@article{Isberg2016,
abstract = {Recent developments in G protein-coupled receptor (GPCR) structural biology and pharmacology have greatly enhanced our knowledge of receptor structure-function relations, and have helped improve the scientific foundation for drug design studies. The GPCR database, GPCRdb, serves a dual role in disseminating and enabling new scientific developments by providing reference data, analysis tools and interactive diagrams. This paper highlights new features in the fifth major GPCRdb release: (i) GPCR crystal structure browsing, superposition and display of ligand interactions; (ii) direct deposition by users of point mutations and their effects on ligand binding; (iii) refined snake and helix box residue diagram looks; and (iii) phylogenetic trees with receptor classification colour schemes. Under the hood, the entire GPCRdb front- and back-ends have been recoded within one infrastructure, ensuring a smooth browsing experience and development. GPCRdb is available at http://www.gpcrdb.org/ and it's open source code at https://bitbucket.org/gpcr/protwis.},
annote = {Generic residue number tables},
author = {Isberg, Vignir and Mordalski, Stefan and Munk, Christian and Rataj, Krzysztof and Harps{\o}e, Kasper and Hauser, Alexander S. and Vroling, Bas and Bojarski, Andrzej J. and Vriend, Gert and Gloriam, David E.},
doi = {10.1093/nar/gkv1178},
issn = {13624962},
journal = {Nucleic Acids Research},
month = {jan},
number = {D1},
pages = {D356--D364},
pmid = {26582914},
title = {{GPCRdb: An information system for G protein-coupled receptors}},
url = {https://academic.oup.com/nar/article-lookup/doi/10.1093/nar/gkv1178},
volume = {44},
year = {2016}
}
@article{Kooistra2021,
abstract = {G protein-coupled receptors (GPCRs) form both the largest family of membrane proteins and drug targets, mediating the action of one-third of medicines. The GPCR database, GPCRdb serves >4 000 researchers every month and offers reference data, analysis of own or literature data, experiment design and dissemination of published datasets. Here, we describe new and updated GPCRdb resources with a particular focus on integration of sequence, structure and function. GPCRdb contains all human non-olfactory GPCRs (and >27 000 orthologs), G-proteins and arrestins. It includes over 2 000 drug and in-trial agents and nearly 200 000 ligands with activity and availability data. GPCRdb annotates all published GPCR structures (updated monthly), which are also offered in a refined version (with re-modeled missing/distorted regions and reverted mutations) and provides structure models of all human non-olfactory receptors in inactive, intermediate and active states. Mutagenesis data in the GPCRdb spans natural genetic variants, GPCR-G protein interfaces, ligand sites and thermostabilising mutations. A new sequence signature tool for identification of functional residue determinants has been added and two data driven tools to design ligand site mutations and constructs for structure determination have been updated extending their coverage of receptors and modifications. The GPCRdb is available at https://gpcrdb.org.},
annote = {Most current GPCRdb reference},
author = {Kooistra, Albert J. and Mordalski, Stefan and P{\'{a}}ndy-Szekeres, G{\'{a}}sp{\'{a}}r and Esguerra, Mauricio and Mamyrbekov, Alibek and Munk, Christian and Keserű, Gy{\"{o}}rgy M. and Gloriam, David E.},
doi = {10.1093/nar/gkaa1080},
issn = {13624962},
journal = {Nucleic Acids Research},
month = {jan},
number = {D1},
pages = {D335--D343},
pmid = {33270898},
title = {{GPCRdb in 2021: Integrating GPCR sequence, structure and function}},
url = {https://academic.oup.com/nar/article/49/D1/D335/6018432 https://doi.org/10.1093/NAR/GKAA1080},
volume = {49},
year = {2021}
}
@article{Isberg2015,
abstract = {Generic residue numbers facilitate comparisons of, for example, mutational effects, ligand interactions, and structural motifs. The numbering scheme by Ballesteros and Weinstein for residues within the class A GPCRs (G protein-coupled receptors) has more than 1100 citations, and the recent crystal structures for classes B, C, and F now call for a community consensus in residue numbering within and across these classes. Furthermore, the structural era has uncovered helix bulges and constrictions that offset the generic residue numbers. The use of generic residue numbers depends on convenient access by pharmacologists, chemists, and structural biologists. We review the generic residue numbering schemes for each GPCR class, as well as a complementary structure-based scheme, and provide illustrative examples and GPCR database (GPCRDB) web tools to number any receptor sequence or structure.},
