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Journal articles on the topic 'Protein discovery'

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Author: Grafiati

Published: 10 December 2022

Last updated: 29 January 2023

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1

Cheng, Miaomiao, Lizhen Liu, Hanshi Wang, Chao Du, and Wei Song. "Essential Proteins Discovery from Weighted Protein–Protein Interaction Networks." Journal of Bionanoscience 8, no. 4 (August 1, 2014): 293–97. http://dx.doi.org/10.1166/jbns.2014.1239.

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2

Oláh, Judit, Tibor Szénási, Attila Lehotzky, Victor Norris, and Judit Ovádi. "Challenges in Discovering Drugs That Target the Protein–Protein Interactions of Disordered Proteins." International Journal of Molecular Sciences 23, no. 3 (January 28, 2022): 1550. http://dx.doi.org/10.3390/ijms23031550.

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Protein–protein interactions (PPIs) outnumber proteins and are crucial to many fundamental processes; in consequence, PPIs are associated with several pathological conditions including neurodegeneration and modulating them by drugs constitutes a potentially major class of therapy. Classically, however, the discovery of small molecules for use as drugs entails targeting individual proteins rather than targeting PPIs. This is largely because discovering small molecules to modulate PPIs has been seen as extremely challenging. Here, we review the difficulties and limitations of strategies to discover drugs that target PPIs directly or indirectly, taking as examples the disordered proteins involved in neurodegenerative diseases.

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3

Li, Meijing, Tsendsuren Munkhdalai, Xiuming Yu, and Keun Ho Ryu. "A Novel Approach for Protein-Named Entity Recognition and Protein-Protein Interaction Extraction." Mathematical Problems in Engineering 2015 (2015): 1–10. http://dx.doi.org/10.1155/2015/942435.

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Many researchers focus on developing protein-named entity recognition (Protein-NER) or PPI extraction systems. However, the studies about these two topics cannot be merged well; then existing PPI extraction systems’ Protein-NER still needs to improve. In this paper, we developed the protein-protein interaction extraction system named PPIMiner based on Support Vector Machine (SVM) and parsing tree. PPIMiner consists of three main models: natural language processing (NLP) model, Protein-NER model, and PPI discovery model. The Protein-NER model, which is named ProNER, identifies the protein names based on two methods: dictionary-based method and machine learning-based method. ProNER is capable of identifying more proteins than dictionary-based Protein-NER model in other existing systems. The final discovered PPIs extracted via PPI discovery model are represented in detail because we showed the protein interaction types and the occurrence frequency through two different methods. In the experiments, the result shows that the performances achieved by our ProNER and PPI discovery model are better than other existing tools. PPIMiner applied this protein-named entity recognition approach and parsing tree based PPI extraction method to improve the performance of PPI extraction. We also provide an easy-to-use interface to access PPIs database and an online system for PPIs extraction and Protein-NER.

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4

Fischer, P. "Protein-Protein Interactions in Drug Discovery." Drug Design Reviews - Online 2, no. 3 (May 1, 2005): 179–207. http://dx.doi.org/10.2174/1567269053828837.

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5

Llabrés, Mercè, and Gabriel Valiente. "Alignment of virus-host protein-protein interaction networks by integer linear programming: SARS-CoV-2." PLOS ONE 15, no. 12 (December 7, 2020): e0236304. http://dx.doi.org/10.1371/journal.pone.0236304.

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Motivation Beside socio-economic issues, coronavirus pandemic COVID-19, the infectious disease caused by the newly discovered coronavirus SARS-CoV-2, has caused a deep impact in the scientific community, that has considerably increased its effort to discover the infection strategies of the new virus. Among the extensive and crucial research that has been carried out in the last months, the analysis of the virus-host relationship plays an important role in drug discovery. Virus-host protein-protein interactions are the active agents in virus replication, and the analysis of virus-host protein-protein interaction networks is fundamental to the study of the virus-host relationship. Results We have adapted and implemented a recent integer linear programming model for protein-protein interaction network alignment to virus-host networks, and obtained a consensus alignment of the SARS-CoV-1 and SARS-CoV-2 virus-host protein-protein interaction networks. Despite the lack of shared human proteins in these virus-host networks, and the low number of preserved virus-host interactions, the consensus alignment revealed aligned human proteins that share a function related to viral infection, as well as human proteins of high functional similarity that interact with SARS-CoV-1 and SARS-CoV-2 proteins, whose alignment would preserve these virus-host interactions.

