ALPHAFOLD: DERİN ÖĞRENME VE SİNİR AĞLARI YOLUYLA PROTEİN KATLAMASINDA DEVRİM YARATMAK

Yıl 2023, Cilt: 22 Sayı: 44, 445 - 466, 31.12.2023

Burcu Tekin , Rafig Gurbanov

https://doi.org/10.55071/ticaretfbd.1323165

Öz

AlphaFold, bir protein dizisinin üç boyutlu yapısını tahmin etmek için derin sinir ağlarını ve gelişmiş makine öğrenimi tekniklerini kullanan, DeepMind ekibi tarafından geliştirilmiş bir protein katlama tahmin aracıdır. Protein katlanmasının tahmini, hesaplamalı biyolojide uzun süredir devam eden bir sorun olmuştur ve doğru protein yapısı tahmin yöntemlerinin geliştirilmesi, bilim camiasının büyük ilgisini çekmiştir. AlphaFold, önce bir proteinin yerel yapısını tahmin ettiği ve ardından genel yapıyı bir araya getirdiği iki aşamalı bir yaklaşım kullanır. AlphaFold, iki yılda bir yapılan CASP (Yapı Tahmininin Kritik Değerlendirmesi) deneylerinde diğer son teknoloji yöntemleri geride bırakarak çok çeşitli proteinlerin yapısını tahmin etmede kayda değer bir başarı elde etmiştir. AlphaFold'un tahminlerinin doğruluğu, protein işlevini ve hastalık mekanizmalarını, ilaç keşfini ve sentetik biyolojiyi anlamak için önemli etkilere sahiptir. Bu derlemede, AlphaFold'un geliştirilmesine, temel metodolojisine ve CASP deneylerindeki performansına genel bir bakış sunulmaktadır. Ek olarak, AlphaFold'un protein mühendisliği, ilaç keşfi ve yapısal biyolojideki potansiyel uygulamaları da tartışılmaktadır.

Anahtar Kelimeler

Protein yapı tahmini, Protein katlanması, AlphaFold, Makine öğrenmesi

ALPHAFOLD: REVOLUTIONIZING PROTEIN FOLDING THROUGH DEEP LEARNING AND NEURAL NETWORKS

Öz

AlphaFold is a protein folding prediction tool developed by the DeepMind team, which leverages deep neural networks and advanced machine learning techniques to predict the three-dimensional structure of a protein sequence. The prediction of protein folding has been a long-standing challenge in computational biology, and the development of accurate protein structure prediction methods has been of great interest to the scientific community. AlphaFold employs a two-stage approach, in which it first predicts the local structure of a protein and then assembles the global configuration. AlphaFold has achieved remarkable success in predicting the structure of a wide range of proteins, outperforming other state-of-the-art methods in the biennial CASP (Critical Assessment of Structure Prediction) experiments. The accuracy of AlphaFold's predictions has significant implications for understanding protein function and disease mechanisms, drug discovery, and synthetic biology. This review provides an overview of AlphaFold's development, basic methodology, and performance in CASP experiments. Moreover, potential applications of AlphaFold in protein engineering, drug discovery, and structural biology are also discussed.

