Fast end-to-end learning on protein surfaces

Sverrisson, Freyr; Feydy, Jean; Correia, Bruno E.; Bronstein, Michael M.

doi:10.1109/cvpr46437.2021.01502

Cited by 79 publications

(79 citation statements)

References 31 publications

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“…Protein Surface Representation Structure-based encoders of protein molecular surfaces have been successfully applied to predicting protein-protein interaction sites and identifying potential binding partners in protein docking (Gainza et al, 2019;Sverrisson et al, 2021;Somnath et al, 2021). Molecular surface interaction fingerprinting (MaSIF) (Gainza et al, 2019) pioneered learning geometric surface descriptors for solving protein interaction-related tasks.…”

Section: Related Workmentioning

confidence: 99%

“…Protein Pockets To retrieve the protein pocket, we first generate a surface of the protein and compute embeddings for each point on the surface using dMaSIF (Sverrisson et al, 2021). For each ligand atom, we select the closest point on the protein surface.…”

Section: Dataset Preparationmentioning

confidence: 99%

“…In Table 1, we report the fractions of pockets from the query set for which the methods returned correct ligands in top-1, top-5, top-10, top-20, and top-50. Our best performing method operates on pockets built with neighborhood radius r = 2 Å and using a dMaSIF-search model trained (for 33 epochs) with the standard parameters (Sverrisson et al, 2021) except subsampling (set to 150), resolution (set to 0.7 Å), and embedding size (set to 16). We report metrics computed after the first and last stages of the search process.…”

Section: Ligand Searchmentioning

confidence: 99%

“…In case of dMaSIF-ligand and dMaSIF-affinity, we slightly adjusted the code for new purposes. dMaSIF-search models were trained on the same dataset as in Sverrisson et al (2021). dMaSIF-ligand was trained on the dataset that was used for training MaSIF-ligand Gainza et al (2019).…”

Section: A Ligand Search Parametersmentioning

confidence: 99%

“…It computes geometric and chemical features on a triangulated surface mesh and aggregates local information using a geometric deep learning architecture based on geodesic convolution operators (Bronstein et al, 2017). Differential MaSIF (dMaSIF) (Sverrisson et al, 2021) is a derived method that sidesteps the expensive mesh generation and feature pre-computation steps and creates a lightweight point cloud representation of the molecular surface solely from raw atom coordinates and types. Feature vectors at each surface point are updated by applying approximate geodesic convolutions resulting in the final embedding vectors that can be further used in various downstream tasks.…”

Section: Introductionmentioning

confidence: 99%

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Decoding Surface Fingerprints for Protein-Ligand Interactions

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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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Section: Related Workmentioning

confidence: 99%

Section: Dataset Preparationmentioning

confidence: 99%

Section: Ligand Searchmentioning

confidence: 99%

Section: A Ligand Search Parametersmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Decoding Surface Fingerprints for Protein-Ligand Interactions

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Jamasb

Sadek

et al. 2022

Preprint

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Structure‐Based Drug Discovery with Deep Learning**

et al. 2023

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Artificial intelligence (AI) in the form of deep learning has promise for drug discovery and chemical biology, for example, to predict protein structure and molecular bioactivity, plan organic synthesis, and design molecules de novo. While most of the deep learning efforts in drug discovery have focused on ligand‐based approaches, structure‐based drug discovery has the potential to tackle unsolved challenges, such as affinity prediction for unexplored protein targets, binding‐mechanism elucidation, and the rationalization of related chemical kinetic properties. Advances in deep‐learning methodologies and the availability of accurate predictions for protein tertiary structure advocate for a renaissance in structure‐based approaches for drug discovery guided by AI. This review summarizes the most prominent algorithmic concepts in structure‐based deep learning for drug discovery, and forecasts opportunities, applications, and challenges ahead.

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Protein engineering in the deep learning era

Zhou,

Tan,

et al. 2024

mLife

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Advances in deep learning have significantly aided protein engineering in addressing challenges in industrial production, healthcare, and environmental sustainability. This review frames frequently researched problems in protein understanding and engineering from the perspective of deep learning. It provides a thorough discussion of representation methods for protein sequences and structures, along with general encoding pipelines that support both pre‐training and supervised learning tasks. We summarize state‐of‐the‐art protein language models, geometric deep learning techniques, and the combination of distinct approaches to learning from multi‐modal biological data. Additionally, we outline common downstream tasks and relevant benchmark datasets for training and evaluating deep learning models, focusing on satisfying the particular needs of protein engineering applications, such as identifying mutation sites and predicting properties for candidates' virtual screening. This review offers biologists the latest tools for assisting their engineering projects while providing a clear and comprehensive guide for computer scientists to develop more powerful solutions by standardizing problem formulation and consolidating data resources. Future research can foresee a deeper integration of the communities of biology and computer science, unleashing the full potential of deep learning in protein engineering and driving new scientific breakthroughs.

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Fast end-to-end learning on protein surfaces

Cited by 79 publications

References 31 publications

Decoding Surface Fingerprints for Protein-Ligand Interactions

Decoding Surface Fingerprints for Protein-Ligand Interactions

Structure‐Based Drug Discovery with Deep Learning**

Protein engineering in the deep learning era

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