Relevant bibliographies by topics / Algorithms- Protein

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Algorithms- Protein'

Author: Grafiati
Published: 6 September 2023

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Journal articles on the topic "Algorithms- Protein"

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Hulianytskyi, Leonid, and Sergii Chornozhuk. "Genetic Algorithm with New Stochastic Greedy Crossover Operator for Protein Structure Folding Problem." Cybernetics and Computer Technologies, no. 2 (July 24, 2020): 19–29. http://dx.doi.org/10.34229/2707-451x.20.2.3.

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Introduction. The spatial protein structure folding is an important and actual problem in biology. Considering the mathematical model of the task, we can conclude that it comes down to the combinatorial optimization problem. Therefore, genetic and mimetic algorithms can be used to find a solution. The article proposes a genetic algorithm with a new greedy stochastic crossover operator, which differs from classical approaches with paying attention to qualities of possible ancestors. The purpose of the article is to describe a genetic algorithm with a new greedy stochastic crossover operator, reveal its advantages and disadvantages, compare the proposed algorithm with the best-known implementations of genetic and memetic algorithms for the spatial protein structure prediction, and make conclusions with future steps suggestion afterward. Result. The work of the proposed algorithm is compared with others on the basis of 10 known chains with a length of 48 first proposed in [13]. For each of the chain, a global minimum of free energy was already precalculated. The algorithm found 9 out of 10 spatial structures on which a global minimum of free energy is achieved and also demonstrated a better average value of solutions than the comparing algorithms. Conclusion. The quality of the genetic algorithm with the greedy stochastic crossover operator has been experimentally confirmed. Consequently, its further research is promising. For example, research on the selection of optimal algorithm parameters, improving the speed and quality of solutions found through alternative coding or parallelization. Also, it is worth testing the proposed algorithm on datasets with proteins of other lengths for further checks of the algorithm’s validity. Keywords: spatial protein structure, combinatorial optimization, genetic algorithms, crossover operator, stochasticity.
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Cavanaugh, David, and Krishnan Chittur. "A hydrophobic proclivity index for protein alignments." F1000Research 4 (October 21, 2015): 1097. http://dx.doi.org/10.12688/f1000research.6348.1.

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Sequence alignment algorithms are fundamental to modern bioinformatics. Sequence alignments are widely used in diverse applications such as phylogenetic analysis, database searches for related sequences to aid identification of unknown protein domain structures and classification of proteins and protein domains. Additionally, alignment algorithms are integral to the location of related proteins to secure understanding of unknown protein functions, to suggest the folded structure of proteins of unknown structure from location of homologous proteins and/or by locating homologous domains of known 3D structure. For proteins, alignment algorithms depend on information about amino acid substitutions that allows for matching sequences that are similar, but not exact. When primary sequence percent identity falls below about 25%, algorithms often fail to identify proteins that may have similar 3D structure. We have created a hydrophobicity scale and a matching dynamic programming algorithm called TMATCH (unpublished report) that is able to match proteins with remote homologs with similar secondary/tertiary structure, even with very low primary sequence matches. In this paper, we describe how we arrived at the hydrophobic scale, how it provides much more information than percent identity matches and some of the implications for better alignments and understanding protein structure.
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Cavanaugh, David, and Krishnan Chittur. "A hydrophobic proclivity index for protein alignments." F1000Research 4 (October 15, 2020): 1097. http://dx.doi.org/10.12688/f1000research.6348.2.

