Статті в журналах: "Target binding"

1

Cheung, S. H., G. E. Legge, S. T. L. Chung, and B. S. Tjan. "Target-flanker binding releases crowding." Journal of Vision 6, no. 6 (March 24, 2010): 807. http://dx.doi.org/10.1167/6.6.807.

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2

POOLSAP, UNYANEE, YUKI KATO, KENGO SATO, and TATSUYA AKUTSU. "USING BINDING PROFILES TO PREDICT BINDING SITES OF TARGET RNAs." Journal of Bioinformatics and Computational Biology 09, no. 06 (December 2011): 697–713. http://dx.doi.org/10.1142/s0219720011005628.

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Prediction of RNA–RNA interaction is a key to elucidating possible functions of small non-coding RNAs, and a number of computational methods have been proposed to analyze interacting RNA secondary structures. In this article, we focus on predicting binding sites of target RNAs that are expected to interact with regulatory antisense RNAs in a general form of interaction. For this purpose, we propose bistaRNA, a novel method for predicting multiple binding sites of target RNAs. bistaRNA employs binding profiles that represent scores for hybridized structures, leading to reducing the computational cost for interaction prediction. bistaRNA considers an ensemble of equilibrium interacting structures and seeks to maximize expected accuracy using dynamic programming. Experimental results on real interaction data validate good accuracy and fast computation time of bistaRNA as compared with several competitive methods. Moreover, we aim to find new targets given specific antisense RNAs, which provides interesting insights into antisense RNA regulation. bistaRNA is implemented in C++. The program and Supplementary Material are available at .

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3

JOHNSTON, Angus, and Eva VAN DER MAREL. "How Binding are the EU’s ‘Binding’ Renewables Targets?" Cambridge Yearbook of European Legal Studies 18 (August 9, 2016): 176–214. http://dx.doi.org/10.1017/cel.2016.7.

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AbstractThe EU’s current renewable energy legislation sets a binding EU target for renewables as a share of overall energy consumption, allied with binding national targets for renewables as well. Yet the precise implications of having imposed such ‘mandatory’ binding targets have received little attention to date. This contribution examines the history and evolution of such targets, the context within which they must be pursued and applied, and some of the problems in and prospects for their enforcement and effectiveness. Comparisons are drawn with other areas of EU law where appropriate and some tentative lessons learned, as well as challenges still to be faced, are offered by way of conclusion.

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4

Park, Keunwan, Young-Joon Ko, Prasannavenkatesh Durai, and Cheol-Ho Pan. "Machine learning-based chemical binding similarity using evolutionary relationships of target genes." Nucleic Acids Research 47, no. 20 (August 31, 2019): e128-e128. http://dx.doi.org/10.1093/nar/gkz743.

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Abstract Chemical similarity searching is a basic research tool that can be used to find small molecules which are similar in shape to known active molecules. Despite its popularity, the retrieval of local molecular features that are critical to functional activity related to target binding often fails. To overcome this limitation, we developed a novel machine learning-based chemical binding similarity score by using various evolutionary relationships of binding targets. The chemical similarity was defined by the probability of chemical compounds binding to identical targets. Comprehensive and heterogeneous multiple target-binding chemical data were integrated into a paired data format and processed using multiple classification similarity-learning models with various levels of target evolutionary information. Encoding evolutionary information to chemical compounds through their binding targets substantially expanded available chemical-target interaction data and significantly improved model performance. The output probability of our integrated model, referred to as ensemble evolutionary chemical binding similarity (ensECBS), was effective for finding hidden chemical relationships. The developed method can serve as a novel chemical similarity tool that uses evolutionarily conserved target binding information.

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5

Lipovsek, D. "Adnectins: engineered target-binding protein therapeutics." Protein Engineering Design and Selection 24, no. 1-2 (November 10, 2010): 3–9. http://dx.doi.org/10.1093/protein/gzq097.

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6

Chen, Zihao, Long Hu, Bao-Ting Zhang, Aiping Lu, Yaofeng Wang, Yuanyuan Yu, and Ge Zhang. "Artificial Intelligence in Aptamer–Target Binding Prediction." International Journal of Molecular Sciences 22, no. 7 (March 30, 2021): 3605. http://dx.doi.org/10.3390/ijms22073605.

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Aptamers are short single-stranded DNA, RNA, or synthetic Xeno nucleic acids (XNA) molecules that can interact with corresponding targets with high affinity. Owing to their unique features, including low cost of production, easy chemical modification, high thermal stability, reproducibility, as well as low levels of immunogenicity and toxicity, aptamers can be used as an alternative to antibodies in diagnostics and therapeutics. Systematic evolution of ligands by exponential enrichment (SELEX), an experimental approach for aptamer screening, allows the selection and identification of in vitro aptamers with high affinity and specificity. However, the SELEX process is time consuming and characterization of the representative aptamer candidates from SELEX is rather laborious. Artificial intelligence (AI) could help to rapidly identify the potential aptamer candidates from a vast number of sequences. This review discusses the advancements of AI pipelines/methods, including structure-based and machine/deep learning-based methods, for predicting the binding ability of aptamers to targets. Structure-based methods are the most used in computer-aided drug design. For this part, we review the secondary and tertiary structure prediction methods for aptamers, molecular docking, as well as molecular dynamic simulation methods for aptamer–target binding. We also performed analysis to compare the accuracy of different secondary and tertiary structure prediction methods for aptamers. On the other hand, advanced machine-/deep-learning models have witnessed successes in predicting the binding abilities between targets and ligands in drug discovery and thus potentially offer a robust and accurate approach to predict the binding between aptamers and targets. The research utilizing machine-/deep-learning techniques for prediction of aptamer–target binding is limited currently. Therefore, perspectives for models, algorithms, and implementation strategies of machine/deep learning-based methods are discussed. This review could facilitate the development and application of high-throughput and less laborious in silico methods in aptamer selection and characterization.

