Equivariant Graph Neural Networks for 3D Macromolecular Structure

Jing, Bowen; Eismann, Stephan; Soni, Pratham N.; Dror, Ron O.

doi:10.48550/arxiv.2106.03843

Cited by 27 publications

(50 citation statements)

References 10 publications

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“…Each edge also encodes both the scalar and the vector features. We then apply the geometric vector perceptrons (GVP) [Jing et al 2020, 2021] to embed the protein and arrive at feature h p ∈ ℝ n × s after graph propagation, where n is the number of nodes and s is the embedding size.…”

Section: Tankbind Modelmentioning

confidence: 99%

“…There has been a surge of interest in integrating geometric priors for representation learning in the domain of drug discovery Jumper et al [2021], Baek et al [2021], Jing et al [2021], Ganea et al [2021], Jin et al [2021], Ingraham et al [2019], AlQuraishi 2019], [Schütt et al [2017], Somnath et al [2021]. Recent researches have incorporated geometric information and symmetry properties of the input signals to improve the spatial perception of the learned representations.…”

Section: Introductionmentioning

confidence: 99%

“…Recent researches have incorporated geometric information and symmetry properties of the input signals to improve the spatial perception of the learned representations. These works have been shown great potential in various applications like protein structure modeling Jumper et al [2021], Baek et al [2021], Jing et al [2021], Ganea et al [2021], molecular low-energy generation prediction [Shi et al 2021], Xu et al [2022], Méndez-Lucio et al [2021], property/function prediction Schütt et al [2017], Somnath et al [2021] and molecule design Jin et al [2021], Ingraham et al [2019]. Among which, AlphaFold 2 achieved outstanding performance in protein structure prediction Jumper et al [2021], representing the state-of-the-art geometry-aware method.…”

Section: Introductionmentioning

confidence: 99%

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TANKBind: Trigonometry-Aware Neural NetworKs for Drug-Protein Binding Structure Prediction

Lu¹,

Zhang³

et al. 2022

Preprint

102

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Illuminating interactions between proteins and small drug molecules is a long-standing challenge in the field of drug discovery. Despite the importance of understanding these interactions, most previous works are limited by hand-designed scoring functions and insufficient conformation sampling. The recently-proposed graph neural network-based methods provides alternatives to predict protein-ligand complex conformation in a one-shot manner. However, these methods neglect the geometric constraints of the complex structure and weaken the role of local functional regions. As a result, they might produce unreasonable conformations for challenging targets and generalize poorly to novel proteins. In this paper, we propose Trigonometry-Aware Neural networKs for binding structure prediction, TANKBind, that builds trigonometry constraint as a vigorous inductive bias into the model and explicitly attends to all possible binding sites for each protein by segmenting the whole protein into functional blocks. We construct novel contrastive losses with local region negative sampling to jointly optimize the binding interaction and affinity. Extensive experiments show substantial performance gains in comparison to state-of-the-art physics-based and deep learning-based methods on commonly-used benchmark datasets for both binding structure and affinity predictions with variant settings. We release our code at https://github.com/luwei0917/TankBind.

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Section: Tankbind Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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TANKBind: Trigonometry-Aware Neural NetworKs for Drug-Protein Binding Structure Prediction

Lu¹,

Zhang³

et al. 2022

Preprint

102

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“…We also compare GNN variants such as GCN (Kipf & Welling, 2016), GIN (Xu et al, 2018) and GAT (Veličković et al, 2017) with our DW-GCN, DW-GIN and DW-GAT models. In addition, we include GVP-GNN (Jing et al, 2021) discussed above as well.…”

Section: Residue Identificationmentioning

confidence: 99%

Directed Weight Neural Networks for Protein Structure Representation Learning

Li¹,

Luo²,

Deng³

et al. 2022

Preprint

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A protein performs biological functions by folding to a particular 3D structure. To accurately model the protein structures, both the overall geometric topology and local fine-grained relations between amino acids (e.g. side-chain torsion angles and inter-amino-acid orientations) should be carefully considered. In this work, we propose the Directed Weight Neural Network for better capturing geometric relations among different amino acids. Extending a single weight from a scalar to a 3D directed vector, our new framework supports a rich set of geometric operations on both classical and SO(3)-representation features, on top of which we construct a perceptron unit for processing amino-acid information. In addition, we introduce an equivariant message passing paradigm on proteins for plugging the directed weight perceptrons into existing Graph Neural Networks, showing superior versatility in maintaining SO(3)-equivariance at the global scale. Experiments show that our network has remarkably better expressiveness in representing geometric relations in comparison to classical neural networks and the (globally) equivariant networks. It also achieves state-of-the-art performance on various computational biology applications related to protein 3D structures. All codes and models will be published upon acceptance.

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“…However, none of those DL approaches consider a temporal perspective and take advantage of molecular dynamics simulations to describe the joint flexibility of proteins and ligands. [65]…”

Section: Protein-ligand Modelingmentioning

confidence: 99%

Pre‐Training of Equivariant Graph Matching Networks with Conformation Flexibility for Drug Binding

Jin

Jiang³

et al. 2022

Advanced Science

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The latest biological findings observe that the motionless “lock‐and‐key” theory is not generally applicable and that changes in atomic sites and binding pose can provide important information for understanding drug binding. However, the computational expenditure limits the growth of protein trajectory‐related studies, thus hindering the possibility of supervised learning. A spatial‐temporal pre‐training method based on the modified equivariant graph matching networks, dubbed ProtMD which has two specially designed self‐supervised learning tasks: atom‐level prompt‐based denoising generative task and conformation‐level snapshot ordering task to seize the flexibility information inside molecular dynamics (MD) trajectories with very fine temporal resolutions is presented. The ProtMD can grant the encoder network the capacity to capture the time‐dependent geometric mobility of conformations along MD trajectories. Two downstream tasks are chosen to verify the effectiveness of ProtMD through linear detection and task‐specific fine‐tuning. A huge improvement from current state‐of‐the‐art methods, with a decrease of 4.3% in root mean square error for the binding affinity problem and an average increase of 13.8% in the area under receiver operating characteristic curve and the area under the precision‐recall curve for the ligand efficacy problem is observed. The results demonstrate a strong correlation between the magnitude of conformation's motion in the 3D space and the strength with which the ligand binds with its receptor.

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Equivariant Graph Neural Networks for 3D Macromolecular Structure

Cited by 27 publications

References 10 publications

TANKBind: Trigonometry-Aware Neural NetworKs for Drug-Protein Binding Structure Prediction

TANKBind: Trigonometry-Aware Neural NetworKs for Drug-Protein Binding Structure Prediction

Directed Weight Neural Networks for Protein Structure Representation Learning

Pre‐Training of Equivariant Graph Matching Networks with Conformation Flexibility for Drug Binding

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