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Arayüz Mutasyonlarının Protein Etkileşimlerine Tesirini Tahmin Eden Algoritmalarla HADDOCK’un Performansının Karşılaştırılması

Year 2021, Volume: 33 Issue: 4, 592 - 608, 30.12.2021

Mehdi Koşaca , Eda Şamiloğlu , Ezgi Karaca

https://doi.org/10.7240/jeps.920075

Abstract

Hücresel süreçler proteinlerin birbirleriyle yaptıkları etkileşimlerinin üzerinden ilerler. Bilinen protein-protein etkileşimleri, etkileşim arayüzlerinde meydana gelen nokta mutasyonları ile yeniden düzenlenebilir. Bu düzenleme sonucunda, mevcut etkileşimler bozulabilir ve bu durum, kanser ve nörodejenaratif hastalıkların oluşmasına yol açabilir. Mutasyonların bu kadar hayati bir etkisinin olabilmesi, onların protein etkileşimleri üzerindeki etkisinin tahminini, hesaplamalı biyolojinin aktif çalışma alanlarından biri haline getirmiştir. Mevcut mutasyon etki tahmin algoritmalarının yanında, ünlü kenetlenme programı HADDOCK, protein-protein etkileşim arayüzünde görülen mutasyonların, ayrıntılı bir şekilde modellenmesine olanak sağlamaktadır. Bu çalışmamızda, HADDOCK’un literatürde önerilen kullanım parametrelerini optimize ederek, mutasyon tahmin performansını iyileştirmeyi hedefledik. Bu kapsamda yaptığımız karşılaştırma çalışmamızda, HADDOCK’un en optimum parametre seçkisi ile bile alternatif bir mutasyon tahmin algoritması olan EvoEF1’in performansını geçemediğini ortaya koyduk. Bunun yanında, EvoEF1’in performansını EvoEF2, FoldX ve UEP tahmin algoritmalarınınki ile karşılaştırdığımızda, EvoEF1’in en iyi performansı gösterdiğini gözlemledik. Dolayısıyla, bu çalışmamızın sonucu olarak, EvoEF1 programının protein-protein etkileşimlerinde nokta mutasyonunun etkisini tahmininde öncelikli olarak kullanılmasını önermekteyiz.

Keywords

Nokta mutasyonu modellemesi Bağlanma afinitesi HADDOCK protein-protein etkileşimi örnekleme optimizasyonu

Supporting Institution

TÜSEB

Project Number

3393

Thanks

2019-TA-02 Çağrı kodlu ve 3393 proje numaralı bu çalışma, Türkiye Sağlık Enstitüleri Başkanlığı (TÜSEB) tarafından desteklenmiştir. Desteklerinden ötürü TÜSEB’e ve Bilim Akademisi Genç Bilim İnsanları Ödül Programları’na (BAGEP) teşekkür ederiz. Yardımlarından ve yol göstericiliğinden dolayı Mehmet Ergüven’e, makalenin kritik okumasını ve düzenlemelerini yaptığı için Ayşe Berçin Barlas’a ve Büşra Savaş’a teşekkür ederiz.

