Journal articles: 'Small molecule-protein interactions'

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Ottmann, Christian. "Small-molecule modulation of protein–protein interactions." Drug Discovery Today: Technologies 10, no. 4 (2013): e499-e500. http://dx.doi.org/10.1016/j.ddtec.2013.08.001.

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Kuusk, Ave, Helen Boyd, Hongming Chen, and Christian Ottmann. "Small-molecule modulation of p53 protein-protein interactions." Biological Chemistry 401, no. 8 (2020): 921–31. http://dx.doi.org/10.1515/hsz-2019-0405.

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AbstractSmall-molecule modulation of protein-protein interactions (PPIs) is a very promising but also challenging area in drug discovery. The tumor suppressor protein p53 is one of the most frequently altered proteins in human cancers, making it an attractive target in oncology. 14-3-3 proteins have been shown to bind to and positively regulate p53 activity by protecting it from MDM2-dependent degradation or activating its DNA binding affinity. PPIs can be modulated by inhibiting or stabilizing specific interactions by small molecules. Whereas inhibition has been widely explored by the pharmaceutical industry and academia, the opposite strategy of stabilizing PPIs still remains relatively underexploited. This is rather interesting considering the number of natural compounds like rapamycin, forskolin and fusicoccin that exert their activity by stabilizing specific PPIs. In this review, we give an overview of 14-3-3 interactions with p53, explain isoform specific stabilization of the tumor suppressor protein, explore the approach of stabilizing the 14-3-3σ-p53 complex and summarize some promising small molecules inhibiting the p53-MDM2 protein-protein interaction.

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Pollock, Julie A., Courtney L. Labrecque, Cassidy N. Hilton, et al. "Small Molecule Modulation of MEMO1 Protein-Protein Interactions." Journal of the Endocrine Society 5, Supplement_1 (2021): A1031. http://dx.doi.org/10.1210/jendso/bvab048.2110.

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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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Guo, Z. "Designing Small-Molecule Switches for Protein-Protein Interactions." Science 288, no. 5473 (2000): 2042–45. http://dx.doi.org/10.1126/science.288.5473.2042.

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Li, Xiyan, Xin Wang, and Michael Snyder. "Systematic investigation of protein-small molecule interactions." IUBMB Life 65, no. 1 (2012): 2–8. http://dx.doi.org/10.1002/iub.1111.

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D’Abramo, C. M. "Small Molecule Inhibitors of Human Papillomavirus Protein - Protein Interactions." Open Virology Journal 5, no. 1 (2011): 80–95. http://dx.doi.org/10.2174/1874357901105010080.

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Linhares, Brian M., Jolanta Grembecka, and Tomasz Cierpicki. "Targeting epigenetic protein–protein interactions with small-molecule inhibitors." Future Medicinal Chemistry 12, no. 14 (2020): 1305–26. http://dx.doi.org/10.4155/fmc-2020-0082.

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Epigenetic protein–protein interactions (PPIs) play essential roles in regulating gene expression, and their dysregulations have been implicated in many diseases. These PPIs are comprised of reader domains recognizing post-translational modifications on histone proteins, and of scaffolding proteins that maintain integrities of epigenetic complexes. Targeting PPIs have become focuses for development of small-molecule inhibitors and anticancer therapeutics. Here we summarize efforts to develop small-molecule inhibitors targeting common epigenetic PPI domains. Potent small molecules have been reported for many domains, yet small domains that recognize methylated lysine side chains on histones are challenging in inhibitor development. We posit that the development of potent inhibitors for difficult-to-prosecute epigenetic PPIs may be achieved by interdisciplinary approaches and extensive explorations of chemical space.

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Song, Yun, and Peter Buchwald. "TNF Superfamily Protein-Protein Interactions: Feasibility of Small- Molecule Modulation." Current Drug Targets 16, no. 4 (2015): 393–408. http://dx.doi.org/10.2174/1389450116666150223115628.

