Improved protein structure refinement guided by deep learning based accuracy estimation

Hiranuma, Naozumi; Park, Hahnbeom; Baek, Minkyung; Anishchenko, Ivan; Dauparas, Justas; Baker, David

doi:10.1038/s41467-021-21511-x

Cited by 199 publications

(247 citation statements)

References 35 publications

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“…To obtain the final refined structures, we need to assess and rank the generated models. Many methods for estimation of model accuracy have been described such as MULTICOM_CLUSTER [ 10 ], Pcons [ 58 ], PRESCO [ 59 ], DeepAccNet [ 60 ], and ProQ3D [ 61 ]. Here, for a quick ranking purpose, we use a widely used knee-based ranking method [ 49 ] in a multi-objective optimization problem to rank those models.…”

Section: Methodsmentioning

confidence: 99%

Protein Structure Refinement Using Multi-Objective Particle Swarm Optimization with Decomposition Strategy

Zhou

Wang

Pan

et al. 2021

IJMS

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Protein structure refinement is a crucial step for more accurate protein structure predictions. Most existing approaches treat it as an energy minimization problem to intuitively improve the quality of initial models by searching for structures with lower energy. Considering that a single energy function could not reflect the accurate energy landscape of all the proteins, our previous AIR 1.0 pipeline uses multiple energy functions to realize a multi-objectives particle swarm optimization-based model refinement. It is expected to provide a general balanced conformation search protocol guided from different energy evaluations. However, AIR 1.0 solves the multi-objective optimization problem as a whole, which could not result in good solution diversity and convergence on some targets. In this study, we report a decomposition-based method AIR 2.0, which is an updated version of AIR, for protein structure refinement. AIR 2.0 decomposes a multi-objective optimization problem into a number of subproblems and optimizes them simultaneously using particle swarm optimization algorithm. The solutions yielded by AIR 2.0 show better convergence and diversity compared to its previous version, which increases the possibilities of digging out better structure conformations. The experimental results on CASP13 refinement benchmark targets and blind tests in CASP 14 demonstrate the efficacy of AIR 2.0.

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Section: Methodsmentioning

confidence: 99%

Protein Structure Refinement Using Multi-Objective Particle Swarm Optimization with Decomposition Strategy

Zhou

Wang

Pan

et al. 2021

IJMS

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“…Baker’s group recently developed a DL framework called DeepAccNEt [ 72 ] that estimates the error in every residue–residue distance along with the local residue contact error. DeepAccNEt consists of a series of 3D and 2D convolutional layers and predicts (i) error histogram (Cβ–Cβ distance error distribution), (ii) mask (native Cβ contact map with a threshold of 15 Å, (iii) and per residue Cβ local distance difference score (Cβ I-DDT) score.…”

Section: Deep Learning-based Advances In Various Steps Of Protein Structure Prediction Pipelinementioning

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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“…Treating a protein structural model as a graph, ProteinGCN [13], GraphQA [14] and VoroCNN [15] applied graph convolutional networks (GCN) to estimate the model accuracy. ResNetQA [16] and DeepAccNet [17] used deep residue networks to address the problem.…”

Section: Introductionmentioning

confidence: 99%

“…In CSAP13, DeepRank [5] demonstrated that accurate residueresidue contacts (a simplified representation of distances between residues) predicted by deep learning improved the prediction of the quality of protein structural models, suggesting that more detailed residue-residue distance predictions could further improve EMA. However, only a few methods [6], [16], [17], use residue-residue distances to estimate the accuracy of protein structural models.…”

Section: Introductionmentioning

confidence: 99%

DISTEMA: distance map-based estimation of single protein model accuracy with attentive 2D convolutional neural network

Chen

Cheng

2021

Preprint

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Estimation of the accuracy (quality) of protein structural models is important for both prediction and use of protein structural models. Deep learning methods have been used to integrate protein structure features to predict the quality of protein models. Inter-residue distances are key information for predicting protein's tertiary structures and therefore have good potentials to predict the quality of protein structural models. However, few methods have been developed to fully take advantage of predicted inter-residue distance maps to estimate the accuracy of a single protein structural model. We developed an attentive 2D convolutional neural network (CNN) with channel-wise attention to take only a raw difference map between the inter-residue distance map calculated from a single protein model and the distance map predicted from the protein sequence as input to predict the quality of the model. The network comprises multiple convolutional layers, batch normalization layers, dense layers, and Squeeze-and-Excitation blocks with attention to automatically extract features relevant to protein model quality from the raw input without using any expert-curated features. We evaluated DISTEMA's capability of selecting the best models for CASP13 targets in terms of ranking loss of GDT-TS score. The ranking loss of DISTEMA is 0.079, lower than several state-of-the-art single-model quality assessment methods. The work demonstrates that using raw inter-residue distance information alone with deep learning can predict the quality of protein structural models reasonably well.

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Improved protein structure refinement guided by deep learning based accuracy estimation

Cited by 199 publications

References 35 publications

Protein Structure Refinement Using Multi-Objective Particle Swarm Optimization with Decomposition Strategy

Protein Structure Refinement Using Multi-Objective Particle Swarm Optimization with Decomposition Strategy

Deep Learning-Based Advances in Protein Structure Prediction

DISTEMA: distance map-based estimation of single protein model accuracy with attentive 2D convolutional neural network

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