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Journal articles on the topic 'Protein; Ligands'

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Published: 4 June 2021

Last updated: 16 February 2022

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1

Karasev, Dmitry, Boris Sobolev, Alexey Lagunin, Dmitry Filimonov, and Vladimir Poroikov. "Prediction of Protein–ligand Interaction Based on Sequence Similarity and Ligand Structural Features." International Journal of Molecular Sciences 21, no. 21 (2020): 8152. http://dx.doi.org/10.3390/ijms21218152.

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Computationally predicting the interaction of proteins and ligands presents three main directions: the search of new target proteins for ligands, the search of new ligands for targets, and predicting the interaction of new proteins and new ligands. We proposed an approach providing the fuzzy classification of protein sequences based on the ligand structural features to analyze the latter most complicated case. We tested our approach on five protein groups, which represented promised targets for drug-like ligands and differed in functional peculiarities. The training sets were built with the or

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2

Southern, Craig, Jennifer M. Cook, Zaynab Neetoo-Isseljee та ін. "Screening β-Arrestin Recruitment for the Identification of Natural Ligands for Orphan G-Protein–Coupled Receptors". Journal of Biomolecular Screening 18, № 5 (2013): 599–609. http://dx.doi.org/10.1177/1087057113475480.

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A variety of G-protein–coupled receptor (GPCR) screening technologies have successfully partnered a number of GPCRs with their cognate ligands. GPCR-mediated β-arrestin recruitment is now recognized as a distinct intracellular signaling pathway, and ligand-receptor interactions may show a bias toward β-arrestin over classical GPCR signaling pathways. We hypothesized that the failure to identify native ligands for the remaining orphan GPCRs may be a consequence of biased β-arrestin signaling. To investigate this, we assembled 10 500 candidate ligands and screened 82 GPCRs using PathHunter β-arr

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Finkina, Ekaterina I., Daria N. Melnikova, Ivan V. Bogdanov, et al. "Impact of Different Lipid Ligands on the Stability and IgE-Binding Capacity of the Lentil Allergen Len c 3." Biomolecules 10, no. 12 (2020): 1668. http://dx.doi.org/10.3390/biom10121668.

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Previously, we isolated the lentil allergen Len c 3, belonging to the class of lipid transfer proteins, cross-reacting with the major peach allergen Pru p 3 and binding lipid ligands. In this work, the allergenic capacity of Len c 3 and effects of different lipid ligands on the protein stability and IgE-binding capacity were investigated. Impacts of pH and heat treating on ligand binding with Len c 3 were also studied. It was shown that the recombinant Len c 3 (rLen c 3) IgE-binding capacity is sensitive to heating and simulating of gastroduodenal digestion. While being heated or digested, the

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4

Raingeval, Claire, and Isabelle Krimm. "NMR investigation of protein–ligand interactions for G-protein coupled receptors." Future Medicinal Chemistry 11, no. 14 (2019): 1811–25. http://dx.doi.org/10.4155/fmc-2018-0312.

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In this review, we report NMR studies of ligand–GPCR interactions, including both ligand-observed and protein-observed NMR experiments. Published studies exemplify how NMR can be used as a powerful tool to design novel GPCR ligands and investigate the ligand-induced conformational changes of GPCRs. The strength of NMR also lies in its capability to explore the diverse signaling pathways and probe the allosteric modulation of these highly dynamic receptors. By offering unique opportunities for the identification, structural and functional characterization of GPCR ligands, NMR will likely play a

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Hutchens, T. W., and J. O. Porath. "Protein recognition of immobilized ligands: promotion of selective adsorption." Clinical Chemistry 33, no. 9 (1987): 1502–8. http://dx.doi.org/10.1093/clinchem/33.9.1502.

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Abstract We are using simple immobilized ligands to evaluate the biochemistry and mechanisms of selective, high-affinity, protein adsorption events. Several specific means have recently been developed to more selectively utilize the favorable entropy changes associated with the displacement of protein-bound water during the formation and stabilization of protein-ligand recognition events. For protein and peptide immobilization these include, besides hydrophobic interaction, for example, metal ion, pi-electron-mediated, and thiophilic interactions. This latter type of protein-ligand recognition

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6

Mary, Sophie, Jean-Alain Fehrentz, Marjorie Damian, et al. "How ligands and signalling proteins affect G-protein-coupled receptors' conformational landscape." Biochemical Society Transactions 41, no. 1 (2013): 144–47. http://dx.doi.org/10.1042/bst20120267.

