DeepNitro: Prediction of Protein Nitration and Nitrosylation Sites by Deep Learning

DeepPhos: prediction of protein phosphorylation sites with deep learning

Motivation

Phosphorylation is the most studied post-translational modification, which is crucial for multiple biological processes. Recently, many efforts have been taken to develop computational predictors for phosphorylation site prediction, but most of them are based on feature selection and discriminative classification. Thus, it is useful to develop a novel and highly accurate predictor that can unveil intricate patterns automatically for protein phosphorylation sites.

Results

In this study we present DeepPhos, a novel deep learning architecture for prediction of protein phosphorylation. Unlike multi-layer convolutional neural networks, DeepPhos consists of densely connected convolutional neuron network blocks which can capture multiple representations of sequences to make final phosphorylation prediction by intra block concatenation layers and inter block concatenation layers. DeepPhos can also be used for kinase-specific prediction varying from group, family, subfamily and individual kinase level. The experimental results demonstrated that DeepPhos outperforms competitive predictors in general and kinase-specific phosphorylation site prediction.

Availability and implementation

The source code of DeepPhos is publicly deposited at https://github.com/USTCHIlab/DeepPhos.

Supplementary information

Supplementary data are available at Bioinformatics online.

The crosstalk of NAD, ROS and autophagy in cellular health and ageing

Cellular adaptation to various types of stress requires a complex network of steps that altogether lead to reconstitution of redox balance, degradation of damaged macromolecules and restoration of cellular metabolism. Advances in our understanding of the interplay between cellular signalling and signal translation paint a complex picture of multi-layered paths of regulation. In this review we explore the link between cellular adaptation to metabolic and oxidative stresses by activation of autophagy, a crucial cellular catabolic pathway. Metabolic stress can lead to changes in the redox state of nicotinamide adenine dinucleotide (NAD), a co-factor in a variety of enzymatic reactions and thus trigger autophagy that acts to sequester intracellular components for recycling to support cellular growth. Likewise, autophagy is activated by oxidative stress to selectively recycle damaged macromolecules and organelles and thus maintain cellular viability. Multiple proteins that help regulate or execute autophagy are targets of post-translational modifications (PTMs) that have an effect on their localization, binding affinity or enzymatic activity. These PTMs include acetylation, a reversible enzymatic modification of a protein’s lysine residues, and oxidation, a set of reversible and irreversible modifications by free radicals. Here we highlight the latest findings and outstanding questions on the interplay of autophagy with metabolic stress, presenting as changes in NAD levels, and oxidative stress, with a focus on autophagy proteins that are regulated by both, oxidation and acetylation. We further explore the relevance of this multi-layered signalling to healthy human ageing and their potential role in human disease.

Deep learning for mining protein data

The recent emergence of deep learning to characterize complex patterns of protein big data reveals its potential to address the classic challenges in the field of protein data mining. Much research has revealed the promise of deep learning as a powerful tool to transform protein big data into valuable knowledge, leading to scientific discoveries and practical solutions. In this review, we summarize recent publications on deep learning predictive approaches in the field of mining protein data. The application architectures of these methods include multilayer perceptrons, stacked autoencoders, deep belief networks, two- or three-dimensional convolutional neural networks, recurrent neural networks, graph neural networks, and complex neural networks and are described from five perspectives: residue-level prediction, sequence-level prediction, three-dimensional structural analysis, interaction prediction, and mass spectrometry data mining. The advantages and deficiencies of these architectures are presented in relation to various tasks in protein data mining. Additionally, some practical issues and their future directions are discussed, such as robust deep learning for protein noisy data, architecture optimization for specific tasks, efficient deep learning for limited protein data, multimodal deep learning for heterogeneous protein data, and interpretable deep learning for protein understanding. This review provides comprehensive perspectives on general deep learning techniques for protein data analysis.

Characterization and Identification of Lysine Succinylation Sites based on Deep Learning Method

