Login Register Cart 0
Generic placeholder image

Current Protein & Peptide Science

Editor-in-Chief

ISSN (Print): 1389-2037
ISSN (Online): 1875-5550

Back
Journal Home About Journal Editorial Board Journal Insight Current Issue Volumes/Issues
Subscribe
Research Article

Structural Insights of PD-1/PD-L1 Axis: An In silico Approach

Author(s): Shishir Rohit, Mehul Patel*, Yogesh Jagtap, Umang Shah, Ashish Patel, Swayamprakash Patel and Nilay Solanki

Volume 25, Issue 8, 2024

Published on: 03 May, 2024

Page: [638 - 650] Pages: 13

DOI: 10.2174/0113892037297012240408063250

Price: $65

Abstract

Background: Interaction of PD-1 protein (present on immune T-cell) with its ligand PD-L1 (over-expressed on cancerous cell) makes the cancerous cell survive and thrive. The association of PD-1/PD-L1 represents a classical protein-protein interaction (PPI), where receptor and ligand binding through a large flat surface. Blocking the PD-1/PDL-1 complex formation can restore the normal immune mechanism, thereby destroying cancerous cells. However, the PD-1/PDL1 interactions are only partially characterized.

Objective: We aim to comprehend the time-dependent behavior of PD-1 upon its binding with PD-L1.

Methods: The current work focuses on a molecular dynamics simulation (MDs) simulation study of apo and ligand bound PD-1.

Results: Our simulation reveals the flexible nature of the PD-1, both in apo and bound form. Moreover, the current study also differentiates the type of strong and weak interactions which could be targeted to overcome the complex formation.

Conclusion: The current article could provide a valuable structural insight about the target protein (PD-1) and its ligand (PD-L1) which could open new opportunities in developing small molecule inhibitors (SMIs) targeting either PD-1 or PD-L1.