annote = {Ref for Generic residue Numbering},
author = {Isberg, Vignir and {De Graaf}, Chris and Bortolato, Andrea and Cherezov, Vadim and Katritch, Vsevolod and Marshall, Fiona H. and Mordalski, Stefan and Pin, Jean Philippe and Stevens, Raymond C. and Vriend, Gerrit and Gloriam, David E.},
doi = {10.1016/j.tips.2014.11.001},
issn = {18733735},
journal = {Trends in Pharmacological Sciences},
keywords = {G protein-coupled receptor,ligand binding,mutational effects,sequence alignments,structural motifs},
month = {jan},
number = {1},
pages = {22--31},
pmid = {25541108},
publisher = {Elsevier Current Trends},
title = {{Generic GPCR residue numbers - Aligning topology maps while minding the gaps}},
volume = {36},
year = {2015}
}
@article{Wootten2013,
abstract = {Recently, the concept of ligand-directed signaling-the ability of different ligands of an individual receptor to promote distinct patterns of cellular response-has gained much traction in the field of drug discovery, with the potential to sculpt biological response to favor therapeutically beneficial signaling pathways over those leading to harmful effects. However, there is limited understanding of the mechanistic basis underlying biased signaling. The glucagon-like peptide-1 receptor is a major target for treatment of type-2 diabetes and is subject to ligand-directed signaling. Here, we demonstrate the importance of polar transmembrane residues conserved within family B G protein-coupled receptors, not only for protein folding and expression, but also in controlling activation transition, ligand-biased, and pathway-biased signaling. Distinct clusters of polar residues were important for receptor activation and signal preference, globally changing the profile of receptor response to distinct peptide ligands, including endogenous ligands glucagon-like peptide-1, oxyntomodulin, and the clinically used mimetic exendin-4. {\textcopyright} PNAS 2013.},
annote = {B},
author = {Wootten, Denise and Simms, John and Miller, Laurence J. and Christopoulos, Arthur and Sexton, Patrick M.},
doi = {10.1073/pnas.1221585110},
issn = {00278424},
journal = {Proceedings of the National Academy of Sciences of the United States of America},
month = {mar},
number = {13},
pages = {5211--5216},
pmid = {23479653},
title = {{Polar transmembrane interactions drive formation of ligand-specific and signal pathway-biased family B G protein-coupled receptor conformations}},
volume = {110},
year = {2013}
}
@article{Pin2003,
abstract = {G-protein-coupled receptors (GPCRs) represent one of the largest gene families in the animal genome. These receptors can be classified into several groups based on the sequence similarity of their common heptahelical domain. The family 3 (or C) GPCRs are receptors for the main neurotransmitters glutamate and $\gamma$-aminobutyric acid, for Ca2+, for sweet and amino acid taste compounds, and for some pheromone molecules, as well as for odorants in fish. Although none of these family 3 receptors have been found in plants, members have been identified in ancient organisms, such as slime molds (Dictyostelium) and sponges. Like any other GPCRs, family 3 receptors possess a transmembrane heptahelical domain responsible for G-protein activation. However, most of these identified receptors also possess a large extracellular domain that is responsible for ligand recognition, is structurally similar to bacterial periplasmic proteins involved in the transport of small molecules, and is called a Venus Flytrap module. The recent resolution of the structure of this binding domain in one of these receptors, the metabotropic glutamate 1 receptor, together with the recent demonstration that these receptors are dimers, revealed a unique mechanism of activation for these GPCRs. Such data open new possibilities in the development of drugs aimed at modulating these receptors, and raise a number of interesting questions on the activation mechanism of the other GPCRs. {\textcopyright} 2003 Elsevier Science Inc. All rights reserved.},
