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E-pRSA:嵌入改进了蛋白质序列中残基相对溶剂可及性的预测。

E-pRSA: Embeddings Improve the Prediction of Residue Relative Solvent Accessibility in Protein Sequence.

机构信息

Biocomputing Group, Dept. of Pharmacy and Biotechnology, University of Bologna, Italy.

Biocomputing Group, Dept. of Pharmacy and Biotechnology, University of Bologna, Italy.

出版信息

J Mol Biol. 2024 Sep 1;436(17):168494. doi: 10.1016/j.jmb.2024.168494. Epub 2024 Feb 15.

Abstract

Knowledge of the solvent accessibility of residues in a protein is essential for different applications, including the identification of interacting surfaces in protein-protein interactions and the characterization of variations. We describe E-pRSA, a novel web server to estimate Relative Solvent Accessibility values (RSAs) of residues directly from a protein sequence. The method exploits two complementary Protein Language Models to provide fast and accurate predictions. When benchmarked on different blind test sets, E-pRSA scores at the state-of-the-art, and outperforms a previous method we developed, DeepREx, which was based on sequence profiles after Multiple Sequence Alignments. The E-pRSA web server is freely available at https://e-prsa.biocomp.unibo.it/main/ where users can submit single-sequence and batch jobs.

摘要

蛋白质中残基的溶剂可及性知识对于不同的应用至关重要,包括识别蛋白质-蛋白质相互作用中的相互作用表面和特征变化。我们描述了 E-pRSA,这是一种新颖的网络服务器,可以直接从蛋白质序列估算残基的相对溶剂可及性值(RSAs)。该方法利用两种互补的蛋白质语言模型来提供快速准确的预测。在不同的盲测集上进行基准测试时,E-pRSA 的得分达到了最新水平,并且优于我们之前基于多重序列比对后的序列图谱开发的 DeepREx 方法。E-pRSA 网络服务器可在 https://e-prsa.biocomp.unibo.it/main/ 上免费获得,用户可以在此处提交单序列和批处理作业。

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