A Structure-based Data Set of Protein-peptide Affinities and its Nonredundant Benchmark: Potential Applications in Computational Peptidology
- 作者: Wang S.1, Ye H.1, Shang S.2, Li Z.1, Peng Y.1, Zhou P.1
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隶属关系:
- Center for Informational Biology, University of Electronic Science and Technology of China
- Institute of Ecological Environment Protection, Chengdu Normal University
- 期: 卷 31, 编号 26 (2024)
- 页面: 4127-4137
- 栏目: Anti-Infectives and Infectious Diseases
- URL: https://rjpbr.com/0929-8673/article/view/644939
- DOI: https://doi.org/10.2174/0929867331666230908102925
- ID: 644939
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Background::Peptides play crucial roles in diverse cellular functions and participate in many biological processes by interacting with a variety of proteins, which have also been exploited as a promising class of therapeutic agents to target druggable proteins over the past decades. Understanding the intrinsic association between the structure and affinity of protein-peptide interactions (PpIs) should be considerably valuable for the computational peptidology area, such as guiding protein-peptide docking calculations, developing protein-peptide affinity scoring functions, and designing peptide ligands for specific protein receptors.
Objective::We attempted to create a data source for relating PpI structure to affinity.
Methods::By exhaustively surveying the whole protein data bank (PDB) database as well as the ontologically enriched literature information, we manually curated a structure- based data set of protein-peptide affinities, PpI[S/A]DS, which assembled over 350 PpI complex samples with both the experimentally measured structure and affinity data. The data set was further reduced to a nonredundant benchmark consisting of 102 culled samples, PpI[S/A]BM, which only selected those of structurally reliable, functionally diverse and evolutionarily nonhomologous.
Results::The collected structures were resolved at a high-resolution level with either Xray crystallography or solution NMR, while the deposited affinities were characterized by dissociation constant, i.e. Kd value, which is a direct biophysical measure of the intermolecular interaction strength between protein and peptide, ranging from subnanomolar to millimolar levels. The PpI samples in the set/benchmark were arbitrarily classified into α-helix, partial α-helix, β-sheet formed through binding, β-strand formed through selffolding, mixed, and other irregular ones, totally resulting in six classes according to the secondary structure of their peptide ligands. In addition, we also categorized these PpIs in terms of their biological function and binding behavior.
Conclusion::The PpI[S/A]DS set and PpI[S/A]BM benchmark can be considered a valuable data source in the computational peptidology community, aiming to relate the affinity to structure for PpIs.
作者简介
Shaozhou Wang
Center for Informational Biology, University of Electronic Science and Technology of China
Email: info@benthamscience.net
Haiyang Ye
Center for Informational Biology, University of Electronic Science and Technology of China
Email: info@benthamscience.net
Shuyong Shang
Institute of Ecological Environment Protection, Chengdu Normal University
Email: info@benthamscience.net
Zilong Li
Center for Informational Biology, University of Electronic Science and Technology of China
Email: info@benthamscience.net
Yue Peng
Center for Informational Biology, University of Electronic Science and Technology of China
Email: info@benthamscience.net
Peng Zhou
Center for Informational Biology, University of Electronic Science and Technology of China
编辑信件的主要联系方式.