annote = {C},
author = {Pin, Jean Philippe and Galvez, Thierry and Pr{\'{e}}zeau, Laurent},
doi = {10.1016/S0163-7258(03)00038-X},
issn = {01637258},
journal = {Pharmacology and Therapeutics},
keywords = {Dimerization,G-protein-coupled receptors,GABAB receptors,Metabotropic glutamate receptors,Pheromones,Taste},
month = {jun},
number = {3},
pages = {325--354},
pmid = {12782243},
publisher = {Elsevier Inc.},
title = {{Evolution, structure, and activation mechanism of family 3/C G-protein-coupled receptors}},
volume = {98},
year = {2003}
}
@article{Wu2014,
abstract = {The excitatory neurotransmitter glutamate induces modulatory actions via the metabotropic glutamate receptors (mGlus), which are class C G protein-coupled receptors (GPCRs). We determined the structure of the human mGlu1 receptor seven-transmembrane (7TM) domain bound to a negative allosteric modulator, FITM, at a resolution of 2.8 angstroms. The modulator binding site partially overlaps with the orthosteric binding sites of class A GPCRs but is more restricted than most other GPCRs. We observed a parallel 7TM dimer mediated by cholesterols, which suggests that signaling initiated by glutamate's interaction with the extracellular domain might be mediated via 7TM interactions within the full-length receptor dimer. A combination of crystallography, structure-activity relationships, mutagenesis, and full-length dimer modeling provides insights about the allosteric modulation and activation mechanism of class C GPCRs.},
annote = {F},
author = {Wu, Huixian and Wang, Chong and Gregory, Karen J. and Han, Gye Won and Cho, Hyekyung P. and Xia, Yan and Niswender, Colleen M. and Katritch, Vsevolod and Meiler, Jens and Cherezov, Vadim and Conn, P. Jeffrey and Stevens, Raymond C.},
doi = {10.1126/science.1249489},
issn = {10959203},
journal = {Science},
number = {6179},
pages = {58--64},
pmid = {24603153},
publisher = {American Association for the Advancement of Science},
title = {{Structure of a class C GPCR metabotropic glutamate receptor 1 bound to an allosteric modulator}},
volume = {344},
year = {2014}
}
@article{Ballesteros1995,
abstract = {This chapter discusses the integrated methods for the construction of three-dimensional models and computational probing of structure–function relations in G protein-coupled receptors (GPCR). The rapid pace of cloning and expression of G protein-coupled receptors offers attractive opportunities to probe the structural basis of signal transduction mechanisms at the level of these cell-surface receptors. Major insights have emerged from comparisons and classifications of the amino acid sequences of GPCRs into families defined by evolutionary developments and adapted to perform selective functions. Structural data on GPCRs, based on biochemical, immunological, and biophysical approaches have validated consensus architecture of GPCRs with an extracellular N-terminus, a cytoplasmic C-terminus, and a transmembrane portion comprised of seven-transmembrane helical domains connected by loops. Developments in the molecular modeling and computational exploration of GPCR proteins indicate a tantalizing potential to alleviate some of these difficulties. These expectations are based on the increased rate of success achieved by molecular modeling and computational simulation methods in providing structural insights relevant to the functions of biological molecules. {\textcopyright} 1995, Elsevier Inc.},
annote = {BW},
author = {Ballesteros, Juan A. and Weinstein, Harel},
doi = {10.1016/S1043-9471(05)80049-7},
issn = {10439471},
journal = {Methods in Neurosciences},
month = {jan},
number = {C},
pages = {366--428},
title = {{Integrated methods for the construction of three-dimensional models and computational probing of structure-function relations in G protein-coupled receptors}},
volume = {25},
year = {1995}
}
@article{Baldwin1997,