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de Vink, Pim J., Sebastian A. Andrei, Yusuke Higuchi, Christian Ottmann, Lech-Gustav Milroy, and Luc Brunsveld. "Cooperativity basis for small-molecule stabilization of protein–protein interactions." Chemical Science 10, no. 10 (2019): 2869–74. http://dx.doi.org/10.1039/c8sc05242e.

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A cooperativity framework to describe and interpret small-molecule stabilization of protein–protein interactions (PPI) is presented, which allows elucidating structure–activity relationships regarding cooperativity and intrinsic affinity.

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Aeluri, Madhu, Srinivas Chamakuri, Bhanudas Dasari, et al. "Small Molecule Modulators of Protein–Protein Interactions: Selected Case Studies." Chemical Reviews 114, no. 9 (2014): 4640–94. http://dx.doi.org/10.1021/cr4004049.

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Ottmann, Christian. "Small-molecule modulators of 14-3-3 protein–protein interactions." Bioorganic & Medicinal Chemistry 21, no. 14 (2013): 4058–62. http://dx.doi.org/10.1016/j.bmc.2012.11.028.

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Fry, David C. "Protein–protein interactions as targets for small molecule drug discovery." Biopolymers 84, no. 6 (2006): 535–52. http://dx.doi.org/10.1002/bip.20608.

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Vargas, Carolyn, Gerald Radziwill, Gerd Krause, et al. "Small-Molecule Inhibitors of AF6 PDZ-Mediated Protein-Protein Interactions." ChemMedChem 9, no. 7 (2014): 1458–62. http://dx.doi.org/10.1002/cmdc.201300553.

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Balci, Hamza, Sujay Ray, Jagat Budhathoki, and Parastoo Maleki. "Single Molecule Studies on G-Quadruplex, Protein, and Small Molecule Interactions." Biophysical Journal 112, no. 3 (2017): 170a. http://dx.doi.org/10.1016/j.bpj.2016.11.940.

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Nagatoishi, Satoru, Jose M. M. Caaveiro, and Kouhei Tsumoto. "Biophysical Analysis of the Protein-Small Molecule Interactions to Develop Small Molecule Drug Discovery." YAKUGAKU ZASSHI 138, no. 8 (2018): 1033–41. http://dx.doi.org/10.1248/yakushi.17-00211-2.

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Berwanger, Anja, Susanne Eyrisch, Inge Schuster, Volkhard Helms, and Rita Bernhardt. "Polyamines: Naturally occurring small molecule modulators of electrostatic protein–protein interactions." Journal of Inorganic Biochemistry 104, no. 2 (2010): 118–25. http://dx.doi.org/10.1016/j.jinorgbio.2009.10.007.

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Arkin, Michelle R., and James A. Wells. "Small-molecule inhibitors of protein–protein interactions: progressing towards the dream." Nature Reviews Drug Discovery 3, no. 4 (2004): 301–17. http://dx.doi.org/10.1038/nrd1343.

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Arkin, Michelle R., Yinyan Tang, and James A. Wells. "Small-Molecule Inhibitors of Protein-Protein Interactions: Progressing toward the Reality." Chemistry & Biology 21, no. 9 (2014): 1102–14. http://dx.doi.org/10.1016/j.chembiol.2014.09.001.

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Hashimoto, Chie, and Jutta Eichler. "Turning Peptide Ligands into Small-Molecule Inhibitors of Protein-Protein Interactions." ChemBioChem 16, no. 13 (2015): 1855–56. http://dx.doi.org/10.1002/cbic.201500298.

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Huang, Da, Aaron D. Robison, Yiquan Liu, and Paul S. Cremer. "Monitoring protein–small molecule interactions by local pH modulation." Biosensors and Bioelectronics 38, no. 1 (2012): 74–78. http://dx.doi.org/10.1016/j.bios.2012.05.023.

