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Journal articles on the topic 'Bioinformatics. Proteins'

Author: Grafiati

Published: 4 June 2021

Last updated: 14 February 2022

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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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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 proteinâ€™s structure and functions rather than using different technologies/experimental tools which canâ€™t completely explains proteins, its structure and role in several diseases. Prostate Cancer is the leading cause of cancer deaths in males worldwide, itâ€™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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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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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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Hernández, Sergio, Alejandra Calvo, Gabriela Ferragut, et al. "Can bioinformatics help in the identification of moonlighting proteins?" Biochemical Society Transactions 42, no. 6 (2014): 1692–97. http://dx.doi.org/10.1042/bst20140241.

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Protein multitasking or moonlighting is the capability of certain proteins to execute two or more unique biological functions. This ability to perform moonlighting functions helps us to understand one of the ways used by cells to perform many complex functions with a limited number of genes. Usually, moonlighting proteins are revealed experimentally by serendipity, and the proteins described probably represent just the tip of the iceberg. It would be helpful if bioinformatics could predict protein multifunctionality, especially because of the large amounts of sequences coming from genome projects. In the present article, we describe several approaches that use sequences, structures, interactomics and current bioinformatics algorithms and programs to try to overcome this problem. The sequence analysis has been performed: (i) by remote homology searches using PSI-BLAST, (ii) by the detection of functional motifs, and (iii) by the co-evolutionary relationship between amino acids. Programs designed to identify functional motifs/domains are basically oriented to detect the main function, but usually fail in the detection of secondary ones. Remote homology searches such as PSI-BLAST seem to be more versatile in this task, and it is a good complement for the information obtained from protein–protein interaction (PPI) databases. Structural information and mutation correlation analysis can help us to map the functional sites. Mutation correlation analysis can be used only in very restricted situations, but can suggest how the evolutionary process of the acquisition of the second function took place.

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12

Ramakrishnan, Reshmi, Bert Houben, Frederic Rousseau, and Joost Schymkowitz. "Differential proteostatic regulation of insoluble and abundant proteins." Bioinformatics 35, no. 20 (2019): 4098–107. http://dx.doi.org/10.1093/bioinformatics/btz214.

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Abstract Motivation Despite intense effort, it has been difficult to explain chaperone dependencies of proteins from sequence or structural properties. Results We constructed a database collecting all publicly available data of experimental chaperone interaction and dependency data for the Escherichia coli proteome, and enriched it with an extensive set of protein-specific as well as cell-context-dependent proteostatic parameters. Employing this new resource, we performed a comprehensive meta-analysis of the key determinants of chaperone interaction. Our study confirms that GroEL client proteins are biased toward insoluble proteins of low abundance, but for client proteins of the Trigger Factor/DnaK axis, we instead find that cellular parameters such as high protein abundance, translational efficiency and mRNA turnover are key determinants. We experimentally confirmed the finding that chaperone dependence is a function of translation rate and not protein-intrinsic parameters by tuning chaperone dependence of Green Fluorescent Protein (GFP) in E.coli by synonymous mutations only. The juxtaposition of both protein-intrinsic and cell-contextual chaperone triage mechanisms explains how the E.coli proteome achieves combining reliable production of abundant and conserved proteins, while also enabling the evolution of diverging metabolic functions. Availability and implementation The database will be made available via http://phdb.switchlab.org. Supplementary information Supplementary data are available at Bioinformatics online.

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13

Gromiha, M. M., and Y. Y. Ou. "Bioinformatics approaches for functional annotation of membrane proteins." Briefings in Bioinformatics 15, no. 2 (2013): 155–68. http://dx.doi.org/10.1093/bib/bbt015.

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14

Tate, Christopher G. "Identifying Thermostabilizing Mutations in Membrane Proteins by Bioinformatics." Biophysical Journal 109, no. 7 (2015): 1307–8. http://dx.doi.org/10.1016/j.bpj.2015.08.020.

