Computational Design of Peptides with Improved Recognition of the Focal Adhesion Kinase FAT Domain.
Computational peptide design
Computational protein design
Molecular mechanics
Monte Carlo
Proteus program
Journal
Methods in molecular biology (Clifton, N.J.)
ISSN: 1940-6029
Titre abrégé: Methods Mol Biol
Pays: United States
ID NLM: 9214969
Informations de publication
Date de publication:
2022
2022
Historique:
entrez:
17
3
2022
pubmed:
18
3
2022
medline:
22
3
2022
Statut:
ppublish
Résumé
We describe a two-stage computational protein design (CPD) methodology for the design of peptides binding to the FAT domain of the protein focal adhesion kinase. The first stage involves high-throughput CPD calculations with the Proteus software. The energies of the folded state are described by a physics-based energy function and of the unfolded peptides by a knowledge-based model that reproduces aminoacid compositions consistent with a helicity scale. The obtained sequences are filtered in terms of the affinity and the stability of the complex. In the second stage, design sequences are further evaluated by all-atom molecular dynamics simulations and binding free energy calculations with a molecular mechanics/implicit solvent free energy function.
Identifiants
pubmed: 35298823
doi: 10.1007/978-1-0716-1855-4_18
doi:
Substances chimiques
Peptides
0
Focal Adhesion Protein-Tyrosine Kinases
EC 2.7.10.2
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
383-402Informations de copyright
© 2022. The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.
Références
Schaller MD (2010) Cellular functions of FAK kinases: insight into molecular mechanisms and novel functions. J Cell Sci 123(7):1007–1013
pubmed: 20332118
Walkiewicz KW, Girault J, Arold ST (2015) How to awaken your nanomachines: site-specific activation of focal adhesion kinases through ligand interactions. Prog Biophys. Mol. Bio 119(1):60–71
Naser R, Aldehaiman A, Díaz-Galicia E, Arold ST (2018) Endogenous control mechanisms of FAK and PYK2 and their relevance to cancer development. Cancers 10(6):196
pmcid: 6025627
Sulzmaier FJ, Jean C, Schlaepfer DD (2014) FAK in cancer: mechanistic findings and clinical applications. Nat Rev Cancer 14(9):598–610
pubmed: 25098269
pmcid: 4365862
Shen T, Guo Q (2018) Role of Pyk2 in human cancers. Med Sci Monitor 24:8172–8182
Liu S, Chen L, Xu Y (2018) Significance of PYK2 level as a prognosis predictor in patients with colon adenocarcinoma after surgical resection. Oncotargets Ther 11:7625–7634
Quiroga MN, Díaz MR, Moreno J, Aguilar RG, Ibarra ML, Sánchez PP, Cabrero IA, Gómez GV, Zavaleta LR, Aranda DA, Gómez FS (2019) Increased expression of FAK isoforms as potential cancer biomarkers in ovarian cancer. Oncol Lett 17:4779–4786
Pan M-R, Wu C-C, Kan J-Y, Li Q-L, Chang S-J, Wu C-C, Li C-L, Ou-Yang F, Hou M-F, Yip H-K, Luo C-W (2019) Impact of FAK expression on the cytotoxic effects of CIK therapy in triple-negative breast cancer. Cancers 12(1):94
pmcid: 7017032
Golubovskaya VM, Ho B, Zheng M, Magis A, Ostrov D, Morrison C, Cance WG (2013) Disruption of focal adhesion kinase and p53 interaction with small molecule compound r2 reactivated p53 and blocked tumor growth. BMC Cancer 13(1):342
pubmed: 23841915
pmcid: 3712010
Golubovskaya VM, Palma NL, Zheng M, Ho B, Magis A, Ostrov D, Cance WG (2013) A small-molecule inhibitor, 5
Ucar DA, Magis AT, He D, Lawrence NJ, Sebti SM, Kurenova E, Zajac-Kaye M, Zhang J, Hochwald SN (2013) Inhibiting the interaction of cMET and IGF-1r with FAK effectively reduces growth of pancreatic cancer cells in vitro and in vivo. Anti-Cancer Agent Me 13(4):595–602
Ucar DA, Kurenova E, Garrett TJ, Cance WG, Nyberg C, Cox A, Massoll N, Ostrov DA, Lawrence N, Sebti SM, Zajac-Kaye M, Hochwald SN (2014) Disruption of the protein interaction between FAK and IGF-1R inhibits melanoma tumor growth. Cell Cycle 11(17):3250–3259
