Enhancing HDAC Inhibitor Screening: Addressing Zinc Parameterization and Ligand Protonation in Docking Studies DOI Open Access

Rocco Buccheri,

Alessandro Coco, Lorella Pasquinucci

и другие.

International Journal of Molecular Sciences, Год журнала: 2025, Номер 26(2), С. 850 - 850

Опубликована: Янв. 20, 2025

Precise binding free-energy predictions for ligands targeting metalloproteins, especially zinc-containing histone deacetylase (HDAC) enzymes, require specialized computational approaches due to the unique interactions at metal-binding sites. This study evaluates a docking algorithm optimized zinc coordination determine whether it could accurately differentiate between protonated and deprotonated states of hydroxamic acid ligands, key functional group in HDAC inhibitors (HDACi). By systematically analyzing both protonation states, we sought identify which state produces poses energy estimates most closely aligned with experimental values. The was applied across 2, 4, 8, comparing ligand correlations data. results demonstrate that consistently yielded stronger data, R2 values outperforming counterparts all targets (average = 0.80 compared form where 0.67). These findings emphasize significance proper molecular studies zinc-binding particularly HDACs, suggest deprotonation enhances predictive accuracy. study’s methodology provides robust foundation improved virtual screening protocols evaluate large libraries efficiently. approach supports streamlined discovery high-affinity, HDACi, advancing therapeutic exploration metalloprotein targets. A comprehensive, step-by-step tutorial is provided facilitate thorough understanding enable reproducibility results.

Язык: Английский

The Histone Deacetylase Family: Structural Features and Application of Combined Computational Methods DOI Creative Commons
Antonio Curcio, Roberta Rocca, Stefano Alcaro

и другие.

Pharmaceuticals, Год журнала: 2024, Номер 17(5), С. 620 - 620

Опубликована: Май 10, 2024

Histone deacetylases (HDACs) are crucial in gene transcription, removing acetyl groups from histones. They also influence the deacetylation of non-histone proteins, contributing to regulation various biological processes. Thus, HDACs play pivotal roles diseases, including cancer, neurodegenerative disorders, and inflammatory conditions, highlighting their potential as therapeutic targets. This paper reviews structure function four classes human HDACs. While HDAC inhibitors currently available for treating hematological malignancies, numerous others undergoing clinical trials. However, non-selective toxicity necessitates ongoing research into safer more efficient class-selective or isoform-selective inhibitors. Computational methods have aided discovery with desired potency and/or selectivity. These include ligand-based approaches, such scaffold hopping, pharmacophore modeling, three-dimensional quantitative structure–activity relationships, structure-based virtual screening (molecular docking). Moreover, recent developments field molecular dynamics simulations, combined Poisson–Boltzmann/molecular mechanics generalized Born surface area techniques, improved prediction ligand binding affinity. In this review, we delve ways which these contributed designing identifying

Язык: Английский

Процитировано

15
Reliability of AlphaFold2 Models in Virtual Drug Screening: A Focus on Selected Class A GPCRs DOI Open Access

Nada K. Alhumaid,

Essam A. Tawfik

International Journal of Molecular Sciences, Год журнала: 2024, Номер 25(18), С. 10139 - 10139

Опубликована: Сен. 21, 2024

Protein three-dimensional (3D) structure prediction is one of the most challenging issues in field computational biochemistry, which has overwhelmed scientists for almost half a century. A significant breakthrough structural biology been established by developing artificial intelligence (AI) system AlphaFold2 (AF2). The AF2 provides state-of-the-art protein structures from nearly all known sequences with high accuracy. This study examined reliability models compared to experimental drug discovery, focusing on common drug-targeted classes as G protein-coupled receptors (GPCRs) class A. total 32 representative targets were selected, including X-ray crystallographic and Cryo-EM their corresponding models. quality was assessed using different validation tools, pLDDT score, RMSD value, MolProbity percentage Ramachandran favored, QMEAN Z-score, QMEANDisCo Global. molecular docking performed Genetic Optimization Ligand Docking (GOLD) software. models’ virtual screening determined ability predict ligand binding poses closest native pose assessing Root Mean Square Deviation (RMSD) metric scoring function. function evaluated enrichment factor (EF). Furthermore, capability identify hits key protein–ligand interactions analyzed. posing power results showed that successfully predicted (RMSD < 2 Å). However, they exhibited lower power, average EF values 2.24, 2.42, 1.82 X-ray, Cryo-EM, structures, respectively. Moreover, our revealed can competitive inhibitors. In conclusion, this found provided comparable particularly certain GPCR targets, could potentially significantly impact discovery.

Язык: Английский

Процитировано

8
Discovery of a Novel Chemo-Type for TAAR1 Agonism via Molecular Modeling DOI Creative Commons
Giancarlo Grossi, Naomi Scarano, Francesca Musumeci

и другие.

Molecules, Год журнала: 2024, Номер 29(8), С. 1739 - 1739

Опубликована: Апрель 11, 2024

The search for novel effective TAAR1 ligands continues to draw great attention due the wide range of pharmacological applications related targeting. Herein, molecular docking studies known ligands, characterized by an oxazoline core, have been performed in order identify promising chemo-types discovery more active agonists. In particular, oxazoline-based compound S18616 has taken as a reference computational study, leading development quite flat and conformationally locked ligands. choice “Y-shape” conformation was suggested design interacting with protein cavity delimited ASP103 aromatic residues such PHE186, PHE195, PHE268, PHE267. obtained results allowed us preliminary silico screen in-house series pyrimidinone-benzimidazoles (1a–10a) scaffold target TAAR1. Combined ligand-based (LBCM) structure based (SBCM) methods biological evaluation compounds 1a–10a, identification derivatives 1a–3a (hTAAR1 EC50 = 526.3–657.4 nM)

Язык: Английский

Процитировано

6
Comparative Structure Based Virtual Screening Utilizing Optimized AlphaFold Model Identifies Selective HDAC11 Inhibitor DOI Creative Commons
Fady Baselious, Sebastian Hilscher, Dina Robaa

и другие.

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

Процитировано

5
Enhancing HDAC Inhibitor Screening: Addressing Zinc Parameterization and Ligand Protonation in Docking Studies DOI Open Access

Rocco Buccheri,

Alessandro Coco, Lorella Pasquinucci

и другие.

International Journal of Molecular Sciences, Год журнала: 2025, Номер 26(2), С. 850 - 850

Опубликована: Янв. 20, 2025

Precise binding free-energy predictions for ligands targeting metalloproteins, especially zinc-containing histone deacetylase (HDAC) enzymes, require specialized computational approaches due to the unique interactions at metal-binding sites. This study evaluates a docking algorithm optimized zinc coordination determine whether it could accurately differentiate between protonated and deprotonated states of hydroxamic acid ligands, key functional group in HDAC inhibitors (HDACi). By systematically analyzing both protonation states, we sought identify which state produces poses energy estimates most closely aligned with experimental values. The was applied across 2, 4, 8, comparing ligand correlations data. results demonstrate that consistently yielded stronger data, R2 values outperforming counterparts all targets (average = 0.80 compared form where 0.67). These findings emphasize significance proper molecular studies zinc-binding particularly HDACs, suggest deprotonation enhances predictive accuracy. study’s methodology provides robust foundation improved virtual screening protocols evaluate large libraries efficiently. approach supports streamlined discovery high-affinity, HDACi, advancing therapeutic exploration metalloprotein targets. A comprehensive, step-by-step tutorial is provided facilitate thorough understanding enable reproducibility results.

Язык: Английский

Процитировано

0