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15

Han, Xi, Xiaonan Wang, and Kang Zhou. "Develop machine learning-based regression predictive models for engineering protein solubility." Bioinformatics 35, no. 22 (2019): 4640–46. http://dx.doi.org/10.1093/bioinformatics/btz294.

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Abstract Motivation Protein activity is a significant characteristic for recombinant proteins which can be used as biocatalysts. High activity of proteins reduces the cost of biocatalysts. A model that can predict protein activity from amino acid sequence is highly desired, as it aids experimental improvement of proteins. However, only limited data for protein activity are currently available, which prevents the development of such models. Since protein activity and solubility are correlated for some proteins, the publicly available solubility dataset may be adopted to develop models that can predict protein solubility from sequence. The models could serve as a tool to indirectly predict protein activity from sequence. In literature, predicting protein solubility from sequence has been intensively explored, but the predicted solubility represented in binary values from all the developed models was not suitable for guiding experimental designs to improve protein solubility. Here we propose new machine learning (ML) models for improving protein solubility in vivo. Results We first implemented a novel approach that predicted protein solubility in continuous numerical values instead of binary ones. After combining it with various ML algorithms, we achieved a R2 of 0.4115 when support vector machine algorithm was used. Continuous values of solubility are more meaningful in protein engineering, as they enable researchers to choose proteins with higher predicted solubility for experimental validation, while binary values fail to distinguish proteins with the same value—there are only two possible values so many proteins have the same one. Availability and implementation We present the ML workflow as a series of IPython notebooks hosted on GitHub (https://github.com/xiaomizhou616/protein_solubility). The workflow can be used as a template for analysis of other expression and solubility datasets. Supplementary information Supplementary data are available at Bioinformatics online.

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16

Sverud, O., and R. M. MacCallum. "Towards optimal views of proteins." Bioinformatics 19, no. 7 (2003): 882–88. http://dx.doi.org/10.1093/bioinformatics/btg100.

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17

Shidhi, P. R., Prashanth Suravajhala, Aysha Nayeema, Achuthsankar S. Nair, Shailja Singh, and Pawan K. Dhar. "Making novel proteins from pseudogenes." Bioinformatics 31, no. 1 (2014): 33–39. http://dx.doi.org/10.1093/bioinformatics/btu615.

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18

Cameron, J. M., T. Hurd, and B. H. Robinson. "Computational identification of human mitochondrial proteins based on homology to yeast mitochondrially targeted proteins." Bioinformatics 21, no. 9 (2005): 1825–30. http://dx.doi.org/10.1093/bioinformatics/bti280.

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19

Facchiano, Angelo. "Bioinformatic resources for the investigation of proteins and proteomes." Peptidomics 3, no. 1 (2017): 1–10. http://dx.doi.org/10.1515/ped-2017-0001.

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AbstractExperimental techniques in omics sciences need strong support of bioinformatics tools for the data management, analysis and interpretation. Scientific community develops continuously new databases and tools. They make it possible the comparison of new experimental data with the existing ones, to gain new knowledge. Bioinformatics assists proteomics scientists for protein identification from experimental data, management of the huge data produced, investigation of molecular mechanisms of protein functions, their roles in biochemical pathways, and functional interpretation of biological processes. This article introduces the main bioinformatics resources for investigation in the protein world, with references to analyses performed by means of free tools available on the net.

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20

Rao, Allam Appa, Hanuman Thota, Ramamurthy Adapala, et al. "Proteomic Analysis in Diabetic Cardiomyopathy using Bioinformatics Approach." Bioinformatics and Biology Insights 2 (January 2008): BBI.S313. http://dx.doi.org/10.4137/bbi.s313.