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The dynamic character of GPCRs (G-protein-coupled receptors) is essential to their function. However, the details of how ligands and signalling proteins stabilize a receptor conformation to trigger the activation of a given signalling pathway remain largely unexplored. Multiple data, including recent results obtained with the purified ghrelin receptor, suggest a model where ligand efficacy and functional selectivity are directly related to different receptor conformations. Importantly, distinct effector proteins (G-proteins and arrestins) as well as ligands are likely to affect the conformatio

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7

Galano-Frutos, Juan J., M. Carmen Morón, and Javier Sancho. "The mechanism of water/ion exchange at a protein surface: a weakly bound chloride in Helicobacter pylori apoflavodoxin." Physical Chemistry Chemical Physics 17, no. 43 (2015): 28635–46. http://dx.doi.org/10.1039/c5cp04504e.

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8

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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9

Mehta, Simpi, and Seema R. Pathak. "INSILICO DRUG DESIGN AND MOLECULAR DOCKING STUDIES OF NOVEL COUMARIN DERIVATIVES AS ANTI-CANCER AGENTS." Asian Journal of Pharmaceutical and Clinical Research 10, no. 4 (2017): 335. http://dx.doi.org/10.22159/ajpcr.2017.v10i4.16826.

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Objective: Cancer is the major worldwide problem. It arises due to uncontrolled growth of cells. In the present study a series of novel coumarin derivatives were designed and computationallyoptimized to investigate the interaction between designed ligands and 10 pdb files of five selected proteins. The objective here was to analyse in silico anticancerous activity of designed ligands to reduce cost and time for getting novel anticancerous drug with minimum side effects.Methods: Docking studies were performed to find outmaximum interaction between designed ligands and selected five proteins usi

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10

GORETZKI, Lothar, and Barbara M. MUELLER. "Low-density-lipoprotein-receptor-related protein (LRP) interacts with a GTP-binding protein." Biochemical Journal 336, no. 2 (1998): 381–86. http://dx.doi.org/10.1042/bj3360381.

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The low-density-lipoprotein-receptor-related protein (LRP) binds and internalizes numerous ligands, including lipoproteins, proteinase–inhibitor complexes and others. We have shown previously that LRP-mediated ligand internalization is dependent on cAMP-dependent protein kinase (PKA) activity. Here, we investigated whether ligation of LRP increases the intracellular cAMP level and PKA activity via a stimulatory GTP-binding protein. Treatment of LRP-expressing cell lines with the LRP ligands lactoferrin or urokinase-type plasminogen activator caused a significant elevation in cAMP and stimulate

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11

Féau, Clémentine, Leggy A. Arnold, Aaron Kosinski, and R. Kiplin Guy. "A High-Throughput Ligand Competition Binding Assay for the Androgen Receptor and Other Nuclear Receptors." Journal of Biomolecular Screening 14, no. 1 (2008): 43–48. http://dx.doi.org/10.1177/1087057108326662.

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Standardized, automated ligand-binding assays facilitate evaluation of endocrine activities of environmental chemicals and identification of antagonists of nuclear receptor ligands. Many current assays rely on fluorescently labeled ligands that are significantly different from the native ligands. The authors describe a radiolabeled ligand competition scintillation proximity assay (SPA) for the androgen receptor (AR) using Ni-coated 384-well FlashPlates® and liganded AR-LBD protein. This highly reproducible, low-cost assay is well suited for automated high-throughput screening. In addition, the

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12

Shoda, Takuji, Nobumichi Ohoka, Genichiro Tsuji, et al. "Targeted Protein Degradation by Chimeric Compounds using Hydrophobic E3 Ligands and Adamantane Moiety." Pharmaceuticals 13, no. 3 (2020): 34. http://dx.doi.org/10.3390/ph13030034.

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Targeted protein degradation using small chimeric molecules, such as proteolysis-targeting chimeras (PROTACs) and specific and nongenetic inhibitors of apoptosis protein [IAP]-dependent protein erasers (SNIPERs), is a promising technology in drug discovery. We recently developed a novel class of chimeric compounds that recruit the aryl hydrocarbon receptor (AhR) E3 ligase complex and induce the AhR-dependent degradation of target proteins. However, these chimeras contain a hydrophobic AhR E3 ligand, and thus, degrade target proteins even in cells that do not express AhR. In this study, we synt

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13

Chavan, Tanmay, Merritt Maduke, and Kenton Swartz. "Protein ligands for studying ion channel proteins." Journal of General Physiology 149, no. 4 (2017): 407–11. http://dx.doi.org/10.1085/jgp.201711776.

