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Journal articles on the topic 'Sequence design'

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Published: 4 June 2021
Last updated: 1 February 2022

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1

Jedwab, Jonathan, and Mark Strange. "Wavelength Isolation Sequence Design." IEEE Transactions on Information Theory 59, no. 5 (May 2013): 3210–14. http://dx.doi.org/10.1109/tit.2013.2241391.

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2

Norn, Christoffer, Basile I. M. Wicky, David Juergens, Sirui Liu, David Kim, Doug Tischer, Brian Koepnick, Ivan Anishchenko, David Baker, and Sergey Ovchinnikov. "Protein sequence design by conformational landscape optimization." Proceedings of the National Academy of Sciences 118, no. 11 (March 12, 2021): e2017228118. http://dx.doi.org/10.1073/pnas.2017228118.

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The protein design problem is to identify an amino acid sequence that folds to a desired structure. Given Anfinsen’s thermodynamic hypothesis of folding, this can be recast as finding an amino acid sequence for which the desired structure is the lowest energy state. As this calculation involves not only all possible amino acid sequences but also, all possible structures, most current approaches focus instead on the more tractable problem of finding the lowest-energy amino acid sequence for the desired structure, often checking by protein structure prediction in a second step that the desired structure is indeed the lowest-energy conformation for the designed sequence, and typically discarding a large fraction of designed sequences for which this is not the case. Here, we show that by backpropagating gradients through the transform-restrained Rosetta (trRosetta) structure prediction network from the desired structure to the input amino acid sequence, we can directly optimize over all possible amino acid sequences and all possible structures in a single calculation. We find that trRosetta calculations, which consider the full conformational landscape, can be more effective than Rosetta single-point energy estimations in predicting folding and stability of de novo designed proteins. We compare sequence design by conformational landscape optimization with the standard energy-based sequence design methodology in Rosetta and show that the former can result in energy landscapes with fewer alternative energy minima. We show further that more funneled energy landscapes can be designed by combining the strengths of the two approaches: the low-resolution trRosetta model serves to disfavor alternative states, and the high-resolution Rosetta model serves to create a deep energy minimum at the design target structure.
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3

Moinuddin, Mohd Moazzam, Mallikarjuna Reddy. Y, Pasha I. A ., and Lal Kishore K. "Mono-alphabetic Poly-semantic Sequence Design for HRR Target Detection." International Journal of Engineering and Technology 2, no. 3 (2010): 238–44. http://dx.doi.org/10.7763/ijet.2010.v2.127.

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4

Carter, Charles W. "Sequence-Leveled Experimental Designs. Part II. Design Construction." Quality Engineering 8, no. 2 (December 1995): 361–66. http://dx.doi.org/10.1080/08982119508904633.

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5

Mach, Paul, and Patrice Koehl. "Capturing protein sequence-structure specificity using computational sequence design." Proteins: Structure, Function, and Bioinformatics 81, no. 9 (June 20, 2013): 1556–70. http://dx.doi.org/10.1002/prot.24307.

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6

Jonsson, Jögen, Torbjörn Norberg, Lena Carlsson, Claes Gustafsson, and Svante Wold. "Quantitative sequence-activity models (QSAM)—tools for sequence design." Nucleic Acids Research 21, no. 3 (1993): 733–39. http://dx.doi.org/10.1093/nar/21.3.733.

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7

Helander, Martin G., and Li Lin. "Optimal Sequence in Product Design." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 42, no. 6 (October 1998): 569–73. http://dx.doi.org/10.1177/154193129804200610.

