Deep Learning–Based Advances In Protein Posttranslational Modification Site and Protein Cleavage Prediction

Pakhrin, Subash C.; Pokharel, Suresh; Saigo, Hiroto; Kc, Dukka B.

doi:10.1007/978-1-0716-2317-6_15

Cited by 15 publications

(11 citation statements)

References 92 publications

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“…Recently, deep learning-based approaches have also been developed for prediction of protein succinylation sites 19 . CNN-SuccSite 20 uses four feature encoding techniques as input to a convolutional neural network (CNN)-based architecture to predict succinylation sites.…”

Section: Introductionmentioning

confidence: 99%

Improving protein succinylation sites prediction using embeddings from protein language model

Pokharel

Pratyush

Heinzinger

et al. 2022

Sci Rep

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Protein succinylation is an important post-translational modification (PTM) responsible for many vital metabolic activities in cells, including cellular respiration, regulation, and repair. Here, we present a novel approach that combines features from supervised word embedding with embedding from a protein language model called ProtT5-XL-UniRef50 (hereafter termed, ProtT5) in a deep learning framework to predict protein succinylation sites. To our knowledge, this is one of the first attempts to employ embedding from a pre-trained protein language model to predict protein succinylation sites. The proposed model, dubbed LMSuccSite, achieves state-of-the-art results compared to existing methods, with performance scores of 0.36, 0.79, 0.79 for MCC, sensitivity, and specificity, respectively. LMSuccSite is likely to serve as a valuable resource for exploration of succinylation and its role in cellular physiology and disease.

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Section: Introductionmentioning

confidence: 99%

Improving protein succinylation sites prediction using embeddings from protein language model

Pokharel

Pratyush

Heinzinger

et al. 2022

Sci Rep

Self Cite

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“…CNNs are less computationally intensive models than sequence-oriented models and facilitate the training of deeper networks as significantly fewer parameters are needed to be learned. The usage of CNNs is prevalent in several PTM prediction tasks [ 13 , 15 , 27 ]. In our case, we use CNN to process the feature representation of the protein sequence obtained from the word embedding layer as described in the previous section.…”

Section: Methodsmentioning

confidence: 99%

“…Moreover, iSNO-PseAAC [ 11 ] is another approach developed by Xu et al that uses PseAAC to represent protein sequences for prediction of protein S-nitrosylation sites. Recently, various deep learning-based methods [ 13 , 14 ] have been developed for prediction of various post-translation modification sites including SNO sites. In that regard, DeepNitro [ 15 ], a deep learning-based approach, developed by Xie et al for the prediction of protein S-nitrosylation sites uses four different types of features: one-hot encoding, Property Factor Representation (PFR), k-space spectrum, and PSSM encoding.…”

Section: Introductionmentioning

confidence: 99%

pLMSNOSite: an ensemble-based approach for predicting protein S-nitrosylation sites by integrating supervised word embedding and embedding from pre-trained protein language model

et al. 2023

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Background Protein S-nitrosylation (SNO) plays a key role in transferring nitric oxide-mediated signals in both animals and plants and has emerged as an important mechanism for regulating protein functions and cell signaling of all main classes of protein. It is involved in several biological processes including immune response, protein stability, transcription regulation, post translational regulation, DNA damage repair, redox regulation, and is an emerging paradigm of redox signaling for protection against oxidative stress. The development of robust computational tools to predict protein SNO sites would contribute to further interpretation of the pathological and physiological mechanisms of SNO. Results Using an intermediate fusion-based stacked generalization approach, we integrated embeddings from supervised embedding layer and contextualized protein language model (ProtT5) and developed a tool called pLMSNOSite (protein language model-based SNO site predictor). On an independent test set of experimentally identified SNO sites, pLMSNOSite achieved values of 0.340, 0.735 and 0.773 for MCC, sensitivity and specificity respectively. These results show that pLMSNOSite performs better than the compared approaches for the prediction of S-nitrosylation sites. Conclusion Together, the experimental results suggest that pLMSNOSite achieves significant improvement in the prediction performance of S-nitrosylation sites and represents a robust computational approach for predicting protein S-nitrosylation sites. pLMSNOSite could be a useful resource for further elucidation of SNO and is publicly available at https://github.com/KCLabMTU/pLMSNOSite.

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“…It refers to changes in the polypeptide chain as a result of adding distinct chemical parts to amino acid residues To be blunt, PTMs are the foundation of complicated cellular processes, such as cell division, growth, differentiation, signaling, and regulation, the same as various processes included in the maintenance of protein structure and integrity [48]. PTMs also regulate the metabolism and defense processes, cellular recognition, and morphology alternation [49]. Consequently, analysis of PTMs is important for the study of cell biology and disease diagnostics and prevention.…”

Section: Ms-based Quantitative Strategies and Analysis Of Proteome Ge...mentioning

confidence: 99%

Mass Spectrometry-Based Techniques Towards OMICS 2.0

Šoša¹

2023

Preprint

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The literature was scoped to help place mass spectrometric (MS) platforms in the spotlight of the life sciences, as a tool of OMICS and OMICS 2.0 technologies. Moreover, today’s appliances are so handy, that they literally place a whole lab on a single chip. Methods of OMICS (and the most recently developed term OMICS 2.0) are tackled, having in mind their aptness for educational purposes, as well as their feasibility. This same topic was highlighted in many reviews that have been periodically published. The author’s initial interest was the forensic application of MS, but since these interests changed, terms such as “biosensors” were also examined, so the review included means of discovery of efficient biomarkers and clinical therapeutic targets. Aside from simply reviewing the research status and the future perspectives, this article tackles terms such as Society 5.0 and OMICS 2.0. However, within the framework of this review, physical chemistry, bioinformatics, or specific software solutions were not specifically studied. Since the topic of generating permanent collections of digital content for retrospective analysis was also reviewed, the need for loads of data – cloud storage was discussed in the context of MS. These large quantities of data are easier to operate with if generated by MS, rather than by any other commercially available assay. In this manner, the review has strengthened an in-depth understanding of the applications of MS.

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Deep Learning–Based Advances In Protein Posttranslational Modification Site and Protein Cleavage Prediction

Cited by 15 publications

References 92 publications

Improving protein succinylation sites prediction using embeddings from protein language model

Improving protein succinylation sites prediction using embeddings from protein language model

pLMSNOSite: an ensemble-based approach for predicting protein S-nitrosylation sites by integrating supervised word embedding and embedding from pre-trained protein language model

Mass Spectrometry-Based Techniques Towards OMICS 2.0

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