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1. Designer high-density lipoprotein particles enhance endothelial barrier function and suppress inflammation.

Author

Lin, Yueh-Chien, Swendeman, Steven, Moreira, Irina S., Ghosh, Avishek, Kuo, Andrew, Rosário-Ferreira, Nícia, Guo, Shihui, Culbertson, Alan, Levesque, Michel V., Cartier, Andreane, Seno, Takahiro, Schmaier, Alec, Galvani, Sylvain, Inoue, Asuka, Parikh, Samir M., FitzGerald, Garret A., Zurakowski, David, Liao, Maofu, Flaumenhaft, Robert, and Gümüş, Zeynep H.

Subjects
HIGH density lipoproteins, CYTOSKELETAL proteins, INFLAMMATORY mediators, CHIMERIC proteins, MOLECULAR dynamics, CYTOKINE release syndrome, VASCULAR endothelium
Abstract

High-density lipoprotein (HDL) nanoparticles promote endothelial cell (EC) function and suppress inflammation, but their utility in treating EC dysfunction has not been fully explored. Here, we describe a fusion protein named ApoA1-ApoM (A1M) consisting of apolipoprotein A1 (ApoA1), the principal structural protein of HDL that forms lipid nanoparticles, and ApoM, a chaperone for the bioactive lipid sphingosine 1-phosphate (S1P). A1M forms HDL-like particles, binds to S1P, and is signaling competent. Molecular dynamics simulations showed that the S1P-bound ApoM moiety in A1M efficiently activated EC surface receptors. Treatment of human umbilical vein ECs with A1M-S1P stimulated barrier function either alone or cooperatively with other barrier-enhancing molecules, including the stable prostacyclin analog iloprost, and suppressed cytokine-induced inflammation. A1M-S1P injection into mice during sterile inflammation suppressed neutrophil influx and inflammatory mediator secretion. Moreover, systemic A1M administration led to a sustained increase in circulating HDL-bound S1P and suppressed inflammation in a murine model of LPS-induced endotoxemia. We propose that A1M administration may enhance vascular endothelial barrier function, suppress cytokine storm, and promote resilience of the vascular endothelium. Editor's summary: Endothelial cell function is enhanced by bioactive lipids such as prostacyclin and sphingosine 1-phosphate (S1P) when bound to its chaperone apolipoprotein M (ApoM). Lin et al. generated a fusion protein called A1M to promote both S1P and prostacyclin signaling. A1M consisted of ApoM and ApoA1, a structural component of high-density lipoprotein (HDL) that stabilizes prostacyclin. When bound to S1P or a prostacyclin analog, A1M suppressed inflammation and increased the barrier function of cultured endothelial cells. In mouse models of acute or systemic inflammation, S1P-loaded A1M suppressed neutrophil infiltration, increases in circulating proinflammatory factors, and cytokine storm. The ApoA1 moiety of A1M therefore provides additional benefits over previously engineered S1P chaperones. —Wei Wong [ABSTRACT FROM AUTHOR]

Published
2024
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4. Recent Advances on Computational Proteomics

Author

Sousa, Sérgio F., Cerqueira, Nuno M. F. S. A., Perez, Marta A. S., Moreira, Irina S., M.Ribeiro, António J., Neves, Ana R. A. P., Ramos, Maria J., and Fernandes, Pedro A.