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McFedries, Amanda, Adam Schwaid, and Alan Saghatelian. "Methods for the Elucidation of Protein-Small Molecule Interactions." Chemistry & Biology 20, no. 5 (2013): 667–73. http://dx.doi.org/10.1016/j.chembiol.2013.04.008.

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Benes, Madeleine, and Nathan T. Wright. "BPS2025 - Characterizing small-molecule/desmoplakin interactions preventing protein degradation." Biophysical Journal 124, no. 3 (2025): 221a—222a. https://doi.org/10.1016/j.bpj.2024.11.1221.

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Bai, Bing, Rongfeng Zou, H. C. Stephen Chan, Hongchun Li, and Shuguang Yuan. "MolADI: A Web Server for Automatic Analysis of Protein–Small Molecule Dynamic Interactions." Molecules 26, no. 15 (2021): 4625. http://dx.doi.org/10.3390/molecules26154625.

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Protein–ligand interaction analysis is important for drug discovery and rational protein design. The existing online tools adopt only a single conformation of the complex structure for calculating and displaying the interactions, whereas both protein residues and ligand molecules are flexible to some extent. The interactions evolved with time in the trajectories are of greater interest. MolADI is a user-friendly online tool which analyzes the protein–ligand interactions in detail for either a single structure or a trajectory. Interactions can be viewed easily with both 2D graphs and 3D representations. MolADI is available as a web application.

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Guan, Yan, Xiaonan Shan, Fenni Zhang, Shaopeng Wang, Hong-Yuan Chen, and Nongjian Tao. "Kinetics of small molecule interactions with membrane proteins in single cells measured with mechanical amplification." Science Advances 1, no. 9 (2015): e1500633. http://dx.doi.org/10.1126/sciadv.1500633.

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Measuring small molecule interactions with membrane proteins in single cells is critical for understanding many cellular processes and for screening drugs. However, developing such a capability has been a difficult challenge. We show that molecular interactions with membrane proteins induce a mechanical deformation in the cellular membrane, and real-time monitoring of the deformation with subnanometer resolution allows quantitative analysis of small molecule–membrane protein interaction kinetics in single cells. This new strategy provides mechanical amplification of small binding signals, making it possible to detect small molecule interactions with membrane proteins. This capability, together with spatial resolution, also allows the study of the heterogeneous nature of cells by analyzing the interaction kinetics variability between different cells and between different regions of a single cell.

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Zhao, Yujun, Denzil Bernard, and Shaomeng Wang. "Small Molecule Inhibitors of MDM2-p53 and MDMX-p53 Interactions as New Cancer Therapeutics." BioDiscovery 8 (July 8, 2013): e8950. https://doi.org/10.7750/BioDiscovery.2013.8.4.

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Inactivation of the function of tumor suppressor p53 is common in human cancers. In approximately half of human cancers, the tumor suppressor function of p53 is inactivated by deletion or mutation of TP53, the gene encoding p53 protein. In the remaining 50% of human cancers, p53 tumor suppressor function can be effectively inhibited by oncoprotein MDM2 or its homolog MDMX. Since inhibition of p53 by MDM2 or MDMX protein is mediated by their direct interaction with p53, small-molecule inhibitors designed to block the MDM2-p53 or MDMX-p53 protein-protein interaction (MDM2 or MDMX inhibitors) can activate p53 in tumor cells retaining wild-type p53. In the last few years, several classes of potent, selective, and efficacious small molecule MDM2 inhibitors have been designed and developed, and six such compounds are being evaluated in clinical trials as new anticancer drugs. Additionally, non-peptide, small-molecule MDMX inhibitors have been reported. We review herein the design and development of potent small-molecule MDM2 and MDMX inhibitors.

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Li, Fengzhi, Ieman A. M. Aljahdali, and Xiang Ling. "Molecular Glues: Capable Protein-Binding Small Molecules That Can Change Protein–Protein Interactions and Interactomes for the Potential Treatment of Human Cancer and Neurodegenerative Diseases." International Journal of Molecular Sciences 23, no. 11 (2022): 6206. http://dx.doi.org/10.3390/ijms23116206.