Email: info@benthamscience.net
参考
- Lucchese, G.; Stufano, A.; Trost, B.; Kusalik, A.; Kanduc, D. Peptidology: Short amino acid modules in cell biology and immunology. Amino Acids, 2007, 33(4), 703-707. doi: 10.1007/s00726-006-0458-z PMID: 17077961
- Akbarian, M.; Khani, A.; Eghbalpour, S.; Uversky, V.N. Bioactive peptides: Synthesis, sources, applications, and proposed mechanisms of Action. Int. J. Mol. Sci., 2022, 23(3), 1445. doi: 10.3390/ijms23031445 PMID: 35163367
- Fosgerau, K.; Hoffmann, T. Peptide therapeutics: Current status and future directions. Drug Discov. Today, 2015, 20(1), 122-128. doi: 10.1016/j.drudis.2014.10.003 PMID: 25450771
- Wang, L.; Wang, N.; Zhang, W.; Cheng, X.; Yan, Z.; Shao, G.; Wang, X.; Wang, R.; Fu, C. Therapeutic peptides: Current applications and future directions. Signal Transduct. Target. Ther., 2022, 7(1), 48. doi: 10.1038/s41392-022-00904-4 PMID: 35165272
- Neduva, V.; Russell, R.B. Linear motifs: Evolutionary interaction switches. FEBS Lett., 2005, 579(15), 3342-3345. doi: 10.1016/j.febslet.2005.04.005 PMID: 15943979
- London, N.; Raveh, B.; Schueler-Furman, O. Druggable proteinprotein interactions-from hot spots to hot segments. Curr. Opin. Chem. Biol., 2013, 17(6), 952-959. doi: 10.1016/j.cbpa.2013.10.011 PMID: 24183815
- Neduva, V.; Russell, R.B. Peptides mediating interaction networks: New leads at last. Curr. Opin. Biotechnol., 2006, 17(5), 465-471. doi: 10.1016/j.copbio.2006.08.002 PMID: 16962311
- Petsalaki, E.; Russell, R.B. Peptide-mediated interactions in biological systems: New discoveries and applications. Curr. Opin. Biotechnol., 2008, 19(4), 344-350. doi: 10.1016/j.copbio.2008.06.004 PMID: 18602004
- Lin, J.; Wang, S.; Wen, L.; Ye, H.; Shang, S.; Li, J.; Shu, J.; Zhou, P. Targeting peptide-mediated interactions in omics. Proteomics, 2023, 23(6), 2200175. doi: 10.1002/pmic.202200175 PMID: 36461811
- London, N.; Raveh, B.; Movshovitz-Attias, D.; Schueler-Furman, O. Can self-inhibitory peptides be derived from the interfaces of globular protein-protein interactions? Proteins, 2010, 78(15), 3140-3149. doi: 10.1002/prot.22785 PMID: 20607702
- Yang, C.; Zhang, S.; He, P.; Wang, C.; Huang, J.; Zhou, P. Self-binding peptides: Folding or binding? J. Chem. Inf. Model., 2015, 55(2), 329-342. doi: 10.1021/ci500522v PMID: 25643174
- Zhou, P.; Wang, C.; Ren, Y.; Yang, C.; Tian, F. Computational peptidology: A new and promising approach to therapeutic peptide design. Curr. Med. Chem., 2013, 20(15), 1985-1996. doi: 10.2174/0929867311320150005 PMID: 23317161
- London, N.; Raveh, B.; Schueler-Furman, O. Peptide docking and structure-based characterization of peptide binding: from knowledge to know-how. Curr. Opin. Struct. Biol., 2013, 23(6), 894-902. doi: 10.1016/j.sbi.2013.07.006 PMID: 24138780
- Ciemny, M.; Kurcinski, M.; Kamel, K.; Kolinski, A.; Alam, N.; Schueler-Furman, O.; Kmiecik, S. Proteinpeptide docking: Opportunities and challenges. Drug Discov. Today, 2018, 23(8), 1530-1537. doi: 10.1016/j.drudis.2018.05.006 PMID: 29733895
- Romero-Molina, S.; Ruiz-Blanco, Y.B.; Mieres-Perez, J.; Harms, M.; Münch, J.; Ehrmann, M.; Sanchez-Garcia, E. PPI-Affinity: A web tool for the prediction and optimization of proteinpeptide and proteinprotein binding affinity. J. Proteome Res., 2022, 21(8), 1829-1841. doi: 10.1021/acs.jproteome.2c00020 PMID: 35654412
- Zhou, P.; Wen, L.; Lin, J.; Mei, L.; Liu, Q.; Shang, S.; Li, J.; Shu, J. Integrated unsupervisedsupervised modeling and prediction of proteinpeptide affinities at structural level. Brief. Bioinform., 2022, 23(3), bbac097. doi: 10.1093/bib/bbac097 PMID: 35352094
- Berman, H.M.; Westbrook, J.; Feng, Z.; Gilliland, G.; Bhat, T.N.; Weissig, H.; Shindyalov, I.N.; Bourne, P.E. The protein data bank. Nucleic Acids Res., 2000, 28(1), 235-242. doi: 10.1093/nar/28.1.235 PMID: 10592235
- Vanhee, P.; Reumers, J.; Stricher, F.; Baeten, L.; Serrano, L.; Schymkowitz, J.; Rousseau, F. PepX: A structural database of non-redundant proteinpeptide complexes. Nucleic Acids Res., 2010, 38(S1), D545-D551. doi: 10.1093/nar/gkp893 PMID: 19880386
- London, N.; Movshovitz-Attias, D.; Schueler-Furman, O. The structural basis of peptide-protein binding strategies. Structure, 2010, 18(2), 188-199. doi: 10.1016/j.str.2009.11.012 PMID: 20159464
- Frappier, V.; Duran, M.; Keating, A.E.; Pixel, D.B. PixelDB: Protein-peptide complexes annotated with structural conservation of the peptide binding mode. Protein Sci., 2018, 27(1), 276-285. doi: 10.1002/pro.3320 PMID: 29024246
- Wen, Z; He, J; Tao, H; Huang, SY PepBDB: A comprehensive structural database of biological peptide-protein interactions. Bioinformatics., 2019, 35(1), 175-177.