« Previous Next »
Graphical Abstract

[1]
Esfahani, K.; Roudaia, L.; Buhlaiga, N.; Del Rincon, S.V.; Papneja, N.; Miller, W.H., Jr A review of cancer immunotherapy: From the past, to the present, to the future. Curr. Oncol., 2020, 27(12), 87-97.
[http://dx.doi.org/10.3747/co.27.5223] [PMID: 32368178]
[2]
Pardoll, D.M. The blockade of immune checkpoints in cancer immunotherapy. Nat. Rev. Cancer, 2012, 12(4), 252-264.
[http://dx.doi.org/10.1038/nrc3239] [PMID: 22437870]
[3]
Hoos, A. Development of immuno-oncology drugs — from CTLA4 to PD1 to the next generations. Nat. Rev. Drug Discov., 2016, 15(4), 235-247.
[http://dx.doi.org/10.1038/nrd.2015.35] [PMID: 26965203]
[4]
Khalil, D.N.; Smith, E.L.; Brentjens, R.J.; Wolchok, J.D. The future of cancer treatment: Immunomodulation, CARs and combination immunotherapy. Nat. Rev. Clin. Oncol., 2016, 13(5), 273-290.
[http://dx.doi.org/10.1038/nrclinonc.2016.25] [PMID: 26977780]
[5]
Ishida, Y.; Agata, Y.; Shibahara, K.; Honjo, T. Induced expression of PD-1, a novel member of the immunoglobulin gene superfamily, upon programmed cell death. EMBO J., 1992, 11(11), 3887-3895.
[http://dx.doi.org/10.1002/j.1460-2075.1992.tb05481.x] [PMID: 1396582]
[6]
Liu, C.; Seeram, N.P.; Ma, H. Small molecule inhibitors against PD-1/PD-L1 immune checkpoints and current methodologies for their development: A review. Cancer Cell Int., 2021, 21(1), 239.
[http://dx.doi.org/10.1186/s12935-021-01946-4] [PMID: 33906641]
[7]
Sasikumar, P.G.; Ramachandra, M. Small molecule agents targeting PD-1 checkpoint pathway for cancer immunotherapy: Mechanisms of action and other considerations for their advanced development. Front. Immunol., 2022, 13, 752065.
[http://dx.doi.org/10.3389/fimmu.2022.752065] [PMID: 35585982]
[8]
Shaabani, S.; Huizinga, H.P.S.; Butera, R.; Kouchi, A.; Guzik, K.; Mularz, M.K.; Holak, T.A.; Dömling, A. A patent review on PD-1/PD-L1 antagonists: Small molecules, peptides, and macrocycles (2015-2018). Expert Opin. Ther. Pat., 2018, 28(9), 665-678.
[http://dx.doi.org/10.1080/13543776.2018.1512706] [PMID: 30107136]
[9]
Muszak, D.; Surmiak, E.; Plewka, J.; Mularz, M.K.; Krol, K.J.; Musielak, B.; Sala, D.; Kitel, R.; Stec, M.; Weglarczyk, K.; Siedlar, M.; Dömling, A.; Skalniak, L.; Holak, T.A. Terphenyl-based small-molecule inhibitors of programmed cell death-1/programmed death-ligand 1 protein–protein interaction. J. Med. Chem., 2021, 64(15), 11614-11636.
[http://dx.doi.org/10.1021/acs.jmedchem.1c00957] [PMID: 34313116]
[10]
Magid, A.F.A. Inhibitors of the PD-1/PD-L1 pathway can mobilize the immune system: An innovative potential therapy for cancer and chronic infections. ACS Med. Chem. Lett., 2015, 6(5), 489-490.
[11]
Musielak, B.; Kocik, J.; Skalniak, L.; Mularz, M.K.; Sala, D.; Czub, M.; Stec, M.; Siedlar, M.; Holak, T.A.; Plewka, J. CA-170 – A potent small-molecule PD-L1 inhibitor or not? Molecules, 2019, 24(15), 2804.
[http://dx.doi.org/10.3390/molecules24152804] [PMID: 31374878]
[12]
Lin, D.Y.; Tanaka, Y.; Iwasaki, M.; Gittis, A.G.; Su, H.P.; Mikami, B.; Okazaki, T.; Honjo, T.; Minato, N.; Garboczi, D.N. The PD-1/PD-L1 complex resembles the antigen-binding Fv domains of antibodies and T cell receptors. Proc. Natl. Acad. Sci., 2008, 105(8), 3011-3016.