References

  • [1] Stites, W. (1997). Protein−Protein Interactions: Interface Structure, Binding Thermodynamics, and Mutational Analysis. Chemical Reviews, 97(5), 1233-1250. https://doi.org/10.1021/cr960387h
  • [2] Hein, M. Y., Hubner, N. C., Poser, I., Cox, J., Nagaraj, N., Toyoda, Y., Gak, I. A., Weisswange, I., Mansfeld, J., Buchholz, F., Hyman, A. A., & Mann, M. (2015). A human interactome in three quantitative dimensions organized by stoichiometries and abundances. Cell, 163(3), 712–723. https://doi.org/10.1016/j.cell.2015.09.053
  • [3] Subramanian, S., & Kumar, S. (2006). Evolutionary anatomies of positions and types of disease-associated and neutral amino acid mutations in the human genome. BMC genomics, 7, 306. https://doi.org/10.1186/1471-2164-7-306
  • [4] Gonzalez, M. W., & Kann, M. G. (2012). Chapter 4: Protein interactions and disease. PLoS computational biology, 8(12), e1002819. https://doi.org/10.1371/journal.pcbi.1002819
  • [5] Krohl, P. J., Ludwig, S. D., & Spangler, J. B. (2019). Emerging technologies in protein interface engineering for biomedical applications. Current opinion in biotechnology, 60, 82–88. https://doi.org/10.1016/j.copbio.2019.01.017
  • [6] Karaca, E., & Bonvin, A. M. (2013). Advances in integrative modeling of biomolecular complexes. Methods (San Diego, Calif.), 59(3), 372–381. https://doi.org/10.1016/j.ymeth.2012.12.004
  • [7] Jankauskaite, J., Jiménez-García, B., Dapkunas, J., Fernández-Recio, J., & Moal, I. H. (2019). SKEMPI 2.0: an updated benchmark of changes in protein-protein binding energy, kinetics and thermodynamics upon mutation. Bioinformatics (Oxford, England), 35(3), 462–469. https://doi.org/10.1093/bioinformatics/bty635
  • [8] Geng, C., Xue, L., Roel‐Touris, J. and Bonvin, A. (2021). Finding the ΔΔ G spot: Are predictors of binding affinity changes upon mutations in protein–protein interactions ready for it? WIREs Computational Molecular Science, 2019. 9(5). https://doi.org/10.1002/wcms.1410
  • [9] Geng, C., Vangone, A., & Bonvin, A. (2016). Exploring the interplay between experimental methods and the performance of predictors of binding affinity change upon mutations in protein complexes. Protein engineering, design & selection : PEDS, 29(8), 291–299. https://doi.org/10.1093/protein/gzw020
  • [10] Amengual-Rigo, P., Fernández-Recio, J., & Guallar, V. (2020). UEP: an open-source and fast classifier for predicting the impact of mutations in protein-protein complexes. Bioinformatics (Oxford, England), btaa708. Advance online publication. https://doi.org/10.1093/bioinformatics/btaa708
  • [11] Mosca, R., Céol, A., & Aloy, P. (2013). Interactome3D: adding structural details to protein networks. Nature methods, 10(1), 47–53. https://doi.org/10.1038/nmeth.2289
  • [12] Schymkowitz, J., Borg, J., Stricher, F., Nys, R., Rousseau, F., & Serrano, L. (2005). The FoldX web server: an online force field. Nucleic acids research, 33(Web Server issue), W382–W388. https://doi.org/10.1093/nar/gki387
  • [13] Pearce, R., Huang, X., Setiawan, D., & Zhang, Y. (2019). EvoDesign: Designing Protein-Protein Binding Interactions Using Evolutionary Interface Profiles in Conjunction with an Optimized Physical Energy Function. Journal of molecular biology, 431(13), 2467–2476. https://doi.org/10.1016/j.jmb.2019.02.028
  • [14] Rodrigues, J., Barrera-Vilarmau, S., M C Teixeira, J., Sorokina, M., Seckel, E., Kastritis, P. L., & Levitt, M. (2020). Insights on cross-species transmission of SARS-CoV-2 from structural modeling. PLoS computational biology, 16(12), e1008449. https://doi.org/10.1371/journal.pcbi.1008449
