Protein-protein docking with simultaneous optimization of rigid-body displacement and side-chain conformations

Jeffrey J. Gray, Stewart Moughon, Chu Wang, Ora Schueler-Furman, Brian Kuhlman, Carol A. Rohl, David Baker*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

923 Scopus citations


Protein-protein docking algorithms provide a means to elucidate structural details for presently unknown complexes. Here, we present and evaluate a new method to predict protein-protein complexes from the coordinates of the unbound monomer components. The method employs a low-resolution, rigid-body, Monte Carlo search followed by simultaneous optimization of backbone displacement and side-chain conformations using Monte Carlo minimization. Up to 105 independent simulations are carried out, and the resulting "decoys" are ranked using an energy function dominated by van der Waals interactions, an implicit solvation model, and an orientation-dependent hydrogen bonding potential. Top-ranking decoys are clustered to select the final predictions. Small-perturbation studies reveal the formation of binding funnels in 42 of 54 cases using coordinates derived from the bound complexes and in 32 of 54 cases using independently determined coordinates of one or both monomers. Experimental binding affinities correlate with the calculated score function and explain the predictive success or failure of many targets. Global searches using one or both unbound components predict at least 25% of the native residue-residue contacts in 28 of the 32 cases where binding funnels exist. The results suggest that the method may soon be useful for generating models of biologically important complexes from the structures of the isolated components, but they also highlight the challenges that must be met to achieve consistent and accurate prediction of protein-protein interactions.

Original languageAmerican English
Pages (from-to)281-299
Number of pages19
JournalJournal of Molecular Biology
Issue number1
StatePublished - 1 Aug 2003
Externally publishedYes

Bibliographical note

Funding Information:
The authors thank the many scientists who have participated in the development of the suite of computational tools used in the Baker laboratory for computations on the structure of proteins. In particular, Kira Misura & William Wedemeyer refined the Lennard-Jones model, Jerry Tsai implemented the surface area solvation calculation and assisted with the logistic regression of scoring weights, and William Schief developed methods to treat disulfide bonds. Discussions with Tanja Kortemme on the nature of protein–protein interfaces were helpful and stimulating. Keith Laidig of Formix, Inc. built and maintained reliable, state-of-the-art computing resources. J.J.G. thanks NIH/NHGRI for support through award K01-HG02316. O.S.-F. carries Damon-Runyon Fellowship DRG-1704-02 from the Damon Runyon Cancer Research Foundation. Additional funding was provided by NIH.


  • Biomolecular free energy functions
  • Biomolecular modeling
  • Conformational change
  • Protein binding
  • Protein-protein docking


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