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14

Karasev, Dmitry, Boris Sobolev, Alexey Lagunin, Dmitry Filimonov, and Vladimir Poroikov. "Prediction of Protein–Ligand Interaction Based on the Positional Similarity Scores Derived from Amino Acid Sequences." International Journal of Molecular Sciences 21, no. 1 (2019): 24. http://dx.doi.org/10.3390/ijms21010024.

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The affinity of different drug-like ligands to multiple protein targets reflects general chemical–biological interactions. Computational methods estimating such interactions analyze the available information about the structure of the targets, ligands, or both. Prediction of protein–ligand interactions based on pairwise sequence alignment provides reasonable accuracy if the ligands’ specificity well coincides with the phylogenic taxonomy of the proteins. Methods using multiple alignment require an accurate match of functionally significant residues. Such conditions may not be met in the case o

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15

Kuldeep Patel, Richa Dubey, Shaifali Soni, Jagdish Chandra Rathi, and Neerupma Dhiman. "Molecular Docking and ADME Study of Quinoline and Chalcone based Derivatives for Anti-Cancer Activity." International Journal of Research in Pharmaceutical Sciences 12, no. 3 (2021): 2252–64. http://dx.doi.org/10.26452/ijrps.v12i3.4849.

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Cancer is a big issue that affects people all over the world. It develops as a result of uncontrolled cell growth. The interaction between developed ligands and thymine phosphorylation was investigated in this study, which was computationally optimized. The aim of this study was to examine the anticancerous activity of designed ligands in thymine phosphorylation (PDB ID: 1UOU) in order to minimize the cost and time required to develop a novel anticancer drug with minimal side effects. All the designed ligands showed mild to excellent binding with proteins. Most of the ligands exhibited better

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16

Stefansson, S., M. Z. Kounnas, J. Henkin, et al. "gp330 on type II pneumocytes mediates endocytosis leading to degradation of pro-urokinase, plasminogen activator inhibitor-1 and urokinase-plasminogen activator inhibitor-1 complex." Journal of Cell Science 108, no. 6 (1995): 2361–68. http://dx.doi.org/10.1242/jcs.108.6.2361.

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Glycoprotein 330 (gp330) is a member of a family of receptors related to the low density lipoprotein receptor (LDLR). Although several ligands have been shown to bind gp330 in solid-phase assays, the ability of gp330 to mediate ligand endocytosis has not been demonstrated. To develop a cellular model for gp330 function we screened a variety of cultured cell lines and identified several that expressed this protein, including immortalized rat type II pneumocytes and a human and two rodent tumor cell lines. Using type II pneumocytes, endocytosis of a previously described gp330 ligand, urokinase (

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17

Marsh, Lorraine. "Strong Ligand-Protein Interactions Derived from Diffuse Ligand Interactions with Loose Binding Sites." BioMed Research International 2015 (2015): 1–6. http://dx.doi.org/10.1155/2015/746980.

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Many systems in biology rely on binding of ligands to target proteins in a single high-affinity conformation with a favorableΔG. Alternatively, interactions of ligands with protein regions that allow diffuse binding, distributed over multiple sites and conformations, can exhibit favorableΔGbecause of their higher entropy. Diffuse binding may be biologically important for multidrug transporters and carrier proteins. A fine-grained computational method for numerical integration of total bindingΔGarising from diffuse regional interaction of a ligand in multiple conformations using a Markov Chain

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18

Srb, Pavel, Michal Svoboda, Ladislav Benda, et al. "Capturing a dynamically interacting inhibitor by paramagnetic NMR spectroscopy." Physical Chemistry Chemical Physics 21, no. 10 (2019): 5661–73. http://dx.doi.org/10.1039/c9cp00416e.

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19

Nero, Tracy L., Michael W. Parker, and Craig J. Morton. "Protein structure and computational drug discovery." Biochemical Society Transactions 46, no. 5 (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 w

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20

Rother, Kristian, Mathias Dunkel, Elke Michalsky, et al. "A structural keystone for drug design." Journal of Integrative Bioinformatics 3, no. 1 (2006): 21–31. http://dx.doi.org/10.1515/jib-2006-19.