[http://dx.doi.org/10.1073/pnas.0712278105] [PMID: 18287011]
[13]
Cheng, X.; Veverka, V.; Radhakrishnan, A.; Waters, L.C.; Muskett, F.W.; Morgan, S.H.; Huo, J.; Yu, C.; Evans, E.J.; Leslie, A.J.; Griffiths, M.; Stubberfield, C.; Griffin, R.; Henry, A.J.; Jansson, A.; Ladbury, J.E.; Ikemizu, S.; Carr, M.D.; Davis, S.J. Structure and interactions of the human programmed cell death 1 receptor. J. Biol. Chem., 2013, 288(17), 11771-11785.
[http://dx.doi.org/10.1074/jbc.M112.448126] [PMID: 23417675]
[14]
Zak, K.M.; Grudnik, P.; Magiera, K.; Dömling, A.; Dubin, G.; Holak, T.A. Structural biology of the immune checkpoint receptor PD-1 and its ligands PD-L1/PD-L2. Structure, 2017, 25(8), 1163-1174.
[http://dx.doi.org/10.1016/j.str.2017.06.011] [PMID: 28768162]
[15]
Zak, K.M.; Kitel, R.; Przetocka, S.; Golik, P.; Guzik, K.; Musielak, B.; Dömling, A.; Dubin, G.; Holak, T.A. Structure of the complex of human programmed death 1, PD-1, and its ligand PD-L1. Structure, 2015, 23(12), 2341-2348.
[http://dx.doi.org/10.1016/j.str.2015.09.010] [PMID: 26602187]
[16]
Guo, W.; Wisniewski, J.A.; Ji, H. Hot spot-based design of small-molecule inhibitors for protein–protein interactions. Bioorg. Med. Chem. Lett., 2014, 24(11), 2546-2554.
[http://dx.doi.org/10.1016/j.bmcl.2014.03.095] [PMID: 24751445]
[17]
Koes, D.; Khoury, K.; Huang, Y.; Wang, W.; Bista, M.; Popowicz, G.M.; Wolf, S.; Holak, T.A.; Dömling, A.; Camacho, C.J. Enabling large-scale design, synthesis and validation of small molecule protein-protein antagonists. PLoS One, 2012, 7(3), e32839.
[http://dx.doi.org/10.1371/journal.pone.0032839] [PMID: 22427896]
[18]
Mittal, L.; Srivastava, M.; Kumari, A.; Tonk, R.K.; Awasthi, A.; Asthana, S. Interplay among structural stability, plasticity, and energetics determined by conformational attuning of flexible loops in PD-1. J. Chem. Inf. Model., 2021, 61(1), 358-384.
[http://dx.doi.org/10.1021/acs.jcim.0c01080] [PMID: 33433201]
[19]
Ahmed, M.; Barakat, K. The too many faces of PD-L1: A comprehensive conformational analysis study. Biochemistry, 2017, 56(40), 5428-5439.
[20]
Viricel, C.; Ahmed, M.; Barakat, K. Human PD-1 binds differently to its human ligands: A comprehensive modeling study. J. Mol. Graph. Model., 2015, 57, 131-142.
[http://dx.doi.org/10.1016/j.jmgm.2015.01.015] [PMID: 25723350]
[21]
Du, J.; Qin, Y.; Wu, Y.; Zhao, W.; Zhai, W.; Qi, Y.; Wang, C.; Gao, Y. The design of high affinity human PD-1 mutants by using molecular dynamics simulations (MD). Cell Commun. Signal., 2018, 16(1), 25.
[http://dx.doi.org/10.1186/s12964-018-0239-9] [PMID: 29879980]
[22]
Mehler, E.L. Self-consistent, free energy based approximation to calculate pH dependent electrostatic effects in proteins. J. Phys. Chem., 1996, 100(39), 16006-16018.
[http://dx.doi.org/10.1021/jp9537926]
[23]
Mehler, E.L.; Guarnieri, F. A self-consistent, microenvironment modulated screened coulomb potential approximation to calculate pH-dependent electrostatic effects in proteins. Biophys. J., 1999, 77(1), 3-22.
[http://dx.doi.org/10.1016/S0006-3495(99)76868-2] [PMID: 10388736]
[24]