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Molecular glue (MG) compounds are a type of unique small molecule that can change the protein–protein interactions (PPIs) and interactomes by degrading, stabilizing, or activating the target protein after their binging. These small-molecule MGs are gradually being recognized for their potential application in treating human diseases, including cancer. Evidence suggests that small-molecule MG compounds could essentially target any proteins, which play critical roles in human disease etiology, where many of these protein targets were previously considered undruggable. Intriguingly, most MG compounds with high efficacy for cancer treatment can glue on and control multiple key protein targets. On the other hand, a single key protein target can also be glued by multiple MG compounds with distinct chemical structures. The high flexibility of MG–protein interaction profiles provides rich soil for the growth and development of small-molecule MG compounds that can be used as molecular tools to assist in unraveling disease mechanisms, and they can also facilitate drug development for the treatment of human disease, especially human cancer. In this review, we elucidate this concept by using various types of small-molecule MG compounds and their corresponding protein targets that have been documented in the literature.

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Xin, Dongyue, Andreas Holzenburg, and Kevin Burgess. "Small molecule probes that perturb a protein–protein interface in antithrombin." Chem. Sci. 5, no. 12 (2014): 4914–21. http://dx.doi.org/10.1039/c4sc01295j.

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Small molecule probes for perturbing protein–protein interactions (PPIs) in vitro can be useful if they cause the target proteins to undergo biomedically relevant changes to their tertiary and quaternary structures.

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C. Fry, David. "Small-Molecule Inhibitors of Protein-Protein Interactions: How to Mimic a Protein Partner." Current Pharmaceutical Design 18, no. 30 (2012): 4679–84. http://dx.doi.org/10.2174/138161212802651634.

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Cossins, Benjamin, and Alastair Lawson. "Small Molecule Targeting of Protein–Protein Interactions through Allosteric Modulation of Dynamics." Molecules 20, no. 9 (2015): 16435–45. http://dx.doi.org/10.3390/molecules200916435.

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Horswill, A. R., S. N. Savinov, and S. J. Benkovic. "A systematic method for identifying small-molecule modulators of protein-protein interactions." Proceedings of the National Academy of Sciences 101, no. 44 (2004): 15591–96. http://dx.doi.org/10.1073/pnas.0406999101.

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Lee, Wan-Hung, Zhili Xu, Nicole M. Ashpole, et al. "Small molecule inhibitors of PSD95-nNOS protein–protein interactions as novel analgesics." Neuropharmacology 97 (October 2015): 464–75. http://dx.doi.org/10.1016/j.neuropharm.2015.05.038.

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Arkin, Michelle. "Finding Small Molecule Ligands for Protein-Protein Interactions and Other “Undruggable” Targets." Biophysical Journal 98, no. 3 (2010): 411a. http://dx.doi.org/10.1016/j.bpj.2009.12.2216.

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Guo, Wenxing, John A. Wisniewski, and Haitao Ji. "Hot spot-based design of small-molecule inhibitors for protein–protein interactions." Bioorganic & Medicinal Chemistry Letters 24, no. 11 (2014): 2546–54. http://dx.doi.org/10.1016/j.bmcl.2014.03.095.

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Cummings, Christopher G., та Andrew D. Hamilton. "Disrupting protein–protein interactions with non-peptidic, small molecule α-helix mimetics". Current Opinion in Chemical Biology 14, № 3 (2010): 341–46. http://dx.doi.org/10.1016/j.cbpa.2010.04.001.

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Xu, David, Khuchtumur Bum-Erdene, Yubing Si, Donghui Zhou, Mona K. Ghozayel, and Samy O. Meroueh. "Mimicking Intermolecular Interactions of Tight Protein-Protein Complexes for Small-Molecule Antagonists." ChemMedChem 12, no. 21 (2017): 1794–809. http://dx.doi.org/10.1002/cmdc.201700572.

