Skip to main content
SpringerLink
Log in

Development of SAAP3D force field and the application to replica-exchange Monte Carlo simulation for chignolin and C-peptide

  • Published:
Journal of Computer-Aided Molecular Design Aims and scope Submit manuscript

Abstract

Single amino acid potential (SAAP) would be a prominent factor to determine peptide conformations. To prove this hypothesis, we previously developed SAAP force field for molecular simulation of polypeptides. In this study, the force field was renovated to SAAP3D force field by applying more accurate three-dimensional main-chain parameters, instead of the original two-dimensional ones, for the amino acids having a long side-chain. To demonstrate effectiveness of the SAAP3D force field, replica-exchange Monte Carlo (REMC) simulation was performed for two benchmark short peptides, chignolin (H-GYDPETGTWG-OH) and C-peptide (CHO-AETAAAKFLRAHA-NH2). For chignolin, REMC/SAAP3D simulation correctly produced native β-turn structures, whose minimal all-atom root-mean-square deviation value measured from the native NMR structure (except for H) was 1.2 Å, at 300 K in implicit water, along with misfolded β-hairpin structures with unpacked aromatic side chains of Tyr2 and Trp9. Similar results were obtained for chignolin analog [G1Y,G10Y], which folded more tightly to the native β-turn structure than chignolin did. For C-peptide, on the other hand, the α-helix content was larger than the β content on average, suggesting a significant helix-forming propensity. When the imidazole side chain of His12 was protonated (i.e., [His12Hip]), the α content became larger. These observations as well as the representative structures obtained by clustering analysis were in reasonable agreement not only with the structures of C-peptide that were determined in this study by NMR in 30% CD3CD in H2O at 298 K but also with the experimental and theoretical behaviors having been reported for protonated C-peptide. Thus, accuracy of the SAAP force field was improved by applying three-dimensional main-chain parameters, supporting prominent importance of SAAP for peptide conformations.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

References

  1. Ramachandran GN, Sasisekharan V (1968) Conformation of polypeptides and proteins. Adv Protein Chem 23:283–437. https://doi.org/10.1016/S0065-3233(08)60402-7

    Article  CAS  Google Scholar 

  2. Head-Gordon T, Head-Gordon M, Frisch MJ et al (1991) Theoretical study of blocked glycine and alanine peptide analogues. J Am Chem Soc 113:5989–5997. https://doi.org/10.1021/ja00016a010

    Article  CAS  Google Scholar 

  3. Shang HS, Head-Gordon T (1994) Stabilization of helices in glycine and alanine dipeptides in a reaction field model of solvent. J Am Chem Soc 116:1528–1532. https://doi.org/10.1021/ja00083a042

    Article  CAS  Google Scholar 

  4. Gould IR, Cornell WD, Hillier IH (1994) A quantum mechanical investigation of the conformational energetics of the alanine and glycine dipeptides in the gas phase and in aqueous solution. J Am Chem Soc 116:9250–9256. https://doi.org/10.1021/ja00099a048

    Article  CAS  Google Scholar 

  5. Hudáky I, Hudáky P, Perczel A (2004) Solvation model induced structural changes in peptides. A quantum chemical study on Ramachandran surfaces and conformers of alanine diamide using the polarizable continuum model. J Comput Chem 25:1522–1531. https://doi.org/10.1002/jcc.20073

    Article  Google Scholar 

  6. Wang ZX, Duan Y (2004) Solvation effects on alanine dipeptide: A MP2/cc-pVTZ//MP2/6-31G** study of (Φ, Ψ) energy maps and conformers in the gas phase, ether, and water. J Comput Chem 25:1699–1716. https://doi.org/10.1002/jcc.20092

    Article  CAS  Google Scholar 

  7. Iwaoka M, Okada M, Tomoda S (2002) Solvent effects on the φ-ψ potential surfaces of glycine and alanine dipeptides studied by PCM and I-PCM methods. J Mol Struct THEOCHEM 586:111–124. https://doi.org/10.1016/S0166-1280(02)00076-3

    Article  CAS  Google Scholar 

  8. Iwaoka M, Yosida D, Kimura N (2006) Importance of the single amino acid potential in water for secondary and tertiary structures of proteins. J Phys Chem B 110:14475–14482. https://doi.org/10.1021/jp062196g

