کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
10962008 | 1102326 | 2014 | 8 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Bayesian models for screening and TB Mobile for target inference with Mycobacterium tuberculosis
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کلمات کلیدی
موضوعات مرتبط
علوم زیستی و بیوفناوری
ایمنی شناسی و میکروب شناسی
میکروبیولوژی و بیوتکنولوژی کاربردی
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چکیده انگلیسی
The search for compounds active against Mycobacterium tuberculosis is reliant upon high-throughput screening (HTS) in whole cells. We have used Bayesian machine learning models which can predict anti-tubercular activity to filter an internal library of over 150,000 compounds prior to in vitro testing. We used this to select and test 48 compounds in vitro; 11 were active with MIC values ranging from 0.4 μM to 10.2 μM, giving a high hit rate of 22.9%. Among the hits, we identified several compounds belonging to the same series including five quinolones (including ciprofloxacin), three molecules with long aliphatic linkers and three singletons. This approach represents a rapid method to prioritize compounds for testing that can be used alongside medicinal chemistry insight and other filters to identify active molecules. Such models can significantly increase the hit rate of HTS, above the usual 1% or lower rates seen. In addition, the potential targets for the 11 molecules were predicted using TB Mobile and clustering alongside a set of over 740 molecules with known M. tuberculosis target annotations. These predictions may serve as a mechanism for prioritizing compounds for further optimization.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Tuberculosis - Volume 94, Issue 2, March 2014, Pages 162-169
Journal: Tuberculosis - Volume 94, Issue 2, March 2014, Pages 162-169
نویسندگان
Sean Ekins, Allen C. Casey, David Roberts, Tanya Parish, Barry A. Bunin,