abstract = {A model for the alpha-carbon positions in the seven transmembrane helices in the rhodopsin family of G-protein-coupled receptors is presented. The model incorporates structural information derived from the analysis of $\sim$ 500 sequences in this family. The location, relative to the centre of the lipid bilayer, of each of the seven helical sequence segments and their probable lengths are deduced from sequence analysis, along with the orientation, relative to the centre of the helix bundle, of each helical segment around its axis. The packing of the helices in the model is guided by the density in a three-dimensional map of frog rhodopsin determined by electron cryo-microscopy. The model suggests which of the residues that are highly conserved in this family of receptors interact with each other. Helices III, V and VI are predicted to protrude more than the others from the central lipid core towards the aqueous phase on the intracellular side of the membrane. This feature could be a property of the receptor structure in some but not all of the conformations that it adopts, since recent studies suggest that relative movement occurs between these helices on photoactivation of rhodopsin. Results from other techniques, including the creation of metal-binding sites and disulphide bridges, site-directed spin-labelling studies, the substituted-cysteine accessibility method and other site-directed mutagenesis studies, are discussed in terms of the model.},
author = {Baldwin, Joyce M. and Schertler, Gebhard F.X. and Unger, Vinzenz M.},
doi = {10.1006/jmbi.1997.1240},
issn = {00222836},
journal = {Journal of Molecular Biology},
keywords = {G-protein-coupled receptor,Rhodopsin,Sequence analysis,Structure prediction},
month = {sep},
number = {1},
pages = {144--164},
pmid = {9299344},
publisher = {Academic Press},
title = {{An alpha-carbon template for the transmembrane helices in the rhodopsin family of G-protein-coupled receptors}},
volume = {272},
year = {1997}
}
@article{Oliveira1993,
author = {Oliveira, L. and Paiva, A. C. M. and Vriend, G.},
doi = {10.1007/BF00125323},
issn = {0920-654X},
journal = {Journal of Computer-Aided Molecular Design},
month = {dec},
number = {6},
pages = {649--658},
title = {{A common motif in G-protein-coupled seven transmembrane helix receptors}},
url = {http://link.springer.com/10.1007/BF00125323},
volume = {7},
year = {1993}
}
@article{Baldwin1993,
abstract = {G protein-coupled receptors form a large family of integral membrane proteins whose amino acid sequences have seven hydrophobic segments containing distinctive sequence patterns. Rhodopsin, a member of the family, is known to have transmembrane alpha-helices. The probable arrangement of the seven helices, in all receptors, was deduced from structural information extracted from a detailed analysis of the sequences. Constraints established include: (1) each helix must be positioned next to its neighbours in the sequence; (2) helices I, IV and V must be most exposed to the lipid surrounding the receptor and helix m least exposed. (l) is established from the lengths of the shortest loops. (2) is determined by considering: (i) sites of the most conserved residues; (ii) other sites where variability is restricted; (iii) sites that accommodate polar residues; (iv) sites of differences in sequence between pairs or within groups of closely related receptors. Most sites in the last category should be in unimportant positions and are most useful in determining the position and extent of lipid-facing surface in each helix. The structural constraints for the receptors are used to allocate particular helices to the peaks in the recently published projection map of rhodopsin and to propose a tentative three-dimensional arrangement of the helices in G protein-coupled receptors.},
author = {Baldwin, J.M.},
doi = {10.1002/J.1460-2075.1993.TB05814.X},
issn = {1460-2075},
journal = {The EMBO Journal},
keywords = {G protein-coupled receptor,Rhodopsin,Sequence comparisons,Structure prediction},
month = {apr},
number = {4},
pages = {1693--1703},
publisher = {John Wiley & Sons, Ltd},
title = {{The probable arrangement of the helices in G protein-coupled receptors.}},
url = {https://onlinelibrary.wiley.com/doi/full/10.1002/j.1460-2075.1993.tb05814.x https://onlinelibrary.wiley.com/doi/abs/10.1002/j.1460-2075.1993.tb05814.x https://www.embopress.org/doi/abs/10.1002/j.1460-2075.1993.tb05814.x},
volume = {12},
year = {1993}
}
@article{Schwartz1994,