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Diabetic cardiomyopathy is a distinct clinical entity that produces asymptomatic heart failure in diabetic patients without evidence of coronary artery disease and hypertension. Abnormalities in diabetic cardiomyopathy include: myocardial hypertrophy, impairment of contractile proteins, accumulation of extracellular matrix proteins, formation of advanced glycation end products, and decreased left ventricular compliance. These abnormalities lead to the most common clinical presentation of diabetic cardiomyopathy in the form of diastolic dysfunction. We evaluated the role of various proteins that are likely to be involved in diabetic cardiomyopathy by employing multiple sequence alignment using ClustalW tool and constructed a Phylogenetic tree using functional protein sequences extracted from NCBI. Phylogenetic tree was constructed using Neighbour—Joining Algorithm in bioinformatics approach. These results suggest a causal relationship between altered calcium homeostasis and diabetic cardiomyopathy that implies that efforts directed to normalize calcium homeostasis could form a novel therapeutic approach.

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21

Mayol, Eduardo, Mercedes Campillo, Arnau Cordomí, and Mireia Olivella. "Inter-residue interactions in alpha-helical transmembrane proteins." Bioinformatics 35, no. 15 (2018): 2578–84. http://dx.doi.org/10.1093/bioinformatics/bty978.

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Abstract Motivation The number of available membrane protein structures has markedly increased in the last years and, in parallel, the reliability of the methods to detect transmembrane (TM) segments. In the present report, we characterized inter-residue interactions in α-helical membrane proteins using a dataset of 3462 TM helices from 430 proteins. This is by far the largest analysis published to date. Results Our analysis of residue–residue interactions in TM segments of membrane proteins shows that almost all interactions involve aliphatic residues and Phe. There is lack of polar–polar, polar–charged and charged–charged interactions except for those between Thr or Ser sidechains and the backbone carbonyl of aliphatic and Phe residues. The results are discussed in the context of the preferences of amino acids to be in the protein core or exposed to the lipid bilayer and to occupy specific positions along the TM segment. Comparison to datasets of β-barrel membrane proteins and of α-helical globular proteins unveils the specific patterns of interactions and residue composition characteristic of α-helical membrane proteins that are the clue to understanding their structure. Availability and implementation Results data and datasets used are available at http://lmc.uab.cat/TMalphaDB/interactions.php. Supplementary information Supplementary data are available at Bioinformatics online.

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22

Khan, Abdul Arif, and Zakir Khan. "COVID-2019-associated overexpressed Prevotella proteins mediated host–pathogen interactions and their role in coronavirus outbreak." Bioinformatics 36, no. 13 (2020): 4065–69. http://dx.doi.org/10.1093/bioinformatics/btaa285.

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Abstract Motivation The outbreak of COVID-2019 initiated at Wuhan, China has become a global threat by rapid transmission and severe fatalities. Recent studies have uncovered whole genome sequence of SARS-CoV-2 (causing COVID-2019). In addition, lung metagenomic studies on infected patients revealed overrepresented Prevotella spp. producing certain proteins in abundance. We performed host–pathogen protein–protein interaction analysis between SARS-CoV-2 and overrepresented Prevotella proteins with human proteome. We also performed functional overrepresentation analysis of interacting proteins to understand their role in COVID-2019 severity. Results It was found that overexpressed Prevotella proteins can promote viral infection. As per the results, Prevotella proteins, but not viral proteins, are involved in multiple interactions with NF-kB, which is involved in increasing clinical severity of COVID-2019. Prevotella may have role in COVID-2019 outbreak and should be given importance for understanding disease mechanisms and improving treatment outcomes. Supplementary information Supplementary data are available at Bioinformatics online.

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23

Kumari, Bandana, Ravindra Kumar, and Manish Kumar. "Identifying residues that determine palmitoylation using association rule mining." Bioinformatics 35, no. 17 (2019): 2887–90. http://dx.doi.org/10.1093/bioinformatics/btz003.

