Department of Computer Science, Rheinland Pfälzische Technische Universität, Kaiserslautern, 67663, Germany.
German Research Center for Artificial Intelligence, Kaiserslautern, 67663, Germany.
Brief Funct Genomics. 2024 Mar 20;23(2):163-179. doi: 10.1093/bfgp/elad018.
Post-translational modifications (PTMs) either enhance a protein's activity in various sub-cellular processes, or degrade their activity which leads toward failure of intracellular processes. Tyrosine nitration (NT) modification degrades protein's activity that initiates and propagates various diseases including neurodegenerative, cardiovascular, autoimmune diseases and carcinogenesis. Identification of NT modification supports development of novel therapies and drug discoveries for associated diseases. Identification of NT modification in biochemical labs is expensive, time consuming and error-prone. To supplement this process, several computational approaches have been proposed. However these approaches fail to precisely identify NT modification, due to the extraction of irrelevant, redundant and less discriminative features from protein sequences. This paper presents the NTpred framework that is competent in extracting comprehensive features from raw protein sequences using four different sequence encoders. To reap the benefits of different encoders, it generates four additional feature spaces by fusing different combinations of individual encodings. Furthermore, it eradicates irrelevant and redundant features from eight different feature spaces through a Recursive Feature Elimination process. Selected features of four individual encodings and four feature fusion vectors are used to train eight different Gradient Boosted Tree classifiers. The probability scores from the trained classifiers are utilized to generate a new probabilistic feature space, which is used to train a Logistic Regression classifier. On the BD1 benchmark dataset, the proposed framework outperforms the existing best-performing predictor in 5-fold cross validation and independent test evaluation with combined improvement of 13.7% in MCC and 20.1% in AUC. Similarly, on the BD2 benchmark dataset, the proposed framework outperforms the existing best-performing predictor with combined improvement of 5.3% in MCC and 1.0% in AUC. NTpred is publicly available for further experimentation and predictive use at: https://sds_genetic_analysis.opendfki.de/PredNTS/.
翻译后修饰（PTMs）要么增强蛋白质在各种亚细胞过程中的活性，要么降低其活性，从而导致细胞内过程失败。酪氨酸硝化（NT）修饰会降低蛋白质的活性，引发和传播包括神经退行性疾病、心血管疾病、自身免疫性疾病和癌症发生在内的各种疾病。NT修饰的鉴定有助于开发针对相关疾病的新疗法和药物。在生化实验室中鉴定NT修饰既昂贵、耗时又容易出错。为了补充这一过程，已经提出了几种计算方法。然而，由于从蛋白质序列中提取了不相关、冗余和缺乏区分性的特征，这些方法无法精确鉴定NT修饰。本文提出了NTpred框架，该框架能够使用四种不同的序列编码器从原始蛋白质序列中提取全面的特征。为了利用不同编码器的优势，它通过融合单个编码的不同组合生成了四个额外的特征空间。此外，它通过递归特征消除过程从八个不同的特征空间中消除不相关和冗余的特征。四个单独编码和四个特征融合向量的选定特征用于训练八个不同的梯度提升树分类器。训练后的分类器的概率分数用于生成一个新的概率特征空间，该空间用于训练逻辑回归分类器。在BD1基准数据集上，所提出的框架在5折交叉验证和独立测试评估中优于现有的最佳预测器，马修斯相关系数（MCC）综合提高了13.7%，曲线下面积（AUC）综合提高了20.1%。同样，在BD2基准数据集上，所提出的框架优于现有的最佳预测器，MCC综合提高了5.3%，AUC综合提高了1.0%。NTpred可在以下网址公开获取，以供进一步实验和预测使用：https://sds_genetic_analysis.opendfki.de/PredNTS/ 。