abstract = {Over the past year, mutational analysis of peptide receptors has started to change our understanding of the interaction between G protein coupled receptors and their ligands, an area previously almost totally dominated by results from studies of monoamine receptors. A picture is currently emerging, in which small ligands appear to bind in three (more or less) overlapping ligand-binding pockets in between the transmembrane segments. In contrast, contact residues for peptide and protein ligands have mainly been found in exterior regions of peptide and protein receptors. It is also becoming increasingly clear that agonists and antagonists may interact in vastly different manners, even though they are competitive ligands for a common receptor. {\textcopyright} 1994.},
author = {Schwartz, Thue W.},
doi = {10.1016/0958-1669(94)90054-X},
issn = {09581669},
journal = {Current Opinion in Biotechnology},
month = {aug},
number = {4},
pages = {434--444},
pmid = {7765177},
publisher = {Elsevier Current Trends},
title = {{Locating ligand-binding sites in 7tm receptors by protein engineering}},
volume = {5},
year = {1994}
}
@article{Schwartz1995,
author = {Schwartz, Thue W. and Gether, U. and Schambye, H. T. and Hjorth, S. A.},
journal = {Current Pharmaceutical Design},
pages = {325--342},
title = {{Molecular mechanism of action of non-peptide ligands for peptide receptors}},
volume = {1},
year = {1995}
}
@article{Flock2015,
abstract = {G protein-coupled receptors (GPCRs) allosterically activate heterotrimeric G proteins and trigger GDP release. Given that there are ∼800 human GPCRs and 16 different G$\alpha$ genes, this raises the question of whether a universal allosteric mechanism governs G$\alpha$ activation. Here we show that different GPCRs interact with and activate G$\alpha$ proteins through a highly conserved mechanism. Comparison of G$\alpha$ with the small G protein Ras reveals how the evolution of short segments that undergo disorder-to-order transitions can decouple regions important for allosteric activation from receptor binding specificity. This might explain how the GPCR–G$\alpha$ system diversified rapidly, while conserving the allosteric activation mechanism. There are ∼800 human GPCRs and 16 different G$\alpha$ proteins; this study revealed the molecular details of G$\alpha$ activation by GPCRs and suggests that a universal activation mechanism governs G$\alpha$ activation—the details of this mechanism can explain how the GPCR–G$\alpha$ system diversified rapidly, while conserving the allosteric activation mechanism. G-protein-coupled receptors (GPCRs) are membrane proteins that function as sensors for a wide range of extracellular signals. They act via heterotrimeric G proteins — guanine nucleotide-binding proteins that act as intracellular molecular switches — which they activate allosterically to trigger GDP release. There are hundreds of human GPCRs acting upon 16 different G$\alpha$ proteins. In this Analysis, Madan Babu and colleagues set out to establish whether there is a universal allosteric mechanism governing G$\alpha$ activation. They find that there is: different GPCRs interact with and activate G$\alpha$ proteins through a highly conserved mechanism, which might explain how the GPCR–G$\alpha$ system diversified rapidly while conserving its allosteric properties.},
author = {Flock, Tilman and Ravarani, Charles N. J. and Sun, Dawei and Venkatakrishnan, A. J. and Kayikci, Melis and Tate, Christopher G. and Veprintsev, Dmitry B. and Babu, M. Madan},
doi = {10.1038/nature14663},
issn = {1476-4687},
journal = {Nature 2015 524:7564},
keywords = {Computational biology and bioinformatics,G protein,Structural biology,coupled receptors},
month = {jul},
number = {7564},
pages = {173--179},
publisher = {Nature Publishing Group},
title = {{Universal allosteric mechanism for G$\alpha$ activation by GPCRs}},
url = {https://www.nature.com/articles/nature14663},
volume = {524},
year = {2015}
}
@article{Berman2000,
abstract = {The Protein Data Bank (PDB; http://www.rcsb.org/pdb/) is the single worldwide archive of structural data of biological macromolecules. This paper describes the goals of the PDB, the systems in place for data deposition and access, how to obtain further information, and near-term plans for the future development of the resource.},
author = {Berman, Helen M. and Westbrook, John and Feng, Zukang and Gilliland, Gary and Bhat, T. N. and Weissig, Helge and Shindyalov, Ilya N. and Bourne, Philip E.},
doi = {10.1093/NAR/28.1.235},
issn = {0305-1048},
journal = {Nucleic Acids Research},
keywords = {protein data bank},
month = {jan},
