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Protein-Protein Interaction Studies Using Molecular Dynamics Simulation

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Advanced Methods in Structural Biology

Part of the book series: Methods in Molecular Biology ((MIMB,volume 2652))

Abstract

Protein-protein interaction (PPI) is a crucial event for many biological functions. Studying the molecular details of PPI requires structure determination using X-ray crystallography, nuclear magnetic resistance (NMR), and single particle Cryo-EM. However, sometimes it is not easy to solve the complex structure for various reasons. For example, complex may be unstable, not enough protein expression for structural studies, etc. Further, PPI are intricate processes, and its molecular details cannot be fully explained by experimental observations. Here, we describe a quick and simple method to study the PPI using the combinatorial approach of molecular dynamics simulation and biophysical methods.

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References

  1. Chothia C, Janin J (1975) Principles of protein-protein recognition. Nature 256:705–708

    Article  CAS  PubMed  Google Scholar 

  2. Jones S, Thornton JM (1996) Principles of protein-protein interactions. Proc Natl Acad Sci U S A 93:13–20

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Titeca K, Lemmens I, Tavernier J et al (2019) Discovering cellular protein-protein interactions: technological strategies and opportunities. Mass Spectrom Rev 38:79–111

    Article  CAS  PubMed  Google Scholar 

  4. Scott DE, Bayly AR, Abell C et al (2016) Small molecules, big targets: drug discovery faces the protein–protein interaction challenge. Nat Rev Drug Discov 15(8):533–550

    Article  CAS  PubMed  Google Scholar 

  5. Lu H, Zhou Q, He J et al (2020) Recent advances in the development of protein–protein interactions modulators: mechanisms and clinical trials. Signal Transduct Target Ther 5(1):1–23

    Google Scholar 

  6. Alder BJ, Wainwright TE (1957) Phase transition for a hard sphere system ARTICLES YOU MAY BE INTERESTED IN. J Chem Phys 27:1208

    Article  CAS  Google Scholar 

  7. McCammon JA, Gelin BR, Karplus M (1977) Dynamics of folded proteins. Nature 267:585–590

    Article  CAS  PubMed  Google Scholar 

  8. Hollingsworth SA, Dror RO (2018) Molecular dynamics simulation for all. Neuron 99:1129

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Heinz H, Lin TJ, Kishore Mishra R et al (2013) Thermodynamically consistent force fields for the assembly of inorganic, organic, and biological nanostructures: the INTERFACE force field. Langmuir 29:1754–1765

    Article  CAS  PubMed  Google Scholar 

  10. Sun H, Mumby SJ, Maple JR et al (2002) An ab initio CFF93 all-atom force field for polycarbonates. J Am Chem Soc 116:2978–2987

    Article  Google Scholar 

  11. Wang J, Wolf RM, Caldwell JW et al (2004) Development and testing of a general amber force field. J Comput Chem 25:1157–1174

    Article  CAS  PubMed  Google Scholar 

  12. Jorgensen WL, Tirado-Rives J (2005) Potential energy functions for atomic-level simulations of water and organic and biomolecular systems. Proc Natl Acad Sci U S A 102:6665–6670

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. MacKerell AD, Bashford D, Bellott M et al (1998) All-atom empirical potential for molecular modeling and dynamics studies of proteins. J Phys Chem B 102:3586–3616

    Article  CAS  PubMed  Google Scholar 

  14. Pearlman DA, Case DA, Caldwell JW et al (1995) AMBER, a package of computer programs for applying molecular mechanics, normal mode analysis, molecular dynamics and free energy calculations to simulate the structural and energetic properties of molecules. Comput Phys Commun 91:1–41

    Article  CAS  Google Scholar 

  15. Case DA, Cheatham TE, Darden T et al (2005) The Amber biomolecular simulation programs. J Comput Chem 26:1668–1688

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Brooks BR, Brooks CL, Mackerell AD et al (2009) CHARMM: the biomolecular simulation program. J Comput Chem 30:1545–1614

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Karplus M, McCammon JA (2002) Molecular dynamics simulations of biomolecules. Nat Struct Biol 9:646–652

    Article  CAS  PubMed  Google Scholar 

  18. Karplus M, Kuriyan J (2005) Molecular dynamics and protein function. Proc Natl Acad Sci U S A 102:6679–6685

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Özen A, Haliloğlu T, Schiffer CA (2011) Dynamics of preferential substrate recognition in HIV-1 protease: redefining the substrate envelope. J Mol Biol 410:726–744

    Article  PubMed  PubMed Central  Google Scholar 

  20. Adiyaman R, McGuffin LJ (2019) Methods for the refinement of protein structure 3D models. Int J Mol Sci 20:2301

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Baker D, Sali A (2001) Protein structure prediction and structural genomics. Science 294(1979):93–96

    Article  CAS  PubMed  Google Scholar 

  22. Henzler-Wildman KA, Lei M, Thai V et al (2007) A hierarchy of timescales in protein dynamics is linked to enzyme catalysis. Nature 450:913–916

