Abstract
X-linked inhibitor of apoptosis (XIAP) is a member of inhibitor of apoptosis (IAP) family and involved in the suppression of apoptosis in cancer cells. This property makes it a therapeutic target for the cancer therapy. In the present study, we have developed QSAR models using chemical descriptors, fingerprints, principal components, docking energy parameters and similarity-based approach against XIAP. We have achieved correlation (R) of 0.803 with R2 value of 0.645 at 10-fold cross validation using SMOreg algorithm. We have evaluated these models on independent dataset to ascertain its robustness and achieved correlation (R) of 0.793 with R2 value of 0.628. Further, we have used these models for the screening of FDA approved drugs and drug-like molecules from ZINC database and prioritized them on the basis of their predicted pIC50 values. Docking studies of top hits with XIAP-BIR3 domain shows that Iodixanol (DB01249) and ZINC68678304 have higher binding affinities than well-known tetrapeptide inhibitor, AVPI. We have integrated these models in a web server named as “XIAPin”. We hope that this web server will contribute in the designing of nifty antagonists against XIAP.
Keywords: Chemical descriptors, peptidomimetics, QSAR, virtual screening and principal components, X-linked inhibitor of apoptosis.
Current Cancer Drug Targets
Title:In Silico Designing and Screening of Antagonists against Cancer Drug Target XIAP
Volume: 15 Issue: 9
Author(s): Rahul Kumar, Jagat Singh Chauhan and Gajendra Pal Singh Raghava
Affiliation:
Keywords: Chemical descriptors, peptidomimetics, QSAR, virtual screening and principal components, X-linked inhibitor of apoptosis.
Abstract: X-linked inhibitor of apoptosis (XIAP) is a member of inhibitor of apoptosis (IAP) family and involved in the suppression of apoptosis in cancer cells. This property makes it a therapeutic target for the cancer therapy. In the present study, we have developed QSAR models using chemical descriptors, fingerprints, principal components, docking energy parameters and similarity-based approach against XIAP. We have achieved correlation (R) of 0.803 with R2 value of 0.645 at 10-fold cross validation using SMOreg algorithm. We have evaluated these models on independent dataset to ascertain its robustness and achieved correlation (R) of 0.793 with R2 value of 0.628. Further, we have used these models for the screening of FDA approved drugs and drug-like molecules from ZINC database and prioritized them on the basis of their predicted pIC50 values. Docking studies of top hits with XIAP-BIR3 domain shows that Iodixanol (DB01249) and ZINC68678304 have higher binding affinities than well-known tetrapeptide inhibitor, AVPI. We have integrated these models in a web server named as “XIAPin”. We hope that this web server will contribute in the designing of nifty antagonists against XIAP.
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Cite this article as:
Kumar Rahul, Chauhan Singh Jagat and Raghava Pal Singh Gajendra, In Silico Designing and Screening of Antagonists against Cancer Drug Target XIAP , Current Cancer Drug Targets 2015; 15 (9) . https://dx.doi.org/10.2174/1568009615666150706103537
DOI https://dx.doi.org/10.2174/1568009615666150706103537 |
Print ISSN 1568-0096 |
Publisher Name Bentham Science Publisher |
Online ISSN 1873-5576 |
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