Lv P-C, Jiang A-Q, Zhang W-M, Zhu H-L (2018) FAK inhibitors in cancer, a patent review. Expert Opinion Therapeutic Patents 28(2):139–145. PMID: 29210300
Alvarado C, Stahl E, Koessel K, Rivera A, Cherry BR, Pulavarti SVSRK, Szyperski T, Cance W, Marlowe T (2019) Development of a fragment-based screening assay for the focal adhesion targeting domain using SPR and NMR. Molecules 24(18):3352
pmcid: 6766811
Mabonga L, Kappo AP (2020) Peptidomimetics: a synthetic tool for inhibiting protein–protein interactions in cancer. Int J Peptide Res Therapeutics 26(1):225–241
Michael E, Polydorides S, Simonson T, Archontis G (2017) Simple models for nonpolar solvation: parameterization and testing. J Comp Chem 38(29):2509–2519
Michael E, Polydorides S, Promponas V, Skourides P, Archontis G (2021) Recognition of LD motifs by the focal adhesion targeting domains of focal adhesion kinase and proline-rich tyrosine kinase 2beta: insights from molecular dynamics simulations. Proteins 89(29):29–52
Munoz V, Serrano L (1995) Elucidating the folding problem of helical peptides using empirical parameters. ii. helix macrodipole effects and rational modification of the helical content of natural peptides. J Mol Biol 245(3):275–296
pubmed: 7844817
Mignon D, Druart K, Michael E, Opuu V, Polydorides S, Villa F, Gaillard T, Panel N, Archontis G, Simonson T (2020) Physics-based computational protein design: an update. J Phys Chem A 2020:10637–10648
Simonson T, Gaillard T, Mignon D, am Busch MS, Lopes A, Amara N, Polydorides S, Sedano A, Druart K, Archontis G (2013) Computational protein design: the proteus software and selected applications. J Comput Chem 34(28):2472–2484
Phillips JC, Hardy DJ, Maia JDC, Stone JE, Ribeiro JV, Bernardi RC, Buch R, Fiorin G, Henin J, Jiang W, McGreevy R, Melo MCR, Radak BK, Skeel RD, Singharoy A, Wang Y, Roux B, Aksimentiev A, Luthey-Schulten Z, Kale LV, Schulten K, Chipot C, Tajkhorshid E (2020) Scalable molecular dynamics on CPU and GPU architectures with NAMD. J Chem Phys 153:044130
pubmed: 32752662
pmcid: 7395834
Hayashi I, Vuori K, Liddington RC (2002) The focal adhesion targeting (FAT) region of focal adhesion kinase is a four-helix bundle that binds paxillin. Nat Struct Mol Biol 9(2):101–106
Arold ST, Hoellerer MK, Noble MEM (2002) The structural basis of localization and signaling by the focal adhesion targeting domain. Structure 10(3):319–327
pubmed: 12005431
Lulo J, Yuzawa S, Schlessinger J (2009) Crystal structures of free and ligand-bound focal adhesion targeting domain of Pyk2. Biochem Bioph Res Co 383(3):347–352
Alam T, Alazmi M, Gao X, Arold ST (2014) How to find a leucine in a haystack? structure, ligand recognition and regulation of leucine–aspartic acid (LD) motifs. Biochem J 460(3):317–329
pubmed: 24870021
Liu G, Guibao CD, Zheng J (2002) Structural insight into the mechanisms of targeting and signaling of focal adhesion kinase. Mol Cell Biol 22(8):2751–2760
pubmed: 11909967
pmcid: 133741
Hoellerer MK, Noble MEM, Labesse G, Campbell ID, Werner JM, Arold ST (2003) Molecular recognition of paxillin LD motifs by the focal adhesion targeting domain. Structure 11(10):1207–1217
pubmed: 14527389
Gao G, Prutzman KC, King ML, Scheswohl DM, DeRose EF, London RE, Schaller MD, Campbell SL (2004) NMR solution structure of the focal adhesion targeting domain of focal adhesion kinase in complex with a paxillin LD peptide: evidence for a two-site binding model. J Biolog Chem 279(9):8441–8451
Bertolucci CM, Guibao CD, Zheng J (2005) Structural features of the focal adhesion kinase-paxillin complex give insight into the dynamics of focal adhesion assembly. Prot Sci 14(3):644–652
