Статті в журналах з теми "Computational analyses protein"
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Chen, Yu, and Dong Xu. "Computational Analyses of High-Throughput Protein-Protein Interaction Data." Current Protein & Peptide Science 4, no. 3 (June 1, 2003): 159–80. http://dx.doi.org/10.2174/1389203033487225.
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Gruber, Jan, Alexander Zawaira, Rhodri Saunders, C. Paul Barrett, and Martin E. M. Noble. "Computational analyses of the surface properties of protein–protein interfaces." Acta Crystallographica Section D Biological Crystallography 63, no. 1 (December 13, 2006): 50–57. http://dx.doi.org/10.1107/s0907444906046762.
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Gao, Xinjiao, Changjiang Jin, Yu Xue, and Xuebiao Yao. "Computational Analyses of TBC Protein Family in Eukaryotes." Protein & Peptide Letters 15, no. 5 (June 1, 2008): 505–9. http://dx.doi.org/10.2174/092986608784567483.
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Sarkar, Anita, Sonu Kumar, Abhinav Grover, and Durai Sundar. "Protein Aggregation in Neurodegenerative Diseases: Insights from Computational Analyses." Current Bioinformatics 7, no. 1 (March 1, 2012): 87–95. http://dx.doi.org/10.2174/157489312799304495.
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Gumerov, Vadim M., and Igor B. Zhulin. "TREND: a platform for exploring protein function in prokaryotes based on phylogenetic, domain architecture and gene neighborhood analyses." Nucleic Acids Research 48, W1 (April 13, 2020): W72—W76. http://dx.doi.org/10.1093/nar/gkaa243.
Повний текст джерелаАнотація:
Abstract Key steps in a computational study of protein function involve analysis of (i) relationships between homologous proteins, (ii) protein domain architecture and (iii) gene neighborhoods the corresponding proteins are encoded in. Each of these steps requires a separate computational task and sets of tools. Currently in order to relate protein features and gene neighborhoods information to phylogeny, researchers need to prepare all the necessary data and combine them by hand, which is time-consuming and error-prone. Here, we present a new platform, TREND (tree-based exploration of neighborhoods and domains), which can perform all the necessary steps in automated fashion and put the derived information into phylogenomic context, thus making evolutionary based protein function analysis more efficient. A rich set of adjustable components allows a user to run the computational steps specific to his task. TREND is freely available at http://trend.zhulinlab.org.
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MORIMOTO, Yasumasa, Takashi NAKAGAWA, and Masaki KOJIMA. "Computational Analyses of Protein Structures by Solution X-ray Scattering." Seibutsu Butsuri 51, no. 2 (2011): 088–91. http://dx.doi.org/10.2142/biophys.51.088.
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Mahmood, Niaz, and Nahid Tamanna. "Analyses of Physcomitrella patens Ankyrin Repeat Proteins by Computational Approach." Molecular Biology International 2016 (June 27, 2016): 1–8. http://dx.doi.org/10.1155/2016/9156735.
Повний текст джерелаАнотація:
Ankyrin (ANK) repeat containing proteins are evolutionary conserved and have functions in crucial cellular processes like cell cycle regulation and signal transduction. In this study, through an entirely in silico approach using the first release of the moss genome annotation, we found that at least 54 ANK proteins are present in P. patens. Based on their differential domain composition, the identified ANK proteins were classified into nine subfamilies. Comparative analysis of the different subfamilies of ANK proteins revealed that P. patens contains almost all the known subgroups of ANK proteins found in the other angiosperm species except for the ones having the TPR domain. Phylogenetic analysis using full length protein sequences supported the subfamily classification where the members of the same subfamily almost always clustered together. Synonymous divergence (dS) and nonsynonymous divergence (dN) ratios showed positive selection for the ANK genes of P. patens which probably helped them to attain significant functional diversity during the course of evolution. Taken together, the data provided here can provide useful insights for future functional studies of the proteins from this superfamily as well as comparative studies of ANK proteins.
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Santiago, Luis, and Ravinder Abrol. "Understanding G Protein Selectivity of Muscarinic Acetylcholine Receptors Using Computational Methods." International Journal of Molecular Sciences 20, no. 21 (October 24, 2019): 5290. http://dx.doi.org/10.3390/ijms20215290.
Повний текст джерелаАнотація:
The neurotransmitter molecule acetylcholine is capable of activating five muscarinic acetylcholine receptors, M1 through M5, which belong to the superfamily of G-protein-coupled receptors (GPCRs). These five receptors share high sequence and structure homology; however, the M1, M3, and M5 receptor subtypes signal preferentially through the Gαq/11 subset of G proteins, whereas the M2 and M4 receptor subtypes signal through the Gαi/o subset of G proteins, resulting in very different intracellular signaling cascades and physiological effects. The structural basis for this innate ability of the M1/M3/M5 set of receptors and the highly homologous M2/M4 set of receptors to couple to different G proteins is poorly understood. In this study, we used molecular dynamics (MD) simulations coupled with thermodynamic analyses of M1 and M2 receptors coupled to both Gαi and Gαq to understand the structural basis of the M1 receptor’s preference for the Gαq protein and the M2 receptor’s preference for the Gαi protein. The MD studies showed that the M1 and M2 receptors can couple to both Gα proteins such that the M1 receptor engages with the two Gα proteins in slightly different orientations and the M2 receptor engages with the two Gα proteins in the same orientation. Thermodynamic studies of the free energy of binding of the receptors to the Gα proteins showed that the M1 and M2 receptors bind more strongly to their cognate Gα proteins compared to their non-cognate ones, which is in line with previous experimental studies on the M3 receptor. A detailed analysis of receptor–G protein interactions showed some cognate-complex-specific interactions for the M2:Gαi complex; however, G protein selectivity determinants are spread over a large overlapping subset of residues. Conserved interaction between transmembrane helices 5 and 6 far away from the G-protein-binding receptor interface was found only in the two cognate complexes and not in the non-cognate complexes. An analysis of residues implicated previously in G protein selectivity, in light of the cognate and non-cognate structures, shaded a more nuanced role of those residues in affecting G protein selectivity. The simulation of both cognate and non-cognate receptor–G protein complexes fills a structural gap due to difficulties in determining non-cognate complex structures and provides an enhanced framework to probe the mechanisms of G protein selectivity exhibited by most GPCRs.
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Carija, Pinheiro, Iglesias, and Ventura. "Computational Assessment of Bacterial Protein Structures Indicates a Selection Against Aggregation." Cells 8, no. 8 (August 8, 2019): 856. http://dx.doi.org/10.3390/cells8080856.
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The aggregation of proteins compromises cell fitness, either because it titrates functional proteins into non-productive inclusions or because it results in the formation of toxic assemblies. Accordingly, computational proteome-wide analyses suggest that prevention of aggregation upon misfolding plays a key role in sequence evolution. Most proteins spend their lifetimes in a folded state; therefore, it is conceivable that, in addition to sequences, protein structures would have also evolved to minimize the risk of aggregation in their natural environments. By exploiting the AGGRESCAN3D structure-based approach to predict the aggregation propensity of >600 Escherichia coli proteins, we show that the structural aggregation propensity of globular proteins is connected with their abundance, length, essentiality, subcellular location and quaternary structure. These data suggest that the avoidance of protein aggregation has contributed to shape the structural properties of proteins in bacterial cells.
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Bottini, Silvia, David Pratella, Valerie Grandjean, Emanuela Repetto, and Michele Trabucchi. "Recent computational developments on CLIP-seq data analysis and microRNA targeting implications." Briefings in Bioinformatics 19, no. 6 (June 12, 2017): 1290–301. http://dx.doi.org/10.1093/bib/bbx063.
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AbstractCross-Linking Immunoprecipitation associated to high-throughput sequencing (CLIP-seq) is a technique used to identify RNA directly bound to RNA-binding proteins across the entire transcriptome in cell or tissue samples. Recent technological and computational advances permit the analysis of many CLIP-seq samples simultaneously, allowing us to reveal the comprehensive network of RNA–protein interaction and to integrate it to other genome-wide analyses. Therefore, the design and quality management of the CLIP-seq analyses are of critical importance to extract clean and biological meaningful information from CLIP-seq experiments. The application of CLIP-seq technique to Argonaute 2 (Ago2) protein, the main component of the microRNA (miRNA)-induced silencing complex, reveals the direct binding sites of miRNAs, thus providing insightful information about the role played by miRNA(s). In this review, we summarize and discuss the most recent computational methods for CLIP-seq analysis, and discuss their impact on Ago2/miRNA-binding site identification and prediction with a regard toward human pathologies.