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7

Molina, Daniel Martinez, Rozbeh Jafari, Marina Ignatushchenko, Takahiro Seki, E. Andreas Larsson, Chen Dan, Lekshmy Sreekumar, Yihai Cao, and Pär Nordlund. "Monitoring Drug Target Engagement in Cells and Tissues Using the Cellular Thermal Shift Assay." Science 341, no. 6141 (July 4, 2013): 84–87. http://dx.doi.org/10.1126/science.1233606.

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The efficacy of therapeutics is dependent on a drug binding to its cognate target. Optimization of target engagement by drugs in cells is often challenging, because drug binding cannot be monitored inside cells. We have developed a method for evaluating drug binding to target proteins in cells and tissue samples. This cellular thermal shift assay (CETSA) is based on the biophysical principle of ligand-induced thermal stabilization of target proteins. Using this assay, we validated drug binding for a set of important clinical targets and monitored processes of drug transport and activation, off-target effects and drug resistance in cancer cell lines, as well as drug distribution in tissues. CETSA is likely to become a valuable tool for the validation and optimization of drug target engagement.

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8

Yim, Hyung-Soon, and Jae-Hak Lee. "Prediction of Hypoxia-inducible Factor Binding Site in Whale Genome and Analysis of Target Genes Regulated by Predicted Sites." Journal of Marine Bioscience and Biotechnology 7, no. 2 (December 31, 2015): 35–41. http://dx.doi.org/10.15433/ksmb.2015.7.2.035.

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9

Ganotra, Gaurav K., and Rebecca C. Wade. "Prediction of Drug–Target Binding Kinetics by Comparative Binding Energy Analysis." ACS Medicinal Chemistry Letters 9, no. 11 (October 4, 2018): 1134–39. http://dx.doi.org/10.1021/acsmedchemlett.8b00397.

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10

Henrich, Stefan, Isabella Feierberg, Ting Wang, Niklas Blomberg, and Rebecca C. Wade. "Comparative binding energy analysis for binding affinity and target selectivity prediction." Proteins: Structure, Function, and Bioinformatics 78, no. 1 (August 17, 2009): 135–53. http://dx.doi.org/10.1002/prot.22579.

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11

Briskin, Daniel, Peter Y. Wang, and David P. Bartel. "The biochemical basis for the cooperative action of microRNAs." Proceedings of the National Academy of Sciences 117, no. 30 (July 13, 2020): 17764–74. http://dx.doi.org/10.1073/pnas.1920404117.

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In cells, closely spaced microRNA (miRNA) target sites within a messenger RNA (mRNA) can act cooperatively, leading to more repression of the target mRNA than expected by independent action at each site. Using purified miRNA-Argonaute (AGO2) complexes, synthetic target RNAs, and a purified domain of TNRC6B (GW182 in flies) that is able to simultaneously bind multiple AGO proteins, we examined both the occupancies and binding affinities of miRNA-AGO2 complexes and target RNAs with either one site or two cooperatively spaced sites. On their own, miRNA-AGO2 complexes displayed little if any cooperative binding to dual sites. In contrast, in the presence of the AGO-binding region of TNRC6B, we observed strong cooperative binding to dual sites, with almost no singly bound target RNAs and substantially increased binding affinities and Hill coefficients. Cooperative binding was retained when the two sites were for two different miRNAs or when the two sites were bound to miRNAs loaded into two different AGO paralogs, AGO1 and AGO2. The improved binding affinity was attributable primarily to a reduced rate of dissociation between miRNA-AGO complexes and their dual-site targets. Thus, the multivalent binding of TNRC6 enables cooperative binding of miRNA-AGO complexes to target RNAs, thereby explaining the basis of cooperative action.

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12

Julio, Ashley R., and Keriann M. Backus. "New approaches to target RNA binding proteins." Current Opinion in Chemical Biology 62 (June 2021): 13–23. http://dx.doi.org/10.1016/j.cbpa.2020.12.006.

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13

Kadonosono, Tetsuya. "A smart design of target-binding molecules." Japanese Journal of Pesticide Science 46, no. 2 (August 20, 2021): 168–72. http://dx.doi.org/10.1584/jpestics.w21-33.

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14

Holmberg, Eric, Kazuo Maruyama, Stephen Kennel, Alexander Klibanov, Vladimir Torchilin, Una Ryan, and Leaf Huang. "Target-Specific Binding of Immunoliposomes in Vivo." Journal of Liposome Research 1, no. 4 (January 1990): 393–406. http://dx.doi.org/10.3109/08982109009036003.

