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Journal articles on the topic 'Protein bioinformatics'

Author: Grafiati

Published: 4 June 2021

Last updated: 7 September 2023

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1

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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DiTursi, M. K., S. J. Kwon, P. J. Reeder, and J. S. Dordick. "Bioinformatics-driven, rational engineering of protein thermostability." Protein Engineering Design and Selection 19, no. 11 (2006): 517–24. http://dx.doi.org/10.1093/protein/gzl039.

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3

Laskowski, Roman, and A. W. Chan. "Bioinformatics and Protein Design." Current Pharmaceutical Biotechnology 3, no. 4 (2002): 317–27. http://dx.doi.org/10.2174/1389201023378157.

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4

Nugent, Timothy, and David T. Jones. "Membrane protein structural bioinformatics." Journal of Structural Biology 179, no. 3 (2012): 327–37. http://dx.doi.org/10.1016/j.jsb.2011.10.008.

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5

Gibson, James. "Bioinformatics of Protein Allergenicity." Molecular Nutrition & Food Research 50, no. 7 (2006): 591. http://dx.doi.org/10.1002/mnfr.200690020.

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6

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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Deng, M., Z. Tu, F. Sun, and T. Chen. "Mapping gene ontology to proteins based on protein-protein interaction data." Bioinformatics 20, no. 6 (2004): 895–902. http://dx.doi.org/10.1093/bioinformatics/btg500.

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LU, Liang, Dong LI, and Fu-Chu HE. "Bioinformatics advances in protein ubiquitination." Hereditas (Beijing) 35, no. 1 (2013): 17–26. http://dx.doi.org/10.3724/sp.j.1005.2013.00017.

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9

Mohabatkar, Hassan, Mehrnaz Keyhanfar, and Mandana Behbahani. "Protein Bioinformatics Applied to Virology." Current Protein & Peptide Science 13, no. 6 (2012): 547–59. http://dx.doi.org/10.2174/138920312803582988.

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10

Radivojac, P. "Protein Structure Prediction: Bioinformatics Approach." Briefings in Bioinformatics 5, no. 2 (2004): 207–9. http://dx.doi.org/10.1093/bib/5.2.207.

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11

Kirac, M., G. Ozsoyoglu, and J. Yang. "Annotating proteins by mining protein interaction networks." Bioinformatics 22, no. 14 (2006): e260-e270. http://dx.doi.org/10.1093/bioinformatics/btl221.

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12

VAN BERLO, ROGIER J. P., LODEWYK F. A. WESSELS, DICK DE RIDDER, and MARCEL J. T. REINDERS. "PROTEIN COMPLEX PREDICTION USING AN INTEGRATIVE BIOINFORMATICS APPROACH." Journal of Bioinformatics and Computational Biology 05, no. 04 (2007): 839–64. http://dx.doi.org/10.1142/s0219720007002953.

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Since protein complexes play a crucial role in biological cells, one of the major goals in bioinformatics is the elucidation of protein complexes. A general approach is to build a prediction rule based on multiple data sources, e.g. gene expression data and protein interaction data, to assess the likelihood of two proteins having complex association.a We critically revisit the step of predictor construction, i.e. the determination of a proper training set, an optimal classifier, and, most importantly, an optimal feature set. We use an exhaustive set of features, which includes the 2hop-feature as introduced by Wong et al.23 for predicting synthetic sick or lethal interactions. Post-processing of the likelihoods of protein interaction is then required to extract protein complexes. We propose a new protocol for combining these likelihood estimates. The protocol interprets the probabilities of complex association as output by the prediction rule as distances and employs hierarchical clustering to find groups of interacting proteins. In contrast to the computationally expensive search-and-score approach of Sharan et al.,19 this protocol is very fast and can be applied to fully connected graphs. The protocol identifies trusted protein complexes with high confidence. We show that the 2hop-feature is relevant for predicting protein complexes. Furthermore, several interesting hypotheses about new protein complexes have been generated. For example, our approach linked the protein FYV4 to the mitochondrial ribosomal subunit. Interestingly, it is known that this protein is located in the mitochondrion, but its biological role is unknown. Vid22 and YGR071C were also linked, which corresponds to the new TAP data of Krogan et al.14

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13

Philipp, Oliver, Heinz D. Osiewacz, and Ina Koch. "Path2PPI: an R package to predict protein–protein interaction networks for a set of proteins." Bioinformatics 32, no. 9 (2016): 1427–29. http://dx.doi.org/10.1093/bioinformatics/btv765.