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This paper introduces Axiomatic Design (AD) for top-down design of ergonomics systems. The Independence Axiom was used to demonstrate how design activity can be structured to simplify the design process. Functional Requirements (FR) are first specified at a high level of abstraction and corresponding high level Design Parameters (DP) are generated. Then lower level Functional requirements are generated, and corresponding lower level Design Parameters. To avoid iterative improvements of design, one should strive for a decoupled design. This means that for each Functional Requirement there should ideally be only one Design Parameter - and this design parameter should satisfy only one functional requirement. Two case studies were used to illustrate the AD procedure.
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8

Kusui, Yoichi, Takeshi Kohno, Takashi Takayanagi, Hiroyuki Ikemoto, and Yoshinori Tsuchiya. "System Sequence Design Tool "SSD"." IFAC Proceedings Volumes 20, no. 9 (August 1987): 481–86. http://dx.doi.org/10.1016/s1474-6670(17)55753-5.

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9

Arita, Masanori, and Satoshi Kobayashi. "DNA sequence design using templates." New Generation Computing 20, no. 3 (September 2002): 263–77. http://dx.doi.org/10.1007/bf03037360.

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10

Todd Monroe, W., and Frederick R. Haselton. "Molecular Beacon Sequence Design Algorithm." BioTechniques 34, no. 1 (January 2003): 68–73. http://dx.doi.org/10.2144/03341st02.

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11

Guan, Chengyu, Zemin Zhou, and Xinwu Zeng. "A Phase-Coded Sequence Design Method for Active Sonar." Sensors 20, no. 17 (August 19, 2020): 4659. http://dx.doi.org/10.3390/s20174659.

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Phase-coded sequences are widely studied as the transmitted signals of active sonars. Recently, several design methods have been developed to generate phased-coded sequences satisfying specific aperiodic or periodic autocorrelation sidelobe level metrics. In this paper, based on the majorization–minimization strategy and the squared iterative acceleration scheme, we propose a method to generate sequences with the periodic weighted integrated sidelobe level metric. Numerical simulations illustrate that the proposed method can effectively suppress the periodic autocorrelation sidelobe levels in specific time lags. Compared with other sequence design methods satisfying the periodic weighted integrated sidelobe level metric, our method improves the computational efficiency significantly. In addition, the proposed sequence demonstrates better matched filter performance in specific range intervals compared with its counterpart. The results suggest that the method could be applied as a valid and real-time design method for transmitted signals of active sonars.
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12

Wan, Shao Song, Qun Song Zhu, and Jian Cao. "Design Modeling and Simulation by Interpolation Algorithm of Zadoff-Chu Sequence." Advanced Materials Research 989-994 (July 2014): 2617–20. http://dx.doi.org/10.4028/www.scientific.net/amr.989-994.2617.

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HF channel sounding requires perfect autocorrelation property of the sounding sequences. Compared with other pseudonoise sounding sequences, Zadoff-Chu sequence gets great attention due to its predominant periodic autocorrelation property. In order to adapt the spectrum property of Zadoff-Chu sequence to the requirement of HF channel sounding transmitter-receiver, the Formula Direct Interpolation Algorithm (FDIA) is put forward by which the amplitude frequency characteristic of Zadoff-Chu sequence can be enhanced. Application examples show that, this system can significantly improve the efficiency of aerodynamic design.
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13

Niu, Ying, Hangyu Zhou, Shida Wang, Kai Zhao, Xiaoxiao Wang, and Xuncai Zhang. "Improved Multi-Objective Particle Swarm Optimization Algorithm for DNA Sequence Design." Journal of Nanoelectronics and Optoelectronics 15, no. 12 (December 1, 2020): 1450–59. http://dx.doi.org/10.1166/jno.2020.2882.