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Kroll, Alexander, Sahasra Ranjan, and Martin J. Lercher. "A multimodal Transformer Network for protein-small molecule interactions enhances predictions of kinase inhibition and enzyme-substrate relationships." PLOS Computational Biology 20, no. 5 (2024): e1012100. http://dx.doi.org/10.1371/journal.pcbi.1012100.

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The activities of most enzymes and drugs depend on interactions between proteins and small molecules. Accurate prediction of these interactions could greatly accelerate pharmaceutical and biotechnological research. Current machine learning models designed for this task have a limited ability to generalize beyond the proteins used for training. This limitation is likely due to a lack of information exchange between the protein and the small molecule during the generation of the required numerical representations. Here, we introduce ProSmith, a machine learning framework that employs a multimodal Transformer Network to simultaneously process protein amino acid sequences and small molecule strings in the same input. This approach facilitates the exchange of all relevant information between the two molecule types during the computation of their numerical representations, allowing the model to account for their structural and functional interactions. Our final model combines gradient boosting predictions based on the resulting multimodal Transformer Network with independent predictions based on separate deep learning representations of the proteins and small molecules. The resulting predictions outperform recently published state-of-the-art models for predicting protein-small molecule interactions across three diverse tasks: predicting kinase inhibitions; inferring potential substrates for enzymes; and predicting Michaelis constants KM. The Python code provided can be used to easily implement and improve machine learning predictions involving arbitrary protein-small molecule interactions.

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Kroll, Alexander, Sahasra Ranjan, and Martin Lercher. "A multimodal Transformer Network for protein-small molecule interactions enhances predictions of kinase inhibition and enzyme-substrate relationships." PLOS Computational Biology 20, no. 5 (2024): e1012100. https://doi.org/10.5281/zenodo.14825175.

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The activities of most enzymes and drugs depend on interactions between proteins and small molecules. Accurate prediction of these interactions could greatly accelerate pharmaceutical and biotechnological research. Current machine learning models designed for this task have a limited ability to generalize beyond the proteins used for training. This limitation is likely due to a lack of information exchange between the protein and the small molecule during the generation of the required numerical representations. Here, we introduce ProSmith, a machine learning framework that employs a multimodal Transformer Network to simultaneously process protein amino acid sequences and small molecule strings in the same input. This approach facilitates the exchange of all relevant information between the two molecule types during the computation of their numerical representations, allowing the model to account for their structural and functional interactions. Our final model combines gradient boosting predictions based on the resulting multimodal Transformer Network with independent predictions based on separate deep learning representations of the proteins and small molecules. The resulting predictions outperform recently published state-of-the-art models for predicting protein-small molecule interactions across three diverse tasks: pre- dicting kinase inhibitions; inferring potential substrates for enzymes; and predicting Michaelis constants KM. The Python code provided can be used to easily implement and improve machine learning predictions involving arbitrary protein-small molecule interactions.

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Yamaguchi, A. "Het-PDB Navi.: A Database for Protein-Small Molecule Interactions." Journal of Biochemistry 135, no. 1 (2004): 79–84. http://dx.doi.org/10.1093/jb/mvh009.

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Nieuwenhuijsen, Bart W., Youping Huang, Yuren Wang, Fernando Ramirez, Gary Kalgaonkar, and Kathleen H. Young. "A Dual Luciferase Multiplexed High-Throughput Screening Platform for Protein-Protein Interactions." Journal of Biomolecular Screening 8, no. 6 (2003): 676–84. http://dx.doi.org/10.1177/1087057103258287.

