Combining fragment docking with graph theory to improve ligand docking for homology model structures
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Combining fragment docking with graph theory to improve ligand docking for homology model structures Sara Sarfaraz1 · Iqra Muneer1 · Haiyan Liu1 Received: 30 June 2020 / Accepted: 24 September 2020 © Springer Nature Switzerland AG 2020
Abstract Computational protein–ligand docking is well-known to be prone to inaccuracies in input receptor structures, and it is challenging to obtain good docking results with computationally predicted receptor structures (e.g. through homology modeling). Here we introduce a fragment-based docking method and test if it reduces requirements on the accuracy of an input receptor structures relative to non-fragment docking approaches. In this method, small rigid fragments are docked first using AutoDock Vina to generate a large number of favorably docked poses spanning the receptor binding pocket. Then a graph theory maximum clique algorithm is applied to find combined sets of docked poses of different fragment types onto which the complete ligand can be properly aligned. On the basis of these alignments, possible binding poses of complete ligand are determined. This docking method is first tested for bound docking on a series of Cytochrome P450 (CYP450) enzyme–substrate complexes, in which experimentally determined receptor structures are used. For all complexes tested, ligand poses of less than 1 Å root mean square deviations (RMSD) from the actual binding positions can be recovered. Then the method is tested for unbound docking with modeled receptor structures for a number of protein–ligand complexes from different families including the very recent severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) protease. For all complexes, poses with RMSD less than 3 Å from actual binding positions can be recovered. Our results suggest that for docking with approximately modeled receptor structures, fragment-based methods can be more effective than common complete ligand docking approaches. Keywords Fragment docking · Homology modeling · Graph theory · Maximum clique · Cytochrome P450-substrate complex
Introduction Computational docking is extensively applied to predict protein–ligand binding, both for the prediction and understanding of inter-molecular interactions and for virtual screening Electronic supplementary material The online version of this article (https://doi.org/10.1007/s10822-020-00345-7) contains supplementary material, which is available to authorized users. * Haiyan Liu [email protected] Sara Sarfaraz [email protected] Iqra Muneer [email protected] 1
School of life sciences, University of Science and Technology of China, Hefei 230026, Anhui, China
in structure-based drug design [1–3]. Docking calculations require prior knowledge of the three-dimensional structure of the receptor protein. It is not uncommon that a suitable experimental structure of the target receptor is unavailable and one has to manage with a computationally modeled one. Most commonly, the modeled structures are obtained through comparative modeling, i.e., by using existing exp
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