Literature DB >> 32271577

Structure- and Ligand-Based Virtual Screening on DUD-E+: Performance Dependence on Approximations to the Binding Pocket.

Ann E Cleves1, Ajay N Jain2.   

Abstract

Using the DUD-E+ benchmark, we explore the impact of using a single protein pocket or ligand for virtual screening compared with using ensembles of alternative pockets, ligands, and sets thereof. For both structure-based and ligand-based approaches, the precise characterization of the binding site in question had a significant impact on screening performance. Using the single original DUD-E protein, Surflex-Dock yielded mean ROC area of 0.81 ± 0.11. Using the cognate ligand instead, with the eSim method for screening, yielded 0.77 ± 0.14. Moving to ensembles of five protein pocket variants increased docking performance to 0.84 ± 0.09. Results for the analogous ligand-based approach (using the five crystallographically aligned cognate ligands) was 0.83 ± 0.11. Using the same ligands, but making use of an automatically generated mutual alignment, yielded mean AUC nearly as good as from single-structure docking: 0.80 ± 0.12. Detailed results and statistical analyses show that structure- and ligand-based methods are complementary and can be fruitfully combined to enhance screening efficiency. A hybrid approach combining ensemble docking with eSim-based screening produced the best and most consistent performance (mean ROC area of 0.89 ± 0.08 and 1% early enrichment of 46-fold). Based on results from both the docking and ligand-similarity approaches, it is clearly unwise to make use of a single arbitrarily chosen protein structure for docking or single ligand query for similarity-based screening.

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Year:  2020        PMID: 32271577     DOI: 10.1021/acs.jcim.0c00115

Source DB:  PubMed          Journal:  J Chem Inf Model        ISSN: 1549-9596            Impact factor:   4.956


  10 in total

1.  Fine tuning for success in structure-based virtual screening.

Authors:  Emilie Pihan; Martin Kotev; Obdulia Rabal; Claudia Beato; Constantino Diaz Gonzalez
Journal:  J Comput Aided Mol Des       Date:  2021-11-20       Impact factor: 3.686

2.  fingeRNAt-A novel tool for high-throughput analysis of nucleic acid-ligand interactions.

Authors:  Natalia A Szulc; Zuzanna Mackiewicz; Janusz M Bujnicki; Filip Stefaniak
Journal:  PLoS Comput Biol       Date:  2022-06-02       Impact factor: 4.779

3.  Predicting target-ligand interactions with graph convolutional networks for interpretable pharmaceutical discovery.

Authors:  Paola Ruiz Puentes; Laura Rueda-Gensini; Natalia Valderrama; Isabela Hernández; Cristina González; Laura Daza; Carolina Muñoz-Camargo; Juan C Cruz; Pablo Arbeláez
Journal:  Sci Rep       Date:  2022-05-19       Impact factor: 4.996

4.  Property-Unmatched Decoys in Docking Benchmarks.

Authors:  Reed M Stein; Ying Yang; Trent E Balius; Matt J O'Meara; Jiankun Lyu; Jennifer Young; Khanh Tang; Brian K Shoichet; John J Irwin
Journal:  J Chem Inf Model       Date:  2021-01-25       Impact factor: 4.956

5.  New machine learning and physics-based scoring functions for drug discovery.

Authors:  Isabella A Guedes; André M S Barreto; Diogo Marinho; Eduardo Krempser; Mélaine A Kuenemann; Olivier Sperandio; Laurent E Dardenne; Maria A Miteva
Journal:  Sci Rep       Date:  2021-02-04       Impact factor: 4.379

6.  Virtual screening of potentially endocrine-disrupting chemicals against nuclear receptors and its application to identify PPARγ-bound fatty acids.

Authors:  Chaitanya K Jaladanki; Yang He; Li Na Zhao; Sebastian Maurer-Stroh; Lit-Hsin Loo; Haiwei Song; Hao Fan
Journal:  Arch Toxicol       Date:  2020-09-09       Impact factor: 5.153

7.  A Comparison between Enrichment Optimization Algorithm (EOA)-Based and Docking-Based Virtual Screening.

Authors:  Jacob Spiegel; Hanoch Senderowitz
Journal:  Int J Mol Sci       Date:  2021-12-21       Impact factor: 5.923

8.  Leveraging nonstructural data to predict structures and affinities of protein-ligand complexes.

Authors:  Joseph M Paggi; Julia A Belk; Scott A Hollingsworth; Nicolas Villanueva; Alexander S Powers; Mary J Clark; Augustine G Chemparathy; Jonathan E Tynan; Thomas K Lau; Roger K Sunahara; Ron O Dror
Journal:  Proc Natl Acad Sci U S A       Date:  2021-12-21       Impact factor: 11.205

9.  AtomNet PoseRanker: Enriching Ligand Pose Quality for Dynamic Proteins in Virtual High-Throughput Screens.

Authors:  Kate A Stafford; Brandon M Anderson; Jon Sorenson; Henry van den Bedem
Journal:  J Chem Inf Model       Date:  2022-03-02       Impact factor: 4.956

Review 10.  Computer-Aided Drug Design Boosts RAS Inhibitor Discovery.

Authors:  Ge Wang; Yuhao Bai; Jiarui Cui; Zirui Zong; Yuan Gao; Zhen Zheng
Journal:  Molecules       Date:  2022-09-05       Impact factor: 4.927

  10 in total

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