Design of target specific peptide inhibitors using generative deep learning and molecular dynamics simulations.
Journal
Nature communications
ISSN: 2041-1723
Titre abrégé: Nat Commun
Pays: England
ID NLM: 101528555
Informations de publication
Date de publication:
21 Feb 2024
21 Feb 2024
Historique:
received:
31
08
2022
accepted:
04
02
2024
medline:
22
2
2024
pubmed:
22
2
2024
entrez:
21
2
2024
Statut:
epublish
Résumé
We introduce a computational approach for the design of target-specific peptides. Our method integrates a Gated Recurrent Unit-based Variational Autoencoder with Rosetta FlexPepDock for peptide sequence generation and binding affinity assessment. Subsequently, molecular dynamics simulations are employed to narrow down the selection of peptides for experimental assays. We apply this computational strategy to design peptide inhibitors that specifically target β-catenin and NF-κB essential modulator. Among the twelve β-catenin inhibitors, six exhibit improved binding affinity compared to the parent peptide. Notably, the best C-terminal peptide binds β-catenin with an IC
Identifiants
pubmed: 38383543
doi: 10.1038/s41467-024-45766-2
pii: 10.1038/s41467-024-45766-2
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
1611Informations de copyright
© 2024. The Author(s).
Références
Nguyen, P. T. et al. Computational design of peptides to target Na
pubmed: 36576241
pmcid: 9831606
doi: 10.7554/eLife.81727
Han, Y. & Král, P. Computational design of ACE2-based peptide inhibitors of sars-cov-2. ACS Nano 14, 5143–5147 (2020).
pubmed: 32286790
doi: 10.1021/acsnano.0c02857
Hosseinzadeh, P. et al. Anchor extension: a structure-guided approach to design cyclic peptides targeting enzyme active sites. Nat. Commun. 12, 3384 (2021).
pubmed: 34099674
pmcid: 8185074
doi: 10.1038/s41467-021-23609-8
Vanhee, P. et al. Computational design of peptide ligands. Trends Biotechnol. 29, 231–239 (2011).
pubmed: 21316780
doi: 10.1016/j.tibtech.2011.01.004
Raveh, B., London, N. & Schueler-Furman, O. Sub-angstrom modeling of complexes between flexible peptides and globular proteins. Proteins 78, 2029–2040 (2010).
pubmed: 20455260
doi: 10.1002/prot.22716
London, N., Raveh, B., Cohen, E., Fathi, G. & Schueler-Furman, O. Rosetta FlexPepDock web server–high resolution modeling of peptide-protein interactions. Nucleic Acids Res. 39, W249–53 (2011).
pubmed: 21622962
pmcid: 3125795
doi: 10.1093/nar/gkr431
Sood, V. D. & Baker, D. Recapitulation and design of protein binding peptide structures and sequences. J. Mol. Biol. 357, 917–927 (2006).
pubmed: 16473368
doi: 10.1016/j.jmb.2006.01.045
Rooklin, D. et al. Targeting unoccupied surfaces on protein-protein interfaces. J. Am. Chem. Soc. 139, 15560–15563 (2017).
pubmed: 28759230
pmcid: 5677581
doi: 10.1021/jacs.7b05960
Jenson, J. M. et al. Peptide design by optimization on a data-parameterized protein interaction landscape. Proc. Natl. Acad. Sci. USA 115, E10342–E10351 (2018).
pubmed: 30322927
pmcid: 6217393
doi: 10.1073/pnas.1812939115
Karplus, M. & McCammon, J. A. Molecular dynamics simulations of biomolecules. Nat. Struct. Biol. 9, 646–652 (2002).
pubmed: 12198485
doi: 10.1038/nsb0902-646
Raveh, B., London, N., Zimmerman, L. & Schueler-Furman, O. Rosetta FlexPepDock ab-initio: simultaneous folding, docking and refinement of peptides onto their receptors. PLoS ONE 6, e18934 (2011).
pubmed: 21572516
pmcid: 3084719
doi: 10.1371/journal.pone.0018934
Morrone, J. A., Perez, A., MacCallum, J. & Dill, K. A. Computed binding of peptides to proteins with MELD-accelerated molecular dynamics. J. Chem. Theory Comput. 13, 870–876 (2017).
