Docking rigid macrocycles using Convex-PL, AutoDock Vina, and RDKit in the D3R Grand Challenge 4

Kadukova, Maria; Chupin, Vladimir; Grudinin, Sergei

doi:10.1007/s10822-019-00263-3

Cited by 11 publications

(10 citation statements)

References 33 publications

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“…We have already successfully applied earlier versions of this protocol in the D3R Grand Challenges 2 80 and 4. 81…”

Section: Model Assessment and Discussionmentioning

confidence: 99%

“…successfully applied earlier versions of this protocol in the D3R Grand Challenges 2 80 and 4. 81 AutoDock Vina, Smina, and VinaCPL were launched with the exhaustiveness parameter set to 10, other parameters except for the number of output conformations in the case of VinaCPL were left to their defaults. We have tested several ways to define the docking binding boxes.…”

Section: Pose Predictionmentioning

confidence: 99%

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Convex-PL^R – Revisiting affinity predictions and virtual screening using physics-informed machine learning

Kadukova

Chupin

Grudinin

2021

Preprint

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Virtual screening is an essential part of the modern drug design pipeline, which significantly accelerates the discovery of new drug candidates. Structure-based virtual screening involves ligand conformational sampling, which is often followed by re-scoring of docking poses. A great variety of scoring functions have been designed for this purpose. The advent of structural and affinity databases and the progress in machine-learning methods have recently boosted scoring function performance. Nonetheless, the most successful scoring functions are typically designed for specific tasks or systems. All-purpose scoring functions still perform poorly on the virtual screening tests, compared to precision with which they are able to predict co-crystal binding poses. Another limitation is the low interpretability of the heuristics being used. We analyzed scoring functions' performance in the CASF benchmarks and discovered that the vast majority of them have a strong bias towards predicting larger binding interfaces. This motivated us to develop a physical model with additional entropic terms with the aim of penalizing such a preference. We parameterized the new model using affinity and structural data, solving a classification problem followed by regression. The new model, called Convex-PLR, demonstrated high-quality results on multiple tests and a substantial improvement over its predecessor Convex-PL. Convex-PLR can be used for molecular docking together with VinaCPL, our version of AutoDock Vina, with Convex-PL integrated as a scoring function. Convex-PLR, Convex-PL, and VinaCPL are available at https://team.inria.fr/nano-d/convex-pl/.

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“…We have already successfully applied earlier versions of this protocol in the D3R Grand Challenges 2 80 and 4. 81…”

Section: Model Assessment and Discussionmentioning

confidence: 99%

Section: Pose Predictionmentioning

confidence: 99%

Convex-PL^R – Revisiting affinity predictions and virtual screening using physics-informed machine learning

Kadukova

Chupin

Grudinin

2021

Preprint

Self Cite

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show abstract

“…For the second protocol, we used our in-house modification of AutoDock Vina and the Convex-PL [ 48 ] scoring function for rescoring, augmented with additional descriptors that account for conformational flexibility and solvation. We have recently applied this protocol for pose prediction in [ 49 ], where it is discussed in more detail. We ran several AutoDock Vina simulations to obtain more diverse docking poses and clustered all resulting conformations with a 1 Å threshold using RDKit (Open-source cheminformatics, ).…”

Section: Methodsmentioning

confidence: 99%

Hydroxylation of Antitubercular Drug Candidate, SQ109, by Mycobacterial Cytochrome P450

Bukhdruker

Varaksa

Grabovec

et al. 2020

IJMS

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Spreading of the multidrug-resistant (MDR) strains of the one of the most harmful pathogen Mycobacterium tuberculosis (Mtb) generates the need for new effective drugs. SQ109 showed activity against resistant Mtb and already advanced to Phase II/III clinical trials. Fast SQ109 degradation is attributed to the human liver Cytochrome P450s (CYPs). However, no information is available about interactions of the drug with Mtb CYPs. Here, we show that Mtb CYP124, previously assigned as a methyl-branched lipid monooxygenase, binds and hydroxylates SQ109 in vitro. A 1.25 Å-resolution crystal structure of the CYP124–SQ109 complex unambiguously shows two conformations of the drug, both positioned for hydroxylation of the ω-methyl group in the trans position. The hydroxylated SQ109 presumably forms stabilizing H-bonds with its target, Mycobacterial membrane protein Large 3 (MmpL3). We anticipate that Mtb CYPs could function as analogs of drug-metabolizing human CYPs affecting pharmacokinetics and pharmacodynamics of antitubercular (anti-TB) drugs.

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“…For the second protocol, we used our in-house modification of AutoDock Vina and the Convex-PL [47] scoring function for rescoring, augmented with additional descriptors that account for conformational flexibility and solvation. We have recently applied this protocol for pose prediction in [48], where it is discussed in more detail. We ran several AutoDock Vina simulations to obtain more diverse docking poses and clustered all resulting conformations with a 1 Å threshold using RDKit [49].…”

Section: Molecular Dockingmentioning

confidence: 99%

Hydroxylation of antitubercular drug candidate, SQ109, by mycobacterial cytochrome P450

Bukhdruker¹,

Varaksa²,

Grabovec³

et al. 2020

Preprint

Self Cite

View full text Add to dashboard Cite

Spreading of the multidrug-resistant (MDR) strains of the deadliest pathogen Mycobacterium tuberculosis (Mtb) generates the need for new effective drugs. SQ109 showed activity against resistant Mtb and already advanced to Phase II/III clinical trials. Fast SQ109 degradation is attributed to the human liver Cytochrome P450s (CYPs). However, no information is available about interactions of the drug with Mtb CYPs. Here, we show that Mtb CYP124, previously assigned as a methyl-branched lipid monooxygenase, binds and hydroxylates SQ109 in vitro. A 1.25 Å-resolution crystal structure of the CYP124–SQ109 complex unambiguously shows two conformations of the drug, both positioned for hydroxylation of the ω-methyl group in the trans position. The hydroxylated SQ109 presumably forms stabilizing H-bonds with its target, the Mycobacterial membrane protein Large 3 (MmpL3). We anticipate that Mtb CYPs could function as analogs of drug-metabolizing human CYPs affecting pharmacokinetics and pharmacodynamics of antitubercular (anti-TB) drugs.

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Docking rigid macrocycles using Convex-PL, AutoDock Vina, and RDKit in the D3R Grand Challenge 4

Cited by 11 publications

References 33 publications

Convex-PL^R – Revisiting affinity predictions and virtual screening using physics-informed machine learning

Convex-PL^R – Revisiting affinity predictions and virtual screening using physics-informed machine learning

Hydroxylation of Antitubercular Drug Candidate, SQ109, by Mycobacterial Cytochrome P450

Hydroxylation of antitubercular drug candidate, SQ109, by mycobacterial cytochrome P450

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Docking rigid macrocycles using Convex-PL, AutoDock Vina, and RDKit in the D3R Grand Challenge 4

Cited by 11 publications

References 33 publications

Convex-PLR – Revisiting affinity predictions and virtual screening using physics-informed machine learning

Convex-PLR – Revisiting affinity predictions and virtual screening using physics-informed machine learning

Hydroxylation of Antitubercular Drug Candidate, SQ109, by Mycobacterial Cytochrome P450

Hydroxylation of antitubercular drug candidate, SQ109, by mycobacterial cytochrome P450

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Product

Resources

About

Convex-PL^R – Revisiting affinity predictions and virtual screening using physics-informed machine learning

Convex-PL^R – Revisiting affinity predictions and virtual screening using physics-informed machine learning