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6

Huston, James S. "Antibody discovery and the arrow of time." Protein Engineering, Design and Selection 31, no. 7-8 (July 1, 2018): 231–32. http://dx.doi.org/10.1093/protein/gzy026.

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7

Zhu, LingZhi, Junling Zhang, Lingya He, Jun Wang, Zhenwu Peng, and Zixin Jian. "Essential Proteins Discovery Methods based on the Protein-Protein Interaction Networks." American Journal of Biochemistry and Biotechnology 13, no. 4 (April 1, 2017): 242–51. http://dx.doi.org/10.3844/ajbbsp.2017.242.251.

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8

B, Joshi, Boraste A, Khairnar Y, Vamsi KK, Jhadav A, Patil P, Trivedi S, et al. "Protein Based Drug Discovery." International Journal of Drug Discovery 1, no. 2 (December 30, 2009): 40–51. http://dx.doi.org/10.9735/0975-4423.1.2.40-51.

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9

Perry, Sarah. "Protein discovery goes global." Nature Methods 12, S1 (September 10, 2015): 19. http://dx.doi.org/10.1038/nmeth.3534.

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10

Stein, Richard A. "Protein-Specific Discovery Strategies." Genetic Engineering & Biotechnology News 34, no. 6 (March 15, 2014): 1, 12, 13, 15. http://dx.doi.org/10.1089/gen.34.06.01.

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11

Glick, Yair, Ya’ara Ben-Ari, Nir Drayman, Michal Pellach, Gregory Neveu, Jim Boonyaratanakornkit, Dorit Avrahami, Shirit Einav, Ariella Oppenheim, and Doron Gerber. "Pathogen receptor discovery with a microfluidic human membrane protein array." Proceedings of the National Academy of Sciences 113, no. 16 (April 4, 2016): 4344–49. http://dx.doi.org/10.1073/pnas.1518698113.

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The discovery of how a pathogen invades a cell requires one to determine which host cell receptors are exploited. This determination is a challenging problem because the receptor is invariably a membrane protein, which represents an Achilles heel in proteomics. We have developed a universal platform for high-throughput expression and interaction studies of membrane proteins by creating a microfluidic-based comprehensive human membrane protein array (MPA). The MPA is, to our knowledge, the first of its kind and offers a powerful alternative to conventional proteomics by enabling the simultaneous study of 2,100 membrane proteins. We characterized direct interactions of a whole nonenveloped virus (simian virus 40), as well as those of the hepatitis delta enveloped virus large form antigen, with candidate host receptors expressed on the MPA. Selected newly discovered membrane protein–pathogen interactions were validated by conventional methods, demonstrating that the MPA is an important tool for cellular receptor discovery and for understanding pathogen tropism.

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12

Celis, Sergio, Fruzsina Hobor, Thomas James, Gail J. Bartlett, Amaurys A. Ibarra, Deborah K. Shoemark, Zsófia Hegedüs, et al. "Query-guided protein–protein interaction inhibitor discovery." Chemical Science 12, no. 13 (2021): 4753–62. http://dx.doi.org/10.1039/d1sc00023c.

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13

Nero, Tracy L., Michael W. Parker, and Craig J. Morton. "Protein structure and computational drug discovery." Biochemical Society Transactions 46, no. 5 (September 21, 2018): 1367–79. http://dx.doi.org/10.1042/bst20180202.

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The first protein structures revealed a complex web of weak interactions stabilising the three-dimensional shape of the molecule. Small molecule ligands were then found to exploit these same weak binding events to modulate protein function or act as substrates in enzymatic reactions. As the understanding of ligand–protein binding grew, it became possible to firstly predict how and where a particular small molecule might interact with a protein, and then to identify putative ligands for a specific protein site. Computer-aided drug discovery, based on the structure of target proteins, is now a well-established technique that has produced several marketed drugs. We present here an overview of the various methodologies being used for structure-based computer-aided drug discovery and comment on possible future developments in the field.