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15

Wu, Yung-Peng, Chee Ying Chew, Tian-Neng Li, Tzu-Hsuan Chung, En-Hao Chang, Chak Hin Lam, and Kui-Thong Tan. "Target-activated streptavidin–biotin controlled binding probe." Chemical Science 9, no. 3 (2018): 770–76. http://dx.doi.org/10.1039/c7sc04014h.

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The streptavidin–biotin controlled binding probe has several advantages for the detection of enzymes and reactive small molecules, such as minimal background, multiple signal amplification steps, and wide selection of the optimal dyes for detection.

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16

GuhaThakurta, D., and G. D. Stormo. "Identifying target sites for cooperatively binding factors." Bioinformatics 17, no. 7 (July 1, 2001): 608–21. http://dx.doi.org/10.1093/bioinformatics/17.7.608.

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17

Öztürk, Hakime, Arzucan Özgür, and Elif Ozkirimli. "DeepDTA: deep drug–target binding affinity prediction." Bioinformatics 34, no. 17 (September 1, 2018): i821—i829. http://dx.doi.org/10.1093/bioinformatics/bty593.

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18

de la Rosa, Mario A. Diaz, Elena F. Koslover, Peter J. Mulligan, and Andrew J. Spakowitz. "Target-Site Search of DNA-Binding Proteins." Biophysical Journal 98, no. 3 (January 2010): 221a. http://dx.doi.org/10.1016/j.bpj.2009.12.1194.

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19

Oğul, Hasan, Sinan U. Umu, Y. Yener Tuncel, and Mahinur S. Akkaya. "A probabilistic approach to microRNA-target binding." Biochemical and Biophysical Research Communications 413, no. 1 (September 2011): 111–15. http://dx.doi.org/10.1016/j.bbrc.2011.08.065.

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20

Loach, Daniel, and Paloma Marí-Beffa. "Post-target inhibition: A temporal binding mechanism?" Visual Cognition 10, no. 5 (June 2003): 513–26. http://dx.doi.org/10.1080/13506280244000203.

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21

Drwal, Malgorzata N., Guillaume Bret, and Esther Kellenberger. "Multi-target Fragments Display Versatile Binding Modes." Molecular Informatics 36, no. 10 (July 10, 2017): 1700042. http://dx.doi.org/10.1002/minf.201700042.

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22

Robers, M. B., R. Friedman-Ohana, K. V. M. Huber, L. Kilpatrick, J. D. Vasta, B. T. Berger, C. Chaudhry, et al. "Quantifying Target Occupancy of Small Molecules Within Living Cells." Annual Review of Biochemistry 89, no. 1 (June 20, 2020): 557–81. http://dx.doi.org/10.1146/annurev-biochem-011420-092302.

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The binding affinity and kinetics of target engagement are fundamental to establishing structure–activity relationships (SARs) for prospective therapeutic agents. Enhancing these binding parameters for operative targets, while minimizing binding to off-target sites, can translate to improved drug efficacy and a widened therapeutic window. Compound activity is typically assessed through modulation of an observed phenotype in cultured cells. Quantifying the corresponding binding properties under common cellular conditions can provide more meaningful interpretation of the cellular SAR analysis. Consequently, methods for assessing drug binding in living cells have advanced and are now integral to medicinal chemistry workflows. In this review, we survey key technological advancements that support quantitative assessments of target occupancy in cultured cells, emphasizing generalizable methodologies able to deliver analytical precision that heretofore required reductionist biochemical approaches.

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23

Tan, Zhixin Cyrillus, Brian T. Orcutt-Jahns, and Aaron S. Meyer. "A quantitative view of strategies to engineer cell-selective ligand binding." Integrative Biology 13, no. 11 (November 2021): 269–82. http://dx.doi.org/10.1093/intbio/zyab019.

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Abstract A critical property of many therapies is their selective binding to target populations. Exceptional specificity can arise from high-affinity binding to surface targets expressed exclusively on target cell types. In many cases, however, therapeutic targets are only expressed at subtly different levels relative to off-target cells. More complex binding strategies have been developed to overcome this limitation, including multi-specific and multivalent molecules, creating a combinatorial explosion of design possibilities. Guiding strategies for developing cell-specific binding are critical to employ these tools. Here, we employ a uniquely general multivalent binding model to dissect multi-ligand and multi-receptor interactions. This model allows us to analyze and explore a series of mechanisms to engineer cell selectivity, including mixtures of molecules, affinity adjustments, valency changes, multi-specific molecules and ligand competition. Each of these strategies can optimize selectivity in distinct cases, leading to enhanced selectivity when employed together. The proposed model, therefore, provides a comprehensive toolkit for the model-driven design of selectively binding therapies.

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24

Bandorowicz-Pikuła, J., M. Danieluk, A. Wrzosek, R. Buś, R. Buchet, and S. Pikuła. "Annexin VI: an intracellular target for ATP." Acta Biochimica Polonica 46, no. 3 (September 30, 1999): 801–12. http://dx.doi.org/10.18388/abp.1999_4152.

