ArticleBioMed research international2014
iMethyl-PseAAC: identification of protein methylation sites via a pseudo amino acid composition approach.
Article in BioMed research international, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 41 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
41 citing papers in PubMed, 192 citations in OpenAlex.
- Leveraging learned representations and multitask learning for lysine methylation site discovery.Scientific reports · 2026Article
- Leveraging learned representations and multitask learning for lysine methylation site discovery.bioRxiv : the preprint server for biology · 2025Article
- Targeting the YXXΦ Motifs of the SARS Coronaviruses 1 and 2 ORF3a Peptides by In Silico Analysis to Predict Novel Virus-Host Interactions.Biomolecules · 2022Article
- CNNArginineMe: A CNN structure for training models for predicting arginine methylation sites based on the One-Hot encoding of peptide sequence.Frontiers in genetics · 2022Article
- Calmodulin7: recent insights into emerging roles in plant development and stress.Plant molecular biology · 2021Review
- Article
- SSMFN: a fused spatial and sequential deep learning model for methylation site prediction.PeerJ. Computer science · 2021Article
- DeepRMethylSite: a deep learning based approach for prediction of arginine methylation sites in proteins.Molecular omics · 2020Article
- Mal-Light: Enhancing Lysine Malonylation Sites Prediction Problem Using Evolutionary-based Features.IEEE access : practical innovations, open solutions · 2020Article
- Large-scale comparative assessment of computational predictors for lysine post-translational modification sites.Briefings in bioinformatics · 2019Review
- iMethylK_pseAAC: Improving Accuracy of Lysine Methylation Sites Identification by Incorporating Statistical Moments and Position Relative Features into General PseAACCurrent genomics · 2019Article
- iSulfoTyr-PseAAC: Identify Tyrosine Sulfation Sites by Incorporating Statistical MomentsCurrent genomics · 2019Article
- Article
- RAACBook: a web server of reduced amino acid alphabet for sequence-dependent inference by using Chou's five-step rule.Database : the journal of biological databases and curation · 2019Article
- An Information Entropy-Based Approach for Computationally Identifying Histone Lysine Butyrylation.Frontiers in genetics · 2019Article
- LAIPT: Lysine Acetylation Site Identification with Polynomial Tree.International journal of molecular sciences · 2018Article
- iPhosY-PseAAC: identify phosphotyrosine sites by incorporating sequence statistical moments into PseAAC.Molecular biology reports · 2018Article
- Detecting Succinylation sites from protein sequences using ensemble support vector machine.BMC bioinformatics · 2018Article
- Success: evolutionary and structural properties of amino acids prove effective for succinylation site prediction.BMC genomics · 2018Article
- Article
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Before becoming the native proteins during the biosynthesis, their polypeptide chains created by ribosome's translating mRNA will undergo a series of "product-forming" steps, such as cutting, folding, and posttranslational modification (PTM). Knowledge of PTMs in proteins is crucial for dynamic proteome analysis of various human diseases and epigenetic inheritance. One of the most important PTMs is the Arg- or Lys-methylation that occurs on arginine or lysine, respectively. Given a protein, which site of its Arg (or Lys) can be methylated, and which site cannot? This is the first important problem for understanding the methylation mechanism and drug development in depth. With the avalanche of protein sequences generated in the postgenomic age, its urgency has become self-evident. To address this problem, we proposed a new predictor, called iMethyl-PseAAC. In the prediction system, a peptide sample was formulated by a 346-dimensional vector, formed by incorporating its physicochemical, sequence evolution, biochemical, and structural disorder information into the general form of pseudo amino acid composition. It was observed by the rigorous jackknife test and independent dataset test that iMethyl-PseAAC was superior to any of the existing predictors in this area.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.