  • [15] Sorokina, M., M C Teixeira, J., Barrera-Vilarmau, S., Paschke, R., Papasotiriou, I., Rodrigues, J., & Kastritis, P. L. (2020). Structural models of human ACE2 variants with SARS-CoV-2 Spike protein for structure-based drug design. Scientific data, 7(1), 309. https://doi.org/10.1038/s41597-020-00652-6
  • [16] Rodrigues, J., Teixeira, J., Trellet, M., & Bonvin, A. (2018). pdb-tools: a swiss army knife for molecular structures. F1000Research, 7, 1961. https://doi.org/10.12688/f1000research.17456.1
  • [17] Huang, X., Pearce, R., & Zhang, Y. (2020). EvoEF2: accurate and fast energy function for computational protein design. Bioinformatics (Oxford, England), 36(4), 1135–1142. https://doi.org/10.1093/bioinformatics/btz740
  • [18] van Zundert, G., Rodrigues, J., Trellet, M., Schmitz, C., Kastritis, P. L., Karaca, E., Melquiond, A., van Dijk, M., de Vries, S. J., & Bonvin, A. (2016). The HADDOCK2.2 Web Server: User-Friendly Integrative Modeling of Biomolecular Complexes. Journal of molecular biology, 428(4), 720–725. https://doi.org/10.1016/j.jmb.2015.09.01489
  • [19] Geng, C., Vangone, A., Folkers, G. E., Xue, L. C., & Bonvin, A. (2019). iSEE: Interface structure, evolution, and energy-based machine learning predictor of binding affinity changes upon mutations. Proteins, 87(2), 110–119. https://doi.org/10.1002/prot.25630
  • [20] Karaca, E., Rodrigues, J., Graziadei, A., Bonvin, A., & Carlomagno, T. (2017). M3: an integrative framework for structure determination of molecular machines. Nature methods, 14(9), 897–902. https://doi.org/10.1038/nmeth.4392
  • [21] Brünger, A. T., Adams, P. D., Clore, G. M., DeLano, W. L., Gros, P., Grosse-Kunstleve, R. W., Jiang, J. S., Kuszewski, J., Nilges, M., Pannu, N. S., Read, R. J., Rice, L. M., Simonson, T., & Warren, G. L. (1998). Crystallography & NMR system: A new software suite for macromolecular structure determination. Acta crystallographica. Section D, Biological crystallography, 54(Pt 5), 905–921. https://doi.org/10.1107/s0907444998003254
  • [22] Jorgensen, W. L., & Tirado-Rives, J. (1988). The OPLS [optimized potentials for liquid simulations] potential functions for proteins, energy minimizations for crystals of cyclic peptides and crambin. Journal of the American Chemical Society, 110(6), 1657–1666. https://doi.org/10.1021/ja00214a001
  • [23] Dominguez, C., Boelens, R., & Bonvin, A. M. (2003). HADDOCK: a protein-protein docking approach based on biochemical or biophysical information. Journal of the American Chemical Society, 125(7), 1731–1737. https://doi.org/10.1021/ja026939x
  • [24] Sitkoff, D., Sharp, K., & Honig, B. (1994). Accurate Calculation of Hydration Free Energies Using Macroscopic Solvent Models. The Journal Of Physical Chemistry, 98(7), 1978-1988. https://doi.org/10.1021/j100058a043
  • [25] Brooks, B. R., Brooks, C. L., 3rd, Mackerell, A. D., Jr, Nilsson, L., Petrella, R. J., Roux, B., Won, Y., Archontis, G., Bartels, C., Boresch, S., Caflisch, A., Caves, L., Cui, Q., Dinner, A. R., Feig, M., Fischer, S., Gao, J., Hodoscek, M., Im, W., Kuczera, K., … Karplus, M. (2009). CHARMM: the biomolecular simulation program. Journal of computational chemistry, 30(10), 1545–1614. https://doi.org/10.1002/jcc.21287
  • [26] Kastritis, P. L., & Bonvin, A. M. (2012). On the binding affinity of macromolecular interactions: daring to ask why proteins interact. Journal of the Royal Society, Interface, 10(79), 20120835. https://doi.org/10.1098/rsif.2012.0835

