Unlock your biological data


Try: RNA sequencing CRISPR Genomic databases DESeq

1 - 17 of 17 results
filter_list Filters
language Programming Language
settings_input_component Operating System
tv Interface
computer Computer Skill
copyright License
1 - 17 of 17 results
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.
Identifies multiple lysine post-translational modification (PTM) sites and their different types. iPTM-mLys represents the first multi-label PTM predictor ever established. The novel predictor is featured by incorporating the sequence-coupled effects into the general PseAAC, and by fusing an array of basic random forest classifiers into an ensemble system. Rigorous cross-validations via a set of multi-label metrics indicate that the first multi-label PTM predictor is very promising and encouraging.
A homology-based pipeline that allows identification of potential modification sites for most of the proteomes lacking post-translational modifications (PTMs) data. PTMProber provides a unique functionality for constructing customized models (such as organism-specific and modification-specific models) from user-provided data sets. Cross-promotion E-value (CPE) as stringent benchmark has been used in PTMProber to evaluate homology to known modification sites. Independent-validation tests show that PTMProber achieves over 58.8% recall with high precision by CPE benchmark. Comparisons with other machine-learning tools show that PTMProber pipeline performs better on general predictions.
A position-specific method for lysine acetylation prediction based on support vector machines. The residues around the acetylation sites were selected or excluded based on their entropy values. We incorporated features of amino acid composition information, evolutionary similarity and physicochemical properties to predict lysine acetylation sites. The prediction model achieved an accuracy of 79.84% and a Matthews correlation coefficient of 59.72% using the 10-fold cross-validation on balanced positive and negative samples.
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.
KA-predictor / lysine (K) Acetylation predictor
Predicts species-specific lysine acetylation sites based on support vector machine (SVM) classifier. KA-predictor was designed for four species, H. sapiens, M. musculus, E. coli, and S. typhimurium. It employs an efficient feature selection on each type to form the final optimal feature set for model learning. The results indicates that the predictor is highly competitive for the majority of species when compared with other existing methods.
PAIL / Prediction of Acetylation on Internal Lysines
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.
A plugin implemented with the commonly used visualization software PyMOL. PyTMs enables users to introduce a set of common post-translational modifications (PTMs) into protein/peptide models and can be used to address research questions related to PTMs. Ten types of modification are currently supported, including acetylation, carbamylation, citrullination, cysteine oxidation, malondialdehyde adducts, methionine oxidation, methylation, nitration, proline hydroxylation and phosphorylation.
EnCOUNTer / Extraction and Calculation Of Unbiased N-Termini
Scores all characterized peptides using discriminating parameters to identify bona fide mature protein N-termini. EnCOUNTer determines the N-terminus acetylation yield of the most reliable ones. It provides a unique way to extract accurately the most relevant mature proteins N-terminal peptides from large scale experimental datasets. The tool can help researchers to determine accurately and rapidly the influence of various stresses on protein N-terminal status and N-terminal modification yield.
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.
BRABSB-PHKA / Bi-Relative Binomial Score Bayes- Prediction of potential Human lysine (K) Acetylation
An in silico online tool for prediction of potential human lysine (K) acetylation (PHKA) sites from protein sequences. The computational methodology is based on bi-relative binomial score Bayes (BRBSB) combined with support vector machines (SVMs). BRBSB-PHKA yields, on average, a sensitivity of 83.91%, a specificity of 87.25% and an accuracy of 85.58% in the case of 5-fold cross validation, together with the results on independent test data sets, suggesting that BRBSB-PHKA presented here can facilitate the identification of human lysine acetylation sites and more confident annotation.
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.
0 - 0 of 0 results
1 - 1 of 1 result

By using OMICtools you acknowledge that you have read and accepted the terms of the end user license agreement.