Protein Model Accuracy Estimation Empowered by Deep Learning and Inter-residue Distance Prediction in CASP14

Chen, Xiao; Liu, Jian; Guo, Zhiye; Wu, Tai Tsun; Hou, Jie; Cheng, Jianlin

doi:10.21203/rs.3.rs-228012/v1

Cited by 5 publications

(7 citation statements)

References 29 publications

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“…The CASP assessors provided access to full label information before it was publicly available (i.e., prior to PDB release) for many of the targets which enabled us to analyze our predictions across 61 protein targets. We evaluated these targets based on residue-residue contacts, which are defined by CASP as having a Cβ (or Cα for glycine) distance less than 8 Å [ 18 ]. Predicted contact probabilities were straightforward to derive from our binned distance predictions; we summed the probabilities of the first three bins since their distances correspond to those less than 8 Å.…”

Section: Evaluation and Resultsmentioning

confidence: 99%

Evaluation of Deep Neural Network ProSPr for Accurate Protein Distance Predictions on CASP14 Targets

Stern

Hedelius

Fisher

et al. 2021

IJMS

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The field of protein structure prediction has recently been revolutionized through the introduction of deep learning. The current state-of-the-art tool AlphaFold2 can predict highly accurate structures; however, it has a prohibitively long inference time for applications that require the folding of hundreds of sequences. The prediction of protein structure annotations, such as amino acid distances, can be achieved at a higher speed with existing tools, such as the ProSPr network. Here, we report on important updates to the ProSPr network, its performance in the recent Critical Assessment of Techniques for Protein Structure Prediction (CASP14) competition, and an evaluation of its accuracy dependency on sequence length and multiple sequence alignment depth. We also provide a detailed description of the architecture and the training process, accompanied by reusable code. This work is anticipated to provide a solid foundation for the further development of protein distance prediction tools.

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Section: Evaluation and Resultsmentioning

confidence: 99%

Evaluation of Deep Neural Network ProSPr for Accurate Protein Distance Predictions on CASP14 Targets

Stern

Hedelius

Fisher

et al. 2021

IJMS

View full text Add to dashboard Cite

show abstract

“…We evaluated these targets based on residue-residue contacts, which are defined by CASP as having a Cβ (or Cα for glycine) distance less than 8 Å. [13] Predicted contact probabilities were straightforward to derive from our binned distance predictions: we summed the probabilities of the first three bins since their distances correspond to those less than 8 Å.…”

Section: Evaluation and Resultsmentioning

confidence: 99%

Evaluation of Deep Neural Network ProSPr for Accurate Protein Distance Predictions on CASP14 Targets

Stern

Hedelius

Fisher

et al. 2021

Preprint

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The field of protein structure prediction has recently been revolutionized through the introduction of deep learning. The current state-of-the-art tool AlphaFold2 can predict highly accurate structures, however, it has a prohibitively long inference time for applications that require the folding of hundreds of sequences. The prediction of protein structure annotations, such as amino acid distances, can be achieved at a higher speed with existing tools, such as the ProSPr network. Here, we report on important updates to the ProSPr network, its performance on the recent Critical Assessment of Structure Prediction (CASP14) competition, and an evaluation of its accuracy dependency on multiple sequence alignment depth. We also provide a detailed description of the architecture and the training process, accompanied by reusable code. This work is anticipated to provide a solid foundation for the further development of protein distance prediction tools.

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“…• Sixths lowest average LOSS (Table 3 [58][59][60][61][62][63] by three measures: MCC(50) (Table 1), RMSE (Table 2), and LOSS (Table 3). Results are reproduced from the CASP website at (https://predictioncenter.org/casp14).…”

Section: Casp14: Meshi_consensusmentioning

confidence: 99%

Estimation of Model Accuracy By A Unique Set of Features And Tree-Based Regressor

Bitton

Keasar

2022

Preprint

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Computationally generated models of protein structures bridge the gap between the practically negligible price tag of sequencing and the high cost of experimental structure determination. By providing a low-cost (often free) partial alternative to experimentally determined structures, these models help biologists design and interpret their experiments. Obviously, the more accurate the models the more useful they are. However, methods for protein structure prediction generate many structural models (called decoys) of various qualities, necessitating means for the estimation of their accuracy. Here we present MESHI_consensus, a new method for the estimation of model accuracy. The method uses a tree based regressor and a set of structural, target-based, and consensus-based features. The new method achieved high performance in the EMA (Estimation of Model Accuracy) track of the recent CASP14 community-wide experiment (https://predictioncenter.org/casp14/index.cgi). The tertiary structure prediction track of that experiment revealed an unprecedented leap in prediction performance by a single prediction group/method, namely AlphaFold2. This achievement would inevitably have a profound impact on the field of protein structure prediction, including the accuracy estimation sub-task. We conclude this manuscript with some speculations regarding the future role of accuracy estimation in a new era of accurate protein structure prediction

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Protein Model Accuracy Estimation Empowered by Deep Learning and Inter-residue Distance Prediction in CASP14

Cited by 5 publications

References 29 publications

Evaluation of Deep Neural Network ProSPr for Accurate Protein Distance Predictions on CASP14 Targets

Evaluation of Deep Neural Network ProSPr for Accurate Protein Distance Predictions on CASP14 Targets

Evaluation of Deep Neural Network ProSPr for Accurate Protein Distance Predictions on CASP14 Targets

Estimation of Model Accuracy By A Unique Set of Features And Tree-Based Regressor

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