Year 2021, Volume: 33 Issue: 4, 592 - 608, 30.12.2021

Mehdi Koşaca , Eda Şamiloğlu , Ezgi Karaca

https://doi.org/10.7240/jeps.920075

Abstract

Project Number

3393

References

  • [1] Stites, W. (1997). Protein−Protein Interactions: Interface Structure, Binding Thermodynamics, and Mutational Analysis. Chemical Reviews, 97(5), 1233-1250. https://doi.org/10.1021/cr960387h
  • [2] Hein, M. Y., Hubner, N. C., Poser, I., Cox, J., Nagaraj, N., Toyoda, Y., Gak, I. A., Weisswange, I., Mansfeld, J., Buchholz, F., Hyman, A. A., & Mann, M. (2015). A human interactome in three quantitative dimensions organized by stoichiometries and abundances. Cell, 163(3), 712–723. https://doi.org/10.1016/j.cell.2015.09.053
  • [3] Subramanian, S., & Kumar, S. (2006). Evolutionary anatomies of positions and types of disease-associated and neutral amino acid mutations in the human genome. BMC genomics, 7, 306. https://doi.org/10.1186/1471-2164-7-306
  • [4] Gonzalez, M. W., & Kann, M. G. (2012). Chapter 4: Protein interactions and disease. PLoS computational biology, 8(12), e1002819. https://doi.org/10.1371/journal.pcbi.1002819
  • [5] Krohl, P. J., Ludwig, S. D., & Spangler, J. B. (2019). Emerging technologies in protein interface engineering for biomedical applications. Current opinion in biotechnology, 60, 82–88. https://doi.org/10.1016/j.copbio.2019.01.017
  • [6] Karaca, E., & Bonvin, A. M. (2013). Advances in integrative modeling of biomolecular complexes. Methods (San Diego, Calif.), 59(3), 372–381. https://doi.org/10.1016/j.ymeth.2012.12.004
  • [7] Jankauskaite, J., Jiménez-García, B., Dapkunas, J., Fernández-Recio, J., & Moal, I. H. (2019). SKEMPI 2.0: an updated benchmark of changes in protein-protein binding energy, kinetics and thermodynamics upon mutation. Bioinformatics (Oxford, England), 35(3), 462–469. https://doi.org/10.1093/bioinformatics/bty635
  • [8] Geng, C., Xue, L., Roel‐Touris, J. and Bonvin, A. (2021). Finding the ΔΔ G spot: Are predictors of binding affinity changes upon mutations in protein–protein interactions ready for it? WIREs Computational Molecular Science, 2019. 9(5). https://doi.org/10.1002/wcms.1410
  • [9] Geng, C., Vangone, A., & Bonvin, A. (2016). Exploring the interplay between experimental methods and the performance of predictors of binding affinity change upon mutations in protein complexes. Protein engineering, design & selection : PEDS, 29(8), 291–299. https://doi.org/10.1093/protein/gzw020
  • [10] Amengual-Rigo, P., Fernández-Recio, J., & Guallar, V. (2020). UEP: an open-source and fast classifier for predicting the impact of mutations in protein-protein complexes. Bioinformatics (Oxford, England), btaa708. Advance online publication. https://doi.org/10.1093/bioinformatics/btaa708
  • [11] Mosca, R., Céol, A., & Aloy, P. (2013). Interactome3D: adding structural details to protein networks. Nature methods, 10(1), 47–53. https://doi.org/10.1038/nmeth.2289
  • [12] Schymkowitz, J., Borg, J., Stricher, F., Nys, R., Rousseau, F., & Serrano, L. (2005). The FoldX web server: an online force field. Nucleic acids research, 33(Web Server issue), W382–W388. https://doi.org/10.1093/nar/gki387
  • [13] Pearce, R., Huang, X., Setiawan, D., & Zhang, Y. (2019). EvoDesign: Designing Protein-Protein Binding Interactions Using Evolutionary Interface Profiles in Conjunction with an Optimized Physical Energy Function. Journal of molecular biology, 431(13), 2467–2476. https://doi.org/10.1016/j.jmb.2019.02.028
  • [14] Rodrigues, J., Barrera-Vilarmau, S., M C Teixeira, J., Sorokina, M., Seckel, E., Kastritis, P. L., & Levitt, M. (2020). Insights on cross-species transmission of SARS-CoV-2 from structural modeling. PLoS computational biology, 16(12), e1008449. https://doi.org/10.1371/journal.pcbi.1008449