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To study the biology of regulators of G-protein signaling (RGS) proteins and to facilitate the identification of small molecule modulators of RGS proteins, the authors recently developed an advanced yeast 2-hybrid (YTH) assay format for GαZand RGS-Z1. Moreover, they describe the development of a multiplexed luciferase-based assay that has been successfully adapted to screen large numbers of small molecule modulators of protein-protein interactions. They generated and evaluated 2 different luciferase reporter gene systems for YTH interactions, a Gal4 responsive firefly luciferase reporter gene and a Gal4 responsive Renilla luciferase reporter gene. Both the firefly and Renilla luciferase reporter genes demonstrated a 40-to 50-fold increase in luminescence in strains expressing interacting YTH fusion proteins versus negative control strains. Because the firefly and Renilla luciferase proteins have different substrate specificity, the assays were multiplexed. The multiplexed luciferase-based YTH platform adds speed, sensitivity, simplicity, quantification, and efficiency to YTH high-throughput applications and therefore greatly facilitates the identification of small molecule modulators of protein-protein interactions as tools or potential leads for drug discovery efforts.

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Ferreira de Freitas, Renato, and Matthieu Schapira. "A systematic analysis of atomic protein–ligand interactions in the PDB." MedChemComm 8, no. 10 (2017): 1970–81. http://dx.doi.org/10.1039/c7md00381a.

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We compiled a list of 11 016 unique structures of small-molecule ligands bound to proteins representing 750 873 protein–ligand atomic interactions, and analyzed the frequency, geometry and the impact of each interaction type. The most frequent ligand–protein atom pairs can be clustered into seven interaction types.

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Xu, David, Shadia I. Jalal, George W. Sledge, and Samy O. Meroueh. "Small-molecule binding sites to explore protein–protein interactions in the cancer proteome." Molecular BioSystems 12, no. 10 (2016): 3067–87. http://dx.doi.org/10.1039/c6mb00231e.

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Pedram Fatemi, Roya, Sultan Salah-Uddin, Farzaneh Modarresi, Nathalie Khoury, Claes Wahlestedt, and Mohammad Ali Faghihi. "Screening for Small-Molecule Modulators of Long Noncoding RNA-Protein Interactions Using AlphaScreen." Journal of Biomolecular Screening 20, no. 9 (2015): 1132–41. http://dx.doi.org/10.1177/1087057115594187.

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Long non–protein coding RNAs (lncRNAs) are an important class of molecules that help orchestrate key cellular events. Although their functional roles in cells are not well understood, thousands of lncRNAs and a number of possible mechanisms by which they act have been reported. LncRNAs can exert their regulatory function in cells by interacting with epigenetic enzymes. In this study, we developed a tool to study lncRNA-protein interactions for high-throughput screening of small-molecule modulators using AlphaScreen technology. We tested the interaction of two lncRNAs: brain-derived neurotrophic factor antisense ( BDNF-AS) and Hox transcript antisense RNA ( HOTAIR), with Enhancer of zeste homolog 2 (EZH2), a histone methyltransferase against a phytochemical library, to look for small-molecule inhibitors that can alter the expression of downstream target genes. We identified ellipticine, a compound that up-regulates BDNF transcription. Our study shows the feasibility of using high-throughput screening to identify modulators of lncRNA-protein interactions and paves the road for targeting lncRNAs that are dysregulated in human disorders using small-molecule therapies.

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Stringer, Bas, Hans de Ferrante, Sanne Abeln, Jaap Heringa, K. Anton Feenstra, and Reza Haydarlou. "PIPENN: protein interface prediction from sequence with an ensemble of neural nets." Bioinformatics 38, no. 8 (2022): 2111–18. http://dx.doi.org/10.1093/bioinformatics/btac071.

