MetalProGNet: a structure-based deep graph model for metalloprotein–ligand interaction predictions

Jiang, Dejun; Ye, Zhaofeng; Hsieh, Chang‐Yu; Yang, Ziyi; Zhang, Xujun; Kang, Yu; Du, Hongyan; Wu, Zhenxing; Wang, Jike; Zeng, Yiyu; Zhang, Haotian; Wang, Xiaorui; Wang, Mingyang; Yao, Xiao-Jun; Zhang, Shengyu; Wu, Jian; Hou, Tingjun

doi:10.1039/d2sc06576b

Cited by 6 publications

(16 citation statements)

References 59 publications

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“…Also, Jian et al reported for their data set a Pearson coefficient of 0.70 and an RMSE of 1.28. 15 In our evaluated data set, we obtained a comparable RMSE value of 1.07, whereas for MDB, it was lower with 0.73.…”

Section: Discussionmentioning

confidence: 54%

“…14 Despite their relevance and potential as targets, the search for highaffinity ligands targeting metalloproteins has been delayed. 15 Concerning, docking programs, only a few of them, such as GPDOCK, 16 FlexX, 17 AutoDockZn, 18 MpsDockZn, 19 and GM-DockZn, 20 are specially developed for metalloproteins, and most of them are predominantly specific for zinc metalloproteins. Recently, some neural network (NN)-based ML methodologies emerged as an alternative.…”

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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Section: Discussionmentioning

confidence: 54%

Section: Introductionmentioning

confidence: 99%

Unlocking Precision Docking for Metalloproteins

Clemente,

Prieto,

Martí

2024

J. Chem. Inf. Model.

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“…This specific inclusion is crucial as hydrogen bonding represents one of the most prevalent interactions observed in NA–ligand complexes. Previous studies have also demonstrated its effectiveness in predicting binding modes for metalloproteins. , LeDock employs a hybrid approach of simulated annealing (SA) and genetic algorithm (GA) to optimize the position, orientation, and rotatable bonds of the docked ligand. Similarly, rDock combines stochastic and deterministic search techniques (GA and MC) to generate low-energy ligand poses.…”

Section: Resultsmentioning

confidence: 99%

How Good Are Current Docking Programs at Nucleic Acid–Ligand Docking? A Comprehensive Evaluation

Jiang

Zhao

et al. 2023

J. Chem. Theory Comput.

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Nucleic acid (NA)–ligand interactions are of paramount importance in a variety of biological processes, including cellular reproduction and protein biosynthesis, and therefore, NAs have been broadly recognized as potential drug targets. Understanding NA–ligand interactions at the atomic scale is essential for investigating the molecular mechanism and further assisting in NA-targeted drug discovery. Molecular docking is one of the predominant computational approaches for predicting the interactions between NAs and small molecules. Despite the availability of versatile docking programs, their performance profiles for NA–ligand complexes have not been thoroughly characterized. In this study, we first compiled the largest structure-based NA–ligand binding data set to date, containing 800 noncovalent NA–ligand complexes with clearly identified ligands. Based on this extensive data set, eight frequently used docking programs, including six protein–ligand docking programs (LeDock, Surflex-Dock, UCSF Dock6, AutoDock, AutoDock Vina, and PLANTS) and two specific NA–ligand docking programs (rDock and RLDOCK), were systematically evaluated in terms of binding pose and binding affinity predictions. The results demonstrated that some protein–ligand docking programs, specifically PLANTS and LeDock, produced more promising or comparable results compared with the specialized NA–ligand docking programs. Among the programs evaluated, PLANTS, rDock, and LeDock showed the highest performance in binding pose prediction, and their top-1 and best root-mean-square deviation (rmsd) success rates were as follows: PLANTS (35.93 and 76.05%), rDock (27.25 and 72.16%), and LeDock (27.40 and 64.37%). Compared with the moderate level of binding pose prediction, few programs were successful in binding affinity prediction, and the best correlation (R p = −0.461) was observed with PLANTS. Finally, further comparison with the latest NA–ligand docking program (NLDock) on four well-established data sets revealed that PLANTS and LeDock outperformed NLDock in terms of binding pose prediction on all data sets, demonstrating their significant potential for NA–ligand docking. To the best of our knowledge, this study is the most comprehensive evaluation of popular molecular docking programs for NA–ligand systems.

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“…77 The development of high-affinity metalloprotein ligands could provide a potential treatment for these pathologies. Recently, Jiang et al 78 developed a structure-based deep graph model, MetalProGNet, which explicitly models the coordination interactions between metalloproteins and ligand atoms to predict the binding affinities of metalloprotein ligands. We next investigated the impact of incorporating MetalProGNet's scoring as the reward feedback in 3D-MCTS on the search results.…”

Section: Resultsmentioning

confidence: 99%

“…The structures for three metalloproteins are 1M2X (blaB1), 4MHY (QCs) and 2JKE (SusB). To evaluate the binding affinities between ligands and metalloproteins, we adopted the model proposed by Jiang et al 78 The binding affinity was represented by pIC50, with higher values indicating stronger binding capability. For each ligand, we calculated three pIC 50 values and took their average.…”

Section: Molecular Generation With the Pharmacophore Model And Metal ...mentioning

confidence: 99%

A flexible data-free framework for structure-based de novo drug design with reinforcement learning

Du,

Jiang,

Zhang

et al. 2023

Chem. Sci.

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MetalProGNet: a structure-based deep graph model for metalloprotein–ligand interaction predictions

Abstract: Metalloproteins play indispensable roles in various biological processes ranging from reaction catalyzing to free radicals scavenging, and it is also pertinent to numerous pathologies including cancer, HIV infection, neurodegeneration, and...

Cited by 6 publications

References 59 publications

Unlocking Precision Docking for Metalloproteins

Unlocking Precision Docking for Metalloproteins

How Good Are Current Docking Programs at Nucleic Acid–Ligand Docking? A Comprehensive Evaluation

A flexible data-free framework for structure-based de novo drug design with reinforcement learning

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