کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
2076936 | 1545000 | 2007 | 21 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Improving the efficiency of evolutionary de novo peptide design: Strategies for probing configuration and parameter settings
دانلود مقاله + سفارش ترجمه
دانلود مقاله ISI انگلیسی
رایگان برای ایرانیان
کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
ریاضیات
مدلسازی و شبیه سازی
پیش نمایش صفحه اول مقاله
چکیده انگلیسی
Evolutionary molecular design based on genetic algorithms (GAs) has been demonstrated to be a flexible and efficient optimization approach with potential for locating global optima. Its efficacy and efficiency are largely dependent on the operations and control parameters of the GAs. Accordingly, we have explored new operations and probed good parameter setting through simulations. The findings have been evaluated in a helical peptide design according to “Parameter setting by analogy” strategy; highly helical peptides have been successfully obtained with a population of only 16 peptides and 5 iterative cycles. The results indicate that new operations such as multi-step crossover-mutation are able to improve the explorative efficiency and to reduce the sensitivity to crossover and mutation rates (CR-MR). The efficiency of the peptide design has been furthermore improved by setting the GAs at the good CR-MR setting determined through simulation. These results suggest that probing the operations and parameter settings through simulation in combination with “Parameter setting by analogy” strategy provides an effective framework for improving the efficiency of the approach. Consequently, we conclude that this framework will be useful for contributing to practical peptide design, and gaining a better understanding of evolutionary molecular design.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Biosystems - Volume 88, Issues 1â2, March 2007, Pages 35-55
Journal: Biosystems - Volume 88, Issues 1â2, March 2007, Pages 35-55
نویسندگان
Wuming Zhang, Kazuyoshi Yano, Isao Karube,