In CAD 2.0 Database Literature

Details of Literature

  • [1].
    Reddy AS, Sastry GM, Sastry GN. Cation-Aromatic Database. Proteins 2007;67(4):1179-89.

    https://doi.org/10.1002/prot.21202

  • [2].
    Mahadevi AS, Sastry GN. Cation-π interaction: Its role and relevance in chemistry, biology, and material science. Chem Rev 2013;113(3):2100-38.

    https://doi.org/10.1021/cr300222d

  • [3].
    Mahadevi AS, Sastry GN. Cooperativity in non-covalent interactions. Chem Rev 2016;116(5):2775-825.

    https://doi.org/10.1021/cr500344e

  • [4].
    Ma JC, Dougherty DA. The Catio-π Interaction. Chem Rev 1997;97(5):1303-1324.

    https://doi.org/10.1021/cr9603744

  • [5].
    Wouters J. Cation-π (Na+-Trp) interactions in the crystal structure of tetragonal lysozyme. Protein Sci 1998;7(11):2472-2475.

    https://doi.org/10.1002/pro.5560071127

  • [6].
    Chaturvedi UC, Shrivastava R. Interaction of viral proteins with metal ions: role in maintaining the structure and functions of viruses. FEMS Immunol Med Microbiol 2005;43(3):105-114.

    https://doi.org/10.1016/j.femsim.2004.11.004

  • [7].
    Bindu PH, Sastry GM, Sastry GN. Characterization of calcium and magnesium binding domains of human 5-lipoxygenase. Biochem Biophys Res Commun 2004;320(1):461-467.

    https://doi.org/10.1016/j.bbrc.2004.05.194

  • [8].
    Chourasia M, Sastry GM, Sastry GN. Proton binding sites and conformational analysis of H+ K+-ATPase. Biochem Biophys Res Commun 2005;336(2):961-966.

    https://doi.org/10.1016/j.bbrc.2005.08.205

  • [9].
    Gallivan JP, Dougherty DA. A Computational Study of Cation-π Interactions vs Salt Bridges in Aqueous Media: Implications for Protein Engineering. J Am Chem Soc 2000;122(5):870-874.

    https://doi.org/10.1021/ja991755c

  • [10].
    Meyer EA, Castellano RK, Diederich F. Interactions with aromatic rings in chemical and biological recognition. Angew Chem Int Ed 2003;42(11):1210-1250.

    https://doi.org/10.1002/anie.200390319

  • [11].
    Dougherty DA. The cationπ interaction. Acc Chem Res 2013;46(4):885-893.

    https://doi.org/10.1021/ar300265y

  • [12].
    Kumar N, Gaur AS, Sastry GN. A perspective on the nature of cation-π interactions. J Chem Sci 2021; 133:1-3.

    https://doi.org/10.1007/s12039-021-01959-6

  • [13].
    Dougherty DA. Cation-π interactions involving aromatic amino acids. J Nutr 2007;137(6):1504S-1508S.

    https://doi.org/10.1093/jn/137.6.1504S

  • [14].
    Sunner J, Nishizawa K, Kebarle P. Ion-solvent molecule interactions in the gas phase. The potassium ion and benzene. J Phys Chem 1981; 85:1814-1820.

    https://doi.org/10.1021/j150613a011

  • [15].
    Cabarcos OM, Weinheimer CJ, Lisy JM. Size selectivity by cation-π interactions: solvation of K⁺ and Na⁺ by benzene and water. J Chem Phys 1999; 110:8429-8435.

    https://doi.org/10.1063/1.478752

  • [16].
    Cabarcos OM, Weinheimer CJ, Lisy JM. Competitive solvation of K⁺ by benzene and water: cation-π interactions and π-hydrogen bonds. J Chem Phys 1998; 108:5151-5154.

    https://doi.org/10.1063/1.476310

  • [17].
    Amicangelo JC, Armentrout PB. Absolute binding energies of alkali-metal cation complexes with benzene determined by threshold collision-induced dissociation experiments and ab initio theory. J Phys Chem A 2000; 104:11420–11432.