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Gosset, Simon, Annie Glatigny, Mélina Gallopin, Zhou Yi, Marion Salé, and Marie-Hélène Mucchielli-Giorgi. "APPINetwork: an R package for building and computational analysis of protein–protein interaction networks." PeerJ 10 (November 4, 2022): e14204. http://dx.doi.org/10.7717/peerj.14204.
Повний текст джерелаАнотація:
Background Protein–protein interactions (PPIs) are essential to almost every process in a cell. Analysis of PPI networks gives insights into the functional relationships among proteins and may reveal important hub proteins and sub-networks corresponding to functional modules. Several good tools have been developed for PPI network analysis but they have certain limitations. Most tools are suited for studying PPI in only a small number of model species, and do not allow second-order networks to be built, or offer relevant functions for their analysis. To overcome these limitations, we have developed APPINetwork (Analysis of Protein–protein Interaction Networks). The aim was to produce a generic and user-friendly package for building and analyzing a PPI network involving proteins of interest from any species as long they are stored in a database. Methods APPINetwork is an open-source R package. It can be downloaded and installed on the collaborative development platform GitLab (https://forgemia.inra.fr/GNet/appinetwork). A graphical user interface facilitates its use. Graphical windows, buttons, and scroll bars allow the user to select or enter an organism name, choose data files and network parameters or methods dedicated to network analysis. All functions are implemented in R, except for the script identifying all proteins involved in the same biological process (developed in C) and the scripts formatting the BioGRID data file and generating the IDs correspondence file (implemented in Python 3). PPI information comes from private resources or different public databases (such as IntAct, BioGRID, and iRefIndex). The package can be deployed on Linux and macOS operating systems (OS). Deployment on Windows is possible but it requires the prior installation of Rtools and Python 3. Results APPINetwork allows the user to build a PPI network from selected public databases and add their own PPI data. In this network, the proteins have unique identifiers resulting from the standardization of the different identifiers specific to each database. In addition to the construction of the first-order network, APPINetwork offers the possibility of building a second-order network centered on the proteins of interest (proteins known for their role in the biological process studied or subunits of a complex protein) and provides the number and type of experiments that have highlighted each PPI, as well as references to articles containing experimental evidence. Conclusion More than a tool for PPI network building, APPINetwork enables the analysis of the resultant network, by searching either for the community of proteins involved in the same biological process or for the assembly intermediates of a protein complex. Results of these analyses are provided in easily exportable files. Examples files and a user manual describing each step of the process come with the package.
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Steffen, Raphael, Lynn Ogoniak, Norbert Grundmann, Anna Pawluchin, Oliver Soehnlein, and Jürgen Schmitz. "paPAML: An Improved Computational Tool to Explore Selection Pressure on Protein-Coding Sequences." Genes 13, no. 6 (June 18, 2022): 1090. http://dx.doi.org/10.3390/genes13061090.
Повний текст джерелаАнотація:
Evolution is change over time. Although neutral changes promoted by drift effects are most reliable for phylogenetic reconstructions, selection-relevant changes are of only limited use to reconstruct phylogenies. On the other hand, comparative analyses of neutral and selected changes of protein-coding DNA sequences (CDS) retrospectively tell us about episodic constrained, relaxed, and adaptive incidences. The ratio of sites with nonsynonymous (amino acid altering) versus synonymous (not altering) mutations directly measures selection pressure and can be analysed by using the Phylogenetic Analysis by Maximum Likelihood (PAML) software package. We developed a CDS extractor for compiling protein-coding sequences (CDS-extractor) and parallel PAML (paPAML) to simplify, amplify, and accelerate selection analyses via parallel processing, including detection of negatively selected sites. paPAML compiles results of site, branch-site, and branch models and detects site-specific negative selection with the output of a codon list labelling significance values. The tool simplifies selection analyses for casual and inexperienced users and accelerates computing speeds up to the number of allocated computer threads. We then applied paPAML to examine the evolutionary impact on a new GINS Complex Subunit 3 exon, and neutrophil-associated as well as lysin and apolipoprotein genes. Compared with codeml (PAML version 4.9j) and HyPhy (HyPhy FEL version 2.5.26), all paPAML test runs performed with 10 computing threads led to identical selection pressure results, whereas the total selection analysis via paPAML, including all model comparisons, was about 3 to 5 times faster than the longest running codeml model and about 7 to 15 times faster than the entire processing time of these codeml runs.
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Navakauskienė, Rūta, Sandra Baronaitė, Dalius Matuzevičius, Natalija Krasovskaja, Gražina Treigytė, Audronė Arlauskienė, and Dalius Navakauskas. "Comparative Proteomic Assessment of Normal vs. Polyhydramnios Amniotic Fluid Based on Computational Analysis." Biomedicines 10, no. 8 (July 28, 2022): 1821. http://dx.doi.org/10.3390/biomedicines10081821.
Повний текст джерелаАнотація:
Mass spectrometry-based proteomics have become a valued tool for conducting comprehensive analyses in amniotic fluid samples with pathologies. Our research interest is the finding and characterization of proteins related to normal vs. polyhydramnios (non-immune hydrops) pregnancy. Proteomic analysis was performed on proteins isolated from fresh amniotic fluid samples. Proteins were fractionated by 2DE using a different pI range (pI 3–11, pI 4–7) and analyzed with MALDI-TOF-MS. Furthermore, by using computational analysis, identified proteins in protein maps specific to normal vs. polyhydramnios pregnancy were compared and the quantities of expressed proteins were evaluated mathematically. Comparative analysis of proteome characteristic for the same polyhydramnios pregnancy fractionated by 2DE in different pI range (3–11 and 4–7) was performed and particular protein groups were evaluated for the quantification of changes within the same protein level. Proteins of normal and polyhydramnios pregnancies were fractionated by 2DE in pI range 3–11 and in pI range 4–7. Mass spectrometry analysis of proteins has revealed that the quantity changes of the main identified proteins in normal vs. polyhydramnios pregnancy could be assigned to immune response and inflammation proteins, cellular signaling and regulation proteins, metabolic proteins, etc. Specifically, we have identified and characterized proteins associated with heart function and circulatory system and proteins associated with abnormalities in prenatal medicine. The following are: serotransferrin, prothrombin, haptoglobin, transthyretin, alpha-1-antitrypsin, zinc-alpha-2-glycprotein, haptoglobin kininogen-1, hemopexin, clusterin, lumican, afamin, gelsolin. By using computational analysis, we demonstrated that some of these proteins increased a few times in pathological pregnancy. Computer assistance analysis of 2DE images suggested that, for the better isolation of the proteins’ isoforms, those levels increased/decreased in normal vs. polyhydramnios pregnancy, and the fractionation of proteins in pI rage 3–11 and 4–7 could be substantial. We analyzed and identified by MS proteins specific for normal and polyhydramnios pregnancies. Identified protein levels increased and/or modification changed in case of non-immune hydrops fetus and in cases of cardiovascular, anemia, growth restriction, and metabolic disorders. Computational analysis for proteomic characterization empower to estimate the quantitative changes of proteins specific for normal vs. polyhydramnios pregnancies.
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Sowdhamini, R. "Biography of a scientist with strength, substance, sincerity and service: Late N. Srinivasan (1962-2021)." Bioinformation 18, no. 6 (June 30, 2022): 600–604. http://dx.doi.org/10.6026/97320630018600.
Повний текст джерелаАнотація:
Late N. Srinivasan belongs to the GN Ramachandran lineage of protein structural analysts. His role in the advancement of the structure based understanding of signal transduction, protein kinase analyses and host-pathogen interactions both developing and using Bioinformatics tools for protein-protein interactions, protein dynamics, remote homology detection and polypeptide stereochemistry is well documented in the literature. Thus, his contribution to the understanding of protein function through structural analysis, using computational models and tools, is exceptional.
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Zhou, Jianfu, Alexandra E. Panaitiu, and Gevorg Grigoryan. "A general-purpose protein design framework based on mining sequence–structure relationships in known protein structures." Proceedings of the National Academy of Sciences 117, no. 2 (December 31, 2019): 1059–68. http://dx.doi.org/10.1073/pnas.1908723117.