Subjects
Enzyme, Protein, Quantum Mechanics, Molecular Dynamics
Abstract

In this work we report the recent progresses that have been achieved by our group in the last half decade on the field of computational proteomics. Specifically, we discuss the application of Molecular Dynamics Simulations and Electronic Structure Calculations in drug design, in the clarification of the structural and dynamic properties of proteins and enzymes and in the understanding of the catalytic and inhibition mechanism of cancer-related enzymes. A set of examples illustrate the concepts and help to introduce the reader into this important and fast moving field., {"references":["J. A. McCammon, B. R. Gelin, and M. Karplus, \"Dynamics of folded\nproteins,\" Nature, vol. 267, pp. 585-590, 1977.","M. Karplus and J. A. McCammon, \"Molecular dynamics simulations of\nbiomolecules,\" Nat. Struct. Biol., vol. 9, pp. 646-652, 2002.","A. Warshel, \"Molecular dynamics simulations of biological reactions,\"\nAcc. Chem. Res., vol. 35, pp. 385-395, 2002.","S. J. Weiner, P. A. Kollman, D. A. Case, U. C. Singh, C. Ghio, G.\nAlagona, et al., \"A new force-field for molecular mechanical simulation\nof nucleic-acids and proteins,\" J. Am. Chem. Soc., vol. 106, pp. 765-\n784, 1984.","S. J. Weiner, P. A. Kollman, D. T. Nguyen, and D. A. Case, \"An allatom\nforce field for simulations of protein and nucleic acids,\" J.\nComput. Chem., vol. 7, pp. 230-252, 1986.","W. D. Cornell, P. Cieplak, C. I. Bayly, I. R. Gould, K. M. Merz, D. M.\nFergunson, et al., \"A 2nd generation force-field for the simulation of\nproteins, nucleic-acids, and organic-molecules,\" J. Am. Chem. Soc., vol.\n117, pp. 5179-5197, 1995.","B. R. Brooks, R. E. Bruccoleri, B. D. Olafson, D. J. States, S.\nSwaminathan, and M. Karplus, \"CHARMM - a programm for\nmacromolecular energy, minimization, and dynamics calculations,\" J.\nComput. Chem., vol. 4, pp. 187-217, 1983.","A. D. MacKerell, D. Bashford, M. Bellott, R. L. Dunbrack, J. D.\nEvanseck, M. J. Field et al., \"All-atom empirical potential for molecular\nmodeling and dynamics studies of proteins,\" J. Phys. Chem. B, vol. 102,\npp. 3586-3616, 1998.","A. D. MacKerell, N. Banavali, and N. Foloppe, \"Development and\ncurrent status of the CHARMM force field for nucleic acids,\"\nBiopolymers, vol. 56, pp. 257-265, 2000.\n[10] W. L. Jorgensen and J. Tirado-Rives, \"The OPLS [optimized potentials\nfor liquid simulations] potential functions for proteins, energy\nminimizations for crystals of cyclic peptides and crambin,\" J. Am. Chem. Soc., vol. 110, pp. 1657-1666, 1988.\n[11] J. Pranata, S. G. Wierschke, and W. L. Jorgensen, \"Opls Potential\nFunctions for Nucleotide Bases - Relative Association Constants of\nHydrogen-Bonded Base-Pairs in Chloroform,\" J. Am. Chem. Soc., vol. 113, pp. 2810-2819, 1991.\n[12] W. L. Jorgensen, D. S. Maxwell, and J. Tirado-Rives, \"Development\nand testing of the OPLS all-atom force field on conformational\nenergetics and properties of organic liquids,\" J. Am. Chem. Soc., vol.\n118, pp. 11225-11236, 1996.\n[13] A. T. Marques, P. A. Fernandes, and M. J. Ramos, \"Molecular dynamics\nsimulations of the amyloid-beta binding alcohol dehydrogenase\n(ABAD) enzyme,\" Bioorgan. Med. Chem., vol. 16, pp. 9511-9518, 2008.\n[14] N. F. Bras, N. M. F. S. Cerqueira, P. A. Fernandes, and M. J. Ramos,\n\"Carbohydrate-binding modules from family 11: Understanding the\nbinding mode of polysaccharides,\" Int. J. Quantum Chem., vol. 108, pp.\n2030-2040, 2008.\n[15] A. Viegas, N. F. Bras, N. M. F. S. Cerqueira, P. A. Fernandes, J. A. M.\nPrates, C. M. G. A. Fontes, et al., \"Molecular determinants of ligand specificity in family 11 carbohydrate binding modules - an NMR, X-ray\ncrystallography and computational chemistry approach,\" FEBS J., vol.\n275, pp. 2524-2535, 2008.\n[16] A. T. P. Carvalho, M. Swart, J. N. P. van Stralen, P. A. Fernandes, M. J.\nRamos, and F. M. Bickelhaupt, \"Mechanism of thioredoxin-catalyzed\ndisulfide reduction. Activation of the buried thiol and role of the variable active-site residues,\" J. Phys. Chem. B, vol. 112, pp. 2511-2523, 2008.\n[17] A. T. P. Carvalho, P. A. Fernandes, and M. J. Ramos, \"Insights on\nresistance to reverse transcriptase: The different patterns of interaction\nof the nucleoside reverse transcriptase inhibitors in the\ndeoxyribonucleotide triphosphate binding site relative to the normal\nsubstrate,\" J. Med. Chem., vol. 49, pp. 7675-7682, 2006.