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The DNA sequence design is a vital step in reducing undesirable biochemical reactions and incorrect computations in successful DNA computing. To this end, many studies had concentrated on how to design higher quality DNA sequences. However, DNA sequences involve some thermodynamic and conflicting conditions, which in turn reflect the evolutionary algorithm process implemented through chemical reactions. In the present study, we applied an improved multi-objective particle swarm optimization (IMOPSO) algorithm to DNA sequence design, in which a chaotic map is combined with this algorithm to avoid falling into local optima. The experimental simulation and statistical results showed that the DNA sequence design method based on IMOPSO has higher reliability than the existing sequence design methods such as traditional evolutionary algorithm, invasive weed algorithm, and specialized methods.
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14

Saitou, Kazuhiro, and Mark J. Jakiela. "Subassembly Generation via Mechanical Conformational Switches." Artificial Life 2, no. 4 (July 1995): 377–416. http://dx.doi.org/10.1162/artl.1995.2.4.377.

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A question is posed on how a particular subassembly sequence is generated in randomized assembly. An extended design of mechanical conformational switches [16] is proposed that can encode several subassembly sequences. A particular subassembly sequence is generated due to conformational changes of parts during one-dimensional randomized assembly. The optimal subassembly sequence that maximizes the yield of a desired assembly can be found via genetic search over a space of parameterized conformational switch designs, rather than a space of subassembly sequences. The resulting switch design encodes the optimal subassembly sequence so that the desired assemblies are put together only in the optimal sequence. The results of genetic search and rate equation analyses reveal that the optimal subassembly sequence depends on the initial concentration of parts and the defect probabilities during randomized assembly. The results indicate that abundant parts and parts with high defect probabilities should be assembled earlier rather than later.
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15

TAKADA, Shoji. "Protein Allostery and Sequence Design Principle." Seibutsu Butsuri 52, no. 3 (2012): 150–51. http://dx.doi.org/10.2142/biophys.52.150.

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16

Jienan Chen, Jianhao Hu, and Zijun Xin. "The Sequence Design for Stochastic Computation." International Journal of Digital Content Technology and its Applications 5, no. 10 (October 31, 2011): 199–210. http://dx.doi.org/10.4156/jdcta.vol5.issue10.24.

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17

Cordes, Matthew HJ, Alan R. Davidson, and Robert T. Sauer. "Sequence space, folding and protein design." Current Opinion in Structural Biology 6, no. 1 (February 1996): 3–10. http://dx.doi.org/10.1016/s0959-440x(96)80088-1.

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18

Goler, Jonathan A., Brian W. Bramlett, and Jean Peccoud. "Genetic design: rising above the sequence." Trends in Biotechnology 26, no. 10 (October 2008): 538–44. http://dx.doi.org/10.1016/j.tibtech.2008.06.003.

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19

Sandhya, Sankaran, Richa Mudgal, Gayatri Kumar, Ramanathan Sowdhamini, and Narayanaswamy Srinivasan. "Protein sequence design and its applications." Current Opinion in Structural Biology 37 (April 2016): 71–80. http://dx.doi.org/10.1016/j.sbi.2015.12.004.

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20

Chertovich, A. V., E. N. Govorun, V. A. Ivanov, P. G. Khalatur, and A. R. Khokhlov. "Conformation-dependent sequence design: evolutionary approach." European Physical Journal E 13, no. 1 (January 2004): 15–25. http://dx.doi.org/10.1140/epje/e2004-00036-1.

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21

Betancourt, Marcos R., and D. Thirumalai. "Protein Sequence Design by Energy Landscaping." Journal of Physical Chemistry B 106, no. 3 (January 2002): 599–609. http://dx.doi.org/10.1021/jp012014c.

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22

Khokhlov, Alexei R., and Pavel G. Khalatur. "Conformation-Dependent Sequence Design (Engineering) ofABCopolymers." Physical Review Letters 82, no. 17 (April 26, 1999): 3456–59. http://dx.doi.org/10.1103/physrevlett.82.3456.

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23

Everson, Bernard H., Cooper A. French, Andrew C. Mutter, Vikas Nanda, and Ronald L. Koder. "Hemoprotein Design using Minimal Sequence Information." Biophysical Journal 104, no. 2 (January 2013): 661a. http://dx.doi.org/10.1016/j.bpj.2012.11.3649.