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14

Zhou, Yu, Hao Zou, Christina Yau, Lequn Zhao, Steven C. Hall, Daryl C. Drummond, Shauna Farr-Jones, John W. Park, Christopher C. Benz, and James D. Marks. "Discovery of internalizing antibodies to basal breast cancer cells." Protein Engineering, Design and Selection 31, no. 1 (December 28, 2017): 17–28. http://dx.doi.org/10.1093/protein/gzx063.

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15

He, M., and M. J. Taussig. "Rapid discovery of protein interactions by cell-free protein technologies." Biochemical Society Transactions 35, no. 5 (October 25, 2007): 962–65. http://dx.doi.org/10.1042/bst0350962.

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Cell-free transcription and translation provides an open, controllable environment for production of correctly folded, soluble proteins and allows the rapid generation of proteins from DNA without the need for cloning. Thus it is becoming an increasingly attractive alternative to conventional in vivo expression systems, especially when parallel expression of multiple proteins is required. Through novel design and exploitation, powerful cell-free technologies of ribosome display and protein in situ arrays have been developed for in vitro production and isolation of protein-binding molecules from large libraries. These technologies can be combined for rapid detection of protein interactions.

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16

Zhang, Changsheng, Bo Tang, Qian Wang, and Luhua Lai. "Discovery of binding proteins for a protein target using protein-protein docking-based virtual screening." Proteins: Structure, Function, and Bioinformatics 82, no. 10 (June 3, 2014): 2472–82. http://dx.doi.org/10.1002/prot.24611.

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17

Liu, Y., W. Gong, J. Breinholt, L. Norskov-Lauritsen, J. Zhang, Q. Ma, J. Chen, et al. "Discovery of the improved antagonistic prolactin variants by library screening." Protein Engineering Design and Selection 24, no. 11 (September 27, 2011): 855–60. http://dx.doi.org/10.1093/protein/gzr047.

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18

Milligan, G., G. Feng, R. Ward, N. Sartania, D. Ramsay, A. McLean, and J. Carrillo. "G Protein-Coupled Receptor Fusion Proteins in Drug Discovery." Current Pharmaceutical Design 10, no. 17 (July 1, 2004): 1989–2001. http://dx.doi.org/10.2174/1381612043384295.

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19

Patrone, James D., J. Phillip Kennedy, Andreas O. Frank, Michael D. Feldkamp, Bhavatarini Vangamudi, Nicholas F. Pelz, Olivia W. Rossanese, Alex G. Waterson, Walter J. Chazin, and Stephen W. Fesik. "Discovery of Protein–Protein Interaction Inhibitors of Replication Protein A." ACS Medicinal Chemistry Letters 4, no. 7 (May 21, 2013): 601–5. http://dx.doi.org/10.1021/ml400032y.

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20

Liu, Lizhen, Miaomiao Cheng, Hanshi Wang, and Wei Song. "Complexes Discovery from Weighted Protein–Protein Interaction Networks." Journal of Bionanoscience 9, no. 1 (February 1, 2015): 55–62. http://dx.doi.org/10.1166/jbns.2015.1275.

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21

Andrei, Sebastian A., Eline Sijbesma, Michael Hann, Jeremy Davis, Gavin O’Mahony, Matthew W. D. Perry, Anna Karawajczyk, et al. "Stabilization of protein-protein interactions in drug discovery." Expert Opinion on Drug Discovery 12, no. 9 (July 11, 2017): 925–40. http://dx.doi.org/10.1080/17460441.2017.1346608.

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22

Boucher, Lauren E., Daisy D. Colon Lopez, Alexia S. Miller, Serge M. Stamm, and Jürgen Bosch. "Targeting Protein-Protein-Interactions for Antimalarial Drug Discovery." Biophysical Journal 108, no. 2 (January 2015): 148a. http://dx.doi.org/10.1016/j.bpj.2014.11.818.

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23

Canduri, Fernanda, and Walter de Azevedo Jr. "Protein Crystallography in Drug Discovery." Current Drug Targets 9, no. 12 (December 1, 2008): 1048–53. http://dx.doi.org/10.2174/138945008786949423.

