Relevant bibliographies by topics / Bioinformatics. Proteins

Contents

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

Academic literature on the topic 'Bioinformatics. Proteins'

Author: Grafiati
Published: 4 June 2021
Last updated: 14 February 2022

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Journal articles on the topic "Bioinformatics. Proteins"

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Kopec, Klaus O., Vikram Alva, and Andrei N. Lupas. "Bioinformatics of the TULIP domain superfamily." Biochemical Society Transactions 39, no. 4 (2011): 1033–38. http://dx.doi.org/10.1042/bst0391033.

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Proteins of the BPI (bactericidal/permeability-increasing protein)-like family contain either one or two tandem copies of a fold that usually provides a tubular cavity for the binding of lipids. Bioinformatic analyses show that, in addition to its known members, which include BPI, LBP [LPS (lipopolysaccharide)-binding protein)], CETP (cholesteryl ester-transfer protein), PLTP (phospholipid-transfer protein) and PLUNC (palate, lung and nasal epithelium clone) protein, this family also includes other, more divergent groups containing hypothetical proteins from fungi, nematodes and deep-branching unicellular eukaryotes. More distantly, BPI-like proteins are related to a family of arthropod proteins that includes hormone-binding proteins (Takeout-like; previously described to adopt a BPI-like fold), allergens and several groups of uncharacterized proteins. At even greater evolutionary distance, BPI-like proteins are homologous with the SMP (synaptotagmin-like, mitochondrial and lipid-binding protein) domains, which are found in proteins associated with eukaryotic membrane processes. In particular, SMP domain-containing proteins of yeast form the ERMES [ER (endoplasmic reticulum)-mitochondria encounter structure], required for efficient phospholipid exchange between these organelles. This suggests that SMP domains themselves bind lipids and mediate their exchange between heterologous membranes. The most distant group of homologues we detected consists of uncharacterized animal proteins annotated as TM (transmembrane) 24. We propose to group these families together into one superfamily that we term as the TULIP (tubular lipid-binding) domain superfamily.
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Droit, Arnaud, Guy G. Poirier, and Joanna M. Hunter. "Experimental and bioinformatic approaches for interrogating protein–protein interactions to determine protein function." Journal of Molecular Endocrinology 34, no. 2 (2005): 263–80. http://dx.doi.org/10.1677/jme.1.01693.

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An ambitious goal of proteomics is to elucidate the structure, interactions and functions of all proteins within cells and organisms. One strategy to determine protein function is to identify the protein–protein interactions. The increasing use of high-throughput and large-scale bioinformatics-based studies has generated a massive amount of data stored in a number of different databases. A challenge for bioinformatics is to explore this disparate data and to uncover biologically relevant interactions and pathways. In parallel, there is clearly a need for the development of approaches that can predict novel protein–protein interaction networks in silico. Here, we present an overview of different experimental and bioinformatic methods to elucidate protein–protein interactions.
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TALAT, ROHA, Mohammad Zahid Mustafa, Zunera Tanveer, et al. "Bioinformatics Analysis of Serologic Proteins of Prostate Cancer Patients Separated by SDS-PAGE." Pak-Euro Journal of Medical and Life Sciences 1, no. 1 (2019): 5–11. http://dx.doi.org/10.31580/pjmls.v1i1.940.

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One of the main goals of bioinformatics is to understand and analyze the 3D structure of proteins and the relationship between amino acid sequences. With the help of amino acid sequences, the protein structure can easily be predicted as proteins are essential in natural science research and they are linked with evolution, drug development, mutation and the occurrence of different diseases directly or indirectly. Biologists used bioinformatics tools to discover different diseases by knowing protein’s structure and functions rather than using different technologies/experimental tools which can’t completely explains proteins, its structure and role in several diseases. Prostate Cancer is the leading cause of cancer deaths in males worldwide, it’s least common in Asia and more common in western countries. The study was conducted for the bioinformatics analysis of Prostate cancer proteins.
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Hossen, Md Sakib, Taebun Nahar, Siew Hua Gan, and Md Ibrahim Khalil. "Bioinformatics and Therapeutic Insights on Proteins in Royal Jelly." Current Proteomics 16, no. 2 (2019): 84–101. http://dx.doi.org/10.2174/1570164615666181012113130.