Повний текст джерелаАнотація:
Current state-of-the-art approaches to computational protein design (CPD) aim to capture the determinants of structure from physical principles. While this has led to many successful designs, it does have strong limitations associated with inaccuracies in physical modeling, such that a reliable general solution to CPD has yet to be found. Here, we propose a design framework—one based on identifying and applying patterns of sequence–structure compatibility found in known proteins, rather than approximating them from models of interatomic interactions. We carry out extensive computational analyses and an experimental validation for our method. Our results strongly argue that the Protein Data Bank is now sufficiently large to enable proteins to be designed by using only examples of structural motifs from unrelated proteins. Because our method is likely to have orthogonal strengths relative to existing techniques, it could represent an important step toward removing remaining barriers to robust CPD.
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Cho, Hyunju, Francesca Stanzione, Ryan LaMarca, Amadeu K. Sum та Christina Chan. "Computational and Experimental Analyses of the Transmembrane Domain Dimerization of IRE1α Protein". Biophysical Journal 104, № 2 (січень 2013): 406a. http://dx.doi.org/10.1016/j.bpj.2012.11.2265.
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Salas, Gicela G. Saucedo, Alan E. Lopez Hernandez, Jiadi He, Chitra Karki, Yixin Xie, Shengjie Sun, Yuejiao Xian, and Lin Li. "Using computational approaches to study dengue virus capsid assembly." Computational and Mathematical Biophysics 7, no. 1 (December 13, 2019): 64–72. http://dx.doi.org/10.1515/cmb-2019-0005.
Повний текст джерелаАнотація:
AbstractDengue viral capsid plays a significant role in viral life cycle of dengue, especially in vial genome protection and virus-cell fusion. Revealing mechanisms of the viral capsid protein assembly may lead to the discovery of anti-viral drugs that inhibit the assembly of the viral capsid. The E and M-proteins are arranged into heterotetramers, which consists of two copies of E and M-protein. The heterotetramers are assembled into a highly ordered capsid. While many investigations of the interactions between E and M-proteins have been performed, there are very few studies on the interactions between the heterotetramers and their roles in capsid assembly. Utilizing a series of computational approaches, this study focuses on the assembly mechanism of the heterotetramers. Our electrostatic analyses lead to the identification of four binding modes between each two dengue heterotetramers that repeat periodically throughout the virus capsid. Among these four binding modes, heterotetramers in binding modes I, II and IV are attractive. But in the binding mode III the heterotetramers repel each other, making mode III a suitable target for drug design. Furthermore, MD simulations were performed following by salt bridges analysis. This study demonstrates that using computational approaches is a promising direction to study the dengue virus.
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Rajneesh, Soumila Mondal, Jainendra Pathak, Prashant R. Singh, Shailendra P. Singh, and Rajeshwar P. Sinha. "Computational studies on photolyase (Phr) proteins of cyanobacteria." Canadian Journal of Microbiology 68, no. 2 (February 2022): 111–37. http://dx.doi.org/10.1139/cjm-2021-0167.
Повний текст джерелаАнотація:
Photolyases (Phrs) are enzymes that utilize the blue/ultraviolet (UV-A) region of light for repairing UV-induced cyclopyramidine dimers. We studied Phr groups by bioinformatic analyses as well as active-site and structural modeling. Analysis of 238 amino acid sequences from 85 completely sequenced cyanobacterial genomes revealed five classes of Phrs, CPD Gr I, 6-4 Phrs/cryptochrome, Cry-DASH, Fe-S bacteria Phrs, and a group with fewer amino acids (276–385) in length. The distribution of Phr groups in cyanobacteria belonging to the order Synechococcales was found to be influenced by the habitats of the organisms. Class V Phrs are exclusively present in cyanobacteria. Unique motifs and binding sites were reported in groups II and III. The Fe-S protein binding site was only present in group V and the active site residues and putative CPD/6-4PP binding residues are charged amino acids present on the surface of the proteins. The majority of hydrophilic amino acid residues were present on the surface of the Phrs. Sequence analysis confirmed the diverse nature of Phrs, although sequence diversity did not affect the overall three-dimensional structure. Protein–ligand interaction analysis identified novel CPD/6-4PP binding sites on Phrs. This structural information of Phrs can be used for the preparation of efficient Phr-based formulations.
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Greller, Larry D., and Frank L. Tobin. "Detecting Selective Expression of Genes and Proteins." Genome Research 9, no. 3 (March 1, 1999): 282–96. http://dx.doi.org/10.1101/gr.9.3.282.
Повний текст джерелаАнотація:
Selective expression of a gene product (mRNA or protein) is a pattern in which the expression is markedly high, or markedly low, in one particular tissue compared with its level in other tissues or sources. We present a computational method for the identification of such patterns. The method combines assessments of the reliability of expression quantitation with a statistical test of expression distribution patterns. The method is applicable to small studies or to data mining of abundance data from expression databases, whether mRNA or protein. Though the method was developed originally for gene-expression analyses, the computational method is, in fact, rather general. It is well suited for the identification of exceptional values in many sorts of intensity data, even noisy data, for which assessments of confidences in the sources of the intensities are available. Moreover, the method is indifferent as to whether the intensities are experimentally or computationally derived. We show details of the general method and examples of computational results on gene abundance data.
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Drago, Valentina, Luisa Di Paola, Claire Lesieur, Renato Bernardini, Claudio Bucolo, and Chiara Bianca Maria Platania. "In-Silico Characterization of von Willebrand Factor Bound to FVIII." Applied Sciences 12, no. 15 (August 4, 2022): 7855. http://dx.doi.org/10.3390/app12157855.
Повний текст джерелаАнотація:
Factor VIII belongs to the coagulation cascade and is expressed as a long pre-protein (mature form, 2351 amino acids long). FVIII is deficient or defective in hemophilic A patients, who need to be treated with hemoderivatives or recombinant FVIII substitutes, i.e., biologic drugs. The interaction between FVIII and von Willebrand factor (VWF) influences the pharmacokinetics of FVIII medications. In vivo, full-length FVIII (FL-FVIII) is secreted in a plasma-inactive form, which includes the B domain, which is then proteolyzed by thrombin protease activity, leading to an inactive plasma intermediate. In this work, we analyzed through a computational approach the binding of VWF with two structure models of FVIII (secreted full-length with B domain, and B domain-deleted FVIII). We included in our analysis the atomic model of efanesoctocog alfa, a novel and investigational recombinant FVIII medication, in which the VWF is covalently linked to FVIII. We carried out a structural analysis of VWF/FVIII interfaces by means of protein–protein docking, PISA (Proteins, Interfaces, Structures and Assemblies), and protein contact networks (PCN) analyses. Accordingly, our computational approaches to previously published experimental data demonstrated that the domains A3-C1 of B domain-deleted FVIII (BDD-FVIII) is the preferential binding site for VWF. Overall, our computational approach applied to topological analysis of protein–protein interface can be aimed at the rational design of biologic drugs other than FVIII medications.
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Mawuenyega, Kwasi G., Christian V. Forst, Karen M. Dobos, John T. Belisle, Jin Chen, E. Morton Bradbury, Andrew R. M. Bradbury, and Xian Chen. "Mycobacterium tuberculosisFunctional Network Analysis by Global Subcellular Protein Profiling." Molecular Biology of the Cell 16, no. 1 (January 2005): 396–404. http://dx.doi.org/10.1091/mbc.e04-04-0329.
Повний текст джерелаАнотація:
Trends in increased tuberculosis infection and a fatality rate of ∼23% have necessitated the search for alternative biomarkers using newly developed postgenomic approaches. Here we provide a systematic analysis of Mycobacterium tuberculosis (Mtb) by directly profiling its gene products. This analysis combines high-throughput proteomics and computational approaches to elucidate the globally expressed complements of the three subcellular compartments (the cell wall, membrane, and cytosol) of Mtb. We report the identifications of 1044 proteins and their corresponding localizations in these compartments. Genome-based computational and metabolic pathways analyses were performed and integrated with proteomics data to reconstruct response networks. From the reconstructed response networks for fatty acid degradation and lipid biosynthesis pathways in Mtb, we identified proteins whose involvements in these pathways were not previously suspected. Furthermore, the subcellular localizations of these expressed proteins provide interesting insights into the compartmentalization of these pathways, which appear to traverse from cell wall to cytoplasm. Results of this large-scale subcellular proteome profile of Mtb have confirmed and validated the computational network hypothesis that functionally related proteins work together in larger organizational structures.