number = {1},
pages = {235--242},
publisher = {Oxford Academic},
title = {{The Protein Data Bank}},
url = {https://academic.oup.com/nar/article/28/1/235/2384399},
volume = {28},
year = {2000}
}
@article{Bateman2021,
author = {Bateman, Alex and Martin, Maria Jesus and Orchard, Sandra and Magrane, Michele and Agivetova, Rahat and Ahmad, Shadab and Alpi, Emanuele and Bowler-Barnett, Emily H. and Britto, Ramona and Bursteinas, Borisas and Bye-A-Jee, Hema and Coetzee, Ray and Cukura, Austra and da Silva, Alan and Denny, Paul and Dogan, Tunca and Ebenezer, Thank God and Fan, Jun and Castro, Leyla Garcia and Garmiri, Penelope and Georghiou, George and Gonzales, Leonardo and Hatton-Ellis, Emma and Hussein, Abdulrahman and Ignatchenko, Alexandr and Insana, Giuseppe and Ishtiaq, Rizwan and Jokinen, Petteri and Joshi, Vishal and Jyothi, Dushyanth and Lock, Antonia and Lopez, Rodrigo and Luciani, Aurelien and Luo, Jie and Lussi, Yvonne and MacDougall, Alistair and Madeira, Fabio and Mahmoudy, Mahdi and Menchi, Manuela and Mishra, Alok and Moulang, Katie and Nightingale, Andrew and Oliveira, Carla Susana and Pundir, Sangya and Qi, Guoying and Raj, Shriya and Rice, Daniel and Lopez, Milagros Rodriguez and Saidi, Rabie and Sampson, Joseph and Sawford, Tony and Speretta, Elena and Turner, Edward and Tyagi, Nidhi and Vasudev, Preethi and Volynkin, Vladimir and Warner, Kate and Watkins, Xavier and Zaru, Rossana and Zellner, Hermann and Bridge, Alan and Poux, Sylvain and Redaschi, Nicole and Aimo, Lucila and Argoud-Puy, Ghislaine and Auchincloss, Andrea and Axelsen, Kristian and Bansal, Parit and Baratin, Delphine and Blatter, Marie Claude and Bolleman, Jerven and Boutet, Emmanuel and Breuza, Lionel and Casals-Casas, Cristina and de Castro, Edouard and Echioukh, Kamal Chikh and Coudert, Elisabeth and Cuche, Beatrice and Doche, Mikael and Dornevil, Dolnide and Estreicher, Anne and Famiglietti, Maria Livia and Feuermann, Marc and Gasteiger, Elisabeth and Gehant, Sebastien and Gerritsen, Vivienne and Gos, Arnaud and Gruaz-Gumowski, Nadine and Hinz, Ursula and Hulo, Chantal and Hyka-Nouspikel, Nevila and Jungo, Florence and Keller, Guillaume and Kerhornou, Arnaud and Lara, Vicente and {Le Mercier}, Philippe and Lieberherr, Damien and Lombardot, Thierry and Martin, Xavier and Masson, Patrick and Morgat, Anne and Neto, Teresa Batista and Paesano, Salvo and Pedruzzi, Ivo and Pilbout, Sandrine and Pourcel, Lucille and Pozzato, Monica and Pruess, Manuela and Rivoire, Catherine and Sigrist, Christian and Sonesson, Karin and Stutz, Andre and Sundaram, Shyamala and Tognolli, Michael and Verbregue, Laure and Wu, Cathy H. and Arighi, Cecilia N. and Arminski, Leslie and Chen, Chuming and Chen, Yongxing and Garavelli, John S. and Huang, Hongzhan and Laiho, Kati and McGarvey, Peter and Natale, Darren A. and Ross, Karen and Vinayaka, C. R. and Wang, Qinghua and Wang, Yuqi and Yeh, Lai Su and Zhang, Jian},
doi = {10.1093/NAR/GKAA1100},
issn = {0305-1048},
journal = {Nucleic Acids Research},
keywords = {chief complaint,community,coronavirus pandemic,knowledge bases,licensure,proteome,redundancy,uniprot knowledgebase},
month = {jan},
number = {D1},
pages = {D480--D489},
pmid = {33237286},
publisher = {Oxford Academic},
title = {{UniProt: the universal protein knowledgebase in 2021}},
url = {https://academic.oup.com/nar/article/49/D1/D480/6006196},
volume = {49},
year = {2021}
}
@article{Kanev2021,
abstract = {Kinases are a prime target of drug development efforts with >60 drug approvals in the past two decades. Due to the research into this protein family, a wealth of data has been accumulated that keeps on growing. KLIFS-Kinase-Ligand Interaction Fingerprints and Structures-is a structural database focusing on how kinase inhibitors interact with their targets. The aim of KLIFS is to support (structure-based) kinase research through the systematic collection, annotation, and processing of kinase structures. Now, 5 years after releasing the initial KLIFS website, the database has undergone a complete overhaul with a new website, new logo, and new functionalities. In this article, we start by looking back at how KLIFS has been used by the research community, followed by a description of the renewed KLIFS, and conclude with showcasing the functionalities of KLIFS. Major changes include the integration of approved drugs and inhibitors in clinical trials, extension of the coverage to atypical kinases, and a RESTful API for programmatic access. KLIFS is available at the new domain https://klifs.net.},
