CapsNh-Kcr: Capsule network-based prediction of lysine crotonylation sites in human non-histone proteins

Current computational tools for protein lysine acylation site prediction

As a main subtype of post-translational modification (PTM), protein lysine acylations (PLAs) play crucial roles in regulating diverse functions of proteins. With recent advancements in proteomics technology, the identification of PTM is becoming a data-rich field. A large amount of experimentally verified data is urgently required to be translated into valuable biological insights. With computational approaches, PLA can be accurately detected across the whole proteome, even for organisms with small-scale datasets. Herein, a comprehensive summary of 166 in silico PLA prediction methods is presented, including a single type of PLA site and multiple types of PLA sites. This recapitulation covers important aspects that are critical for the development of a robust predictor, including data collection and preparation, sample selection, feature representation, classification algorithm design, model evaluation, and method availability. Notably, we discuss the application of protein language models and transfer learning to solve the small-sample learning issue. We also highlight the prediction methods developed for functionally relevant PLA sites and species/substrate/cell-type-specific PLA sites. In conclusion, this systematic review could potentially facilitate the development of novel PLA predictors and offer useful insights to researchers from various disciplines.

ILYCROsite: Identification of lysine crotonylation sites based on FCM-GRNN undersampling technique

Protein lysine crotonylation is an important post-translational modification that regulates various cellular activities. For example, histone crotonylation affects chromatin structure and promotes histone replacement. Identification and understanding of lysine crotonylation sites is crucial in the field of protein research. However, due to the increasing amount of non-histone crotonylation sites, existing classifiers based on traditional machine learning may encounter performance limitations. In order to address this problem, a novel deep learning-based model for identifying crotonylation sites is presented in this study, given the unique advantages of deep learning techniques for sequence data analysis. In this study, an MLP-Attention-based model was developed for the identification of crotonylation sites. Firstly, three feature extraction strategies, namely Amino Acid Composition, K-mer, and Distance-based residue features extraction strategy, were used to encode crotonylated and non-crotonylated sequences. Then, in order to balance the training dataset, the FCM-GRNN undersampling algorithm combining fuzzy clustering and generalized neural network approaches was introduced. Finally, to improve the effectiveness of crotonylation site identification, we explored various classification algorithms, and based on the relevant experimental performance comparisons, the multilayer perceptron (MLP) combined with the superimposed self-attention mechanism was finally selected to construct the prediction model ILYCROsite. The results obtained from independent testing and five-fold cross-validation demonstrated that the model proposed in this study, ILYCROsite, had excellent performance. Notably, on the independent test set, ILYCROsite achieves an AUC value of 87.93 %, which is significantly better than the existing state-of-the-art models. In addition, SHAP (Shapley Additive exPlanations) values were used to analyze the importance of features and their impact on model predictions. Meanwhile, in order to facilitate researchers to use the prediction model constructed in this study, we developed a prediction program to identify the crotonylation sites in a given protein sequence. The data and code for this program are available at: https://github.com/wmqskr/ILYCROsite.

Advancing Peptide-Based Cancer Therapy with AI: In-Depth Analysis of State-of-the-Art AI Models

Anticancer peptides (ACPs) play a vital role in selectively targeting and eliminating cancer cells. Evaluating and comparing predictions from various machine learning (ML) and deep learning (DL) techniques is challenging but crucial for anticancer drug research. We conducted a comprehensive analysis of 15 ML and 10 DL models, including the models released after 2022, and found that support vector machines (SVMs) with feature combination and selection significantly enhance overall performance. DL models, especially convolutional neural networks (CNNs) with light gradient boosting machine (LGBM) based feature selection approaches, demonstrate improved characterization. Assessment using a new test data set (ACP10) identifies ACPred, MLACP 2.0, AI4ACP, mACPred, and AntiCP2.0_AAC as successive optimal predictors, showcasing robust performance. Our review underscores current prediction tool limitations and advocates for an omnidirectional ACP prediction framework to propel ongoing research.

N-GlycoPred: A hybrid deep learning model for accurate identification of N-glycosylation sites

Studies have shown that protein glycosylation in cells reflects the real-time dynamics of biological processes, and the occurrence and development of many diseases are closely related to protein glycosylation. Abnormal protein glycosylation can be used as a potential diagnostic and prognostic marker of a disease, as well as a therapeutic target and a new breakthrough point for exploring pathogenesis. To address the issue of significant differences in the prediction results of previous models for different species, we constructed a hybrid deep learning model N-GlycoPred on the basis of dual-layer convolution, a paired attention mechanism and BiLSTM for accurate identification of N-glycosylation sites. By adopting one-hot encoding or the AAindex, we specifically selected the optimum combination of features and deep learning frameworks for human and mouse to refine the models. Based on six independent test datasets, our N-GlycoPred model achieved an average AUC of 0.9553, which is 0.23% higher than MusiteDeep. The comparison results indicate that our model can serve as a powerful tool for N-glycosylation site prescreening for biological researchers.

LMCrot: an enhanced protein crotonylation site predictor by leveraging an interpretable window-level embedding from a transformer-based protein language model

MOTIVATION

Recent advancements in natural language processing have highlighted the effectiveness of global contextualized representations from protein language models (pLMs) in numerous downstream tasks. Nonetheless, strategies to encode the site-of-interest leveraging pLMs for per-residue prediction tasks, such as crotonylation (Kcr) prediction, remain largely uncharted.