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14

Gomez, S. M., W. S. Noble, and A. Rzhetsky. "Learning to predict protein-protein interactions from protein sequences." Bioinformatics 19, no. 15 (2003): 1875–81. http://dx.doi.org/10.1093/bioinformatics/btg352.

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15

Burgoyne, Nicholas J., and Richard M. Jackson. "Predicting protein interaction sites: binding hot-spots in protein–protein and protein–ligand interfaces." Bioinformatics 22, no. 11 (2006): 1335–42. http://dx.doi.org/10.1093/bioinformatics/btl079.

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16

Pruess, Manuela, and Rolf Apweiler. "Bioinformatics Resources for In Silico Proteome Analysis." Journal of Biomedicine and Biotechnology 2003, no. 4 (2003): 231–36. http://dx.doi.org/10.1155/s1110724303209219.

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In the growing field of proteomics, tools for the in silico analysis of proteins and even of whole proteomes are of crucial importance to make best use of the accumulating amount of data. To utilise this data for healthcare and drug development, first the characteristics of proteomes of entire species—mainly the human—have to be understood, before secondly differentiation between individuals can be surveyed. Specialised databases about nucleic acid sequences, protein sequences, protein tertiary structure, genome analysis, and proteome analysis represent useful resources for analysis, characterisation, and classification of protein sequences. Different from most proteomics tools focusing on similarity searches, structure analysis and prediction, detection of specific regions, alignments, data mining, 2D PAGE analysis, or protein modelling, respectively, comprehensive databases like the proteome analysis database benefit from the information stored in different databases and make use of different protein analysis tools to provide computational analysis of whole proteomes.

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17

Kara, Altan, Martin Vickers, Martin Swain, David E. Whitworth, and Narcis Fernandez-Fuentes. "MetaPred2CS: a sequence-based meta-predictor for protein–protein interactions of prokaryotic two-component system proteins." Bioinformatics 32, no. 21 (2016): 3339–41. http://dx.doi.org/10.1093/bioinformatics/btw403.

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18

Walter, Peter, Ozlem Ulucan, Jennifer Metzger, and Volkhard Helms. "Bioinformatics of Protein-Protein Interfaces and Small Molecule Effectors." Current Bioinformatics 7, no. 2 (2012): 159–72. http://dx.doi.org/10.2174/157489312800604444.

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19

Friedhoff, Peter. "Mapping protein?protein interactions by bioinformatics and cross-linking." Analytical and Bioanalytical Chemistry 381, no. 1 (2004): 78–80. http://dx.doi.org/10.1007/s00216-004-2892-7.

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20

Ananta Kusuma, Wisnu. "Bioinformatics Approaches to Natural Product Discovery." BIO Web of Conferences 41 (2021): 02004. http://dx.doi.org/10.1051/bioconf/20214102004.

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Introduction: Bioinformatics is a multi-disciplinary field that usually uses approaches in Computer Science such as algorithms and machine learning to solve problems in the domains of Biology, Biochemistry, and other domains involving molecular biology data. This approach can also be used to screen natural products that have certain properties. Jamu or Indonesian herbal medicine works with the principle of multi-component multi-target. This principle focuses on the complex interactions of system components that describe how multi-components (compounds) can work together to affect multi-targets (protein targets). This mechanism is also popularly called Network Pharmacology. In this study, we introduce a workflow to screen herbal compounds based on Network Pharmacology and machine learning approach. Methods: The workflow starts by screening for proteins that have an important role in relation to a certain disease. The screening was conducted by applying clustering and utilizing network topological features which were represented as graphs [1]. Furthermore, we performed enrichment analysis by integrating the protein-protein interaction network with the Gene Ontology (GO) network covering biological processes, molecular functions, and cellular components into k-partite graph and analyzing them using soft clustering method [2]. From the results of this enrichment analysis, we determined which proteins are really relevant and have important role in a certain disease [3]. Next, from these screened proteins, we built a predictive models of compound-protein interactions from drug data collected from the DrugBank and SuperTarget databases and train the models using machine learning or deep learning methods [4]. This model was then used to predict Indonesian herbal compounds from the HerbalDB database (http://herbaldb.farmasi.ui.ac.id/v3/) and IJAH Analytics. Results: To demonstrate the effectiveness of the workflow, we applied it to analize some diseases, such as hyperinflamation in Covid-19 and obesity. We found several potential plants such as Andrographis paniculata (Sambiloto) to reduce the inflammatory effect on Covid-19 and Murraya paniculata (Kemuning) to activate Brown Adipose Tissue (BAT) which has the potential to treat obesity. Certainly all of this requires proof through in vitro, in vivo, and clinical trials. We have also implemented several processes in the workflow into the IJAH Analytics application. Some of the features of IJAH are finding herbal compounds or plant formulas based on specific disease or protein targets; and otherwise looking for the efficacy of several combinations of plants or herbal compounds. In addition, IJAH Analytics can also visualize pharmacological networks from plants-compound-protein-diseases. IJAH is available to the public at https://ijah.apps.cs.ipb.ac.id for free. Conclusion: This study shows the potential of using bioinformatics approaches based on network pharmacology and machine learning in discovering the potential of natural products from Indonesia’s biodiversity. In addition, IJAH Analytics, although still in the refinement stage, can be an alternative application that can support researchers to screen potential Indonesian natural products.

