Pharmacophore Modeling Guided by Conformational Dynamics Reveals Potent Anticancer Agents

NIGAR ÇARSIBASI (Üsküdar Üniversitesi, Mühendislik Fakültesi, Kimya Mühendisliği Bölümü, İstanbul, Türkiye)
Süleyman Demirel Üniversitesi Fen Bilimleri Enstitüsü Dergisi
6 2
Yıl: 2023 Cilt: 27 Sayı: 1 Sayfa Aralığı: 51 - 63 Metin Dili: İngilizce DOI: 10.19113/sdufenbed.1121167 İndeks Tarihi: 03-05-2023

Pharmacophore Modeling Guided by Conformational Dynamics Reveals Potent Anticancer Agents

Öz:
Targeting the interaction between tumor suppressor p53 and murine double minute 2(MDM2) has been an attractive therapeutic strategy of recent cancer research. There are a few number of MDM2-targeted anticancer drug molecules undergoing clinical trials, yet none of them have been approved so far. In this study, a new approach is employed in which dynamics of MDM2 obtained by elastic network models are used as a guide in the generation of the ligand-based pharmacophore model prior to virtual screening. Hit molecules exhibiting high affinity to MDM2 were captured and tested by rigid and induced-fit molecular docking. The knowledge of the binding mechanism was used while creating the induced-fit docking criteria. Application of Molecular Mechanics-Generalized Born Surface Area (MM-GBSA) method provided an accurate prediction of the binding free energy values. Two leading hit molecules which have shown better docking scores, binding free energy values and drug-like molecular properties were identified. These hits exhibited extra intermolecular interactions with MDM2, indicating a stable complex formation and hence would be further tested in vitro. Finally, the combined computational strategy employed in this study can be a promising tool in drug design for the discovery of potential new hits.
Anahtar Kelime: elastic network model drug repurposing induced-fit docking MDM2 p53

Konformasyonel Dinamik Yönlendirmeli Farmakofor Modelleme ile Güçlü Antikanser Ajanlarının Belirlenmesi

Öz:
Tümör baskılayıcı p53 ile Murine Double Minute 2 (MDM2) proteinleri arasındaki etkileşimi hedeflemek, son kanser araştırmalarında öne çıkan bir terapötik strateji olmuştur. Şu anda klinik deneylerde çalışılan birkaç MDM2 hedefli antikanser ilaç molekülü bulunmakla beraber hiçbiri henüz onay alamamıştır. Bu çalışmada, elastik ağ modelleri ile elde edilen MDM2 dinamiklerinin, ligand bazlı farmakofor modelinin oluşturulmasında ardından sanal tarama yürütülerek yeni MDM2 inhibitörlerinin araştırılmasında kılavuz olarak kullanıldığı bir yaklaşım yürütülmüştür. Sanal tarama sonucu elde edilen öncü moleküllerin MDM2'ye afiniteleri sabit ve uyarılmış-uyumlu moleküler yerleştirme (induced-fit docking) metodları ile test edilmiştir. İndüklenmiş yerleştirme kriterleri oluşturulurken bağlanma mekanizması bilgisi kullanılmıştır. Bağlanma serbest enerji değerlerinin doğru tahminini sağlayan Moleküler Mekanik Generalized Born-Surface Area (MM-GBSA) yönteminin uygulanması ile, yüksek yerleştirme puanları, bağlanma serbest enerjileri ve ilaca benzer fizikokimyasal özelliklere sahip iki adet lider molekül belirlenmiştir. Bu lider moleküller, MDM2 ile ekstra etkileşimler sergilerken kararlı kompleks oluşturmaktadırlar ve sonraki aşamada in vitro çalışmalarda inceleneceklerdir. Sonuç olarak, burada uygulanan kombine bilgisayar destekli strateji, yeni ilaç adaylarının keşfinde başarılı bir yöntem olarak uygulanabilir.
Anahtar Kelime: elastik ağ modeli ilaç yeniden konumlandırma uyarılmış-uyumlu moleküler yerleştirme MDM2 p53

