Wiehe, al. The performance of ZDOCK and ZRANK in rounds 6-11 of CAPRI. Proteins69, 719-725

Bioinformatics Program, Boston University, Boston, Massachusetts 02215, USA.
Proteins Structure Function and Bioinformatics (Impact Factor: 2.63). 12/2007; 69(4):719-25. DOI: 10.1002/prot.21747
Source: PubMed


We present an evaluation of our protein-protein docking approach using the ZDOCK and ZRANK algorithms, in combination with structural clustering and filtering, utilizing biological data in Rounds 6-11 of the CAPRI docking experiment. We achieved at least one prediction of acceptable accuracy for five of six targets submitted. In addition, two targets resulted in medium-accuracy predictions. In the new scoring portion of the CAPRI exercise, we were able to attain at least one acceptable prediction for the three targets submitted and achieved three medium-accuracy predictions for Target 26. Scoring was performed using ZRANK, a new algorithm for reranking initial-stage docking predictions using a weighted energy function and no structural refinement. Here we outline a practical and successful docking strategy, given limited prior biological knowledge of the complex to be predicted.

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    • "The 3D structure of CD147 monomer (3B5H, chain C) was then docked to the Fv model of metuximab using the program ZDOCK with framework region blocked [27]. The results were evaluated using ZRANK and optimized using RDOCK [28] [29]. The top one docking model from RDOCK was picked up as the theoretical metuximab-CD147 complex. "
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    ABSTRACT: Metuximab is the generic name of Licartin, a new drug for radioimmunotherapy of hepatocellular carcinoma. Although it is known to be a mouse monoclonal antibody against CD147, the complete epitope mediating the binding of metuximab to CD147 remains unknown. We panned the Ph.D.-12 phage display peptide library against metuximab and got six mimotopes. The following bioinformatics analysis based on mimotopes suggested that metuximab recognizes a conformational epitope composed of more than 20 residues. The residues of its epitope may include T28, V30, K36, L38, K57, F74, D77, S78, D79, D80, Q81, G83, S86, N98, Q100, L101, H102, G103, P104, V131, P132, and K191. The homology modeling of metuximab and the docking of CD147 to metuximab were also performed. Based on the top one docking model, the epitope was predicted to contain 28 residues: AGTVFTTV (23-30), I37, D45, E84, V88, EPMGTANIQLH (92-102), VPP (131-133), Q164, and K191. Almost half of the residues predicted on the basis of mimotope analysis also appear in the docking result, indicating that both results are reliable. As the predicted epitopes of metuximab largely overlap with interfaces of CD147-CD147 interactions, a structural mechanism of metuximab is proposed as blocking the formation of CD147 dimer.
    Full-text · Article · Jun 2013 · Computational and Mathematical Methods in Medicine
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    • "PPI residues for a pair of interacting proteins can be identified by experiments including point mutation such as the alanine scanning [35-38], chemical modification of residues [39,40], NMR [41], hydrogen/deuterium exchange [42], and disulfide cross-linking [43]. If several PPI residues are known, they can be simply used for filtering, i.e. to select docking decoys which have the known PPI residues at their docking interface [44,45]. Alternatively, known PPI residues from interacting proteins can be incorporated as distant constraints [14]. "
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    ABSTRACT: Many important cellular processes are carried out by protein complexes. To provide physical pictures of interacting proteins, many computational protein-protein prediction methods have been developed in the past. However, it is still difficult to identify the correct docking complex structure within top ranks among alternative conformations. We present a novel protein docking algorithm that utilizes imperfect protein-protein binding interface prediction for guiding protein docking. Since the accuracy of protein binding site prediction varies depending on cases, the challenge is to develop a method which does not deteriorate but improves docking results by using a binding site prediction which may not be 100% accurate. The algorithm, named PI-LZerD (using Predicted Interface with Local 3D Zernike descriptor-based Docking algorithm), is based on a pair wise protein docking prediction algorithm, LZerD, which we have developed earlier. PI-LZerD starts from performing docking prediction using the provided protein-protein binding interface prediction as constraints, which is followed by the second round of docking with updated docking interface information to further improve docking conformation. Benchmark results on bound and unbound cases show that PI-LZerD consistently improves the docking prediction accuracy as compared with docking without using binding site prediction or using the binding site prediction as post-filtering. We have developed PI-LZerD, a pairwise docking algorithm, which uses imperfect protein-protein binding interface prediction to improve docking accuracy. PI-LZerD consistently showed better prediction accuracy over alternative methods in the series of benchmark experiments including docking using actual docking interface site predictions as well as unbound docking cases.
    Full-text · Article · Jan 2012 · BMC Bioinformatics
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    • "Needless to say, the approach works best if the starting structures are identical to the bound conformation; although this is not too common in biological complexes, good results can nonetheless be obtained in several cases. Various research groups have used this approach [46–52]; amongst them the program ZDock [53] has achieved good results in the CAPRI experiment [54–57]. "
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    ABSTRACT: Antibodies play an increasingly important role in both basic research and the pharmaceutical industry. Since their efficiency depends, in ultimate analysis, on their atomic interactions with an antigen, studying such interactions is important to understand how they function and, in the long run, to design new molecules with desired properties. Computational docking, the process of predicting the conformation of a complex from its separated components, is emerging as a fast and affordable technique for the structural characterization of antibody-antigen complexes. In this manuscript, we first describe the different computational strategies for the modeling of antibodies and docking of their complexes, and then predict the binding of two antibodies to the stalk region of influenza hemagglutinin, an important pharmaceutical target. The purpose is two-fold: on a general note, we want to illustrate the advantages and pitfalls of computational docking with a practical example, using different approaches and comparing the results to known experimental structures. On a more specific note, we want to assess if docking can be successful in characterizing the binding to the same influenza epitope of other antibodies with unknown structure, which has practical relevance for pharmaceutical and biological research. The paper clearly shows that some of the computational docking predictions can be very accurate, but the algorithm often fails to discriminate them from inaccurate solutions. It is of paramount importance, therefore, to use rapidly obtained experimental data to validate the computational results.
    Full-text · Article · Dec 2011 · International Journal of Molecular Sciences
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