EquiBind: Geometric Deep Learning for Drug Binding Structure Prediction

Stark, Henry; Ganea, Octavian-Eugen; Pattanaik, Lagnajit; Barzilay, Regina; Jaakkola, Tommi S.

doi:10.48550/arxiv.2202.05146

Cited by 37 publications

(67 citation statements)

References 55 publications

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“…Notably, all chains in the complex are represented within the same graph topology, where connected pairs of atoms from the same chain are distinguished using a binary edge feature, as described in the following sections. DPROQ models protein complexes in this manner to facilitate explicit information flow between chains, which has proven useful for other tasks on macromolecular structures [44,45]. We note that DPROQ is not trained using any coevolutionary features, making it an end-to-end geometric deep learning method for protein complex structures.…”

Section: Dproq Pipelinementioning

confidence: 99%

A Gated Graph Transformer for Protein Complex Structure Quality Assessment and its Performance in CASP15

Chen

Morehead

Liu

et al. 2022

Preprint

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Proteins interact to form complexes to carry out essential biological functions. Computational methods have been developed to predict the structures of protein complexes. However, an important challenge in protein complex structure prediction is to estimate the quality of predicted protein complex structures without any knowledge of the corresponding native structures. Such estimations can then be used to select high-quality predicted complex structures to facilitate biomedical research such as protein function analysis and drug discovery. We challenge this significant task with DProQ, which introduces a gated neighborhood-modulating Graph Transformer (GGT) designed to predict the quality of 3D protein complex structures. Notably, we incorporate node and edge gates within a novel Graph Transformer framework to control information flow during graph message passing. We train and evaluate DProQ on four newly-developed datasets that we make publicly available in this work. Our rigorous experiments demonstrate that DProQ achieves state-of-the-art performance in ranking protein complex structures.3

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Section: Dproq Pipelinementioning

confidence: 99%

A Gated Graph Transformer for Protein Complex Structure Quality Assessment and its Performance in CASP15

Chen

Morehead

Liu

et al. 2022

Preprint

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“…However, using fast computational inference, new DL methods have now made it possible to determine the structures of proteins and other biomolecules in a matter of minutes rather than weeks or months [14,15]. Such methods have promoted the widespread adoption of structure prediction software [16,17] and have inspired several new works in DL-driven structure prediction [18,19].…”

Section: Related Workmentioning

confidence: 99%

“…As such, for data efficiency and generalization capabilities, we then turn to designing a neural network capable of capturing within its hidden layers E(3)-transformations of any input protein, especially since for this task we are left to train on a relatively small number of input examples. Towards this end, we propose the Equivariant Graph Refiner (EGR) model, which combines insights from Equivariant Graph Neural Networks [12], EquiDock [35], and EquiBind [19]. The EGR model learns to transform node features and node positions in R 3 to perform graph message passing across each input complex graph.…”

Section: E(3)-equivariant Transformationsmentioning

confidence: 99%

EGR: Equivariant Graph Refinement and Assessment of 3D Protein Complex Structures

Morehead¹,

Chen²,

Wu³

et al. 2022

Preprint

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Protein complexes are macromolecules essential to the functioning and well-being of all living organisms. As the structure of a protein complex, in particular its region of interaction between multiple protein subunits (i.e., chains), has a notable influence on the biological function of the complex, computational methods that can quickly and effectively be used to refine and assess the quality of a protein complex's 3D structure can directly be used within a drug discovery pipeline to accelerate the development of new therapeutics and improve the efficacy of future vaccines. In this work, we introduce the Equivariant Graph Refiner (EGR), a novel E(3)-equivariant graph neural network (GNN) for multi-task structure refinement and assessment of protein complexes. Our experiments on new, diverse protein complex datasets, all of which we make publicly available in this work, demonstrate the state-of-the-art effectiveness of EGR for atomistic refinement and assessment of protein complexes and outline directions for future work in the field. In doing so, we establish a baseline for future studies in macromolecular refinement and structure analysis. 1 Preprint. Under review.

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“…Recently, pocket-centered methods for conditional molecule generation (Masuda et al, 2020; Méndez-Lucio et al, 2021) have been proposed. Stärk et al (2022) developed a geometric deep learning method for predicting the receptor binding location and the ligand’s bound pose and orientation.…”

Section: Introductionmentioning

confidence: 99%

Decoding Surface Fingerprints for Protein-Ligand Interactions

Igashov

Jamasb

Sadek

et al. 2022

Preprint

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Small molecules have been the preferred modality for drug development and therapeutic interventions. This molecular format presents a number of advantages, e.g. long half-lives and cell permeability, making it possible to access a wide range of therapeutic targets. However, finding small molecules that engage “hard-to-drug” protein targets specifically and potently remains an arduous process, requiring experimental screening of extensive compound libraries to identify candidate leads. The search continues with further optimization of compound leads to meet the required potency and toxicity thresholds for clinical applications. Here, we propose a new computational workflow for high-throughput fragment-based screening and binding affinity prediction where we leverage the available protein-ligand complex structures using a state-of-the-art protein surface embedding framework (dMaSIF). We developed a tool capable of finding suitable ligands and fragments for a given protein pocket solely based on protein surface descriptors, that capture chemical and geometric features of the target pocket. The identified fragments can be further combined into novel ligands. Using the structural data, our ligand discovery pipeline learns the signatures of interactions between surface patches and small pharmacophores. On a query target pocket, the algorithm matches known target pockets and returns either potential ligands or identifies multiple ligand fragments in the binding site. Our binding affinity predictor is capable of predicting the affinity of a given protein-ligand pair, requiring only limited information about the ligand pose. This enables screening without the costly step of first docking candidate molecules. Our framework will facilitate the design of ligands based on the target’s surface information. It may significantly reduce the experimental screening load and ultimately reveal novel chemical compounds for targeting challenging proteins.

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EquiBind: Geometric Deep Learning for Drug Binding Structure Prediction

Cited by 37 publications

References 55 publications

A Gated Graph Transformer for Protein Complex Structure Quality Assessment and its Performance in CASP15

A Gated Graph Transformer for Protein Complex Structure Quality Assessment and its Performance in CASP15

EGR: Equivariant Graph Refinement and Assessment of 3D Protein Complex Structures

Decoding Surface Fingerprints for Protein-Ligand Interactions

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