  • [15] Sorokina, M., M C Teixeira, J., Barrera-Vilarmau, S., Paschke, R., Papasotiriou, I., Rodrigues, J., & Kastritis, P. L. (2020). Structural models of human ACE2 variants with SARS-CoV-2 Spike protein for structure-based drug design. Scientific data, 7(1), 309. https://doi.org/10.1038/s41597-020-00652-6
  • [16] Rodrigues, J., Teixeira, J., Trellet, M., & Bonvin, A. (2018). pdb-tools: a swiss army knife for molecular structures. F1000Research, 7, 1961. https://doi.org/10.12688/f1000research.17456.1
  • [17] Huang, X., Pearce, R., & Zhang, Y. (2020). EvoEF2: accurate and fast energy function for computational protein design. Bioinformatics (Oxford, England), 36(4), 1135–1142. https://doi.org/10.1093/bioinformatics/btz740
  • [18] van Zundert, G., Rodrigues, J., Trellet, M., Schmitz, C., Kastritis, P. L., Karaca, E., Melquiond, A., van Dijk, M., de Vries, S. J., & Bonvin, A. (2016). The HADDOCK2.2 Web Server: User-Friendly Integrative Modeling of Biomolecular Complexes. Journal of molecular biology, 428(4), 720–725. https://doi.org/10.1016/j.jmb.2015.09.01489
  • [19] Geng, C., Vangone, A., Folkers, G. E., Xue, L. C., & Bonvin, A. (2019). iSEE: Interface structure, evolution, and energy-based machine learning predictor of binding affinity changes upon mutations. Proteins, 87(2), 110–119. https://doi.org/10.1002/prot.25630
  • [20] Karaca, E., Rodrigues, J., Graziadei, A., Bonvin, A., & Carlomagno, T. (2017). M3: an integrative framework for structure determination of molecular machines. Nature methods, 14(9), 897–902. https://doi.org/10.1038/nmeth.4392
  • [21] Brünger, A. T., Adams, P. D., Clore, G. M., DeLano, W. L., Gros, P., Grosse-Kunstleve, R. W., Jiang, J. S., Kuszewski, J., Nilges, M., Pannu, N. S., Read, R. J., Rice, L. M., Simonson, T., & Warren, G. L. (1998). Crystallography & NMR system: A new software suite for macromolecular structure determination. Acta crystallographica. Section D, Biological crystallography, 54(Pt 5), 905–921. https://doi.org/10.1107/s0907444998003254
  • [22] Jorgensen, W. L., & Tirado-Rives, J. (1988). The OPLS [optimized potentials for liquid simulations] potential functions for proteins, energy minimizations for crystals of cyclic peptides and crambin. Journal of the American Chemical Society, 110(6), 1657–1666. https://doi.org/10.1021/ja00214a001
  • [23] Dominguez, C., Boelens, R., & Bonvin, A. M. (2003). HADDOCK: a protein-protein docking approach based on biochemical or biophysical information. Journal of the American Chemical Society, 125(7), 1731–1737. https://doi.org/10.1021/ja026939x
  • [24] Sitkoff, D., Sharp, K., & Honig, B. (1994). Accurate Calculation of Hydration Free Energies Using Macroscopic Solvent Models. The Journal Of Physical Chemistry, 98(7), 1978-1988. https://doi.org/10.1021/j100058a043
  • [25] Brooks, B. R., Brooks, C. L., 3rd, Mackerell, A. D., Jr, Nilsson, L., Petrella, R. J., Roux, B., Won, Y., Archontis, G., Bartels, C., Boresch, S., Caflisch, A., Caves, L., Cui, Q., Dinner, A. R., Feig, M., Fischer, S., Gao, J., Hodoscek, M., Im, W., Kuczera, K., … Karplus, M. (2009). CHARMM: the biomolecular simulation program. Journal of computational chemistry, 30(10), 1545–1614. https://doi.org/10.1002/jcc.21287
  • [26] Kastritis, P. L., & Bonvin, A. M. (2012). On the binding affinity of macromolecular interactions: daring to ask why proteins interact. Journal of the Royal Society, Interface, 10(79), 20120835. https://doi.org/10.1098/rsif.2012.0835
There are 26 citations in total.