pubmed: 28042966
doi: 10.1021/acs.jctc.6b00977
Liu, C., Brini, E., Perez, A. & Dill, K. A. Computing ligands bound to proteins using MELD-acceleratedMD. J. Chem. Theory Comput. 16, 6377–6382 (2020).
pubmed: 32910647
pmcid: 7572789
doi: 10.1021/acs.jctc.0c00543
Yu, L., Barros, S. A., Sun, C. & Somani, S. Cyclic peptide linker design and optimization by molecular dynamics simulations. J. Chem. Inf. Model. 63, 6863–6876 (2023).
pubmed: 37903231
doi: 10.1021/acs.jcim.3c01359
Wang, J., Alekseenko, A., Kozakov, D. & Miao, Y. Improved modeling of peptide-protein binding through global docking and accelerated molecular dynamics simulations. Front. Mol. Biosci. 6, 112 (2019).
Miao, Y., Yu, L. & Blunsom, P. Neural variational inference for text processing. In Proceedings of the 33rd International Conference on International Conference on Machine Learning, 1727–1736 (PMLR, 2016).
Brunner, G., Wang, Y., Wattenhofer, R. & Weigelt, M. Disentangling the latent space of (variational) autoencoders for NLP. In Advances in Intelligent Systems and Computing, 163–168 (eds. Lotfi, A., Bouchachia, H., Gegov, A., Langensiepen, C. and McGinnity, M)(Springer International Publishing, 2018).
Fang, L., Li, C., Gao, J., Dong, W. & Chen, C. Implicit deep latent variable models for text generation. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), 3946–3956 (Association for Computational Linguistics (ACL), 2019).
Sutskever, I., Vinyals, O. & Le, Q. V. Sequence to sequence learning with neural networks. In Proceedings of the 27th International Conference on Neural Information Processing Systems, 3104–3112 (Curran Associates, Inc., 2014).
Hong, S. H., Ryu, S., Lim, J. & Kim, W. Y. Molecular generative model based on an adversarially regularized autoencoder. J. Chem. Inf. Model. 60, 29–36 (2020).
pubmed: 31820983
doi: 10.1021/acs.jcim.9b00694
Das, P. et al. Accelerated antimicrobial discovery via deep generative models and molecular dynamics simulations. Nat. Biomed. Eng. 5, 613–623 (2021).
pubmed: 33707779
doi: 10.1038/s41551-021-00689-x
Vaswani, A. et al. Attention is all you need. In Advances in Neural Information Processing Systems (eds. von Luxburg, U., Bengio, S., Fergus, R., Garnett, R., Guyon, I., Wallach, H., & Vishwanathan, S.) 5999–6009 (Curran Associates, 2017).
Ko, J. & Lee, J. Can alphafold2 predict protein-peptide complex structures accurately? Preprint at https://www.biorxiv.org/content/early/2021/07/27/2021.07.27.453972.full.pdf (2021).
Chang, L. & Perez, A. Ranking peptide binders by affinity with AlphaFold. Angew Chem. Int. Ed. Engl. 62, e202213362 (2023).
pubmed: 36542066
doi: 10.1002/anie.202213362
Jumper, J. et al. Highly accurate protein structure prediction with AlphaFold. Nature 596, 583–589 (2021).
pubmed: 34265844
pmcid: 8371605
doi: 10.1038/s41586-021-03819-2
Wen, Z., He, J., Tao, H. & Huang, S.-Y. PepBDB: a comprehensive structural database of biological peptide-protein interactions. Bioinformatics 35, 175–177 (2019).
pubmed: 29982280
doi: 10.1093/bioinformatics/bty579
Ferruz, N., Schmidt, S. & Höcker, B. Protgpt2 is a deep unsupervised language model for protein design. Nat. Commun. 13, 4348 (2022).
pubmed: 35896542
pmcid: 9329459
doi: 10.1038/s41467-022-32007-7
Radford, A., Narasimhan, K., Salimans, T. & Sutskever, I. Improving language understanding by generative pre-training. https://api.semanticscholar.org/CorpusID:49313245 (2018).
