Relevant bibliographies by topics / Proteins Bioinformatics

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  1. Journal articles

Academic literature on the topic 'Proteins Bioinformatics'

Author: Grafiati
Published: 4 June 2021
Last updated: 2 February 2022

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Journal articles on the topic "Proteins Bioinformatics"

1

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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2

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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3

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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4

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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5

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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6

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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7

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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8

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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9

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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10

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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