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24

Ullrich, Florian. "Cryo-EM for protein discovery." Nature Structural & Molecular Biology 28, no. 12 (December 2021): 958. http://dx.doi.org/10.1038/s41594-021-00701-7.

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25

Selinger, Zvi. "Discovery of G Protein Signaling." Annual Review of Biochemistry 77, no. 1 (June 2008): 1–13. http://dx.doi.org/10.1146/annurev.biochem.76.082906.094316.

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26

Ranade, Vasant. "Protein Crystallography in Drug Discovery." American Journal of Therapeutics 11, no. 3 (May 2004): 242. http://dx.doi.org/10.1097/00045391-200405000-00016.

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27

Cirillo, Davide, Carmen Maria Livi, Federico Agostini, and Gian Gaetano Tartaglia. "Discovery of protein–RNA networks." Mol. BioSyst. 10, no. 7 (2014): 1632–42. http://dx.doi.org/10.1039/c4mb00099d.

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28

Conklin, Darrell. "Machine discovery of protein motifs." Machine Learning 21, no. 1-2 (1995): 125–50. http://dx.doi.org/10.1007/bf00993382.

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29

Tsutsumi, Ryouhei, Yuko Fukata, and Masaki Fukata. "Discovery of protein-palmitoylating enzymes." Pflügers Archiv - European Journal of Physiology 456, no. 6 (January 30, 2008): 1199–206. http://dx.doi.org/10.1007/s00424-008-0465-x.

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30

Berman, Helen. "Protein crystallography in drug discovery." Biochemistry and Molecular Biology Education 32, no. 4 (July 2004): 285–86. http://dx.doi.org/10.1002/bmb.2004.494032049995.

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31

Ciulli, Alessio, and William Farnaby. "Protein degradation for drug discovery." Drug Discovery Today: Technologies 31 (April 2019): 1–3. http://dx.doi.org/10.1016/j.ddtec.2019.04.002.

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32

Maziarz, Marcin, Stefan Broselid, Vincent DiGiacomo, Jong-Chan Park, Alex Luebbers, Lucia Garcia-Navarrete, Juan B. Blanco-Canosa, George S. Baillie, and Mikel Garcia-Marcos. "A biochemical and genetic discovery pipeline identifies PLCδ4b as a nonreceptor activator of heterotrimeric G-proteins." Journal of Biological Chemistry 293, no. 44 (September 7, 2018): 16964–83. http://dx.doi.org/10.1074/jbc.ra118.003580.

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Recent evidence has revealed that heterotrimeric G-proteins can be activated by cytoplasmic proteins that share an evolutionarily conserved sequence called the Gα-binding-and-activating (GBA) motif. This mechanism provides an alternative to canonical activation by G-protein–coupled receptors (GPCRs) and plays important roles in cell function, and its dysregulation is linked to diseases such as cancer. Here, we describe a discovery pipeline that uses biochemical and genetic approaches to validate GBA candidates identified by sequence similarity. First, putative GBA motifs discovered in bioinformatics searches were synthesized on peptide arrays and probed in batch for Gαi3 binding. Then, cDNAs encoding proteins with Gαi3-binding sequences were expressed in a genetically-modified yeast strain that reports mammalian G-protein activity in the absence of GPCRs. The resulting GBA motif candidates were characterized by comparison of their biochemical, structural, and signaling properties with those of all previously described GBA motifs in mammals (GIV/Girdin, DAPLE, Calnuc, and NUCB2). We found that the phospholipase Cδ4 (PLCδ4) GBA motif binds G-proteins with high affinity, has guanine nucleotide exchange factor activity in vitro, and activates G-protein signaling in cells, as indicated by bioluminescence resonance energy transfer (BRET)-based biosensors of G-protein activity. Interestingly, the PLCδ4 isoform b (PLCδ4b), which lacks the domains required for PLC activity, bound and activated G-proteins more efficiently than the full-length isoform a, suggesting that PLCδ4b functions as a G-protein regulator rather than as a PLC. In summary, we have identified PLCδ4 as a nonreceptor activator of G-proteins and established an experimental pipeline to discover and characterize GBA motif–containing proteins.