Anahtar Kelimeler

Protein structure prediction, Protein folding, AlphaFold, Machine learning

Kaynakça

• Akdel, M., Pires, D. E., Pardo, E. P., Jänes, J., Zalevsky, A. O., Mészáros, B., Bryant, P., Good, L. L., Laskowski, R. A. & Pozzati, G. (2022). A structural biology community assessment of AlphaFold2 applications. Nature Structural & Molecular Biology, 1-12.
• AlQuraishi, M. (2019). AlphaFold at CASP13. Bioinformatics, 35(22), 4862-4865.
• AlQuraishi, M. (2021). Machine learning in protein structure prediction. Current opinion in chemical biology, 65, 1-8.
• Anfinsen, C. & Scheraga, H. (1975). Experimental and theoretical aspects of protein folding. Advances in protein chemistry, 29, 205-300.
• Anfinsen, C. B. (1973). Principles that govern the folding of protein chains. Science, 181(4096), 223-230.
• Anfinsen, C. B., Haber, E., Sela, M. & White Jr, F. (1961). The kinetics of formation of native ribonuclease during oxidation of the reduced polypeptide chain. Proceedings of the National Academy of Sciences, 47(9), 1309-1314.
• Arnold, K., Bordoli, L., Kopp, J. & Schwede, T. (2006). The SWISS-MODEL workspace: A web-based environment for protein structure homology modelling. Bioinformatics, 22(2), 195-201.
• Bolen, D. & Baskakov, I. V. (2001). The osmophobic effect: natural selection of a thermodynamic force in protein folding. Journal of molecular biology, 310(5), 955-963.
• Borkakoti, N. & Thornton, J. M. (2023). AlphaFold2 protein structure prediction: Implications for drug discovery. Current Opinion in Structural Biology, 78, 102526.
• Buel, G. R. & Walters, K. J. (2022). Can AlphaFold2 predict the impact of missense mutations on structure? Nature Structural & Molecular Biology, 29(1), 1-2.
• Burley, S. & Petsko, G. (1988). Weakly polar interactions in proteins. Advances in protein chemistry, 39, 125-189.
• Burley, S. K., Bhikadiya, C., Bi, C., Bittrich, S., Chen, L., Crichlow, G. V., Duarte, J. M., Dutta, S., Fayazi, M. & Feng, Z. (2022). RCSB Protein Data Bank: Celebrating 50 years of the PDB with new tools for understanding and visualizing biological macromolecules in 3D. Protein Science, 31(1), 187-208.
• Chen, I.-M. A., Markowitz, V. M., Chu, K., Palaniappan, K., Szeto, E., Pillay, M., Ratner, A., Huang, J., Andersen, E. & Huntemann, M. (2016). IMG/M: Integrated genome and metagenome comparative data analysis system. Nucleic acids research, 507-516.
• Cheng, J., Roy, R. S., Liu, J., Giri, N. & Guo, Z. (2023). Combining pairwise structural similarity and deep learning interface contact prediction to estimate protein complex model accuracy in CASP15. bioRxiv, 2023.2003.2008.531814.
• de Almeida Paiva, V., de Souza Gomes, I., Monteiro, C. R., Mendonça, M. V., Martins, P. M., Santana, C. A., Gonçalves-Almeida, V., Izidoro, S. C., de Melo-Minardi, R. C. & de Azevedo Silveira, S. (2022). Protein structural bioinformatics: An overview. Computers in Biology and Medicine, 105695.
• Dhingra, S., Sowdhamini, R., Cadet, F. & Offmann, B. (2020). A glance into the evolution of template-free protein structure prediction methodologies. Biochimie, 175, 85-92.
• Dill, K. A. (1990). Dominant forces in protein folding. Biochemistry, 29(31), 7133-7155.
• Dill, K. A. & MacCallum, J. L. (2012). The protein-folding problem, 50 years on. Science, 338(6110), 1042-1046.
• Dobson, L., Szekeres, L. I., Gerdán, C., Langó, T., Zeke, A. & Tusnády, G. E. (2023). TmAlphaFold database: membrane localization and evaluation of AlphaFold2 predicted alpha-helical transmembrane protein structures. Nucleic acids research, 51(D1), 517-522.