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Ranjpour, Maryam, Deepshikha P. Katare, Saima Wajid, and Swatantra K. Jain. "HCC Specific Protein Network Involving Interactions of EGFR with A-Raf and Transthyretin: Experimental Analysis and Computational Biology Correlates." Anti-Cancer Agents in Medicinal Chemistry 18, no. 8 (December 28, 2018): 1163–76. http://dx.doi.org/10.2174/1871520618666180507141632.
Повний текст джерелаАнотація:
Background: The network interactions link human disease proteins to regulatory cellular pathways leading to better understanding of protein functions and cellular processes. Revealing the network of signaling pathways in cancer through protein-protein interactions at molecular level enhances our understanding of Hepatocellular Carcinoma (HCC). Objective: A rodent model for study of HCC was developed to identify differentially expressed proteins at very early stage of cancer initiation and throughout its progression. Methodology: HCC was induced by administrating N-Nitrosodiethylamine (DEN) and 2-aminoacetylfluorine (2-AAF) to male Wistar rats. Proteomic approaches such as 2D-Electrophoresis, PD-Quest, MALDI-TOF-MS and Western blot analyses have been used to identify, characterize and validate the differentially expressed proteins in HCC-bearing animals vis-a-vis controls. Results: The step-wise analysis of morphological and histological parameters revealed HCC induction and tumorigenesis at 1 and 4 months after carcinogen treatment, respectively. We report a novel protein network of 735 different proteins out of which eight proteins are characterized by MALDI-TOF-MS analysis soon after HCC was chemically induced in rats. We have analyzed four different novel routes representing the association of experimentally identified proteins with HCC progression. Conclusion: The study suggests that A-Raf, transthyretin and epidermal growth factor receptor play major role in HCC progression by regulating MAPK signaling pathway and lipid metabolism leading to continuous proliferation, neoplastic transformation and tumorigenesis.
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Kasai, Takuma, and Takanori Kigawa. "Selective isotope labeling strategy and computational interpretation of spectra for protein NMR analyses." Journal of Physics: Conference Series 1036 (June 2018): 012007. http://dx.doi.org/10.1088/1742-6596/1036/1/012007.
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Essadssi, Soukaina, Al Mehdi Krami, Lamiae Elkhattabi, Zouhair Elkarhat, Ghita Amalou, Houria Abdelghaffar, Hassan Rouba, and Abdelhamid Barakat. "Computational Analysis of nsSNPs of ADA Gene in Severe Combined Immunodeficiency Using Molecular Modeling and Dynamics Simulation." Journal of Immunology Research 2019 (November 3, 2019): 1–14. http://dx.doi.org/10.1155/2019/5902391.
Повний текст джерелаАнотація:
Severe combined immunodeficiency (SCID) is the most severe form of primary immunodeficiency (PID), characterized by fatal opportunistic infections. The ADA gene encodes adenosine deaminase, an enzyme that catalyzes the irreversible deamination of adenosine and deoxyadenosine in the catabolic pathway of purine. Mutations of the ADA gene have been identified in patients with severe combined immunodeficiency. In this study, we performed a bioinformatics analysis of the human ADA gene to identify potentially harmful nonsynonymous SNPs and their effect on protein structure and stability. Using eleven prediction tools, we identified 15 nsSNPs (H15D, H15P, H17Q, H17Y, D19N, T26I, G140E, C153F, A183D, G216R, H258Y, C262Y, S291L, S291W, and K34OE) as harmful. The results of ConSurf’s analysis revealed that all these nsSNPs are localised in the highly conserved positions and affect the structure of the native proteins. In addition, our computational analysis showed that the H15D, G140E, G216R, and S291L mutations identified as being associated with severe combined immunodeficiency affect protein structure. Similarly, the results of the analyses of Rmsd, Rmsf, and Rg showed that all these factors influence protein stability, flexibility, and compaction with different levels of impact. This study is the first comprehensive computational analysis of nsSNPs of the ADA gene. However, functional analyses are needed to elucidate the biological mechanisms of these polymorphisms in severe combined immunodeficiency.
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Hazra, Anjan. "Computational Fishing and Structural Analysis of MIPS Protein from Two Important Plant Groups." International Letters of Natural Sciences 42 (July 2015): 18–27. http://dx.doi.org/10.18052/www.scipress.com/ilns.42.18.
Повний текст джерелаАнотація:
Myo Inositol 1-Phosphate Synthase (MIPS), which catalyzes the first step of inositol metabolism, has been reported from a diverse range of organism like bacteria to human including different groups of plants and animals. The present work is carried out to explore and analyze structural forms of the respective MIPS proteins from complete sequenced genome or proteome available on database of one representative from two important plant groups viz. bryophyte (Physcomitrella patens) and pteridophyte (Selaginella moellendorffii). Previously reported characteristic MIPS sequences was used to identify it’s homolog ones from those members under study. The explored sequences compared with a number of MIPS varieties from other plant members to study the conserveness or evolution of the protein/enzyme. ProtParam tool provided necessary theoretical physicochemical data of the predicted proteins, the three-dimensional structures were predicted through homology modelling with identified amino acid data. Structural evaluation and stereochemical analyses were performed using ProSA-webdisplaying Z-scores and Molprobityvisualising Ramachandran plot.
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Hazra, Anjan. "Computational Fishing and Structural Analysis of MIPS Protein from Two Important Plant Groups." International Letters of Natural Sciences 42 (July 7, 2015): 18–27. http://dx.doi.org/10.56431/p-vg0b0a.
Повний текст джерелаАнотація:
Myo Inositol 1-Phosphate Synthase (MIPS), which catalyzes the first step of inositol metabolism, has been reported from a diverse range of organism like bacteria to human including different groups of plants and animals. The present work is carried out to explore and analyze structural forms of the respective MIPS proteins from complete sequenced genome or proteome available on database of one representative from two important plant groups viz. bryophyte (Physcomitrella patens) and pteridophyte (Selaginella moellendorffii). Previously reported characteristic MIPS sequences was used to identify it’s homolog ones from those members under study. The explored sequences compared with a number of MIPS varieties from other plant members to study the conserveness or evolution of the protein/enzyme. ProtParam tool provided necessary theoretical physicochemical data of the predicted proteins, the three-dimensional structures were predicted through homology modelling with identified amino acid data. Structural evaluation and stereochemical analyses were performed using ProSA-webdisplaying Z-scores and Molprobityvisualising Ramachandran plot.
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Torres, Camilo, Simon Dumas, Valentina Palacio-Castañeda, Stéphanie Descroix, Roland Brock, and Wouter P. R. Verdurmen. "A Computational Investigation of In Vivo Cytosolic Protein Delivery for Cancer Therapy." Pharmaceutics 13, no. 4 (April 15, 2021): 562. http://dx.doi.org/10.3390/pharmaceutics13040562.
Повний текст джерелаАнотація:
The ability to specifically block or degrade cytosolic targets using therapeutic proteins would bring tremendous therapeutic opportunities in cancer therapy. Over the last few years, significant progress has been made with respect to tissue targeting, cytosolic delivery, and catalytic inactivation of targets, placing this aim within reach. Here, we developed a mathematical model specifically built for the evaluation of approaches towards cytosolic protein delivery, involving all steps from systemic administration to translocation into the cytosol and target engagement. Focusing on solid cancer tissues, we utilized the model to investigate the effects of microvascular permeability, receptor affinity, the cellular density of targeted receptors, as well as the mode of activity (blocking/degradation) on therapeutic potential. Our analyses provide guidance for the rational optimization of protein design for enhanced activity and highlight the importance of tuning the receptor affinity as a function of receptor density as well as the receptor internalization rate. Furthermore, we provide quantitative insights into how enzymatic cargoes can enhance the distribution, extent, and duration of therapeutic activity, already at very low catalytic rates. Our results illustrate that with current protein engineering approaches, the goal of delivery of cytosolic delivery of proteins for therapeutic effects is well within reach.
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Newaz, Khalique, Gabriel Wright, Jacob Piland, Jun Li, Patricia L. Clark, Scott J. Emrich, and Tijana Milenković. "Network analysis of synonymous codon usage." Bioinformatics 36, no. 19 (July 1, 2020): 4876–84. http://dx.doi.org/10.1093/bioinformatics/btaa603.
