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

• Published in: BMC bioinformatics 2023-02, Vol.24 (1), p.41-41, Article 41
• Main Authors: Pratyush, Pawel, Pokharel, Suresh, Saigo, Hiroto, Kc, Dukka B
• Format: Article
• Language: English
• Subjects: Algorithms; Amino acids; Animals; Bioinformatics; Biological activity; Cell signaling; Chemical properties; Computational linguistics; Computer applications; Convolutional neural network; Datasets; Deep learning; DNA damage; DNA repair; Embedding; Gene regulation; Genetic transcription; Health aspects; Immune response; Language; Language processing; Machine learning; Methods; Natural language interfaces; Natural language processing; Nitric oxide; Nitric Oxide - metabolism; Oxidation-Reduction; Oxidative stress; Physiological aspects; Post-translational modification; Protein language model; Protein Processing, Post-Translational; Protein S; Protein structure prediction; Proteins; Proteins - metabolism; Robustness (mathematics); S-nitrosylation; Signal Transduction; Signaling; Software; Structure; Word embedding
• ISSN: 1471-2105; 1471-2105
• DOI: 10.1186/s12859-023-05164-9

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. 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. 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 .