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24

Khokhlov, Alexei R., and Pavel G. Khalatur. "Biomimetic sequence design in functional copolymers." Current Opinion in Solid State and Materials Science 8, no. 1 (January 2004): 3–10. http://dx.doi.org/10.1016/j.cossms.2003.08.001.

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25

Khalatur, Pavel G., Alexei R. Khokhlov, and Maria K. Krotova. "Evolutionary Approach in Copolymer Sequence Design." Macromolecular Symposia 252, no. 1 (May 2007): 36–46. http://dx.doi.org/10.1002/masy.200750604.

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26

Bergman Ärlebäck, Jonas, and Helen M. Doerr. "Moving beyond descriptive models: Research issues for design and implementation." Avances de Investigación en Educación Matemática, no. 17 (May 1, 2020): 5–20. http://dx.doi.org/10.35763/aiem.v0i17.307.

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In this paper, we draw on a models and modeling perspective to describe the design of a sequence of tasks, known as a model development sequence, that has been used to research the teaching and learning of mathematics. A central research goal of a models and modeling perspective is the development of principles for the design of sequences of modeling tasks and for the teaching of such sequences. We extend our earlier research by elaborating how a model development sequence can be used to support students in developing models that are not only descriptive but also have explanatory power when connected to existing mathematical models. In so doing, we elaborate language issues about representations and context as well as the implementation strategies used by the teacher.
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27

QIN, MENG, JUN WANG, TANPING LI, and WEI WANG. "SEQUENCE DESIGN AND FOLDING DYNAMICS OF LATTICE PROTEIN-LIKE MODELS." International Journal of Modern Physics B 16, no. 04 (February 10, 2002): 631–37. http://dx.doi.org/10.1142/s0217979202009950.

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A sequence design method based on maximizing the thermodynamic occupying probability of the target structure is investigated. Some model-protein sequences are designed using the occupying-probability-maximized procedure on a 3×3×3 lattice. The thermodynamic and dynamic features of these sequences show their great improvement comparing with those of the sequences designed by an energy-minimized method. A better foldability is achieved for the occupying-probability-maximized sequences. These results suggest that the native occupying probability rather than the energy would be a better judgment for protein-like models.
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28

Liu, Wen-li, and Qing-biao Wu. "Analysis method and algorithm design of biological sequence problem based on generalized k-mer vector." Applied Mathematics-A Journal of Chinese Universities 36, no. 1 (March 2021): 114–27. http://dx.doi.org/10.1007/s11766-021-4033-x.

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AbstractK-mer can be used for the description of biological sequences and k-mer distribution is a tool for solving sequences analysis problems in bioinformatics. We can use k-mer vector as a representation method of the k-mer distribution of the biological sequence. Problems, such as similarity calculations or sequence assembly, can be described in the k-mer vector space. It helps us to identify new features of an old sequence-based problem in bioinformatics and develop new algorithms using the concepts and methods from linear space theory. In this study, we defined the k-mer vector space for the generalized biological sequences. The meaning of corresponding vector operations is explained in the biological context. We presented the vector/matrix form of several widely seen sequence-based problems, including read quantification, sequence assembly, and pattern detection problem. Its advantages and disadvantages are discussed. Also, we implement a tool for the sequence assembly problem based on the concepts of k-mer vector methods. It shows the practicability and convenience of this algorithm design strategy.
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29

Huyen, Do Thi, Nguyen Minh Giang, Nguyen Thu Nguyet, and Truong Nam Hai. "Probe design for mining and selection of genes coding endo 1- 4 xylanase from dna metagenome data." TAP CHI SINH HOC 40, no. 1 (January 25, 2018): 39–50. http://dx.doi.org/10.15625/0866-7160/v40n1.9200.