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Background: To date, there is no x-ray crystallography or structures from nuclear magnetic resonance (NMR) on royal jelly proteins available in the online data banks. In addition, characterization of proteins in royal jelly is not fully accomplished to date. Although new investigations unravel novel proteins in royal jelly, the majority of a protein family is present in high amounts (80-90%). Objective: In this review, we attempted to predict the three-dimensional structure of royal jelly proteins (especially the major royal jelly proteins) to allow visualization of the four protein surface properties (aromaticity, hydrophobicity, ionizability and (hydrogen (H)-bond) by using bioinformatics tools. Furthermore, we gathered the information on available therapeutic activities of crude royal jelly and its proteins. Methods: For protein modeling, prediction and analysis, the Phyre2 web portal systematically browsed in which the modeling mode was intensive. On the other side, to build visualized understanding of surface aromaticity, hydrophobicity, ionizability and H-bond of royal jelly proteins, the Discovery Studio 4.1 (Accelrys Software Inc.) was used. Results: Our in silico study confirmed that all proteins treasure these properties, including aromaticity, hydrophobicity, ionizability and (hydrogen (H)-bond. Another finding was that newly discovered proteins in royal jelly do not belong to the major royal jelly protein group. Conclusion: In conclusion, the three dimensional structure of royal jelly proteins along with its major characteristics were successfully elucidated in this review. Further studies are warranted to elucidate the detailed physiochemical properties and pharmacotherapeutics of royal jelly proteins.
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Pavlović-Lažetić, Gordana M., Nenad S. Mitić, Jovana J. Kovačević, Zoran Obradović, Saša N. Malkov, and Miloš V. Beljanski. "Bioinformatics analysis of disordered proteins in prokaryotes." BMC Bioinformatics 12, no. 1 (2011): 66. http://dx.doi.org/10.1186/1471-2105-12-66.

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Bhardwaj, Nitin, Robert V. Stahelin, Robert E. Langlois, Wonhwa Cho, and Hui Lu. "Structural Bioinformatics Prediction of Membrane-binding Proteins." Journal of Molecular Biology 359, no. 2 (2006): 486–95. http://dx.doi.org/10.1016/j.jmb.2006.03.039.

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Collins, Kodi, and Tandy Warnow. "PASTA for proteins." Bioinformatics 34, no. 22 (2018): 3939–41. http://dx.doi.org/10.1093/bioinformatics/bty495.

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Jia, Yan, Jinshan Cao, and Zhanyong Wei. "Bioinformatics Analysis of Spike Proteins of Porcine Enteric Coronaviruses." BioMed Research International 2021 (July 1, 2021): 1–11. http://dx.doi.org/10.1155/2021/6689471.

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This article is aimed at analyzing the structure and function of the spike (S) proteins of porcine enteric coronaviruses, including transmissible gastroenteritis virus (TGEV), porcine epidemic diarrhea virus (PEDV), porcine deltacoronavirus (PDCoV), and swine acute diarrhea syndrome coronavirus (SADS-CoV) by applying bioinformatics methods. The physical and chemical properties, hydrophilicity and hydrophobicity, transmembrane region, signal peptide, phosphorylation and glycosylation sites, epitope, functional domains, and motifs of S proteins of porcine enteric coronaviruses were predicted and analyzed through online software. The results showed that S proteins of TGEV, PEDV, SADS-CoV, and PDCoV all contained transmembrane regions and signal peptide. TGEV S protein contained 139 phosphorylation sites, 24 glycosylation sites, and 53 epitopes. PEDV S protein had 143 phosphorylation sites, 22 glycosylation sites, and 51 epitopes. SADS-CoV S protein had 109 phosphorylation sites, 20 glycosylation sites, and 43 epitopes. PDCoV S protein had 124 phosphorylation sites, 18 glycosylation sites, and 52 epitopes. Moreover, TGEV, PEDV, and PDCoV S proteins all contained two functional domains and two motifs, spike_rec_binding and corona_S2. The corona_S2 consisted of S2 subunit heptad repeat 1 (HR1) and S2 subunit heptad repeat 2 (HR2) region profiles. Additionally, SADS-CoV S protein was predicted to contain only one functional domain, the corona_S2. This analysis of the biological functions of porcine enteric coronavirus spike proteins can provide a theoretical basis for the design of antiviral drugs.
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Peng, Fang, Xianquan Zhan, Mao-Yu Li, et al. "Proteomic and Bioinformatics Analyses of Mouse Liver Microsomes." International Journal of Proteomics 2012 (March 20, 2012): 1–24. http://dx.doi.org/10.1155/2012/832569.