Повний текст джерелаАнотація:
Abstract Motivation Most amino acids are encoded by multiple synonymous codons, some of which are used more rarely than others. Analyses of positions of such rare codons in protein sequences revealed that rare codons can impact co-translational protein folding and that positions of some rare codons are evolutionarily conserved. Analyses of their positions in protein 3-dimensional structures, which are richer in biochemical information than sequences alone, might further explain the role of rare codons in protein folding. Results We model protein structures as networks and use network centrality to measure the structural position of an amino acid. We first validate that amino acids buried within the structural core are network-central, and those on the surface are not. Then, we study potential differences between network centralities and thus structural positions of amino acids encoded by conserved rare, non-conserved rare and commonly used codons. We find that in 84% of proteins, the three codon categories occupy significantly different structural positions. We examine protein groups showing different codon centrality trends, i.e. different relationships between structural positions of the three codon categories. We see several cases of all proteins from our data with some structural or functional property being in the same group. Also, we see a case of all proteins in some group having the same property. Our work shows that codon usage is linked to the final protein structure and thus possibly to co-translational protein folding. Availability and implementation https://nd.edu/∼cone/CodonUsage/. Supplementary information Supplementary data are available at Bioinformatics online.
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Janes, R. W. "Bioinformatics analyses of circular dichroism protein reference databases." Bioinformatics 21, no. 23 (September 27, 2005): 4230–38. http://dx.doi.org/10.1093/bioinformatics/bti690.
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Mei, Suyu, and Kun Zhang. "Neglog: Homology-Based Negative Data Sampling Method for Genome-Scale Reconstruction of Human Protein–Protein Interaction Networks." International Journal of Molecular Sciences 20, no. 20 (October 12, 2019): 5075. http://dx.doi.org/10.3390/ijms20205075.
Повний текст джерелаАнотація:
Rapid reconstruction of genome-scale protein–protein interaction (PPI) networks is instrumental in understanding the cellular processes and disease pathogenesis and drug reactions. However, lack of experimentally verified negative data (i.e., pairs of proteins that do not interact) is still a major issue that needs to be properly addressed in computational modeling. In this study, we take advantage of the very limited experimentally verified negative data from Negatome to infer more negative data for computational modeling. We assume that the paralogs or orthologs of two non-interacting proteins also do not interact with high probability. We coin an assumption as “Neglog” this assumption is to some extent supported by paralogous/orthologous structure conservation. To reduce the risk of bias toward the negative data from Negatome, we combine Neglog with less biased random sampling according to a certain ratio to construct training data. L2-regularized logistic regression is used as the base classifier to counteract noise and train on a large dataset. Computational results show that the proposed Neglog method outperforms pure random sampling method with sound biological interpretability. In addition, we find that independent test on negative data is indispensable for bias control, which is usually neglected by existing studies. Lastly, we use the Neglog method to validate the PPIs in STRING, which are supported by gene ontology (GO) enrichment analyses.
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Sun, Xiaolin, Nawar Malhis, Bi Zhao, Bin Xue, Joerg Gsponer, and Erik H. A. Rikkerink. "Computational Disorder Analysis in Ethylene Response Factors Uncovers Binding Motifs Critical to Their Diverse Functions." International Journal of Molecular Sciences 21, no. 1 (December 20, 2019): 74. http://dx.doi.org/10.3390/ijms21010074.
Повний текст джерелаАнотація:
APETALA2/ETHYLENE RESPONSE FACTOR transcription factors (AP2/ERFs) play crucial roles in adaptation to stresses such as those caused by pathogens, wounding and cold. Although their name suggests a specific role in ethylene signalling, some ERF members also co-ordinate signals regulated by other key plant stress hormones such as jasmonate, abscisic acid and salicylate. We analysed a set of ERF proteins from three divergent plant species for intrinsically disorder regions containing conserved segments involved in protein–protein interaction known as Molecular Recognition Features (MoRFs). Then we correlated the MoRFs identified with a number of known functional features where these could be identified. Our analyses suggest that MoRFs, with plasticity in their disordered surroundings, are highly functional and may have been shuffled between related protein families driven by selection. A particularly important role may be played by the alpha helical component of the structured DNA binding domain to permit specificity. We also present examples of computationally identified MoRFs that have no known function and provide a valuable conceptual framework to link both disordered and ordered structural features within this family to diverse function.
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Bennani, Fatima Ezzahra, Khalid Karrouchi, Latifa Doudach, Mario Scrima, Noor Rahman, Luca Rastrelli, Trina Ekawati Tallei, Christopher E. Rudd, My El Abbes Faouzi, and M’hammed Ansar. "In Silico Identification of Promising New Pyrazole Derivative-Based Small Molecules for Modulating CRMP2, C-RAF, CYP17, VEGFR, C-KIT, and HDAC—Application towards Cancer Therapeutics." Current Issues in Molecular Biology 44, no. 11 (October 31, 2022): 5312–51. http://dx.doi.org/10.3390/cimb44110361.
Повний текст джерелаАнотація:
Despite continual efforts being made with multiple clinical studies and deploying cutting-edge diagnostic tools and technologies, the discovery of new cancer therapies remains of severe worldwide concern. Multiple drug resistance has also emerged in several cancer cell types, leaving them unresponsive to the many cancer treatments. Such a condition always prompts the development of next-generation cancer therapies that have a better chance of inhibiting selective target macromolecules with less toxicity. Therefore, in the present study, extensive computational approaches were implemented combining molecular docking and dynamic simulation studies for identifying potent pyrazole-based inhibitors or modulators for CRMP2, C-RAF, CYP17, c-KIT, VEGFR, and HDAC proteins. All of these proteins are in some way linked to the development of numerous forms of cancer, including breast, liver, prostate, kidney, and stomach cancers. In order to identify potential compounds, 63 in-house synthesized pyrazole-derivative compounds were docked with each selected protein. In addition, single or multiple standard drug compounds of each protein were also considered for docking analyses and their results used for comparison purposes. Afterward, based on the binding affinity and interaction profile of pyrazole compounds of each protein, potentially strong compounds were filtered out and further subjected to 1000 ns MD simulation analyses. Analyzing parameters such as RMSD, RMSF, RoG and protein–ligand contact maps were derived from trajectories of simulated protein–ligand complexes. All these parameters turned out to be satisfactory and within the acceptable range to support the structural integrity and interaction stability of the protein–ligand complexes in dynamic state. Comprehensive computational analyses suggested that a few identified pyrazole compounds, such as M33, M36, M72, and M76, could be potential inhibitors or modulators for HDAC, C-RAF, CYP72 and VEGFR proteins, respectively. Another pyrazole compound, M74, turned out to be a very promising dual inhibitor/modulator for CRMP2 and c-KIT proteins. However, more extensive study may be required for further optimization of the selected chemical framework of pyrazole derivatives to yield improved inhibitory activity against each studied protein receptor.
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Kim, Oanh, K. Yura, and N. Go. "2P304 Structure-based bioinformatics analyses of protein-RNA interface toward developing a computational method to predict protein-RNA interface." Seibutsu Butsuri 45, supplement (2005): S195. http://dx.doi.org/10.2142/biophys.45.s195_4.
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Brazão, João M., Peter G. Foster, and Cymon J. Cox. "Data-specific substitution models improve protein-based phylogenetics." PeerJ 11 (August 8, 2023): e15716. http://dx.doi.org/10.7717/peerj.15716.
Повний текст джерелаАнотація:
Calculating amino-acid substitution models that are specific for individual protein data sets is often difficult due to the computational burden of estimating large numbers of rate parameters. In this study, we tested the computational efficiency and accuracy of five methods used to estimate substitution models, namely Codeml, FastMG, IQ-TREE, P4 (maximum likelihood), and P4 (Bayesian inference). Data-specific substitution models were estimated from simulated alignments (with different lengths) that were generated from a known simulation model and simulation tree. Each of the resulting data-specific substitution models was used to calculate the maximum likelihood score of the simulation tree and simulated data that was used to calculate the model, and compared with the maximum likelihood scores of the known simulation model and simulation tree on the same simulated data. Additionally, the commonly-used empirical models, cpREV and WAG, were assessed similarly. Data-specific models performed better than the empirical models, which under-fitted the simulated alignments, had the highest difference to the simulation model maximum-likelihood score, clustered further from the simulation model in principal component analysis ordination, and inferred less accurate trees. Data-specific models and the simulation model shared statistically indistinguishable maximum-likelihood scores, indicating that the five methods were reasonably accurate at estimating substitution models by this measure. Nevertheless, tree statistics showed differences between optimal maximum likelihood trees. Unlike other model estimating methods, trees inferred using data-specific models generated with IQ-TREE and P4 (maximum likelihood) were not significantly different from the trees derived from the simulation model in each analysis, indicating that these two methods alone were the most accurate at estimating data-specific models. To show the benefits of using data-specific protein models several published data sets were reanalysed using IQ-TREE-estimated models. These newly estimated models were a better fit to the data than the empirical models that were used by the original authors, often inferred longer trees, and resulted in different tree topologies in more than half of the re-analysed data sets. The results of this study show that software availability and high computation burden are not limitations to generating better-fitting data-specific amino-acid substitution models for phylogenetic analyses.
