mLysPTMpred: Multiple Lysine PTM Site Prediction Using Combination of SVM with Resolving Data Imbalance Issue

Abstract

Post-translational modification (PTM) increases the functional diversity of proteins by introducing new functional groups to the side chain of amino acid of a protein. Among all amino acid residues, the side chain of lysine (K) can undergo many types of PTM, called K-PTM, such as "acetylation", "crotonylation", "methylation" and "succinylation" and also responsible for occurring multiple PTM in the same lysine of a protein which leads to the requirement of multi-label PTM site identification. However, most of the existing computational methods have been established to predict various single-label PTM sites and a very few have been developed to solve multi-label issue which needs further improvement. Here, we have developed a computational tool termed mLysPTMpred to predict multi-label lysine PTM sites by 1) incorporating the sequence-coupled information into the general pseudo amino acid composition, 2) balancing the effect of skewed training dataset by Different Error Cost method, and 3) constructing a multi-label predictor using a combination of support vector machine (SVM). This predictor achieved 83.73% accuracy in predicting the multi-label PTM site of K-PTM types. Moreover, all the experimental results along with accuracy outperformed than the existing predictor iPTM-mLys. A user-friendly web server of mLysPTMpred is available at http://research.ru.ac.bd/mLysPTMpred/.

predPhogly-Site: Predicting phosphoglycerylation sites by incorporating probabilistic sequence-coupling information into PseAAC and addressing data imbalance

“…To formulate the phosphoglycerylation site sequences more meticulously and comprehensively, Chou’s scheme [ 9 , 13 , 33 ] was adopted. According to this scheme, a potential phosphoglycerylation site containing sequence fragment could be expressed as: Where Q 1 to Q ζ denote the leftward and Q ζ +1 to Q 2 ζ +1 denote the rightward amino acid residues, respectively, while ζ being an integer and centered ‘K’ indicating “lysine” [ 14 ]. Furthermore, the peptide sequences Θ ζ ( K ) can be categorized into two types: and , where the first one denotes phosphoglycerylated peptide and the later one denotes non-phosphoglycerylated peptide with a lysine residue at its center [ 9 , 14 ].…”

“…According to this scheme, a potential phosphoglycerylation site containing sequence fragment could be expressed as: Where Q 1 to Q ζ denote the leftward and Q ζ +1 to Q 2 ζ +1 denote the rightward amino acid residues, respectively, while ζ being an integer and centered ‘K’ indicating “lysine” [ 14 ]. Furthermore, the peptide sequences Θ ζ ( K ) can be categorized into two types: and , where the first one denotes phosphoglycerylated peptide and the later one denotes non-phosphoglycerylated peptide with a lysine residue at its center [ 9 , 14 ]. The sliding window method [ 9 ] was adopted to segment the phosphoglycerylation protein sequences with different window size where ζ = 1, 2, 3, …32.…”

“…For extracting pertinent features hidden in amino acid sequences, different sequence encoding methods such as amino acid composition, pseudo amino acid composition were used initially. However, in the proposed predictor predPhogly-Site, the vectorized sequence-coupled model [ 3 , 14 – 16 , 35 ] has been incorporated into general PseAAC [ 3 , 14 , 33 , 35 – 39 ] to extract features from the phosphoglycerylation sites conserving the sequence pattern information. According to this conception, the peptide sample in Eq (2) can be expressed as: where, where, Θ + ( Q 1 | Q 2 ) denotes the conditional probability of amino acid Q 1 at the leftmost position given that its adjacent right member is Q 2 and the same applies for remaining indices of leftward residues [ 24 ].…”

“…In this study, we are introducing a novel computational tool predPhogly-Site for predicting phosphoglycerylation sites by blending vectorized sequence coupling information with PseAAC [ 3 , 13 – 16 ]. After generating necessary features from the protein sequences adopted from Bigram-PGK [ 11 ], a cost-sensitive SVM [ 14 , 17 – 19 ] classifier has been used to predict phosphoglycerylation sites by minimizing class-level imbalance in benchmark dataset. The workflow of our proposed predictor is shown in Fig 1 .…”

“…Highlighted in a series of recently published predictors [ 3 , 6 , 14 , 19 – 23 ], to develop an efficient predictor with regards to computational biology, one should go through Chou’s five-step [ 14 , 24 , 25 ] guidelines: i) generating an acceptable benchmark dataset for training and testing the system, ii) formulating the sequences using proper mathematical representations, iii) developing a prediction approach or introducing a robust prediction algorithm, iv) conducting rigorous cross-validation tests to evaluate predictive accuracy, and v) providing an accessible and easy-to-use web-server. Following these steps, details of materials, methods, results, and analysis will be discussed in the following sections.…”