iCrotoK-PseAAC: Identify lysine crotonylation sites by blending position relative statistical features according to the Chou’s 5-step rule

English version

Autoři: Sharaf Jameel Malebary ^aff001; Muhammad Safi ur Rehman ^aff002; Yaser Daanial Khan ^aff002
Působiště autorů: Department of Information Technology, King Abdul Aziz University, Rabigh, Kingdom of Saudi Arabia ^aff001; Department of Computer Science, School of Systems and Technology, University of Management and Technology, Lahore, Pakistan ^aff002
Vyšlo v časopise: PLoS ONE 14(11)
Kategorie: Research Article
doi: https://doi.org/10.1371/journal.pone.0223993

Souhrn

Among different post-translational modifications (PTMs), one of the most important one is the lysine crotonylation in proteins. Its importance cannot be undermined related to different diseases and essential biological practice. The key step for finding the hidden mechanisms of crotonylation along with their occurrence sites is to completely apprehend the mechanism behind this biological process. In previously reported studies, researchers have used different techniques, like position weighted matrix (PWM), support vector machine (SVM), k nearest neighbors (KNN), and many others. However, the maximum prediction accuracy achieved was not such high. To address this, herein, we propose an improved predictor for lysine crotonylation sites named iCrotoK-PseAAC, in which we have incorporated various position and composition relative features along with statistical moments into PseAAC. The results of self-consistency testing were 100% accurate, while the 10-fold cross validation gave 99.0% accuracy. Based on the validation and comparison of model, it is concluded that the iCrotoK-PseAAC is more accurate than the previously proposed models.

Klíčová slova:

Artificial neural networks – Database searching – Lysine – Post-translational modification – Protein sequencing – Sequence databases – Support vector machines

Zdroje

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iCrotoK-PseAAC: Identify lysine crotonylation sites by blending position relative statistical features according to the Chou’s 5-step rule

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