Deep protein representations enable recombinant protein expression prediction

Martiny, Hannah-Marie; Armenteros, José Juan Almagro; Salomon, Jesper; Nielsen, Henrik

doi:10.1101/2021.05.13.443426

Cited by 3 publications

(3 citation statements)

References 40 publications

(63 reference statements)

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“…The objective of this core is to calculate the embedding representations for protein sequences. For protein sequence encoding/embedding, recent studies have shown the superior performance of deep learning-based methods compared to traditional methods [ 31 , 32 ]. Accordingly, we only compared one-hot encoding to show the difference between these 2 kinds of embedding in this study.…”

Section: Methodsmentioning

confidence: 99%

Enzyme Commission Number Prediction and Benchmarking with Hierarchical Dual-core Multitask Learning Framework

Shi,

Deng,

Yuan

et al. 2023

Research

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Enzyme commission (EC) numbers, which associate a protein sequence with the biochemical reactions it catalyzes, are essential for the accurate understanding of enzyme functions and cellular metabolism. Many ab initio computational approaches were proposed to predict EC numbers for given input protein sequences. However, the prediction performance (accuracy, recall, and precision), usability, and efficiency of existing methods decreased seriously when dealing with recently discovered proteins, thus still having much room to be improved. Here, we report HDMLF, a hierarchical dual-core multitask learning framework for accurately predicting EC numbers based on novel deep learning techniques. HDMLF is composed of an embedding core and a learning core; the embedding core adopts the latest protein language model for protein sequence embedding, and the learning core conducts the EC number prediction. Specifically, HDMLF is designed on the basis of a gated recurrent unit framework to perform EC number prediction in the multi-objective hierarchy, multitasking manner. Additionally, we introduced an attention layer to optimize the EC prediction and employed a greedy strategy to integrate and fine-tune the final model. Comparative analyses against 4 representative methods demonstrate that HDMLF stably delivers the highest performance, which improves accuracy and F1 score by 60% and 40% over the state of the art, respectively. An additional case study of tyrB predicted to compensate for the loss of aspartate aminotransferase aspC, as reported in a previous experimental study, shows that our model can also be used to uncover the enzyme promiscuity. Finally, we established a web platform, namely, ECRECer ( https://ecrecer.biodesign.ac.cn ), using an entirely could-based serverless architecture and provided an offline bundle to improve usability.

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Section: Methodsmentioning

confidence: 99%

Enzyme Commission Number Prediction and Benchmarking with Hierarchical Dual-core Multitask Learning Framework

Shi,

Deng,

Yuan

et al. 2023

Research

View full text Add to dashboard Cite

show abstract

“…The objective of this core is to calculate the embedding representations for protein sequences. For protein sequence encoding/embedding, recent studies have shown the superior performance of deep learning-based methods compared to traditional methods [23,24]. Accordingly, we only compared one-hot encoding to show the difference between these two kinds of embedding in this study.…”

Section: Proposed Frameworkmentioning

confidence: 99%

ECRECer: Enzyme Commission Number Recommendation and Benchmarking based on Multiagent Dual-core Learning

Shi¹,

Yuan²,

Wang³

et al. 2022

Preprint

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Enzyme Commission (EC) numbers, which associate a protein sequence with the biochemical reactions it catalyzes, are essential for the accurate understanding of enzyme functions and cellular metabolism. Many ab-initio computational approaches were proposed to predict EC numbers for given input sequences directly. However, the prediction performance (accuracy, recall, precision), usability, and efficiency of existing methods still have much room to be improved. Here, we report ECRECer, a cloud platform for accurately predicting EC numbers based on novel deep learning techniques. To build ECRECer, we evaluate different protein representation methods and adopt a protein language model for protein sequence embedding. After embedding, we propose a multi-agent hierarchy deep learning-based framework to learn the proposed tasks in a multi-task manner. Specifically, we used an extreme multilabel classifier to perform the EC prediction and employed a greedy strategy to integrate and fine-tune the final model. Comparative analyses against four representative methods demonstrate that ECRECer delivers the highest performance, which improves accuracy and F1 score by 70% and 20% over the state-of-the-the-art, respectively. With ECRECer, we can annotate numerous enzymes in the Swiss-Prot database with incomplete EC numbers to their full fourth level. Take UniPort protein "A0A0U5GJ41" as an example (1.14.-.-), ECRECer annotated it with "1.14.11.38", which supported by further protein structure analysis based on AlphaFold2. Finally, we established a webserver (https://ecrecer.biodesign.ac.cn) and provided an offline bundle to improve usability.

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“…By using the masked language model objective it is able to build a context around each position and learns to “attend” or “focus” on amino acids and peptides that are relevant in the given context. These language models have been found to encode contact maps, taxonomy, and biophysical characteristics in their distributed representations [Rives et al, 2021, Rao et al, 2021, 2020, Elnaggar et al, 2020, Vig et al, 2020, Brandes et al, 2021, Martiny et al, 2021]. In this study, we use a protein language model to predict two objectives, solubility and practical usability for purification of proteins in E. coli , and obtain state-of-the-art performance.…”

Section: Introductionmentioning

confidence: 99%

NetSolP: predicting protein solubility in E. coli using language models

Thumuluri

Martiny

Armenteros

et al. 2021

Preprint

Self Cite

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Solubility and expression levels of proteins can be a limiting factor for large-scale studies and industrial production. By determining the solubility and expression directly from the protein sequence, the success rate of wet-lab experiments can be increased. In this study, we focus on predicting the solubility and usability for purification of proteins expressed in Escherichia coli directly from the sequence. Our model NetSolP is based on deep-learning protein language models called transformers and we show that it achieves state-of-the-art performance and improves extrapolation across datasets. As we find current methods are built on biased datasets, we curate existing datasets by using strict sequence-identity partitioning and ensure that there is minimal bias in the sequences.

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Deep protein representations enable recombinant protein expression prediction

Cited by 3 publications

References 40 publications

Enzyme Commission Number Prediction and Benchmarking with Hierarchical Dual-core Multitask Learning Framework

Enzyme Commission Number Prediction and Benchmarking with Hierarchical Dual-core Multitask Learning Framework

ECRECer: Enzyme Commission Number Recommendation and Benchmarking based on Multiagent Dual-core Learning

NetSolP: predicting protein solubility in E. coli using language models

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