Belge Türü: Makale Makale Türü: Araştırma Makalesi Erişim Türü: Erişime Açık
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APA ÇARSIBASI N (2023). Pharmacophore Modeling Guided by Conformational Dynamics Reveals Potent Anticancer Agents. , 51 - 63. 10.19113/sdufenbed.1121167
Chicago ÇARSIBASI NIGAR Pharmacophore Modeling Guided by Conformational Dynamics Reveals Potent Anticancer Agents. (2023): 51 - 63. 10.19113/sdufenbed.1121167
MLA ÇARSIBASI NIGAR Pharmacophore Modeling Guided by Conformational Dynamics Reveals Potent Anticancer Agents. , 2023, ss.51 - 63. 10.19113/sdufenbed.1121167
AMA ÇARSIBASI N Pharmacophore Modeling Guided by Conformational Dynamics Reveals Potent Anticancer Agents. . 2023; 51 - 63. 10.19113/sdufenbed.1121167
Vancouver ÇARSIBASI N Pharmacophore Modeling Guided by Conformational Dynamics Reveals Potent Anticancer Agents. . 2023; 51 - 63. 10.19113/sdufenbed.1121167
IEEE ÇARSIBASI N "Pharmacophore Modeling Guided by Conformational Dynamics Reveals Potent Anticancer Agents." , ss.51 - 63, 2023. 10.19113/sdufenbed.1121167
ISNAD ÇARSIBASI, NIGAR. "Pharmacophore Modeling Guided by Conformational Dynamics Reveals Potent Anticancer Agents". (2023), 51-63. https://doi.org/10.19113/sdufenbed.1121167
APA ÇARSIBASI N (2023). Pharmacophore Modeling Guided by Conformational Dynamics Reveals Potent Anticancer Agents. Süleyman Demirel Üniversitesi Fen Bilimleri Enstitüsü Dergisi, 27(1), 51 - 63. 10.19113/sdufenbed.1121167
Chicago ÇARSIBASI NIGAR Pharmacophore Modeling Guided by Conformational Dynamics Reveals Potent Anticancer Agents. Süleyman Demirel Üniversitesi Fen Bilimleri Enstitüsü Dergisi 27, no.1 (2023): 51 - 63. 10.19113/sdufenbed.1121167
MLA ÇARSIBASI NIGAR Pharmacophore Modeling Guided by Conformational Dynamics Reveals Potent Anticancer Agents. Süleyman Demirel Üniversitesi Fen Bilimleri Enstitüsü Dergisi, vol.27, no.1, 2023, ss.51 - 63. 10.19113/sdufenbed.1121167
AMA ÇARSIBASI N Pharmacophore Modeling Guided by Conformational Dynamics Reveals Potent Anticancer Agents. Süleyman Demirel Üniversitesi Fen Bilimleri Enstitüsü Dergisi. 2023; 27(1): 51 - 63. 10.19113/sdufenbed.1121167
Vancouver ÇARSIBASI N Pharmacophore Modeling Guided by Conformational Dynamics Reveals Potent Anticancer Agents. Süleyman Demirel Üniversitesi Fen Bilimleri Enstitüsü Dergisi. 2023; 27(1): 51 - 63. 10.19113/sdufenbed.1121167
IEEE ÇARSIBASI N "Pharmacophore Modeling Guided by Conformational Dynamics Reveals Potent Anticancer Agents." Süleyman Demirel Üniversitesi Fen Bilimleri Enstitüsü Dergisi, 27, ss.51 - 63, 2023. 10.19113/sdufenbed.1121167
ISNAD ÇARSIBASI, NIGAR. "Pharmacophore Modeling Guided by Conformational Dynamics Reveals Potent Anticancer Agents". Süleyman Demirel Üniversitesi Fen Bilimleri Enstitüsü Dergisi 27/1 (2023), 51-63. https://doi.org/10.19113/sdufenbed.1121167
Sayfa Oluşturulma Tarihi
24-04-2024 6:35

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