• Evans, R., O’Neill, M., Pritzel, A., Antropova, N., Senior, A., Green, T., Žídek, A., Bates, R., Blackwell, S. & Yim, J. (2021). Protein complex prediction with AlphaFold-Multimer. bioRxiv, 2021.2010.2004.463034.
• Faure, A. J., Domingo, J., Schmiedel, J. M., Hidalgo-Carcedo, C., Diss, G. & Lehner, B. (2022). Mapping the energetic and allosteric landscapes of protein binding domains. Nature, 604(7904), 175-183.
• Fontana, P., Dong, Y., Pi, X., Tong, A. B., Hecksel, C. W., Wang, L., Fu, T.-M., Bustamante, C. & Wu, H. (2022). Structure of cytoplasmic ring of nuclear pore complex by integrative cryo-EM and AlphaFold. Science, 376(6598), 1-29.
• Freschlin, C. R., Fahlberg, S. A. & Romero, P. A. (2022). Machine learning to navigate fitness landscapes for protein engineering. Current Opinion in Biotechnology, 75, 102713.
• Gasic, A. G., Sarkar, A. & Cheung, M. S. (2021). Understanding protein-complex assembly through grand canonical maximum entropy modeling. Physical Review Research, 3(3), 033220.
• Gogoi, C. R., Rahman, A., Saikia, B. & Baruah, A. (2023). Protein Dihedral Angle Prediction: The State of the Art. ChemistrySelect, 8(5), e202203427.
• Goverde, C., Wolf, B., Khakzad, H., Rosset, S. & Correia, B. E. (2022). De novo protein design by inversion of the AlphaFold structure prediction network. bioRxiv, 2022.2012. 2013.520346.
• He, K., Zhang, X., Ren, S. & Sun, J. (2016). Deep residual learning for image recognition. Proceedings of the IEEE conference on computer vision and pattern recognition, 770-778.
• Hey, T., Butler, K., Jackson, S. & Thiyagalingam, J. (2020). Machine learning and big scientific data. Philosophical Transactions of the Royal Society A, 378(2166), 20190054.
• Higgins, M. K. (2021). Can we AlphaFold our way out of the next pandemic? Journal of molecular biology, 433(20), 167093.
• Hong, H., Choi, H.-K. & Yoon, T.Y. (2022). Untangling the complexity of membrane protein folding. Current Opinion in Structural Biology, 72, 237-247.
• Hornak, V., Abel, R., Okur, A., Strockbine, B., Roitberg, A. & Simmerling, C. (2006). Comparison of multiple Amber force fields and development of improved protein backbone parameters. Proteins: Structure, Function, and Bioinformatics, 65(3), 712-725.
• Hsu, C., Verkuil, R., Liu, J., Lin, Z., Hie, B., Sercu, T., Lerer, A. & Rives, A. (2022). Learning inverse folding from millions of predicted structures. International Conference on Machine Learning, 1-25.
• Jendrusch, M., Korbel, J. O. & Sadiq, S. K. (2021). AlphaDesign: A de novo protein design framework based on AlphaFold. bioRxiv, 2021.2010. 2011.463937.
• Jiang, F., & Wu, Y.-D. (2014). Folding of fourteen small proteins with a residue-specific force field and replica-exchange molecular dynamics. Journal of the American Chemical Society, 136(27), 9536-9539.
• Jiang, L., Chakraborty, P., Zhang, L., Wong, M., Hill, S. E., Webber, C. J., Libera, J., Blair, L. J., Wolozin, B. & Zweckstetter, M. (2023). Chaperoning of specific tau structure by immunophilin FKBP12 regulates the neuronal resilience to extracellular stress. Science Advances, 9(5), eadd9789.
• Jisna, V. & Jayaraj, P. (2021). Protein structure prediction: conventional and deep learning perspectives. The Protein Journal, 40(4), 522-544.
• Jumper, J., Evans, R., Pritzel, A., Green, T., Figurnov, M., Ronneberger, O., Tunyasuvunakool, K., Bates, R., Žídek, A. & Potapenko, A. (2021). Highly accurate protein structure prediction with AlphaFold. Nature, 596(7873), 583-589.
