Dergiler / Bilecik Şeyh Edebali Üniversitesi Fen Bilimleri Dergisi / 2026 / Cilt: 13 Sayı: 1

Kronik Miyeloid Lösemi (KML) ile İlişkili ABL1 Genindeki Tek Nükleotid Varyasyonlarının (SNV'ler) ve Tirozin Kinaz İnhibitörlerinin (TKI'ler) Etkinliğinin Biyoinformatik Araçlarla Değerlendirilmesi

Evaluation of Single Nucleotide Variations in the Chronic Myeloid Leukemia (CML) Associated ABL1 Gene and the Efficacy of Tyrosine Kinase Inhibitors by Bioinformatics Tools

Ahmet Burak Doğanoğlu(ÜSKÜDAR ÜNİVERSİTESİ)0009-0005-8215-2319
Tuğba Kaman(ÜSKÜDAR ÜNİVERSİTESİ)0000-0002-5885-0193
Ömer Faruk Karasakal(ÜSKÜDAR ÜNİVERSİTESİ)0000-0001-7803-3249
Sayfa
1–16
Yayın Tarihi
2026-05-31
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Yapay Zeka Hızlı Bakış & Çıkarımlar

AI Insight
⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

M351 ve F359 bölgesindeki varyasyonlar potansiyel direnç mekanizmaları açısından klinik takipte öncelikli değerlendirilmelidir.

📌 3 Maddede Bu Makale

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Amaç & Hipotez:ABL1 proteini, kronik miyeloid lösemi (KML) patogenezinde kritik rol oynamaktadır.
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Temel Bulgular & Veri:KML tedavisinde, ABL1’in ATP ve miristoil bağlanma ceplerini hedef alan tirozin kinaz inhibitörleri (TKI’ler) kullanılmaktadır.
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Sonuç & Katkı:M351 ve F359 bölgesindeki varyasyonlar potansiyel direnç mekanizmaları açısından klinik takipte öncelikli değerlendirilmelidir.
🔬Tür:Modelleme & Sayısal Simülasyon
📍Kapsam:Genişletilmiş Akademik Örneklem
⚙️Değişkenler:ABL1 geni · Tek Nükleotid Varyasyonu (SNV) · Kronik Miyeloid Lösemi (KML)

Özet

ABL1 proteini, kronik miyeloid lösemi (KML) patogenezinde kritik rol oynamaktadır. KML tedavisinde, ABL1’in ATP ve miristoil bağlanma ceplerini hedef alan tirozin kinaz inhibitörleri (TKI’ler) kullanılmaktadır. Bu çalışmada, ABL1 protein kinaz domainindeki missense varyasyonların protein stabilitesi ve TKI bağlanma afiniteleri üzerindeki etkilerini değerlendirmek amaçlanmıştır. ABL1 protein kinaz domaininde yer alan 201 missense varyasyonu incelenmiş, ATP ve miristoil bağlanma bölgelerini etkileyebilecek toplam 224 aminoasit değişimi analiz edilmiştir. Protein stabilitesine etkiler MAESTRO-web, I-Mutant 2.0, DUET, SDM, CUPSAT ve MUpro ile tahmin edilmiştir. Tüm araçlarda destabilize edici bulunan varyasyonlar Swiss-Model DeepView ile modellenmiş, yabanıl tip ABL1 referans alınarak AutoDock Vina ile Imatinib , Dasatinib , Nilotinib , Bosutinib , Ponatinib ve Asciminib bağlanma afiniteleri hesaplanmıştır. Fonksiyonel etkiler PolyPhen2, PHD-SNP, PredictProtein ve PANTHER ile belirlenmiştir. 224 varyasyondan 74’ü tüm araçlarca destabilize edici bulunmuştur. Bu varyasyonlardan 29’u altı TKI’nin bağlanma afinitesini olumsuz etkilemiştir. Yabanıl tip proteinin bağlanma afiniteleri sırasıyla -10,7, -9,8, -10,6, -9,1, -10,8 ve -11,1 kcal/mol’dur. I242T ve V268M varyasyonları en zayıf bağlanma değerlerine sahipken, A350T, M351V, Y353S, L354Q, I360T ve H361R varyasyonları M351/F359 bölgesine yakınlıkları nedeniyle kritik bulunmuştur. Çoğu varyasyonlu görüntüde gerçekleştirilen kenetlenme T315 aminoasidi ile hidrojen bağı oluşturmuştur. ABL1 varyasyonları, TKI bağlanmasını zayıflatarak tedavi etkinliğini azaltabilir. M351 ve F359 bölgesindeki varyasyonlar potansiyel direnç mekanizmaları açısından klinik takipte öncelikli değerlendirilmelidir.

