97,639 research outputs found

    DockPro: A VR-Based Tool for Protein-Protein Docking Problem

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    Proteins are large molecules that are vital for all living organisms and they are essential components of many industrial products. The process of binding a protein to another is called protein-protein docking. Many automated algorithms have been proposed to find docking configurations that might yield promising protein-protein complexes. However, these automated methods are likely to come up with false positives and have high computational costs. Consequently, Virtual Reality has been used to take advantage of user's experience on the problem; and proposed applications can be further improved. Haptic devices have been used for molecular docking problems; but they are inappropriate for protein-protein docking due to their workspace limitations. Instead of haptic rendering of forces, we provide a novel visual feedback for simulating physicochemical forces of proteins. We propose an interactive 3D application, DockPro, which enables domain experts to come up with dockings of protein-protein couples by using magnetic trackers and gloves in front of a large display

    Import of honeybee prepromelittin into the endoplasmic reticulum

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    Honeybee prepromelittin is correctly processed and imported by dog pancreas microsomes. Insertion of prepromelittin into microsomal membranes, as assayed by signal sequence removal, does not depend on signal recognition particle (SRP) and docking protein. We addressed the question as to how prepromelittin bypasses the SRP/docking protein system. Hybrid proteins between prepromelittin, or carboxy-terminally truncated derivatives, and the cytoplasmic protein dihydrofolate reductase from mouse were constructed. These hybrid proteins were analysed for membrane insertion and sequestration into microsomes. The results suggest the following: (i) The signal sequence of prepromelittin is capable of interacting with the SRP/docking protein system, but this interaction is not mandatory for membrane insertion; this is related to the small size of prepromelittin. (ii) In prepromelittin a cluster of negatively charged amino acids must be balanced by a cluster of positively charged amino acids in order to allow membrane insertion. (iii) In general, a signal sequence can be sufficient to mediate membrane insertion independently of SRP and docking protein in the case of short precursor proteins; however, the presence and distribution of charged amino acids within the mature part of these precursors can play distinct roles

    A Novel Scoring Based Distributed Protein Docking Application to Improve Enrichment

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    Molecular docking is a computational technique which predicts the binding energy and the preferred binding mode of a ligand to a protein target. Virtual screening is a tool which uses docking to investigate large chemical libraries to identify ligands that bind favorably to a protein target. We have developed a novel scoring based distributed protein docking application to improve enrichment in virtual screening. The application addresses the issue of time and cost of screening in contrast to conventional systematic parallel virtual screening methods in two ways. Firstly, it automates the process of creating and launching multiple independent dockings on a high performance computing cluster. Secondly, it uses a N˙ aive Bayes scoring function to calculate binding energy of un-docked ligands to identify and preferentially dock (Autodock predicted) better binders. The application was tested on four proteins using a library of 10,573 ligands. In all the experiments, (i). 200 of the 1000 best binders are identified after docking only 14% of the chemical library, (ii). 9 or 10 best-binders are identified after docking only 19% of the chemical library, and (iii). no significant enrichment is observed after docking 70% of the chemical library. The results show significant increase in enrichment of potential drug leads in early rounds of virtual screening

    A New Multi-Objective Approach for Molecular Docking Based on RMSD and Binding Energy

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    Ligand-protein docking is an optimization problem based on predicting the position of a ligand with the lowest binding energy in the active site of the receptor. Molecular docking problems are traditionally tackled with single-objective, as well as with multi-objective approaches, to minimize the binding energy. In this paper, we propose a novel multi-objective formulation that considers: the Root Mean Square Deviation (RMSD) difference in the coordinates of ligands and the binding (intermolecular) energy, as two objectives to evaluate the quality of the ligand-protein interactions. To determine the kind of Pareto front approximations that can be obtained, we have selected a set of representative multi-objective algorithms such as NSGA-II, SMPSO, GDE3, and MOEA/D. Their performances have been assessed by applying two main quality indicators intended to measure convergence and diversity of the fronts. In addition, a comparison with LGA, a reference single-objective evolutionary algorithm for molecular docking (AutoDock) is carried out. In general, SMPSO shows the best overall results in terms of energy and RMSD (value lower than 2A for successful docking results). This new multi-objective approach shows an improvement over the ligand-protein docking predictions that could be promising in in silico docking studies to select new anticancer compounds for therapeutic targets that are multidrug resistant.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

    Predicting the accuracy of protein-ligand docking on homology models

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    Ligand-protein docking is increasingly used in Drug Discovery. The initial limitations imposed by a reduced availability of target protein structures have been overcome by the use of theoretical models, especially those derived by homology modeling techniques. While this greatly extended the use of docking simulations, it also introduced the need for general and robust criteria to estimate the reliability of docking results given the model quality. To this end, a large-scale experiment was performed on a diverse set including experimental structures and homology models for a group of representative ligand-protein complexes. A wide spectrum of model quality was sampled using templates at different evolutionary distances and different strategies for target-template alignment and modeling. The obtained models were scored by a selection of the most used model quality indices. The binding geometries were generated using AutoDock, one of the most common docking programs. An important result of this study is that indeed quantitative and robust correlations exist between the accuracy of docking results and the model quality, especially in the binding site. Moreover, state-of-the-art indices for model quality assessment are already an effective tool for an a priori prediction of the accuracy of docking experiments in the context of groups of proteins with conserved structural characteristics.Contract/grant sponsor: National Institutes of Health; contract/grant numbers: ES00768
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