author = {Kanev, Georgi K. and de Graaf, Chris and Westerman, Bart A. and de Esch, Iwan J.P. and Kooistra, Albert J.},
doi = {10.1093/NAR/GKAA895},
issn = {0305-1048},
journal = {Nucleic Acids Research},
keywords = {kinase inhibitors,ligands,phosphotransferases},
month = {jan},
number = {D1},
pages = {D562--D569},
pmid = {33084889},
publisher = {Oxford Academic},
title = {{KLIFS: an overhaul after the first 5 years of supporting kinase research}},
url = {https://academic.oup.com/nar/article/49/D1/D562/5934416},
volume = {49},
year = {2021}
}
@article{VanLinden2014,
abstract = {Protein kinases regulate the majority of signal transduction pathways in cells and have become important targets for the development of designer drugs. We present a systematic analysis of kinase-ligand interactions in all regions of the catalytic cleft of all 1252 human kinase-ligand cocrystal structures present in the Protein Data Bank (PDB). The kinase-ligand interaction fingerprints and structure database (KLIFS) contains a consistent alignment of 85 kinase ligand binding site residues that enables the identification of family specific interaction features and classification of ligands according to their binding modes. We illustrate how systematic mining of kinase-ligand interaction space gives new insights into how conserved and selective kinase interaction hot spots can accommodate the large diversity of chemical scaffolds in kinase ligands. These analyses lead to an improved understanding of the structural requirements of kinase binding that will be useful in ligand discovery and design studies. {\textcopyright} 2013 American Chemical Society.},
author = {Van Linden, Oscar P.J. and Kooistra, Albert J. and Leurs, Rob and {De Esch}, Iwan J.P. and {De Graaf}, Chris},
doi = {10.1021/JM400378W},
issn = {00222623},
journal = {Journal of Medicinal Chemistry},
month = {jan},
number = {2},
pages = {249--277},
pmid = {23941661},
title = {{KLIFS: A knowledge-based structural database to navigate kinase-ligand interaction space}},
volume = {57},
year = {2014}
}
@article{Kooistra2016,
abstract = {Protein kinases play a crucial role in cell signaling and are important drug targets in several therapeutic areas. The KLIFS database contains detailed structural kinase-ligand interaction information derived from all ({\textgreater}2900) structures of catalytic domains of human and mouse protein kinases deposited in the Protein Data Bank in order to provide insights into the structural determinants of kinase-ligand binding and selectivity. The kinase structures have been processed in a consistent manner by systematically analyzing the structural features and molecular interaction fingerprints (IFPs) of a predefined set of 85 binding site residues with bound ligands. KLIFS has been completely rebuilt and extended ({\textgreater}65{\%} more structures) since its first release as a data set, including: novel automated annotation methods for (i) the assessment of ligand-targeted subpockets and the analysis of (ii) DFG and (iii) $\alpha$C-helix conformations; improved and automated protocols for (iv) the generation of sequence/structure alignments, (v) the curation of ligand atom and bond typing for accurate IFP analysis and (vi) weekly database updates. KLIFS is now accessible via a website (http://klifs.vucompmedchem.nl) that provides a comprehensive visual presentation of different types of chemical, biological and structural chemogenomics data, and allows the user to easily access, compare, search and download the data.},
author = {Kooistra, Albert J. and Kanev, Georgi K. and {Van Linden}, Oscar P.J. and Leurs, Rob and {De Esch}, Iwan J.P. and {De Graaf}, Chris},
doi = {10.1093/NAR/GKV1082},
issn = {0305-1048},
journal = {Nucleic Acids Research},
keywords = {atom,binding (molecular function),binding sites,fingerprints,helix (snails),ligands,mice,protein kinase},
month = {jan},
number = {D1},
pages = {D365--D371},
pmid = {26496949},
publisher = {Oxford Academic},
title = {{KLIFS: a structural kinase-ligand interaction database}},
url = {https://academic.oup.com/nar/article/44/D1/D365/2502606},
volume = {44},
year = {2016}
}

