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Ligand-biased ensemble receptor docking (LigBEnD): a hybrid ligand/receptor structure-based approach.
Ligand docking to flexible protein molecules can be efficiently carried out through ensemble docking to multiple protein conformations, either from experimental X-ray structures or from in silico simulations. The success of ensemble docking often requires the careful selection of complementary protein conformations, through docking and scoring of known co-crystallized ligands. False positives, in which a ligand in a wrong pose achieves a better docking score than that of native pose, arise as additional protein conformations are added. In the current study, we developed a new ligand-biased ensemble receptor docking method and composite scoring function which combine the use of ligand-based atomic property field (APF) method with receptor structure-based docking. This method helps us to correctly dock 30 out of 36 ligands presented by the D3R docking challenge. For the six mis-docked ligands, the cognate receptor structures prove to be too different from the 40 available experimental Pocketome conformations used for docking and could be identified only by receptor sampling beyond experimentally explored conformational subspace
DockPro: A VR-Based Tool for Protein-Protein Docking Problem
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
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
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
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
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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