    Article  CAS  PubMed  Google Scholar 

  23. Ode H, Nakashima M, Kitamura S et al (2012) Molecular dynamics simulation in virus research. Front Microbiol 3:258

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. McCorvy JD, Butler KV, Kelly B et al (2018) Structure-inspired design of β-arrestin-biased ligands for aminergic GPCRs. Nat Chem Biol 14:126–134

    Article  CAS  PubMed  Google Scholar 

  25. Manglik A, Lin H, Aryal DK et al (2016) Structure-based discovery of opioid analgesics with reduced side effects. Nature 537:185–190

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Dawe GB, Musgaard M, Aurousseau MRP et al (2016) Distinct structural pathways coordinate the activation of AMPA receptor-auxiliary subunit complexes. Neuron 89:1264–1276

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Wu C, Shea JE (2013) Structural similarities and differences between amyloidogenic and non-amyloidogenic islet amyloid polypeptide (IAPP) sequences and implications for the dual physiological and pathological activities of these peptides. PLoS Comput Biol 9:e1003211

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Stone JE, Hallock MJ, Phillips JC et al (2016) Evaluation of emerging energy-efficient heterogeneous computing platforms for biomolecular and cellular simulation workloads. In: Proceedings – 2016 IEEE 30th international parallel and distributed processing symposium, IPDPS 2016, pp 89–100

    Google Scholar 

  29. Arantes PR, Polêto MD, Pedebos C et al (2021) Making it rain: cloud-based molecular simulations for everyone. J Chem Inf Model 61:4852–4856

    Article  CAS  PubMed  Google Scholar 

  30. Biagini T, Chillemi G, Mazzoccoli G et al (2018) Molecular dynamics recipes for genome research. Brief Bioinform 19:853–862

    Article  CAS  PubMed  Google Scholar 

  31. Manavski SA, Valle G (2008) CUDA compatible GPU cards as efficient hardware accelerators for Smith-Waterman sequence alignment. BMC Bioinf 9:S10

    Article  Google Scholar 

  32. Biagini T, Petrizzelli F, Truglio M et al (2019) Are gaming-enabled graphic processing unit cards convenient for molecular dynamics simulation? Evol Bioinformatics Online 15:1–3

    Google Scholar 

  33. Abraham MJ, Murtola T, Schulz R et al (2015) Gromacs: high performance molecular simulations through multi-level parallelism from laptops to supercomputers. SoftwareX 1–2:19–25

    Article  Google Scholar 

  34. Kumari R, Kumar R, Lynn A (2014) G-mmpbsa -a GROMACS tool for high-throughput MM-PBSA calculations. J Chem Inf Model 54:1951–1962

    Article  CAS  PubMed  Google Scholar 

  35. Emsley P, Lohkamp B, Scott WG et al (2010) Features and development of Coot. Acta Crystallogr D Biol Crystallogr D66:486–501

    Article  Google Scholar 

  36. DeLano WL (2002) The PyMOL molecular graphics system, version 1.1. Schrödinger LLC. https://doi.org/10.1038/hr.2014.17

  37. Lee J, Cheng X, Swails JM et al (2016) CHARMM-GUI input generator for NAMD, GROMACS, AMBER, OpenMM, and CHARMM/OpenMM simulations using the CHARMM36 additive force field. J Chem Theory Comput 12:405–413

    Article  CAS  PubMed  Google Scholar 

  38. Jo S, Kim T, Iyer VG et al (2008) CHARMM-GUI: a web-based graphical user interface for CHARMM. J Comput Chem 29:1859–1865

    Article  CAS  PubMed  Google Scholar 

  39. Zhang J, Zhang H, Wu T et al (2017) Comparison of implicit and explicit solvent models for the calculation of solvation free energy in organic solvents. J Chem Theory Comput 13:1034–1043

    Article  CAS  PubMed  Google Scholar 

  40. Yaduvanshi S, Ero R, Kumar V (2021) The mechanism of complex formation between calmodulin and voltage gated calcium channels revealed by molecular dynamics. PLoS One 16:e0258112

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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Author information

Authors and Affiliations

  1. Amity Institute of Molecular Medicine and Stem Cell Research (AIMMSCR), Amity University, Noida, Uttar Pradesh, India

    Veerendra Kumar & Shivani Yaduvanshi

Authors
  1. Veerendra Kumar
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  2. Shivani Yaduvanshi
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Corresponding author

Correspondence to Veerendra Kumar .

Editor information

Editors and Affiliations

  1. Universidade da Beira Interior, CICS-UBI, Covilhã, Portugal

    Ângela Sousa

  2. University of Beira Interior, Covilhã, Portugal

    Luis Passarinha

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© 2023 The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature

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Kumar, V., Yaduvanshi, S. (2023). Protein-Protein Interaction Studies Using Molecular Dynamics Simulation. In: Sousa, Â., Passarinha, L. (eds) Advanced Methods in Structural Biology. Methods in Molecular Biology, vol 2652. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-3147-8_16

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  • DOI: https://doi.org/10.1007/978-1-0716-3147-8_16

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  • Publisher Name: Humana, New York, NY

  • Print ISBN: 978-1-0716-3146-1

  • Online ISBN: 978-1-0716-3147-8

  • eBook Packages: Springer Protocols

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