• Jumper, J., Evans, R., Pritzel, A., Green, T., Figurnov, M., Tunyasuvunakool, K., Ronneberger, O., Bates, R., Žídek, A. & Bridgland, A. (2020). AlphaFold 2. In Fourteenth Critical Assessment of Techniques for Protein Structure Prediction (Abstract Book).
• Ko, K.-T., Lennartz, F., Mekhaiel, D., Guloglu, B., Marini, A., Deuker, D. J., Long, C. A., Jore, M. M., Miura, K. & Biswas, S. (2022). Structure of the malaria vaccine candidate Pfs48/45 and its recognition by transmission blocking antibodies. Nature Communications, 13(1), 5603. Krell, T. & Matilla, M. A. (2022). Antimicrobial resistance: progress and challenges in antibiotic discovery and anti‐infective therapy. Microbial Biotechnology, 15(1), 70-78.
• Kryshtafovych, A., Moult, J., Billings, W. M., Della Corte, D., Fidelis, K., Kwon, S., Olechnovič, K., Seok, C., Venclovas, Č. & Won, J. (2021). Modeling SARS‐CoV‐2 proteins in the CASP‐commons experiment. Proteins: Structure, Function, and Bioinformatics, 89(12), 1987-1996.
• Kryshtafovych, A., Schwede, T., Topf, M., Fidelis, K. & Moult, J. (2019). Critical assessment of methods of protein structure prediction (CASP)—Round XIII. Proteins: Structure, Function, and Bioinformatics, 87(12), 1011-1020.
• Kryshtafovych, A., Schwede, T., Topf, M., Fidelis, K. & Moult, J. (2021). Critical assessment of methods of protein structure prediction (CASP)—Round XIV. Proteins: Structure, Function, and Bioinformatics, 89(12), 1607-1617.
• Kufareva, I. & Abagyan, R. (2012). Methods of protein structure comparison. Homology Modeling: Methods and Protocols, 231-257.
• Kuhlman, B. & Bradley, P. (2019). Advances in protein structure prediction and design. Nature Reviews Molecular Cell Biology, 20(11), 681-697.
• Levinthal, C. (1969). How to fold graciously. Mossbauer spectroscopy in biological systems, 67, 22-24.
• Li, F. & Du, Y. (2018). From AlphaGo to power system AI: What engineers can learn from solving the most complex board game. IEEE Power and Energy Magazine, 16(2), 76-84.
• Li, S., Wu, S., Wang, L., Li, F., Jiang, H. & Bai, F. (2022). Recent advances in predicting protein–protein interactions with the aid of artificial intelligence algorithms. Current Opinion in Structural Biology, 73, 102344.
• Li, Y., Liu, Y. & Yu, D.-J. (2023). Machine learning for protein inter-residue interaction prediction. Machine Learning in Bioinformatics of Protein Sequences: Algorithms, Databases and Resources for Modern Protein Bioinformatics, 183-203. World Scientific.
• Lin, Z., Akin, H., Rao, R., Hie, B., Zhu, Z., Lu, W., Smetanin, N., Verkuil, R., Kabeli, O. & Shmueli, Y. (2023). Evolutionary-scale prediction of atomic-level protein structure with a language model. Science, 379(6637), 1123-1130.
• Liu, W., Wang, G., Wang, Z., Wang, G., Huang, J. & Liu, B. (2022). Repurposing small-molecule drugs for modulating toxic protein aggregates in neurodegenerative diseases. Drug discovery today, 27(7), 1994-2007.
• Lumry, R. & Eyring, H. (1954). Conformation changes of proteins. The Journal of physical chemistry, 58(2), 110-120.
• Marks, D. S., Hopf, T. A. & Sander, C. (2012). Protein structure prediction from sequence variation. Nature biotechnology, 30(11), 1072-1080.
• Marx, V. (2022). Method of the year: Protein structure prediction. Nature methods, 19(1), 5-10.
• Mazurenko, S., Prokop, Z. & Damborsky, J. (2019). Machine learning in enzyme engineering. ACS Catalysis, 10(2), 1210-1223.
