu, “Model karmaşıklığının kontrolü”, İktisadi ve İdari Yaklaşımlar Dergisi, 2(2), 146-162, 2020.
  • B. Ö. Başer, M. Yangın, E.S. SARIDAŞ, “Makine öğrenmesi teknikleriyle diyabet hastalığının sınıflandırılması”, Süleyman Demirel Üniversitesi Fen Bilimleri Enstitüsü Dergisi, 25(1), 112-120,2021.
  • Z. Lyu, Y. Yu, B. Samali, M. Rashidi, M. Mohammadi, T.N. Nguyen, A. Nguyen, “Back-propagation neural network optimized by K-fold cross-validation for prediction of torsional strength of reinforced Concrete beam”, Materials, 15(4), 1477, 2022.
  • J. Xu, “Distance-based protein folding powered by deep learning”, Proceedings of the National Academy of Sciences, 116(34), 16856-16865, 2019.
  • C. Li, B. Liu, “MotifCNN-fold: protein fold recognition based on fold-specific features extracted by motif-based convolutional neural networks”, Briefings in Bioinformatics, 21(6), 2133–2141, 2020.
  • A. Villegas-Morcillo, V. Sanchez, A.M. Gomez, “FoldHSphere: deep hyperspherical embeddings for protein fold Recognition”, BMC bioinformatics, 22(1), 1-21, 2021.

Protein Folding Recognition by Artificial Neural Networks

Year 2023, Volume: 16 Issue: 2, 95 - 105, 30.04.2023
https://doi.org/10.17671/gazibtd.1141468

Abstract

Proteins are made up of amino acid chains and are important for the structure of the cells and their communication with each other, while regulating body chemistry. For a protein to perform its function on a cell basis, it must perform the folding process that converts the molecule into a three-dimensional structure that can interact with its target in the cell. Factors such as temperature, heavy metals or chemical conditions may cause proteins to be folded incorrectly. Incorrectly folded proteins cannot perform their role in the body. Protein folding recognition is considered a problem for biologists. Despite the template-based approaches in the literature, artificial neural networks show high performance in solving the protein folding problem. Artificial neural networks are a computational technique that uses high-specification knowledge in large data sets to help solve the problem that is being addressed and will contribute to the solution of the problem. In this study, protein folding recognition was performed by combining the results from template-based approaches with artificial neural network method using the Scope 2.06, Scope 2.07, Scope 2.08 datasets. The results in the literature were improved by the contribution of the artificial neural network method because of the experiments conducted. This study aims to contribute literature by introducing an innovative approach to the solution of the problem of protein folding recognition in the field of bioinformatics.