GROMACS 2019.6 Source code. 2020. Available from: https://zenodo.org/records/3685922
[25]
Maier, J.A.; Martinez, C.; Kasavajhala, K.; Wickstrom, L.; Hauser, K.E.; Simmerling, C. ff14SB: Improving the accuracy of protein side chain and backbone parameters from ff99SB. J. Chem. Theory Comput., 2015, 11(8), 3696-3713.
[http://dx.doi.org/10.1021/acs.jctc.5b00255] [PMID: 26574453]
[26]
Onufriev, A.V.; Izadi, S. Water models for biomolecular simulations. Wiley Interdiscip. Rev. Comput. Mol. Sci., 2018, 8(2), e1347.
[http://dx.doi.org/10.1002/wcms.1347]
[27]
Harrach, M.F.; Drossel, B. Structure and dynamics of TIP3P, TIP4P, and TIP5P water near smooth and atomistic walls of different hydroaffinity. J. Chem. Phys., 2014, 140(17), 174501.
[http://dx.doi.org/10.1063/1.4872239] [PMID: 24811640]
[28]
Chinnasamy, K.; Saravanan, M.; Poomani, K. Evaluation of binding and antagonism/downregulation of brilanestrant molecule in estrogen receptor-α via quantum mechanics/molecular mechanics, molecular dynamics and binding free energy calculations. J. Biomol. Struct. Dyn., 2020, 38(1), 219-235.
[http://dx.doi.org/10.1080/07391102.2019.1574605] [PMID: 31038398]
[29]
Berendsen, H.J.C.; Postma, J.P.M.; van Gunsteren, W.F.; DiNola, A.; Haak, J.R. Molecular dynamics with coupling to an external bath. J. Chem. Phys., 1984, 81(8), 3684-3690.
[http://dx.doi.org/10.1063/1.448118]
[30]
Cuendet, M.A.; van Gunsteren, W.F. On the calculation of velocity-dependent properties in molecular dynamics simulations using the leapfrog integration algorithm. J. Chem. Phys., 2007, 127(18), 184102.
[http://dx.doi.org/10.1063/1.2779878] [PMID: 18020625]
[31]
Bekker, H.; Dijkstra, E.J.; Renardus, M.K.R.; Berendsen, H.J.C. An efficient, box shape independent non-bonded force and virial algorithm for molecular dynamics. Mol. Simul., 1995, 14(3), 137-151.
[http://dx.doi.org/10.1080/08927029508022012]
[32]
Guenot, J.; Kollman, P.A. Conformational and energetic effects of truncating nonbonded interactions in an aqueous protein dynamics simulation. J. Comput. Chem., 1993, 14(3), 295-311.
[http://dx.doi.org/10.1002/jcc.540140306]
[33]
Essmann, U.; Perera, L.; Berkowitz, M.L.; Darden, T.; Lee, H.; Pedersen, L.G. A smooth particle mesh Ewald method. J. Chem. Phys., 1995, 103(19), 8577-8593.
[http://dx.doi.org/10.1063/1.470117]
[34]
Darden, T.; York, D.; Pedersen, L. Particle mesh Ewald: An N-log(N) method for Ewald sums in large systems. J. Chem. Phys., 1993, 98, 10089-10092.
[35]
Hess, B.; Bekker, H.; Berendsen, H.J.C.; Fraaije, J.G.E.M. LINCS: A linear constraint solver for molecular simulations. J. Comput. Chem., 1997, 18(12), 1463-1472.
[http://dx.doi.org/10.1002/(SICI)1096-987X(199709)18:12<1463::AID-JCC4>3.0.CO;2-H]
[36]
Hess, B. P-LINCS: A parallel linear constraint solver for molecular simulation. J. Chem. Theory Comput., 2008, 4(1), 116-122.
[http://dx.doi.org/10.1021/ct700200b] [PMID: 26619985]
[37]
Mehta, C.C.; Rohit, S.; Patel, S.; Bhatt, H.G. New molecular insights for 4 H -1,2,4-triazole derivatives as inhibitors of tankyrase and Wnt-signaling antagonist: A molecular dynamics simulation study. J. Biomol. Struct. Dyn., 2023, 41(22), 13496-13508.
[http://dx.doi.org/10.1080/07391102.2023.2175376] [PMID: 36755438]