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33

He, Huiqin, Benquan Liu, Hongyi Luo, Tingting Zhang, and Jingwei Jiang. "Big data and artificial intelligence discover novel drugs targeting proteins without 3D structure and overcome the undruggable targets." Stroke and Vascular Neurology 5, no. 4 (March 29, 2020): 381–87. http://dx.doi.org/10.1136/svn-2019-000323.

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The discovery of targeted drugs heavily relies on three-dimensional (3D) structures of target proteins. When the 3D structure of a protein target is unknown, it is very difficult to design its corresponding targeted drugs. Although the 3D structures of some proteins (the so-called undruggable targets) are known, their targeted drugs are still absent. As increasing crystal/cryogenic electron microscopy structures are deposited in Protein Data Bank, it is much more possible to discover the targeted drugs. Moreover, it is also highly probable to turn previous undruggable targets into druggable ones when we identify their hidden allosteric sites. In this review, we focus on the currently available advanced methods for the discovery of novel compounds targeting proteins without 3D structure and how to turn undruggable targets into druggable ones.

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34

Rogers, J., R. J. Schoepp, O. Schroder, T. L. Clements, T. F. Holland, J. Q. Li, J. Li, et al. "Rapid discovery and optimization of therapeutic antibodies against emerging infectious diseases." Protein Engineering Design and Selection 21, no. 8 (May 13, 2008): 495–505. http://dx.doi.org/10.1093/protein/gzn027.

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35

Stafford, R. L., M. L. Matsumoto, G. Yin, Q. Cai, J. J. Fung, H. Stephenson, A. Gill, et al. "In vitro Fab display: a cell-free system for IgG discovery." Protein Engineering Design and Selection 27, no. 4 (February 28, 2014): 97–109. http://dx.doi.org/10.1093/protein/gzu002.

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36

Nomura, Daniel K. "Abstract SY17-03: Reimagining druggability using chemoproteomic platforms." Cancer Research 82, no. 12_Supplement (June 15, 2022): SY17–03—SY17–03. http://dx.doi.org/10.1158/1538-7445.am2022-sy17-03.

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Abstract The Nomura Research Group is focused on reimagining druggability using chemoproteomic platforms to develop transformative medicines. One of the greatest challenges that we face in discovering new disease therapies is that most proteins are considered “undruggable,” in that most proteins do not possess known binding pockets or “ligandable hotspots” that small-molecules can bind to modulate protein function. Our research group addresses this challenge by advancing and applying chemoproteomic platforms to discover and pharmacologically target unique and novel ligandable hotspots for disease therapy. We currently have three major research directions. Our first major focus is on developing and applying chemoproteomics-enabled covalent ligand discovery approaches to rapidly discover small-molecule therapeutic leads that target unique and novel ligandable hotspots for undruggable protein targets and pathways. Our second research area focuses on using chemoproteomic platforms to expand the scope of targeted protein degradation technologies. Our third research area focuses on using chemoproteomics-enabled covalent ligand discovery platforms to develop new induced proximity-based therapeutic modalities. Collectively, our lab is focused on developing next-generation transformative medicines through pioneering innovative chemical technologies to overcome challenges in drug discovery. Citation Format: Daniel K. Nomura. Reimagining druggability using chemoproteomic platforms [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr SY17-03.

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37

Akondi, Kalyana Bharati, Marianne Paolini-Bertrand, and Oliver Hartley. "Precision-engineered Peptide and Protein Analogs: Establishing a New Discovery Platform for Potent GPCR Modulators." CHIMIA International Journal for Chemistry 75, no. 6 (June 30, 2021): 489–94. http://dx.doi.org/10.2533/chimia.2021.489.