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Abstract Motivation In eukaryotes, palmitoylation drives several essential cellular mechanisms like protein sorting, protein stability and protein–protein interaction. Several amino acids namely Cys, Gly, Ser, Thr and Lys undergo palmitoylation. But very little is known about the amino acid patterns that promote palmitoylation. Results We deduced presence of statistically significant amino acids around palmitoylation sites and their association with different palmitoylated residues i.e. Cys, Gly and Ser. The results suggest that palmitoylation, irrespective of its target residue, generally occurs at sites where Cys, Leu, Lys, Arg, Ser and Met are abundant. Furthermore, functional properties of the three types of palmitoylated proteins were compared. We observed similar functional behavior of Cys and Gly palmitoylated proteins but proteins with Ser palmitoylation showed distinctiveness from remaining two. Motif-wise functional conservation was also observed in Cys palmitoylated proteins. We also did functional annotation of predicted human palmitoylome. Supplementary information Supplementary data are available at Bioinformatics online.

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Fuchs, Angelika, Antonio J. Martin-Galiano, Matan Kalman, Sarel Fleishman, Nir Ben-Tal, and Dmitrij Frishman. "Co-evolving residues in membrane proteins." Bioinformatics 23, no. 24 (2007): 3312–19. http://dx.doi.org/10.1093/bioinformatics/btm515.

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Brameier, M., A. Krings, and R. M. MacCallum. "NucPred Predicting nuclear localization of proteins." Bioinformatics 23, no. 9 (2007): 1159–60. http://dx.doi.org/10.1093/bioinformatics/btm066.

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26

Contreras-Moreira, B., and J. Collado-Vides. "Comparative footprinting of DNA-binding proteins." Bioinformatics 22, no. 14 (2006): e74-e80. http://dx.doi.org/10.1093/bioinformatics/btl215.

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27

Bannen, R. M., V. Suresh, G. N. Phillips, S. J. Wright, and J. C. Mitchell. "Optimal design of thermally stable proteins." Bioinformatics 24, no. 20 (2008): 2339–43. http://dx.doi.org/10.1093/bioinformatics/btn450.

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Kulandaisamy, A., S. Binny Priya, R. Sakthivel, et al. "MutHTP: mutations in human transmembrane proteins." Bioinformatics 34, no. 13 (2018): 2325–26. http://dx.doi.org/10.1093/bioinformatics/bty054.

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29

Krzeminski, Mickaël, Joseph A. Marsh, Chris Neale, Wing-Yiu Choy, and Julie D. Forman-Kay. "Characterization of disordered proteins with ENSEMBLE." Bioinformatics 29, no. 3 (2012): 398–99. http://dx.doi.org/10.1093/bioinformatics/bts701.

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30

Ray, Arjun, Erik Lindahl, and Björn Wallner. "Model quality assessment for membrane proteins." Bioinformatics 26, no. 24 (2010): 3067–74. http://dx.doi.org/10.1093/bioinformatics/btq581.

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31

Goffard, N., V. Garcia, F. Iragne, A. Groppi, and A. de Daruvar. "IPPRED: server for proteins interactions inference." Bioinformatics 19, no. 7 (2003): 903–4. http://dx.doi.org/10.1093/bioinformatics/btg091.

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Li, K. B., P. Issac, and A. Krishnan. "Predicting allergenic proteins using wavelet transform." Bioinformatics 20, no. 16 (2004): 2572–78. http://dx.doi.org/10.1093/bioinformatics/bth286.

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Arnold, R., T. Rattei, P. Tischler, M. D. Truong, V. Stumpflen, and W. Mewes. "SIMAP--The similarity matrix of proteins." Bioinformatics 21, Suppl 2 (2005): ii42—ii46. http://dx.doi.org/10.1093/bioinformatics/bti1107.

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Guruprasad, K., M. S. Prasad, and G. R. Kumar. "Database of Structural Motifs in Proteins." Bioinformatics 16, no. 4 (2000): 372–75. http://dx.doi.org/10.1093/bioinformatics/16.4.372.

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35

Fariselli, P., and R. Casadio. "Prediction of disulfide connectivity in proteins." Bioinformatics 17, no. 10 (2001): 957–64. http://dx.doi.org/10.1093/bioinformatics/17.10.957.