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Annexin VI (AnxVI), an Ca2+- and phospholipid-binding protein, interacts in vitro with ATP in a calcium-dependent manner. Experimental evidence indicates that its nucleotide-binding domain which is localized in the C-terminal half of the protein differs structurally from ATP/GTP-binding motifs found in other nucleotide-binding proteins. The amino-acid residues of AnxVI directly involved in ATP binding have not been yet defined. Binding of ATP to AnxVI induces changes in the secondary and tertiary structures of protein, affecting the affinity of AnxVI for Ca2+ and, in consequence, influencing the Ca2+-dependent activities of AnxVI: binding to F-actin and to membranous phospholipids, and self-association of the annexin molecules. These observations suggest that ATP is a functional ligand for AnxVI in vivo, and ATP-sensitive AnxVI may play the role of a factor coupling vesicular transport and calcium homeostasis to cellular metabolism.

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25

Mohebbi, Mohammad, Liang Ding, Russell L. Malmberg, Cory Momany, Khaled Rasheed, and Liming Cai. "Accurate prediction of human miRNA targets via graph modeling of the miRNA-target duplex." Journal of Bioinformatics and Computational Biology 16, no. 04 (August 2018): 1850013. http://dx.doi.org/10.1142/s0219720018500130.

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miRNAs are involved in many critical cellular activities through binding to their mRNA targets, e.g. in cell proliferation, differentiation, death, growth control, and developmental timing. Accurate prediction of miRNA targets can assist efficient experimental investigations on the functional roles of miRNAs. Their prediction, however, remains a challengeable task due to the lack of experimental data about the tertiary structure of miRNA-target binding duplexes. In particular, correlations of nucleotides in the binding duplexes may not be limited to the canonical Watson Crick base pairs (BPs) as they have been perceived; methods based on secondary structure prediction (typically minimum free energy (MFE)) have only had mix success. In this work, we characterized miRNA binding duplexes with a graph model to capture the correlations between pairs of nucleotides of an miRNA and its target sequences. We developed machine learning algorithms to train the graph model to predict the target sites of miRNAs. In particular, because imbalance between positive and negative samples can significantly deteriorate the performance of machine learning methods, we designed a novel method to re-sample available dataset to produce more informative data learning process. We evaluated our model and miRNA target prediction method on human miRNAs and target data obtained from mirTarBase, a database of experimentally verified miRNA-target interactions. The performance of our method in target prediction achieved a sensitivity of 86% with a false positive rate below 13%. In comparison with the state-of-the-art methods miRanda and RNAhybrid on the test data, our method outperforms both of them by a significant margin. The source codes, test sets and model files all are available at http://rna-informatics.uga.edu/?f=software&p=GraB-miTarget .

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26

Lee and Kim. "In-Silico Molecular Binding Prediction for Human Drug Targets Using Deep Neural Multi-Task Learning." Genes 10, no. 11 (November 7, 2019): 906. http://dx.doi.org/10.3390/genes10110906.

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In in-silico prediction for molecular binding of human genomes, promising results have been demonstrated by deep neural multi-task learning due to its strength in training tasks with imbalanced data and its ability to avoid over-fitting. Although the interrelation between tasks is known to be important for successful multi-task learning, its adverse effect has been underestimated. In this study, we used molecular interaction data of human targets from ChEMBL to train and test various multi-task and single-task networks and examined the effectiveness of multi-task learning for different compositions of targets. Targets were clustered based on sequence similarity in their binding domains and various target sets from clusters were chosen. By comparing the performance of deep neural architectures for each target set, we found that similarity within a target set is highly important for reliable multi-task learning. For a diverse target set or overall human targets, the performance of multi-task learning was lower than single-task learning, but outperformed single-task for the target set containing similar targets. From this insight, we developed Multiple Partial Multi-Task learning, which is suitable for binding prediction for human drug targets.

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27

Liao, Jianbo, Qinyu Wang, Fengxu Wu, and Zunnan Huang. "In Silico Methods for Identification of Potential Active Sites of Therapeutic Targets." Molecules 27, no. 20 (October 20, 2022): 7103. http://dx.doi.org/10.3390/molecules27207103.

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Target identification is an important step in drug discovery, and computer-aided drug target identification methods are attracting more attention compared with traditional drug target identification methods, which are time-consuming and costly. Computer-aided drug target identification methods can greatly reduce the searching scope of experimental targets and associated costs by identifying the diseases-related targets and their binding sites and evaluating the druggability of the predicted active sites for clinical trials. In this review, we introduce the principles of computer-based active site identification methods, including the identification of binding sites and assessment of druggability. We provide some guidelines for selecting methods for the identification of binding sites and assessment of druggability. In addition, we list the databases and tools commonly used with these methods, present examples of individual and combined applications, and compare the methods and tools. Finally, we discuss the challenges and limitations of binding site identification and druggability assessment at the current stage and provide some recommendations and future perspectives.

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28

Li, Shiyuan, Duyu Chen, Qingtong Zhou, Wei Wang, Lingfeng Gao, Jie Jiang, Haojun Liang, Yangzhong Liu, Gaolin Liang, and Hua Cui. "A General Chemiluminescence Strategy for Measuring Aptamer–Target Binding and Target Concentration." Analytical Chemistry 86, no. 11 (May 16, 2014): 5559–66. http://dx.doi.org/10.1021/ac501061c.