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Numerous members of the human G protein-coupled receptor (GPCR) superfamily are receptors of therapeutic interest. GPCRs are considered to be highly tractable for drug discovery, representing the targets of approximately one-third of currently licensed drugs. These successful drug discovery outcomes cover only a relatively small subset of the superfamily, however, and many other attractive receptors have proven to present significant challenges. Among these difficult GPCRs are those whose natural ligands are peptides and proteins. In this review we explain the obstacles faced by GPCR drug discovery campaigns, with particular focus on those related to peptide and protein GPCRs. We describe a novel and promising approach for these targets based on engineering of their natural ligands and describe an integrated discovery platform that allows potent ligand analogs to be discovered rapidly and efficiently. Finally, we present a case study involving the chemokine receptor CCR5 to show that this approach can be used to generate new drugs for peptide and protein GPCR targets combining best-in-class potency with tunable signaling activity.

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38

Walter, Peter. "Walking Along the Serendipitous Path of Discovery." Molecular Biology of the Cell 21, no. 1 (January 2010): 15–17. http://dx.doi.org/10.1091/mbc.e09-08-0662.

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Deciphering of the molecular mechanism of the “unfolded protein response” (UPR) provides a wonderful example of how serendipity can shape scientific discovery. Secretory and membrane proteins begin their journey to the cell surface in the endoplasmic reticulum (ER). Before leaving the organelle, proteins are quality-controlled, and only properly folded proteins are transported onwards. The UPR detects an insufficiency in the protein-folding capacity in the ER and in the ways of a finely tuned homeostat adjusts organelle abundance according to need. If the protein-folding defect in the ER cannot be corrected, the UPR switches from a cell-protective to a cell-destructive mode and activates apoptosis in metazoan cells. Such life or death decisions position the UPR in the center of numerous pathologies, including viral infection, protein-folding diseases, diabetes, and cancer. The UPR proved to be a rich field for serendipitous discovery because the molecular machines that transmit information about insufficient protein folding and activate appropriate gene expression programs function in unusual, unprecedented ways. A key regulatory switch in the UPR, for example, is a cytoplasmic, nonconventional mRNA spicing reaction, initiated by a bifunctional transmembrane kinase/endoribonuclease.

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39

Jain, K. K. "Proteomics-based Anticancer Drug Discovery and Development." Technology in Cancer Research & Treatment 1, no. 4 (August 2002): 231–36. http://dx.doi.org/10.1177/153303460200100403.

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Proteins are important targets for drug discovery and this applied to cancer as well because there is a defect in the protein machinery of the cell in malignancy. Proteomic technologies are now being integrated with genomic approaches for cancer drug discovery and target validation. Among the large number of proteomic technologies available for this purpose, the most important ones are 3-D protein structure determination, protein microarrays, laser capture microdissection and study of protein-protein and protein-drug interactions. Cancer biomarkers and several cell pathways are important drug targets. Several companies are involved in using proteomic technologies for drug discovery. Finally, proteomic approaches will play an important role in the discovery and development of personalized medicines.

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40

Patrone, James D., Alex G. Waterson, and Stephen W. Fesik. "Recent advancements in the discovery of protein–protein interaction inhibitors of replication protein A." MedChemComm 8, no. 2 (2017): 259–67. http://dx.doi.org/10.1039/c6md00460a.

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This review summarizes recent work directed toward the discovery of selective inhibitors of the protein–protein interactions between RPA and proteins involved in the initiation of DNA damage response pathways.

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41

Slater, Olivia, Bethany Miller, and Maria Kontoyianni. "Decoding Protein-protein Interactions: An Overview." Current Topics in Medicinal Chemistry 20, no. 10 (May 19, 2020): 855–82. http://dx.doi.org/10.2174/1568026620666200226105312.

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Drug discovery has focused on the paradigm “one drug, one target” for a long time. However, small molecules can act at multiple macromolecular targets, which serves as the basis for drug repurposing. In an effort to expand the target space, and given advances in X-ray crystallography, protein-protein interactions have become an emerging focus area of drug discovery enterprises. Proteins interact with other biomolecules and it is this intricate network of interactions that determines the behavior of the system and its biological processes. In this review, we briefly discuss networks in disease, followed by computational methods for protein-protein complex prediction. Computational methodologies and techniques employed towards objectives such as protein-protein docking, protein-protein interactions, and interface predictions are described extensively. Docking aims at producing a complex between proteins, while interface predictions identify a subset of residues on one protein that could interact with a partner, and protein-protein interaction sites address whether two proteins interact. In addition, approaches to predict hot spots and binding sites are presented along with a representative example of our internal project on the chemokine CXC receptor 3 B-isoform and predictive modeling with IP10 and PF4.