\n[18] S. F. Sousa, P. A. Fernandes, and M. J. Ramos, \"Enzyme Flexibility and\nthe Catalytic Mechanism of Farnesyltransferase: Targeting the\nRelation,\" J. Phys. Chem. B, vol. 112, pp. 8681-8691, 2008.\n[19] S. F. Sousa, P. A. Fernandes, and M. J. Ramos, \"Molecular Dynamics\nSimulations on the Critical States of the Farnesyltransferase Enzyme,\"\nBioorg. Med. Chem., pp. (10.1016/j.bmc.2009.03.055), 2009.\n[20] S. F. Sousa, P. A. Fernandes, and M. J. Ramos, \"Molecular dynamics\nanalysis of farnesyltransferase: A closer look into the amino acid\nbehavior,\" Int. J. Quant. Chem., vol. 108, pp. 1939-1950, 2008.\n[21] N. M. F. S. Cerqueira, N. F. Bras, P. A. Fernandes, and M. J. Ramos,\n\"MADAMM: A multistaged docking with an automated molecular\nmodeling protocol,\" Proteins, vol. 74, pp. 192-206, 2009.\n[22] S. F. Sousa, P. A. Fernandes, and M. J. Ramos, \"Unraveling the\nmechanism of the farnesyltransferase Enzyme,\" J. Biol. Inorg. Chem.,\nvol. 10, pp. 3-10, 2005.\n[23] S. F. Sousa, P. A. Fernandes, and M. J. Ramos, \"Farnesyltransferase\ninhibitors: A detailed chemical view on an elusive biological problem,\"\nCurr. Med. Chem., vol. 15, pp. 1478-1492, 2008.\n[24] S. F. Sousa, P. A. Fernandes, and M. J. Ramos, \"The Search for the\nMechanism of the Reaction Catalyzed by Farnesyltransferase,\"\nChemistry, 2009.\n[25] S. F. Sousa, P. A. Fernandes, and M. J. Ramos, \"Farnesyltransferase--\nNew Insights into the Zinc-Coordination Sphere Paradigm: Evidence for\na Carboxylate-Shift Mechanism,\" Biophys. J., vol. 88, pp. 483-494,2005.\n[26] S. F. Sousa, P. A. Fernandes, and M. J. Ramos, \"Farnesyltransferase: \nTheoretical Studies on Peptide Substrate Entrance - Thiol or Thiolate \nCoordination?,\" J. Mol. Struct. (Theochem), vol. 729, pp. 125-129, \n2005. \n[27] S. F. Sousa, P. A. Fernandes, and M. J. Ramos, \"The carboxylate-shift \nin zinc enzymes: a computational study,\" J. Am. Chem. Soc., vol. 129, \npp. 1378-1385, 2007. \n[28] S. F. Sousa, P. A. Fernandes, and M. J. Ramos, \"Theoretical Studies on \nFarnesyltransferase: The Distances Paradox Explained,\" Proteins, vol. \n66, pp. 205-218, 2007. \n[29] M. J. Ramos and P. A. Fernandes, \"Computational enzymatic catalysis,\" \nAcc. Chem. Res., vol. 41, pp. 689-698, 2008. \n[30] S. B. Long, P. J. Casey, and L. S. Beese, \"Reaction path of protein \nfarnesyltransferase at atomic resolution,\" Nature, vol. 419, pp. 645-650, \n2002. \n[31] H. W. Park, S. R. Boduluri, J. F. Moomaw, P. J. Casey, and L. S. Beese, \n\"Crystal Structure of Protein Farnesyltransferase at 2.25 Angstrom \nResolution,\" Science, vol. 275, pp. 1800-1804, 1997. \n[32] D. A. Tobin, J. S. Pickett, H. L. Hartman, C. A. Fierke, and J. E. \nPenner-Hahn, \"Structural characterization of the zinc site in protein \nfarnesyltransferase,\" J. Am. Chem. Soc., vol. 125, pp. 9962-9969,2003. \n[33] S. F. Sousa, P. A. Fernandes, and M. J. Ramos, \"Theoretical Studies on \nFarnesyltransferase: Evidence for Thioether Product Coordination to the \nActive-Site Zinc Sphere,\" J. Comput. Chem., vol. 28, pp. 1160-1168, \n2007. \n[34] N. M. F. S. A. Cerqueira, S. Pereira, P. A. Fernandes, and M. J. Ramos, \n\"Overview of ribonucleotide reductase inhibitors: an appealing target in \nanti-tumour therapy,\" Curr. Med. Chem., vol. 12, pp. 1283-1294, 2005. \n[35] N. M. F. S. A. Cerqueira, P. A. Fernandes, L. A. Eriksson, and M. J. \nRamos, \"New insights into a critical biological control step of the \nmechanism of ribonucleotide reductase,\" J. Mol. Struct. (Theochem), \nvol. 709, pp. 53-65, 2004. \n[36] N. M. F. S. A. Cerqueira, P. A. Fernandes, L. A. Eriksson, and M. J. \nRamos, \"Ribonucleotide activation by enzyme ribonucleotide reductase: \nunderstanding the role of the enzyme,\" J. Comput. Chem., vol. 25, pp. \n2031-2037, 2004. \n[37] N. M. F. S. A. Cerqueira, P. A. Fernandes, L. A. Eriksson, and M. J. \nRamos, \"Dehydration of ribonucleotides catalysed by ribonucleotide \nreductase: the role of the enzyme,\" Biophys. J., vol. 90, pp. 2109-2119, \n2006. \n[38] P. A. Fernandes, L. A. Eriksson, and M. J. Ramos, \"The reduction of \nribonucleotides catalyzed by the enzyme ribonucleotide reductase,\" \nTheor. Chem. Acc., vol. 108, pp. 352-364, 2002. \n[39] S. Pereira, N. M. F. S. A. Cerqueira, P. A. Fernandes, and M. J. Ramos, \n\"Computational studies on class I ribonucleotide reductase: \nUnderstanding the mechanism of action and inhibition of a cornerstone \nenzyme for the treatment of cancer,\" Eur. Biophys. J., vol. 35, pp. 125-\n135, 2006. \n[40] P. A. Fernandes and M. J. Ramos, \"Theoretical studies on the mode of \ninhibition of ribonucleotide