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Microsomes are derived mostly from endoplasmic reticulum and are an ideal target to investigate compound metabolism, membrane-bound enzyme functions, lipid-protein interactions, and drug-drug interactions. To better understand the molecular mechanisms of the liver and its diseases, mouse liver microsomes were isolated and enriched with differential centrifugation and sucrose gradient centrifugation, and microsome membrane proteins were further extracted from isolated microsomal fractions by the carbonate method. The enriched microsome proteins were arrayed with two-dimensional gel electrophoresis (2DE) and carbonate-extracted microsome membrane proteins with one-dimensional gel electrophoresis (1DE). A total of 183 2DE-arrayed proteins and 99 1DE-separated proteins were identified with tandem mass spectrometry. A total of 259 nonredundant microsomal proteins were obtained and represent the proteomic profile of mouse liver microsomes, including 62 definite microsome membrane proteins. The comprehensive bioinformatics analyses revealed the functional categories of those microsome proteins and provided clues into biological functions of the liver. The systematic analyses of the proteomic profile of mouse liver microsomes not only reveal essential, valuable information about the biological function of the liver, but they also provide important reference data to analyze liver disease-related microsome proteins for biomarker discovery and mechanism clarification of liver disease.
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Aszói, A., and W. R. Taylor. "Connection topology of proteins." Bioinformatics 9, no. 5 (1993): 523–29. http://dx.doi.org/10.1093/bioinformatics/9.5.523.

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Dissertations / Theses on the topic "Bioinformatics. Proteins"

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Hvidsten, Torgeir R. "Predicting Function of Genes and Proteins from Sequence, Structure and Expression Data." Doctoral thesis, Uppsala : Acta Universitatis Upsaliensis : Univ.-bibl. [distributör], 2004. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-4490.

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Muhammad, Ashfaq. "Design and Development of a Database for the Classification of Corynebacterium glutamicum Genes, Proteins, Mutants and Experimental Protocols." Thesis, University of Skövde, School of Humanities and Informatics, 2006. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-23.

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<p>Coryneform bacteria are largely distributed in nature and are rod like, aerobic soil bacteria capable of growing on a variety of sugars and organic acids. Corynebacterium glutamicum is a nonpathogenic species of Coryneform bacteria used for industrial production of amino acids. There are three main publicly available genome annotations, Cg, Cgl and NCgl for C. glutamicum. All these three annotations have different numbers of protein coding genes and varying numbers of overlaps of similar genes. The original data is only available in text files. In this format of genome data, it was not easy to search and compare the data among different annotations and it was impossible to make an extensive multidimensional customized formal search against different protein parameters. Comparison of all genome annotations for construction deletion, over-expression mutants, graphical representation of genome information, such as gene locations, neighboring genes, orientation (direct or complementary strand), overlapping genes, gene lengths, graphical output for structure function relation by comparison of predicted trans-membrane domains (TMD) and functional protein domains protein motifs was not possible when data is inconsistent and redundant on various publicly available biological database servers. There was therefore a need for a system of managing the data for mutants and experimental setups. In spite of the fact that the genome sequence is known, until now no databank providing such a complete set of information has been available. We solved these problems by developing a standalone relational database software application covering data processing, protein-DNA sequence extraction and</p><p>management of lab data. The result of the study is an application named, CORYNEBASE, which is a software that meets our aims and objectives.</p>
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Reddy, Joseph. "Identification and Analysis of Important Proteins in Protein Interaction Networks Using Functional and Topological Information." Thesis, University of Skövde, School of Life Sciences, 2008. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-2395.