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21

Marcotte, E. M., I. Xenarios, and D. Eisenberg. "Mining literature for protein-protein interactions." Bioinformatics 17, no. 4 (2001): 359–63. http://dx.doi.org/10.1093/bioinformatics/17.4.359.

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22

Liu, S., Y. Gao, and I. A. Vakser. "DOCKGROUND protein-protein docking decoy set." Bioinformatics 24, no. 22 (2008): 2634–35. http://dx.doi.org/10.1093/bioinformatics/btn497.

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23

Letovsky, S., and S. Kasif. "Predicting protein function from protein/protein interaction data: a probabilistic approach." Bioinformatics 19, Suppl 1 (2003): i197—i204. http://dx.doi.org/10.1093/bioinformatics/btg1026.

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24

Maurer-Stroh, Sebastian, Nora L. Krutz, Petra S. Kern, et al. "AllerCatPro—prediction of protein allergenicity potential from the protein sequence." Bioinformatics 35, no. 17 (2019): 3020–27. http://dx.doi.org/10.1093/bioinformatics/btz029.

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Abstract Motivation Due to the risk of inducing an immediate Type I (IgE-mediated) allergic response, proteins intended for use in consumer products must be investigated for their allergenic potential before introduction into the marketplace. The FAO/WHO guidelines for computational assessment of allergenic potential of proteins based on short peptide hits and linear sequence window identity thresholds misclassify many proteins as allergens. Results We developed AllerCatPro which predicts the allergenic potential of proteins based on similarity of their 3D protein structure as well as their amino acid sequence compared with a data set of known protein allergens comprising of 4180 unique allergenic protein sequences derived from the union of the major databases Food Allergy Research and Resource Program, Comprehensive Protein Allergen Resource, WHO/International Union of Immunological Societies, UniProtKB and Allergome. We extended the hexamer hit rule by removing peptides with high probability of random occurrence measured by sequence entropy as well as requiring 3 or more hexamer hits consistent with natural linear epitope patterns in known allergens. This is complemented with a Gluten-like repeat pattern detection. We also switched from a linear sequence window similarity to a B-cell epitope-like 3D surface similarity window which became possible through extensive 3D structure modeling covering the majority (74%) of allergens. In case no structure similarity is found, the decision workflow reverts to the old linear sequence window rule. The overall accuracy of AllerCatPro is 84% compared with other current methods which range from 51 to 73%. Both the FAO/WHO rules and AllerCatPro achieve highest sensitivity but AllerCatPro provides a 37-fold increase in specificity. Availability and implementation https://allercatpro.bii.a-star.edu.sg/ Supplementary information Supplementary data are available at Bioinformatics online.

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25

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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26

Chautard, Emilie, Lionel Ballut, Nicolas Thierry-Mieg, and Sylvie Ricard-Blum. "MatrixDB, a database focused on extracellular protein–protein and protein–carbohydrate interactions." Bioinformatics 25, no. 5 (2009): 690–91. http://dx.doi.org/10.1093/bioinformatics/btp025.

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27

Winterhalter, C., R. Nicolle, A. Louis, C. To, F. Radvanyi, and M. Elati. "Pepper: cytoscape app for protein complex expansion using protein–protein interaction networks." Bioinformatics 30, no. 23 (2014): 3419–20. http://dx.doi.org/10.1093/bioinformatics/btu517.

