Comparative analysis of nucleotide translocation through protein nanopores using steered molecular dynamics and an adaptive biasing force

Hugh S.C. Martin, Shantenu Jha, Peter V. Coveney

Research output: Contribution to journalArticlepeer-review

19 Scopus citations


The translocation of nucleotide molecules across biological and synthetic nanopores has attracted attention as a next generation technique for sequencing DNA. Computer simulations have the ability to provide atomistic-level insight into important states and processes, delivering a means to develop a fundamental understanding of the translocation event, for example, by extracting the free energy of the process. Even with current supercomputing facilities, the simulation of many-atom systems in fine detail is limited to shorter timescales than the real events they attempt to recreate. This imposes the need for enhanced simulation techniques that expand the scope of investigation in a given timeframe. There are numerous free energy calculation and translocation methodologies available, and it is by no means clear which method is best applied to a particular problem. This article explores the use of two popular free energy calculation methodologies in a nucleotide-nanopore translocation system, using the α-hemolysin nanopore. The first uses constant velocity-steered molecular dynamics (cv-SMD) in conjunction with Jarzynski's equality. The second applies an adaptive biasing force (ABF), which has not previously been applied to the nucleotide-nanpore system. The purpose of this study is to provide a comprehensive comparison of these methodologies, allowing for a detailed comparative assessment of the scientific merits, the computational cost, and the statistical quality of the data obtained from each technique. We find that the ABF method produces results that are closer to experimental measurements than those from cv-SMD, whereas the net errors are smaller for the same computational cost.

Original languageEnglish (US)
Pages (from-to)692-702
Number of pages11
JournalJournal of Computational Chemistry
Issue number9
StatePublished - Apr 5 2014

All Science Journal Classification (ASJC) codes

  • Chemistry(all)
  • Computational Mathematics


  • DNA
  • adaptive biasing force
  • molecular dynamics
  • nanopore
  • protein

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