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Rachman, Helmy, Michael Strong, Timo Ulrichs, Leander Grode, Johannes Schuchhardt, Hans Mollenkopf, George A. Kosmiadi, David Eisenberg, and Stefan H. E. Kaufmann. "Unique Transcriptome Signature of Mycobacterium tuberculosis in Pulmonary Tuberculosis." Infection and Immunity 74, no. 2 (February 2006): 1233–42. http://dx.doi.org/10.1128/iai.74.2.1233-1242.2006.
Повний текст джерелаАнотація:
ABSTRACT Although tuberculosis remains a substantial global threat, the mechanisms that enable mycobacterial persistence and replication within the human host are ill defined. This study represents the first genome-wide expression analysis of Mycobacterium tuberculosis from clinical lung samples, which has enabled the identification of M. tuberculosis genes actively expressed during pulmonary tuberculosis. To obtain optimal information from our DNA array analyses, we analyzed the differentially expressed genes within the context of computationally inferred protein networks. Protein networks were constructed using functional linkages established by the Rosetta stone, phylogenetic profile, conserved gene neighbor, and operon computational methods. This combined approach revealed that during pulmonary tuberculosis, M. tuberculosis actively transcribes a number of genes involved in active fortification and evasion from host defense systems. These genes may provide targets for novel intervention strategies.
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Fereig, Ragab M., and Hanan H. Abdelbaky. "Comparative study on Toxoplasma gondii dense granule protein 7, peroxiredoxin 1 and 3 based on bioinformatic analysis tools." German Journal of Microbiology 2, no. 1 (2022): 30–38. http://dx.doi.org/10.51585/gjm.2022.1.0013.
Повний текст джерелаАнотація:
Toxoplasmosis remains a devastating protozoan disease induced by Toxoplasma gondii (T. gondii) that induces extreme hazards in both medical and veterinary fields. Our previous studies revealed the high immunogenicity and antigenicity of T. gondii peroxiredoxin (TgPrx) 1, 3, and TgGRA7. Herein, the comparison of TgPrx1, TgPrx3, and TgGRA7 was conducted using bioinformatics analysis tools. In this computational comparison, the physico-chemical, morphometric, immunogenic, and antigenic properties were analyzed. Analyses of complete coding sequences showed the probability of signal peptides and transmembrane domains only in the case of TgGRA7. NetPhos server-based prediction revealed 23, 11, and 39 phosphorylation sites in TgGRA7, TgPrx1, and TgPrx3 proteins, respectively. The secondary structure of TgGRA7, TgPrx1, and TgPrx3 proteins were analyzed by PSIPRED servers. The percentage of the random coil and alpha-helix amino acids was higher in TgGRA7 (99.15%), followed by TgPrx3 (85.87%) and TgPrx1 (77.55%). The antigenic epitopes of the protein were predicted by analyzing the features of the IEDB server. The linear B-cell epitope regions prediction of TgGRA7 showed the maximum estimated length (118 amino acid residues). In addition, antigenicity and hydrophilicity index showed similar tendencies among the three tested proteins, TgGRA7, TgPrx1, and TgPrx3. Thus, the current computational analyses represented TgGRA7, TgPrx1, and TgPrx3 proteins as efficient diagnostic and vaccine candidates suggesting further research and assessments. Additional validation of bioinformatic analysis tools in predicting potent diagnostic and vaccine antigens will greatly contribute to the success of control policies against T. gondii and other infectious agents.
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Tzul, Franco O., Katrina L. Schweiker, and George I. Makhatadze. "Modulation of folding energy landscape by charge–charge interactions: Linking experiments with computational modeling." Proceedings of the National Academy of Sciences 112, no. 3 (January 6, 2015): E259—E266. http://dx.doi.org/10.1073/pnas.1410424112.
Повний текст джерелаАнотація:
The kinetics of folding–unfolding of a structurally diverse set of four proteins optimized for thermodynamic stability by rational redesign of surface charge–charge interactions is characterized experimentally. The folding rates are faster for designed variants compared with their wild-type proteins, whereas the unfolding rates are largely unaffected. A simple structure-based computational model, which incorporates the Debye–Hückel formalism for the electrostatics, was used and found to qualitatively recapitulate the experimental results. Analysis of the energy landscapes of the designed versus wild-type proteins indicates the differences in refolding rates may be correlated with the degree of frustration of their respective energy landscapes. Our simulations indicate that naturally occurring wild-type proteins have frustrated folding landscapes due to the surface electrostatics. Optimization of the surface electrostatics seems to remove some of that frustration, leading to enhanced formation of native-like contacts in the transition-state ensembles (TSE) and providing a less frustrated energy landscape between the unfolded and TS ensembles. Macroscopically, this results in faster folding rates. Furthermore, analyses of pairwise distances and radii of gyration suggest that the less frustrated energy landscapes for optimized variants are a result of more compact unfolded and TS ensembles. These findings from our modeling demonstrates that this simple model may be used to: (i) gain a detailed understanding of charge–charge interactions and their effects on modulating the energy landscape of protein folding and (ii) qualitatively predict the kinetic behavior of protein surface electrostatic interactions.
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Kikuchi, Takeshi. "Decoding an Amino Acid Sequence to Extract Information on Protein Folding." Molecules 27, no. 9 (May 7, 2022): 3020. http://dx.doi.org/10.3390/molecules27093020.
Повний текст джерелаАнотація:
Protein folding is a complicated phenomenon including various time scales (μs to several s), and various structural indices are required to analyze it. The methodologies used to study this phenomenon also have a wide variety and employ various experimental and computational techniques. Thus, a simple speculation does not serve to understand the folding mechanism of a protein. In the present review, we discuss the recent studies conducted by the author and their colleagues to decode amino acid sequences to obtain information on protein folding. We investigate globin-like proteins, ferredoxin-like fold proteins, IgG-like beta-sandwich fold proteins, lysozyme-like fold proteins and β-trefoil-like fold proteins. Our techniques are based on statistics relating to the inter-residue average distance, and our studies performed so far indicate that the information obtained from these analyses includes data on the protein folding mechanism. The relationships between our results and the actual protein folding phenomena are also discussed.
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SARAI, AKINORI, JORG SIEBERS, SAMUEL SELVARAJ, M. MICHAEL GROMIHA, and HIDETOSHI KONO. "INTEGRATION OF BIOINFORMATICS AND COMPUTATIONAL BIOLOGY TO UNDERSTAND PROTEIN-DNA RECOGNITION MECHANISM." Journal of Bioinformatics and Computational Biology 03, no. 01 (February 2005): 169–83. http://dx.doi.org/10.1142/s0219720005000965.
Повний текст джерелаАнотація:
Transcription factors play essential role in the gene regulation in higher organisms, binding to multiple target sequences and regulating multiple genes in a complex manner. In order to decipher the mechanism of gene regulation, it is important to understand the molecular mechanism of protein-DNA recognition. Here we describe a strategy to approach this problem, using various methods in bioinformatics and computational biology. We have used a knowledge-based approach, utilizing rapidly increasing structural data of protein-DNA complexes, to derive empirical potential functions for the specific interactions between bases and amino acids as well as for DNA conformation, from the statistical analyses on the structural data. Then these statistical potentials are used to quantify the specificity of protein-DNA recognition. The quantification of specificity has enabled us to establish the structure-function analysis of transcription factors, such as the effects of binding cooperativity on target recognition. The method is also applied to real genome sequences, predicting potential target sites. We are also using computer simulations of protein-DNA interactions and DNA conformation in order to complement the empirical method. The integration of these approaches together will provide deeper insight into the mechanism of protein-DNA recognition and improve the target prediction of transcription factors.