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Abstract 3D-structures of proteins and potential ligands are the cornerstones of rational drug design. The first brick to build upon is selecting a protein target and finding out whether biologically active compounds are known. Both tasks require more information than the structures themselves provide. For this purpose we have built a web resource bridging protein and ligand databases. It consists of three parts: i) A data warehouse on annotation of protein structures that integrates many well-known databases such as Swiss-Prot, SCOP, ENZYME and others. ii) A conformational library of structur

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21

Emsley, Paul. "Protein-Ligand Analysis and Validation." Acta Crystallographica Section A Foundations and Advances 70, a1 (2014): C1480. http://dx.doi.org/10.1107/s2053273314085192.

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A number of tools related to handling of ligands have been added to Coot in recent years - these include 2D depictions, ligand binding pocket layout and a ligand scoring system. Coot also incorporates a number interface to other tools (CCP4's Refmac, Molprobity's probe and reduce and the CCDC's Mogul) to generate score for protein ligand complexes. This scoring system has been applied to models (with data) from the PDB. The details of the ligand scoring, and its application to one's own complex structure will be discussed.

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22

Devgan, Manish. "STRUCTURE PREDICTION AND IN SILICO DESIGNING OF DRUGS AGAINST KALLIKREIN PROTEIN 12." International Journal of Current Pharmaceutical Research 9, no. 2 (2017): 64. http://dx.doi.org/10.22159/ijcpr.2017v9i2.17387.

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Objective: Human Kallikrein protein 12 (hK12) might serve as a novel diagnostic and prognostic biomarker, as well as a potential therapeutic target, in gastric cancer.Methods: In this work, a theoretical model of hK12 receptor protein was generated using the concepts of homology modeling and loop modeling. The resulting model was validated with Ramachandran plot analysis. The ligands generated with the help of Drug bank were docked against hK12 receptor protein using AutoDock Vina in PyRx 0.8. The structure of ligand DB04786 (Suramin), with least binding energy, was varied by using ACD/ChemSke

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23

Loftis, Alexander R., Genwei Zhang, Coralie Backlund, et al. "An in vivo selection-derived d-peptide for engineering erythrocyte-binding antigens that promote immune tolerance." Proceedings of the National Academy of Sciences 118, no. 34 (2021): e2101596118. http://dx.doi.org/10.1073/pnas.2101596118.

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When displayed on erythrocytes, peptides and proteins can drive antigen-specific immune tolerance. Here, we investigated a straightforward approach based on erythrocyte binding to promote antigen-specific tolerance to both peptides and proteins. We first identified a robust erythrocyte-binding ligand. A pool of one million fully d-chiral peptides was injected into mice, blood cells were isolated, and ligands enriched on these cells were identified using nano-liquid chromatography–tandem mass spectrometry. One round of selection yielded a murine erythrocyte-binding ligand with an 80 nM apparent

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24

Lu, Sumin, Wonjo Jang, Asuka Inoue, and Nevin A. Lambert. "Constitutive G protein coupling profiles of understudied orphan GPCRs." PLOS ONE 16, no. 4 (2021): e0247743. http://dx.doi.org/10.1371/journal.pone.0247743.

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A large number of GPCRs are potentially valuable drug targets but remain understudied. Many of these lack well-validated activating ligands and are considered “orphan” receptors, and G protein coupling profiles have not been defined for many orphan GPCRs. Here we asked if constitutive receptor activity can be used to determine G protein coupling profiles of orphan GPCRs. We monitored nucleotide-sensitive interactions between 48 understudied orphan GPCRs and five G proteins (240 combinations) using bioluminescence resonance energy transfer (BRET). No receptor ligands were used, but GDP was used

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25

Kuznetsov, Aleksei, and Jaak Järv. "Ligand structure controlled allostery in cAMP-dependent protein kinase catalytic subunit." Open Life Sciences 4, no. 2 (2009): 131–41. http://dx.doi.org/10.2478/s11535-009-0012-6.

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AbstractProtein kinase A (cAMP dependent protein kinase catalytic subunit, EC 2.7.11.11) binds simultaneously ATP and a phosphorylatable peptide. These structurally dissimilar allosteric ligands influence the binding effectiveness of each other. The same situation is observed with substrate congeners, which reversibly inhibit the enzyme. In this review these allosteric effects are quantified using the interaction factor, which compares binding effectiveness of ligands with the free enzyme and the pre-loaded enzyme complex containing another ligand. This analysis revealed that the allosteric ef

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Jayanna, P. K., D. Bedi, P. Deinnocentes, R. C. Bird, and V. A. Petrenko. "Landscape phage ligands for PC3 prostate carcinoma cells." Protein Engineering Design and Selection 23, no. 6 (2010): 423–30. http://dx.doi.org/10.1093/protein/gzq011.

