Lysine acetylation site detection software tools | Post-translational modification data analysis
Lysine acetylation is a crucial type of protein post-translational modification, which is involved in many important cellular processes and serious diseases. However, identification of protein acetylated sites through traditional experiment methods is time-consuming and laborious. Those methods are not suitable to identify a large number of acetylated sites quickly. Therefore, computational methods are still very valuable to accelerate lysine acetylated site finding.
A web tool for predicting the protein Acetylation site based on support vector machine (SVM), which is training depend on the amino acid sequence and other structural characteristics, such as accessible surface area, absolute entropy, non-bonded energy, size, amino acid composition, steric parameter, hydrophobicity, volume, mean polarity, electric charge, heat capacity and isoelectric point which is surrounding the modification site and implemented two stages SVM method. N-Ace not only provides a user-friendly input/output interface but also is a creative method for predicting protein acetylation sites.
A neural network based method for prediction of N-terminal acetylation-by far the most abundant post-translational modification in eukaryotes. The method was developed on a yeast dataset for N-acetyltransferase A (NatA) acetylation, which is the type of N-acetylation for which most examples are known and for which orthologs have been found in several eukaryotes. We obtain correlation coefficients close to 0.7 on yeast data and a sensitivity up to 74% on mammalian data, suggesting that the method is valid for eukaryotic NatA orthologs.
A protein acetylation prediction program implemented in a BDM (Bayesian discriminant method) algorithm. The accuracies of PAIL are 85.13%, 87.97%, and 89.21% at low, medium, and high thresholds, respectively. Both Jack-Knife validation and n-fold cross-validation have been performed to show that PAIL is accurate and robust. Taken together, we propose that PAIL is a novel predictor for identification of protein acetylation sites and may serve as an important tool to study the function of protein acetylation.
Allows prediction of the acetylation state, the responsible the lysine (K)-acetyl-transferase (KAT) family and silent mating type information regulator 2 homolog 1 (SIRT1) substrates. ASEB is a web-server that can be useful for scientists in the acetylation field. The efficacy of the software was validated via independent methods, including biological experiments.
A lysine acetylation site prediction system based on a logistic regression model. In practice, the amino acid sequence of the acetylated sites, the physicochemical property of the amino acids and the transition probability of adjacent amino acids were utilized as features of LAceP. When compared with existing methods, LAceP overwhelms most of state-of-the-art methods. Especially, LAceP has a more balanced prediction capability for positive and negative datasets.
A lysine acetylation prediction algorithm. When compared with other methods or existing tools, LysAcet is the best predictor of lysine acetylation, with K-fold (5- and 10-) and jackknife cross-validation accuracies of 75.89%, 76.73%, and 77.16%, respectively. LysAcet's superior predictive accuracy is attributed primarily to the use of sequence coupling patterns, which describe the relative position of two amino acids. LysAcet contributes to the limited PTM prediction research on lysine epsilon-acetylation, and may serve as a complementary in-silicon approach for exploring acetylation on proteomes.
Predicts lysine methylation and lysine acetylation only from the protein primary sequence. PLMLA is an algorithm implemented in a web app that consider not only protein sequence information but also physicochemical properties of amino acids and residue secondary structure within the lysine regions. The prediction model achieved a promising performance and outperformed other prediction tools. This resource could help to understand the lysine methylation and acetylation mechanism and guide the related experimental validation.