    https://doi.org/10.1021/jp002652f

  • [18].
    Spontarelli K, Infield DT, Nielsen HN, Holm R, Young VC, Galpin JD, Ahern CA, Vilsen B, Artigas P. Role of a conserved ion-binding site tyrosine in ion selectivity of the Na+/K+ pump. J Gen Physiol 2022; 154(7):e202113039.

    https://doi.org/10.1085/jgp.202113039

  • [19].
  • [20].
    Agranoff DD, Krishna S. Metal ion homeostasis and intracellular parasitism. Mol Microbiol 1998; 28:403-412.

    https://doi.org/10.1046/j.1365-2958.1998.00790.x

  • [21].
    Pyle A. Metal ions in the structure and function of RNA. J Biol Inorg Chem 2002; 7:679-690.

    https://doi.org/10.1007/s00775-002-0387-6

  • [22].
    De Wall SL, Meadows ES, Barbour LJ, Gokel GW. Synthetic receptors as models for alkali metal cation-π binding sites in proteins. Proc Natl Acad Sci 2000;97(12):6271-6276.

    https://doi.org/10.1073/pnas.97.12.6271

  • [23].
    Hu J, Barbour LJ, Gokel GW. Probing alkali metal–π interactions with the side chain residue of tryptophan. Proc Natl Acad Sci 2002;99(8):5121-5126.

    https://doi.org/10.1073/pnas.082645599

  • [24].
    Masson E, Schlosser M. π-Arene/metal binding: an issue not only of structure but also of reactivity. Org Lett 2005;7(10):1923-1925.

    https://doi.org/10.1021/ol0502580

  • [25].
    Ruan C, Rodgers MT. Cation- π interactions: structures and energetics of complexation of Na+ and K+ with the aromatic amino acids, phenylalanine, tyrosine, and tryptophan. J Am Chem Soc 2004;126(44):14600-14610.

    https://doi.org/10.1021/ja048297e

  • [26].
    Priyakumar UD, Sastry GN. Cation-π interactions of curved polycyclic systems: M+ (M= Li and Na) ion complexation with buckybowls. Tetrahedron Lett 2003;44(32):6043-6046.

    https://doi.org/10.1016/S0040-4039(03)01512-0

  • [27].
    Priyakumar UD, Punnagai M, Mohan GK, Sastry GN. A computational study of cation–π interactions in polycyclic systems: exploring the dependence on the curvature and electronic factors. Tetrahedron 2004;60(13):3037-3043.

    https://doi.org/10.1016/j.tet.2004.01.086

  • [28].
    Reddy AS, Sastry GN. Cation [M= H+, Li+, Na+, K+, Ca2+, Mg2+, NH4+, and NMe4+] interactions with the aromatic motifs of naturally occurring amino acids: a theoretical study. J Phys Chem A 2005;109(39):8893-8899.

    https://doi.org/10.1021/jp0525179

  • [29].
    Reddy AS, Vijay D, Sastry GM, Sastry GN. From subtle to substantial: role of metal ions on π- π interactions. J Phys Chem B 2006;110(6):2479-2481.

    https://doi.org/10.1021/jp060018h

  • [30].
    Vijay D, Sastry GN. A Computational Study on π and σ Modes of Metal Ion Binding to Heteroaromatics (CH) 5-m X m and (CH) 6-m X m (X= N and P): Contrasting Preferences Between Nitrogen-and Phosphorous-Substituted Rings. J Phys Chem A 2006;110(33):10148-10154.

    https://doi.org/10.1021/jp062448d

  • [31].
    Sharma B, Umadevi D, Sastry GN. Contrasting preferences of N and P substituted heteroaromatics towards metal binding: probing the regioselectivity of Li+ and Mg2+ binding to (CH)6-m-nNmPn. Phys Chem Chem Phys 2012;14(40):13922-13932.

    https://doi.org/10.1039/C2CP41834G

  • [32].
    Kumar N, Saha S, Sastry GN. Towards developing a criterion to characterize non-covalent bonds: a quantum mechanical study. Phys Chem Chem Phys 2021;23(14):8478-8488.