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Winkler, Robert. "An evolving computational platform for biological mass spectrometry: workflows, statistics and data mining with MASSyPup64." PeerJ 3 (November 17, 2015): e1401. http://dx.doi.org/10.7717/peerj.1401.
Повний текст джерелаАнотація:
In biological mass spectrometry, crude instrumental data need to be converted into meaningful theoretical models. Several data processing and data evaluation steps are required to come to the final results. These operations are often difficult to reproduce, because of too specific computing platforms. This effect, known as ‘workflow decay’, can be diminished by using a standardized informatic infrastructure. Thus, we compiled an integrated platform, which contains ready-to-use tools and workflows for mass spectrometry data analysis. Apart from general unit operations, such as peak picking and identification of proteins and metabolites, we put a strong emphasis on the statistical validation of results and Data Mining. MASSyPup64 includes e.g., the OpenMS/TOPPAS framework, the Trans-Proteomic-Pipeline programs, the ProteoWizard tools, X!Tandem, Comet and SpiderMass. The statistical computing language R is installed with packages for MS data analyses, such as XCMS/metaXCMS and MetabR. The R package Rattle provides a user-friendly access to multiple Data Mining methods. Further, we added the non-conventional spreadsheet program teapot for editing large data sets and a command line tool for transposing large matrices. Individual programs, console commands and modules can be integrated using the Workflow Management System (WMS) taverna. We explain the useful combination of the tools by practical examples: (1) A workflow for protein identification and validation, with subsequent Association Analysis of peptides, (2) Cluster analysis and Data Mining in targeted Metabolomics, and (3) Raw data processing, Data Mining and identification of metabolites in untargeted Metabolomics. Association Analyses reveal relationships between variables across different sample sets. We present its application for finding co-occurring peptides, which can be used for target proteomics, the discovery of alternative biomarkers and protein–protein interactions. Data Mining derived models displayed a higher robustness and accuracy for classifying sample groups in targeted Metabolomics than cluster analyses. Random Forest models do not only provide predictive models, which can be deployed for new data sets, but also the variable importance. We demonstrate that the later is especially useful for tracking down significant signals and affected pathways in untargeted Metabolomics. Thus, Random Forest modeling supports the unbiased search for relevant biological features in Metabolomics. Our results clearly manifest the importance of Data Mining methods to disclose non-obvious information in biological mass spectrometry . The application of a Workflow Management System and the integration of all required programs and data in a consistent platform makes the presented data analyses strategies reproducible for non-expert users. The simple remastering process and the Open Source licenses of MASSyPup64 (http://www. bioprocess.org/massypup/) enable the continuous improvement of the system.
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Wang, Qiankun, Aamir Mehmood, Heng Wang, Qin Xu, Yi Xiong, and Dong-Qing Wei. "Computational Screening and Analysis of Lung Cancer Related Non-Synonymous Single Nucleotide Polymorphisms on the Human Kirsten Rat Sarcoma Gene." Molecules 24, no. 10 (May 21, 2019): 1951. http://dx.doi.org/10.3390/molecules24101951.
Повний текст джерелаАнотація:
The human KRAS (Kirsten rat sarcoma) is an oncogene, involved in the regulation of cell growth and division. The mutations in the KRAS gene have the potential to cause normal cells to become cancerous in human lungs. In the present study, we focus on non-synonymous single nucleotide polymorphisms (nsSNPs), which are point mutations in the DNA sequence leading to the amino acid variants in the encoded protein. To begin with, we developed a pipeline to utilize a set of computational tools in order to obtain the most deleterious nsSNPs (Q22K, Q61P, and Q61R) associated with lung cancer in the human KRAS gene. Furthermore, molecular dynamics simulation and structural analyses of the 3D structures of native and mutant proteins confirmed the impact of these nsSNPs on the stability of the protein. Finally, the experimental results demonstrated that the structural stability of the mutant proteins was worse than that of the native protein. This study provides significant guidance for narrowing down the number of KRAS mutations to be screened as potential diagnostic biomarkers and to better understand the structural and functional mechanisms of the KRAS protein.
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Pollock, Julie A., Courtney L. Labrecque, Cassidy N. Hilton, Justin Airas, Alexis Blake, Kristen J. Rubenstein, and Carol A. Parish. "Small Molecule Modulation of MEMO1 Protein-Protein Interactions." Journal of the Endocrine Society 5, Supplement_1 (May 1, 2021): A1031. http://dx.doi.org/10.1210/jendso/bvab048.2110.
Повний текст джерелаАнотація:
Abstract MEMO1 (mediator of ErbB2-driven cell motility) is upregulated in breast tumors and has been correlated with poor prognosis in patients. As a scaffolding protein that binds to phosphorylated-tyrosine residues on receptors such as estrogen receptor and ErbB2, MEMO1 levels can influence phosphorylation cascades. Using our previously developed fluorescence polarization assay, we have identified small molecules with the ability to disrupt the interactions of MEMO1. We have performed limited structure-activity-relationship studies and computational analyses to investigate the molecular requirements for MEMO1 inhibition. The most promising compounds exhibit slowed migration of breast cancer cell lines (T47D and SKBR3) in a wound-healing assay emulating results obtained from the knockdown of MEMO1 protein. To our knowledge, these are the first small molecules targeting the MEMO1 protein-protein interface and therefore, will be invaluable tools for the investigation of the role of the MEMO1 in breast cancer and other biological contexts.
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Abdur Razzak, Md, Ji Eun Lee, Hee Ho Park, Tai Hyun Park, and Shin Sik Choi. "Exploring Binding Mechanisms between Curcumin and Silkworm 30Kc19 Protein Using Spectroscopic Analyses and Computational Simulations." Biotechnology and Bioprocess Engineering 23, no. 5 (September 2018): 605–16. http://dx.doi.org/10.1007/s12257-018-0285-6.
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Su, Taojunfeng, Michael A. R. Hollas, Ryan T. Fellers, and Neil L. Kelleher. "Identification of Splice Variants and Isoforms in Transcriptomics and Proteomics." Annual Review of Biomedical Data Science 6, no. 1 (August 10, 2023): 357–76. http://dx.doi.org/10.1146/annurev-biodatasci-020722-044021.
Повний текст джерелаАнотація:
Alternative splicing is pivotal to the regulation of gene expression and protein diversity in eukaryotic cells. The detection of alternative splicing events requires specific omics technologies. Although short-read RNA sequencing has successfully supported a plethora of investigations on alternative splicing, the emerging technologies of long-read RNA sequencing and top-down mass spectrometry open new opportunities to identify alternative splicing and protein isoforms with less ambiguity. Here, we summarize improvements in short-read RNA sequencing for alternative splicing analysis, including percent splicing index estimation and differential analysis. We also review the computational methods used in top-down proteomics analysis regarding proteoform identification, including the construction of databases of protein isoforms and statistical analyses of search results. While many improvements in sequencing and computational methods will result from emerging technologies, there should be future endeavors to increase the effectiveness, integration, and proteome coverage of alternative splicing events.
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Notari, Daniel Luis, Samuel Brando Oldra, Mauricio Adami Mariani, Cristian Reolon, and Diego Bonatto. "Dis2PPI." International Journal of Knowledge Discovery in Bioinformatics 3, no. 3 (July 2012): 67–85. http://dx.doi.org/10.4018/jkdb.2012070104.
Повний текст джерелаАнотація:
Experiments in bioinformatics are based on protocols that employ different steps for data mining and data integration, collectively known as computational workflows. Considering the use of databases in the biomedical sciences software that is able to query multiple databases is desirable. Systems biology, which encompasses the design of interactomic networks to understand complex biological processes, can benefit from computational workflows. Unfortunately, the use of computational workflows in systems biology is still very limited, especially for applications associated with the study of disease. To address this limitation, we designed Dis2PPI, a workflow that integrates information retrieved from genetic disease databases and interactomes. Dis2PPI extracts protein names from a disease report and uses this information to mine protein-protein interaction (PPI) networks. The data gathered from this mining can be used in systems biology analyses. To demonstrate the functionality of Dis2PPI for systems biology analyses, the authors mined information about xeroderma pigmentosum and Cockayne syndrome, two monogenic diseases that lead to skin cancer when the patients are exposed to sunlight and neurodegeneration.
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Li, Hongchun, Fen Pei, D. Lansing Taylor, and Ivet Bahar. "QuartataWeb: Integrated Chemical–Protein-Pathway Mapping for Polypharmacology and Chemogenomics." Bioinformatics 36, no. 12 (March 28, 2020): 3935–37. http://dx.doi.org/10.1093/bioinformatics/btaa210.