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28

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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29

Tabassum Khan, Nida. "The Emerging Role of Bioinformatics in Biotechnology." Journal of Biotechnology and Biomedical Science 1, no. 3 (2018): 13–24. http://dx.doi.org/10.14302/issn.2576-6694.jbbs-18-2173.

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Bioinformatic tools is widely used to manage the enormous genomic and proteomic data involving DNA/protein sequences management, drug designing, homology modelling, motif/domain prediction ,docking, annotation and dynamic simulation etc. Bioinformatics offers a wide range of applications in numerous disciplines such as genomics. Proteomics, comparative genomics, nutrigenomics, microbial genome, biodefense, forensics etc. Thus it offers promising future to accelerate scientific research in biotechnology

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30

Bock, J. R., and D. A. Gough. "Predicting protein-protein interactions from primary structure." Bioinformatics 17, no. 5 (2001): 455–60. http://dx.doi.org/10.1093/bioinformatics/17.5.455.

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31

Douguet, D., H. C. Chen, A. Tovchigrechko, and I. A. Vakser. "DOCKGROUND resource for studying protein-protein interfaces." Bioinformatics 22, no. 21 (2006): 2612–18. http://dx.doi.org/10.1093/bioinformatics/btl447.

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32

Kowalsman, N., and M. Eisenstein. "Inherent limitations in protein-protein docking procedures." Bioinformatics 23, no. 4 (2006): 421–26. http://dx.doi.org/10.1093/bioinformatics/btl524.

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33

Futschik, Matthias E., Gautam Chaurasia, and Hanspeter Herzel. "Comparison of human protein–protein interaction maps." Bioinformatics 23, no. 5 (2007): 605–11. http://dx.doi.org/10.1093/bioinformatics/btl683.

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34

Soong, Ta-tsen, Kazimierz O. Wrzeszczynski, and Burkhard Rost. "Physical protein–protein interactions predicted from microarrays." Bioinformatics 24, no. 22 (2008): 2608–14. http://dx.doi.org/10.1093/bioinformatics/btn498.

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35

Reynolds, C., D. Damerell, and S. Jones. "ProtorP: a protein-protein interaction analysis server." Bioinformatics 25, no. 3 (2008): 413–14. http://dx.doi.org/10.1093/bioinformatics/btn584.

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36

Ramírez-Aportela, Erney, José Ramón López-Blanco, and Pablo Chacón. "FRODOCK 2.0: fast protein–protein docking server." Bioinformatics 32, no. 15 (2016): 2386–88. http://dx.doi.org/10.1093/bioinformatics/btw141.

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37

Pons, Carles, Daniel Jiménez-González, Cecilia González-Álvarez, et al. "Cell-Dock: high-performance protein–protein docking." Bioinformatics 28, no. 18 (2012): 2394–96. http://dx.doi.org/10.1093/bioinformatics/bts454.

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38

Martin, S., D. Roe, and J. L. Faulon. "Predicting protein-protein interactions using signature products." Bioinformatics 21, no. 2 (2004): 218–26. http://dx.doi.org/10.1093/bioinformatics/bth483.

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39

Ben-Hur, A., and W. S. Noble. "Kernel methods for predicting protein-protein interactions." Bioinformatics 21, Suppl 1 (2005): i38—i46. http://dx.doi.org/10.1093/bioinformatics/bti1016.

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40

Pagel, P., S. Kovac, M. Oesterheld, et al. "The MIPS mammalian protein-protein interaction database." Bioinformatics 21, no. 6 (2004): 832–34. http://dx.doi.org/10.1093/bioinformatics/bti115.

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41

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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42

Kuroda, Daisuke, and Jeffrey J. Gray. "Shape complementarity and hydrogen bond preferences in protein–protein interfaces: implications for antibody modeling and protein–protein docking." Bioinformatics 32, no. 16 (2016): 2451–56. http://dx.doi.org/10.1093/bioinformatics/btw197.

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43

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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44

Ovek, Damla, Ameer Taweel, Zeynep Abali, et al. "SARS-CoV-2 Interactome 3D: A Web interface for 3D visualization and analysis of SARS-CoV-2–human mimicry and interactions." Bioinformatics 38, no. 5 (2021): 1455–57. http://dx.doi.org/10.1093/bioinformatics/btab799.