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42

Korf, Ulrike, and Stefan Wiemann. "Protein microarrays as a discovery tool for studying protein–protein interactions." Expert Review of Proteomics 2, no. 1 (January 2005): 13–26. http://dx.doi.org/10.1586/14789450.2.1.13.

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43

Gemovic, Branislava, Neven Sumonja, Radoslav Davidovic, Vladimir Perovic, and Nevena Veljkovic. "Mapping of Protein-Protein Interactions: Web-Based Resources for Revealing Interactomes." Current Medicinal Chemistry 26, no. 21 (September 19, 2019): 3890–910. http://dx.doi.org/10.2174/0929867325666180214113704.

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Background: The significant number of protein-protein interactions (PPIs) discovered by harnessing concomitant advances in the fields of sequencing, crystallography, spectrometry and two-hybrid screening suggests astonishing prospects for remodelling drug discovery. The PPI space which includes up to 650 000 entities is a remarkable reservoir of potential therapeutic targets for every human disease. In order to allow modern drug discovery programs to leverage this, we should be able to discern complete PPI maps associated with a specific disorder and corresponding normal physiology. Objective: Here, we will review community available computational programs for predicting PPIs and web-based resources for storing experimentally annotated interactions. Methods: We compared the capacities of prediction tools: iLoops, Struck2Net, HOMCOS, COTH, PrePPI, InterPreTS and PRISM to predict recently discovered protein interactions. Results: We described sequence-based and structure-based PPI prediction tools and addressed their peculiarities. Additionally, since the usefulness of prediction algorithms critically depends on the quality and quantity of the experimental data they are built on; we extensively discussed community resources for protein interactions. We focused on the active and recently updated primary and secondary PPI databases, repositories specialized to the subject or species, as well as databases that include both experimental and predicted PPIs. Conclusion: PPI complexes are the basis of important physiological processes and therefore, possible targets for cell-penetrating ligands. Reliable computational PPI predictions can speed up new target discoveries through prioritization of therapeutically relevant protein–protein complexes for experimental studies.

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44

Carro, Laura. "Protein–protein interactions in bacteria: a promising and challenging avenue towards the discovery of new antibiotics." Beilstein Journal of Organic Chemistry 14 (November 21, 2018): 2881–96. http://dx.doi.org/10.3762/bjoc.14.267.

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Antibiotics are potent pharmacological weapons against bacterial infections; however, the growing antibiotic resistance of microorganisms is compromising the efficacy of the currently available pharmacotherapies. Even though antimicrobial resistance is not a new problem, antibiotic development has failed to match the growth of resistant pathogens and hence, it is highly critical to discover new anti-infective drugs with novel mechanisms of action which will help reducing the burden of multidrug-resistant microorganisms. Protein–protein interactions (PPIs) are involved in a myriad of vital cellular processes and have become an attractive target to treat diseases. Therefore, targeting PPI networks in bacteria may offer a new and unconventional point of intervention to develop novel anti-infective drugs which can combat the ever-increasing rate of multidrug-resistant bacteria. This review describes the progress achieved towards the discovery of molecules that disrupt PPI systems in bacteria for which inhibitors have been identified and whose targets could represent an alternative lead discovery strategy to obtain new anti-infective molecules.

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Jubb, Harry, Alicia P. Higueruelo, Anja Winter, and Tom L. Blundell. "Structural biology and drug discovery for protein–protein interactions." Trends in Pharmacological Sciences 33, no. 5 (May 2012): 241–48. http://dx.doi.org/10.1016/j.tips.2012.03.006.

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Cukuroglu, Engin, H. Billur Engin, Attila Gursoy, and Ozlem Keskin. "Hot spots in protein–protein interfaces: Towards drug discovery." Progress in Biophysics and Molecular Biology 116, no. 2-3 (November 2014): 165–73. http://dx.doi.org/10.1016/j.pbiomolbio.2014.06.003.