reductase by 2'-substituted substrate \nanalogues,\" Chem. Eur. J., vol. 9, pp. 5916-5925, 2003. \n[41] N. M. F. S. Cerqueira, P. A. Fernandes, and M. J. Ramos, \"Enzyme \nribonucleotide reductase: Unraveling an enigmatic paradigm of enzyme \ninhibition by furanone derivatives,\" J. Phys. Chem. B, vol. 110, pp. \n21272-21281, 2006. \n[42] S. Pereira, P. A. Fernandes, and M. J. Ramos, \"Theoretical study of \nribonucleotide reductase mechanism-based inhibition by 2'-azido-2'-\ndeoxyribonucleoside 5'-diphosphates,\" J. Comput. Chem., vol. 25, pp. \n227-237, 2004. \n[43] S. Pereira, P. A. Fernandes, and M. J. Ramos, \"Mechanism for \nribonucleotide reductase inactivation by the anticancer drug \ngemcitabine,\" J. Comput. Chem., vol. 25, pp. 1286-1294, 2004. \n[44] S. Pereira, P. A. Fernandes, and M. J. Ramos, \"Theoretical study on the \ninhibition of ribonucleotide reductase by 2'-mercapto-2'-\ndeoxyribonucleoside-5,\" J. Am. Chem. Soc., vol. 127, pp. 5174-5179, \n2005. \n[45] P. A. Fernandes and M. J. Ramos, \"Theoretical studies on the \nmechanism of inhibition of Ribonucleotide Reductase by (E)-2'-\nFluoromethylene-2'-deoxycitidine-5'-diphosphate,\" J. Am. Chem. Soc., \nvol. 125, pp. 6311-6322, 2003. \n[46] A. Ghosh, \"Just how good is DFT?,\" J. Biol. Inorg. Chem., vol. 11, pp. \n671-673, 2006. \n[47] N. E. Schultz, Y. Zhao, and D. G. Truhlar, \"Density functionals for \ninorganometallic and organometallic chemistry,\" J. Phys. Chem. A, vol. \n109, pp. 11127-11143, 2005. \n[48] P. Hohenberg and W. Kohn, \"Inhomogeneous Electron Gas,\" Phys. Rev. \nB, vol. 136, pp. B864, 1964. \n[49] Y. Zhao and D. G. Truhlar, \"Density functionals with broad \napplicability in chemistry,\" Acc. Chem. Res., vol. 41, pp. 157-167, \n2008. \n[50] S. F. Sousa, P. A. Fernandes, and M. J. Ramos, \"General Performance of \nDensity Functionals,\" J. Phys. Chem A, vol. 111, pp. 10439-10452, \n2007. \n[51] S. F. Sousa, P. A. Fernandes, and M. J. Ramos, \"Comparative \nassessment of theoretical methods for the determination of geometrical \nproperties in biological zinc complexes,\" J. Phys. Chem. B, vol. 111, pp. \n9146-9152, 2007. \n[52] P. Jurecka, J. Cerny, P. Hobza, and D. R. Salahub, \"Density functional \ntheory augmented with an empirical dispersion term. Interaction \nenergies and geometries of 80 noncovalent complexes compared with ab \ninitio quantum mechanics calculations,\" J. Comput. Chem., vol. 28, pp. \n555-569, 2007. \n[53] A. Karton, A. Tarnopolsky, J. F. Lamere, G. C. Schatz, and J. M. L. \nMartin, \"Highly Accurate First-Principles Benchmark Data Sets for the \nParametrization and Validation of Density Functional and Other \nApproximate Methods. Derivation of a Robust, Generally Applicable, \nDouble-Hybrid Functional for Thermochemistry and Thermochemical \nKinetics,\" J. Phys. Chem. A, vol. 112, pp. 12868-12886, 2008. \n[54] Y. Zhao and D. G. Truhlar, \"Density functionals for noncovalent \ninteraction energies of biological importance,\" J. Chem. Theor. \nComput., vol. 3, pp. 289-300, 2007. \n[55] T. van Mourik, \"Assessment of Density Functionals for Intramolecular \nDispersion-Rich Interactions,\" J. Chem. Theor. Comput., vol. 4, pp. \n1610-1619, 2008. \n[56] R. Peverati and K. K. Baldridge, \"Implementation and Performance of \nDFT-D with Respect to Basis Set and Functional for Study of \nDispersion Interactions in Nanoscale Aromatic Hydrocarbons,\" J. Chem. \nTheor. Comput., vol. 4, pp. 2030-2048, 2008. \n[57] N. F. Bras, S. A. Moura-Tamames, P. A. Fernandes, and M. J. Ramos, \n\"Mechanistic Studies on the Formation of Glycosidase-Substrate and \nGlycosidase-Inhibitor Covalent Intermediates,\" J. Comput. Chem., vol. \n29, pp. 2565-2574, 2008. \n[58] I. S. Moreira, P. A. Fernandes, and M. J. Ramos, \"Hot spots-A review of \nthe protein-protein interface determinant amino-acid residues,\" Proteins, \nvol. 68, pp. 803-812, 007. \n[59] S. Jones and J. M. Thornton, \"Principles of protein-protein interactions,\" \nProc. Natl. Acad. Sci. USA, vol. 93, pp. 13-20, 1996. \n[60] A. A. Bogan and K. S. Thorn, \"Anatomy of hot spots in protein \ninterfaces,\" J. Mol. Biol., vol. 280, pp. 1-9, 1998. \n[61] T. Clackson and J. A. Wells, \"A Hot-Spot of Binding-Energy in A \nHormone-Receptor Interface,\" Science, vol. 267, pp. 383-386, 1995. \n[62] W. L. Delano, \"Unraveling hot spots in binding interfaces: progress and \nchallenges,\" Curr. Opin. Struct. Biol., vol. 12, pp. 14-20, 2002. \n[63] I. S. Moreira, P. A. Fernandes, and M. J. Ramos, \"Detailed microscopic \nstudy of the