[38]
Grace Home. Available from: https://plasma-gate.weizmann.ac.il/Grace/ (Accessed on: 2024-02-16).
[39]
Hernández, P.G.; Hildebrand, P.W. Mdciao: Accessible analysis and visualization of molecular dynamics simulation data. bioRxiv, 2022, 2022.07.
[http://dx.doi.org/10.1101/2022.07.15.500163]
[40]
Carugo, O.; Pongor, S. A normalized root-mean-spuare distance for comparing protein three-dimensional structures. Protein Sci., 2001, 10(7), 1470-1473.
[http://dx.doi.org/10.1110/ps.690101] [PMID: 11420449]
[41]
Carugo, O. Statistical validation of the root-mean-square-distance, a measure of protein structural proximity. Protein Eng. Des. Sel., 2007, 20(1), 33-37.
[http://dx.doi.org/10.1093/protein/gzl051] [PMID: 17218333]
[42]
Damm, K.L.; Carlson, H.A. Gaussian-weighted RMSD superposition of proteins: A structural comparison for flexible proteins and predicted protein structures. Biophys. J., 2006, 90(12), 4558-4573.
[http://dx.doi.org/10.1529/biophysj.105.066654] [PMID: 16565070]
[43]
Kufareva, I.; Abagyan, R. Methods of protein structure comparison. Methods Mol. Biol., 2011, 857, 231-257.
[http://dx.doi.org/10.1007/978-1-61779-588-6_10] [PMID: 22323224]
[44]
Fuglebakk, E.; Echave, J.; Reuter, N. Measuring and comparing structural fluctuation patterns in large protein datasets. Bioinformatics, 2012, 28(19), 2431-2440.
[http://dx.doi.org/10.1093/bioinformatics/bts445] [PMID: 22796957]
[45]
Humphrey, W.; Dalke, A.; Schulten, K. VMD: Visual molecular dynamics. J Mol Graph, 1996, 14(1), 33-38.
[http://dx.doi.org/10.1016/0263-7855(96)00018-5]
[46]
Kabsch, W.; Sander, C. Dictionary of protein secondary structure: Pattern recognition of hydrogen-bonded and geometrical features. Biopolymers, 1983, 22(12), 2577-2637.
[http://dx.doi.org/10.1002/bip.360221211] [PMID: 6667333]
[47]
Kim, R.; Choi, C.Y. A linear function for the approximation of accessible surface area of proteins. Protein Pept. Lett., 2006, 13(6), 549-553.
[http://dx.doi.org/10.2174/092986606777145869] [PMID: 16842107]
[48]
Ali, S.; Hassan, M.; Islam, A.; Ahmad, F. A review of methods available to estimate solvent-accessible surface areas of soluble proteins in the folded and unfolded states. Curr. Protein Pept. Sci., 2014, 15(5), 456-476.
[http://dx.doi.org/10.2174/1389203715666140327114232] [PMID: 24678666]
[49]
Lu, S.; Wagaman, A.S. On methods for determining solvent accessible surface area for proteins in their unfolded state. BMC Res. Notes, 2014, 7(1), 602.
[http://dx.doi.org/10.1186/1756-0500-7-602] [PMID: 25187400]
[50]
How to calculate solvent accessible surface (SASA)? Available from: https://www.researchgate.net/post/How-to-calculate-solvent-accessible-surface-SASA (Accessed on: 2024-02-16).
[51]
Lobanov, M.Y.; Bogatyreva, N.S.; Galzitskaya, O.V. Radius of gyration as an indicator of protein structure compactness. Mol. Biol., 2008, 42(4), 623-628.
[http://dx.doi.org/10.1134/S0026893308040195] [PMID: 18856071]
[52]
Ivankov, D.N.; Finkelstein, A.V. Prediction of protein folding rates from the amino acid sequence-predicted secondary structure. Proc. Natl. Acad. Sci. USA, 2004, 101(24), 8942-8944.
[http://dx.doi.org/10.1073/pnas.0402659101] [PMID: 15184682]