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According to the CAZY classification, endo 1- 4 xylanase belongs to GH 5, 8, 10, 11, 30, 51, 98. However only 03 sequences of GH8, 27 sequences of GH10, 18 sequence of GH11, only one sequence of each GH30 and GH51 from CAZy and NCBI database were thouroughly experimentally studied for biological activity and characteristics of the enzyme. Through the collected sequences, two probes for endo 1- 4 xylanase of GH10 and GH11 were designed, based on the sequence homology. The GH10 probe was 338 amino acids lenghth contained all the conserved amino acid residues (16 conserved residues in all sequences, 13 residues similar in almost sequences, 14 residues conserved in many sequences) with the lowest maxscore of 189, coverage of 88% and identity of 39%. The GH11 probe was 204 amino acids contained all the conserved amino acid residues (54 conserved residues were identity in all sequences, 25 residues similar in almost sequences, 24 residues conserved in many sequences) with the lowest maxscore of 165, coverage of 84% and identity of 50%. Using the two probes, we mined only one sequence (GL0018509) for endo 1- 4 xylanase from metagenomic DNA data of free-living bacteria in Coptotermes termite gut. Prediction of three-dimention structure of GL0018509 sequence by Phyre2 and Swiss Prot showed that this sequence was high similarity (95% by Phyre2 and 93,4% by Swiss Prot) with endo 1- 4 xylanase with the 100% confidence.
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30

WU, CATHY H., HONGZHAN HUANG, and JERRY MCLARTY. "GENE FAMILY IDENTIFICATION NETWORK DESIGN FOR PROTEIN SEQUENCE ANALYSIS." International Journal on Artificial Intelligence Tools 08, no. 04 (December 1999): 419–32. http://dx.doi.org/10.1142/s0218213099000282.

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With the exponential accumulation of sequence data, continued progress in the Human Genome Project will depend increasingly on advanced computational tools to manage and analyze the data. Utilizing information embedded within families of homologous sequences, a gene family identification approach may facilitate the understanding of gene functions. We have developed a GeneFIND (Gene Family Identification Network Design) system for database searching against gene families. It provides rapid and accurate protein family identification by combining global and motif sequence similarities and incorporating ProClass family information. Multi-level filters are used, starting with the MOTIFIND neural networks and BLAST search, followed by SSEARCH alignment, motif pattern match, hidden Markov modeling of motifs and ClustalW motif alignment. GeneFIND has been implemented as a full-scale system for the classification of more than 1200 ProSite and 6000 PIR families. It has been used to identify thousands of new family members and is well suited for genomic sequence analysis. The system is available for on-line family identification from our WWW server ().
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31

Sutton, John M., Joshua D. Millwood, A. Case McCormack, and Janna L. Fierst. "Optimizing experimental design for genome sequencing and assembly with Oxford Nanopore Technologies." Gigabyte 2021 (July 13, 2021): 1–26. http://dx.doi.org/10.46471/gigabyte.27.

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High quality reference genome sequences are the core of modern genomics. Oxford Nanopore Technologies (ONT) produces inexpensive DNA sequences, but has high error rates, which make sequence assembly and analysis difficult as genome size and complexity increases. Robust experimental design is necessary for ONT genome sequencing and assembly, but few studies have addressed eukaryotic organisms. Here, we present novel results using simulated and empirical ONT and DNA libraries to identify best practices for sequencing and assembly for several model species. We find that the unique error structure of ONT libraries causes errors to accumulate and assembly statistics plateau as sequence depth increases. High-quality assembled eukaryotic sequences require high-molecular-weight DNA extractions that increase sequence read length, and computational protocols that reduce error through pre-assembly correction and read selection. Our quantitative results will be helpful for researchers seeking guidance for de novo assembly projects.
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32

Miao, Di. "Design of power network fault diagnosis based on time series matching." Thermal Science 23, no. 5 Part A (2019): 2595–604. http://dx.doi.org/10.2298/tsci181126148m.