    https://doi.org/10.1039/D0CP05689H

  • [33].
    Kumar YB, Pandey A, Kumar N, Sastry GN. Binding propensity and selectivity of cationic, anionic, and neutral guests with model hydrophobic hosts: A first principles study. J Comput Chem 2023;44(3):432-441.

    https://doi.org/10.1002/jcc.26977

  • [34].
    Kumar N, Kumar YB, Sarma H, Sastry GN. Fate of Sc-Ion Interaction With Water: A Computational Study to Address Splitting Water Versus Solvating Sc Ion. Front Chem 2021; 9:738852.

    https://doi.org/10.3389/fchem.2021.738852

  • [35].
    Kumar YB, Kumar N, Sastry GN. First-principles calculations on the micro-solvation of 3d-transition metal ions: solvation versus splitting water. Theor Chem Acc 2023;142(4):1-17.

    https://doi.org/10.1007/s00214-023-02974-1

  • [36].
    Chourasia M, Sastry GM, Sastry GN. Aromatic–aromatic interactions database, A2ID: an analysis of aromatic π-networks in proteins. Int J Biol Macromol 2011;48(4):540-552.

    https://doi.org/10.1016/j.ijbiomac.2011.01.008

  • [37].
    Gaur AS, Bhardwaj A, Sharma A, John L, Vivek MR, Tripathi N, Bharatam PV, Kumar R, Janardhan S, Mori A, Banerji A, et al. Assessing therapeutic potential of molecules: molecular property diagnostic suite for tuberculosis MPDSTB (MPDS TB). J Chem Sci 2017; 129:515-531.

    https://doi.org/10.1007/s12039-017-1268-4

  • [38].
    Kumar N, Sarma H, Sastry GN. Repurposing of approved drug molecules for viral infectious diseases: a molecular modelling approach. J Biomol Struct Dyn 2022;40(17):8056-8072.

    https://doi.org/10.1080/07391102.2021.1905558

  • [39].
    Kumar N, Sastry GN. Study of lipid heterogeneity on bilayer membranes using molecular dynamics simulations. J Mol Graphics Model 2021; 108:108000.

    https://doi.org/10.1016/j.jmgm.2021.108000

  • [40].
    Crowley PB, Golovin A. Cation-π interactions in protein-protein interfaces. Proteins 2005; 59(2):231-239.

    https://doi.org/10.1002/prot.20417

  • [41].
    Yang JF, Wang F, Wang MY, Wang D, Zhou ZS, Hao GF, Li QX, Yang GF. CIPDB: a biological structure databank for studying cation-π interactions. Drug Discov Today 2023;103546.

    https://doi.org/10.1016/j.drudis.2023.103546

  • [42].
    Clementel D, Del Conte A, Monzon AM, Camagni GF, Minervini G, Piovesan D, Tosatto SC. RING 3.0: fast generation of probabilistic residue interaction networks from structural ensembles. Nucleic Acids Res 2022; 50 (W1):W651-W656.

    https://doi.org/10.1093/nar/gkac365

  • [43].
    Ding K, Yin S, Li Z, Jiang S, Yang Y, Zhou W, Zhang Y, Huang B. Observing Noncovalent Interactions in Experimental Electron Density for Macromolecular Systems: A Novel Perspective for Protein-Ligand Interaction Research. J Chem Inf Model 2022;62(7):1734-1743.

    https://doi.org/10.1021/acs.jcim.1c01406

  • [44].
    Sparrow ZM, Ernst BG, Joo PT, Lao KU, DiStasio Jr RA. NENCI-2021. I. A large benchmark database of non-equilibrium non-covalent interactions emphasizing close intermolecular contacts. J Chem Phys 2021; 155(18):184303.