[53]
Finkelstein, A.V.; Galzitskaya, O.V. Physics of protein folding. Phys. Life Rev., 2004, 1(1), 23-56.
[http://dx.doi.org/10.1016/j.plrev.2004.03.001]
[54]
Tsai, J.; Taylor, R.; Chothia, C.; Gerstein, M. The packing density in proteins. J. Molecul. Biol., 1999, 290(1), 253-266.
[http://dx.doi.org/10.1006/jmbi.1999.2829]
[55]
Peng, J.; Wang, W.; Yu, Y.; Gu, H.; Huang, X. Clustering algorithms to analyze molecular dynamics simulation trajectories for complex chemical and biological systems. Chin. J. Chem. Phys., 2018, 31(4), 404-420.
[http://dx.doi.org/10.1063/1674-0068/31/cjcp1806147]
[56]
Saxena, A.; Prasad, M.; Gupta, A.; Bharill, N.; Patel, O.P.; Tiwari, A.; Er, M.J.; Ding, W.; Lin, C.T. A review of clustering techniques and developments. Neurocomputing, 2017, 267, 664-681.
[http://dx.doi.org/10.1016/j.neucom.2017.06.053]
[57]
Abramyan, T.M.; Snyder, J.A.; Thyparambil, A.A.; Stuart, S.J.; Latour, R.A. Cluster analysis of molecular simulation trajectories for systems where both conformation and orientation of the sampled states are important. J. Comput. Chem., 2016, 37(21), 1973-1982.
[http://dx.doi.org/10.1002/jcc.24416] [PMID: 27292100]
[58]
Bissantz, C.; Kuhn, B.; Stahl, M. A medicinal chemist’s guide to molecular interactions. J. Med. Chem., 2010, 53(14), 5061-5084.
[http://dx.doi.org/10.1021/jm100112j] [PMID: 20345171]
[59]
Bernstein, F.C.; Koetzle, T.F.; Williams, G.J.B.; Meyer, E.F., Jr; Brice, M.D.; Rodgers, J.R.; Kennard, O.; Shimanouchi, T.; Tasumi, M. The protein data bank: A computer-based archival file for macromolecular structures. J. Mol. Biol., 1977, 112(3), 535-542.
[http://dx.doi.org/10.1016/S0022-2836(77)80200-3] [PMID: 875032]
[60]
Gilson, M.K.; Zhou, H.X. Calculation of protein-ligand binding affinities. Annu. Rev. Biophys. Biomol. Struct., 2007, 36(1), 21-42.
[http://dx.doi.org/10.1146/annurev.biophys.36.040306.132550] [PMID: 17201676]
[61]
Almahmoud, S.; Zhong, H.A. Molecular modeling studies on the binding mode of the PD-1/PD-L1 complex inhibitors. Int. J. Mol. Sci., 2019, 20(18), 4654.
[http://dx.doi.org/10.3390/ijms20184654] [PMID: 31546905]
[62]
Sartori, G.R.; Albuquerque, A. de O.; Costa, S.A.H.; Andrade, L.M.; Almeida, D. da S.; Gaieta, E.M.; Sampaio, J.V.; Albuquerque, V. In silico mapping of the dynamic interactions and structure-activity relationship of flavonoid compounds against the immune checkpoint programmed-cell death 1 pathway. Front. Drug. Discov., 2022, 2, 1032587.
[63]
Hubbard, R.E.; Haider, K.M. Hydrogen bonds in proteins: Role and strength. In: Encyclopedia of Life Sciences; John Wiley & Sons, Ltd, 2010.
[http://dx.doi.org/10.1002/9780470015902.a0003011.pub2]
[64]
Chen, D.; Oezguen, N.; Urvil, P.; Ferguson, C.; Dann, S.M.; Savidge, T.C. Regulation of protein-ligand binding affinity by hydrogen bond pairing. Sci. Adv., 2016, 2(3), e1501240.
[http://dx.doi.org/10.1126/sciadv.1501240] [PMID: 27051863]
[65]
Jiang, L.; Lai, L. CH...O hydrogen bonds at protein-protein interfaces. J. Biol. Chem., 2002, 277(40), 37732-37740.
[http://dx.doi.org/10.1074/jbc.M204514200] [PMID: 12119293]
[66]