    Article  CAS  Google Scholar 

  9. Smith LJ, Fiebig KM, Schwalbe H, Dobson CM (1996) The concept of a random coil. Residual structure in peptides and denatured proteins. Fold Des 1:R95–R106. https://doi.org/10.1016/S1359-0278(96)00046-6

    Article  CAS  Google Scholar 

  10. Fiebig KM, Schwalbe H, Buck M et al (1996) Toward a description of the conformations of denatured states of proteins. Comparison of a random coil model with NMR measurements. J Phys Chem 100:2661–2666. https://doi.org/10.1021/jp952747v

    Article  CAS  Google Scholar 

  11. Iwaoka M, Tomoda S (2003) The SAAP force field. A simple approach to a new all-atom protein force field by using single amino acid potential (SAAP) functions in various solvents. J Comput Chem 24:1192–1200. https://doi.org/10.1002/jcc.10259

    Article  CAS  Google Scholar 

  12. Iwaoka M, Kimura N, Yosida D, Minezaki T (2009) The SAAP force field: development of the single amino acid potentials for 20 proteinogenic amino acids and monte carlo molecular simulation for short peptides. J Comput Chem 30:2039–2055. https://doi.org/10.1002/jcc.21196

    Article  CAS  Google Scholar 

  13. Takei T, Urabe Y, Asahina Y et al (2014) Model study using designed selenopeptides on the importance of the catalytic triad for the antioxidative functions of glutathione peroxidase. J Phys Chem B 118:492–500. https://doi.org/10.1021/jp4113975

    Article  CAS  Google Scholar 

  14. Dedachi K, Shimosato T, Minezaki T, Iwaoka M (2013) Toward structure prediction for short peptides using the improved SAAP force field parameters. J Chem 407862. https://doi.org/10.1155/2013/407862

  15. Frisch MJ, Trucks GW, Schlegel HB et al (2010) Gaussian 09, Revision B.01

  16. Metropolis N, Rosenbluth AW, Rosenbluth MN et al (1953) Equation of state calculations by fast computing machines. J Chem Phys 21:1087–1092. https://doi.org/10.1063/1.1699114

    Article  CAS  Google Scholar 

  17. Binder K (1984) Applications of the Monte Carlo method in statistical physics. Springer. https://doi.org/10.1007/978-3-642-96788-7

    Google Scholar 

  18. Hastings WK (1970) Monte Carlo sampling methods using Markov chains and their applications. Biometrika 57:97–109. https://doi.org/10.2307/2334940

    Article  Google Scholar 

  19. Honda S, Yamasaki K, Sawada Y, Morii H (2004) 10 Residue folded peptide designed by segment statistics. Structure 12:1507–1518. https://doi.org/10.1016/j.str.2004.05.022

    Article  CAS  Google Scholar 

  20. Honda S, Akiba T, Kato YS et al (2008) Crystal structure of a ten-amino acid protein. J Am Chem Soc 130:15327–15331. https://doi.org/10.1021/ja8030533

    Article  CAS  Google Scholar 

  21. Osterhout JJ, Baldwin RL, York EJ et al (1989) 1H NMR studies of the solution conformations of an analogue of the C-peptide of ribonuclease A. BioChemistry 28:7059–7064. https://doi.org/10.1021/bi00443a042

    Article  CAS  Google Scholar 

  22. Shao J, Tanner SW, Thompson N, Cheatham TE (2007) Clustering molecular dynamics trajectories: 1. Characterizing the performance of different clustering algorithms. J Chem Theory Comput 3:2312–2334. https://doi.org/10.1021/ct700119m

    Article  CAS  Google Scholar 

  23. Case DA, Darden TA, Cheatham TE III et al (2008) AMBER 10. University of California, San Francisco, California

  24. King DS, Fields CG, Fields GB (1990) A cleavage method which minimizes side reactions following Fmoc solid phase peptide synthesis. Int J Pept Protein Res 36:255–266. https://doi.org/10.1111/j.1399-3011.1990.tb00976.x

    Article  CAS  Google Scholar 

  25. Greenfield N, Fasman GD (1969) Computed circular dichroism spectra for the evaluation of protein conformation. BioChemistry 8:4108–4116. https://doi.org/10.1021/bi00838a031

    Article  CAS  Google Scholar 

  26. Shoemaker KR, Kim PS, York EJ et al (1987) Tests of the helix dipole model for stabilization of α-helices. Nature 326:563–567. https://doi.org/10.1038/326563a0