• Meng, B., Abdullahi, A., Ferreira, I. A., Goonawardane, N., Saito, A., Kimura, I., Yamasoba, D., Gerber, P. P., Fatihi, S. & Rathore, S. (2022). Altered TMPRSS2 usage by SARS-CoV-2 Omicron impacts infectivity and fusogenicity. Nature, 603(7902), 706-714.
• Miserez, A., Yu, J. & Mohammadi, P. (2023). Protein-based biological materials: Molecular design and artificial production. Chemical Reviews, 123(5), 2049-2111.
• Miyazawa, T., Hiratsuka, Y., Toda, M., Hatakeyama, N., Ozawa, H., Abe, C., Cheng, T.-Y., Matsushima, Y., Miyawaki, Y. & Ashida, K. (2022). Artificial intelligence in food science and nutrition: a narrative review. Nutrition Reviews, 80(12), 2288-2300.
• Nussinov, R., Zhang, M., Liu, Y. & Jang, H. (2022). AlphaFold, artificial intelligence (AI), and allostery. The Journal of Physical Chemistry B, 126(34), 6372-6383.
• Ongie, G., Jalal, A., Metzler, C. A., Baraniuk, R. G., Dimakis, A. G. & Willett, R. (2020). Deep learning techniques for inverse problems in imaging. IEEE Journal on Selected Areas in Information Theory, 1(1), 39-56.
• Ovchinnikov, S., Kim, D. E., Wang, R. Y. R., Liu, Y., DiMaio, F. & Baker, D. (2016). Improved de novo structure prediction in CASP 11 by incorporating coevolution information into Rosetta. Proteins: Structure, Function, and Bioinformatics, 84, 67-75.
• Pakhrin, S. C., Shrestha, B., Adhikari, B. & Kc, D. B. (2021). Deep learning-based advances in protein structure prediction. International Journal of Molecular Sciences, 22(11), 5553.
• Pearce, R. & Zhang, Y. (2021). Deep learning techniques have significantly impacted protein structure prediction and protein design. Current Opinion in Structural Biology, 68, 194-207.
• Pei, J. & Cong, Q. (2023). AFTM: a database of transmembrane regions in the human proteome predicted by AlphaFold. Database, 2023, baad008.
• Perrakis, A. & Sixma, T. K. (2021). AI revolutions in biology: The joys and perils of AlphaFold. EMBO reports, 22(11), e54046.
• Pinheiro, F., Santos, J. & Ventura, S. (2021). AlphaFold and the amyloid landscape. Journal of Molecular Biology, 433(20), 167059.
• Ponting, C. P. & Russell, R. R. (2002). The natural history of protein domains. Annual Review of Biophysics and Biomolecular Structure, 31(1), 45-71.
• Ren, F., Ding, X., Zheng, M., Korzinkin, M., Cai, X., Zhu, W., Mantsyzov, A., Aliper, A., Aladinskiy, V. & Cao, Z. (2023). AlphaFold accelerates artificial intelligence powered drug discovery: efficient discovery of a novel CDK20 small molecule inhibitor. Chemical Science, 14(6), 1443-1452.
• Romero, P. A. & Arnold, F. H. (2009). Exploring protein fitness landscapes by directed evolution. Nature Reviews Molecular Cell Biology, 10(12), 866-876.
• Sadek, A., Zaha, D. & Ahmed, M. S. (2021). Structural insights of SARS-CoV-2 spike protein from Delta and Omicron variants. bioRxiv, 2021.2012. 2008.471777.
• Salahuddin, P., Siddiqi, M. K., Khan, S., Abdelhameed, A. S. & Khan, R. H. (2016). Mechanisms of protein misfolding: Novel therapeutic approaches to protein-misfolding diseases. Journal of Molecular Structure, 1123, 311-326.
• Savytskyi, O. V., Sirmans, T. N., Coban, M. A., Weber, C. A., Murray, M. E. & Caulfield, T. R. (2023). Computational modeling and molecular mapping of serine protease inhibitor family A5 (SERPINA5) structure, associated with tau expression and Alzheimer's disease. Biophysical Journal, 122(3), 471a.
• Senior, A. W., Evans, R., Jumper, J., Kirkpatrick, J., Sifre, L., Green, T., Qin, C., Žídek, A., Nelson, A. W. & Bridgland, A. (2020). Improved protein structure prediction using potentials from deep learning. Nature, 577(7792), 706-710.