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Abstract Motivation The interactions between proteins and other molecules are essential to many biological and cellular processes. Experimental identification of interface residues is a time-consuming, costly and challenging task, while protein sequence data are ubiquitous. Consequently, many computational and machine learning approaches have been developed over the years to predict such interface residues from sequence. However, the effectiveness of different Deep Learning (DL) architectures and learning strategies for protein–protein, protein–nucleotide and protein–small molecule interface prediction has not yet been investigated in great detail. Therefore, we here explore the prediction of protein interface residues using six DL architectures and various learning strategies with sequence-derived input features. Results We constructed a large dataset dubbed BioDL, comprising protein–protein interactions from the PDB, and DNA/RNA and small molecule interactions from the BioLip database. We also constructed six DL architectures, and evaluated them on the BioDL benchmarks. This shows that no single architecture performs best on all instances. An ensemble architecture, which combines all six architectures, does consistently achieve peak prediction accuracy. We confirmed these results on the published benchmark set by Zhang and Kurgan (ZK448), and on our own existing curated homo- and heteromeric protein interaction dataset. Our PIPENN sequence-based ensemble predictor outperforms current state-of-the-art sequence-based protein interface predictors on ZK448 on all interaction types, achieving an AUC-ROC of 0.718 for protein–protein, 0.823 for protein–nucleotide and 0.842 for protein–small molecule. Availability and implementation Source code and datasets are available at https://github.com/ibivu/pipenn/. Supplementary information Supplementary data are available at Bioinformatics online.

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Zhuang, Chunlin, Zhongli Wu, Chengguo Xing, and Zhenyuan Miao. "Small molecules inhibiting Keap1–Nrf2 protein–protein interactions: a novel approach to activate Nrf2 function." MedChemComm 8, no. 2 (2017): 286–94. http://dx.doi.org/10.1039/c6md00500d.

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Wang, Lei, Lixiao Zhang, Li Li, et al. "Small-molecule inhibitor targeting the Hsp90-Cdc37 protein-protein interaction in colorectal cancer." Science Advances 5, no. 9 (2019): eaax2277. http://dx.doi.org/10.1126/sciadv.aax2277.

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Disrupting the interactions between Hsp90 and Cdc37 is emerging as an alternative and specific way to regulate the Hsp90 chaperone cycle in a manner not involving adenosine triphosphatase inhibition. Here, we identified DDO-5936 as a small-molecule inhibitor of the Hsp90-Cdc37 protein-protein interaction (PPI) in colorectal cancer. DDO-5936 disrupted the Hsp90-Cdc37 PPI both in vitro and in vivo via binding to a previously unknown site on Hsp90 involving Glu47, one of the binding determinants for the Hsp90-Cdc37 PPI, leading to selective down-regulation of Hsp90 kinase clients in HCT116 cells. In addition, inhibition of Hsp90-Cdc37 complex formation by DDO-5936 resulted in a remarkable cyclin-dependent kinase 4 decrease and consequent inhibition of cell proliferation through Cdc37-dependent cell cycle arrest. Together, our results demonstrated DDO-5936 as an identified specific small-molecule inhibitor of the Hsp90-Cdc37 PPI that could be used to comprehensively investigate alternative approaches targeting Hsp90 chaperone cycles for cancer therapy.

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Mai, Deborah, Jennifer Jones, John W. Rodgers, John L. Hartman, Olaf Kutsch, and Adrie J. C. Steyn. "A Screen to Identify Small Molecule Inhibitors of Protein–Protein Interactions in Mycobacteria." ASSAY and Drug Development Technologies 9, no. 3 (2011): 299–310. http://dx.doi.org/10.1089/adt.2010.0326.

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Kaplan, Andrew, Barbara Morquette, Antje Kroner, et al. "Small-Molecule Stabilization of 14-3-3 Protein-Protein Interactions Stimulates Axon Regeneration." Neuron 93, no. 5 (2017): 1082–93. http://dx.doi.org/10.1016/j.neuron.2017.02.018.

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Thiel, Philipp, Markus Kaiser, and Christian Ottmann. "Small-Molecule Stabilization of Protein-Protein Interactions: An Underestimated Concept in Drug Discovery?" Angewandte Chemie International Edition 51, no. 9 (2012): 2012–18. http://dx.doi.org/10.1002/anie.201107616.

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Zhang, Changsheng, and Luhua Lai. "Towards structure-based protein drug design." Biochemical Society Transactions 39, no. 5 (2011): 1382–86. http://dx.doi.org/10.1042/bst0391382.