    Article  CAS  Google Scholar 

  27. Satoh D, Shimizu K, Nakamura S, Terada T (2006) Folding free-energy landscape of a 10-residue mini-protein, chignolin. FEBS Lett 580:3422–3426. https://doi.org/10.1016/j.febslet.2006.05.015

    Article  CAS  Google Scholar 

  28. Terada T, Satoh D, Mikawa T et al (2008) Understanding the roles of amino acid residues in tertiary structure formation of chignolin by using molecular dynamics simulation. Proteins 73:621–631. https://doi.org/10.1002/prot.22100

    Article  CAS  Google Scholar 

  29. Terada T, Shimizu K (2008) A comparison of generalized Born methods in folding simulations. Chem Phys Lett 460:295–299. https://doi.org/10.1016/j.cplett.2008.05.066

    Article  CAS  Google Scholar 

  30. Moritsugu K, Terada T, Kidera A (2010) Scalable free energy calculation of proteins via multiscale essential sampling. J Chem Phys 133:224105. https://doi.org/10.1063/1.3510519

    Article  Google Scholar 

  31. Moritsugu K, Terada T, Kidera A (2014) Multiscale enhanced sampling driven by multiple coarse-grained models. Chem Phys Lett 616:20–24. https://doi.org/10.1016/j.cplett.2014.10.009

    Article  Google Scholar 

  32. Moritsugu K, Terada T, Kidera A (2016) Multiscale enhanced sampling for protein systems: An extension via adiabatic separation. Chem Phys Lett 661:279–283. https://doi.org/10.1016/j.cplett.2016.08.075

    Article  CAS  Google Scholar 

  33. Seibert MM, Patriksson A, Hess B, van der Spoel D (2005) Reproducible polypeptide folding and structure prediction using molecular dynamics simulations. J Mol Biol 354:173–183. https://doi.org/10.1016/j.jmb.2005.09.030

    Article  CAS  Google Scholar 

  34. van der Spoel D, Seibert MM (2006) Protein folding kinetics and thermodynamics from atomistic simulations. Phys Rev Lett 96:238102. https://doi.org/10.1103/PhysRevLett.96.238102

    Article  Google Scholar 

  35. Roy S, Goedecker S, Field MJ, Penev E (2009) A minima hopping study of all-atom protein folding and structure prediction. J Phys Chem B 113:7315–7321. https://doi.org/10.1021/jp8106793

    Article  CAS  Google Scholar 

  36. Kannan S, Zacharias M (2007) Enhanced sampling of peptide and protein conformations using replica exchange simulations with a peptide backbone biasing-potential. Proteins 66:697–706. https://doi.org/10.1002/prot.21258

    Article  CAS  Google Scholar 

  37. Xu W, Lai T, Yang Y, Mu Y (2008) Reversible folding simulation by hybrid Hamiltonian replica exchange. J Chem Phys 128:175105. https://doi.org/10.1063/1.2911693

    Article  Google Scholar 

  38. Suenaga A, Narumi T, Futatsugi N et al (2007) Folding dynamics of 10-residue beta-hairpin peptide chignolin. Chem Asian J 2:591–598. https://doi.org/10.1002/asia.200600385

    Article  CAS  Google Scholar 

  39. Dou X, Wang J (2008) Folding free energy landscape of the decapeptide chignolin. Mod Phys Lett B 22:3087–3098. https://doi.org/10.1142/S0217984908017606

    Article  CAS  Google Scholar 

  40. Brüschweiler R, Morikis D, Wright PE (1995) Hydration of the partially folded peptide RN-24 studied by multidimensional NMR. J Biomol NMR 5:353–356. https://doi.org/10.1007/BF00182277

    Article  Google Scholar 

  41. Armstrong KM, Fairman R, Baldwin RL (1993) The (i,i + 4) Phe-His interaction studied in an alanine-based α-helix. J Mol Biol 230:284–291. https://doi.org/10.1006/jmbi.1993.1142

    Article  CAS  Google Scholar 

  42. Hansmann UHE, Okamoto Y (1999) Effects of side-chain charges on α-helix stability in C-peptide of ribonuclease A studied by multicanonical algorithm. J Phys Chem B 103:1595–1604. https://doi.org/10.1021/JP983479E

    Article  CAS  Google Scholar 

  43. Gökoğlu G, Çeli̇k T (2007) Alpha-helix formation in C-peptide RNase-A investigated by parallel tempering simulations. Int J Mod Phys C 18:91–98. https://doi.org/10.1142/S0129183107010292

