In-Pero: Exploiting Deep Learning Embeddings of Protein Sequences to Predict the Localisation of Peroxisomal Proteins

Anteghini, Marco; Santos, Vítor A. P. Martins dos; Saccenti, Edoardo

doi:10.3390/ijms22126409

Cited by 25 publications

(39 citation statements)

References 54 publications

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“…The performance in terms of accuracy is 0.83. The in-depth analysis of the In-Mito predictor is available in the original paper [16]. Datasets are available at https://github.com/MarcoAnteghini/.…”

Section: Processing Methodsmentioning

confidence: 99%

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OrganelX Web Server for Sub-Peroxisomal and Sub-Mitochondrial protein localisation

Anteghini

Haja

Santos

et al. 2022

Preprint

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Computational approaches for sub-organelle protein localisation and identification are often neglected while general methods, not suitable for specific use cases, are promoted instead. In particular, organelle-specific research lacks user-friendly and easily accessible computational tools that allow researchers to perform computational analysis before starting time-consuming and expensive wet-lab experiments. We present the Organelx e-Science Web Server which hosts three sequence localisation predictive algorithms: In-Pero and In-Mito for classifying sub-peroxisomal and sub-mitochondrial protein localisations given their FASTA sequences, as well as the Is-PTS1 algorithm for detecting and validating potential peroxisomal proteins carrying a PTS1 signal. These tools can be used for a fast and accurate screening while looking for new peroxisomal and mitochondrial proteins. To our knowledge, this is the only service that provides these functionalities and can fasten the daily research of the peroxisomal science community.

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Section: Processing Methodsmentioning

confidence: 99%

“…SeqVec showed similar results and optimal performance for predicting subcellular localisation [10]. However, their potential has been explored for highly specific tasks, such as sub-organelle localisation, just recently [16]. In particular, their usage can be adapted for sub-peroxisomal and sub-mitochondrial protein localisation.…”

Section: Introductionmentioning

confidence: 99%

OrganelX Web Server for Sub-Peroxisomal and Sub-Mitochondrial protein localisation

Anteghini

Haja

Santos

et al. 2022

Preprint

Self Cite

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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 [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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“…The PTS1 receptor PEX5 recognises a C-terminal tripeptide (SKL-type), whereas PEX7 recognises a nonapeptide within the N-terminus ( Walter and Erdmann, 2019 ; Kunze, 2020 ). Several predictors have been developed to identify peroxisomal proteins, their PTS, and their sub-peroxisomal location ( Kunze, 2018 ; Anteghini et al, 2021 ).…”

Section: Introductionmentioning

confidence: 99%

Insights Into the Peroxisomal Protein Inventory of Zebrafish

et al. 2022

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Peroxisomes are ubiquitous, oxidative subcellular organelles with important functions in cellular lipid metabolism and redox homeostasis. Loss of peroxisomal functions causes severe disorders with developmental and neurological abnormalities. Zebrafish are emerging as an attractive vertebrate model to study peroxisomal disorders as well as cellular lipid metabolism. Here, we combined bioinformatics analyses with molecular cell biology and reveal the first comprehensive inventory of Danio rerio peroxisomal proteins, which we systematically compared with those of human peroxisomes. Through bioinformatics analysis of all PTS1-carrying proteins, we demonstrate that D. rerio lacks two well-known mammalian peroxisomal proteins (BAAT and ZADH2/PTGR3), but possesses a putative peroxisomal malate synthase (Mlsl) and verified differences in the presence of purine degrading enzymes. Furthermore, we revealed novel candidate peroxisomal proteins in D. rerio, whose function and localisation is discussed. Our findings confirm the suitability of zebrafish as a vertebrate model for peroxisome research and open possibilities for the study of novel peroxisomal candidate proteins in zebrafish and humans.

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In-Pero: Exploiting Deep Learning Embeddings of Protein Sequences to Predict the Localisation of Peroxisomal Proteins

Cited by 25 publications

References 54 publications

OrganelX Web Server for Sub-Peroxisomal and Sub-Mitochondrial protein localisation

OrganelX Web Server for Sub-Peroxisomal and Sub-Mitochondrial protein localisation

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

Insights Into the Peroxisomal Protein Inventory of Zebrafish

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