GlyStruct: glycation prediction using structural properties of amino acid residues

Abstract

Background: Glycation is a one of the post-translational modifications (PTM) where sugar molecules and residues in protein sequences are covalently bonded. It has become one of the clinically important PTM in recent times attributed to many chronic and age related complications. Being a non-enzymatic reaction, it is a great challenge when it comes to its prediction due to the lack of significant bias in the sequence motifs. Results: We developed a classifier, GlyStruct based on support vector machine, to predict glycated and non-glycated lysine residues using structural properties of amino acid residues. The features used were secondary structure, accessible surface area and the local backbone torsion angles. For this work, a benchmark dataset was extracted containing 235 glycated and 303 non-glycated lysine residues. GlyStruct demonstrated improved performance of approximately 10% in comparison to benchmark method of Gly-PseAAC. The performance for GlyStruct on the metrics, sensitivity, specificity, accuracy and Mathew's correlation coefficient were 0.7013, 0.7989, 0.7562, and 0.5065, respectively for 10-fold crossvalidation.Conclusion: Glycation has emerged to be one of the clinically important PTM of proteins in recent times. Therefore, the development of computational tools become necessary to predict glycation, which could help medical professionals administer drugs and manage patients more effectively. The proposed predictor manages to classify glycated and nonglycated lysine residues with promising results consistently on various cross-validation schemes and outperforms other state of the art methods.

Machine Learning Enables Accurate Prediction of Asparagine Deamidation Probability and Rate

“…Computational tools already exist to facilitate drug candidate screening by the identification of sequence liabilities 6 , 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45. In the case of asparagine deamidation, 36 , 40, 41, 42, 43, 44, 45 currently available tools suffer from several limitations: they provide only a binary (yes, high risk to deamidate; or no, low risk to deamidate) prediction, 36 , 40 , 42 , 43 require an experimental crystal structure,42, 43, 44, 45 or are applicable only to antibody asparagines 36 , 40 .…”

PupStruct: Prediction of Pupylated Lysine Residues Using Structural Properties of Amino Acids

“…To achieve this, we employed the toolbox SPIDER2 [48,49] which has previously obtained good outcomes for prediction using accessible surface area [50][51][52][53], secondary structure [54][55][56][57], and backbone torsion angles [50,[58][59][60][61]. SPIDER2 has also been used to extract the structural properties for other predictions [12,18,[61][62][63][64]. The details for these structural properties are explained in the succeeding sections.…”

APBioNet's annual International Conference on Bioinformatics (InCoB) returns to India in 2018

“…Several novel methods for predicting sites involved in sumoylation [ 11 ], phosphoglyceration [ 12 ], glycation [ 13 ] and lysine glutarylation [ 14 ] from sequence data add valuable approaches to functional annotation of proteins. Since protein function is derived from structure, the functional characterization of intrinsically disordered proteins has remained difficult.…”