References

  • M. Levitt, C. Chothia, “Structural patterns in globular proteins.” Nature, 261(5561), 552-558, 1976.
  • P. Sudha, D. Ramyachitra, P. Manikandan, “Enhanced artificial neural network for protein fold recognition and structural class prediction”, Gene Reports, 12, 261-275, 2018.
  • J. S. Butler, S. N. Loh, “Folding and misfolding mechanisms of the p53 DNA binding domain at physiological temperature”, Protein science, 15(11), 2457-2465, 2006.
  • Y. Kaya, R. Tekin, “Epileptik nöbetlerin tespiti için aşırı öğrenme makinesi tabanlı uzman bir sistem”, Bilişim Teknolojileri Dergisi, 5(2), 33-40, 2012.
  • A. Haltaş, A. Alkan, “Medline veritabanı üzerinde bulunan tıbbi dokümanların kanser türlerine göre otomatik sınıflandırılması”, Bilişim Teknolojileri Dergisi, 9(2), 181, 2016.
  • G. Akgül, A.A. Çelik, Z.E. Aydın, Z. K. Öztürk, “Hipotiroidi Hastalığı Teşhisinde Sınıflandırma Algoritmalarının Kullanımı”, Bilişim Teknolojileri Dergisi, 13(3), 255-268, 2020.
  • A. Şenol, Y. Canbay, M. Kaya, “Makine Öğrenmesi Yaklaşımlarını Kullanarak Salgınları Erken Evrede Tespit Etme Alanındaki Eğilimler”, Bilişim Teknolojileri Dergisi, 14(4), 2021.
  • M. AlQuraishi, “Machine learning in protein structure prediction”, Current opinion in chemical biology, 65, 1-8, 2021.
  • C. Ekenna, S. Thomas, N.M. Amato, “Adaptive local learning in sampling based motion planning for protein Folding”, BMC systems biology, 10(2), 165-179, 2016.
  • J. Zhu, H. Zhang, S.C. Li, C. Wang, L. Kong, S. Sun, D. Bu, ”Improving protein fold recognition by extracting fold-specific features from predicted residue–residue contacts” , Bioinformatics, 33(23), 3749-3757, 2017.
  • K. Yan, J. Wen, J. X. Liu, Y. Xu, B. Liu, “Protein fold recognition by combining support vector machines and pairwise sequence similarity scores”, IEEE/ACM Transactions on Computational Biology and Bioinformatics, 18(5), 2008-2016, 2020.
  • Y. Yang, E. Faraggi, H. Zhao, Y. Zhou, “Improving protein fold recognition and template-based modeling by employing probabilistic-based matching between predicted one-dimensional structural properties of query and corresponding native properties of templates”, Bioinformatics, 27(15), 2076-2082, 2011.
  • S. Makigaki, T. Ishida, “Improvement of template-based protein structure prediction by using chimera alignment”, In Proceedings of the 2018 8th International Conference on Bioscience, Biochemistry and Bioinformatics, Tokyo, Japonya, 32-37, Ocak 2018.
  • J. Söding, A. Biegert, A.N. Lupas, “The HHpred interactive server for protein homology detection and structure prediction” , Nucleic acids research, 33(2), 244-248,2005.
  • B. Liu,Y. Zhu, “ProtDec-LTR3.0: protein remote homology detection by incorporating profile-based features into learning to rank”, Ieee Access, 7, 102499-102507, 2019.
  • L. Wei, Q. Zou, “Recent progress in machine learning-based methods for protein fold Recognition”, International journal of molecular sciences, 17(12), 2118, 2016.
  • M. Corrales, P. Cusco, D. R. Usmanova, H.C. Chen, N.S. Bogatyreva, G.J. Filion, D.N. Ivankov, “Machine learning: how much does it tell about protein folding rates?”, PloS one, 10(11), 2015.
  • F. Noé, G. De Fabritiis, C. Clementi, “Machine learning for protein folding and Dynamics”, Current opinion in structural biology, 60, 77-84, 2020.
  • Internet: SCOPe: Structural Classification of Proteins — extended, https://scop.berkeley.edu, 15.05.2022.
  • D. M. Halaby, A. Poupon, J. P. Mornon, “The immunoglobulin fold family: sequence analysis and 3D structure comparisons”, Protein engineering, 12(7), 563-571,1999.
  • T. J. Richmond, F. M. Richards, “Packing of α-helices: Geometrical constraints and contact area”, Journal of molecular biology, 119(4), 537-555, 1978.