• Sezgin, E. & Tekin, B. (2023). Molecular evolution and population genetics of glutamate decarboxylase acid resistance pathway in lactic acid bacteria. Frontiers in Genetics, 1-14.
• Shen, Y. & Bax, A. (2013). Protein backbone and sidechain torsion angles predicted from NMR chemical shifts using artificial neural networks. Journal of biomolecular NMR, 56, 227-241.
• Tan, S., Tan, H. T. & Chung, M. C. (2008). Membrane proteins and membrane proteomics. Proteomics, 8(19), 3924-3932.
• Thompson, J. D., Plewniak, F., Ripp, R., Thierry, J.-C. & Poch, O. (2001). Towards a reliable objective function for multiple sequence alignments. Journal of Molecular Biology, 314(4), 937-951.
• Tunyasuvunakool, K., Adler, J., Wu, Z., Green, T., Zielinski, M., Žídek, A., Bridgland, A., Cowie, A., Meyer, C. & Laydon, A. (2021). Highly accurate protein structure prediction for the human proteome. Nature, 596(7873), 590-596.
• UniProt: the universal protein knowledgebase in 2021. (2021). Nucleic acids research, 49(D1), D480-D489.
• UniProt: the Universal Protein knowledgebase in 2023. (2023). Nucleic acids research, 51(D1), D523-D531.
• Varadi, M. & Velankar, S. (2022). The impact of AlphaFold Protein Structure Database on the fields of life sciences. Proteomics, 2200128.
• Wang, G., Wu, Z., Fang, X., Xiang, Y., Liu, Y., Yu, D. & Ma, Y. (2022). Efficient AlphaFold2 Training using Parallel Evoformer and Branch Parallelism. arXiv preprint arXiv:2211.00235. Wang, Y. & Huang, R. (2023). Identification of Artemisia Argyi (AA) Therapy in Alzheimer's Disease (AD) Using Network Pharmacology and Molecular Docking. Advanced Biology, 2200256.
• Waterhouse, A., Bertoni, M., Bienert, S., Studer, G., Tauriello, G., Gumienny, R., Heer, F. T., de Beer, T. A. P., Rempfer, C. & Bordoli, L. (2018). SWISS-MODEL: homology modelling of protein structures and complexes. Nucleic acids research, 46(W1), W296-W303.
• Wodak, S. J., Vajda, S., Lensink, M. F., Kozakov, D. & Bates, P. A. (2022). Critical Assessment of Methods for Predicting the 3D Structure of Proteins and Protein Complexes. Annual Review of Biophysics, 52.
• Xu, Z., Cen, Y.-K., Zou, S.-P., Xue, Y.-P. & Zheng, Y.-G. (2020). Recent advances in the improvement of enzyme thermostability by structure modification. Critical reviews in biotechnology, 40(1), 83-98.
• Yang, J., Anishchenko, I., Park, H., Peng, Z., Ovchinnikov, S. & Baker, D. (2020). Improved protein structure prediction using predicted interresidue orientations. Proceedings of the National Academy of Sciences, 117(3), 1496-1503.
• Yang, Q., Jian, X., Syed, A. A. S., Fahira, A., Zheng, C., Zhu, Z., Wang, K., Zhang, J., Wen, Y. & Li, Z. (2022). Structural comparison and drug screening of spike proteins of ten SARS-CoV-2 variants. Research.
• Yu, D., Chojnowski, G., Rosenthal, M. & Kosinski, J. (2023). AlphaPulldown—a python package for protein–protein interaction screens using AlphaFold-Multimer. Bioinformatics, 39(1), btac749.
• Zhang, J. & Chen, B. (2022). Fighting SARS-CoV-2 with structural biology methods. Nature methods, 19(4), 381-383.
• Ziegler, S. J., Mallinson, S. J., John, P. C. S. & Bomble, Y. J. (2021). Advances in integrative structural biology: Towards understanding protein complexes in their cellular context. Computational and Structural Biotechnology Journal, 19, 214-225.
• Zwanzig, R., Szabo, A. & Bagchi, B. (1992). Levinthal's paradox. Proceedings of the National Academy of Sciences, 89(1), 20-22.