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<p>Studying protein interaction networks using functional and topological information is important for understanding cellular organization and functionality. This study deals with identifying important proteins in protein interaction networks using SWEMODE (Lubovac, et al, 2006) and analyzing topological and functional properties of these proteins with the help of information derived from modular organization in protein interaction networks as well as information available in public resources, in this case, annotation sources describing the functionality of proteins. Multi-modular proteins are short-listed from the modules generated by SWEMODE. Properties of these short-listed proteins are then analyzed using functional information from SGD Gene Ontology(GO) (Dwight, et al., 2002) and MIPS functional categories (Ruepp, et al., 2004). Topological features such as lethality and centrality of these proteins are also investigated, using graph theoretic properties and information on lethal genes from Yeast Hub (Kei-Hoi, et al., 2005). The findings of the study based on GO terms reveal that these important proteins are mostly involved in the biological process of “organelle organization and biogenesis” and a majority of these proteins belong to MIPS “cellular organization” and “transcription” functional categories. A study of lethality reveals that multi-modular proteins are more likely to be lethal than proteins present only in a single module. An examination of centrality (degree of connectivity of proteins) in the network reveals that the ratio of number of important proteins to number of hubs at different hub sizes increases with the hub size (degree).</p>
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Callahan, Nicholas. "Bioinformatics-Driven Enzyme Engineering: Work On Adenylate Kinase." The Ohio State University, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=osu1420802270.

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Tsirigos, Konstantinos. "Bioinformatics Methods for Topology Prediction of Membrane Proteins." Doctoral thesis, Stockholms universitet, Institutionen för biokemi och biofysik, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-138479.

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Membrane proteins are key elements of the cell since they are associated with a variety of very important biological functions crucial to its survival. They are implicated in cellular recognition and adhesion, act as molecular receptors, transport substrates through membranes and exhibit specific enzymatic activity.This thesis is focused on integral membrane proteins, most of which contain transmembrane segments that form an alpha helix and are composed of mainly hydrophobic residues, spanning the lipid bilayer. A more specialized and less well-studied case, is the case of integral membrane proteins found in the outer membrane of Gram-negative bacteria and (presumably) in the outer envelope of mitochondria and chloroplasts, proteins whose transmembrane segments are formed by amphipathic beta strands that create a closed barrel (beta-barrels). The importance of transmembrane proteins, as well as the inherent difficulties in crystallizing and obtaining three-dimensional structures of these, dictates the need for developing computational algorithms and tools that will allow for a reliable and fast prediction of their structural and functional features. In order to elucidate their function, we must acquire knowledge about their structure and topology with relation to the membrane. Therefore, a large number of computational methods have been developed in order to predict the transmembrane segments and the overall topology of transmembrane proteins. In this thesis, I initially describe a large-scale benchmark of many topology prediction tools in order to devise a strategy that will allow for better detection of alpha-helical membrane proteins in a proteome. Then, I give a description of construction of improved machine-learning algorithms and computer software for accurate topology prediction of transmembrane proteins and discrimination of such proteins from non-transmembrane proteins. Finally, I introduce a fast way to obtain a position-specific scoring matrix, which is essential for modern topology prediction methods.<br><p>At the time of the doctoral defense, the following paper was unpublished and had a status as follows: Paper 3: Manuscript.</p>
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Viklund, Håkan. "Formalizing life : Towards an improved understanding of the sequence-structure relationship in alpha-helical transmembrane proteins." Doctoral thesis, Stockholm University, Department of Biochemistry and Biophysics, 2007. http://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-7144.