  • T. J. P. Hubbard, T. L. Blundell, “Comparison of solvent-inaccessible cores of homologous proteins: definitions useful for protein modelling. Protein Engineering”, Design and Selection, 1(3), 159-171, 1987.
  • J. Rozewicki,, S. Li,, K. M. Amada,D. M. Standley, K. Katoh, “MAFFT-DASH: integrated protein sequence and structural alignment”, Nucleic acids research, 47(W1), W5-W10, 2019.
  • V. Adar, Protein-ligand etkileşimleri, http://www.magum.hacettepe.edu.tr/MMKurs/KURS1Proteinligand.pdf, 24.06.2022.
  • Y. N. Imai, Y. Inoue, L. Nakanishi, K. Kitaura, “Cl–π interactions in protein–ligand complexes”, Protein Science, 17(7), 1129-1137, 2008.
  • R. Rojas, Neural Network A Systematic Introduction, Springer, Heidelberg, Almanya, 1996.
  • M. M. Yılmaz, Periferik sinir defekt onarımında biyolojik kondüit modeli: de-epitelize insan amniyotik membranı ve adipoz kökenli mezenkimal kök hücre tabakası içeren sinir kondüit modelinin sinir iyileşmesine etkisinin değerlendirilmesi, Uzmanlık Tezi, Hacettepe Üniversitesi, Tıp Fakültesi, 2020.
  • J. Ma, J. Tang, “A review for dynamics in neuron and neuronal network”, Nonlinear Dynamics, 89(3), 1569-1578, 2017.
  • A. Eliasy, J. Przychodzen, “The role of AI in capital structure to enhance corporate funding strategies”. Array, 6, 2020.
  • I. H. Sarker, “Deep cybersecurity: a comprehensive overview from neural network and deep learning perspective”, SN Computer Science, 2(3), 1-16, 2021.
  • V. Nair, G. E.Hinton, “Rectified linear units improve restricted boltzmann machines”, Icml, 2010.
  • X. Glorot, A. Bordes, Y. Bengio,” Deep sparse rectifier neural networks”, In Proceedings of the fourteenth international conference on artificial intelligence and statistics, Fort Lauderdale, A.B.D, 315-323, Nisan 2011.
  • I. Goodfellow, Y. Bengio, A. Courville, “Deep learning (adaptive computation and machine learning series”, Cambridge Massachusetts, 321-359, 2011.
  • M. Bağ, Derin öğrenme kullanarak IP üzerinden ses hizmeti veren şebekelerde sahtekarlığa yönelik çağrıların tespiti, Yüksek Lisans Tezi, Ankara Üniversitesi, Fen Bilimleri Enstitüsü, 2019.
  • Y.N.Fu’adah, N.K.C. Pratiwi, M.A. Pramudito, N. İbrahim,”Convolutional neural network (CNN) for automatic skin cancer classification system”, IOP Conf. Ser. Mater. Sci. Eng., 982, 12005, 2020.
  • G. Korkmaz, E. Eroğlu, “Model karmaşıklığının kontrolü”, İktisadi ve İdari Yaklaşımlar Dergisi, 2(2), 146-162, 2020.
  • B. Ö. Başer, M. Yangın, E.S. SARIDAŞ, “Makine öğrenmesi teknikleriyle diyabet hastalığının sınıflandırılması”, Süleyman Demirel Üniversitesi Fen Bilimleri Enstitüsü Dergisi, 25(1), 112-120,2021.
  • Z. Lyu, Y. Yu, B. Samali, M. Rashidi, M. Mohammadi, T.N. Nguyen, A. Nguyen, “Back-propagation neural network optimized by K-fold cross-validation for prediction of torsional strength of reinforced Concrete beam”, Materials, 15(4), 1477, 2022.
  • J. Xu, “Distance-based protein folding powered by deep learning”, Proceedings of the National Academy of Sciences, 116(34), 16856-16865, 2019.
  • C. Li, B. Liu, “MotifCNN-fold: protein fold recognition based on fold-specific features extracted by motif-based convolutional neural networks”, Briefings in Bioinformatics, 21(6), 2133–2141, 2020.
  • A. Villegas-Morcillo, V. Sanchez, A.M. Gomez, “FoldHSphere: deep hyperspherical embeddings for protein fold Recognition”, BMC bioinformatics, 22(1), 1-21, 2021.
There are 41 citations in total.

Details

Primary Language Turkish
Subjects Computer Software
Journal Section Articles
Authors

Sena Dikici 0000-0002-1759-6045

Volkan Altuntaş 0000-0003-3144-8724

Publication Date April 30, 2023
Submission Date July 6, 2022
Published in Issue Year 2023 Volume: 16 Issue: 2

Cite

APA Dikici, S., & Altuntaş, V. (2023). Yapay Sinir Ağları Kullanılarak Protein Katlanması Tanıma. Bilişim Teknolojileri Dergisi, 16(2), 95-105. https://doi.org/10.17671/gazibtd.1141468