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Common grid fault diagnosis does not fully utilize the alarm timing information generated by the fault. To solve this problem, this paper proposes a fault diagnosis method based on time series. The method analyzes the alarm hypothesis sequence generated by the grid fault and the time sequence actually received by the dispatch center, and utilizes the discrete characteristics of the edit distance and reflects the event discreteness and time continuity of the alarm information by adding the time distance. The calculated data of the similarity between the two sequences and the confidence of the alarm hypothesis sequence determine the faulty component.
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33

Yamamoto, Hidehiko, Etsuo Marui, and Kenichi Oota. "Development of Network System For Sequence Circuit By Database(Digital design and digital manufacturing)." Proceedings of International Conference on Leading Edge Manufacturing in 21st century : LEM21 2005.1 (2005): 29–34. http://dx.doi.org/10.1299/jsmelem.2005.1.29.

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34

Huang, Xiaoqiang, Robin Pearce, and Yang Zhang. "EvoEF2: accurate and fast energy function for computational protein design." Bioinformatics 36, no. 4 (October 7, 2019): 1135–42. http://dx.doi.org/10.1093/bioinformatics/btz740.

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Abstract Motivation The accuracy and success rate of de novo protein design remain limited, mainly due to the parameter over-fitting of current energy functions and their inability to discriminate incorrect designs from correct designs. Results We developed an extended energy function, EvoEF2, for efficient de novo protein sequence design, based on a previously proposed physical energy function, EvoEF. Remarkably, EvoEF2 recovered 32.5%, 47.9% and 22.3% of all, core and surface residues for 148 test monomers, and was generally applicable to protein–protein interaction design, as it recapitulated 30.9%, 42.4%, 31.3% and 21.4% of all, core, interface and surface residues for 88 test dimers, significantly outperforming EvoEF on the native sequence recapitulation. We further used I-TASSER to evaluate the foldability of the 148 designed monomer sequences, where all of them were predicted to fold into structures with high fold- and atomic-level similarity to their corresponding native structures, as demonstrated by the fact that 87.8% of the predicted structures shared a root-mean-square-deviation less than 2 Å to their native counterparts. The study also demonstrated that the usefulness of physical energy functions is highly correlated with the parameter optimization processes, and EvoEF2, with parameters optimized using sequence recapitulation, is more suitable for computational protein sequence design than EvoEF, which was optimized on thermodynamic mutation data. Availability and implementation The source code of EvoEF2 and the benchmark datasets are freely available at https://zhanglab.ccmb.med.umich.edu/EvoEF. Supplementary information Supplementary data are available at Bioinformatics online.
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35

Meng, Xin, Ruo Mei Wang, and She Xiang Ma. "Training Sequence Design Based on Channel Estimation." Advanced Materials Research 787 (September 2013): 1097–104. http://dx.doi.org/10.4028/www.scientific.net/amr.787.1097.

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Channel estimation is an important research direction in wireless communications; channel estimation based on the training sequence is the most commonly used method. Different training sequence is very different from the performance of channel estimation, in order to improve channel estimation accuracy. This paper analyzes the common m sequence and from the optimal channel estimated performance point design of applicable in any given length of sequence. The sequence, respectively, under the traditional channel estimated algorithm and based on compressed sensing to channel estimation algorithm has the criterion of Minimum Mean Square Error. Meanwhile, the sequence obtained after the demodulated training sequence through different modulation systems are different, so as to have greater flexibility.
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36

Lee, Chang-Ho, and Richard Sause. "Sequence Control for Integrated Structural Design Models." Journal of Computing in Civil Engineering 10, no. 3 (July 1996): 213–25. http://dx.doi.org/10.1061/(asce)0887-3801(1996)10:3(213).

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37

Chen, Jiun-Kai, and Cheng-Ching Yu. "Optimal input design using generalized binary sequence." Automatica 33, no. 11 (November 1997): 2081–84. http://dx.doi.org/10.1016/s0005-1098(97)00122-2.