Abstract

The ABL1 protein plays a critical role in the pathogenesis of chronic myeloid leukaemia (CML). In the treatment of CML, tyrosine kinase inhibitors (TKIs) targeting the ATP and myristoyl binding pockets of ABL1 are used. In this study, we aimed to evaluate the effects of missense variations in the ABL1 protein kinase domain on protein stability and TKI binding affinities. A total of 201 missense variations in the ABL1 protein kinase domain were examined, and 224 amino acid variations that could affect the ATP and myristoyl binding regions were analysed. Effects on protein stability were predicted using MAESTRO-web, I-Mutant 2.0, DUET, SDM, CUPSAT, and MUpro. Variants identified as destabilising in all tools were modelled using Swiss-Model DeepView, and binding affinities for Imatinib , Dasatinib , Nilotinib , Bosutinib , Ponatinib , and Asciminib were calculated using AutoDock Vina with wild-type ABL1 as a reference. Functional effects were determined using PolyPhen2, PHD-SNP, PredictProtein, and PANTHER. Of the 224 variations, 74 were found to be destabilising by all tools. Of these variations, 29 negatively affected the binding affinity of six TKIs. The binding affinities of the wild-type protein are -10.7, -9.8, -10.6, -9.1, -10.8, and -11.1 kcal/mol, respectively. The I242T and V268M variations had the weakest binding values, while the A350T, M351V, Y353S, L354Q, I360T, and H361R variations were considered critical due to their proximity to the M351/F359 region. Most of the docking performed on the variant structures formed a hydrogen bond with the T315 amino acid. ABL1 variants may weaken TKI binding, thereby reducing treatment efficacy. Variants in the M351 and F359 region should be prioritised for clinical monitoring in terms of potential resistance mechanisms.

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Bu Makaleye Atıf Yap

Doğanoğlu, A. B., Kaman, T., Karasakal, Ö. F. (2026). Kronik Miyeloid Lösemi (KML) ile İlişkili ABL1 Genindeki Tek Nükleotid Varyasyonlarının (SNV'ler) ve Tirozin Kinaz İnhibitörlerinin (TKI'ler) Etkinliğinin Biyoinformatik Araçlarla Değerlendirilmesi. Bilecik Şeyh Edebali Üniversitesi Fen Bilimleri Dergisi, Cilt: 13 Sayı: 1, 1–16. https://doi.org/10.35193/bseufbd.1681493

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Kaynakça (38)

