Progress on Open Chemoinformatic Tools for Drug Discovery

Medina-Franco, José L.; Gutiérrez-Nieto, Rodrigo; Gómez-Velasco, Homero

doi:10.1007/978-3-030-95895-4_9

Cited by 1 publication

(2 citation statements)

References 59 publications

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“…Therefore, understanding the key interactions within these specific protein−ligand complexes is of uttermost relevance, playing an essential role in the long process of drug development, among others. 1 It is important to note that currently, drug discovery processes are linked to the use of various in silico methods for analyzing and predicting protein− ligand interactions, 2,3 and where the so-called "docking" methods are widely adopted. 3,4 Docking methods aim to predict the protein−ligand complex structure, starting from the structure of the protein and ligand separately.…”

Section: Introductionmentioning

confidence: 99%

“…1 It is important to note that currently, drug discovery processes are linked to the use of various in silico methods for analyzing and predicting protein− ligand interactions, 2,3 and where the so-called "docking" methods are widely adopted. 3,4 Docking methods aim to predict the protein−ligand complex structure, starting from the structure of the protein and ligand separately. This task is usually called pose prediction since the method seeks the correct "pose" of the ligand inside the protein.…”

Section: Introductionmentioning

confidence: 99%

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Unlocking Precision Docking for Metalloproteins

Clemente,

Prieto,

Martí

2024

J. Chem. Inf. Model.

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Metalloproteins play a fundamental role in molecular biology, contributing to various biological processes. However, the discovery of highaffinity ligands targeting metalloproteins has been delayed due, in part, to a lack of suitable tools and data. Molecular docking, a widely used technique for virtual screening of small-molecule ligand interactions with proteins, often faces challenges when applied to metalloproteins due to the particular nature of the ligand metal bond. To address these limitations associated with docking metalloproteins, we introduce a knowledge-driven docking approach known as "metalloprotein bias docking" (MBD), which extends the AutoDock Bias technique. We assembled a comprehensive data set of metalloprotein−ligand complexes from 15 different metalloprotein families, encompassing Ca, Co, Fe, Mg, Mn, and Zn metal ions. Subsequently, we conducted a performance analysis of our MBD method and compared it to the conventional docking (CD) program AutoDock4, applied to various metalloprotein targets within our data set. Our results demonstrate that MBD outperforms CD, significantly enhancing accuracy, selectivity, and precision in ligand pose prediction. Additionally, we observed a positive correlation between our predicted ligand free energies and the corresponding experimental values. These findings underscore the potential of MBD as a valuable tool for the effective exploration of metalloprotein−ligand interactions.

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