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Sequence alignment algorithms are fundamental to modern bioinformatics. Sequence alignments are widely used in diverse applications such as phylogenetic analysis, database searches for related sequences to aid identification of unknown protein domain structures and classification of proteins and protein domains. Additionally, alignment algorithms are integral to the location of related proteins to secure understanding of unknown protein functions, to suggest the folded structure of proteins of unknown structure from location of homologous proteins and/or by locating homologous domains of known 3D structure. For proteins, alignment algorithms depend on information about amino acid substitutions that allows for matching sequences that are similar, but not exact. When primary sequence percent identity falls below about 25%, algorithms often fail to identify proteins that may have similar 3D structure. We have created a hydrophobicity scale and a matching dynamic programming algorithm called TMATCH (preprint report) that is able to match proteins with remote homologs with similar secondary/tertiary structure, even with very low primary sequence matches. In this paper, we describe how we arrived at the hydrophobic scale, how it provides much more information than percent identity matches and some of the implications for better alignments and understanding protein structure.
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Begleiter, R., R. El-Yaniv, and G. Yona. "On Prediction Using Variable Order Markov Models." Journal of Artificial Intelligence Research 22 (December 1, 2004): 385–421. http://dx.doi.org/10.1613/jair.1491.

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This paper is concerned with algorithms for prediction of discrete sequences over a finite alphabet, using variable order Markov models. The class of such algorithms is large and in principle includes any lossless compression algorithm. We focus on six prominent prediction algorithms, including Context Tree Weighting (CTW), Prediction by Partial Match (PPM) and Probabilistic Suffix Trees (PSTs). We discuss the properties of these algorithms and compare their performance using real life sequences from three domains: proteins, English text and music pieces. The comparison is made with respect to prediction quality as measured by the average log-loss. We also compare classification algorithms based on these predictors with respect to a number of large protein classification tasks. Our results indicate that a ``decomposed'' CTW (a variant of the CTW algorithm) and PPM outperform all other algorithms in sequence prediction tasks. Somewhat surprisingly, a different algorithm, which is a modification of the Lempel-Ziv compression algorithm, significantly outperforms all algorithms on the protein classification problems.
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Moschopoulos, Charalampos, Grigorios Beligiannis, Spiridon Likothanassis, and Sophia Kossida. "Using a Genetic Algorithm and Markov Clustering on Protein–Protein Interaction Graphs." International Journal of Systems Biology and Biomedical Technologies 1, no. 2 (April 2012): 35–47. http://dx.doi.org/10.4018/ijsbbt.2012040103.

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In this paper, a Genetic Algorithm is applied on the filter of the Enhanced Markov Clustering algorithm to optimize the selection of clusters having a high probability to represent protein complexes. The filter was applied on the results (obtained by experiments made on five different yeast datasets) of three different algorithms known for their efficiency on protein complex detection through protein interaction graphs. The results are compared with three popular clustering algorithms, proving the efficiency of the proposed method according to metrics such as successful prediction rate and geometrical accuracy.
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Wang, Derui, and Jingyu Hou. "Explore the hidden treasure in protein–protein interaction networks — An iterative model for predicting protein functions." Journal of Bioinformatics and Computational Biology 13, no. 05 (October 2015): 1550026. http://dx.doi.org/10.1142/s0219720015500262.

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Protein–protein interaction networks constructed by high throughput technologies provide opportunities for predicting protein functions. A lot of approaches and algorithms have been applied on PPI networks to predict functions of unannotated proteins over recent decades. However, most of existing algorithms and approaches do not consider unannotated proteins and their corresponding interactions in the prediction process. On the other hand, algorithms which make use of unannotated proteins have limited prediction performance. Moreover, current algorithms are usually one-off predictions. In this paper, we propose an iterative approach that utilizes unannotated proteins and their interactions in prediction. We conducted experiments to evaluate the performance and robustness of the proposed iterative approach. The iterative approach maximally improved the prediction performance by 50%–80% when there was a high proportion of unannotated neighborhood protein in the network. The iterative approach also showed robustness in various types of protein interaction network. Importantly, our iterative approach initially proposes an idea that iteratively incorporates the interaction information of unannotated proteins into the protein function prediction and can be applied on existing prediction algorithms to improve prediction performance.
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Dandekar, Thomas, and Patrick Argos. "Potential of genetic algorithms in protein folding and protein engineering simulations." "Protein Engineering, Design and Selection" 5, no. 7 (1992): 637–45. http://dx.doi.org/10.1093/protein/5.7.637.

