Comparative Structure Based Virtual Screening Utilizing Optimized AlphaFold Model Identifies Selective HDAC11 Inhibitor

Опубликована: Янв. 18, 2024

HDAC11 is a class IV histone deacylase with no crystal structure reported so far. The catalytic domain of shares low sequence identity other HDAC isoforms which makes the conventional homology modeling less reliable. AlphaFold neural network machine learning approach that can predict 3D proteins high accuracy even in absence similar structures. However fact models are predicted small molecules and ions/cofactors complicate their utilization for drug design. Previously we optimized an model by adding zinc ion minimization presence inhibitors. In current study implement comparative structure-based virtual screening utilizing previously to identify novel selective stepwise was successful identifying hit subsequently tested using vitro enzymatic assay. compound showed IC50 value 3.5 µM could selectively inhibit over subtypes at 10 concentration. addition carried out molecular dynamics simulations further confirm binding hypothesis obtained docking study. These results reinforce presented optimization applicability search inhibitors discovery.

Язык: Английский

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Empowering AlphaFold2 for protein conformation selective drug discovery with AlphaFold2-RAVE

eLife, Год журнала: 2024, Номер 13

Опубликована: Июль 1, 2024

Small molecule drug design hinges on obtaining co-crystallized ligand-protein structures. Despite AlphaFold2's strides in protein native structure prediction, its focus apo structures overlooks ligands and associated holo Moreover, designing selective drugs often benefits from the targeting of diverse metastable conformations. Therefore, direct application AlphaFold2 models virtual screening discovery remains tentative. Here, we demonstrate an based framework combined with all-atom enhanced sampling molecular dynamics induced fit docking, named AF2RAVE-Glide, to conduct computational model small binding kinase conformations, initiated sequences. We AF2RAVE-Glide workflow three different kinases their type I II inhibitors, special emphasis known inhibitors which target classical DFG-out state. These states are not easy sample AlphaFold2. Here how AF2RAVE these conformations can be sampled for high enough accuracy enable subsequent docking more than 50% success rates across calculations. believe protocol should deployable other proteins generally.

Язык: Английский

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AlphaFold-latest: revolutionizing protein structure prediction for comprehensive biomolecular insights and therapeutic advancements

Beni-Suef University Journal of Basic and Applied Sciences, Год журнала: 2024, Номер 13(1)

Опубликована: Май 17, 2024

Abstract Breakthrough achievements in protein structure prediction have occurred recently, mostly due to the advent of sophisticated machine learning methods and significant advancements algorithmic approaches. The most recent version AlphaFold model, known as “AlphaFold-latest,” which expands functionalities groundbreaking AlphaFold2, is subject this article. goal novel model predict three-dimensional structures various biomolecules, such ions, proteins, nucleic acids, small molecules, non-standard residues. We demonstrate notable gains precision, surpassing specialized tools across multiple domains, including protein–ligand interactions, protein–nucleic acid antibody–antigen predictions. In conclusion, framework has ability yield atomically-accurate structural predictions for a variety biomolecular hence facilitating drug discovery.

Язык: Английский

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Predicting therapeutic and side effects from drug binding affinities to human proteome structures

iScience, Год журнала: 2024, Номер 27(6), С. 110032 - 110032

Опубликована: Май 20, 2024

Evaluation of the binding affinities drugs to proteins is a crucial process for identifying drug pharmacological actions, but it requires three dimensional structures proteins. Herein, we propose novel computational methods predict therapeutic indications and side effects candidate compounds from human protein on proteome-wide scale. Large-scale docking simulations were performed 7,582 with 19,135 revealed by AlphaFold (including experimentally unresolved proteins), machine learning models affinity score (PBAS) profiles constructed. We demonstrated usefulness method predicting 559 diseases 285 toxicities. The enabled which related had not been determined successfully extract eliciting effects. proposed will be useful in various applications discovery.

Язык: Английский

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Empowering AlphaFold2 for protein conformation selective drug discovery with AlphaFold2-RAVE

Опубликована: Авг. 14, 2024

Small molecule drug design hinges on obtaining co-crystallized ligand-protein structures. Despite AlphaFold2’s strides in protein native structure prediction, its focus apo structures overlooks ligands and associated holo Moreover, designing selective drugs often benefits from the targeting of diverse metastable conformations. Therefore, direct application AlphaFold2 models virtual screening dis-covery remains tentative. Here, we demonstrate an based framework combined with all-atom enhanced sampling molecular dynamics induced fit docking, named AF2RAVE-Glide, to conduct computational model small binding kinase conformations, initiated sequences. We AF2RAVE-Glide workflow three different kinases their type I II inhibitors, special emphasis known inhibitors which target classical DFG-out state. These states are not easy sample AlphaFold2. Here how AF2RAVE these conformations can be sampled for high enough ac- curacy enable subsequent docking more than 50% success rates across calculations. believe protocol should deployable other proteins generally.

Язык: Английский

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