Details

Primary Language Turkish
Subjects Engineering
Journal Section Research Articles
Authors

Mehdi Koşaca 0000-0003-2075-2480

Eda Şamiloğlu 0000-0003-2053-8900

Ezgi Karaca 0000-0002-4926-7991

Project Number 3393
Publication Date December 30, 2021
Published in Issue Year 2021 Volume: 33 Issue: 4

Cite

APA Koşaca, M., Şamiloğlu, E., & Karaca, E. (2021). Arayüz Mutasyonlarının Protein Etkileşimlerine Tesirini Tahmin Eden Algoritmalarla HADDOCK’un Performansının Karşılaştırılması. International Journal of Advances in Engineering and Pure Sciences, 33(4), 592-608. https://doi.org/10.7240/jeps.920075
AMA Koşaca M, Şamiloğlu E, Karaca E. Arayüz Mutasyonlarının Protein Etkileşimlerine Tesirini Tahmin Eden Algoritmalarla HADDOCK’un Performansının Karşılaştırılması. JEPS. December 2021;33(4):592-608. doi:10.7240/jeps.920075
Chicago Koşaca, Mehdi, Eda Şamiloğlu, and Ezgi Karaca. “Arayüz Mutasyonlarının Protein Etkileşimlerine Tesirini Tahmin Eden Algoritmalarla HADDOCK’un Performansının Karşılaştırılması”. International Journal of Advances in Engineering and Pure Sciences 33, no. 4 (December 2021): 592-608. https://doi.org/10.7240/jeps.920075.
EndNote Koşaca M, Şamiloğlu E, Karaca E (December 1, 2021) Arayüz Mutasyonlarının Protein Etkileşimlerine Tesirini Tahmin Eden Algoritmalarla HADDOCK’un Performansının Karşılaştırılması. International Journal of Advances in Engineering and Pure Sciences 33 4 592–608.
IEEE M. Koşaca, E. Şamiloğlu, and E. Karaca, “Arayüz Mutasyonlarının Protein Etkileşimlerine Tesirini Tahmin Eden Algoritmalarla HADDOCK’un Performansının Karşılaştırılması”, JEPS, vol. 33, no. 4, pp. 592–608, 2021, doi: 10.7240/jeps.920075.
ISNAD Koşaca, Mehdi et al. “Arayüz Mutasyonlarının Protein Etkileşimlerine Tesirini Tahmin Eden Algoritmalarla HADDOCK’un Performansının Karşılaştırılması”. International Journal of Advances in Engineering and Pure Sciences 33/4 (December 2021), 592-608. https://doi.org/10.7240/jeps.920075.
JAMA Koşaca M, Şamiloğlu E, Karaca E. Arayüz Mutasyonlarının Protein Etkileşimlerine Tesirini Tahmin Eden Algoritmalarla HADDOCK’un Performansının Karşılaştırılması. JEPS. 2021;33:592–608.
MLA Koşaca, Mehdi et al. “Arayüz Mutasyonlarının Protein Etkileşimlerine Tesirini Tahmin Eden Algoritmalarla HADDOCK’un Performansının Karşılaştırılması”. International Journal of Advances in Engineering and Pure Sciences, vol. 33, no. 4, 2021, pp. 592-08, doi:10.7240/jeps.920075.
Vancouver Koşaca M, Şamiloğlu E, Karaca E. Arayüz Mutasyonlarının Protein Etkileşimlerine Tesirini Tahmin Eden Algoritmalarla HADDOCK’un Performansının Karşılaştırılması. JEPS. 2021;33(4):592-608.