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Structure-based drug design for chemical molecules has been widely used in drug discovery in the last 30 years. Many successful applications have been reported, especially in the field of virtual screening based on molecular docking. Recently, there has been much progress in fragment-based as well as de novo drug discovery. As many protein–protein interactions can be used as key targets for drug design, one of the solutions is to design protein drugs based directly on the protein complexes or the target structure. Compared with protein–ligand interactions, protein–protein interactions are more complicated and present more challenges for design. Over the last decade, both sampling efficiency and scoring accuracy of protein–protein docking have increased significantly. We have developed several strategies for structure-based protein drug design. A grafting strategy for key interaction residues has been developed and successfully applied in designing erythropoietin receptor-binding proteins. Similarly to small-molecule design, we also tested de novo protein-binder design and a virtual screen of protein binders using protein–protein docking calculations. In comparison with the development of structure-based small-molecule drug design, we believe that structure-based protein drug design has come of age.

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Zhu, Yan, and Xiche Hu. "Molecular Recognition of FDA-Approved Small Molecule Protein Kinase Drugs in Protein Kinases." Molecules 27, no. 20 (2022): 7124. http://dx.doi.org/10.3390/molecules27207124.

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Abstract:

Protein kinases are key enzymes that catalyze the covalent phosphorylation of substrates via the transfer of the γ-phosphate of ATP, playing a crucial role in cellular proliferation, differentiation, and various cell regulatory processes. Due to their pivotal cellular role, the aberrant function of kinases has been associated with cancers and many other diseases. Consequently, competitive inhibition of the ATP binding site of protein kinases has emerged as an effective means of curing these diseases. Decades of intense development of protein kinase inhibitors (PKIs) resulted in 71 FDA-approved PKI drugs that target dozens of protein kinases for the treatment of various diseases. How do FDA-approved protein kinase inhibitor PKI drugs compete with ATP in their own binding pocket? This is the central question we attempt to address in this work. Based on modes of non-bonded interactions and their calculated interaction strengths by means of the advanced double hybrid DFT method B2PLYP, the molecular recognition of PKI drugs in the ATP-binding pockets was systematically analyzed. It was found that (1) all the FDA-approved PKI drugs studied here form one or more hydrogen bond(s) with the backbone amide N, O atoms in the hinge region of the ATP binding site, mimicking the adenine base; (2) all the FDA-approved PKI drugs feature two or more aromatic rings. The latter reach far and deep into the hydrophobic regions I and II, forming multiple CH-π interactions with aliphatic residues L(3), V(11), A(15), V(36), G(51), L(77) and π-π stacking interactions with aromatic residues F(47) and F(82), but ATP itself does not utilize these regions extensively; (3) all FDA-approved PKI drugs studied here have one thing in common, i.e., they frequently formed non-bonded interactions with a total of 12 residues L(3),V(11), A(15), K(17), E(24),V(36),T(45), F(47), G(51), L(77), D(81) and F(82) in the ATP binding. Many of those 12 commonly involved residues are highly conserved residues with important structural and catalytic functional roles. K(17) and E(24) are the two highly conserved residues crucial for the catalytic function of kinases. D(81) and F(82) belong to the DFG motif; T(45) was dubbed the gate keeper residue. F(47) is located on the hinge region and G(51) sits on the linker that connects the hinge to the αD-helix. It is this targeting of highly conserved residues in protein kinases that led to promiscuous PKI drugs that lack selectivity. Although the formation of hydrogen bond(s) with the backbone of the hinge gives PKI drugs the added binding affinity and the much-needed directionality, selectivity is sacrificed. That is why so many FDA-approved PKI drugs are known to have multiple targets. Moreover, off-target-mediated toxicity caused by a lack of selectivity was one of the major challenges facing the PKI drug discovery community. This work suggests a road map for future PKI drug design, i.e., targeting non-conserved residues in the ATP binding pocket to gain better selectivity so as to avoid off-target-mediated toxicity.

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Journal articles on the topic 'Small molecule-protein interactions'

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