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29

Schulmeyer, Kayley H., Manisha R. Diaz, Thomas B. Bair, Wes Sanders, Cindy J. Gode, Alain Laederach, Matthew C. Wolfgang, and Timothy L. Yahr. "Primary and Secondary Sequence Structure Requirements for Recognition and Discrimination of Target RNAs by Pseudomonas aeruginosa RsmA and RsmF." Journal of Bacteriology 198, no. 18 (July 5, 2016): 2458–69. http://dx.doi.org/10.1128/jb.00343-16.

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ABSTRACTCsrA family RNA-binding proteins are widely distributed in bacteria and regulate gene expression at the posttranscriptional level.Pseudomonas aeruginosahas a canonical member of the CsrA family (RsmA) and a novel, structurally distinct variant (RsmF). To better understand RsmF binding properties, we performed parallel systematic evolution of ligands by exponential enrichment (SELEX) experiments for RsmA and RsmF. The initial target library consisted of 62-nucleotide (nt) RNA transcripts with central cores randomized at 15 sequential positions. Most targets selected by RsmA and RsmF were the expected size and shared a common consensus sequence (CANGGAYG) that was positioned in a hexaloop region of the stem-loop structure. RsmA and RsmF also selected for longer targets (≥96 nt) that were likely generated by rare PCR errors. Most of the long targets contained two consensus-binding sites. Representative short (single consensus site) and long (two consensus sites) targets were tested for RsmA and RsmF binding. Whereas RsmA bound the short targets with high affinity, RsmF was unable to bind the same targets. RsmA and RsmF both bound the long targets. Mutation of either consensus GGA site in the long targets reduced or eliminated RsmF binding, suggesting a requirement for two tandem binding sites. Conversely, RsmA bound long targets containing only a single GGA site with unaltered affinity. The RsmF requirement for two binding sites was confirmed withtssA1, anin vivoregulatory target of RsmA and RsmF. Our findings suggest that RsmF binding requires two GGA-containing sites, while RsmA binding requirements are less stringent.IMPORTANCEThe CsrA family of RNA-binding proteins is widely conserved in bacteria and plays important roles in the posttranscriptional regulation of protein synthesis.P. aeruginosahas two CsrA proteins, RsmA and RsmF. Although RsmA and RsmF share a few RNA targets, RsmF is unable to bind to other targets recognized by RsmA. The goal of the present study was to better understand the basis for differential binding by RsmF. Our data indicate that RsmF binding requires target RNAs with two consensus-binding sites, while RsmA recognizes targets with just a single binding site. This information should prove useful to future efforts to define the RsmF regulon and its contribution toP. aeruginosaphysiology and virulence.

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Schmidt, Denis, Magdalena M. Scharf, Dominique Sydow, Eva Aßmann, Maria Martí-Solano, Marina Keul, Andrea Volkamer, and Peter Kolb. "Analyzing Kinase Similarity in Small Molecule and Protein Structural Space to Explore the Limits of Multi-Target Screening." Molecules 26, no. 3 (January 26, 2021): 629. http://dx.doi.org/10.3390/molecules26030629.

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While selective inhibition is one of the key assets for a small molecule drug, many diseases can only be tackled by simultaneous inhibition of several proteins. An example where achieving selectivity is especially challenging are ligands targeting human kinases. This difficulty arises from the high structural conservation of the kinase ATP binding sites, the area targeted by most inhibitors. We investigated the possibility to identify novel small molecule ligands with pre-defined binding profiles for a series of kinase targets and anti-targets by in silico docking. The candidate ligands originating from these calculations were assayed to determine their experimental binding profiles. Compared to previous studies, the acquired hit rates were low in this specific setup, which aimed at not only selecting multi-target kinase ligands, but also designing out binding to anti-targets. Specifically, only a single profiled substance could be verified as a sub-micromolar, dual-specific EGFR/ErbB2 ligand that indeed avoided its selected anti-target BRAF. We subsequently re-analyzed our target choice and in silico strategy based on these findings, with a particular emphasis on the hit rates that can be expected from a given target combination. To that end, we supplemented the structure-based docking calculations with bioinformatic considerations of binding pocket sequence and structure similarity as well as ligand-centric comparisons of kinases. Taken together, our results provide a multi-faceted picture of how pocket space can determine the success of docking in multi-target drug discovery efforts.

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31

Shlyakhtenko, Luda S., Alexander Y. Lushnikov, Atsushi Miyagi, and Yuri L. Lyubchenko. "Specificity of Binding of Single-Stranded DNA-Binding Protein to Its Target." Biochemistry 51, no. 7 (February 6, 2012): 1500–1509. http://dx.doi.org/10.1021/bi201863z.

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32

Brokx, Richard D., Maria M. Lopez, Hans J. Vogel, and George I. Makhatadze. "Energetics of Target Peptide Binding by Calmodulin Reveals Different Modes of Binding." Journal of Biological Chemistry 276, no. 17 (January 29, 2001): 14083–91. http://dx.doi.org/10.1074/jbc.m011026200.