    https://doi.org/10.1063/5.0068862

  • [45].
    Takaya D, Watanabe C, Nagase S, Kamisaka K, Okiyama Y, Moriwaki H, Yuki H, Sato T, Kurita N, Yagi Y, Takagi T, Kawashita N, Takaba K, Ozawa T, Takimoto-Kamimura M, Tanaka S, Fukuzawa K, Honma T. FMODB: The World's First Database of Quantum Mechanical Calculations for Biomacromolecules Based on the Fragment Molecular Orbital Method. J Chem Inf Model 2021; 61(2):777-794.

    https://doi.org/10.1021/acs.jcim.0c01062

  • [46].
    Rezac J, Non-Covalent Interactions Atlas Benchmark Data Sets: Hydrogen Bonding. J Chem Theory Comput 2020; 16(4):2355-2368.

    https://doi.org/10.1021/acs.jctc.9b01265

  • [47].
    Rezac J. Non-covalent interactions atlas benchmark data sets 2: Hydrogen bonding in an extended chemical space. J Chem Theory Comput 2020; 16(10):6305-6316.

    https://doi.org/10.1021/acs.jctc.0c00715

  • [48].
    Kriz K, Novacek M, Rezac J. Non-covalent interactions atlas benchmark data sets 3: Repulsive contacts. J Chem Theory Comput 2021; 17(3):1548-1561.

    https://doi.org/10.1021/acs.jctc.0c01341

  • [49].
    Kříž K, Řezáč J. Non-covalent interactions atlas benchmark data sets 4: σ-hole interactions. Phys Chem Chem Phys 2022; 24(24):14794-14804.

    https://doi.org/10.1039/D2CP01600A

  • [50].
    Řezáč J. Non-Covalent Interactions Atlas benchmark data sets 5: London dispersion in an extended chemical space. Phys Chem Chem Phys 2022; 24(24):14780-14793.

    https://doi.org/10.1039/D2CP01602H

  • [51].
    Burns LA, Faver JC, Zheng Z, Marshall MS, Smith DG, Vanommeslaeghe K, MacKerell Jr AD, Merz Jr KM, and Sherrill CD. The BioFragment Database (BFDb): An open-data platform for computational chemistry analysis of noncovalent interactions. The Journal of Chemical Physics 2017; 147(16):161727.

    https://doi.org/10.1063/1.5001028

  • [52].
    Goerigk L, Hansen A, Bauer C, Ehrlich S, Najibi A, Grimme S. A look at the density functional theory zoo with the advanced GMTKN55 database for general main group thermochemistry, kinetics and noncovalent interactions. Phys Chem Chem Phys 2017; 19 (48):32184-32215.

    https://doi.org/10.1039/C7CP04913G

  • [53].
    Bruno IJ, Cole JC, Lommerse JP, Rowland RS, Taylor R, Verdonk ML. IsoStar: a library of information about nonbonded interactions. J Comput Aided Mol Des 1997; 11(6):525-37.

    https://doi.org/10.1023/A:1007934413448

  • [54].
    Novikov AS. IsoStar program suite for studies of noncovalent interactions in crystals of chemical compounds. Crystals 2021;11(2):162.

    https://doi.org/10.3390/cryst11020162

  • [55].
    Adasme M, Linnemann KL, Bolz SN, Kaiser F, Salentin S, Haupt VJ, Schroeder M. PLIP 2021: Expanding the scope of the protein-ligand interaction profiler to DNA and RNA. Nucleic Acids Res 2021; 49(W1):W530-W534.

    https://doi.org/10.1093/nar/gkab294

  • [56].
    Bai B, Zou R, Chan HCS, Li H, Yuan S. MolADI: A Web Server for Automatic Analysis of Protein-Small Molecule Dynamic Interactions. Molecules 2021; 26(15):4625.

    https://doi.org/10.3390/molecules26154625

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    Ferruz N, Schmidt S, Hocker B. ProteinTools: a toolkit to analyze protein structures. Nucleic Acids Res 2021; 49(W1):W559-W566.

    https://doi.org/10.1093/nar/gkab375

  • [58].
    Das S, Chakrabarti S. Classification and prediction of protein-protein interaction interface using machine learning algorithm. Sci Rep 2021; 11(1):1-12.