Valdés-Tresanco, M. S., Valdés-Tresanco, M. E., Valiente, P. A. & Moreno, E. gmx_MMPBSA: a new tool to perform End-State free energy calculations with GROMACS. J. Chem. Theory Comput. 17, 6281–6291 (2021).
pubmed: 34586825
doi: 10.1021/acs.jctc.1c00645
Zhan, T., Rindtorff, N. & Boutros, M. Wnt signaling in cancer. Oncogene 36, 1461–1473 (2017).
pubmed: 27617575
doi: 10.1038/onc.2016.304
Liu, J. et al. Wnt/β-catenin signalling: function, biological mechanisms, and therapeutic opportunities. Signal Transduct. Target. Ther. 7, 3 (2022).
Grossmann, T. N. et al. Inhibition of oncogenic wnt signaling through direct targeting of β-catenin. Proc. Natl. Acad. Sci. USA 109, 17942–17947 (2012).
pubmed: 23071338
pmcid: 3497784
doi: 10.1073/pnas.1208396109
Diderich, P. et al. Phage selection of chemically stabilized α-helical peptide ligands. ACS Chem. Biol. 11, 1422–1427 (2016).
pubmed: 26929989
doi: 10.1021/acschembio.5b00963
Wendt, M. et al. Bicyclic β-sheet mimetics that target the transcriptional coactivator β-catenin and inhibit wnt signaling. Angew. Chem. Int. Edn. 60, 13937–13944 (2021).
doi: 10.1002/anie.202102082
Blosser, S. L., Sawyer, N., Maksimovic, I., Ghosh, B. & Arora, P. S. Covalent and noncovalent targeting of the tcf4/β-catenin strand interface with β-hairpin mimics. ACS Chem. Biol. 16, 1518–1525 (2021).
pubmed: 34286954
pmcid: 8687824
doi: 10.1021/acschembio.1c00389
Schneider, J. A. et al. Design of peptoid-peptide macrocycles to inhibit the β-catenin tcf interaction in prostate cancer. Nat. Commun. 9, 4396 (2018).
pubmed: 30352998
pmcid: 6199279
doi: 10.1038/s41467-018-06845-3
Dougherty, P. G. et al. Enhancing the cell permeability of stapled peptides with a cyclic cell-penetrating peptide. J. Med. Chem. 62, 10098–10107 (2019).
pubmed: 31657556
pmcid: 7291828
doi: 10.1021/acs.jmedchem.9b00456
Jacobs, T. M. & Kuhlman, B. Using anchoring motifs for the computational design of protein–protein interactions. Biochem. Soc. Trans. 41, 1141–1145 (2013).
pubmed: 24059499
pmcid: 4112732
doi: 10.1042/BST20130108
Drew, K. et al. Adding diverse noncanonical backbones to rosetta: enabling peptidomimetic design. PLoS ONE 8, e67051 (2013).
pubmed: 23869206
pmcid: 3712014
doi: 10.1371/journal.pone.0067051
The UniProt Consortium. Uniprot: the universal protein knowledgebase in 2021. Nucleic Acids Res. 49, D480–D489 (2021).
Frappier, V., Duran, M. & Keating, A. E. Pixeldb: protein–peptide complexes annotated with structural conservation of the peptide binding mode. Protein Sci. 27, 276–285 (2018).
pubmed: 29024246
doi: 10.1002/pro.3320
Usmani, S. S. et al. THPdb: database of FDA-approved peptide and protein therapeutics. PLoS ONE 12, e0181748 (2017).
pubmed: 28759605
pmcid: 5536290
doi: 10.1371/journal.pone.0181748
Loomans, E. E., Gribnau, T. C., Bloemers, H. P. & Schielen, W. J. Adsorption studies of tritium-labeled peptides on polystyrene surfaces. J. Immunol. Methods 221, 131–139 (1998).
pubmed: 9894904
doi: 10.1016/S0022-1759(98)00174-4
London, N., Raveh, B., Cohen, E., Fathi, G. & Schueler-Furman, O. Rosetta flexpepdock web server—high resolution modeling of peptide–protein interactions. Nucleic Acids Res. 39, W249–W253 (2011).
pubmed: 21622962
pmcid: 3125795
doi: 10.1093/nar/gkr431
Kollman, P. A. et al. Calculating structures and free energies of complex molecules: combining molecular mechanics and continuum models. Acc. Chem. Res. 33, 889–897 (2000).