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Gainza, Pablo, Hunter M. Nisonoff, and Bruce R. Donald. "Algorithms for protein design." Current Opinion in Structural Biology 39 (August 2016): 16–26. http://dx.doi.org/10.1016/j.sbi.2016.03.006.

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Brown, Michael Scott, Tommy Bennett, and James A. Coker. "Niche Genetic Algorithms are better than traditional Genetic Algorithms for de novo Protein Folding." F1000Research 3 (October 7, 2014): 236. http://dx.doi.org/10.12688/f1000research.5412.1.

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Here we demonstrate that Niche Genetic Algorithms (NGA) are better at computing de novo protein folding than traditional Genetic Algorithms (GA). Previous research has shown that proteins can fold into their active forms in a limited number of ways; however, predicting how a set of amino acids will fold starting from the primary structure is still a mystery. GAs have a unique ability to solve these types of scientific problems because of their computational efficiency. Unfortunately, GAs are generally quite poor at solving problems with multiple optima. However, there is a special group of GAs called Niche Genetic Algorithms (NGA) that are quite good at solving problems with multiple optima. In this study, we use a specific NGA: the Dynamic-radius Species-conserving Genetic Algorithm (DSGA), and show that DSGA is very adept at predicting the folded state of proteins, and that DSGA is better than a traditional GA in deriving the correct folding pattern of a protein.
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Khatami, Mohammad Hassan, Udson C. Mendes, Nathan Wiebe, and Philip M. Kim. "Gate-based quantum computing for protein design." PLOS Computational Biology 19, no. 4 (April 12, 2023): e1011033. http://dx.doi.org/10.1371/journal.pcbi.1011033.

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Protein design is a technique to engineer proteins by permuting amino acids in the sequence to obtain novel functionalities. However, exploring all possible combinations of amino acids is generally impossible due to the exponential growth of possibilities with the number of designable sites. The present work introduces circuits implementing a pure quantum approach, Grover’s algorithm, to solve protein design problems. Our algorithms can adjust to implement any custom pair-wise energy tables and protein structure models. Moreover, the algorithm’s oracle is designed to consist of only adder functions. Quantum computer simulators validate the practicality of our circuits, containing up to 234 qubits. However, a smaller circuit is implemented on real quantum devices. Our results show that using O(N) iterations, the circuits find the correct results among all N possibilities, providing the expected quadratic speed up of Grover’s algorithm over classical methods (i.e., O(N)).
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Dissertations / Theses on the topic "Algorithms- Protein"

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Derevyanko, Georgy. "Structure-based algorithms for protein-protein interactions." Thesis, Grenoble, 2014. http://www.theses.fr/2014GRENY070/document.

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Les phénotypes de tous les organismes vivants connus sont déterminés par les interactions compliquées entre les protéines produites dans ces organismes. La compréhension des réponses des organismes aux stimuli externes ou internes est basée sur la compréhension des interactions des protéines individuelles et des structures de ses complexes. La prédiction d'un complexe de deux ou plus protéines est le problème du domaine du docking protéine-protéine. Les algorithmes du docking ont habituellement deux étapes majeurs: recherche 6D exhaustive suivi par le scoring. Dans ce travail, nous avons contribués aux deus étapes sus indiquées. Nous avons développés le nouvel algorithme pour la recherche 6D exhaustive, HermiteFit. Cela est basé sur la décomposition des fonctions 3D en base Hermite. Nous avons implémenté cet algorithme dans le programme pour le fitting (l'ajustement des donnés) des cartes de densité électronique de résolution faible. Nous avons montrés qu'il surpasse les algorithmes existants en terme de temps par point tandis qu'il maintient la même précision du modèle sortant. Nous avons aussi développés la nouvelle approche de calculation de la fonction du scoring, qui est basé sur les arguments logique simples et qui évite la calculation ambiguë de l'état de référence. Nous avons comparés cela aux fonctions de scoring existantes avec l'aide du docking protéines-protéines benchmarks bien connues. Enfin, nous avons développés une approche permettant l'inclusion des interactions eau-protéine à la fonction du scoring et nous avons validés notre méthode pendant le CAPRI (Critical Assessment of Protein Interactions) tour 47
The phenotype of every known living organism is determined mainly by the complicated interactions between the proteins produced in this organism. Understanding the orchestration of the organismal responses to the external or internal stimuli is based on the understanding of the interactions of individual proteins and their complexes structures. The prediction of a complex of two or more proteins is the problem of the protein-protein docking field. Docking algorithms usually have two major steps: exhaustive 6D rigid-body search followed by the scoring. In this work we made contribution to both of these steps. We developed a novel algorithm for 6D exhaustive search, HermiteFit. It is based on Hermite decomposition of 3D functions into the Hermite basis. We implemented this algorithm in the program for fitting low-resolution electron density maps. We showed that it outperforms existing algorithms in terms of time-per-point while maintaining the same output model accuracy. We also developed a novel approach to computation of a scoring function, which is based on simple logical arguments and avoids an ambiguous computation of the reference state. We compared it to the existing scoring functions on the widely used protein-protein docking benchmarks. Finally, we developed an approach to include water-protein interactions into the scoring functions and validated our method during the Critical Assessment of Protein Interactions round 47
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Lassmann, Timo. "Algorithms for building and evaluating multiple sequence alignments /." Stockholm, 2006. http://diss.kib.ki.se/2006/91-7140-887-8/.