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27

Kurumatani, Natsumi, Hiroyuki Monji, and Takenao Ohkawa. "Binding Site Extraction by Similar Subgraphs Mining from Protein Molecular Surfaces and Its Application to Protein Classification." International Journal on Artificial Intelligence Tools 23, no. 03 (2014): 1460007. http://dx.doi.org/10.1142/s0218213014600070.

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Most proteins express their functions by binding with other proteins or molecular compounds (ligands). Since the characteristics of the local portion involved in binding (binding site) often determine the function of the protein, clarifying the location of the binding site of the protein helps analyze the function of proteins. Binding sites that bind to similar ligands often have common surface structures (surface motifs). Extracting the surface motifs among several proteins with similar functions improves binding site prediction. We propose a method that predicts binding sites by extracting t

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28

Piekarska, B., J. Rybarska, B. Stopa, et al. "Supramolecularity creates nonstandard protein ligands." Acta Biochimica Polonica 46, no. 4 (1999): 841–51. http://dx.doi.org/10.18388/abp.1999_4105.

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Congo red and a group of structurally related dyes long used to stain amyloid proteins are known to associate in water solutions. The self-association of some dyes belonging to this group appears particularly strong. In water solutions their molecules are arranged in ribbon-like micellar forms with liquid crystalline properties. These compounds have recently been found to form complexes with some native proteins in a non-standard way. Gaps formed by the local distribution of beta-sheets in proteins probably represent the receptor sites for these dye ligands. They may result from higher structu

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Senisterra, Guillermo A., Hamed Ghanei, Galina Khutoreskaya, et al. "Assessing the Stability of Membrane Proteins to Detect Ligand Binding Using Differential Static Light Scattering." Journal of Biomolecular Screening 15, no. 3 (2010): 314–20. http://dx.doi.org/10.1177/1087057109357117.

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Protein stabilization upon ligand binding has frequently been used to identify ligands for soluble proteins. Methods such as differential scanning fluorimetry (DSF) and differential static light scattering (DSLS) have been employed in the 384-well format and have been useful in identifying ligands that promote crystallization and 3D structure determination of proteins. However, finding a generic method that is applicable to membrane proteins has been a challenge as the high hydrophobicity of membrane proteins and the presence of detergents essential for their solubilization interfere with fluo

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30

Whitesides, George M., and Vijay M. Krishnamurthy. "Designing ligands to bind proteins." Quarterly Reviews of Biophysics 38, no. 4 (2005): 385–95. http://dx.doi.org/10.1017/s0033583506004240.

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The ability to design drugs (so-called ‘rational drug design’) has been one of the long-term objectives of chemistry for 50 years. It is an exceptionally difficult problem, and many of its parts lie outside the expertise of chemistry. The much more limited problem – how to design tight-binding ligands (rational ligand design) – would seem to be one that chemistry could solve, but has also proved remarkably recalcitrant. The question is ‘Why is it so difficult?’ and the answer is ‘We still don't entirely know’. This perspective discusses some of the technical issues – potential functions, prote

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31

Rosa, Matthew, Timothy Noel, Matthew Harris, and Graham Ladds. "Emerging roles of adhesion G protein-coupled receptors." Biochemical Society Transactions 49, no. 4 (2021): 1695–709. http://dx.doi.org/10.1042/bst20201144.

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Adhesion G protein-coupled receptors (aGPCRs) form a sub-group within the GPCR superfamily. Their distinctive structure contains an abnormally large N-terminal, extracellular region with a GPCR autoproteolysis-inducing (GAIN) domain. In most aGPCRs, the GAIN domain constitutively cleaves the receptor into two fragments. This process is often required for aGPCR signalling. Over the last two decades, much research has focussed on aGPCR-ligand interactions, in an attempt to deorphanize the family. Most ligands have been found to bind to regions N-terminal to the GAIN domain. These receptors may b

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Ortiz-Muñoz, Andrés, Héctor F. Medina-Abarca, and Walter Fontana. "Combinatorial protein–protein interactions on a polymerizing scaffold." Proceedings of the National Academy of Sciences 117, no. 6 (2020): 2930–37. http://dx.doi.org/10.1073/pnas.1912745117.