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33

Xiong, Li, Junfeng Cao, Yixin Qiu, Yinyin Fu, Siyi Chen, Mengjia He, Shengyan Chen, et al. "Exploring the Mechanism of Aspirin in the Treatment of Kawasaki Disease Based on Molecular Docking and Molecular Dynamics." Evidence-Based Complementary and Alternative Medicine 2022 (August 12, 2022): 1–11. http://dx.doi.org/10.1155/2022/9828518.

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Purpose. The research aims to investigate the mechanism of action of aspirin in the treatment of Kawasaki disease. Methods. We predicted the targets of aspirin with the help of the Drugbank and PharmMapper databases, the target genes of Kawasaki disease were mined in the GeneCards and Disgenet databases, the intersection targets were processed in the Venny database, and the gene expression differences were observed in the GEO database. The Drugbank and PharmMapper databases were used to predict the target of aspirin, and the target genes of Kawasaki disease were explored in the GeneCards and Disgenet databases, and the Venny was used for intersection processing. We observed the gene expression differences in the GEO database. The disease-core gene target-drug network was established and molecular docking was used for verification. Molecular dynamics simulation verification was carried out to combine the active ingredient and the target with a stable combination. The supercomputer platform was used to measure and analyze the binding free energy, the number of hydrogen bonds, the stability of the protein target at the residue level, the radius of gyration, and the solvent accessible surface area. Results. Aspirin had 294 gene targets, Kawasaki disease had 416 gene targets, 42 intersecting targets were obtained, we screened 13 core targets by PPI; In the GO analysis, we learned that the biological process of Kawasaki disease involved the positive regulation of chemokine biosynthesis and inflammatory response; pathway enrichment involved PI3K-AKT signaling pathway, tumor necrosis factor signaling pathway, etc. After molecular docking, the data showed that CTSG, ELANE, and FGF1 had the best binding degree to aspirin. Molecular dynamics was used to prove and analyze the binding stability of active ingredients and protein targets, and Aspirin/ELANE combination has the strongest binding energy. Conclusion. In the treatment of Kawasaki disease, aspirin may regulate inflammatory response and vascular remodeling through CTSG, ELANE, and FGF1.

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Klimentová, Eva, Václav Hejret, Ján Krčmář, Katarína Grešová, Ilektra-Chara Giassa, and Panagiotis Alexiou. "miRBind: A Deep Learning Method for miRNA Binding Classification." Genes 13, no. 12 (December 9, 2022): 2323. http://dx.doi.org/10.3390/genes13122323.

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The binding of microRNAs (miRNAs) to their target sites is a complex process, mediated by the Argonaute (Ago) family of proteins. The prediction of miRNA:target site binding is an important first step for any miRNA target prediction algorithm. To date, the potential for miRNA:target site binding is evaluated using either co-folding free energy measures or heuristic approaches, based on the identification of binding ‘seeds’, i.e., continuous stretches of binding corresponding to specific parts of the miRNA. The limitations of both these families of methods have produced generations of miRNA target prediction algorithms that are primarily focused on ‘canonical’ seed targets, even though unbiased experimental methods have shown that only approximately half of in vivo miRNA targets are ‘canonical’. Herein, we present miRBind, a deep learning method and web server that can be used to accurately predict the potential of miRNA:target site binding. We trained our method using seed-agnostic experimental data and show that our method outperforms both seed-based approaches and co-fold free energy approaches. The full code for the development of miRBind and a freely accessible web server are freely available.

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35

Kim, Minjee, and Young Bong Kim. "Uncovering Quercetin’s Effects against Influenza A Virus Using Network Pharmacology and Molecular Docking." Processes 9, no. 9 (September 9, 2021): 1627. http://dx.doi.org/10.3390/pr9091627.

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(1) Background: Re-emerging influenza threats continue to challenge medical and public health systems. Quercetin is a ubiquitous flavonoid found in food and is recognized to possess antioxidant, anti-inflammatory, antiviral, and anticancer activities. (2) Methods: To elucidate the targets and mechanisms underlying the action of quercetin as a therapeutic agent for influenza, network pharmacology and molecular docking were employed. Biological targets of quercetin and target genes associated with influenza were retrieved from public databases. Compound–disease target (C-D) networks were constructed, and targets were further analyzed using KEGG pathway analysis. Potent target genes were retrieved from the compound–disease–pathway (C-D-P) and protein–protein interaction (PPI) networks. The binding affinities between quercetin and the targets were identified using molecular docking. (3) Results: The pathway study revealed that quercetin-associated influenza targets were mainly involved in viral diseases, inflammation-associated pathways, and cancer. Four targets, MAPK1, NFKB1, RELA, and TP53, were identified to be involved in the inhibitory effects of quercetin on influenza. Using the molecular docking method, we evaluated the binding affinity of each ligand (quercetin)–target and discovered that quercetin and MAPK1 showed the strongest calculated binding energy among the four ligand–target complexes. (4) Conclusion: These findings identified potential targets of quercetin and suggest quercetin as a potential drug for influenza treatment.