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Hosur, Raghavendra. "Structure-based algorithms for protein-protein interaction prediction." Thesis, Massachusetts Institute of Technology, 2012. http://hdl.handle.net/1721.1/75843.

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Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Materials Science and Engineering, 2012.
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Cataloged from student submitted PDF version of thesis.
Includes bibliographical references (p. 109-124).
Protein-protein interactions (PPIs) play a central role in all biological processes. Akin to the complete sequencing of genomes, complete descriptions of interactomes is a fundamental step towards a deeper understanding of biological processes, and has a vast potential to impact systems biology, genomics, molecular biology and therapeutics. PPIs are critical in maintenance of cellular integrity, metabolism, transcription/ translation, and cell-cell communication. This thesis develops new methods that significantly advance our efforts at structure- based approaches to predict PPIs and boost confidence in emerging high-throughput (HTP) data. The aims of this thesis are, 1) to utilize physicochemical properties of protein interfaces to better predict the putative interacting regions and increase coverage of PPI prediction, 2) increase confidence in HTP datasets by identifying likely experimental errors, and 3) provide residue-level information that gives us insights into structure-function relationships in PPIs. Taken together, these methods will vastly expand our understanding of macromolecular networks. In this thesis, I introduce two computational approaches for structure-based proteinprotein interaction prediction: iWRAP and Coev2Net. iWRAP is an interface threading approach that utilizes biophysical properties specific to protein interfaces to improve PPI prediction. Unlike previous structure-based approaches that use single structures to make predictions, iWRAP first builds profiles that characterize the hydrophobic, electrostatic and structural properties specific to protein interfaces from multiple interface alignments. Compatibility with these profiles is used to predict the putative interface region between the two proteins. In addition to improved interface prediction, iWRAP provides better accuracy and close to 50% increase in coverage on genome-scale PPI prediction tasks. As an application, we effectively combine iWRAP with genomic data to identify novel cancer related genes involved in chromatin remodeling, nucleosome organization and ribonuclear complex assembly - processes known to be critical in cancer. Coev2Net addresses some of the limitations of iWRAP, and provides techniques to increase coverage and accuracy even further. Unlike earlier sequence and structure profiles, Coev2Net explicitly models long-distance correlations at protein interfaces. By formulating interface co-evolution as a high-dimensional sampling problem, we enrich sequence/structure profiles with artificial interacting homologus sequences for families which do not have known multiple interacting homologs. We build a spanning-tree based graphical model induced by the simulated sequences as our interface profile. Cross-validation results indicate that this approach is as good as previous methods at PPI prediction. We show that Coev2Net's predictions correlate with experimental observations and experimentally validate some of the high-confidence predictions. Furthermore, we demonstrate how analysis of the predicted interfaces together with human genomic variation data can help us understand the role of these mutations in disease and normal cells.
by Raghavendra Hosur.
Ph.D.
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Bazzoli, A. "Protein structure prediction and protein design with evolutionary algorithms." Doctoral thesis, Università degli Studi di Milano, 2009. http://hdl.handle.net/2434/64478.

