MLysPRED: graph-based multi-view clustering and multi-dimensional normal distribution resampling techniques to predict multiple lysine sites

PreMLS: The undersampling technique based on ClusterCentroids to predict multiple lysine sites

The translated protein undergoes a specific modification process, which involves the formation of covalent bonds on lysine residues and the attachment of small chemical moieties. The protein's fundamental physicochemical properties undergo a significant alteration. The change significantly alters the proteins' 3D structure and activity, enabling them to modulate key physiological processes. The modulation encompasses inhibiting cancer cell growth, delaying ovarian aging, regulating metabolic diseases, and ameliorating depression. Consequently, the identification and comprehension of post-translational lysine modifications hold substantial value in the realms of biological research and drug development. Post-translational modifications (PTMs) at lysine (K) sites are among the most common protein modifications. However, research on K-PTMs has been largely centered on identifying individual modification types, with a relative scarcity of balanced data analysis techniques. In this study, a classification system is developed for the prediction of concurrent multiple modifications at a single lysine residue. Initially, a well-established multi-label position-specific triad amino acid propensity algorithm is utilized for feature encoding. Subsequently, PreMLS: a novel ClusterCentroids undersampling algorithm based on MiniBatchKmeans was introduced to eliminate redundant or similar major class samples, thereby mitigating the issue of class imbalance. A convolutional neural network architecture was specifically constructed for the analysis of biological sequences to predict multiple lysine modification sites. The model, evaluated through five-fold cross-validation and independent testing, was found to significantly outperform existing models such as iMul-kSite and predML-Site. The results presented here aid in prioritizing potential lysine modification sites, facilitating subsequent biological assays and advancing pharmaceutical research. To enhance accessibility, an open-access predictive script has been crafted for the multi-label predictive model developed in this study.

RMTLysPTM: recognizing multiple types of lysine PTM sites by deep analysis on sequences

Post-translational modification (PTM) occurs after a protein is translated from ribonucleic acid. It is an important living creature life phenomenon because it is implicated in almost all cellular processes. Identification of PTM sites from a given protein sequence is a hot topic in bioinformatics. Lots of computational methods have been proposed, and they provide good performance. However, most previous methods can only tackle one PTM type. Few methods consider multiple PTM types. In this study, a multi-label classification model, named RMTLysPTM, was developed to recognize four types of lysine (K) PTM sites, including acetylation, crotonylation, methylation and succinylation. The surrounding sites of a lysine site were selected to constitute a peptide segment, representing the lysine at the center. Deep analysis was conducted to count the distribution of 2-residues with fixed location across the four types of lysine PTM sites. By aggregating the distribution information of 2-residues in one peptide segment, the peptide segment was encoded by informative features. Furthermore, a prediction engine that can precisely capture the traits of the above representations was designed to recognize the types of lysine PTM sites. The cross-validation results on two datasets (Qiu and CPLM training datasets) suggested that the model had extremely high performance and RMTLysPTM had strong generalization ability by testing it on protein Q16778 and CPLM testing datasets. The model was found to be generally superior to all previous models and those using popular methods and features. A web server was set up for RMTLysPTM, and it can be accessed at http://119.3.127.138/.

GR-m6A: Prediction of N6-methyladenosine sites in mammals with molecular graph and residual network

RNA N6-methyladenine (m6A), which is produced by the methylation of the N6 position of eukaryotic adenine, is a relatively common post-transcriptional modification on the surface of the molecule, which frequently plays a crucial role in biological processes. Biological experimental methods to identify m6A have been studied and implemented in recent years, but they cannot be promoted widely due to drawbacks such as the time and cost of reagents and equipment. Therefore, researchers have proposed computational strategies for identifying m6A sites, but these strategies do not account for the mechanism of methylation occurrence or the structure of RNA molecules. This study, therefore, proposed a novel deep learning model for predicting m6A sites, GR-m6A, which predicts m6A sites by extracting features from the physicochemical properties and spatial structure of molecules via residual networks. In GR-m6A, each RNA base string is represented by SMILES as two matrices comprising topology structural information and node attributes with molecular physicochemical characteristics. The feature encoding matrix was then obtained by fusing the topology matrix and the node matrix in accordance with the graphical convolutional network principle. Correspondingly, the more discriminative features were extracted from the encoding matrix using the residual neural network and predicted using a multilayer perceptron. As evident from the 5-fold cross-validation and independent validation, the GR-m6A model outperformed other existing methods. Thus, we hope that GR-m6A can aid researchers in predicting mammalian m6A loci. The source code and database are available at https://github.com/YingLiangjxau/GR-m6A.