full ZipA : FtsZ interface,\" Proteins, vol. 63, pp. 811-821, \n2006. \n[64] I. S. Moreira, P. A. Fernandes, and M. J. Ramos, \"Unraveling the \nimportance of protein-protein interaction: Application of a \ncomputational alanine-scanning mutagenesis to the study of the IgG1 \nstreptococcal protein G (C2 fragment) complex,\" J. Phys. Chem. B, vol. \n110, pp. 10962-10969, 2006. \n[65] I. S. Moreira, P. A. Fernandes, and M. J. Ramos, \"Hot spot \ncomputational identification: Application to the complex formed \nbetween the hen egg white lysozyme (HEL) and the antibody HyHEL-\n10,\" Int. J. Quant. Chem., vol. 107, pp. 299-310, 2007. \n[66] I. S. Moreira, P. A. Fernandes, and M. J. Ramos, \"Hot spot occlusion \nfrom bulk water: A comprehensive study of the complex between the \nlysozyme HEL and the antibody FVD1.3,\" J. Phys. Chem. B, vol. 111, \npp. 2697-2706, 2007. \n[67] I. S. Moreira, P. A. Fernandes, and M. J. Ramos, \"Unravelling Hot \nSpots: a comprehensive computational mutagenesis study,\" Theor. \nChem. Acc., vol. 117, pp. 99-113, 2007. \n[68] I. S. Moreira, P. A. Fernandes, and M. J. Ramos, \"Accuracy of the \nnumerical solution of the Poisson-Boltzmann equation,\" J. Mol. Struct. \n(Theochem), vol. 729, pp. 11-18, 2005. \n[69] I. S. Moreira, P. A. Fernandes, and M. J. Ramos, \"Computational \nalanine scanning mutagenesis - An improved methodological approach,\" \nJ. Comput. Chem., vol. 28, pp. 644-654, 2007. \n[70] T. Kortemme and D. Baker, \"A simple physical model for binding \nenergy hot spots in protein-protein complexes,\" Proc. Natl. Acad. Sci. \nUSA, vol. 99, pp. 14116-14121, 2002. \n[71] E. Guney, N. Tuncbag, O. Keskin, and A. Gursoy, \"HotSprint: database \nof computational hot spots in protein interfaces,\" Nucleic Acids Res., \nvol. 36, pp. D662-D666, 2008. \n[72] S. J. Darnell, D. Page, and J. C. Mitchell, \"An automated decision-tree \napproach to predicting protein interaction hot spots,\" Proteins, vol. 68, \npp. 813-823, 2007. \n[73] D. A. Case, T. A. Darden, T. E. Cheatham III, C. L. Simmerling, J. \nWang, R. E. Duke, et al., \"AMBER 8,\" University of California, San \nFrancisco, 2004. \n[74] M. R. Shirts, J. W. Pitera, W. C. Swope, and V. S. Pande, \"Extremely \nprecise free energy calculations of amino acid side chain analogs: \nComparison of common molecular mechanics force fields for proteins,\" \nJ. Chem. Phys., vol. 119, pp. 5740-5761, 2003. \n[75] J. W. Pitera and W. F. van Gunsteren, \"A comparison of non-bonded \nscaling approaches for free energy calculations,\" Mol. Simul., vol. 28, \npp. 45-65, 2002. \n[76] A. Blondel, \"Ensemble variance in free energy calculations by \nthermodynamic integration: Theory, optimal \"Alchemical\" path, and \npractical solutions,\" J. Comput. Chem., vol. 25, pp. 985-993, 2004. \n[77] P. A. Kollman, I. Massova, C. Reyes, B. Kuhn, S. H. Huo, L. Chong, M. \nLee, T. Lee, Y. Duan, W. Wang, O. Donini, P. Cieplak, J. Srinivasan, D. \nA. Case, and T. E. Cheatham, \"Calculating structures and free energies \nof complex molecules: Combining molecular mechanics and continuum \nmodels,\" Acc. Chem. Res., vol. 33, pp. 889-897, 2000. \n[78] M. A. S. Perez, P. A. Fernandes, and M. J. Ramos, \"Drug design: New \ninhibitors for HIV-1 protease based on Nelfinavir as lead,\" J. Mol. \nGraph. Model., vol. 26, pp. 634-642, 2007. \n[79] W. Wang and P. A. Kollman, \"Computational study of protein \nspecificity: The molecular basis of HIV-1 protease drug resistance,\" \nProc. Natl. Acad. Sci. USA, vol. 98, pp. 14937-14942, 2001. \n[80] S. Gupta, L. M. Rodrigues, A. P. Esteves, A. M. F. Oliveira-Campos, M. \nS. J. Nascimento, N. Nazareth, H. Cidade, M. P. Neves, F. Fernandes, \nM. Pinto, N. M. F. S. A. Cerqueira, and N. Bras, \"Synthesis of N-aryl-5-\namino-4-cyanopyrazole derivatives as potent xanthine oxidase \ninhibitors,\" Eur. J. Med. Chem., vol. 43, pp. 771-780, 2007. \n[81] M. L. Lamb and W. L. Jorgensen, \"Computational approaches to \nmolecular recognition,\" Curr. Opin. Chem. Biol., vol. 1, pp. 449-457, \n1997. \n[82] C. N. Cavasotto and N. Singh, \"Docking and high throughput docking: \nSuccesses and the challenge of protein flexibility,\" Curr. Comput. Aided \nDrug Des., vol. 4, pp. 221-234, 2008. \n[83] S. F. Sousa, P. A. Fernandes, and M. J. Ramos, \"Protein-ligand docking: \nCurrent status and future challenges,\" Proteins, vol. 65, pp. 15-26, 2006. \n[84] N. Brooijmans and I. D. Kuntz, \"Molecular recognition and docking \nalgorithms,\" Ann. Rev. Biophys. Biomol. Struct., vol. 32, pp. 335-373, \n2003."]}