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<p>Genes coding for alpha-helical transmembrane proteins constitute roughly 25% of the total number of genes in a typical organism. As these proteins are vital parts of many biological processes, an improved understanding of them is important for achieving a better understanding of the mechanisms that constitute life.</p><p>All proteins consist of an amino acid sequence that fold into a three-dimensional structure in order to perform its biological function. The work presented in this thesis is directed towards improving the understanding of the relationship between sequence and structure for alpha-helical transmembrane proteins. Specifically, five original methods for predicting the topology of alpha-helical transmembrane proteins have been developed: PRO-TMHMM, PRODIV-TMHMM, OCTOPUS, Toppred III and SCAMPI. </p><p>A general conclusion from these studies is that approaches that use multiple sequence information achive the best prediction accuracy. Further, the properties of reentrant regions have been studied, both with respect to sequence and structure. One result of this study is an improved definition of the topological grammar of transmembrane proteins, which is used in OCTOPUS and shown to further improve topology prediction. Finally, Z-coordinates, an alternative system for representation of topological information for transmembrane proteins that is based on distance to the membrane center has been introduced, and a method for predicting Z-coordinates from amino acid sequence, Z-PRED, has been developed.</p>
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Repo, Susanna. "Structural bioinformatics in the study of protein function and evolution /." Turku, Finland : Dept. of Biochemistry and Pharmacy, Abo Akademi University, 2008. http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&doc_number=017048818&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA.

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Adeyemi, Samson Adebowale. "Structural bioinformatics analysis of the Hsp40 and Hsp70 molecular chaperones from humans." Thesis, Rhodes University, 2014. http://hdl.handle.net/10962/d1020962.

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HSP70 is one of the most important families of molecular chaperone that regulate the folding and transport of client proteins in an ATP dependent manner. The ATPase activity of HSP70 is stimulated through an interaction with its family of HSP40 co-chaperones. There is evidence to suggest that specific partnerships occur between the different HSP40 and HSP70 isoforms. While some of the residues involved in the interaction are known, many of the residues governing the specificity of HSP40-HSP70 partnerships are not precisely defined. It is not currently possible to predict which HSP40 and HSP70 isoforms will interact. We attempted to use bioinformatics to identify residues involved in the specificity of the interaction between the J domain from HSP40 and the ATPase domain from the HSP70 isoforms from humans. A total of 49 HSP40 and 13 HSP70 sequences from humans were retrieved and used for subsequent analyses. The HSP40 J domains and HSP70 ATPase domains were extracted using python scripts and classified according to the subcellular localization of the proteins using localization prediction programs. Motif analysis was carried out using the full length HSP40 proteins and Multiple Sequence Alignment (MSA) was performed to identify conserved residues that may contribute to the J domain – ATPase domain interactions. Phylogenetic inference of the proteins was also performed in order to study their evolutionary relationship. Homology models of the J domains and ATPase domains were generated. The corresponding models were docked using HADDOCK server in order to analyze possible putative interactions between the partner proteins using the Protein Interactions Calculator (PIC). The level of residue conservation was found to be higher in Type I and II HSP40 than in Type III J proteins. While highly conserved residues on helixes II and III could play critical roles in J domain interactions with corresponding HSP70s, conserved residues on helixes I and IV seemed to be significant in keeping the J domain in its right orientation for functional interactions with HSP70s. Our results also showed that helixes II and III formed the interaction interface for binding to HSP70 ATPase domain as well as the linker residues. Finally, data based docking procedures, such as applied in this study, could be an effective method to investigate protein-protein interactions complex of biomolecules.
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Dickens, Nicholas J. "Comparisons of proteins and genomes by integrating bioinformatics data." Thesis, University of Oxford, 2006. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.496848.

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Nordström, Karl J. V. "Characterization and Evolution of Transmembrane Proteins with Focus on G-protein coupled receptors in Pre-vertebrate Species." Doctoral thesis, Uppsala universitet, Funktionell farmakologi, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-121696.

