Gene/Protein Disease Symptom Drug Enzyme Compound
Pivot Concepts:   Target Concepts:
Query: EC:3.4.21.6 (thromboplastin)
 13,278 document(s) hit in 31,850,051 MEDLINE articles (0.00 seconds)

A new approach, MOBILE, is presented that models protein binding-sites including bound ligand molecules as restraints. Initially generated, homology models of the target protein are refined iteratively by including information about bioactive ligands as spatial restraints and optimising the mutual interactions between the ligands and the binding-sites. Thus optimised models can be used for structure-based drug design and virtual screening. In a first step, ligands are docked into an averaged ensemble of crude homology models of the target protein. In the next step, improved homology models are generated, considering explicitly the previously placed ligands by defining restraints between protein and ligand atoms. These restraints are expressed in terms of knowledge-based distance-dependent pair potentials, which were compiled from crystallographically determined protein-ligand complexes. Subsequently, the most favourable models are selected by ranking the interactions between the ligands and the generated pockets using these potentials. Final models are obtained by selecting the best-ranked side-chain conformers from various models, followed by an energy optimisation of the entire complex using a common force-field. Application of the knowledge-based pair potentials proved efficient to restrain the homology modelling process and to score and optimise the modelled protein-ligand complexes. For a test set of 46 protein-ligand complexes, taken from the Protein Data Bank (PDB), the success rate of producing near-native binding-site geometries (rmsd<2.0A) with MODELLER is 70% when the ligand restrains the homology modelling process in its native orientation. Scoring these complexes with the knowledge-based potentials, in 66% of the cases a pose with rmsd <2.0A is found on rank 1. Finally, MOBILE has been applied to two case studies modelling factor Xa based on trypsin and aldose reductase based on aldehyde reductase.
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PMID:Ligand-supported homology modelling of protein binding-sites using knowledge-based potentials. 1460 22

In the last decades, molecular docking has emerged as an increasingly useful tool in the modern drug discovery process, but it still needs to overcome many hurdles and limitations such as how to account for protein flexibility and poor scoring function performance. For this reason, it has been recognized that in many cases docking results need to be post-processed to achieve a significant agreement with experimental activities. In this study, we have evaluated the performance of MM-PBSA and MM-GBSA scoring functions, implemented in our post-docking procedure BEAR, in rescoring docking solutions. For the first time, the performance of this post-docking procedure has been evaluated on six different biological targets (namely estrogen receptor, thymidine kinase, factor Xa, adenosine deaminase, aldose reductase, and enoyl ACP reductase) by using i) both a single and a multiple protein conformation approach, and ii) two different software, namely AutoDock and LibDock. The assessment has been based on two of the most important criteria for the evaluation of docking methods, i.e., the ability of known ligands to enrich the top positions of a ranked database with respect to molecular decoys, and the consistency of the docking poses with crystallographic binding modes. We found that, in many cases, MM-PBSA and MM-GBSA are able to yield higher enrichment factors compared to those obtained with the docking scoring functions alone. However, for only a minority of the cases, the enrichment factors obtained by using multiple protein conformations were higher than those obtained by using only one protein conformation.
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PMID:Application of a post-docking procedure based on MM-PBSA and MM-GBSA on single and multiple protein conformations. 2315 14