Tuffery P, Etchebest C, Hazout S, Lavery R (1991) A new approach to the rapid determination of protein side chain conformations. J Biomol Struct Dyn 8(6):1267–1289
pubmed: 1892586
Simonson T (2013) What is the dielectric constant of a protein when its backbone is fixed? JCTC 9:4603–4608
pubmed: 26589172
Cornell W, Cieplak P, Bayly C, Gould I, Merz K, Ferguson D, Spellmeyer D, Fox T, Caldwell J, Kollman P (1995) A second generation force field for the simulation of proteins, nucleic acids, and organic molecules. J Am Chem Soc 117:S179–S197
Still WC, Tempczyk A, Hawley RC, Hendrickson T (1990) Semianalytical treatment of solvation for molecular mechanics and dynamics. J Am Chem Soc 112(16):6127–6129
Hawkins GD, Cramer CJ, Truhlar DG (1995) Pairwise solute descreening of solute charges from a dielectric medium. Chem Phys Lett 246(1–2):122–129
Schaefer M, Karplus M (1996) A comprehensive analytical treatment of continuum electrostatics. J Phys Chem 100(5):1578–1599
Weeks JD, Chandler D, Andersen HC (1971) Role of repulsive forces in determining the equilibrium structure of simple liquids. J Chem Phys 54(12):5237–5247
Aguilar B, Shadrach R, Onufriev AV (2010) Reducing the secondary structure bias in the generalized born model via r6 effective radii. J Chem Theory Comp 6(12):3613–3630
Lazaridis T, Karplus M (1999) Effective energy function for proteins in solution. Proteins 35(2):133–152
pubmed: 10223287
Archontis G, Simonson T (2005) A residue-pairwise generalized born scheme suitable for protein design calculations. J Phys Chem B 109(47):22667–22673
pubmed: 16853951
Villa F, Mignon D, Polydorides S, Simonson T (2017) Comparing pairwise-additive and many-body generalized born models for acid/base calculations and protein design. J Comput Chem 38(28):2396–2410
pubmed: 28749575
Kuhlman B, Dantas G, Ireton GC, Varani G, Stoddard BL, Baker D (2003) Design of a novel globular protein fold with atomic-level accuracy. Science 302:1364–1368
pubmed: 14631033
Ollikainen N, de Jong RM, Kortemme T (2015) Coupling protein side-chain and backbone flexibility improves the re-design of protein-ligand specificity. PLOS Comput Biol 11(9):1–22
Druart K, Bigot J, Audit E, Simonson T (2016) A hybrid Monte Carlo scheme for multibackbone protein design. J Chem Theory Comp 12:6035–6048
Hayes RL, Armacost, KA, Vilseck JZ, Brooks III CL (2017) Adaptive landscape flattening accelerates sampling of alchemical space in multisite λ dynamics. J Phys Chem. B 121(15):3626–3635
pubmed: 28112940
pmcid: 5824625
Simonson T (2020) PROTEUS 3.0 Manual. https://proteus.polytechnique.fr
Mignon D, Simonson T (2016) Comparing three stochastic search algorithms for computational protein design: Monte Carlo, replica exchange Monte Carlo, and a multistart, steepest-descent heuristic. J Comput Chem 37(19):1781–1793
pubmed: 27197555
Mignon D, Panel N, Chen X, Fuentes EJ, Simonson T (2017) Computational design of the Tiam1 PDZ domain and its ligand binding. J Chem Theory Comput 13(5):2271–2289
pubmed: 28394603
Genheden S, Ryde U (2015) The MM/PBSA and MM/GBSA methods to estimate ligand-binding affinities. Expert Opin Drug Dis 10(5):449–461
Jo S, Kim T, Iyer VG, Im W (2008) CHARMM-GUI: a web-based graphical user interface for CHARMM. J Comp Chem 29(11):1859–1865
Polydorides S, Simonson T (2013) Monte Carlo simulations of proteins at constant pH with generalized Born solvent, flexible sidechains, and an effective dielectric boundary. J Comput Chem 34:2742–2756
pubmed: 24122878
Seeber M, Cecchini M, Rao F, Setanni G, Caflisch A (2007) Wordom: a program for efficient analysis of molecular dynamics simulations. Bioinf 23:2625–2627
Grantham R (1974) Amino acid difference formula to help explain protein evolution. Science 185(4154):862–864
pubmed: 4843792
Sneath P (1966) Relations between chemical structure and biological activity in peptides. J Theoret Biol 12(2):157–195