  1. Leak, S., Horne, G. A., & Copland, M. (2023). Targeting BCR-ABL1-positive leukaemias: A review article. Cambridge Prisms. Precision Medicine, 1, e21.
  2. Huang, N., Huang, Z., Gao, M., Luo, Z., Zhou, F., Liu, L., Xiao, Q., Wang, X., & Feng, W. (2018). Induction of apoptosis in imatinib sensitive and resistant chronic myeloid leukemia cells by efficient disruption of BCR-abl oncogene with zinc finger nucleases. Journal of Experimental & Clinical Cancer Research: CR, 37(1), 62.
  3. Perrotti, D., Jamieson, C., Goldman, J., & Skorski, T. (2010). Chronic myeloid leukemia: Mechanisms of blastic transformation. The Journal of Clinical Investigation, 120(7), 2254–2264.
  4. La Rosée, P., & Deininger, M. W. (2010). Resistance to Imatinib: Mutations and Beyond. Seminars in Hematology, 47(4), 335–343.
  5. Miller, G. D., Bruno, B. J., & Lim, C. S. (2014). Resistant mutations in CML and Ph+ALL – role of ponatinib. Biologics: Targets & Therapy, 8, 243–254.
  6. Padala, S., & Cortes, J. (2023). Asciminib in chronic myeloid leukemia: A STAMP for expedited delivery? Haematologica, 108(11), 2913–2918.
  7. De Novellis, D., Cacace, F., Caprioli, V., Wierda, W. G., Mahadeo, K. M., & Tambaro, F. P. (2021). The TKI Era in Chronic Leukemias. Pharmaceutics, 13(12), 2201.
  8. Poudel, G., Tolland, M. G., Hughes, T. P., & Pagani, I. S. (2022). Mechanisms of Resistance and Implications for Treatment Strategies in Chronic Myeloid Leukaemia. Cancers, 14(14), 3300.
  9. Garcia-Gutierrez, V., Huang, F., Ashaye, A., Dalal, M., Laliman-Khara, V., Breccia, M., Rutherford, M., Moradian, H., Patos, P., & Jabbour, E. J. (2024). Ponatinib vs. asciminib in post-second-generation tyrosine kinase inhibitor therapy for chronic-phase chronic myeloid leukemia: a matching-adjusted indirect comparison. Frontiers in oncology, 14, 1455378.
  10. Hochhaus, A., Réa, D., Boquimpani, C., Minami, Y., Cortes, J. E., Hughes, T. P., Apperley, J. F., Lomaia, E., Voloshin, S., Turkina, A., Kim, D. W., Abdo, A., Fogliatto, L. M., le Coutre, P., Sasaki, K., Kim, D. D. H., Saussele, S., Annunziata, M., Chaudhri, N., Chee, L., … Mauro, M. J. (2023). Asciminib vs bosutinib in chronic-phase chronic myeloid leukemia previously treated with at least two tyrosine kinase inhibitors: longer-term follow-up of ASCEMBL. Leukemia, 37(3), 617–626.
  11. Verma, H., & Silakari, O. (2020). Investigating the Role of Missense SNPs on ALDH 1A1 mediated pharmacokinetic resistance to cyclophosphamide. Computers in Biology and Medicine, 125, 103979.
  12. Vyas, B., Choudhary, S., Verma, H., Kumar, M., & Malik, A. K. (2022). Identification of missense SNP-mediated mutations in the regulatory sites of aldose reductase (ALR2) responsible for treatment failure in diabetic complications. Journal of Molecular Modeling, 28(9), 260.
  13. The UniProt Consortium. (2023). UniProt: The Universal Protein Knowledgebase in 2023. Nucleic Acids Research, 51(D1), D523–D531.
  14. Laimer, J., Hiebl-Flach, J., Lengauer, D., & Lackner, P. (2016). MAESTROweb: A web server for structure-based protein stability prediction. Bioinformatics (Oxford, England), 32(9), 1414–1416.
  15. Capriotti, E., Fariselli, P., & Casadio, R. (2005). I-Mutant2.0: Predicting stability changes upon mutation from the protein sequence or structure. Nucleic Acids Research, 33(Web Server issue), W306-310.
  16. Pandurangan, A. P., Ochoa-Montaño, B., Ascher, D. B., & Blundell, T. L. (2017). SDM: A server for predicting effects of mutations on protein stability. Nucleic Acids Research, 45(W1), W229–W235.
  17. Pires, D. E. V., Ascher, D. B., & Blundell, T. L. (2014). DUET: A server for predicting effects of mutations on protein stability using an integrated computational approach. Nucleic Acids Research, 42(Web Server issue), W314-319.
  18. Parthiban, V., Gromiha, M. M., & Schomburg, D. (2006). CUPSAT: Prediction of protein stability upon point mutations. Nucleic Acids Research, 34(Web Server issue), W239-242.
  19. Bon, L., Lucchetti, C., Portolan, F., & Pagan, M. (2002). MUPRO: A multipurpose robot. The International Journal of Neuroscience, 112(7), 855–868.
  20. Guex, N., Peitsch, M. C., & Schwede, T. (2009). Automated comparative protein structure modeling with SWISS-MODEL and Swiss-PdbViewer: A historical perspective. Electrophoresis, 30 Suppl 1, S162-173.
  21. Berman, H. M., Westbrook, J., Feng, Z., Gilliland, G., Bhat, T. N., Weissig, H., Shindyalov, I. N., & Bourne, P. E. (2000). The Protein Data Bank. Nucleic Acids Research, 28(1), 235–242.
  22. Trott, O., & Olson, A. J. (2010). AutoDock Vina: Improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. Journal of Computational Chemistry, 31(2), 455–461.