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Lambert, C., N. Leonard, X. De Bolle, and E. Depiereux. "ESyPred3D: Prediction of proteins 3D structures." Bioinformatics 18, no. 9 (2002): 1250–56. http://dx.doi.org/10.1093/bioinformatics/18.9.1250.

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Leluk, J., L. Konieczny, and I. Roterman. "Search for structural similarity in proteins." Bioinformatics 19, no. 1 (2003): 117–24. http://dx.doi.org/10.1093/bioinformatics/19.1.117.

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Woodhead, Andrea, Andrew Church, Trevor Rapson, Holly Trueman, Jeffrey Church, and Tara Sutherland. "Confirmation of Bioinformatics Predictions of the Structural Domains in Honeybee Silk." Polymers 10, no. 7 (2018): 776. http://dx.doi.org/10.3390/polym10070776.

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Honeybee larvae produce a silk made up of proteins in predominantly a coiled coil molecular structure. These proteins can be produced in recombinant systems, making them desirable templates for the design of advanced materials. However, the atomic level structure of these proteins is proving difficult to determine: firstly, because coiled coils are difficult to crystalize; and secondly, fibrous proteins crystalize as fibres rather than as discrete protein units. In this study, we synthesised peptides from the central structural domain, as well as the N- and C-terminal domains, of the honeybee silk. We used circular dichroism spectroscopy, infrared spectroscopy, and molecular dynamics to investigate the folding behaviour of the central domain peptides. We found that they folded as predicted by bioinformatics analysis, giving the protein engineer confidence in bioinformatics predictions to guide the design of new functionality into these protein templates. These results, along with the infrared structural analysis of the N- and C-terminal domain peptides and the comparison of peptide film properties with those of the full-length AmelF3 protein, provided significant insight into the structural elements required for honeybee silk protein to form into stable materials.

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Zhang, Zheng, Fen Yu, Yuanqiang Zou, et al. "Phage protein receptors have multiple interaction partners and high expressions." Bioinformatics 36, no. 10 (2020): 2975–79. http://dx.doi.org/10.1093/bioinformatics/btaa123.

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Abstract Motivation Receptors on host cells play a critical role in viral infection. How phages select receptors is still unknown. Results Here, we manually curated a high-quality database named phageReceptor, including 427 pairs of phage–host receptor interactions, 341 unique viral species or sub-species and 69 bacterial species. Sugars and proteins were most widely used by phages as receptors. The receptor usage of phages in Gram-positive bacteria was different from that in Gram-negative bacteria. Most protein receptors were located on the outer membrane. The phage protein receptors (PPRs) were highly diverse in their structures, and had little sequence identity and no common protein domain with mammalian virus receptors. Further functional characterization of PPRs in Escherichia coli showed that they had larger node degrees and betweennesses in the protein–protein interaction network, and higher expression levels, than other outer membrane proteins, plasma membrane proteins or other intracellular proteins. These findings were consistent with what observed for mammalian virus receptors reported in previous studies, suggesting that viral protein receptors tend to have multiple interaction partners and high expressions. The study deepens our understanding of virus–host interactions. Availability and implementation phageReceptor is publicly available from: http://www.computationalbiology.cn/phageReceptor/index.html. Supplementary information Supplementary data are available at Bioinformatics online.

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Dai, Bowen, and Chris Bailey-Kellogg. "Protein interaction interface region prediction by geometric deep learning." Bioinformatics 37, no. 17 (2021): 2580–88. http://dx.doi.org/10.1093/bioinformatics/btab154.

