iAcet-Sumo: Identification of lysine acetylation and sumoylation sites in proteins by multi-class transformation methods

iSUMO-RsFPN: A predictor for identifying lysine SUMOylation sites based on multi-features and feature pyramid networks

SUMOylation is a protein post-translational modification that plays an essential role in cellular functions. For predicting SUMO sites, numerous researchers have proposed advanced methods based on ordinary machine learning algorithms. These reported methods have shown excellent predictive performance, but there is room for improvement. In this study, we constructed a novel deep neural network Residual Pyramid Network (RsFPN), and developed an ensemble deep learning predictor called iSUMO-RsFPN. Initially, three feature extraction methods were employed to extract features from samples. Following this, weak classifiers were trained based on RsFPN for each feature type. Ultimately, the weak classifiers were integrated to construct the final classifier. Moreover, the predictor underwent systematically testing on an independent test dataset, where the results demonstrated a significant improvement over the existing state-of-the-art predictors. The code of iSUMO-RsFPN is free and available at https://github.com/454170054/iSUMO-RsFPN.

Multi-omics approach to identifying isoform variants as therapeutic targets in cancer patients

Cancer-specific alternatively spliced events (ASE) play a role in cancer pathogenesis and can be targeted by immunotherapy, oligonucleotide therapy, and small molecule inhibition. However, identifying actionable ASE targets remains challenging due to the uncertainty of its protein product, structure impact, and proteoform (protein isoform) function. Here we argue that an integrated multi-omics profiling strategy can overcome these challenges, allowing us to mine this untapped source of targets for therapeutic development. In this review, we will provide an overview of current multi-omics strategies in characterizing ASEs by utilizing the transcriptome, proteome, and state-of-art algorithms for protein structure prediction. We will discuss limitations and knowledge gaps associated with each technology and informatics analytics. Finally, we will discuss future directions that will enable the full integration of multi-omics data for ASE target discovery.

Thirty years of molecular dynamics simulations on posttranslational modifications of proteins

Posttranslational modifications (PTMs) are an integral component to how cells respond to perturbation. While experimental advances have enabled improved PTM identification capabilities, the same throughput for characterizing how structural changes caused by PTMs equate to altered physiological function has not been maintained. In this Perspective, we cover the history of computational modeling and molecular dynamics simulations which have characterized the structural implications of PTMs. We distinguish results from different molecular dynamics studies based upon the timescales simulated and analysis approaches used for PTM characterization. Lastly, we offer insights into how opportunities for modern research efforts on in silico PTM characterization may proceed given current state-of-the-art computing capabilities and methodological advancements.

ResSUMO: A Deep Learning Architecture Based on Residual Structure for Prediction of Lysine SUMOylation Sites

Lysine SUMOylation plays an essential role in various biological functions. Several approaches integrating various algorithms have been developed for predicting SUMOylation sites based on a limited dataset. Recently, the number of identified SUMOylation sites has significantly increased due to investigation at the proteomics scale. We collected modification data and found the reported approaches had poor performance using our collected data. Therefore, it is essential to explore the characteristics of this modification and construct prediction models with improved performance based on an enlarged dataset. In this study, we constructed and compared 16 classifiers by integrating four different algorithms and four encoding features selected from 11 sequence-based or physicochemical features. We found that the convolution neural network (CNN) model integrated with residue structure, dubbed ResSUMO, performed favorably when compared with the traditional machine learning and CNN models in both cross-validation and independent tests. The area under the receiver operating characteristic (ROC) curve for ResSUMO was around 0.80, superior to that of the reported predictors. We also found that increasing the depth of neural networks in the CNN models did not improve prediction performance due to the degradation problem, but the residual structure could be included to optimize the neural networks and improve performance. This indicates that residual neural networks have the potential to be broadly applied in the prediction of other types of modification sites with great effectiveness and robustness. Furthermore, the online ResSUMO service is freely accessible.

predForm-Site: Formylation site prediction by incorporating multiple features and resolving data imbalance

Formylation is one of the newly discovered post-translational modifications in lysine residue which is responsible for different kinds of diseases. In this work, a novel predictor, named predForm-Site, has been developed to predict formylation sites with higher accuracy. We have integrated multiple sequence features for developing a more informative representation of formylation sites. Moreover, decision function of the underlying classifier have been optimized on skewed formylation dataset during prediction model training for prediction quality improvement. On the dataset used by LFPred and Formator predictor, predForm-Site achieved 99.5% sensitivity, 99.8% specificity and 99.8% overall accuracy with AUC of 0.999 in the jackknife test. In the independent test, it has also achieved more than 97% sensitivity and 99% specificity. Similarly, in benchmarking with recent method CKSAAP_FormSite, the proposed predictor significantly outperformed in all the measures, particularly sensitivity by around 20%, specificity by nearly 30% and overall accuracy by more than 22%. These experimental results show that the proposed predForm-Site can be used as a complementary tool for the fast exploration of formylation sites. For convenience of the scientific community, predForm-Site has been deployed as an online tool, accessible at http://103.99.176.239:8080/predForm-Site.

Prediction of bio-sequence modifications and the associations with diseases

Modifications of protein, RNA and DNA play an important role in many biological processes and are related to some diseases. Therefore, accurate identification and comprehensive understanding of protein, RNA and DNA modification sites can promote research on disease treatment and prevention. With the development of sequencing technology, the number of known sequences has continued to increase. In the past decade, many computational tools that can be used to predict protein, RNA and DNA modification sites have been developed. In this review, we comprehensively summarized the modification site predictors for three different biological sequences and the association with diseases. The relevant web server is accessible at http://lab.malab.cn/∼acy/PTM_data/ some sample data on protein, RNA and DNA modification can be downloaded from that website.

Research progress in protein posttranslational modification site prediction

Posttranslational modifications (PTMs) play an important role in regulating protein folding, activity and function and are involved in almost all cellular processes. Identification of PTMs of proteins is the basis for elucidating the mechanisms of cell biology and disease treatments. Compared with the laboriousness of equivalent experimental work, PTM prediction using various machine-learning methods can provide accurate, simple and rapid research solutions and generate valuable information for further laboratory studies. In this review, we manually curate most of the bioinformatics tools published since 2008. We also summarize the approaches for predicting ubiquitination sites and glycosylation sites. Moreover, we discuss the challenges of current PTM bioinformatics tools and look forward to future research possibilities.