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Lappe, Michael. "Novel algorithms for protein interaction networks." Thesis, University of Cambridge, 2004. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.615625.

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Sajjadi, Sajdeh [Verfasser]. "Step by step in fast protein-protein docking algorithms / Sajdeh Sajjadi." Lübeck : Zentrale Hochschulbibliothek Lübeck, 2014. http://d-nb.info/1060276887/34.

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C, Dukka Bahadur K. "Clique-based algorithms for protein structure prediction." 京都大学 (Kyoto University), 2006. http://hdl.handle.net/2433/143887.

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Thomas, Dallas, and University of Lethbridge Faculty of Arts and Science. "Algorithms & experiments for the protein chain lattice fitting problem." Thesis, Lethbridge, Alta. : University of Lethbridge, Faculty of Arts and Science, 2006, 2006. http://hdl.handle.net/10133/535.

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This study seeks to design algorithms that may be used to determine if a given lattice is a good approximation to a given rigid protein structure. Ideal lattice models discovered using our techniques may then be used in algorithms for protein folding and inverse protein folding. In this study we develop methods based on dynamic programming and branch and bound in an effort to identify “ideal” lattice models. To further our understanding of the concepts behind the methods we have utilized a simple cubic lattice for our analysis. The algorithms may be adapted to work on any lattice. We describe two algorithms. One for aligning the protein backbone to the lattice as a walk. This algorithm runs in polynomial time. The second algorithm for aligning a protein backbone as a path to the lattice. Both the algorithms seek to minimize the CRMS deviation of the alignment. The second problem was recently shown to be NP-Complete, hence it is highly unlikely that an efficient algorithm exists. The first algorithm gives a lower bound on the optimal solution to the second problem, and can be used in a branch and bound procedure. Further, we perform an empirical evaluation of our algorithm on proteins from the Protein Data Bank (PDB).
ix, 47 leaves ; 29 cm.
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Gamalielsson, Jonas. "Models for Protein Structure Prediction by Evolutionary Algorithms." Thesis, University of Skövde, Department of Computer Science, 2001. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-623.

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Evolutionary algorithms (EAs) have been shown to be competent at solving complex, multimodal optimisation problems in applications where the search space is large and badly understood. EAs are therefore among the most promising classes of algorithms for solving the Protein Structure Prediction Problem (PSPP). The PSPP is how to derive the 3D-structure of a protein given only its sequence of amino acids. This dissertation defines, evaluates and shows limitations of simplified models for solving the PSPP. These simplified models are off-lattice extensions to the lattice HP model which has been proposed and is claimed to possess some of the properties of real protein folding such as the formation of a hydrophobic core. Lattice models usually model a protein at the amino acid level of detail, use simple energy calculations and are used mainly for search algorithm development. Off-lattice models usually model the protein at the atomic level of detail, use more complex energy calculations and may be used for comparison with real proteins. The idea is to combine the fast energy calculations of lattice models with the increased spatial possibilities of an off-lattice environment allowing for comparison with real protein structures. A hypothesis is presented which claims that a simplified off-lattice model which considers other amino acid properties apart from hydrophobicity will yield simulated structures with lower Root Mean Square Deviation (RMSD) to the native fold than a model only considering hydrophobicity. The hypothesis holds for four of five tested short proteins with a maximum of 46 residues. Best average RMSD for any model tested is above 6Å, i.e. too high for useful structure prediction and excludes significant resemblance between native and simulated structure. Hence, the tested models do not contain the necessary biological information to capture the complex interactions of real protein folding. It is also shown that the EA itself is competent and can produce near-native structures if given a suitable evaluation function. Hence, EAs are useful for eventually solving the PSPP.

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Parry-Smith, David John. "Algorithms and data structures for protein sequence analysis." Thesis, University of Leeds, 1990. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.277404.