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G protein-coupled receptors (GPCRs) are one of the largest protein families in mammals. GPCRs are instrumental for hormonal and neurotransmitter signalling and are important in all major physiological systems of the body. Paper I describes the repertoire of GPCRs in Branchiostoma floridae, which is one of the species most closely related species to vertebrates. Mining and phylogenetic analysis of the amphioxus genome showed the presence of at least 664 distinct GPCRs distributed among all the main families of GPCRs; Glutamate (18), Rhodopsin (570), Adhesion (37), Frizzled (6) and Secretin (16). Paper II contains studies of the Adhesion, Methuselah and Secretin GPCR families in nine genomes. The Adhesion GPCRs are the most complex gene family among GPCRs with large genomic size, multiple introns and a fascinating flora of functional domains. Phylogenetic analysis showed Adhesion group V (that contains GPR133 and GPR144) to be the closest relative to the Secretin family among the groups in the Adhesion family, which was also supported by splice site setup and conserved motifs. Paper III examines the repertoire of human transmembrane proteins. These form key nodes in mediating the cell’s interaction with the surroundings, which is one of the main reasons why the majority of drug targets are membrane proteins. We identified 6,718 human membrane proteins and classified the majority of them into 234 families of which 151 belong to the three major functional groups; Receptors (63 groups, 1,352 members), Transporters (89 groups, 817 members) or Enzymes (7 groups, 533 members). In addition, 74 Miscellaneous groups were shown to include 697 members. Paper IV clarifies the hierarchy of the main families and evolutionary origin of majority of the metazoan GPCR families. Overall, it suggests common decent of at least 97% of the GPCRs sequences found in humans, including all the main families.
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Books on the topic "Bioinformatics. Proteins"

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Frishman, Dmitrij. Structural bioinformatics of membrane proteins. Springer, 2010.

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Frishman, Dmitrij. Structural Bioinformatics of Membrane Proteins. Springer Vienna, 2010. http://dx.doi.org/10.1007/978-3-7091-0045-5.

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Gromiha, M. Michael. Protein bioinformatics: From sequence to function. Academic Press/Elsevier, 2010.

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Protein modelling with bioinformatics and biophysics. Springer, 2006.

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Rigden, Daniel John. From protein structure to function with bioinformatics. Springer, 2009.

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

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Algorithmic and artificial intelligence methods for protein bioinformatics. Wiley, IEEE Computer Society, 2014.

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Zimmermann, Karl-Heinz. An introduction to protein informatics. Kluwer Academic Publishers, 2003.

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Zimmermann, Karl-Heinz. An introduction to protein informatics. Kluwer Academic Publishers, 2003.

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Zimmermann, Karl-Heinz. An introduction to protein informatics. Springer-Science+Business Media, B.V., 2003.

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Book chapters on the topic "Bioinformatics. Proteins"

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Sidhu, Amandeep S., Matthew I. Bellgard, and Tharam S. Dillon. "Classification of Information About Proteins." In Bioinformatics. Springer New York, 2009. http://dx.doi.org/10.1007/978-0-387-92738-1_12.

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Bornberg-Bauer, Erich. "Simple folding model for HP lattice proteins." In Bioinformatics. Springer Berlin Heidelberg, 1996. http://dx.doi.org/10.1007/bfb0033211.

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Heijne, G. "Bioinformatics of Membrane Proteins." In Bioinformatics and Genome Analysis. Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/978-3-662-04747-7_2.

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Wernisch, Lorenz, and Shoshana J. Wodak. "Identifying Structural Domains in Proteins." In Structural Bioinformatics. John Wiley & Sons, Inc., 2005. http://dx.doi.org/10.1002/0471721204.ch18.

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Massingham, Tim. "Detecting the Presence and Location of Selection in Proteins." In Bioinformatics. Humana Press, 2008. http://dx.doi.org/10.1007/978-1-60327-159-2_15.

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Hyman, Bradley C. "Nucleic Acids and Proteins." In Introduction to Bioinformatics. Humana Press, 2003. http://dx.doi.org/10.1007/978-1-59259-335-4_1.

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Li, Min, Xiaopei Chen, Peng Ni, Jianxin Wang, and Yi Pan. "Identifying Essential Proteins by Purifying Protein Interaction Networks." In Bioinformatics Research and Applications. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-38782-6_9.

