ZRANK: Reranking protein docking predictions with an optimized energy function

Bioinformatics Program, Department of Biomedical Engineering, Boston University, Boston, Massachusetts 02215, USA.
Proteins Structure Function and Bioinformatics (Impact Factor: 2.63). 06/2007; 67(4):1078-86. DOI: 10.1002/prot.21373
Source: PubMed


Protein-protein docking requires fast and effective methods to quickly discriminate correct from incorrect predictions generated by initial-stage docking. We have developed and tested a scoring function that utilizes detailed electrostatics, van der Waals, and desolvation to rescore initial-stage docking predictions. Weights for the scoring terms were optimized for a set of test cases, and this optimized function was then tested on an independent set of nonredundant cases. This program, named ZRANK, is shown to significantly improve the success rate over the initial ZDOCK rankings across a large benchmark. The amount of test cases with No. 1 ranked hits increased from 2 to 11 and from 6 to 12 when predictions from two ZDOCK versions were considered. ZRANK can be applied either as a refinement protocol in itself or as a preprocessing stage to enrich the well-ranked hits prior to further refinement.

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    • "We built an artificial neural network to approximate the relationship between 11 different features, most of which are used as scoring function terms by a wide variety of docking and refinement algorithms (Dominguez et al., 2003; Cheng et al., 2007; Pierce and Weng, 2007; Comeau et al., 2004; Lyskov and Gray, 2008; Akbal-Delibas et al., 2012; Akbal-Delibas and Haspel, 2013; Lopes et al., 2013). Below is a description of features we selected. "
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    ABSTRACT: One of the major challenges for protein-protein docking methods is to accurately discriminate nativelike structures. The protein docking community agrees on the existence of a relationship between various favorable intermolecular interactions (e.g. Van der Waals, electrostatic, desolvation forces, etc.) and the similarity of a conformation to its native structure. Different docking algorithms often formulate this relationship as a weighted sum of selected terms and calibrate their weights against specific training data to evaluate and rank candidate structures. However, the exact form of this relationship is unknown and the accuracy of such methods is impaired by the pervasiveness of false positives. Unlike the conventional scoring functions, we propose a novel machine learning approach that not only ranks the candidate structures relative to each other but also indicates how similar each candidate is to the native conformation. We trained the AccuRMSD neural network with an extensive dataset using the back-propagation learning algorithm. Our method achieved predicting RMSDs of unbound docked complexes with 0.4Å error margin.
    Journal of computational biology: a journal of computational molecular cell biology 09/2015; 22(9):892-904. DOI:10.1089/cmb.2015.0114 · 1.74 Impact Factor
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    • "IL-34 and M-CSF binding were assessed using the ZDOCK [27] [28] protein–protein docking software. The best 2000 poses were re-scored using ZRANK, a scoring function with detailed and weighted electrostatics, van der Waals and desolvation terms [29], and then clustered with an RMSD cutoff of 1 nm. As recommended, starting structures for the ligand protein were displaced from the near-native structure [27]. "
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    ABSTRACT: Interleukin-34 (IL-34) is a newly-discovered homodimeric cytokine that regulates, like Macrophage Colony-Stimulating Factor (M-CSF), the differentiation of the myeloid lineage through M-CSF receptor (M-CSFR) signaling pathways. To date, both cytokines have been considered as competitive cytokines with regard to the M-CSFR. The aim of the present work was to study the functional relationships of these cytokines on cells expressing the M-CSFR. We demonstrate that simultaneous addition of M-CSF and IL-34 led to a specific activation pattern on the M-CSFR, with higher phosphorylation of the tyrosine residues at low concentrations. Similarly, both cytokines showed an additive effect on cellular proliferation or viability. In addition, BIAcore experiments demonstrated that M-CSF binds to IL-34, and molecular docking studies predicted the formation of a heteromeric M-CSF/IL-34 cytokine. A proximity ligation assay confirmed this interaction between the cytokines. Finally, co-expression of the M-CSFR and its ligands differentially regulated M-CSFR trafficking into the cell. This study establishes a new foundation for the understanding of the functional relationship between IL-34 and M-CSF, and gives a new vision for the development of therapeutic approaches targeting the IL-34/M-CSF/M-CSFR axis. Copyright © 2015 Elsevier Ltd. All rights reserved.
    Cytokine 06/2015; 76(2). DOI:10.1016/j.cyto.2015.05.029 · 2.66 Impact Factor
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    • "Intuitively, physical-based scoring functions are particularly attractive since they can be applied to any model by exploiting physiochemical features of the atoms. ZRANK [63] relies on the usage of a combination of three atom-based terms, i.e. van der Waal, electrostatics and desolvation. In order to handle conformational changes upon binding, an extension of ZRANK, IRAD, integrated residue and atom based potentials [64]. "
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    ABSTRACT: Background Since proteins function by interacting with other molecules, analysis of protein-protein interactions is essential for comprehending biological processes. Whereas understanding of atomic interactions within a complex is especially useful for drug design, limitations of experimental techniques have restricted their practical use. Despite progress in docking predictions, there is still room for improvement. In this study, we contribute to this topic by proposing T-PioDock, a framework for detection of a native-like docked complex 3D structure. T-PioDock supports the identification of near-native conformations from 3D models that docking software produced by scoring those models using binding interfaces predicted by the interface predictor, Template based Protein Interface Prediction (T-PIP). Results First, exhaustive evaluation of interface predictors demonstrates that T-PIP, whose predictions are customised to target complexity, is a state-of-the-art method. Second, comparative study between T-PioDock and other state-of-the-art scoring methods establishes T-PioDock as the best performing approach. Moreover, there is good correlation between T-PioDock performance and quality of docking models, which suggests that progress in docking will lead to even better results at recognising near-native conformations. Conclusion Accurate identification of near-native conformations remains a challenging task. Although availability of 3D complexes will benefit from template-based methods such as T-PioDock, we have identified specific limitations which need to be addressed. First, docking software are still not able to produce native like models for every target. Second, current interface predictors do not explicitly consider pairwise residue interactions between proteins and their interacting partners which leaves ambiguity when assessing quality of complex conformations.
    BMC Bioinformatics 06/2014; 15(1):171. DOI:10.1186/1471-2105-15-171 · 2.58 Impact Factor
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