- Books by James Devillers (Author of Genetic Algorithms in Molecular Modeling)
- Genetic Algorithms in Molecular Modeling
Through its up to the minute content, extensive bibliography, and essential information on software availability, this book leads the reader from the theoretical aspects to the practical applications. It enables the uninitiated reader to apply genetic algorithms for modeling the biological activities and properties of chemicals, and provides the trained scientist with the most up to date information on the topic.
He is also the President of the Centre de Traitement de l'Information Scientifique, which is a private company specializing in QSAR studies, drug design, statistical analysis, and data validation. Genetic Algorithms in Molecular Modeling. Whenever the population P is updated, individuals will be rearranged to be from minimal to maximal by the energy values.
Books by James Devillers (Author of Genetic Algorithms in Molecular Modeling)
Finally, the hypothesis h min and minimum energy E min will be used as the optimal values at the end of algorithm. Genetic tabu search algorithm The hybrid algorithm combines genetic algorithm and tabu search algorithm and can deal with multi-extremum and multi-parameter problems. In this section, we describe our experiments by using Fibonacci sequences to test the efficiency of the proposed GATS. A Fibonacci sequence is defined recursively by.
For comparison, we used the same Fibonacci sequences as those used in [ 24 , 26 — 28 ]. For comparison, we also list the minimal energy values obtained by the Simulated Annealing SA [ 28 ], the energy landscape paving minimizer ELP [ 26 ], the conformational space annealing CSA [ 27 ], and the tabu search algorithm TS [ 24 ] respectively.
The corresponding bond angles and the torsional angles at the global minima are shown in Table 2. Although the lowest energy value obtained by GATS is not as low as that obtained by TS for the sequence with length 34, it is smaller than that obtained by TS for the sequences with lengths 55, which shows that GATS has better performance for long sequence.
The lowest-energy ground configurations of Fibonacci sequences listed in Table 1 are presented in Figure 3. In Figure 3 , the solid dots indicate the hydrophobic monomers A while the empty dots indicate the hydrophilic monomers B. Figure 3 shows that all the conformations form single compact hydrophobic cores surrounded by hydrophilic residues, which is observed in real proteins.
The results verify that it is reasonable to use AB model with Fibonacci sequences in three dimensions to mimic the real protein. The lowest energy conformations for the four Fibonacci sequences obtained by GATS algorithm Solid dots indicate hydrophobic monomers A , and open dots indicate hydrophilic monomers B.
In this section, we describe the experimental results using real protein sequences. For comparison, we used the same three protein sequences as those used in [ 29 ].
Because there are few papers dealing with the real protein structure prediction issue using off-lattice AB model, we only compared our experimental results with the results in [ 29 ]. The experimental results for the real proteins are presented in Table 3 , and the corresponding lowest protein landscapes obtained by our GATS are shown in Figure 4. Table 3 shows that the minimal energy values obtained by the proposed GATS are lower than those obtained by TS in [ 29 ], especially for long sequences. From Figure 4 , we find that all the configurations have also formed a hydrophobic core, surrounded by hydrophilic residues.
However, the hydrophobic core of 1AGT, which is the longest among the three real proteins, seems not to be compact enough.
Genetic Algorithms in Molecular Modeling
This may indicate that the performance of the coarse simplified AB off-lattice model is not effective enough for the prediction of the structure for long protein sequences. A hybrid algorithm that combines genetic algorithm and tabu search algorithm is developed for 3-D protein structure prediction using off-lattice AB model. The proposed algorithm can deal with multi-extremum and multi-parameter problems. In the proposed algorithm, different strategies are adopted to make the proposed algorithm have different advantages.
For examples, the variable population size strategy can keep the diversity of the population, and TSM strategy makes it possible to accept poor solution as the current solution and thus makes the algorithm have better hill-climbing capability and stronger local searching capability than many other mutation operators. In addition, TSR strategy can limit the frequency that the offsprings with the same fitness appear, and thus can also keep the diversity of the population and avoid premature convergence of the algorithm.
Compared with the previous algorithms, GATS has stronger capability of global searching. In the future work, we will improve the algorithm and make it more effective for long protein sequence prediction using multi-core computing platforms [ 31 ]. Anfinsen CB: Principles that govern the folding of protein chains. Studies in Computational Intelligence. Springer Berlin.
Dill KA: Theory for the folding and stability of globular proteins. Handbook of Molecular Biology. Edited by: Aluru S. CRC Press. Journal of Computational Biology. The Protein folding problem and tertiary structure prediction. S Birkhauser. Journal of Molecular Biology. Evolutionary Computation in Bioinformatics. Elsevier India. Journal of Molecular Modeling. PAKDD workshops. Holland J: Adaptation in nature and artificial systems.
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University of Michigan Press. Glover F: Future paths for integer programming and links to artificial intelligence. Computers and Operations Research. Zhu J: Non-classical mathematics for Intelligent Systems. Huazhong University of Science and Technology Press. Journal of Systems engineering. Mini-Micro Systems. Master dissertation of Wuhan University of Science and Technlogy.
Mount DW: Bioinformatics: sequence and genome analysis.
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Yang Q M, Yang Jack Y: Lecture notes: and beyond, the decade of high-performance computing for the next-generation sequence analysis. Computational Biology and Drug Design. Download references. We also thank T. Correspondence to Xiaolong Zhang or Jinshan Tang. XZ designed the algorithm and analyzed the experimental results. TW participated in the implementation of algorithm, and did the experiments with the given data. HL took part in the implementation of the algorithm and data processing. JT and YD helped rewriting the paper based on the original version.
JYY and MQY contributed to the development of the algorithm, and provided many useful insights on protein modeling. All authors agreed on the content of the paper. This article is published under license to BioMed Central Ltd. Easily read eBooks on smart phones, computers, or any eBook readers, including Kindle. When you read an eBook on VitalSource Bookshelf, enjoy such features as: Access online or offline, on mobile or desktop devices Bookmarks, highlights and notes sync across all your devices Smart study tools such as note sharing and subscription, review mode, and Microsoft OneNote integration Search and navigate content across your entire Bookshelf library Interactive notebook and read-aloud functionality Look up additional information online by highlighting a word or phrase.
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Extremely topical and timely Sets the foundations for the development of computer-aided tools for solving numerous problems in QSAR and drug design Written to be accessible without prior direct experience in genetic algorithms. Powered by. You are connected as. Connect with:. Use your name:. Thank you for posting a review! We value your input. Share your review so everyone else can enjoy it too. Your review was sent successfully and is now waiting for our team to publish it.
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