  23. Adzhubei, I. A., Schmidt, S., Peshkin, L., Ramensky, V. E., Gerasimova, A., Bork, P., Kondrashov, A. S., & Sunyaev, S. R. (2010). A method and server for predicting Damaging missense mutations. Nature Methods, 7(4), 248–249.
  24. Capriotti, E., Calabrese, R., & Casadio, R. (2006). Predicting the insurgence of human genetic diseases associated to single point protein mutations with support vector machines and evolutionary information. Bioinformatics (Oxford, England), 22(22), 2729–2734.
  25. Bernhofer, M., Dallago, C., Karl, T., Satagopam, V., Heinzinger, M., Littmann, M., Olenyi, T., Qiu, J., Schütze, K., Yachdav, G., Ashkenazy, H., Ben-Tal, N., Bromberg, Y., Goldberg, T., Kajan, L., O’Donoghue, S., Sander, C., Schafferhans, A., Schlessinger, A., … Rost, B. (2021). PredictProtein—Predicting Protein Structure and Function for 29 Years. Nucleic Acids Research, 49(W1), W535–W540.
  26. Tang, H., & Thomas, P. D. (2016). PANTHER-PSEP: Predicting disease-causing genetic variants using position-specific evolutionary preservation. Bioinformatics (Oxford, England), 32(14), 2230–2232.
  27. Discovery Studio Modeling Environment (Version 2017). (2016). [Computer software]. Dassault Syst`emes BIOVIA.
  28. Sultana, T., Mou, S. I., Chatterjee, D., Faruk, M. O., & Hosen, M. I. (2024). Computational exploration of SLC14A1 genetic variants through structure modeling, protein-ligand docking, and molecular dynamics simulation. Biochemistry and biophysics reports, 38, 101703.
  29. Microsoft Excel. (2018). [Computer software]. Microsoft Corporation.
  30. George Priya Doss, C., Sudandiradoss, C., Rajasekaran, R., Purohit, R., Ramanathan, K., & Sethumadhavan, R. (2008). Identification and structural comparison of deleterious mutations in nsSNPs of ABL1 gene in chronic myeloid leukemia: A bio-informatics study. Journal of Biomedical Informatics, 41(4), 607–612.
  31. Müller, M. C., Cortes, J. E., Kim, D.-W., Druker, B. J., Erben, P., Pasquini, R., Branford, S., Hughes, T. P., Radich, J. P., Ploughman, L., Mukhopadhyay, J., & Hochhaus, A. (2009). Dasatinib treatment of chronic-phase chronic myeloid leukemia: Analysis of responses according to preexisting BCR-ABL mutations. Blood, 114(24), 4944–4953.
  32. Kim, H., Kim, S., Kim, H.-J., Kim, Y.-K., Kwak, J.-Y., Yhim, H.-Y., Kim, S.-H., Do, Y. R., Oh, S., Lee, S.-E., Jootar, S., Cui, J. W., & Kim, D.-W. (2018). Comparison of Frequency and Sensitivity of BCR-ABL1 Kinase Domain Mutations in Asian and White Patients with Imatinib-resistant Chronic–Phase Chronic Myeloid Leukemia. Clinical Lymphoma Myeloma and Leukemia, 18(10), e391–e399.
  33. Limsuwanachot, N., Kongruang, A., Rerkamnuaychoke, B., Singdong, R., Niparuck, P., Jootar, S., & Siriboonpiputtana, T. (2020). Practical Laboratory Tools for Monitoring of BCR-ABL1 Transcripts and Tyrosine Kinase (TK) Domain Mutations in Chronic Myeloid Leukemia Patients Undergoing TK Inhibitor Therapy: A Single-Center Experience in Thailand. Asian Pacific Journal of Cancer Prevention: APJCP, 21(7), 2003–2012.
  34. Sun, J., Hu, R., Han, M., Tan, Y., Xie, M., Gao, S., & Hu, J. F. (2024). Mechanisms underlying therapeutic resistance of tyrosine kinase inhibitors in chronic myeloid leukemia. International journal of biological sciences, 20(1), 175–181.
  35. Gleixner, K. V., Filik, Y., Berger, D., Schewzik, C., Stefanzl, G., Sadovnik, I., Degenfeld-Schonburg, L., Eisenwort, G., Schneeweiss-Gleixner, M., Byrgazov, K., Sperr, W. R., Mayer, J., Lion, T., & Valent, P. (2021). Asciminib and ponatinib exert synergistic anti-neoplastic effects on CML cells expressing BCR-ABL1T315I-compound mutations. American Journal of Cancer Research, 11(9), 4470–4484.
  36. Eide, C. A., Zabriskie, M. S., Savage Stevens, S. L., Antelope, O., Vellore, N. A., Than, H., Schultz, A. R., Clair, P., Bowler, A. D., Pomicter, A. D., Yan, D., Senina, A. V., Qiang, W., Kelley, T. W., Szankasi, P., Heinrich, M. C., Tyner, J. W., Rea, D., Cayuela, J.-M., … Deininger, M. W. (2019). Combining the Allosteric Inhibitor Asciminib with Ponatinib Suppresses Emergence of and Restores Efficacy Against Highly Resistant BCR-ABL1 Mutants. Cancer Cell, 36(4), 431-443.e5.
  37. Miller, G. D., Bruno, B. J., & Lim, C. S. (2014). Resistant mutations in CML and Ph+ALL – role of ponatinib. Biologics: Targets & Therapy, 8, 243–254.
  38. Limsuwanachot, N., Kongruang, A., Rerkamnuaychoke, B., Singdong, R., Niparuck, P., Jootar, S., & Siriboonpiputtana, T. (2020). Practical Laboratory Tools for Monitoring of BCR-ABL1 Transcripts and Tyrosine Kinase (TK) Domain Mutations in Chronic Myeloid Leukemia Patients Undergoing TK Inhibitor Therapy: A Single-Center Experience in Thailand. Asian Pacific Journal of Cancer Prevention: APJCP, 21(7), 2003–2012.