Deep Learning in Protein Structural Modeling and Design

Gao, Wenhao; Mahajan, Sai Pooja; Sulam, Jeremias; Gray, Jeffrey J.

doi:10.1016/j.patter.2020.100142

Cited by 168 publications

(133 citation statements)

References 204 publications

(202 reference statements)

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“…As alluded above, the recent and constant progress in cryo-EM techniques is totally changing structural biology ( 179 ). The protein prediction field is now overwhelmed by the achievements of artificial intelligence tools ( 180 , 181 , 182 ). For those raised with the warning of the Levinthal’s paradox ( 183 ), we admire in awe the progress and breakthroughs.…”

Section: In the Light Of Evolutionmentioning

confidence: 99%

An RNA-centric historical narrative around the Protein Data Bank

Westhof

Leontis

2021

Journal of Biological Chemistry

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Some of the amazing contributions brought to the scientific community by the Protein Data Bank (PDB) are described. The focus is on nucleic acid structures with a bias toward RNA. The evolution and key roles in science of the PDB and other structural databases for nucleic acids illustrate how small initial ideas can become huge and indispensable resources with the unflinching willingness of scientists to cooperate globally. The progress in the understanding of the molecular interactions driving RNA architectures followed the rapid increase in RNA structures in the PDB. That increase was consecutive to improvements in chemical synthesis and purification of RNA molecules, as well as in biophysical methods for structure determination and computer technology. The RNA modeling efforts from the early beginnings are also described together with their links to the state of structural knowledge and technological development. Structures of RNA and of its assemblies are physical objects, which, together with genomic data, allow us to integrate present-day biological functions and the historical evolution in all living species on earth.

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Section: In the Light Of Evolutionmentioning

confidence: 99%

An RNA-centric historical narrative around the Protein Data Bank

Westhof

Leontis

2021

Journal of Biological Chemistry

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“…predict a sequence that will fold into a partic-ular structure), has also benefited from deep learning methods [23]. We refer to [21] for a comprehensive overview.…”

Section: Related Workmentioning

confidence: 99%

“…Deep learning methods have increasingly been applied to a broad range of problems in protein science [21], with the particularly notorious success of DeepMind's AlphaFold to predict 3D protein structure from sequence [37]. Recently, Gainza et al [20] introduced MaSIF, one of the first conceptual approaches for geometric deep learning on protein molecular surfaces allowing to predict their binding.…”

Section: Introductionmentioning

confidence: 99%

Fast end-to-end learning on protein surfaces

Sverrisson

Feydy

Correia

et al. 2020

Preprint

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Proteins’ biological functions are defined by the geometric and chemical structure of their 3D molecular surfaces. Recent works have shown that geometric deep learning can be used on mesh-based representations of proteins to identify potential functional sites, such as binding targets for potential drugs. Unfortunately though, the use of meshes as the underlying representation for protein structure has multiple drawbacks including the need to pre-compute the input features and mesh connectivities. This becomes a bottleneck for many important tasks in protein science.In this paper, we present a new framework for deep learning on protein structures that addresses these limitations. Among the key advantages of our method are the computation and sampling of the molecular surface on-the-fly from the underlying atomic point cloud and a novel efficient geometric convolutional layer. As a result, we are able to process large collections of proteins in an end-to-end fashion, taking as the sole input the raw 3D coordinates and chemical types of their atoms, eliminating the need for any hand-crafted pre-computed features.To showcase the performance of our approach, we test it on two tasks in the field of protein structural bioinformatics: the identification of interaction sites and the prediction of protein-protein interactions. On both tasks, we achieve state-of-the-art performance with much faster run times and fewer parameters than previous models. These results will considerably ease the deployment of deep learning methods in protein science and open the door for end-to-end differentiable approaches in protein modeling tasks such as function prediction and design.

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“…The 2D structures of a protein are contact maps and distance maps. There have also been recent review articles highlighting the DL methods in protein structure prediction [ 23 , 24 ]. Torrisi et al [ 23 ] recently reviewed DL-based approaches for 1D protein structural annotations and methods for 2D protein structural annotations.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning-Based Advances in Protein Structure Prediction

Pakhrin

Shrestha

Adhikari

et al. 2021

IJMS

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Obtaining an accurate description of protein structure is a fundamental step toward understanding the underpinning of biology. Although recent advances in experimental approaches have greatly enhanced our capabilities to experimentally determine protein structures, the gap between the number of protein sequences and known protein structures is ever increasing. Computational protein structure prediction is one of the ways to fill this gap. Recently, the protein structure prediction field has witnessed a lot of advances due to Deep Learning (DL)-based approaches as evidenced by the success of AlphaFold2 in the most recent Critical Assessment of protein Structure Prediction (CASP14). In this article, we highlight important milestones and progresses in the field of protein structure prediction due to DL-based methods as observed in CASP experiments. We describe advances in various steps of protein structure prediction pipeline viz. protein contact map prediction, protein distogram prediction, protein real-valued distance prediction, and Quality Assessment/refinement. We also highlight some end-to-end DL-based approaches for protein structure prediction approaches. Additionally, as there have been some recent DL-based advances in protein structure determination using Cryo-Electron (Cryo-EM) microscopy based, we also highlight some of the important progress in the field. Finally, we provide an outlook and possible future research directions for DL-based approaches in the protein structure prediction arena.

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Deep Learning in Protein Structural Modeling and Design

Cited by 168 publications

References 204 publications

An RNA-centric historical narrative around the Protein Data Bank

An RNA-centric historical narrative around the Protein Data Bank

Fast end-to-end learning on protein surfaces

Deep Learning-Based Advances in Protein Structure Prediction

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