pubmed: 11123888
doi: 10.1021/ar000033j
Graham, T. A., Weaver, C., Mao, F., Kimelman, D. & Xu, W. Crystal Structure of a β-Catenin/Tcf Complex. Cell 103, 885–896 (2000).
pubmed: 11136974
doi: 10.1016/S0092-8674(00)00192-6
Sun, J. & Weis, W. I. Biochemical and structural characterization of β-catenin interactions with nonphosphorylated and ck2-phosphorylated lef-1. J. Mol. Biol. 405, 519–530 (2011).
pubmed: 21075118
doi: 10.1016/j.jmb.2010.11.010
Sampietro, J. et al. Crystal structure of a β-catenin/bcl9/tcf4 complex. Mol. Cell 24, 293–300 (2006).
pubmed: 17052462
doi: 10.1016/j.molcel.2006.09.001
Karin, M. & Delhase, M. The IκB kinase (IKK) and NF-κB: key elements of proinflammatory signalling. Seminars in Immunology 12, 85–98 (2000)
Schmidt-Supprian, M. et al. Nemo/ikkγ-deficient mice model incontinentia pigmenti. Mol. Cell 5, 981–992 (2000).
pubmed: 10911992
doi: 10.1016/S1097-2765(00)80263-4
Bonizzi, G. & Karin, M. The two nf-κb activation pathways and their role in innate and adaptive immunity. Trends Immunol. 25, 280–288 (2004).
pubmed: 15145317
doi: 10.1016/j.it.2004.03.008
Makris, C. et al. Female mice heterozygous for ikkγ/nemo deficiencies develop a dermatopathy similar to the human x-linked disorder incontinentia pigmenti. Mol. Cell 5, 969–979 (2000).
pubmed: 10911991
doi: 10.1016/S1097-2765(00)80262-2
Rushe, M. et al. Structure of a NEMO/IKK-associating domain reveals architecture of the interaction site. Structure 16, 798–808 (2008).
pubmed: 18462684
doi: 10.1016/j.str.2008.02.012
Alford, R. F. et al. The rosetta all-atom energy function for macromolecular modeling and design. J. Chem.Theory Comput. 13, 3031–3048 (2017).
pubmed: 28430426
pmcid: 5717763
doi: 10.1021/acs.jctc.7b00125
Gómez-Bombarelli, R. et al. Automatic chemical design using a data-driven continuous representation of molecules. ACS Central Sci. 4, 268–276 (2018).
doi: 10.1021/acscentsci.7b00572
Joo, S., Kim, M. S., Yang, J. & Park, J. Generative model for proposing drug candidates satisfying anticancer properties using a conditional variational autoencoder. ACS Omega 5, 18642–18650 (2020).
pubmed: 32775866
pmcid: 7407547
doi: 10.1021/acsomega.0c01149
Cai, C. et al. Transfer learning for drug discovery. J. Med. Chem. 63, 8683–8694 (2020).
pubmed: 32672961
doi: 10.1021/acs.jmedchem.9b02147
Salem, M., Keshavarzi Arshadi, A. & Yuan, J. S. Ampdeep: hemolytic activity prediction of antimicrobial peptides using transfer learning. BMC Bioinforma. 23, 1–17 (2022).
doi: 10.1186/s12859-022-04952-z
Qvit, N., Rubin, S. J., Urban, T. J., Mochly-Rosen, D. & Gross, E. R. Peptidomimetic therapeutics: scientific approaches and opportunities. Drug Discov. today 22, 454–462 (2017).
pubmed: 27856346
doi: 10.1016/j.drudis.2016.11.003
Nevola, L. & Giralt, E. Modulating protein–protein interactions: the potential of peptides. Chem. Commun. 51, 3302–3315 (2015).
doi: 10.1039/C4CC08565E
Wang, L. et al. Therapeutic peptides: current applications and future directions. Signal Transduct. Target. Ther. 7, 48 (2022).
pubmed: 35165272
pmcid: 8844085
doi: 10.1038/s41392-022-00904-4
Muttenthaler, M., King, G. F., Adams, D. J. & Alewood, P. F. Trends in peptide drug discovery. Nat. Rev. Drug Discov. 20, 309–325 (2021).
