EvolStruct-Phogly: incorporating structural properties and evolutionary information from profile bigrams for the phosphoglycerylation prediction

BMC Genomics

Abdollah Dehzangi¹  Tatushiko Tsunoda²  Abel Avitesh Chandra³  Alok Sharma³ 

[1] Department of Computer Science, Morgan State University;Laboratory for Medical Science Mathematics, RIKEN Center for Integrative Medical Sciences;School of Engineering & Physics, University of the South Pacific;

关键词: Post-translational modification;    Protein sequence;    Amino acids;    Lysine;    Phosphoglycerylation;    Non-phosphoglycerylation;   

DOI  :  10.1186/s12864-018-5383-5

来源: DOAJ

【 摘 要 】

Abstract Background Post-translational modification (PTM), which is a biological process, tends to modify proteome that leads to changes in normal cell biology and pathogenesis. In the recent times, there has been many reported PTMs. Out of the many modifications, phosphoglycerylation has become particularly the subject of interest. The experimental procedure for identification of phosphoglycerylated residues continues to be an expensive, inefficient and time-consuming effort, even with a large number of proteins that are sequenced in the post-genomic period. Computational methods are therefore being anticipated in order to effectively predict phosphoglycerylated lysines. Even though there are predictors available, the ability to detect phosphoglycerylated lysine residues still remains inadequate. Results We have introduced a new predictor in this paper named EvolStruct-Phogly that uses structural and evolutionary information relating to amino acids to predict phosphoglycerylated lysine residues. Benchmarked data is employed containing experimentally identified phosphoglycerylated and non-phosphoglycerylated lysines. We have then extracted the three structural information which are accessible surface area of amino acids, backbone torsion angles, amino acid’s local structure conformations and profile bigrams of position-specific scoring matrices. Conclusion EvolStruct-Phogly showed a noteworthy improvement in regards to the performance when compared with the previous predictors. The performance metrics obtained are as follows: sensitivity 0.7744, specificity 0.8533, precision 0.7368, accuracy 0.8275, and Mathews correlation coefficient of 0.6242. The software package and data of this work can be obtained from https://github.com/abelavit/EvolStruct-Phogly or www.alok-ai-lab.com

【 授权许可】

Unknown