- Wang, R.; Fang, X.; Lu, Y.; Yang, C.Y.; Wang, S. The PDBbind database: Methodologies and updates. J. Med. Chem., 2005, 48(12), 4111-4119. doi: 10.1021/jm048957q PMID: 15943484
- Benson, M.L.; Smith, R.D.; Khazanov, N.A.; Dimcheff, B.; Beaver, J.; Dresslar, P.; Nerothin, J.; Carlson, H.A. Binding MOAD, a high-quality protein-ligand database. Nucleic Acids Res., 2008, 36(Database issue), D674-D678. PMID: 18055497
- Han, K.; Wu, G.; Lv, F. Development of QSAR-improved statistical potential for the structure-based analysis of proteinpeptide binding affinities. Mol. Inform., 2013, 32(9-10), 783-792. doi: 10.1002/minf.201300064 PMID: 27480231
- Zhou, Y.; Ni, Z.; Chen, K.; Liu, H.; Chen, L.; Lian, C.; Yan, L. Modeling protein-peptide recognition based on classical quantitative structure-affinity relationship approach: Implication for proteome-wide inference of peptide-mediated interactions. Protein J., 2013, 32(7), 568-578. doi: 10.1007/s10930-013-9519-9 PMID: 24150505
- Block, P.; Sotriffer, C.A.; Dramburg, I.; Klebe, G. AffinDB: A freely accessible database of affinities for protein-ligand complexes from the PDB. Nucleic Acids Res., 2006, 34(90001), D522-D526. doi: 10.1093/nar/gkj039 PMID: 16381925
- Kastritis, P.L.; Moal, I.H.; Hwang, H.; Weng, Z.; Bates, P.A.; Bonvin, A.M.J.J.; Janin, J. A structure-based benchmark for protein-protein binding affinity. Protein Sci., 2011, 20(3), 482-491. doi: 10.1002/pro.580 PMID: 21213247
- Zhou, P.; Miao, Q.; Yan, F.; Li, Z.; Jiang, Q.; Wen, L.; Meng, Y. Is protein context responsible for peptide-mediated interactions? Mol. Omics, 2019, 15(4), 280-295. doi: 10.1039/C9MO00041K PMID: 31112188
- Kastritis, P.L.; Bonvin, A.M.J.J. Are scoring functions in protein-protein docking ready to predict interactomes? Clues from a novel binding affinity benchmark. J. Proteome Res., 2010, 9(5), 2216-2225. doi: 10.1021/pr9009854 PMID: 20329755
- Sievers, F.; Wilm, A.; Dineen, D.; Gibson, T.J.; Karplus, K.; Li, W.; Lopez, R.; McWilliam, H.; Remmert, M.; Söding, J.; Thompson, J.D.; Higgins, D.G. Fast, scalable generation of high-quality protein multiple sequence alignments using Clustal Omega. Mol. Syst. Biol., 2011, 7(1), 539. doi: 10.1038/msb.2011.75 PMID: 21988835
- Xu, X.; Zou, X. PepPro: A nonredundant structure data set for benchmarking peptideprotein computational docking. J. Comput. Chem., 2020, 41(4), 362-369. doi: 10.1002/jcc.26114 PMID: 31793016
- Jenssen, TK; Laegreid, A; Komorowski, J; Hovig, E A literature network of human genes for high-throughput analysis of gene expression. Nat Genet, 2001, 28, 21-28.
- Blaszczyk, M.; Ciemny, M.P.; Kolinski, A.; Kurcinski, M.; Kmiecik, S. Proteinpeptide docking using CABS-dock and contact information. Brief. Bioinform., 2019, 20(6), 2299-2305. doi: 10.1093/bib/bby080 PMID: 30247502
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