Повний текст джерелаАнотація:
Abstract Summary QuartataWeb is a user-friendly server developed for polypharmacological and chemogenomics analyses. Users can easily obtain information on experimentally verified (known) and computationally predicted (new) interactions between 5494 drugs and 2807 human proteins in DrugBank, and between 315 514 chemicals and 9457 human proteins in the STITCH database. In addition, QuartataWeb links targets to KEGG pathways and GO annotations, completing the bridge from drugs/chemicals to function via protein targets and cellular pathways. It allows users to query a series of chemicals, drug combinations or multiple targets, to enable multi-drug, multi-target, multi-pathway analyses, toward facilitating the design of polypharmacological treatments for complex diseases. Availability and implementation QuartataWeb is freely accessible at http://quartata.csb.pitt.edu. Supplementary information Supplementary data are available at Bioinformatics online.
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Pambudi, S., D. Irawan, A. Danny, T. Widayanti, and Tarwadi. "Computational antigenic epitope prediction of clinical Indonesian Dengue virus NS1 protein." IOP Conference Series: Earth and Environmental Science 948, no. 1 (December 1, 2021): 012080. http://dx.doi.org/10.1088/1755-1315/948/1/012080.
Повний текст джерелаАнотація:
Abstract The identification of human Non-Structural-1 (NS1) protein epitopes will help us better understand Dengue virus (DENV) immunopathogenesis. In this study, several online and offline bioinformatic prediction tools were exploited to predict and analyze T-cell and B-cell epitopes of DENV NS1 consensus sequences originated from Indonesian clinical isolates. We identified a potential peptide at NS1155--163 (VEDYGFGIF) which interact with MHC-I allele HLA-B*40:01 and showed high binding affinity (IC50) scores ranging between 63.8 nM to 183.9 nM for all Indonesian DENV serotypes. Furthermore, we have succeeded identified a region at the C-terminal of Indonesian DENV NS1 protein between 325--344 as part of discontinuous antigenic epitope which conserved for all serotypes. Our analyses showed this region could induce strong and persistent antibody against all DENV serotypes by interacting with MHC-I molecule and also recognized by B-cell receptor. The identification of DENV NS1 T-cell and B-cell epitopes may help in the development of a new vaccine, drug discovery, and diagnostic system to help eradicate dengue infection.
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Tessmer, Maxx H., David M. Anderson, Adam M. Pickrum, Molly O. Riegert, Rocco Moretti, Jens Meiler, Jimmy B. Feix, and Dara W. Frank. "Identification of a ubiquitin-binding interface using Rosetta and DEER." Proceedings of the National Academy of Sciences 115, no. 3 (January 2, 2018): 525–30. http://dx.doi.org/10.1073/pnas.1716861115.
Повний текст джерелаАнотація:
ExoU is a type III-secreted cytotoxin expressing A2 phospholipase activity when injected into eukaryotic target cells by the bacterium Pseudomonas aeruginosa. The enzymatic activity of ExoU is undetectable in vitro unless ubiquitin, a required cofactor, is added to the reaction. The role of ubiquitin in facilitating ExoU enzymatic activity is poorly understood but of significance for designing inhibitors to prevent tissue injury during infections with strains of P. aeruginosa producing this toxin. Most ubiquitin-binding proteins, including ExoU, demonstrate a low (micromolar) affinity for monoubiquitin (monoUb). Additionally, ExoU is a large and dynamic protein, limiting the applicability of traditional structural techniques such as NMR and X-ray crystallography to define this protein–protein interaction. Recent advancements in computational methods, however, have allowed high-resolution protein modeling using sparse data. In this study, we combine double electron–electron resonance (DEER) spectroscopy and Rosetta modeling to identify potential binding interfaces of ExoU and monoUb. The lowest-energy scoring model was tested using biochemical, biophysical, and biological techniques. To verify the binding interface, Rosetta was used to design a panel of mutations to modulate binding, including one variant with enhanced binding affinity. Our analyses show the utility of computational modeling when combined with sensitive biological assays and biophysical approaches that are exquisitely suited for large dynamic proteins.
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Lee, Sanghyun, Andrew Hung, Hong Li, and Angela Wei Hong Yang. "Mechanisms of Action of a Herbal Formula Huangqi Guizhi Wuwu Tang for the Management of Post-Stroke Related Numbness and Weakness: A Computational Molecular Docking Study." Journal of Evidence-Based Integrative Medicine 27 (January 2022): 2515690X2210829. http://dx.doi.org/10.1177/2515690x221082989.
Повний текст джерелаАнотація:
Stroke-related numbness and weakness (SRNW) are resultant symptoms of post-stroke sufferers. Existing research has supported the use of Huangqi Guizhi Wuwu Tang (HGWT) particularly for SRNW; however, their mechanisms of action have not been fully elucidated. Therefore, this study aimed to investigate the mechanisms of action of HGWT components targeting SRNW-related proteins through a computational molecular docking approach. Target proteins associated with SRNW were identified through DrugBank database and Open Targets database. Chemical compounds from each herb of HGWT were identified from the Traditional Chinese Medicine Systems Pharmacology and Analysis Platform (TCMSP). Autodock Vina was utilized and the cut-off criterion applied for protein-ligand complexes was a binding affinity score of ≤ -9.5 kcal/mol; selected protein-ligand complexes were identified using 3D and 2D structural analyses. The protein targets PDE5A and ESR1 have highlighted interactions with compounds (BS040, DZ006, DZ058, DZ118, and HQ066) which are the key molecules in the management of SRNW. PDE5A have bioactivity with the amino acid residues (Val230, Asn252, Gln133 and Thr166) throughout PDE5A-cGMP-PKG pathways which involved reduction in myofilament responsiveness. ESR1 were predicted to be critical active with site residue (Leu346, Glu419 and Leu387) and its proteoglycans pathway involving CD44v3/CD44 that activates rho-associated protein kinase 1 (ROCK1) and ankyrin increasing vascular smooth muscle. In conclusion, HGWT may provide therapeutic benefits through strong interactions between herbal compounds and target proteins of PDE5A and ESR1. Further experimental studies are needed to unequivocally support this result which can be valuable to increase the quality of life of post-stroke patients. Keywords Herbal medicine, Complementary and alternative medicine, Natural product, Post-stroke, Computational analysis
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Cui, Minghui, Limei Lin, Hongyu Guo, Duoduo Zhang, Jie Zhang, Wenwen Cheng, Xin Song, Zhaobin Xing, and Yuehong Long. "In silico/computational analysis of mevalonate pyrophosphate decarboxylase gene families in Campanulids." Open Life Sciences 16, no. 1 (January 1, 2021): 1022–36. http://dx.doi.org/10.1515/biol-2021-0103.
Повний текст джерелаАнотація:
Abstract Mevalonate pyrophosphate decarboxylase (MPD) is a key enzyme in terpenoid biosynthesis. MPD plays an important role in the upstream regulation of secondary plant metabolism. However, studies on the MPD gene are relatively very few despite its importance in plant metabolism. Currently, no systematic analysis has been conducted on the MPD gene in plants under the order Apiales, which comprises important medicinal plants such as Panax ginseng and Panax notoginseng. This study sought to explore the structural characteristics of the MPD gene and the effect of adaptive evolution on the gene by comparing and analyzing MPD gene sequences of different campanulids species. For that, phylogenetic and adaptive evolution analyses were carried out using sequences for 11 Campanulids species. MPD sequence characteristics of each species were then analyzed, and the collinearity analysis of the genes was performed. As a result, a total of 21 MPD proteins were identified in 11 Campanulids species through BLAST analysis. Phylogenetic analysis, physical and chemical properties prediction, gene family analysis, and gene structure prediction showed that the MPD gene has undergone purifying selection and exhibited highly conserved structure. Analysis of physicochemical properties further showed that the MPD protein was a hydrophilic protein without a transmembrane region. Moreover, collinearity analysis in Apiales showed that MPD gene on chromosome 2 of D. carota and chromosome 1 of C. sativum were collinear. The findings showed that MPD gene is highly conserved. This may be a common characteristic of all essential enzymes in the biosynthesis pathways of medicinal plants. Notably, MPD gene is significantly affected by environmental factors which subsequently modulate its expression. The current study’s findings provide a basis for follow-up studies on MPD gene and key enzymes in other medicinal plants.