RESULTS

Herein, we adopt a range of approaches for utilizing pLMs by experimenting with different input sequence types (full-length protein sequence versus window sequence), assessing the implications of utilizing per-residue embedding of the site-of-interest as well as embeddings of window residues centered around it. Building upon these insights, we developed a novel residual ConvBiLSTM network designed to process window-level embeddings of the site-of-interest generated by the ProtT5-XL-UniRef50 pLM using full-length sequences as input. This model, termed T5ResConvBiLSTM, surpasses existing state-of-the-art Kcr predictors in performance across three diverse datasets. To validate our approach of utilizing full sequence-based window-level embeddings, we also delved into the interpretability of ProtT5-derived embedding tensors in two ways: firstly, by scrutinizing the attention weights obtained from the transformer's encoder block; and secondly, by computing SHAP values for these tensors, providing a model-agnostic interpretation of the prediction results. Additionally, we enhance the latent representation of ProtT5 by incorporating two additional local representations, one derived from amino acid properties and the other from supervised embedding layer, through an intermediate fusion stacked generalization approach, using an n-mer window sequence (or, peptide/fragment). The resultant stacked model, dubbed LMCrot, exhibits a more pronounced improvement in predictive performance across the tested datasets.

AVAILABILITY AND IMPLEMENTATION

LMCrot is publicly available at https://github.com/KCLabMTU/LMCrot.

PlantNh-Kcr: a deep learning model for predicting non-histone crotonylation sites in plants

BACKGROUND

Lysine crotonylation (Kcr) is a crucial protein post-translational modification found in histone and non-histone proteins. It plays a pivotal role in regulating diverse biological processes in both animals and plants, including gene transcription and replication, cell metabolism and differentiation, as well as photosynthesis. Despite the significance of Kcr, detection of Kcr sites through biological experiments is often time-consuming, expensive, and only a fraction of crotonylated peptides can be identified. This reality highlights the need for efficient and rapid prediction of Kcr sites through computational methods. Currently, several machine learning models exist for predicting Kcr sites in humans, yet models tailored for plants are rare. Furthermore, no downloadable Kcr site predictors or datasets have been developed specifically for plants. To address this gap, it is imperative to integrate existing Kcr sites detected in plant experiments and establish a dedicated computational model for plants.

RESULTS

Most plant Kcr sites are located on non-histones. In this study, we collected non-histone Kcr sites from five plants, including wheat, tabacum, rice, peanut, and papaya. We then conducted a comprehensive analysis of the amino acid distribution surrounding these sites. To develop a predictive model for plant non-histone Kcr sites, we combined a convolutional neural network (CNN), a bidirectional long short-term memory network (BiLSTM), and attention mechanism to build a deep learning model called PlantNh-Kcr. On both five-fold cross-validation and independent tests, PlantNh-Kcr outperformed multiple conventional machine learning models and other deep learning models. Furthermore, we conducted an analysis of species-specific effect on the PlantNh-Kcr model and found that a general model trained using data from multiple species outperforms species-specific models.

CONCLUSION

PlantNh-Kcr represents a valuable tool for predicting plant non-histone Kcr sites. We expect that this model will aid in addressing key challenges and tasks in the study of plant crotonylation sites.

Large-Scale Identification of Lysine Crotonylation Reveals Its Potential Role in Oral Squamous Cell Carcinoma

Purpose

Lysine crotonylation, an emerging posttranslational modification, has been implicated in the regulation of diverse biological processes. However, its involvement in oral squamous cell carcinoma (OSCC) remains elusive. This study aims to reveal the global crotonylome in OSCC under hypoxic conditions and explore the potential regulatory mechanism of crotonylation in OSCC.

Methods

Liquid-chromatography fractionation, affinity enrichment of crotonylated peptides, and high-resolution mass spectrometry were employed to detect differential crotonylation in CAL27 cells cultured under hypoxia. The obtained data were further subjected to bioinformatics analysis to uncover the involved biological processes and pathways of the dysregulated crotonylated proteins. A site-mutated plasmid was utilized to investigate the effect of crotonylation on Heat Shock Protein 90 Alpha Family Class B Member 1 (HAP90AB1) function.

Results

A large-scale crotonylome analysis revealed 1563 crotonylated modification sites on 605 proteins in CAL27 cells under hypoxia. Bioinformatics analysis revealed a significant decrease in histone crotonylation levels, while up-regulated crotonylated proteins were mainly concentrated in non-histone proteins. Notably, glycolysis-related proteins exhibited prominent up-regulation among the identified crotonylated proteins, with HSP90AB1 displaying the most significant changes. Subsequent experimental findings confirmed that mutating lysine 265 of HSP90AB1 into a silent arginine impaired its function in promoting glycolysis.

Conclusion

Our study provides insights into the crotonylation modification of proteins in OSCC under hypoxic conditions and elucidates the associated biological processes and pathways. Crotonylation of HSP90AB1 in hypoxic conditions may enhance the glycolysis regulation ability in OSCC, offering novel perspectives on the regulatory mechanism of crotonylation in hypoxic OSCC and potential therapeutic targets for OSCC treatment.