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Abstract Summary We present a web-based server for navigating and visualizing possible interactions between SARS-CoV-2 and human host proteins. The interactions are obtained from HMI_Pred which relies on the rationale that virus proteins mimic host proteins. The structural alignment of the viral protein with one side of the human protein–protein interface determines the mimicry. The mimicked human proteins and predicted interactions, and the binding sites are presented. The user can choose one of the 18 SARS-CoV-2 protein structures and visualize the potential 3D complexes it forms with human proteins. The mimicked interface is also provided. The user can superimpose two interacting human proteins in order to see whether they bind to the same site or different sites on the viral protein. The server also tabulates all available mimicked interactions together with their match scores and number of aligned residues. This is the first server listing and cataloging all interactions between SARS-CoV-2 and human protein structures, enabled by our innovative interface mimicry strategy. Availability and implementation The server is available at https://interactome.ku.edu.tr/sars/.

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45

Khatun, Mst Shamima, Watshara Shoombuatong, Md Mehedi Hasan, and Hiroyuki Kurata. "Evolution of Sequence-based Bioinformatics Tools for Protein-protein Interaction Prediction." Current Genomics 21, no. 6 (2020): 454–63. http://dx.doi.org/10.2174/1389202921999200625103936.

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Protein-protein interactions (PPIs) are the physical connections between two or more proteins via electrostatic forces or hydrophobic effects. Identification of the PPIs is pivotal, which contributes to many biological processes including protein function, disease incidence, and therapy design. The experimental identification of PPIs via high-throughput technology is time-consuming and expensive. Bioinformatics approaches are expected to solve such restrictions. In this review, our main goal is to provide an inclusive view of the existing sequence-based computational prediction of PPIs. Initially, we briefly introduce the currently available PPI databases and then review the state-of-the-art bioinformatics approaches, working principles, and their performances. Finally, we discuss the caveats and future perspective of the next generation algorithms for the prediction of PPIs.

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Malod-Dognin, Noël, and Nataša Pržulj. "Functional geometry of protein interactomes." Bioinformatics 35, no. 19 (2019): 3727–34. http://dx.doi.org/10.1093/bioinformatics/btz146.

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Abstract Motivation Protein–protein interactions (PPIs) are usually modeled as networks. These networks have extensively been studied using graphlets, small induced subgraphs capturing the local wiring patterns around nodes in networks. They revealed that proteins involved in similar functions tend to be similarly wired. However, such simple models can only represent pairwise relationships and cannot fully capture the higher-order organization of protein interactomes, including protein complexes. Results To model the multi-scale organization of these complex biological systems, we utilize simplicial complexes from computational geometry. The question is how to mine these new representations of protein interactomes to reveal additional biological information. To address this, we define simplets, a generalization of graphlets to simplicial complexes. By using simplets, we define a sensitive measure of similarity between simplicial complex representations that allows for clustering them according to their data types better than clustering them by using other state-of-the-art measures, e.g. spectral distance, or facet distribution distance. We model human and baker’s yeast protein interactomes as simplicial complexes that capture PPIs and protein complexes as simplices. On these models, we show that our newly introduced simplet-based methods cluster proteins by function better than the clustering methods that use the standard PPI networks, uncovering the new underlying functional organization of the cell. We demonstrate the existence of the functional geometry in the protein interactome data and the superiority of our simplet-based methods to effectively mine for new biological information hidden in the complexity of the higher-order organization of protein interactomes. Availability and implementation Codes and datasets are freely available at http://www0.cs.ucl.ac.uk/staff/natasa/Simplets/. Supplementary information Supplementary data are available at Bioinformatics online.

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Becker, Emmanuelle, Benoît Robisson, Charles E. Chapple, Alain Guénoche, and Christine Brun. "Multifunctional proteins revealed by overlapping clustering in protein interaction network." Bioinformatics 28, no. 1 (2011): 84–90. http://dx.doi.org/10.1093/bioinformatics/btr621.

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Tusnady, G. E., Z. Dosztanyi, and I. Simon. "Transmembrane proteins in the Protein Data Bank: identification and classification." Bioinformatics 20, no. 17 (2004): 2964–72. http://dx.doi.org/10.1093/bioinformatics/bth340.

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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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Champion, E. A., L. Kundrat, L. Regan, and S. J. Baserga. "A structural model for the HAT domain of Utp6 incorporating bioinformatics and genetics." Protein Engineering Design and Selection 22, no. 7 (2009): 431–39. http://dx.doi.org/10.1093/protein/gzp022.

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