Investigating the Precise Identification of Citrullination Sites with High- Performance Score Metrics Using a Powerful Computation Predicting Tool.

Journal: Combinatorial chemistry & high throughput screening
PMID: 37702240

Abstract

BACKGROUND: To elucidate the detailed mechanisms of citrullination at the molecular level and design drugs applicable to major human diseases, predicting protein citrullination sites (PCSs) is essential. Using experimental approaches to predict PCSs is time-consuming and costly. However, there is a limited scope of the current PCS predictors. In particular, most predictors are commonly used for PCS prediction and have limited performance scores.

Authors

  • Fee Faysal Ahmed
    Department of Mathematics, Jashore University of Science and Technology, Jashore, Bangladesh.
  • Anamika Podder
    Department of Mathematics, Jashore University of Science and Technology, Jashore, 7408, Bangladesh.
  • Md Farhad Bulbul
    Department of Mathematics, Jashore University of Science and Technology, Jashore, 7408, Bangladesh.
  • Md Amzad Hossain
    United Graduate School of Agricultural Sciences, Kagoshima University, Kagoshima, 890-0065, Japan.
  • Mahedi Hasan
    Department of Computer Science and Engineering, Jashore University of Science and Technology, Jashore, 7408, Bangladesh.
  • Md Abdur Rauf Sarkar
    Department of Genetic Engineering and Biotechnology, Jashore University of Science and Technology, Jashore 7408, Bangladesh.
  • Daijin Kim
    Department of Computer Science and Engineering, Pohang University of Science and Technology (POSTECH), 77 Cheongam, Pohang 37673, Korea.

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