Protein–Protein Docking: Overview and Performance Analysis

Bioinformatics Program, Boston University, Boston, MA, USA.
Methods in Molecular Biology (Impact Factor: 1.29). 02/2008; 413:283-314. DOI: 10.1007/978-1-59745-574-9_11
Source: PubMed


Protein-protein docking is the computational prediction of protein complex structure given the individually solved component protein structures. It is an important means for understanding the physicochemical forces that underlie macromolecular interactions and a valuable tool for modeling protein complex structures. Here, we report an overview of protein-protein docking with specific emphasis on our Fast Fourier Transform-based rigid-body docking program ZDOCK, which is consistently rated as one of the most accurate docking programs in the Critical Assessment of Predicted Interactions (CAPRI), a series of community-wide blind tests. We also investigate ZDOCK's performance on a non-redundant protein complex benchmark. Finally, we perform regression analysis to better understand the strengths and weaknesses of ZDOCK and to suggest areas of future development for protein-docking algorithms in general.

  • Source
    • "Fast Fourier Transform (FFT) algorithm was applied to perform a global docking to search for potential binding positions of two component proteins (Pierce et al., 2014). Since validity of the ZDOCK analysis is affected by the accuracy of the search algorithm as well as the proteinprotein complex to be predicted, some of the top-scoring predictions resulted from the soft scoring function of the program could be false positives (Wiehe et al., 2008). Combining the results from epitope prediction softwares based on mimotope and ZDOCK may lead to a more reliable result. "

    Full-text · Article · Nov 2014 · Protein & Cell
  • Source
    • "Qualitative protein function annotation using Enzyme Commission (EC) numbers or Gene Ontology (Ashburner et al., 2000) terms is typically followed by a comprehensive functional characterization at the molecular level. The studies of interactions between proteins and other molecular species in a cell are routinely supported by computations involving docking of DNA (Gao and Skolnick, 2009; van Dijk and Bonvin, 2008), other protein partners (Lyskov and Gray, 2008; Wiehe et al., 2008) and small ligands (Goodsell et al., 1996; Moustakas et al., 2006). In the latter case, the docking of specific ligands can be extended to large-scale virtual screening of combinatorial libraries in order to discover novel bioactive compounds (Rajamani and Good, 2007; Seifert et al., 2007). "
    [Show abstract] [Hide abstract]
    ABSTRACT: Exhaustive exploration of molecular interactions at the level of complete proteomes requires efficient and reliable computational approaches to protein function inference. Ligand docking and ranking techniques show considerable promise in their ability to quantify the interactions between proteins and small molecules. Despite the advances in the development of docking approaches and scoring functions, the genome-wide application of many ligand docking/screening algorithms is limited by the quality of the binding sites in theoretical receptor models constructed by protein structure prediction. In this study, we describe a new template-based method for the local refinement of ligand-binding regions in protein models using remotely related templates identified by threading. We designed a Support Vector Regression (SVR) model that selects correct binding site geometries in a large ensemble of multiple receptor conformations. The SVR model employs several scoring functions that impose geometrical restraints on the Cα positions, account for the specific chemical environment within a binding site and optimize the interactions with putative ligands. The SVR score is well correlated with the RMSD from the native structure; in 47% (70%) of the cases, the Pearson's correlation coefficient is >0.5 (>0.3). When applied to weakly homologous models, the average heavy atom, local RMSD from the native structure of the top-ranked (best of top five) binding site geometries is 3.1Å (2.9Å) for roughly half of the targets; this represents a 0.1 (0.3)Å average improvement over the original predicted structure. Focusing on the subset of strongly conserved residues, the average heavy atom RMSD is 2.6Å (2.3Å). Furthermore, we estimate the upper bound of template-based binding site refinement using only weakly related proteins to be ∼2.6Å RMSD. This value also corresponds to the plasticity of the ligand-binding regions in distant homologues. The Binding Site Refinement (BSR) approach is available to the scientific community as a web server that can be accessed at
    Full-text · Article · Mar 2011 · Journal of Structural Biology
  • [Show abstract] [Hide abstract]
    ABSTRACT: This paper proposes a method for identifying and classifying a target from its foveal imagery using a neural network. The method's criterion for identifying a target is based on finding the global minimum of an energy function. This energy function is characterized by matching the candidate target and a library of target models at several levels of resolution of nonuniformly sampled foveal image data. For this purpose, a top-down and bottom-up (concurrent) matching procedure is implemented via a multi-layer Hopfield neural network. The corresponding energy function supports not only connections between cells at the same resolution level, but also interconnections between two sets of nodes at two different resolution levels. The proposed method also utilizes a feature analysis at the higher resolution levels of the target to relocate the center of the fovea to a more salient region of the target (gaze control). The results of an experimental scenario for foveal target recognition are presented
    No preview · Conference Paper · Oct 1996
Show more