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Whitehurst, Charles E., Naim Nazef, D. Allen Annis, Yongmin Hou, Denise M. Murphy, Peter Spacciapoli, Zhiping Yao, et al. "Discovery and Characterization of Orthosteric and Allosteric Muscarinic M2 Acetylcholine Receptor Ligands by Affinity Selection-Mass Spectrometry." Journal of Biomolecular Screening 11, no. 2 (December 16, 2005): 194–207. http://dx.doi.org/10.1177/1087057105284340.

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Abstract:

Screening assays using target-based affinity selection coupled with high-sensitivity detection technologies to identify small-molecule hits from chemical libraries can provide a useful discovery approach that complements traditional assay systems. Affinity selection-mass spectrometry (AS-MS) is one such methodology that holds promise for providing selective and sensitive high-throughput screening platforms. Although AS-MS screening platforms have been used to discover small-molecule ligands of proteins from many target families, they have not yet been used routinely to screen integral membrane proteins. The authors present a proof-of-concept study using size exclusion chromatography coupled to AS-MS to perform a primary screen for small-molecule ligands of the purified muscarinic M2 acetylcholine receptor, a G-protein-coupled receptor. AS-MS is used to characterize the binding mechanisms of 2 newly discovered ligands. NGD-3350 is a novel M2-specific orthosteric antagonist of M2 function. NGD-3366 is an allosteric ligand with binding properties similar to the allosteric antagonist W-84, which decreases the dissociation rate of N-methyl-scopolamine from the M2 receptor. Binding properties of the ligands discerned from AS-MS assays agree with those from in vitro biochemical assays. The authors conclude that when used with appropriate small-molecule libraries, AS-MS may provide a useful high-throughput assay system for the discovery and characterization of all classes of integral membrane protein ligands, including allosteric modulators.

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Kim, Mi Joung, Seong Jun Lim, Youngmin Ko, Hye Eun Kwon, Joo Hee Jung, Hyunwook Kwon, Heounjeong Go, et al. "Urinary Exosomal Cystatin C and Lipopolysaccharide Binding Protein as Biomarkers for Antibody−Mediated Rejection after Kidney Transplantation." Biomedicines 10, no. 10 (September 21, 2022): 2346. http://dx.doi.org/10.3390/biomedicines10102346.

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We aimed to discover and validate urinary exosomal proteins as biomarkers for antibody−mediated rejection (ABMR) after kidney transplantation. Urine and for-cause biopsy samples from kidney transplant recipients were collected and categorized into the discovery cohort (n = 36) and a validation cohort (n = 65). Exosomes were isolated by stepwise ultra-centrifugation for proteomic analysis to discover biomarker candidates for ABMR (n = 12). Of 1820 exosomal proteins in the discovery cohort, four proteins were specifically associated with ABMR: cystatin C (CST3), serum paraoxonase/arylesterase 1, retinol-binding protein 4, and lipopolysaccharide−binding protein (LBP). In the validation cohort, the level of urinary exosomal LBP was significantly higher in the ABMR group (n = 25) compared with the T-cell-mediated rejection (TCMR) group and the no major abnormality (NOMOA) group. Urinary exosomal CST3 level was significantly higher in the ABMR group compared with the control and NOMOA groups. Immunohistochemical staining showed that LBP and CST3 in the glomerulus were more abundant in the ABMR group compared with other groups. The combined prediction probability of urinary exosomal LBP and CST3 was significantly correlated with summed LBP and CST3 intensity scores in the glomerulus and peritubular capillary as well as Banff g + ptc scores. Urinary exosomal CST3 and LBP could be potent biomarkers for ABMR after kidney transplantation.

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Persico, Marco, Antonio Dato, Nausicaa Orteca, Caterina Fattorusso, Ettore Novellino, Mirko Andreoli, and Cristiano Ferlini. "From Protein Communication to Drug Discovery." Current Topics in Medicinal Chemistry 15, no. 20 (July 7, 2015): 2019–31. http://dx.doi.org/10.2174/1568026615666150519102257.

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Blaskovich, Mark. "Drug Discovery and Protein Tyrosine Phosphatases." Current Medicinal Chemistry 16, no. 17 (June 1, 2009): 2095–176. http://dx.doi.org/10.2174/092986709788612693.

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