pubmed: 33536635
doi: 10.1038/s41573-020-00135-8
Frappier, V., Duran, M. & Keating, A. E. PixelDB: protein-peptide complexes annotated with structural conservation of the peptide binding mode. Protein Sci. 27, 276–285 (2017).
pubmed: 29024246
pmcid: 5734312
doi: 10.1002/pro.3320
Stranges, P. B. & Kuhlman, B. A comparison of successful and failed protein interface designs highlights the challenges of designing buried hydrogen bonds. Protein Sci. 22, 74–82 (2013).
pubmed: 23139141
doi: 10.1002/pro.2187
Mikolov, T., Chen, K., Corrado, G. S. & Dean, J. Efficient estimation of word representations in vector space. In The International Conference on Learning Representations https://arxiv.org/abs/1301.3781 (2013).
Doersch, C. Tutorial on variational autoencoders. Preprint at https://arxiv.org/abs/1606.05908 (2016).
Pascanu, R., Mikolov, T. & Bengio, Y. On the difficulty of training recurrent neural networks. In Proceedings of the 30th International Conference on Machine Learning (eds. Dasgupta, S., & McAllester D.) PMLR 28, 1310–1318 (JMLR.org, 2012).
Chung, J., Gulcehre, C., Cho, K. & Bengio, Y. Empirical evaluation of gated recurrent neural networks on sequence modeling. Preprint at https://arxiv.org/abs/1412.3555 (2014).
Chib, S. & Greenberg, E. Understanding the metropolis-hastings algorithm. Am. Stat. 49, 327–335 (1995).
Stranges, P. B. & Kuhlman, B. A comparison of successful and failed protein interface designs highlights the challenges of designing buried hydrogen bonds. Protein Sci. 22, 74–82 (2012).
pubmed: 23139141
pmcid: 3575862
doi: 10.1002/pro.2187
Abraham, M. J. et al. Gromacs: high performance molecular simulations through multi-level parallelism from laptops to supercomputers. SoftwareX 1, 19–25 (2015).
Gfeller, D., Michielin, O. & Zoete, V. SwissSidechain: a molecular and structural database of non-natural sidechains. Nucleic Acids Res. 41, D327–D332 (2012).
pubmed: 23104376
pmcid: 3531096
doi: 10.1093/nar/gks991
Parrinello, M. & Rahman, A. Polymorphic transitions in single crystals: a new molecular dynamics method. J. Appl. Phys. 52, 7182–7190 (1981).
doi: 10.1063/1.328693
Petersen, H. G. Accuracy and efficiency of the particle mesh ewald method. J. Chem. Phys. 103, 3668–3679 (1995).
doi: 10.1063/1.470043
Hess, B., Bekker, H., Berendsen, H. J. & Fraaije, J. G. Lincs: a linear constraint solver for molecular simulations. J. Comput. Chem. 18, 1463–1472 (1997).
doi: 10.1002/(SICI)1096-987X(199709)18:12<1463::AID-JCC4>3.0.CO;2-H
Onufriev, A., Bashford, D. & Case, D. A. Exploring protein native states and large-scale conformational changes with a modified generalized born model. Proteins Struct. Func. Bioinforma. 55, 383–394 (2004).
doi: 10.1002/prot.20033
Weiser, J., Shenkin, P. S. & Still, W. C. Approximate atomic surfaces from linear combinations of pairwise overlaps (lcpo). J. Comput. Chem. 20, 217–230 (1999).
doi: 10.1002/(SICI)1096-987X(19990130)20:2<217::AID-JCC4>3.0.CO;2-A
Lam, K. S., Lebl, M. & Krchňák, V. The “one-bead-one-compound” combinatorial library method. Chem. Rev. 97, 411–448 (1997).
pubmed: 11848877
doi: 10.1021/cr9600114
Sweeney, M. C. & Pei, D. An improved method for rapid sequencing of support-bound peptides by partial edman degradation and mass spectrometry. J. Comb. Chem. 5, 218–222 (2003).
pubmed: 12739936
doi: 10.1021/cc020113+
Youngquist, R. S., Fuentes, G. R., Lacey, M. P. & Keough, T. Generation and screening of combinatorial peptide libraries designed for rapid sequencing by mass spectrometry. J. Am. Chem. Soc. 8, 3900–3906 (1995).
doi: 10.1021/ja00119a002