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36

Otero-Ramirez, Manuel, Toby Passioura, and Hiroaki Suga. "Structural Features and Binding Modes of Thioether-Cyclized Peptide Ligands." Biomedicines 6, no. 4 (December 13, 2018): 116. http://dx.doi.org/10.3390/biomedicines6040116.

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Macrocyclic peptides are an emerging class of bioactive compounds for therapeutic use. In part, this is because they are capable of high potency and excellent target affinity and selectivity. Over the last decade, several biochemical techniques have been developed for the identification of bioactive macrocyclic peptides, allowing for the rapid isolation of high affinity ligands to a target of interest. A common feature of these techniques is a general reliance on thioether formation to effect macrocyclization. Increasingly, the compounds identified using these approaches have been subjected to x-ray crystallographic analysis bound to their respective targets, providing detailed structural information about their conformation and mechanism of target binding. The present review provides an overview of the target bound thioether-closed macrocyclic peptide structures that have been obtained to date.

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Talukder, Amlan, Xiaoman Li, and Haiyan Hu. "Position-wise binding preference is important for miRNA target site prediction." Bioinformatics 36, no. 12 (March 18, 2020): 3680–86. http://dx.doi.org/10.1093/bioinformatics/btaa195.

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Abstract Motivation It is a fundamental task to identify microRNAs (miRNAs) targets and accurately locate their target sites. Genome-scale experiments for miRNA target site detection are still costly. The prediction accuracies of existing computational algorithms and tools are often not up to the expectation due to a large number of false positives. One major obstacle to achieve a higher accuracy is the lack of knowledge of the target binding features of miRNAs. The published high-throughput experimental data provide an opportunity to analyze position-wise preference of miRNAs in terms of target binding, which can be an important feature in miRNA target prediction algorithms. Results We developed a Markov model to characterize position-wise pairing patterns of miRNA–target interactions. We further integrated this model as a scoring method and developed a dynamic programming (DP) algorithm, MDPS (Markov model-scored Dynamic Programming algorithm for miRNA target site Selection) that can screen putative target sites of miRNA-target binding. The MDPS algorithm thus can take into account both the dependency of neighboring pairing positions and the global pairing information. Based on the trained Markov models from both miRNA-specific and general datasets, we discovered that the position-wise binding information specific to a given miRNA would benefit its target prediction. We also found that miRNAs maintain region-wise similarity in their target binding patterns. Combining MDPS with existing methods significantly improves their precision while only slightly reduces their recall. Therefore, position-wise pairing patterns have the promise to improve target prediction if incorporated into existing software tools. Availability and implementation The source code and tool to calculate MDPS score is available at http://hulab.ucf.edu/research/projects/MDPS/index.html. Supplementary information Supplementary data are available at Bioinformatics online.

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Bodén, Mikael, and Timothy L. Bailey. "Associating transcription factor-binding site motifs with target GO terms and target genes." Nucleic Acids Research 36, no. 12 (June 10, 2008): 4108–17. http://dx.doi.org/10.1093/nar/gkn374.

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39

Baguley, Bruce C., Catherine J. Drummond, Ying Yi Chen, and Graeme J. Finlay. "DNA-Binding Anticancer Drugs: One Target, Two Actions." Molecules 26, no. 3 (January 21, 2021): 552. http://dx.doi.org/10.3390/molecules26030552.

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Amsacrine, an anticancer drug first synthesised in 1970 by Professor Cain and colleagues, showed excellent preclinical activity and underwent clinical trial in 1978 under the auspices of the US National Cancer Institute, showing activity against acute lymphoblastic leukaemia. In 1984, the enzyme DNA topoisomerase II was identified as a molecular target for amsacrine, acting to poison this enzyme and to induce DNA double-strand breaks. One of the main challenges in the 1980s was to determine whether amsacrine analogues could be developed with activity against solid tumours. A multidisciplinary team was assembled in Auckland, and Professor Denny played a leading role in this approach. Among a large number of drugs developed in the programme, N-[2-(dimethylamino)-ethyl]-acridine-4-carboxamide (DACA), first synthesised by Professor Denny, showed excellent activity against a mouse lung adenocarcinoma. It underwent clinical trial, but dose escalation was prevented by ion channel toxicity. Subsequent work led to the DACA derivative SN 28049, which had increased potency and reduced ion channel toxicity. Mode of action studies suggested that both amsacrine and DACA target the enzyme DNA topoisomerase II but with a different balance of cellular consequences. As primarily a topoisomerase II poison, amsacrine acts to turn the enzyme into a DNA-damaging agent. As primarily topoisomerase II catalytic inhibitors, DACA and SN 28049 act to inhibit the segregation of daughter chromatids during anaphase. The balance between these two actions, one cell cycle phase specific and the other nonspecific, together with pharmacokinetic, cytokinetic and immunogenic considerations, provides links between the actions of acridine derivatives and anthracyclines such as doxorubicin. They also provide insights into the action of cytotoxic DNA-binding drugs.

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40

Prigozhin, Daniil. "Predicting target binding sites in plant immune receptors." Acta Crystallographica Section A Foundations and Advances 78, a1 (July 29, 2022): a198. http://dx.doi.org/10.1107/s2053273322098011.

