|Minimum Free Energy-Based Amino Acid Sequence Permutation From Amino Acid|
|E. Lloyd-Yemoh1 , H.B. Shi2|
1 College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, China.
2 College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, China.
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Section:Research Paper, Product Type: Journal Paper
Volume-5 , Issue-10 , Page no. 10-15, Oct-2017
Online published on Oct 30, 2017
Copyright © E. Lloyd-Yemoh, H.B. Shi . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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IEEE Style Citation: E. Lloyd-Yemoh, H.B. Shi, “Minimum Free Energy-Based Amino Acid Sequence Permutation From Amino Acid”, International Journal of Computer Sciences and Engineering, Vol.5, Issue.10, pp.10-15, 2017.
MLA Style Citation: E. Lloyd-Yemoh, H.B. Shi "Minimum Free Energy-Based Amino Acid Sequence Permutation From Amino Acid." International Journal of Computer Sciences and Engineering 5.10 (2017): 10-15.
APA Style Citation: E. Lloyd-Yemoh, H.B. Shi, (2017). Minimum Free Energy-Based Amino Acid Sequence Permutation From Amino Acid. International Journal of Computer Sciences and Engineering, 5(10), 10-15.
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|Computationally speaking, there are a few ways to tackle problems of rule-based permutations and iterations. This paper seeks to explore two algorithms and their possible application in the field of bioinformatics and biochemical engineering. We believe that accurately predicting RNA secondary structure formations can only be achieved by extensive analysis of specific RNA folds that have already been documented to occur in nature and others like them that have the same Amino acid sequence structure and similar minimal free energies. This paper focuses on algorithms to extract every single RNA sequence that fits a given amino acid sequence. We concern ourselves mainly with the computation intensive issue of the outputting various permutations of given protein sequences and their respective minimal free energies. Results: We present a way to computationally improve analysis of secondary structure minimization. Using C++, Sequence permutations of amino acids are extracted to be analyzed in terms of minimum free energies. ViennaRNA-2.1.6 is used to facilitate our computation of the RNA fold and the corresponding minimal free energy. The Odometer Weighted Counter (OWC) approach comes in second with its critical length of six amino acids and a computations time of 68 seconds. The Vector Permutation Mapping (VPM) approach comes in as the more desirable approach with a critical length of 10, and a computation time of 26896 seconds. All tests were made on critical path length of sequences. An output of importance to our paper is the minimal free energy of each RNA sequence that the ViennaRNA RNAfold function processes. Analysis of the resulting minimal free energies in comparison to already documented RNA strings in nature is the key to more effective secondary structure prediction.|
|Key-Words / Index Term :|
|RNA, minimal free energy, amino acid, folding, ViennaRNA|
 RF Gesteland, TR Cech, JF Atkins, “The RNA World, edn 3”, Cold Spring Harbor Laboratory Press, 2005.
 J. Li, J. Zhang, J. Wang, W Li, Wang , “Structure Prediction of RNA Loops with a Probabilistic Approach”, PLoS Comput Biol 12(8): e1005032. doi:10.1371/journal.pcbi.1005032 N,2016
 G.M Cooper; R.E Hausman. “The Cell A Molecular Approach (3rd ed.)”, Sinauer. pp. 261–76, 297, 339–44. ISBN 0-87893-214-3,2004
 H Lodish, A Berk, S L Zipursky, P. Matsudaira, D. Baltimore, J,. Darnell, “Molecular Cell Biology. 4th edition”.W. H. Freeman; 2000.
 J. S. Mattick; M. J. Gagen, "The evolution of controlled multitasked gene networks: the role of introns and other noncoding RNAs in the development of complex organisms", Mol. Biol. Evol. 18(9): 1611–30. doi: 10.1093 / oxfordjournals.molbev.a003951, September, 2001
 D. H. Mathews, “Predicting a set of minimal free energy RNA secondary structures common to two sequences”, Vol. 21 no. 10, pages 2246–2253, 2005.
 S. Chen, “RNA folding: conformational statistics, folding kinetics, and ion electrostatics”, Annu. Rev. Biophys. 37:199–214, 2008
 J. Yin， H. Tian, L.Bao, X. Dai. X. Gao, W.Yao, “An alternative method of enhancing the expression level of heterologous protein in Escherichia coli”,Biochemical and Biophysical Research Communications 455 (2014) 198–204, 2014
 S. R Eddy: “How do RNA folding algorithms work?”, Nat Biotechnol 22:1457-1458,2004
 D. H. Mathews, M Zuker, “Predictive methods using RNA sequences in Bioinformatics: A Practical Guide to the Analysis of Genes and Proteins”, edn 3. Edited by Baxevenis A, Oullette F, John.Wiley and Sons:143-170, 2004
 Hajiaghayi et al., “Analysis of energy-based algorithms for RNA secondary structure prediction”. BMC Bioinformatics 13:22, 2012
 R. Lorenz, S. H. Bernhart, C. H. Siederdissen, H. Tafer, C. Flamm, P.F. Stadler,I.L. Hofacker, “ViennaRNA Package 2.0, Algorithms” Mol. Biol. 6, 2011
 J. S. Mattick. "The hidden genetic program of complex organisms". Scientific American. 291 (4): 60–7. PMID 15487671. doi:10.1038/scientificamerican1004-60, October 2004
 Khronos OpenCL Working Group,Aaftab Munshi."The OpenCL Specification".Version: 1.2. Revision: 19,2011
 Matthew Scarpino, “OpenCL in Action”, Manning Publications ISBN 9781617290176,2012