An Improved Computational Prediction Model for Lysine Succinylation Sites Mapping on Homo sapiens by Fusing Three Sequence Encoding Schemes with the Random Forest Classifier.
An Improved Computational Prediction Model for Lysine Succinylation Sites Mapping on Homo sapiens by Fusing Three Sequence Encoding Schemes with the Random Forest Classifier.
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DOI：
10.2174/1389202922666210219114211
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发表时间：
2021-03
期刊：
影响因子：
2.6
通讯作者：
Mollah NH
中科院分区：
文献类型：
--
作者：
Tasmia SA;Ahmed FF;Mosharaf P;Hasan M;Mollah NH
赖氨酸琥珀酰化是可逆蛋白质翻译后修饰 (PTM) 之一，可调节蛋白质的结构和功能。它在各种细胞生理学中发挥着重要作用，包括人类以及许多其他生物体的一些疾病。准确识别琥珀酰化位点对于了解各种生物学功能和药物开发至关重要。 在这项研究中，我们开发了一种改进的方法来预测智人赖氨酸琥珀酰化位点的映射，通过融合三种编码方案，例如二进制、k空间氨基酸对的组成（CKSAAP）和氨基酸组成（AAC）与随机森林（RF）分类器。通过使用 20 倍交叉验证 (CV) 来研究基于融合模型的随机森林 (RF) 在与其他候选模型的比较中的预测性能，并从两个不同来源收集两个独立的测试数据集。 CV结果显示，所提出的预测器在假阳性率（FPR）= 0.10和ROC曲线下面积（AUC）为0.16的情况下，获得了最高的敏感性（SN）得分为0.800，特异性（SP）为0.902，准确性（ACC）为0.919，马修相关系数（MCC）为0.766，部分AUC（pAUC）为0.163 0.958。独立测试蛋白组1的最高性能得分为SN为0.811，SP为0.902，ACC为0.891，MCC为0.629，pAUC为0.139，AUC为0.921，SN为0.772，SP为0.901，ACC为0.836，MCC为0.677，pAUC对于独立测试蛋白组 2，FPR = 0.10 时为 0.141，AUC 为 0.923。它还优于所有其他现有的预测模型。 本文讨论的预测性能表明，所提出的方法可能是人类赖氨酸琥珀酰化位点预测的有用且令人鼓舞的计算资源。
Lysine succinylation is one of the reversible protein post-translational modifications (PTMs), which regulate the structure and function of proteins. It plays a significant role in various cellular physiologies including some diseases of human as well as many other organisms. The accurate identification of succinylation site is essential to understand the various biological functions and drug development. In this study, we developed an improved method to predict lysine succinylation sites mapping on Homo sapiens by the fusion of three encoding schemes such as binary, the composition of k-spaced amino acid pairs (CKSAAP) and amino acid composition (AAC) with the random forest (RF) classifier. The prediction performance of the proposed random forest (RF) based on the fusion model in a comparison of other candidates was investigated by using 20-fold cross-validation (CV) and two independent test datasets were collected from two different sources. The CV results showed that the proposed predictor achieves the highest scores of sensitivity (SN) as 0.800, specificity (SP) as 0.902, accuracy (ACC) as 0.919, Mathew correlation coefficient (MCC) as 0.766 and partial AUC (pAUC) as 0.163 at a false-positive rate (FPR) = 0.10 and area under the ROC curve (AUC) as 0.958. It achieved the highest performance scores of SN as 0.811, SP as 0.902, ACC as 0.891, MCC as 0.629 and pAUC as 0.139 and AUC as 0.921 for the independent test protein set-1 and SN as 0.772, SP as 0.901, ACC as 0.836, MCC as 0.677 and pAUC as 0.141 at FPR = 0.10 and AUC as 0.923 for the independent test protein set-2. It also outperformed all the other existing prediction models. The prediction performances as discussed in this article recommend that the proposed method might be a useful and encouraging computational resource for lysine succinylation site prediction in the case of human population.
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影响因子：
5.8
作者：
Hasan, Md. Mehedi;Schaduangrat, Nalini;Manavalan, Balachandran
通讯作者：
Manavalan, Balachandran
影响因子：
4.4
作者：
Charoenkwan, Phasit;Yana, Janchai;Shoombuatong, Watshara
通讯作者：
Shoombuatong, Watshara
影响因子：
--
作者：
Hasan, Md. Mehedi;Yang, Shiping;Mollah, Md. Nurul Haque
通讯作者：
Mollah, Md. Nurul Haque
影响因子：
1.6
作者：
Islam, Md Merajul;Alam, Md Jahangir;Mollah, Md Nurul Haque
通讯作者：
Mollah, Md Nurul Haque
番茄（Solanum lycopersicum）蛋白质中赖氨酸琥珀酰化的全蛋白质组鉴定
影响因子：
3.7
作者：
Jin W;Wu F
通讯作者：
Wu F