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 9 publications

(18 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%

“…To have a fair assessment of MBD performance against other methods tailored for metalloprotein docking, we also used the following docking methods: Vina Zn, which corresponds to AutoDock VIna, tailored for Zn-containing proteins; 18 GPDOCK, a pose prediction method based on geometric probability; 16 Plants; 34 and MetalProGNet, 15 which provides a rescoring function using a neural network, but does not perform pose prediction. These methods were tested using their default parameters, as described in their corresponding tutorials.…”

Section: Comparison With Other Docking Methodsmentioning

confidence: 99%

“…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%

“…Recently, some neural network (NN)-based ML methodologies emerged as an alternative. 15 The key problem in metalloprotein docking is how to deal with metal− ligand interactions. Most available methods add specific metal to ligand-atom energy terms in the scoring function.…”

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: Comparison With Other Docking Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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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An Effective Plant Small Secretory Peptide Recognition Model Based on Feature Correction Strategy

Wang,

Zhou,

et al. 2023

J. Chem. Inf. Model.

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Plant small secretory peptides (SSPs) play an important role in the regulation of biological processes in plants. Accurately predicting SSPs enables efficient exploration of their functions. Traditional experimental verification methods are very reliable and accurate, but they require expensive equipment and a lot of time. The method of machine learning speeds up the prediction process of SSPs, but the instability of feature extraction will also lead to further limitations of this type of method. Therefore, this paper proposes a new feature-correction-based model for SSP recognition in plants, abbreviated as SE-SSP. The model mainly includes the following three advantages: First, the use of transformer encoders can better reveal implicit features. Second, design a feature correction module suitable for sequences, named 2-D SENET, to adaptively adjust the features to obtain a more robust feature representation. Third, stack multiple linear modules to further dig out the deep information on the sample. At the same time, the training based on a contrastive learning strategy can alleviate the problem of sparse samples. We construct experiments on publicly available data sets, and the results verify that our model shows an excellent performance. The proposed model can be used as a convenient and effective SSP prediction tool in the future. Our data and code are publicly available at https://github.com/wrab12/SE-SSP/.

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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 9 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

An Effective Plant Small Secretory Peptide Recognition Model Based on Feature Correction Strategy

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