Improving succinylation prediction accuracy by incorporating the secondary structure via helix, strand and coil, and evolutionary information from profile bigrams

Dehzangi, Abdollah; López, Yosvany; Lal, Sunder; Taherzadeh, Ghazaleh; Sattar, Abdul; Tsunoda, Tatsuhiko; Sharma, Alok

doi:10.1371/journal.pone.0191900

Cited by 54 publications

(56 citation statements)

References 86 publications

(107 reference statements)

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“…These are helix (ph), strand (pe) and coil (pc) motifs. Information from the secondary structure can contribute constructively to the general three-dimensional configuration of the polypeptide and the affinity for PTM of lysine residues [54,57]. Given a protein sequence, SPIDER2 produces a L × 3 matrix containing the predicted secondary structure, which we call SSpre.…”

Section: Feature Extractionmentioning

confidence: 99%

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GlyStruct: glycation prediction using structural properties of amino acid residues

et al. 2019

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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.

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Section: Feature Extractionmentioning

confidence: 99%

“…Protein sequences are of varying lengths and cannot be used directly in classification. Classifiers require dataset of fixed length [61] therefore we employed a widely used method of truncating the protein sequence into fixed length peptide segments [54,57,[62][63][64][65][66] proposed by Chou [67,68].…”

Section: Feature Vector Constructionmentioning

confidence: 99%

GlyStruct: glycation prediction using structural properties of amino acid residues

et al. 2019

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“…For this reason, dealing with class imbalance is a very crucial action in classification problems. To carry out the imbalance treatment, we utilized the commonly used scheme called the k-nearest neighbor strategy [26,28,32,55,63] where we removed a negative instance when one of its k neighbors was a positive instance. We started out the process by finding the initial value of k by dividing the number of samples in the negative set with the number of samples in the positive set.…”

Section: Data Imbalance Treatmentmentioning

confidence: 99%

“…The feature vector, therefore, consisted of 3 upstream and 3 downstream and 20 upstream and 20 downstream amino acid residues for the two different characteristics corresponding to phosphoglycerylated and non-phosphoglycerylated sites. In the benchmark dataset, there existed a high class imbalance between non-phosphoglycerylated and phosphoglycerylated lysine residues hence we adopted the k-nearest neighbors strategy to carry out the cleaning action [26,32,33]. EvolStruct-Phogly showed a substantial improvement in the detection of phosphoglycerylated and non-phosphoglycerylated residues when compared with the existing predictors [12,29] with sensitivity, specificity, precision, accuracy, and Mathews correlation coefficient equal to 0.7744, 0.8533, 0.7368, 0.8275 and 0.6242, respectively.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2019

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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.

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“…Lysine residue (sumoylated or non-sumoylated) is described by a segment of 31 amino acids (15 upstream and 15 downstream) as done by previous studies [45][46][47][48][49][50][51].…”

Section: Lysine Residue Descriptionmentioning

confidence: 99%

HseSUMO: Sumoylation site prediction using half-sphere exposures of amino acids residues

et al. 2019

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Background: Post-translational modifications are viewed as an important mechanism for controlling protein function and are believed to be involved in multiple important diseases. However, their profiling using laboratorybased techniques remain challenging. Therefore, making the development of accurate computational methods to predict post-translational modifications is particularly important for making progress in this area of research. Results: This work explores the use of four half-sphere exposure-based features for computational prediction of sumoylation sites. Unlike most of the previously proposed approaches, which focused on patterns of amino acid co-occurrence, we were able to demonstrate that protein structural based features could be sufficiently informative to achieve good predictive performance. The evaluation of our method has demonstrated high sensitivity (0.9), accuracy (0.89) and Matthew's correlation coefficient (0.78-0.79). We have compared these results to the recently released pSumo-CD method and were able to demonstrate better performance of our method on the same evaluation dataset. Conclusions: The proposed predictor HseSUMO uses half-sphere exposures of amino acids to predict sumoylation sites. It has shown promising results on a benchmark dataset when compared with the state-of-the-art method. The extracted data of this study can be accessed at https://github.com/YosvanyLopez/HseSUMO.

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Improving succinylation prediction accuracy by incorporating the secondary structure via helix, strand and coil, and evolutionary information from profile bigrams

Cited by 54 publications

References 86 publications

GlyStruct: glycation prediction using structural properties of amino acid residues

GlyStruct: glycation prediction using structural properties of amino acid residues

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

HseSUMO: Sumoylation site prediction using half-sphere exposures of amino acids residues

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