Published
2009
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5. Re(I) and Tc(I) Complexes for Targeting Nitric Oxide Synthase: Influence of the Chelator in the Affinity for the Enzyme.

Author

Oliveira, Bruno L., Morais, Maurício, Mendes, Filipa, Moreira, Irina S., Cordeiro, Carlos, Fernandes, Pedro A., Ramos, Maria J., Alberto, Roger, Santos, Isabel, and Correia, João D. G.

Subjects
NITRIC-oxide synthases, BIOCONJUGATES, RHENIUM compounds, TECHNETIUM compounds, SINGLE-photon emission computed tomography, METAL complexes, MOLECULAR dynamics
Abstract

Aiming to design 99mTc complexes for probing nitric oxide synthase ( NOS) by SPECT, we synthesized conjugates ( L4- L6) comprising a NOS-recognizing moiety connected to a diamino-propionic acid (dap) chelating unit. The conjugates led to complexes of the type fac-[M( CO)3(ĸ3-L)] (M = Re/99mTc; Re4/ Tc4: L = L4; Re5/ Tc5: L = L5; Re6/ Tc6: L = L6). Enzymatic studies showed that L4 and L5, but not L6, gave complexes ( Re4 and Re5) that are less potent than the conjugates. To rationalize these results, we performed docking and molecular dynamics simulations. The high affinity of L4 and L5 is due to the strong interactions between the dap chelator and polar residues of the binding cavity. These interactions are hampered by metallation resulting in complexes with lower affinity. The higher potency of Re5 compared to Re4 was assigned to the increased bulkiness of Re5 and the presence of additional anchoring groups that better fit the active site and provide more extensive contacts. In turn, Re6 is too bulky and its organometallic tail is oriented toward the peripheral pocket of iNOS, leading to loss of contacts and a lower affinity. These results were compared with our previous results obtained with analogue complexes stabilized by a pyrazolyl-diamine chelating unit. [ABSTRACT FROM AUTHOR]

Published
2015
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6. Theoretical studies on the binding of rhenium(I) complexes to inducible nitric oxide synthase.