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Books on the topic "Algorithms- Protein"

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Rangwala, Huzefa. Introduction to protein structure prediction: Methods and algorithms. Hoboken, N.J: Wiley, 2010.

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Rangwala, Huzefa, G. Karypis, and G. Karypis. Introduction to protein structure prediction: Methods and algorithms. Hoboken, N.J: Wiley, 2010.

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Pan, Yi, Jianxin Wang, and Min Li. Algorithmic and Artificial Intelligence Methods for Protein Bioinformatics. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2013. http://dx.doi.org/10.1002/9781118567869.

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Algorithmic and artificial intelligence methods for protein bioinformatics. Hoboken, New Jersey: Wiley, IEEE Computer Society, 2014.

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Donald, Bruce R. Algorithms in structural molecular biology. Cambridge, Mass: MIT Press, 2011.

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Inge, Jonassen, and Taylor W. R, eds. Protein bioinformatics: An algorithmic approach to sequence and structure analysis. New York: J. Wiley & Sons, 2004.

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M, Sansano Allen, and Langley Research Center, eds. Minimizing overhead in parallel algorithms through overlapping communication/computation. Hampton, Va: National Aeronautics and Space Administration, Langley Research Center, 1997.

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Sokolov, Artem, and Oleg Zhdanov. Cryptographic constructions on the basis of functions of multivalued logic. ru: INFRA-M Academic Publishing LLC., 2020. http://dx.doi.org/10.12737/1045434.

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Symmetric encryption algorithms have been successfully used to protect information during transmission on an open channel. The classical approach to the synthesis of modern cryptographic algorithms and cryptographic primitives on which they are based, is the use of mathematical apparatus of Boolean functions. The authors demonstrate that the use to solve this problem of functions of multivalued logic (FML) allows to largely improve the durability of the cryptographic algorithms and to extend the used algebraic structures. On the other hand, the study of functions of multivalued logic in cryptography leads to a better understanding of the principles of cryptographic primitives and the emergence of new methods of describing cryptographic constructions. In the monograph the results of theoretical and experimental studies of the properties of the FML, the presented algorithms for generating high-quality S-blocks for the symmetric encryption algorithms, as well as full-working samples of the cryptographic algorithms ready for practical implementation. For students and teachers and all those interested in issues of information security.
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Kostyukov, Viktor. Molecular mechanics of biopolymers. ru: INFRA-M Academic Publishing LLC., 2020. http://dx.doi.org/10.12737/1010677.

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The monograph is devoted to molecular mechanics simulations of biologically important polymers like proteins and nucleic acids. It is shown that the algorithms based on the classical laws of motion of Newton, with high-quality parameterization and sufficient computing resources is able to correctly reproduce and predict the structure and dynamics of macromolecules in aqueous solution. Summarized the development path of biopolymers molecular mechanics, its theoretical basis, current status and prospects for further progress. It may be useful to researchers specializing in molecular Biophysics and molecular biology, as well as students of senior courses of higher educational institutions, studying the biophysical and related areas of training.
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Information-theoretic evaluation for computational biomedical ontologies. Cham: Springer, 2014.

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Book chapters on the topic "Algorithms- Protein"

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Jothi, Raja, and Teresa M. Przytycka. "Computational Approaches to Predict Protein-Protein and Domain-Domain Interactions." In Bioinformatics Algorithms, 465–91. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2007. http://dx.doi.org/10.1002/9780470253441.ch21.

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Malod-Dognin, Noël, Rumen Andonov, and Nicola Yanev. "Maximum Cliques in Protein Structure Comparison." In Experimental Algorithms, 106–17. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-13193-6_10.

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Yao, Yin, and Martin C. Frith. "Improved DNA-versus-Protein Homology Search for Protein Fossils." In Algorithms for Computational Biology, 146–58. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-74432-8_11.

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Maji, Pradipta, and Sushmita Paul. "Identification of Disease Genes Using Gene Expression and Protein–Protein Interaction Data." In Scalable Pattern Recognition Algorithms, 155–70. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-05630-2_6.