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Abstract Motivation Protein–protein interactions drive wide-ranging molecular processes, and characterizing at the atomic level how proteins interact (beyond just the fact that they interact) can provide key insights into understanding and controlling this machinery. Unfortunately, experimental determination of three-dimensional protein complex structures remains difficult and does not scale to the increasingly large sets of proteins whose interactions are of interest. Computational methods are thus required to meet the demands of large-scale, high-throughput prediction of how proteins interact, but unfortunately, both physical modeling and machine learning methods suffer from poor precision and/or recall. Results In order to improve performance in predicting protein interaction interfaces, we leverage the best properties of both data- and physics-driven methods to develop a unified Geometric Deep Neural Network, ‘PInet’ (Protein Interface Network). PInet consumes pairs of point clouds encoding the structures of two partner proteins, in order to predict their structural regions mediating interaction. To make such predictions, PInet learns and utilizes models capturing both geometrical and physicochemical molecular surface complementarity. In application to a set of benchmarks, PInet simultaneously predicts the interface regions on both interacting proteins, achieving performance equivalent to or even much better than the state-of-the-art predictor for each dataset. Furthermore, since PInet is based on joint segmentation of a representation of a protein surfaces, its predictions are meaningful in terms of the underlying physical complementarity driving molecular recognition. Availability and implementation PInet scripts and models are available at https://github.com/FTD007/PInet. Supplementary information Supplementary data are available at Bioinformatics online.

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Xu, Ying-Ying, Hong-Bin Shen, and Robert F. Murphy. "Learning complex subcellular distribution patterns of proteins via analysis of immunohistochemistry images." Bioinformatics 36, no. 6 (2019): 1908–14. http://dx.doi.org/10.1093/bioinformatics/btz844.

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Abstract Motivation Systematic and comprehensive analysis of protein subcellular location as a critical part of proteomics (‘location proteomics’) has been studied for many years, but annotating protein subcellular locations and understanding variation of the location patterns across various cell types and states is still challenging. Results In this work, we used immunohistochemistry images from the Human Protein Atlas as the source of subcellular location information, and built classification models for the complex protein spatial distribution in normal and cancerous tissues. The models can automatically estimate the fractions of protein in different subcellular locations, and can help to quantify the changes of protein distribution from normal to cancer tissues. In addition, we examined the extent to which different annotated protein pathways and complexes showed similarity in the locations of their member proteins, and then predicted new potential proteins for these networks. Availability and implementation The dataset and code are available at: www.csbio.sjtu.edu.cn/bioinf/complexsubcellularpatterns. Supplementary information Supplementary data are available at Bioinformatics online.

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Wang, Kai, Nan Lyu, Hongjuan Diao, et al. "GM-DockZn: a geometry matching-based docking algorithm for zinc proteins." Bioinformatics 36, no. 13 (2020): 4004–11. http://dx.doi.org/10.1093/bioinformatics/btaa292.

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Abstract Motivation Molecular docking is a widely used technique for large-scale virtual screening of the interactions between small-molecule ligands and their target proteins. However, docking methods often perform poorly for metalloproteins due to additional complexity from the three-way interactions among amino-acid residues, metal ions and ligands. This is a significant problem because zinc proteins alone comprise about 10% of all available protein structures in the protein databank. Here, we developed GM-DockZn that is dedicated for ligand docking to zinc proteins. Unlike the existing docking methods developed specifically for zinc proteins, GM-DockZn samples ligand conformations directly using a geometric grid around the ideal zinc-coordination positions of seven discovered coordination motifs, which were found from the survey of known zinc proteins complexed with a single ligand. Results GM-DockZn has the best performance in sampling near-native poses with correct coordination atoms and numbers within the top 50 and top 10 predictions when compared to several state-of-the-art techniques. This is true not only for a non-redundant dataset of zinc proteins but also for a homolog set of different ligand and zinc-coordination systems for the same zinc proteins. Similar superior performance of GM-DockZn for near-native-pose sampling was also observed for docking to apo-structures and cross-docking between different ligand complex structures of the same protein. The highest success rate for sampling nearest near-native poses within top 5 and top 1 was achieved by combining GM-DockZn for conformational sampling with GOLD for ranking. The proposed geometry-based sampling technique will be useful for ligand docking to other metalloproteins. Availability and implementation GM-DockZn is freely available at www.qmclab.com/ for academic users. Supplementary information Supplementary data are available at Bioinformatics online.

