pLMSNOSite: an ensemble-based approach for predicting protein S-nitrosylation sites by integrating supervised word embedding and embedding from pre-trained 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.
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DOI：
10.1186/s12859-023-05164-9
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发表时间：
2023-02-08
影响因子：
3
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中科院分区：
文献类型：
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在动物和植物中，蛋白质s -亚硝基化（SNO）在一氧化氮介导的信号传递中起着关键作用，并已成为调节蛋白质功能和所有主要蛋白质细胞信号传导的重要机制。它参与多种生物过程，包括免疫反应、蛋白质稳定性、转录调控、翻译后调控、DNA损伤修复、氧化还原调控，是氧化还原信号保护抗氧化应激的新兴范例。开发强大的计算工具来预测蛋白质SNO位点将有助于进一步解释SNO的病理和生理机制。采用基于中间融合的堆叠泛化方法，将监督嵌入层和语境化蛋白质语言模型（ProtT5）的嵌入集成在一起，开发了基于蛋白质语言模型的SNO位点预测工具pLMSNOSite。在实验鉴定的SNO位点的独立测试集上，pLMSNOSite的MCC、敏感性和特异性分别达到0.340、0.735和0.773。这些结果表明，pLMSNOSite在预测s -亚硝基化位点方面优于比较方法。总之，实验结果表明pLMSNOSite在s -亚硝基化位点的预测性能上取得了显著的进步，代表了一种预测蛋白质s -亚硝基化位点的可靠计算方法。pLMSNOSite是进一步阐明SNO的有用资源，可在https://github.com/KCLabMTU/pLMSNOSite公开获取。在线版本包含补充材料，可在10.1186/s12859-023-05164-9获得。
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. The online version contains supplementary material available at 10.1186/s12859-023-05164-9.
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DOI：
10.1007/978-1-0716-2317-6_15
发表时间：
2022-01-01
期刊：
Methods in molecular biology (Clifton, N.J.)
影响因子：
--
作者：
Pakhrin, Subash C;Pokharel, Suresh;Kc, Dukka B
通讯作者：
Kc, Dukka B
影响因子：
64.8
作者：
Jumper J;Evans R;Pritzel A;Green T;Figurnov M;Ronneberger O;Tunyasuvunakool K;Bates R;Žídek A;Potapenko A;Bridgland A;Meyer C;Kohl SAA;Ballard AJ;Cowie A;Romera-Paredes B;Nikolov S;Jain R;Adler J;Back T;Petersen S;Reiman D;Clancy E;Zielinski M;Steinegger M;Pacholska M;Berghammer T;Bodenstein S;Silver D;Vinyals O;Senior AW;Kavukcuoglu K;Kohli P;Hassabis D
通讯作者：
Hassabis D
影响因子：
4.6
作者：
通讯作者：
--
影响因子：
3.7
作者：
Lee TY;Chen YJ;Lu TC;Huang HD;Chen YJ
通讯作者：
Chen YJ
影响因子：
4.6
作者：
通讯作者：
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