    Article  Google Scholar 

  44. Sugita Y, Okamoto Y (2005) Molecular mechanism for stabilizing a short helical peptide studied by generalized-ensemble simulations with explicit solvent. Biophys J 88:3180–3190. https://doi.org/10.1529/biophysj.104.049429

    Article  CAS  Google Scholar 

  45. Jang S, Kim E, Pak Y (2006) Direct folding simulation of α-helices and β-hairpins based on a single all-atom force field with an implicit solvation model. Proteins Struct Funct Bioinforma 66:53–60. https://doi.org/10.1002/prot.21173

    Article  Google Scholar 

  46. Khandogin J, Chen J, Brooks CL (2006) Exploring atomistic details of pH-dependent peptide folding. Proc Natl Acad Sci USA 103:18546–18550. https://doi.org/10.1073/pnas.0605216103

    Article  CAS  Google Scholar 

  47. Caballero-Herrera A, Nordstrand K, Berndt KD, Nilsson L (2005) Effect of urea on peptide conformation in water: molecular dynamics and experimental characterization. Biophys J 89:842–857. https://doi.org/10.1529/biophysj.105.061978

    Article  CAS  Google Scholar 

  48. Monticelli L, Tieleman DP, Colombo G (2005) Mechanism of helix nucleation and propagation: microscopic view from microsecond time scale MD simulations. J Phys Chem B 109:20064–20067. https://doi.org/10.1021/JP054729B

    Article  CAS  Google Scholar 

  49. Shoemaker KR, Fairman R, Schultz DA et al (1990) Side-chain interactions in the C-peptide helix: Phe 8···His 12+. Biopolymers 29:1–11. https://doi.org/10.1002/bip.360290104

    Article  CAS  Google Scholar 

  50. Bartlett GJ, Choudhary A, Raines RT, Woolfson DN (2010) n→π* interactions in proteins. Nat Chem Biol 6:615–620. https://doi.org/10.1038/nchembio.406

    Article  CAS  Google Scholar 

  51. Newberry RW, Raines RT (2016) A prevalent intraresidue hydrogen bond stabilizes proteins. Nat Chem Biol 12:1084–1088. https://doi.org/10.1038/nchembio.2206

    Article  CAS  Google Scholar 

Download references

Acknowledgements

We thank R. Ooka (Tokai University) for assistance of the synthesis of C-peptide. This work was supported by Grant-in-Aid for Scientific Research on Innovative Areas (No. 2120005) from the Ministry of Education, Culture, Sports, Science and Technology, Japan.

Author information

Author notes

  1. Hiroyuki Onuki

    Present address: Tokyo Chemical Industry Co., Ltd. Toshima, Kita, Tokyo, 114-0003, Japan

  2. Hiroshi Hirota

    Present address: Chemical Genomics Research Group, RIKEN CSRS, Wako, Saitama, 351-0198, Japan

Authors and Affiliations

  1. Department of Chemistry, School of Science, Tokai University, Kitakaname, Hiratsuka, Kanagawa, 259-1292, Japan

    Michio Iwaoka, Toshiki Suzuki, Yuya Shoji, Kenichi Dedachi & Taku Shimosato

  2. Laboratory of General Education for Science and Technology, School of Science, Tokai University, Kitakaname, Hiratsuka, Kanagawa, 259-1292, Japan

    Toshiya Minezaki

  3. Institute for Protein Research, Osaka University, Yamadaoka, Suita, Osaka, 565-0871, Japan

    Hironobu Hojo

  4. Genomic Sciences Center, RIKEN, Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa, 230-0045, Japan

    Hiroyuki Onuki & Hiroshi Hirota

Authors
  1. Michio Iwaoka
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Toshiki Suzuki
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yuya Shoji
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Kenichi Dedachi
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Taku Shimosato
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Toshiya Minezaki
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Hironobu Hojo
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Hiroyuki Onuki
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Hiroshi Hirota
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Michio Iwaoka.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material 1 (DOCX 11418 KB)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Iwaoka, M., Suzuki, T., Shoji, Y. et al. Development of SAAP3D force field and the application to replica-exchange Monte Carlo simulation for chignolin and C-peptide. J Comput Aided Mol Des 31, 1039–1052 (2017). https://doi.org/10.1007/s10822-017-0084-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10822-017-0084-8

Keywords

Search

Navigation