    https://doi.org/10.1038/s41598-020-80900-2.pdf

  • [59].
    Heo L, Park S, Seok C. GalaxyWater-wKGB: Prediction of Water Positions on Protein Structure Using wKGB Statistical Potential. J Chem Inf Model 2021; 61(5):2283-2293.

    https://doi.org/10.1021/acs.jcim.0c01434

  • [60].
    Fassio AV, Santos LH, Silveira SA, Ferreira RS, de Melo-Minardi RC. nAPOLI: A Graph-Based Strategy to Detect and Visualize Conserved Protein-Ligand Interactions in Large-Scale. IEEE/ACM Trans Comput Biol Bioinform 2019; 17(4):1317-1328.

    https://doi.org/10.1109/TCBB.2019.2892099

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    Felline A, Seeber M, Fanelli F. webPSN v2.0: a webserver to infer fingerprints of structural communication in biomacromolecules. Nucleic Acids Res 2020; 48(W1):W94-W103.

    https://doi.org/10.1093/nar/gkaa397

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    Gopi S, Devanshu D, Rajasekaran N, Anantakrishnan S, Naganathan AN. pPerturb: A Server for Predicting Long-Distance Energetic Couplings and Mutation-Induced Stability Changes in Proteins via Perturbations. ACS Omega 2020; 5(2):1142-1146.

    https://doi.org/10.1021/acsomega.9b03371

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    Jones DT, Singh T, Kosciolek T, Tetchner S. MetaPSICOV: combining coevolution methods for accurate prediction of contacts and long range hydrogen bonding in proteins. Bioinformatics 2015;31(7):999-1006.

    https://doi.org/10.1093/bioinformatics/btu791

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    Li W, Wang D, Yang Z, Zhang H, Hu L, Chen G. DeepNCI: DFT Noncovalent Interaction Correction with Transferable Multimodal Three-Dimensional Convolutional Neural Networks. J Chem Inf Model 2022; 62(21):5090-5099.

    https://doi.org/10.1021/acs.jcim.1c01305

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    Heidrich J, Exner TE, Boeckler FM. Predicting the magnitude of σ-holes using VmaxPred, a fast and efficient tool supporting the application of halogen bonds in drug discovery. J Chem Inf Model 2018; 59(2):636-643.

    https://doi.org/10.1021/acs.jcim.8b00622

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    Bietz S, Urbaczek S, Schulz B, Rarey M. Protoss: a holistic approach to predict tautomers and protonation states in protein-ligand complexes. Journal of Cheminform 2014; 6:1-12.

    https://doi.org/10.1186/1758-2946-6-12

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    CaPTURE: Capturing Protein Targets Utilizing Residue Energies. Accessed January 15, 2023.

    CaPTURE Program

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    Li S, Wan F, Shu H, Jiang T, Zhao D, Zeng J. MONN: A Multi-Objective Neural Network for Predicting Compound-Protein Interactions and Affinities. Cell Systems 2020; 10(4):308-322.e11.

    https://doi.org/10.1016/j.cels.2020.03.002

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    Protein Data Bank.Nat. New Biol 1971;233:223.

    https://doi.org/10.1038/newbio233223b0

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    Burley SK, Bhikadiya C, Bi C, et al. RCSB Protein Data Bank: powerful new tools for exploring 3D structures of biological macromolecules for basic and applied research and education in fundamental biology, biomedicine, biotechnology, bioengineering and energy sciences. Nucleic Acids Res 2021; 49:D437.

    https://doi.org/10.1093/nar/gkaa1038

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    Biot C, Buisine E, Kwasigroch JM, Wintjens R, Rooman M. Probing the energetic and structural role of amino acid/nucleobase cation-π interactions in protein-ligand complexes. J Biol Chem 2002; 227:40816.

    https://doi.org/10.1074/jbc.M205719200

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    Enzyme Nomenclature. https://iubmb.qmul.ac.uk/enzyme/. Accessed March 20, 2023.

    Enzyme Nomenclature & Database