Protein sequence‐to‐structure learning: Is this the end(‐to‐end revolution)?

Laine, Élodie; Eismann, Stephan; Elofsson, Arne; Grudinin, Sergei

doi:10.1002/prot.26235

Cited by 35 publications

(20 citation statements)

References 190 publications

(398 reference statements)

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“…Yet it was not until 2020 and CASP14 when the real revolution happened with the introduction of the end-to-end models and self-attention mechanisms . The main drawback of the previous implementation was due to crucial pivot points during structure prediction.…”

Section: Computational Algorithms and Application Of Machine Learning...mentioning

confidence: 99%

Protein Design: From the Aspect of Water Solubility and Stability

et al. 2022

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Water solubility and structural stability are key merits for proteins defined by the primary sequence and 3D-conformation. Their manipulation represents important aspects of the protein design field that relies on the accurate placement of amino acids and molecular interactions, guided by underlying physiochemical principles. Emulated designer proteins with well-defined properties both fuel the knowledge-base for more precise computational design models and are used in various biomedical and nanotechnological applications. The continuous developments in protein science, increasing computing power, new algorithms, and characterization techniques provide sophisticated toolkits for solubility design beyond guess work. In this review, we summarize recent advances in the protein design field with respect to water solubility and structural stability. After introducing fundamental design rules, we discuss the transmembrane protein solubilization and de novo transmembrane protein design. Traditional strategies to enhance protein solubility and structural stability are introduced. The designs of stable protein complexes and high-order assemblies are covered. Computational methodologies behind these endeavors, including structure prediction programs, machine learning algorithms, and specialty software dedicated to the evaluation of protein solubility and aggregation, are discussed. The findings and opportunities for Cryo-EM are presented. This review provides an overview of significant progress and prospects in accurate protein design for solubility and stability.

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Section: Computational Algorithms and Application Of Machine Learning...mentioning

confidence: 99%

Protein Design: From the Aspect of Water Solubility and Stability

et al. 2022

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“…Protein structures can be represented in multiple ways: surfaces describing electrochemical properties [7,16,17], volumes using voxels of densities [18,19], graphs of connected atoms [20], or point clouds. The choice of a representation for a specific problem is important as it will affect the overall performance of a machine learning model [21]. Scalar quantities of proteins such as the energy or interactions interfaces are intrinsically independent of the choice of origin for the coordinate system.…”

Section: Introductionmentioning

confidence: 99%

PeSTo: parameter-free geometric deep learning for accurate prediction of protein interacting interfaces

Krapp

Abriata²,

Rodríguez³

et al. 2022

Preprint

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Predicting the interactions that a protein can establish with other molecules from its structure remains a major challenge. As shown by recent applications to tertiary structure prediction and opposite to current mainstream methods for interaction interface prediction, low-level, geometry-based, physicochemical-agnostic representations of structures have several advantages over methods that require pre-calculation of surfaces, charges, hydrophobicity, and other kinds of parameterizations. Here we introduce a new geometric transformer that acts directly on protein atoms labelled with nothing more than element names. The resulting model outperforms the state of the art for the prediction of protein-protein interaction interfaces and distinguishes interfaces with nucleic acids, lipids, small molecules and ions with high confidence. The low computational cost of this method (available online at https://pesto.epfl.ch/) enables processing high volumes of structural data, such as molecular dynamics trajectories allowing the discovery of interfaces that remain inconspicuous in static experimentally solved structures.

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“…Objective testing in CASP14 has shown that the problem of computing atomic accuracy protein structures from amino acid sequence is solved, at least for single, ordered, proteins. The improvements in model accuracy by AlphaFold2 and the other leading groups almost all arise from more advanced use of deep‐learning methods, discussed in Reference 37. At the CASP14 conference, AlphaFold2 outlined four significant changes from their CASP13 methodologies and a detailed methodology paper describing these and many specifics has recently been published 38 .…”

Section: Discussionmentioning

confidence: 99%

Critical assessment of methods of protein structure prediction (CASP)—Round XIV

et al. 2021

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Critical assessment of structure prediction (CASP) is a community experiment to advance methods of computing three-dimensional protein structure from amino acid sequence. Core components are rigorous blind testing of methods and evaluation of the results by independent assessors. In the most recent experiment (CASP14), deeplearning methods from one research group consistently delivered computed structures rivaling the corresponding experimental ones in accuracy. In this sense, the results represent a solution to the classical protein-folding problem, at least for single proteins. The models have already been shown to be capable of providing solutions for problematic crystal structures, and there are broad implications for the rest of structural biology. Other research groups also substantially improved performance.Here, we describe these results and outline some of the many implications. Other related areas of CASP, including modeling of protein complexes, structure refinement, estimation of model accuracy, and prediction of inter-residue contacts and distances, are also described.

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Protein sequence‐to‐structure learning: Is this the end(‐to‐end revolution)?

Cited by 35 publications

References 190 publications

Protein Design: From the Aspect of Water Solubility and Stability

Protein Design: From the Aspect of Water Solubility and Stability

PeSTo: parameter-free geometric deep learning for accurate prediction of protein interacting interfaces

Critical assessment of methods of protein structure prediction (CASP)—Round XIV

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