Author

Oliveira, Bruno L., Moreira, Irina S., Fernandes, Pedro A., Ramos, Maria J., Santos, Isabel, and Correia, João D.G.

Subjects
*RHENIUM, *NITRIC-oxide synthases, *MOLECULAR probes, *MOLECULAR docking, *STRUCTURE-activity relationships, *COMPUTATIONAL biology
Abstract

Highlights: [•] Imaging of NOS in vivo with 99mTc(I) probes would allow earlier diagnosis of diseases. [•] Re(I) non-radioactive surrogates display different degrees of iNOS inhibition. [•] A computational study combining molecular docking, MD simulations and FEP calculations was performed to establish a structure activity relationship. [•] Interactions responsible for iNOS-recognizing ability of the complexes were identified. [•] Electrostatic interactions between the Re(CO)3 core and R260/R382 are a key issue. [Copyright &y& Elsevier]

Published
2013
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7. Protein–protein recognition: a computational mutagenesis study of the MDM2–P53 complex.

Author

Moreira, Irina S., Fernandes, Pedro A., and Ramos, Maria J.

Subjects
*PROTEIN-protein interactions, *MUTAGENESIS, *HYDROGEN bonding, *MOLECULAR dynamics, *EPITOPES, *APOPTOSIS
Abstract

Protein P53 is involved in more than 50% of the human cancers and the P53–MDM2 complex is a target for anticancer drug design. It is possible to engineer small P53 mimics that would be expected to disrupt the P53–MDM2 complex, and release P53 to initiate cell-cycle arrest or apoptosis. These small peptides should bind to the functional epitopes of the protein–protein interface, and prevent the interaction between P53 and MDM2. Here, we apply an improved computational alanine scanning mutagenesis method, which allows the determination of the hot spots present in both monomers, P53 and MDM2, of three protein complexes (the P53-binding domain of human MDM2, its analogue from Xenopus laev is, and the structure of human MDM2 in complex with an optimized P53 peptide). The importance of the hydrogen bonds formed by the protein backbone has been neglected due to the difficulty of measuring experimentally their contribution to the binding free energy. In this study we present a computational approach that allows the estimation of the contribution to the binding free energy of the C=O and N–H groups in the backbone of the P53 and MDM2 proteins. We have noticed that the hydrogen bond between the HE1 atom of the hot spot Trp23 and the O atom of the residue Leu54, as well as the NH-pi hydrogen bond between the Ile57 and Met58 were observed in the Molecular dynamics simulation, and their contribution to the binding free energy measured. This study not only shows the reliability of the computational mutagenesis method to detect hot spots but also demonstrates an excellent correlation between the quantitative calculated binding free energy contribution of the C=O and N–H backbone groups of the interfacial residues and the qualitative values expected for this kind of interaction. The study also increases our understanding of the P53–MDM2 interaction. [ABSTRACT FROM AUTHOR]

Published
2008
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8. Computational alanine scanning mutagenesis—An improved methodological approach.

Author

Moreira, Irina S., Fernandes, Pedro A., and Ramos, Maria J.

Subjects
*PROTEINS, *MUTAGENESIS, *MOLECULAR dynamics, *AMINO acids, *ALANINE
Abstract

Alanine scanning mutagenesis of protein–protein interfacial residues can be applied to a wide variety of protein complexes to understand the structural and energetic characteristics of the hot-spots. Binding free energies have been estimated with reasonable accuracy with empirical methods, such as Molecular Mechanics/Poisson-Boltzmann surface area (MM-PBSA), and with more rigorous computational approaches like Free Energy Perturbation (FEP) and Thermodynamic Integration (TI). The main objective of this work is the development of an improved methodological approach, with less computational cost, that predicts accurately differences in binding free energies between the wild-type and alanine mutated complexes (ΔΔGbinding). The method was applied to three complexes, and a mean unsigned error of 0.80 kcal/mol was obtained in a set of 46 mutations. The computational method presented here achieved an overall success rate of 80% and an 82% success rate in residues for which alanine mutation causes an increase in the binding free energy > 2.0 kcal/mol (warm- and hot-spots). This fully atomistic computational methodological approach consists in a computational Molecular Dynamics simulation protocol performed in a continuum medium using the Generalized Born model. A set of three different internal dielectric constants, to mimic the different degree of relaxation of the interface when different types of amino acids are mutated for alanine, have to be used for the proteins, depending on the type of amino acid that is mutated. This method permits a systematic scanning mutagenesis of protein–protein interfaces and it is capable of anticipating the experimental results of mutagenesis, thus guiding new experimental investigations. © 2006 Wiley Periodicals, Inc. J Comput Chem 28: 644–654, 2007 [ABSTRACT FROM AUTHOR]

Published
2007
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9. A Machine Learning Approach for the Discovery of Ligand-Specific Functional Mechanisms of GPCRs.

