RDOCK: refinement of Rigid-body Protein Docking Predictions

Bioinformatics Program, Boston University, Boston, Massachusetts 02215, USA.
Proteins Structure Function and Bioinformatics (Impact Factor: 2.63). 11/2003; 53(3):693-707. DOI: 10.1002/prot.10460
Source: PubMed


We present a simple and effective algorithm RDOCK for refining unbound predictions generated by a rigid-body docking algorithm ZDOCK, which has been developed earlier by our group. The main component of RDOCK is a three-stage energy minimization scheme, followed by the evaluation of electrostatic and desolvation energies. Ionic side chains are kept neutral in the first two stages of minimization, and reverted to their full charge states in the last stage of brief minimization. Without side chain conformational search or filtering/clustering of resulting structures, RDOCK represents the simplest approach toward refining unbound docking predictions. Despite its simplicity, RDOCK makes substantial improvement upon the top predictions by ZDOCK with all three scoring functions and the improvement is observed across all three categories of test cases in a large benchmark of 49 non-redundant unbound test cases. RDOCK makes the most powerful combination with ZDOCK2.1, which uses pairwise shape complementarity as the scoring function. Collectively, they rank a near-native structure as the number-one prediction for 18 test cases (37% of the benchmark), and within the top 4 predictions for 24 test cases (49% of the benchmark). To various degrees, funnel-like energy landscapes are observed for these 24 test cases. To the best of our knowledge, this is the first report of binding funnels starting from global searches for a broad range of test cases. These results are particularly exciting, given that we have not used any biological information that is specific to individual test cases and the whole process is entirely automated. Among three categories of test cases, the best results are seen for enzyme/inhibitor, with a near-native structure ranked as the number-one prediction for 48% test cases, and within the top 10 predictions for 78% test cases. RDOCK is freely available to academic users at approximately rong/dock.

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    • "Poses with the highest Z-scores were compared to the solved crystal structure of human cathepsin L interacting with human stefin A (PDB 3KSE) to validate the relative binding position and orientation of the two proteins in the predicted complex. Five complexes were chosen and refined through energy minimization using the R-Dock algorithm (Li et al., 2003). The top ranking model was selected for complex visualization and inference of protease and cystatin interacting residues at the binding interface (Sainsbury et al., 2012a). "
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    ABSTRACT: Positive selection is thought to contribute to the functional diversification of insect-inducible protease inhibitors in plants in response to selective pressures exerted by the digestive proteases of their herbivorous enemies. Here we assessed whether a reciprocal evolutionary process takes place on the insect side, and whether ingestion of a positively selected plant inhibitor may translate into a measurable rebalancing of midgut proteases in vivo. Midgut Cys proteases of herbivorous Coleoptera, including the major pest Colorado potato beetle (Leptinotarsa decemlineata), were first compared using a codon-based evolutionary model to look for the occurrence of hypervariable, positively selected amino acid sites among the tested sequences. Hypervariable sites were found, distributed within -or close to- amino acid regions interacting with Cys-type inhibitors of the plant cystatin protein family. A close examination of L. decemlineata sequences indicated a link between their assignment to protease functional families and amino acid identity at positively selected sites. A function-diversifying role for positive selection was further suggested empirically by in vitro protease assays and a shotgun proteomic analysis of L. decemlineata Cys proteases showing a differential rebalancing of protease functional family complements in larvae fed single variants of a model cystatin mutated at positively selected amino acid sites. These data confirm overall the occurrence of hypervariable, positively selected amino acid sites in herbivorous Coleoptera digestive Cys proteases. They also support the idea of an adaptive role for positive selection, useful to generate functionally diverse proteases in insect herbivores ingesting functionally diverse, rapidly evolving dietary cystatins. Copyright © 2015. Published by Elsevier Ltd.
    Full-text · Article · Aug 2015 · Insect biochemistry and molecular biology
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    • "The charges and atom types were assigned using the CHARMm force field [26]. 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. "
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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.
    Full-text · Article · Jun 2015 · Cytokine
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    • "Since docking software produce 100’s to 1000’s of putative models, their exploitation requires the ability to score them accurately [62-64]. Intuitively, physical-based scoring functions are particularly attractive since they can be applied to any model by exploiting physiochemical features of the atoms. "
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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.
    Full-text · Article · Jun 2014 · BMC Bioinformatics
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