First Principles Molecular Modeling of Sensing Material Selection for Hybrid Biomimetic Nanosensors

  • Mario Blanco
  • Michael C. McAlpine
  • James R. Heath
Part of the Integrated Analytical Systems book series (ANASYS)


Hybrid biomimetic nanosensors use selective polymeric and biological materials that integrate flexible recognition moieties with nanometer size transducers. These sensors have the potential to offer the building blocks for a universal sensing platform. Their vast range of chemistries and high conformational flexibility present both a problem and an opportunity. Nonetheless, it has been shown that oligopeptide aptamers from sequenced genes can be robust substrates for the selective recognition of specific chemical species. Here we present first principles molecular modeling approaches tailored to peptide sequences suitable for the selective discrimination of small molecules on nanowire arrays. The modeling strategy is fully atomistic. The excellent performance of these sensors, their potential biocompatibility combined with advanced mechanistic modeling studies, could potentially lead to applications such as: unobtrusive implantable medical sensors for disease diagnostics, light weight multi-purpose sensing devices for aerospace applications, ubiquitous environmental monitoring devices in urban and rural areas, and inexpensive smart packaging materials for active in-situ food safety labeling.


Peptide Sequence Nanowire Array SiNW Array Acetic Acid Lead Human Olfactory Receptor 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



This work was partly supported by the Materials and Process Simulation Center, Beckman Institute at the California Institute of Technology.


  1. 1.
    McAlpine, M. C.; Ahmad, H.; Wang, D.; Heath, J. R., Highly ordered nanowire arrays on plastic substrates for ultrasensitive flexible chemical sensors, Nat. Mat. 2007, 6, 379–384CrossRefGoogle Scholar
  2. 2.
    McAlpine, M. C.; Agnew, H. D.; Rohde, R. D.; Blanco, M.; Ahmad, H.; Stuparu, A. D.; Goddard, W. A.; Heath, J. R., Peptide-nanowire hybrid materials for selective sensing of small molecules, J. Am. Chem. Soc. 2008, 130, 9583–9589CrossRefGoogle Scholar
  3. 3.
    Chan, W. C.; White, P. D., Fmoc Solid Phase Peptide Synthesis: A Practical Approach; Oxford University Press, Oxford, 2000 Google Scholar
  4. 4.
    Belmares, M.; Blanco, M.; Goddard, W. A.; Ross, R. B.; Caldwell, G.; Chou, S. H.; Pham, J.; Olofson, P. M.; Thomas, C., Hildebrand and Hansen solubility parameters from molecular dynamics with applications to electronic nose polymer sensors, J. Comput. Chem. 2004, 25, 1814–1826CrossRefGoogle Scholar
  5. 5.
    Cozmuta, I.; Blanco, M.; Goddard, W. A., Gas sorption and barrier properties of polymeric membranes from molecular dynamics and Monte Carlo simulations, J. Phys. Chem. B 2007, 111, 3151–3166CrossRefGoogle Scholar
  6. 6.
    Wu, T.-Z.; Lo, Y.-R.; Chan, E.-C., Exploring the recognized bio-mimicry materials for gas sensing Biosens. Bioelectron. 2001, 16, 945–953CrossRefGoogle Scholar
  7. 7.
    Mayo, S. L.; Olafson, B. D.; Goddard, W. A., Dreiding – A generic force-field for molecular simulations. J. Phys. Chem. 1990, 94, 8897–8909Google Scholar
  8. 8.
    Lee, C. T.; Yang, W. T.; Parr, R. G., Development of the colle-salvetti correlation-energy formula into a functional of the electron-density, Phys. Rev. B 1988, 37, 785–789CrossRefGoogle Scholar
  9. 9.
    Becke, A. D., Density-functional thermochemistry 3. The role of exact exchange. J. Chem. Phys. 1993, 98, 5648–5652Google Scholar
  10. 10.
    Venkatchalam, C. M., Cerius2 User' Manual, 4.10 edn.; Accelrys, Inc., San Diego, CA, 2005Google Scholar
  11. 11.
    Blanco, M., Molecular silverware. I. General solutions to excluded volume constrained problems, J. Comput. Chem. 1991, 12, 237–247CrossRefGoogle Scholar
  12. 12.
    Accelrys, I.; 4.01 edn.; Accelrys, Inc., San Diego, CA, 2005 Google Scholar
  13. 13.
    Hu, G. Q.; Quaranta, V.; Li, D. Q., Modeling of effects of nutrient gradients on cell proliferation in microfluidic bioreactor, Biotechnol. Prog. 2007, 23, 1347–1354CrossRefGoogle Scholar
  14. 14.
    McAlpine, M.C.; Agnew, H.D.; Rohde, R.D.; Mario Blanco, M.; Ahmad, H.; Stuparu, A.D.; Goddard, W.A.; and Heath, J.R., J. Am. Chem. Soc. 2008, 130 (29), 9583–9589Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2009

Authors and Affiliations

  • Mario Blanco
    • 1
  • Michael C. McAlpine
    • 2
  • James R. Heath
    • 1
  1. 1.Division of Chemistry and Chemical EngineeringCalifornia Institute of TechnologyPasadenaUSA
  2. 2.Department of Mechanical and Aerospace EngineeringPrinceton UniversityPrincetonUSA

Personalised recommendations