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Merugu, Ramchander, Ved Prakash Upadhyay, and Shivaranjani Manda. "Bioinformatics analysis and modelling of mycotoxin patulin induced proteins." International Journal of Bioinformatics and Biological Science 4, no. 1 (2016): 5. http://dx.doi.org/10.5958/2321-7111.2016.00002.0.

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Song, Pei-Ming, Yang Zhang, Yu-Fei He, et al. "Bioinformatics analysis of metastasis-related proteins in hepatocellular carcinoma." World Journal of Gastroenterology 14, no. 38 (2008): 5816. http://dx.doi.org/10.3748/wjg.14.5816.

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Cuthbertson, Jonathan, and Mark S. P. Sansom. "Structural bioinformatics and molecular simulations: Looking at membrane proteins." Biochemist 26, no. 4 (2004): 25–28. http://dx.doi.org/10.1042/bio02604025.

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Membrane proteins account for approximately 25% of all genes, and constitute approximately 50% of potential drug targets. The steady increase in the number of three-dimensional structures for membrane proteins means that the twin disciplines of structural bioinformatics and biomolecular simulations may be applied to this important class of molecules. Bioinformatics studies are starting to reveal, for example, sequence motifs that govern how transmembrane -helices pack together. Simulations are revealing the dynamic behaviour of membrane proteins and the nature of their often transient interactions with the surrounding lipid molecules.

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Franco, Luis, Sergio Hernández, Alejandra Calvo, et al. "Moonlighting proteins: a bioinformatics analysis of their biochemical characteristics." New Biotechnology 33, no. 3 (2016): 432–33. http://dx.doi.org/10.1016/j.nbt.2015.10.052.

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de B. Harrington, Peter. "Bioinformatics: From nucleic acids and proteins to cell metabolism." Chemometrics and Intelligent Laboratory Systems 35, no. 1 (1996): 137. http://dx.doi.org/10.1016/s0169-7439(96)00054-8.

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Elofsson, Arne. "Bioinformatics: From nucleic acids and proteins to cell metabolism." FEBS Letters 388, no. 1 (1996): 87. http://dx.doi.org/10.1016/0014-5793(96)88177-4.

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Fu-Jun, Liu, Wang Hai-Yan, and Li Jian-Yuan. "A new analysis of testicular proteins through integrative bioinformatics." Molecular Biology Reports 39, no. 4 (2011): 3965–70. http://dx.doi.org/10.1007/s11033-011-1176-5.

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Durairaj, Janani, Mehmet Akdel, Dick de Ridder, and Aalt D. J. van Dijk. "Geometricus represents protein structures as shape-mers derived from moment invariants." Bioinformatics 36, Supplement_2 (2020): i718—i725. http://dx.doi.org/10.1093/bioinformatics/btaa839.

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Abstract Motivation As the number of experimentally solved protein structures rises, it becomes increasingly appealing to use structural information for predictive tasks involving proteins. Due to the large variation in protein sizes, folds and topologies, an attractive approach is to embed protein structures into fixed-length vectors, which can be used in machine learning algorithms aimed at predicting and understanding functional and physical properties. Many existing embedding approaches are alignment based, which is both time-consuming and ineffective for distantly related proteins. On the other hand, library- or model-based approaches depend on a small library of fragments or require the use of a trained model, both of which may not generalize well. Results We present Geometricus, a novel and universally applicable approach to embedding proteins in a fixed-dimensional space. The approach is fast, accurate, and interpretable. Geometricus uses a set of 3D moment invariants to discretize fragments of protein structures into shape-mers, which are then counted to describe the full structure as a vector of counts. We demonstrate the applicability of this approach in various tasks, ranging from fast structure similarity search, unsupervised clustering and structure classification across proteins from different superfamilies as well as within the same family. Availability and implementation Python code available at https://git.wur.nl/durai001/geometricus.

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