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von Looz, Moritz, Mario Wolter, Christoph R. Jacob, and Henning Meyerhenke. "Better Partitions of Protein Graphs for Subsystem Quantum Chemistry." In Experimental Algorithms, 353–68. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-38851-9_24.

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Yoo, Paul D., Bing Bing Zhou, and Albert Y. Zomaya. "Protein Domain Boundary Prediction." In Algorithms in Computational Molecular Biology, 501–19. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2010. http://dx.doi.org/10.1002/9780470892107.ch23.

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Yao, Qiuming, Jianjiong Gao, and Dong Xu. "Musite: Tool for Predicting Protein Phosphorylation Sites." In Encyclopedia of Algorithms, 1393–97. New York, NY: Springer New York, 2016. http://dx.doi.org/10.1007/978-1-4939-2864-4_600.

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Li, Shuai Cheng, and Yen Kaow Ng. "On Protein Structure Alignment under Distance Constraint." In Algorithms and Computation, 65–76. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-10631-6_9.

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Yao, Qiuming, Jianjiong Gao, and Dong Xu. "Musite: Tool for Predicting Protein Phosphorylation Sites." In Encyclopedia of Algorithms, 1–5. Berlin, Heidelberg: Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-642-27848-8_600-1.

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Demaine, Erik D., Stefan Langerman, and Joseph O’Rourke. "Geometric Restrictions on Producible Polygonal Protein Chains." In Algorithms and Computation, 395–404. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-540-24587-2_41.

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Conference papers on the topic "Algorithms- Protein"

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Hu, Jing, and Yihang Du. "Predicting Moonlighting Proteins from Protein Sequence." In 14th International Conference on Bioinformatics Models, Methods and Algorithms. SCITEPRESS - Science and Technology Publications, 2023. http://dx.doi.org/10.5220/0011782300003414.

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"PYCOEVOL - A Python Workflow to Study Protein-protein Coevolution." In International Conference on Bioinformatics Models, Methods and Algorithms. SciTePress - Science and and Technology Publications, 2012. http://dx.doi.org/10.5220/0003737901430149.

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Li, Zhao, Zhang Tianchi, and Zhang Jing. "Optimization Algorithms for Flexible Protein-Protein Docking." In 2012 Third International Conference on Digital Manufacturing and Automation (ICDMA). IEEE, 2012. http://dx.doi.org/10.1109/icdma.2012.135.

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"ProRank+ - A Method for Detecting Protein Complexes in Protein Interaction Networks." In International Conference on Bioinformatics Models, Methods and Algorithms. SCITEPRESS - Science and and Technology Publications, 2014. http://dx.doi.org/10.5220/0004910802390244.

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Arikawa, Keisuke. "Investigation of Algorithms for Analyzing Protein Internal Motion From Viewpoint of Robot Kinematics." In ASME 2010 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2010. http://dx.doi.org/10.1115/detc2010-28551.

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Abstract:
We investigate various algorithms for analyzing the characteristics of the internal motion of proteins based on the analogies between their kinematic structures and robotic mechanisms. First, we introduce an artificial simple protein model, planar main chain (PMC), composed of a planar serial link mechanism to investigate the algorithms. Then, we develop algorithms for analyzing the conformational fluctuations by applying the manipulability analysis of robot manipulators and control strategies for redundant manipulators. Next, we develop algorithms for analyzing the conformational deformation caused by the external forces and to evaluate the compliances of the specified parts of proteins. Finally, we show that the proposed algorithms developed by using PMC models are applicable for the three dimensional main chain structures of real proteins, and may be used to analyze their characteristics of the internal motion. We also reveal some preliminary simulation results of the analysis of a real protein.
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"PREDICTION OF CHIMERIC PROTEIN FOLD." In International Conference on Bioinformatics Models, Methods and Algorithms. SciTePress - Science and and Technology Publications, 2012. http://dx.doi.org/10.5220/0003790102340239.