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41

DeBouver, Nicholas, Longxing Cao, Brian Coventry, Asim Bera, Wei Yang, Steffen Bernard, Lance Stewart, et al. "Protein-binding proteins designed from target structural information." Acta Crystallographica Section A Foundations and Advances 78, a1 (July 29, 2022): a281. http://dx.doi.org/10.1107/s2053273322097182.

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42

Murase, T., and T. Iio. "The binding reaction between Calmodulin and Target Peptide." Seibutsu Butsuri 39, supplement (1999): S132. http://dx.doi.org/10.2142/biophys.39.s132_2.

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43

Hong, Ze, Jiahao Mei, Chenhui Li, Guohui Bai, Munire Maimaiti, Haiyang Hu, Wenying Yu, et al. "STING inhibitors target the cyclic dinucleotide binding pocket." Proceedings of the National Academy of Sciences 118, no. 24 (June 7, 2021): e2105465118. http://dx.doi.org/10.1073/pnas.2105465118.

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Cytosolic DNA activates cGAS (cytosolic DNA sensor cyclic AMP-GMP synthase)-STING (stimulator of interferon genes) signaling, which triggers interferon and inflammatory responses that help defend against microbial infection and cancer. However, aberrant cytosolic self-DNA in Aicardi–Goutière’s syndrome and constituently active gain-of-function mutations in STING in STING-associated vasculopathy with onset in infancy (SAVI) patients lead to excessive type I interferons and proinflammatory cytokines, which cause difficult-to-treat and sometimes fatal autoimmune disease. Here, in silico docking identified a potent STING antagonist SN-011 that binds with higher affinity to the cyclic dinucleotide (CDN)-binding pocket of STING than endogenous 2′3′-cGAMP. SN-011 locks STING in an open inactive conformation, which inhibits interferon and inflammatory cytokine induction activated by 2′3′-cGAMP, herpes simplex virus type 1 infection, Trex1 deficiency, overexpression of cGAS-STING, or SAVI STING mutants. In Trex1−/− mice, SN-011 was well tolerated, strongly inhibited hallmarks of inflammation and autoimmunity disease, and prevented death. Thus, a specific STING inhibitor that binds to the STING CDN-binding pocket is a promising lead compound for STING-driven disease.

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44

Grebenkov, Denis S., and Aanjaneya Kumar. "Reversible target-binding kinetics of multiple impatient particles." Journal of Chemical Physics 156, no. 8 (February 28, 2022): 084107. http://dx.doi.org/10.1063/5.0083849.

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Certain biochemical reactions can only be triggered after binding a sufficient number of particles to a specific target region such as an enzyme or a protein sensor. We investigate the distribution of the reaction time, i.e., the first instance when all independently diffusing particles are bound to the target. When each particle binds irreversibly, this is equivalent to the first-passage time of the slowest (last) particle. In turn, reversible binding to the target renders the problem much more challenging and drastically changes the distribution of the reaction time. We derive the exact solution of this problem and investigate the short-time and long-time asymptotic behaviors of the reaction time probability density. We also analyze how the mean reaction time depends on the unbinding rate and the number of particles. Our exact and asymptotic solutions are compared to Monte Carlo simulations.

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45

Smith, Ewan, and Ian Collins. "Photoaffinity labeling in target- and binding-site identification." Future Medicinal Chemistry 7, no. 2 (February 2015): 159–83. http://dx.doi.org/10.4155/fmc.14.152.

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46

Konc, Janez. "Binding site comparisons for target-centered drug discovery." Expert Opinion on Drug Discovery 14, no. 5 (March 11, 2019): 445–54. http://dx.doi.org/10.1080/17460441.2019.1588883.

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47

Orsini, F., E. R. Zanier, R. Gesuete, M. Stravalaci, D. De Blasio, B. Oortwijn, M. L. M. Mannesse, M. Gobbi, and M. G. De Simoni. "Mannose binding lectin as a target for neuroprotection." Molecular Immunology 47, no. 13 (August 2010): 2200. http://dx.doi.org/10.1016/j.molimm.2010.05.020.

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Bernard, Elyse D., Michael A. Beking, Karunanithi Rajamanickam, Eve C. Tsai, and Maria C. DeRosa. "Target binding improves relaxivity in aptamer–gadolinium conjugates." JBIC Journal of Biological Inorganic Chemistry 17, no. 8 (August 19, 2012): 1159–75. http://dx.doi.org/10.1007/s00775-012-0930-z.

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Thevendran, Ramesh, Tholasi Nadhan Navien, Xin Meng, Kechun Wen, Qiao Lin, Shigdar Sarah, Thean-Hock Tang, and Marimuthu Citartan. "Mathematical approaches in estimating aptamer-target binding affinity." Analytical Biochemistry 600 (July 2020): 113742. http://dx.doi.org/10.1016/j.ab.2020.113742.

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Bhattacharya, Shibani, Christopher G. Bunick, and Walter J. Chazin. "Target selectivity in EF-hand calcium binding proteins." Biochimica et Biophysica Acta (BBA) - Molecular Cell Research 1742, no. 1-3 (December 2004): 69–79. http://dx.doi.org/10.1016/j.bbamcr.2004.09.002.

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