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Li, Min, Jianxin Wang, Huan Wang, and Yi Pan. "Essential Proteins Discovery from Weighted Protein Interaction Networks." In Bioinformatics Research and Applications. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-13078-6_11.

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Gulzar, Naila, Hayley Dingerdissen, Cheng Yan, and Raja Mazumder. "Impact of Nonsynonymous Single-Nucleotide Variations on Post-Translational Modification Sites in Human Proteins." In Protein Bioinformatics. Springer New York, 2017. http://dx.doi.org/10.1007/978-1-4939-6783-4_8.

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Li, Hongdong, Yang Zhang, Yuanfang Guan, Rajasree Menon, and Gilbert S. Omenn. "Annotation of Alternatively Spliced Proteins and Transcripts with Protein-Folding Algorithms and Isoform-Level Functional Networks." In Protein Bioinformatics. Springer New York, 2017. http://dx.doi.org/10.1007/978-1-4939-6783-4_20.

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Conference papers on the topic "Bioinformatics. Proteins"

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Wang, Jianxin, Wei Peng, Yingjiao Chen, Yu Lu, and Yi Pan. "Identifying essential proteins based on protein domains in protein-protein interaction networks." In 2013 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2013. http://dx.doi.org/10.1109/bibm.2013.6732476.

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"PROTEINS POCKETS ANALYSIS AND DESCRIPTION." In International Conference on Bioinformatics. SciTePress - Science and and Technology Publications, 2010. http://dx.doi.org/10.5220/0002725302110216.

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Kwan, Alan L., Susan K. Dutcher, and Gary D. Stormo. "Detecting Coevolution of Functionally Related Proteins for Automated Protein Annotation." In 2010 IEEE International Conference on BioInformatics and BioEngineering. IEEE, 2010. http://dx.doi.org/10.1109/bibe.2010.24.

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Tan, Jun, and Donald Adjeroh. "Structure-based protein family signature: Efficient comparison of multidomain proteins." In 2017 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2017. http://dx.doi.org/10.1109/bibm.2017.8217621.

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Jeon, Hyeonseong, Suh-Ryung Kim, and Yun Joo Yoo. "Topological properties of protein interaction network and phylogenetic age of proteins." In 2017 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2017. http://dx.doi.org/10.1109/bibm.2017.8217927.

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Skarzyńska, Agnieszka, Magdalena Pawełkowicz, Tomasz Krzywkowski, Katarzyna Świerkula, Wojciech Pląder, and Zbigniew Przybecki. "Bioinformatics pipeline for functional identification and characterization of proteins." In XXXVI Symposium on Photonics Applications in Astronomy, Communications, Industry, and High-Energy Physics Experiments (Wilga 2015), edited by Ryszard S. Romaniuk. SPIE, 2015. http://dx.doi.org/10.1117/12.2205559.

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Chen, Qi-long, Zi-jun Qiao, He-jian Yang, et al. "Bioinformatics analysis the hub-proteins and co-expression proteins of lung squamous carcinoma and adenocarcinoma based on protein interaction network." In 2011 IEEE International Conference on Computer Science and Automation Engineering (CSAE). IEEE, 2011. http://dx.doi.org/10.1109/csae.2011.5952676.

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Hawkins, J., and M. Boden. "Predicting Peroxisomal Proteins." In 2005 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology. IEEE, 2005. http://dx.doi.org/10.1109/cibcb.2005.1594956.

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Abraham, Aby, N. R. Nambratha, R. K. Amith, H. S. Pavithra, V. B. Pramila, and Savithri Bhat. "Gels from Husk Proteins." In 2008 2nd International Conference on Bioinformatics and Biomedical Engineering. IEEE, 2008. http://dx.doi.org/10.1109/icbbe.2008.51.

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Teng, Zhixia, Maozu Guo, Xiaoyan Liu, Zhen Tian, and Kai Che. "Revealing protein functions based on relationships of interacting proteins and GO terms." In 2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2016. http://dx.doi.org/10.1109/bibm.2016.7822772.

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