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Zhang, Yan-Ping, Yong-Cheng Wang, Li-Na Zhang, and Chen-Chu Xu. "Prediction of protein-protein interaction sites using covering algorithms." In Education (ICCSE 2010). IEEE, 2010. http://dx.doi.org/10.1109/iccse.2010.5593619.

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"SUBSET SEED EXTENSION TO PROTEIN BLAST." In International Conference on Bioinformatics Models, Methods and Algorithms. SciTePress - Science and and Technology Publications, 2011. http://dx.doi.org/10.5220/0003147601490158.

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Winter, Pawel, and Rasmus Fonseca. "Alpha Complexes in Protein Structure Prediction." In International Conference on Bioinformatics Models, Methods and Algorithms. SCITEPRESS - Science and and Technology Publications, 2015. http://dx.doi.org/10.5220/0005251401780182.

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Satou, Kenji, Yoshiki Shimaguchi, Kunti Mahmudah, Ngoc Nguyen, Mera Delimayanti, Bedy Purnama, Mamoru Kubo, Makiko Kakikawa, and Yoichi Yamada. "Prediction of Subnuclear Location for Nuclear Protein." In 10th International Conference on Bioinformatics Models, Methods and Algorithms. SCITEPRESS - Science and Technology Publications, 2019. http://dx.doi.org/10.5220/0007570502760280.

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Reports on the topic "Algorithms- Protein"

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Martin, Shawn Bryan, Kenneth L. Sale, Jean-Loup Michel Faulon, and Diana C. Roe. Developing algorithms for predicting protein-protein interactions of homology modeled proteins. Office of Scientific and Technical Information (OSTI), January 2006. http://dx.doi.org/10.2172/883467.

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Rangwala, Huzefa, and George Karypis. Incremental Window-based Protein Sequence Alignment Algorithms. Fort Belvoir, VA: Defense Technical Information Center, March 2006. http://dx.doi.org/10.21236/ada444856.

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Sapiro, Guillermo. New Forcefields and Algorithms for Computational Protein Design. Fort Belvoir, VA: Defense Technical Information Center, January 2003. http://dx.doi.org/10.21236/ada428012.

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DeRonne, Kevin W., and George Karypis. Effective Optimization Algorithms for Fragment-Assembly Based Protein Structure Prediction. Fort Belvoir, VA: Defense Technical Information Center, March 2006. http://dx.doi.org/10.21236/ada444732.

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Rohrbough, James G., Linda Breci, Nirav Merchant, Susan Miller, and Paul A. Haynes. Verification of Single-Peptide Protein Identifications by the Application of Complementary Database Search Algorithms. Fort Belvoir, VA: Defense Technical Information Center, October 2005. http://dx.doi.org/10.21236/ada439637.

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Kim, Sangtae. Microstructural Models of Interactions That Govern Protein Conformations: Algorithms for High Performance Computer Architectures. Fort Belvoir, VA: Defense Technical Information Center, January 1998. http://dx.doi.org/10.21236/ada360981.

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CARR, ROBERT D., GIUSEPPE LANCIA, and SORIN ISTRAIL. Branch-and-Cut Algorithms for Independent Set Problems: Integrality Gap and An Application to Protein Structure Alignment. Office of Scientific and Technical Information (OSTI), September 2000. http://dx.doi.org/10.2172/764804.

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Gregurick, S. K. AB Initio Protein Tertiary Structure Prediction: Comparative-Genetic Algorithm with Graph Theoretical Methods. Office of Scientific and Technical Information (OSTI), April 2001. http://dx.doi.org/10.2172/834523.

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Gronberg, J., and J. Hollar. Trigger Algorithm Design for a SUSY Lepton Trigger based on Forward Proton Tagging. Office of Scientific and Technical Information (OSTI), March 2010. http://dx.doi.org/10.2172/975215.

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Snihur, Robert Michael. Subjet multiplicity of quark and gluon jets reconstructed with the relative transverse momenta algorithm in proton - anti-proton collisions. Office of Scientific and Technical Information (OSTI), January 2000. http://dx.doi.org/10.2172/1421439.

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