Analysis and prediction of hydrogen bonding in protein-DNA complexes using parallel processors

Graham Campbell, Yuefan Deng, James Glimm, Yuan Wang, Qiqing Yu, Moisés Eisenberg, Arthur Grollman

Research output: Contribution to journalArticlepeer-review


A number of essential biological functions are controlled by proteins that bind to specific sequences in genomic DNA. In this article we present a simplified model for analyzing DNA-protein interactions mediated exclusively by hydrogen bonds. Based on this model, an optimized algorithm for geometric pattern recognition was developed. The large number of local energy minima are efficiently screened by using a geometric approach to pattern matching based on a square-well potential. The second part of the algorithm represents a closed form solution for minimization based on a quadratic potential. A Monte Carlo method applied to a modified Lennard-Jones potential is used as a third step to rank DNA sequences in terms of pattern matching. Using protein structures derived from four DNA-protein complexes with three-dimensional coordinates established by X-ray diffraction analysis, all possible DNA sequences to which these proteins could bind were ranked in terms of binding energies. The algorithm predicts the correct DNA sequence when at least two hydrogen bonds per base pair are involved in binding to the protein, providing a partial solution to the three-dimensional docking problem. This study lays a framework for future refinements of the algorithm in which the number of assumptions made in the present analysis are reduced.

Original languageEnglish (US)
Pages (from-to)1712-1725
Number of pages14
JournalJournal of Computational Chemistry
Issue number15
StatePublished - Nov 30 1996

ASJC Scopus subject areas

  • General Chemistry
  • Computational Mathematics


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