Author

Plante, Ambrose, Shore, Derek M., Morra, Giulia, Khelashvili, George, Weinstein, Harel, and Moreira, Irina S.

Subjects
LEARNING by discovery, G protein coupled receptors, MACHINE learning, MOLECULAR dynamics, DEEP learning
Abstract

G protein-coupled receptors (GPCRs) play a key role in many cellular signaling mechanisms, and must select among multiple coupling possibilities in a ligand-specific manner in order to carry out a myriad of functions in diverse cellular contexts. Much has been learned about the molecular mechanisms of ligand-GPCR complexes from Molecular Dynamics (MD) simulations. However, to explore ligand-specific differences in the response of a GPCR to diverse ligands, as is required to understand ligand bias and functional selectivity, necessitates creating very large amounts of data from the needed large-scale simulations. This becomes a Big Data problem for the high dimensionality analysis of the accumulated trajectories. Here we describe a new machine learning (ML) approach to the problem that is based on transforming the analysis of GPCR function-related, ligand-specific differences encoded in the MD simulation trajectories into a representation recognizable by state-of-the-art deep learning object recognition technology. We illustrate this method by applying it to recognize the pharmacological classification of ligands bound to the 5-HT2A and D2 subtypes of class-A GPCRs from the serotonin and dopamine families. The ML-based approach is shown to perform the classification task with high accuracy, and we identify the molecular determinants of the classifications in the context of GPCR structure and function. This study builds a framework for the efficient computational analysis of MD Big Data collected for the purpose of understanding ligand-specific GPCR activity. [ABSTRACT FROM AUTHOR]

Published
2019
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10. Utilization of Biased G Protein-Coupled Receptor Signaling towards Development of Safer and Personalized Therapeutics.

Author

Ilter, Metehan, Mansoor, Samman, Sensoy, Ozge, and Moreira, Irina S.

Subjects
G protein coupled receptors, ARRESTINS, DRUG side effects, MOLECULAR dynamics, THERAPEUTICS, G proteins
Abstract

G protein-coupled receptors (GPCRs) are involved in a wide variety of physiological processes. Therefore, approximately 40% of currently prescribed drugs have targeted this receptor family. Discovery of β -arrestin mediated signaling and also separability of G protein and β -arrestin signaling pathways have switched the research focus in the GPCR field towards development of biased ligands, which provide engagement of the receptor with a certain effector, thus enriching a specific signaling pathway. In this review, we summarize possible factors that impact signaling profiles of GPCRs such as oligomerization, drug treatment, disease conditions, genetic background, etc. along with relevant molecules that can be used to modulate signaling properties of GPCRs such as allosteric or bitopic ligands, ions, aptamers and pepducins. Moreover, we also discuss the importance of inclusion of pharmacogenomics and molecular dynamics simulations to achieve a holistic understanding of the relation between genetic background and structure and function of GPCRs and GPCR-related proteins. Consequently, specific downstream signaling pathways can be enriched while those that bring unwanted side effects can be prevented on a patient-specific basis. This will improve studies that centered on development of safer and personalized therapeutics, thus alleviating the burden on economy and public health. [ABSTRACT FROM AUTHOR]

Published
2019
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11. A Complete Assessment of Dopamine Receptor- Ligand Interactions through Computational Methods.

Author

Bueschbell, Beatriz, Barreto, Carlos A. V., Preto, António J., Schiedel, Anke C., and Moreira, Irina S.

Subjects
DOPAMINE receptors, LIGANDS (Biochemistry), MOLECULAR dynamics, HYDROGEN bonding, CHARGE-charge interactions
Abstract

Background: Selectively targeting dopamine receptors (DRs) has been a persistent challenge in the last years for the development of new treatments to combat the large variety of diseases involving these receptors. Although, several drugs have been successfully brought to market, the subtype-specific binding mode on a molecular basis has not been fully elucidated. Methods: Homology modeling and molecular dynamics were applied to construct robust conformational models of all dopamine receptor subtypes (D1-like and D2-like). Fifteen structurally diverse ligands were docked. Contacts at the binding pocket were fully described in order to reveal new structural findings responsible for selective binding to DR subtypes. Results: Residues of the aromatic microdomain were shown to be responsible for the majority of ligand interactions established to all DRs. Hydrophobic contacts involved a huge network of conserved and non-conserved residues between three transmembrane domains (TMs), TM2-TM3-TM7. Hydrogen bonds were mostly mediated by the serine microdomain. TM1 and TM2 residues were main contributors for the coupling of large ligands. Some amino acid groups form electrostatic interactions of particular importance for D1R-like selective ligands binding. Conclusions: This in silico approach was successful in showing known receptor-ligand interactions as well as in determining unique combinations of interactions, which will support mutagenesis studies to improve the design of subtype-specific ligands. [ABSTRACT FROM AUTHOR]

Published
2019
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