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Scaffold proteins organize cellular processes by bringing signaling molecules into interaction, sometimes by forming large signalosomes. Several of these scaffolds are known to polymerize. Their assemblies should therefore not be understood as stoichiometric aggregates, but as combinatorial ensembles. We analyze the combinatorial interaction of ligands loaded on polymeric scaffolds, in both a continuum and discrete setting, and compare it with multivalent scaffolds with fixed number of binding sites. The quantity of interest is the abundance of ligand interaction possibilities—the catalytic po

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33

Siligardi, Giuliano, Charlotte S. Hughes, and Rohanah Hussain. "Characterisation of sensor kinase by CD spectroscopy: golden rules and tips." Biochemical Society Transactions 46, no. 6 (2018): 1627–42. http://dx.doi.org/10.1042/bst20180222.

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This is a review that describes the golden rules and tips on how to characterise the molecular interactions of membrane sensor kinase proteins with ligands using mainly circular dichroism (CD) spectroscopy. CD spectroscopy is essential for this task as any conformational change observed in the far-UV (secondary structures (α-helix, β-strands, poly-proline of type II, β-turns, irregular and folding) and near-UV regions [local environment of the aromatic side-chains of amino acid residues (Phe, Tyr and Trp) and ligands (drugs) and prosthetic groups (porphyrins, cofactors and coenzymes (FMN, FAD,

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Logie, Colin, Mark Nichols, Kathy Myles, John W. Funder, and A. Francis Stewart. "Positive and Negative Discrimination of Estrogen Receptor Agonists and Antagonists Using Site-Specific DNA Recombinase Fusion Proteins." Molecular Endocrinology 12, no. 8 (1998): 1120–32. http://dx.doi.org/10.1210/mend.12.8.0155.

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Abstract Activation of the estrogen receptor (ER) by hormone involves at least two steps. First, hormone binding initially relieves repression, a property imposed on ER in cis by its ligand-binding domain (EBD). Subsequently, the derepressed ER binds specific genomic sites and regulates transcription. In addition to the natural hormone, ER binds a broad range of ligands that evoke a spectrum of responses ranging from full ER activation by agonists to partial activation and inhibition by partial or complete antagonists. How these different ligands evoke different ER responses remains unclear. T

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Borodin, E. A., A. P. Chupalov, P. D. Timkin, E. A. Timofeev, and N. Yu Leusova. "Selection of potential ligands for TRPM8 using deep neural networks and intermolecular docking." Bulletin Physiology and Pathology of Respiration, no. 80 (July 16, 2021): 26–33. http://dx.doi.org/10.36604/1998-5029-2021-80-26-33.

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Introduction. TRPM8 has been implicated in the development of bronchial hypersensitivity to cold and is considered a potential target for computer-generated drugs.Aim. Development of a strategy for the selection of ligands for TRPM8 by in silico methods.Materials and methods. Using machine learning tools based on deep neural networks and further verification by intermolecular docking, a strategy has been proposed for predicting potential ligands for TRPM8, which consists in using a neural network to screen out potential drug candidates and thereby reduce the list of candidate ligands for verif

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Perkins, Stephen J., Azubuike I. Okemefuna, and Ruodan Nan. "Unravelling protein–protein interactions between complement factor H and C-reactive protein using a multidisciplinary strategy." Biochemical Society Transactions 38, no. 4 (2010): 894–900. http://dx.doi.org/10.1042/bst0380894.

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Experimental studies of protein–protein interactions are very much affected by whether the complexes are fully formed (strong, with nanomolar dissociation constants) or partially dissociated (weak, with micromolar dissociation constants). The functions of the complement proteins of innate immunity are governed by the weak interactions between the activated proteins and their regulators. Complement is effective in attacking pathogens, but not the human host, and imbalances in this process can lead to disease conditions. The inherent complexity in analysing complement interactions is augmented b

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Schmidt, Uli, Rainer Rudolph, and Gerald Böhm. "Binding of external ligands onto an engineered virus capsid." Protein Engineering, Design and Selection 14, no. 10 (2001): 769–74. http://dx.doi.org/10.1093/protein/14.10.769.