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38

Lacatus, Catalin, David Akopian, and Mehdi Shadaram. "Reduced Complexity Algorithm for Spreading Sequence Design." IEEE Transactions on Circuits and Systems II: Express Briefs 55, no. 12 (December 2008): 1309–13. http://dx.doi.org/10.1109/tcsii.2008.2008056.

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39

Irbäck, Anders. "Sequence Design in Coarse-Grained Protein Models." Progress of Theoretical Physics Supplement 138 (2000): 273–81. http://dx.doi.org/10.1143/ptps.138.273.

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40

Kick, Alfred, Martin Bönsch, and Michael Mertig. "EGNAS: an exhaustive DNA sequence design algorithm." BMC Bioinformatics 13, no. 1 (2012): 138. http://dx.doi.org/10.1186/1471-2105-13-138.

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41

Chung, Wonsuk, Chanhong Kim, Sooyong Choi, and Daesik Hong. "Synchronization Sequence Design for FBMC/OQAM Systems." IEEE Transactions on Wireless Communications 15, no. 10 (October 2016): 7199–211. http://dx.doi.org/10.1109/twc.2016.2598809.

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42

Xu, Han, Tengfei Xiao, Chen-Hao Chen, Wei Li, Clifford A. Meyer, Qiu Wu, Di Wu, et al. "Sequence determinants of improved CRISPR sgRNA design." Genome Research 25, no. 8 (June 10, 2015): 1147–57. http://dx.doi.org/10.1101/gr.191452.115.

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43

Gürleyük, S. S., and Ş. Cinal. "Robust Three-impulse Sequence Input Shaper Design." Journal of Vibration and Control 13, no. 12 (December 2007): 1807–18. http://dx.doi.org/10.1177/1077546307080012.

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44

Liu, Wenbin, Shudong Wang, Lin Gao, Fengyue Zhang, and Jin Xu. "DNA Sequence Design Based on Template Strategy." Journal of Chemical Information and Computer Sciences 43, no. 6 (November 2003): 2014–18. http://dx.doi.org/10.1021/ci025645s.

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45

SHEPPARD, C. M., L. J. BELTRAMINI, and R. L. MOTARD. "CONSTRAINT-DIRECTED NON-SHARP SEPARATION SEQUENCE DESIGN." Chemical Engineering Communications 106, no. 1 (August 1991): 1–32. http://dx.doi.org/10.1080/00986449108911532.

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46

Xiaoming Dai, Hongyuan Zhang, and Yingming Wang. "New Sequence Design Criteria for Multipath Channels." IEEE Transactions on Vehicular Technology 58, no. 8 (October 2009): 4149–57. http://dx.doi.org/10.1109/tvt.2009.2021686.

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47

Fechter, Eric J., Bogdan Olenyuk, and Peter B. Dervan. "Design of a Sequence-Specific DNA Bisintercalator." Angewandte Chemie International Edition 43, no. 27 (July 5, 2004): 3591–94. http://dx.doi.org/10.1002/anie.200454231.

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48

Tian, Pengfei, John M. Louis, James L. Baber, Annie Aniana, and Robert B. Best. "Co-Evolutionary Fitness Landscapes for Sequence Design." Angewandte Chemie International Edition 57, no. 20 (March 25, 2018): 5674–78. http://dx.doi.org/10.1002/anie.201713220.

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49

Liang, Shide, and Nick V. Grishin. "Effective scoring function for protein sequence design." Proteins: Structure, Function, and Bioinformatics 54, no. 2 (December 12, 2003): 271–81. http://dx.doi.org/10.1002/prot.10560.

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Tian, Pengfei, John M. Louis, James L. Baber, Annie Aniana, and Robert B. Best. "Co-Evolutionary Fitness Landscapes for Sequence Design." Angewandte Chemie 130, no. 20 (March 25, 2018): 5776–80. http://dx.doi.org/10.1002/ange.201713220.

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