Quantitative single-molecule conformational distributions: A case study with poly-(L-proline)

Lucas P. Watkins, Hauyee Chang, Haw Yang

Research output: Contribution to journalArticlepeer-review

102 Scopus citations


Precise measurement of the potential of mean force is necessary for a fundamental understanding of the dynamics and chemical reactivity of a biological macromolecule. The unique advantage provided by the recently developed constant-information approach to analyzing time-dependent single-molecule fluorescence measurements was used with maximum entropy deconvolution to create a procedure for the accurate determination of molecular conformational distributions, and analytical expressions for the errors in these distributions were derived. This new method was applied to a derivatized poly(L-proline) series, P nCG 3K-(biotin) (n = 8, 12, 15, 18, and 24), using a modular, server-based single-molecule spectrometer that is capable of registering photon arrival times with a continuous-wave excitation source. To account for potential influence from the microscopic environment, factors that were calibrated and corrected molecule by molecule include background, cross-talk, and detection efficiency. For each single poly(L-proline) molecule, sharply peaked Förster type resonance energy transfer (FRET) efficiency and distance distributions were recovered, indicating a static end-to-end distance on the time scale of measurement. The experimental distances were compared with models of varying rigidity. The results suggest that the 23 A persistence length wormlike chain model derived from experiments with high molecular weight poly(L-proline) is applicable to short chains as well.

Original languageEnglish (US)
Pages (from-to)5191-5203
Number of pages13
JournalJournal of Physical Chemistry A
Issue number15
StatePublished - Apr 20 2006
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Physical and Theoretical Chemistry


Dive into the research topics of 'Quantitative single-molecule conformational distributions: A case study with poly-(L-proline)'. Together they form a unique fingerprint.

Cite this