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Sylte, Ingebrigt, Øyvind Edvardsen, and Svein G. Dahl. "Molecular dynamics of the 5-HT1a receptor and ligands." "Protein Engineering, Design and Selection" 6, no. 7 (1993): 691–700. http://dx.doi.org/10.1093/protein/6.7.691.

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Wikman, M., A. C. Steffen, E. Gunneriusson, et al. "Selection and characterization of HER2/neu-binding affibody ligands." Protein Engineering Design and Selection 17, no. 5 (2004): 455–62. http://dx.doi.org/10.1093/protein/gzh053.

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Bemister-Buffington, Joseph, Alex J. Wolf, Sebastian Raschka, and Leslie A. Kuhn. "Machine Learning to Identify Flexibility Signatures of Class A GPCR Inhibition." Biomolecules 10, no. 3 (2020): 454. http://dx.doi.org/10.3390/biom10030454.

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We show that machine learning can pinpoint features distinguishing inactive from active states in proteins, in particular identifying key ligand binding site flexibility transitions in GPCRs that are triggered by biologically active ligands. Our analysis was performed on the helical segments and loops in 18 inactive and 9 active class A G protein-coupled receptors (GPCRs). These three-dimensional (3D) structures were determined in complex with ligands. However, considering the flexible versus rigid state identified by graph-theoretic ProFlex rigidity analysis for each helix and loop segment wi

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Copoiu, Liviu, Pedro H. M. Torres, David B. Ascher, Tom L. Blundell, and Sony Malhotra. "ProCarbDB: a database of carbohydrate-binding proteins." Nucleic Acids Research 48, no. D1 (2019): D368—D375. http://dx.doi.org/10.1093/nar/gkz860.

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Abstract Carbohydrate-binding proteins play crucial roles across all organisms and viruses. The complexity of carbohydrate structures, together with inconsistencies in how their 3D structures are reported, has led to difficulties in characterizing the protein–carbohydrate interfaces. In order to better understand protein–carbohydrate interactions, we have developed an open-access database, ProCarbDB, which, unlike the Protein Data Bank (PDB), clearly distinguishes between the complete carbohydrate ligands and their monomeric units. ProCarbDB is a comprehensive database containing over 5200 3D

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Narunsky, Aya, Amit Kessel, Ron Solan, Vikram Alva, Rachel Kolodny, and Nir Ben-Tal. "On the evolution of protein–adenine binding." Proceedings of the National Academy of Sciences 117, no. 9 (2020): 4701–9. http://dx.doi.org/10.1073/pnas.1911349117.

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Proteins’ interactions with ancient ligands may reveal how molecular recognition emerged and evolved. We explore how proteins recognize adenine: a planar rigid fragment found in the most common and ancient ligands. We have developed a computational pipeline that extracts protein–adenine complexes from the Protein Data Bank, structurally superimposes their adenine fragments, and detects the hydrogen bonds mediating the interaction. Our analysis extends the known motifs of protein–adenine interactions in the Watson–Crick edge of adenine and shows that all of adenine’s edges may contribute to mol

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Denton, Kyle E., Sijie Wang, Michael C. Gignac, et al. "Robustness of In Vitro Selection Assays of DNA-Encoded Peptidomimetic Ligands to CBX7 and CBX8." SLAS DISCOVERY: Advancing the Science of Drug Discovery 23, no. 5 (2018): 417–28. http://dx.doi.org/10.1177/2472555217750871.

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The identification of protein ligands from a DNA-encoded library is commonly conducted by an affinity selection assay. These assays are often not validated for robustness, raising questions about selections that fail to identify ligands and the utility of enrichment values for ranking ligand potencies. Here, we report a method for optimizing and utilizing affinity selection assays to identify potent and selective peptidic ligands to the highly related chromodomains of CBX proteins. To optimize affinity selection parameters, statistical analyses (Z′ factors) were used to define the ability of s

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Gollan, Timothy J., and Michael R. Green. "Redirecting Retroviral Tropism by Insertion of Short, Nondisruptive Peptide Ligands into Envelope." Journal of Virology 76, no. 7 (2002): 3558–63. http://dx.doi.org/10.1128/jvi.76.7.3558-3563.2002.

