Lessons in Molecular Recognition. 2. Assessing and Improving Cross-Docking Accuracy

Sutherland, Jeffrey J.; Nandigam, Ravi K.; Erickson, Jon A.; Vieth, Michał

doi:10.1021/ci700253h

Cited by 112 publications

(122 citation statements)

References 31 publications

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“…The binding mode predictions are thus performed with either the apo structure or an unrelated ligandbound structure. Docking of a ligand against a non-native structure is often called ''cross-docking'', and cross-docking experiments only have success rates of *50% or less [39,[60][61][62][63][64][65]. These experiments show that small changes in the protein structure are often required to be able to reproduce known binding poses and highlight the deficiencies in the rigid receptor assumption utilized by most docking algorithms.…”

Section: Introductionmentioning

confidence: 99%

Chemical space sampling by different scoring functions and crystal structures

Brooijmans

Humblet

2010

J Comput Aided Mol Des

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Virtual screening has become a popular tool to identify novel leads in the early phases of drug discovery. A variety of docking and scoring methods used in virtual screening have been the subject of active research in an effort to gauge limitations and articulate best practices. However, how to best utilize different scoring functions and various crystal structures, when available, is not yet well understood. In this work we use multiple crystal structures of PI3 K-gamma in both prospective and retrospective virtual screening experiments. Both Glide SP scoring and Prime MM-GBSA rescoring are utilized in the prospective and retrospective virtual screens, and consensus scoring is investigated in the retrospective virtual screening experiments. The results show that each of the different crystal structures that was used, samples a different chemical space, i.e. different chemotypes are prioritized by each structure. In addition, the different (re)scoring functions prioritize different chemotypes as well. Somewhat surprisingly, the Prime MM-GBSA scoring function generally gives lower enrichments than Glide SP. Finally we investigate the impact of different ligand preparation protocols on virtual screening enrichment factors. In summary, different crystal structures and different scoring functions are complementary to each other and allow for a wider variety of chemotypes to be considered for experimental follow-up.

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Section: Introductionmentioning

confidence: 99%

Chemical space sampling by different scoring functions and crystal structures

Brooijmans

Humblet

2010

J Comput Aided Mol Des

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“…Such flexibility is manifested in a variety of ways, ranging from subtle movements in the backbone to significant variations in side-chain conformations and extensive loop movements. Lack of consideration of such flexibility in docking generally leads to inaccuracies in pose predictions, reduced recovery of actives, and by implication poorer correlation with binding affinities [8]. In a previous publication, we described the treatment of induced fit effects that involve small to medium range changes in the flexibility of the active site in terms of a workflow combining the docking program (Glide) and protein refinement program (Prime) [9].…”

Section: Introductionmentioning

confidence: 99%

Improving database enrichment through ensemble docking

Rao

Sanschagrin

Greenwood

et al. 2008

J Comput Aided Mol Des

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While it may seem intuitive that using an ensemble of multiple conformations of a receptor in structure-based virtual screening experiments would necessarily yield improved enrichment of actives relative to using just a single receptor, it turns out that at least in the p38 MAP kinase model system studied here, a very large majority of all possible ensembles do not yield improved enrichment of actives. However, there are combinations of receptor structures that do lead to improved enrichment results. We present here a method to select the ensembles that produce the best enrichments that does not rely on knowledge of active compounds or sophisticated analyses of the 3D receptor structures. In the system studied here, the small fraction of ensembles of up to 3 receptors that do yield good enrichments of actives were identified by selecting ensembles that have the best mean GlideScore for the top 1% of the docked ligands in a database screen of actives and drug-like "decoy" ligands. Ensembles of two receptors identified using this mean GlideScore metric generally outperform single receptors, while ensembles of three receptors identified using this metric consistently give optimal enrichment factors in which, for example, 40% of the known actives outrank all the other ligands in the database.

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“…In recent years, a variety of docking methods taking into account the conformational flexibility of target protein have been reported; 39,49) for example, the docking of the ligand to ensembles of multiple protein structures [50][51][52] or an average protein grid, 53,54) methods using soft docking potentials, 55) approaches exploring side-chain rotamer libraries, 56,57) and current unique methods such as IFREDA 58) and the iterative coupling of Glide and Prime. 59) Every method has both merits and demerits, and there is no decisive method that also shows outstanding effects on virtual screening.…”

Section: N=18)mentioning

confidence: 99%

Prediction of Ligand Binding Affinity Using a Multiple-Conformations-Multiple-Protonation Scheme: Application to Estrogen Receptor .ALPHA.

Mizutani

Takamatsu

Ichinose

et al. 2012

CHEMICAL & PHARMACEUTICAL BULLETIN

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A fast method that can predict the binding affinities of chemicals to a target protein with a high degree of accuracy will be very useful in drug design and regulatory science. We have been developing a scoring function for affinity prediction, which can be applied to extensive protein systems, and also trying to generate a prediction scheme that specializes in each target protein, with as high a predictive power as possible. In this study, we have constructed a prediction scheme with target-specific scores for estimating ligand-binding affinities to human estrogen receptor α (ERα), considering the major conformational change between agonist-and antagonist-bound forms and the change in protonation states of histidine at the ligand-binding site. The generated scheme calibrated with fewer training compounds (23 for the agonist-bound form, 17 for the antagonist-bound form) demonstrated good predictive power (a predictive r 2 of 0.83 for 154 validation compounds); this was also true for compounds with frameworks that were quite different from those of the training compounds. Our prediction scheme will be useful in drug development targeting ERα and in primary screening of endocrine disruptors, and provides a successful method of affinity prediction considering the major conformational changes in a protein.Key words binding affinity; prediction score; docking; protein conformational change; protonatioin state; estrogen receptorIn current drug discovery research, methods of searching for promising seed structures efficiently and optimizing them rationally decide the outcome of pharmaceutical development. The limitations of the high-throughput screening (HTS) technique are becoming clear, and accordingly, rational approaches to seed development by means of computational chemistry have been receiving attention again. In particular, in silico drug design based on the three-dimensional (3D) structure of a target protein has been enthusiastically attempted.1-3) The docking technique, which is the core technique of structurebased drug design (SBDD), has improved considerably over nearly three decades, 4,5) although difficult problems such as protein flexibility and consideration of water molecules remain. Meanwhile, another important technique, a prediction score for the binding affinities of small compounds to a target protein, 6) has not yet met the requirements for accuracy and computational cost. 4,7)One approach for predicting binding affinity is to try to estimate the binding free energy rigorously, e.g., the free energy perturbation (FEP) technique, 8) linear interaction energy method, 9) and Poisson-Boltzmann equation methods. 10,11) However, these rigorous methods have a large computational cost for every ligand-protein complex and would not be suitable for sequential evaluation of a number of compounds. Moreover, the FEP method can be adopted only to compare energies between ligands with the same structural frames. A number of empirical scores have also been developed. 4,12) These scores can handle many compounds ...

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Lessons in Molecular Recognition. 2. Assessing and Improving Cross-Docking Accuracy

Cited by 112 publications

References 31 publications

Chemical space sampling by different scoring functions and crystal structures

Chemical space sampling by different scoring functions and crystal structures

Improving database enrichment through ensemble docking

Prediction of Ligand Binding Affinity Using a Multiple-Conformations-Multiple-Protonation Scheme: Application to Estrogen Receptor .ALPHA.

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