Levy, Y.; Onuchic, J.N. Water and proteins: A love–hate relationship. Proc. Natl. Acad. Sci., 2004, 101(10), 3325-3326.
[http://dx.doi.org/10.1073/pnas.0400157101] [PMID: 14993602]
[67]
Martin, J.; Frezza, E. A dynamical view of protein-protein complexes: Studies by molecular dynamics simulations. Front. Mol. Biosci., 2022, 9, 970109.
[http://dx.doi.org/10.3389/fmolb.2022.970109] [PMID: 36275619]
[68]
Jorgensen, W.L.; Chandrasekhar, J.; Madura, J.D.; Impey, R.W.; Klein, M.L. Comparison of simple potential functions for simulating liquid water. J. Chem. Phys., 1983, 79(2), 926-935.
[http://dx.doi.org/10.1063/1.445869]
[69]
Laage, D.; Elsaesser, T.; Hynes, J.T. Water dynamics in the hydration shells of biomolecules. Chem. Rev., 2017, 117(16), 10694-10725.
[http://dx.doi.org/10.1021/acs.chemrev.6b00765] [PMID: 28248491]
[70]
Ahmad, M.; Gu, W.; Geyer, T.; Helms, V. Adhesive water networks facilitate binding of protein interfaces. Nat. Commun., 2011, 2(1), 261.
[http://dx.doi.org/10.1038/ncomms1258] [PMID: 21448160]
[71]
Huggins, D.J.; Marsh, M.; Payne, M.C. Thermodynamic properties of water molecules at a protein–protein interaction surface. J. Chem. Theory Comput., 2011, 7(11), 3514-3522.
[http://dx.doi.org/10.1021/ct200465z] [PMID: 24554921]
[72]
Papoian, G.A.; Ulander, J.; Wolynes, P.G. Role of water mediated interactions in protein-protein recognition landscapes. J. Am. Chem. Soc., 2003, 125(30), 9170-9178.
[http://dx.doi.org/10.1021/ja034729u] [PMID: 15369374]
[73]
Ban, X.; Lahiri, P.; Dhoble, A.S.; Li, D.; Gu, Z.; Li, C.; Cheng, L.; Hong, Y.; Li, Z.; Kaustubh, B. Evolutionary stability of salt bridges hints its contribution to stability of proteins. Comput. Struct. Biotechnol. J., 2019, 17, 895-903.
[http://dx.doi.org/10.1016/j.csbj.2019.06.022] [PMID: 31333816]
[74]
Donald, J.E.; Kulp, D.W.; DeGrado, W.F. Salt bridges: Geometrically specific, designable interactions. Proteins, 2011, 79(3), 898-915.
[http://dx.doi.org/10.1002/prot.22927] [PMID: 21287621]
[75]
Gribenko, A.V.; Makhatadze, G.I. Role of the charge-charge interactions in defining stability and halophilicity of the CspB proteins. J. Mol. Biol., 2007, 366(3), 842-856.
[http://dx.doi.org/10.1016/j.jmb.2006.11.061] [PMID: 17188709]
[76]
Shao, J.; Kuiper, B.P.; Thunnissen, A.M.W.H.; Cool, R.H.; Zhou, L.; Huang, C.; Dijkstra, B.W.; Broos, J. The role of tryptophan in π interactions in proteins: An experimental approach. J. Am. Chem. Soc., 2022, 144(30), 13815-13822.
[http://dx.doi.org/10.1021/jacs.2c04986] [PMID: 35868012]
[77]
Dougherty, D.A. Cation-pi interactions involving aromatic amino acids. J. Nutr., 2007, 137(6), 1504S-1508S.
[http://dx.doi.org/10.1093/jn/137.6.1504S] [PMID: 17513416]
[78]
Makwana, K.M.; Mahalakshmi, R. Implications of aromatic–aromatic interactions: From protein structures to peptide models. Protein Sci., 2015, 24(12), 1920-1933.
[http://dx.doi.org/10.1002/pro.2814] [PMID: 26402741]
[79]
Vaideeswaran, S.; Ramaiah, S. Investigations on the role of π–π interactions and π–π networks in eNOS and nNOS proteins. Bioorg. Chem., 2013, 49, 16-23.
[http://dx.doi.org/10.1016/j.bioorg.2013.06.005] [PMID: 23845761]

Rights & Permissions Print Cite
© 2024 Bentham Science Publishers | Privacy Policy