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ABSTRACT A potentially powerful approach for in vivo gene delivery is to target retrovirus to specific cells through interactions between cell surface receptors and appropriately modified viral envelope proteins. Previously, relatively large (>100 residues) protein ligands to cell surface receptors have been inserted at or near the N terminus of retroviral envelope proteins. Although viral tropism could be altered, the chimeric envelope proteins lacked full activity, and coexpression of wild-type envelope was required for production of transducing virus. Here we analyze more than 40 derivat

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Hendlich, Manfred. "Databases for Protein–Ligand Complexes." Acta Crystallographica Section D Biological Crystallography 54, no. 6 (1998): 1178–82. http://dx.doi.org/10.1107/s0907444998007124.

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Recent advances in experimental techniques have led to an enormous explosion of available data about protein–ligand complexes. To exploit the information that is hidden in these large data, collection tools for managing and accessing huge data collections are needed. This paper discusses databases for protein–ligand data which are accessibleviathe World Wide Web. A strong focus is placed on the ReLiBase database system which is a new three-dimensional database for storing and analysing structures of protein–ligand complexes currently deposited in the Brookhaven Protein Data Bank (PDB). ReLiBas

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Kobren, Shilpa Nadimpalli, and Mona Singh. "Systematic domain-based aggregation of protein structures highlights DNA-, RNA- and other ligand-binding positions." Nucleic Acids Research 47, no. 2 (2018): 582–93. http://dx.doi.org/10.1093/nar/gky1224.

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Abstract Domains are fundamental subunits of proteins, and while they play major roles in facilitating protein–DNA, protein–RNA and other protein–ligand interactions, a systematic assessment of their various interaction modes is still lacking. A comprehensive resource identifying positions within domains that tend to interact with nucleic acids, small molecules and other ligands would expand our knowledge of domain functionality as well as aid in detecting ligand-binding sites within structurally uncharacterized proteins. Here, we introduce an approach to identify per-domain-position interacti

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Berezhkovskii, Alexander M., Dah-Yen Yang, Sheh-Yi Sheu, and Sheng Hsien Lin. "Stochastic gating in diffusion-influenced ligand binding to proteins: Gated protein versus gated ligands." Physical Review E 54, no. 4 (1996): 4462–64. http://dx.doi.org/10.1103/physreve.54.4462.

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Calderone, Richard A. "Recognition of endothelial cells byCandida albicans: role of complement-binding proteins." Canadian Journal of Botany 73, S1 (1995): 1154–59. http://dx.doi.org/10.1139/b95-372.

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Candida albicans, a commensal of humans, can cause either mucosal or systemic infections. The virulence properties of the organism include cell-surface adhesins that recognize ligands of host cells. Hyphal forms of the organism possess a 60-kDa mannoprotein that recognizes a variety of host-cell ligands including the complement C3 conversion products, C3bi and C3d. In addition, a protein of similar molecular mass also binds to endothelial extracellular matrix proteins such as laminin and fibronectin. While the 60-kDa protein is associated with the cell surface of hyphal forms of the organism,

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Brzezinski, Dariusz, Przemyslaw J. Porebski, Marcin Kowiel, Joanna M. Macnar, and Wladek Minor. "Recognizing and validating ligands with CheckMyBlob." Nucleic Acids Research 49, W1 (2021): W86—W92. http://dx.doi.org/10.1093/nar/gkab296.

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Abstract Structure-guided drug design depends on the correct identification of ligands in crystal structures of protein complexes. However, the interpretation of the electron density maps is challenging and often burdened with confirmation bias. Ligand identification can be aided by automatic methods such as CheckMyBlob, a machine learning algorithm that learns to generalize ligand descriptions from sets of moieties deposited in the Protein Data Bank. Here, we present the CheckMyBlob web server, a platform that can identify ligands in unmodeled fragments of electron density maps or validate li

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Takahashi, Masaki, Ryo Amano, Michiru Ozawa, Anna Martinez, Kazumasa Akita, and Yoshikazu Nakamura. "Nucleic acid ligands act as a PAM and agonist depending on the intrinsic ligand binding state of P2RY2." Proceedings of the National Academy of Sciences 118, no. 18 (2021): e2019497118. http://dx.doi.org/10.1073/pnas.2019497118.

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G protein–coupled receptors (GPCRs) play diverse roles in physiological processes, and hence the ligands to modulate GPCRs have served as important molecules in biological and pharmacological approaches. However, the exploration of novel ligands for GPCR still remains an arduous challenge. In this study, we report a method for the discovery of nucleic acid ligands against GPCRs by an advanced RNA aptamer screening technology that employs a virus-like particle (VLP), exposing the GPCR of interest. An array of biochemical analyses coupled with a cell-based assay revealed that one of the aptamers

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