Succinylation is a type of protein post-translational modification (PTM), which can play important roles in a variety of cellular processes. Due to an increasing number of site-specific succinylated peptides obtained from high-throughput mass spectrometry (MS), various tools have been developed for computationally identifying succinylated sites on proteins. However, most of these tools predict succinylation sites based on traditional machine learning methods. Hence, this work aimed to carry out the succinylation site prediction based on a deep learning model. The abundance of MS-verified succinylated peptides enabled the investigation of substrate site specificity of succinylation sites through sequence-based attributes, such as position-specific amino acid composition, the composition of k-spaced amino acid pairs (CKSAAP), and position-specific scoring matrix (PSSM). Additionally, the maximal dependence decomposition (MDD) was adopted to detect the substrate signatures of lysine succinylation sites by dividing all succinylated sequences into several groups with conserved substrate motifs. According to the results of ten-fold cross-validation, the deep learning model trained using PSSM and informative CKSAAP attributes can reach the best predictive performance and also perform better than traditional machine-learning methods. Moreover, an independent testing dataset that truly did not exist in the training dataset was used to compare the proposed method with six existing prediction tools. The testing dataset comprised of 218 positive and 2621 negative instances, and the proposed model could yield a promising performance with 84.40% sensitivity, 86.99% specificity, 86.79% accuracy, and an MCC value of 0.489. Finally, the proposed method has been implemented as a web-based prediction tool (CNN-SuccSite), which is now freely accessible at http://csb.cse.yzu.edu.tw/CNN-SuccSite/.

Role of Nitric Oxide Carried by Hemoglobin in Cardiovascular Physiology: Developments on a Three-Gas Respiratory Cycle

A continuous supply of oxygen is essential for the survival of multicellular organisms. The understanding of how this supply is regulated in the microvasculature has evolved from viewing erythrocytes (red blood cells [RBCs]) as passive carriers of oxygen to recognizing the complex interplay between Hb (hemoglobin) and oxygen, carbon dioxide, and nitric oxide-the three-gas respiratory cycle-that insures adequate oxygen and nutrient delivery to meet local metabolic demand. In this context, it is blood flow and not blood oxygen content that is the main driver of tissue oxygenation by RBCs. Herein, we review the lines of experimentation that led to this understanding of RBC function; from the foundational understanding of allosteric regulation of oxygen binding in Hb in the stereochemical model of Perutz, to blood flow autoregulation (hypoxic vasodilation governing oxygen delivery) observed by Guyton, to current understanding that centers on S-nitrosylation of Hb (ie, S-nitrosohemoglobin; SNO-Hb) as a purveyor of oxygen-dependent vasodilatory activity. Notably, hypoxic vasodilation is recapitulated by native S-nitrosothiol (SNO)-replete RBCs and by SNO-Hb itself, whereby SNO is released from Hb and RBCs during deoxygenation, in proportion to the degree of Hb deoxygenation, to regulate vessels directly. In addition, we discuss how dysregulation of this system through genetic mutation in Hb or through disease is a common factor in oxygenation pathologies resulting from microcirculatory impairment, including sickle cell disease, ischemic heart disease, and heart failure. We then conclude by identifying potential therapeutic interventions to correct deficits in RBC-mediated vasodilation to improve oxygen delivery-steps toward effective microvasculature-targeted therapies. To the extent that diseases of the heart, lungs, and blood are associated with impaired tissue oxygenation, the development of new therapies based on the three-gas respiratory system have the potential to improve the well-being of millions of patients.

Precise Prediction of Calpain Cleavage Sites and Their Aberrance Caused by Mutations in Cancer

As a widespread post-translational modification of proteins, calpain-mediated cleavage regulates a broad range of cellular processes, including proliferation, differentiation, cytoskeletal reorganization, and apoptosis. The identification of proteins that undergo calpain cleavage in a site-specific manner is the necessary foundation for understanding the exact molecular mechanisms and regulatory roles of calpain-mediated cleavage. In contrast with time-consuming and labor-intensive experimental methods, computational approaches for detecting calpain cleavage sites have attracted wide attention due to their efficiency and convenience. In this study, we established a novel computational tool named DeepCalpain (http://deepcalpain.cancerbio.info/) for predicting the potential calpain cleavage sites by adopting deep neural network and the particle swarm optimization algorithm. Through critical evaluation and comparison, DeepCalpain exhibited superior performance against other existing tools. Meanwhile, we found that protein interactions could enrich the calpain-substrate regulatory relationship. Since calpain-mediated cleavage was critical for cancer development and progression, we comprehensively analyzed the calpain cleavage associated mutations across 11 cancers with the help of DeepCalpain, which demonstrated that the calpain-mediated cleavage events were affected by mutations and heavily implicated in the regulation of cancer cells. These prediction and analysis results might provide helpful information to reveal the regulatory mechanism of calpain cleavage in biological pathways and different cancer types, which might open new avenues for the diagnosis and treatment of cancers.

Computational identification of microbial phosphorylation sites by the enhanced characteristics of sequence information

Protein phosphorylation on serine (S) and threonine (T) has emerged as a key device in the control of many biological processes. Recently phosphorylation in microbial organisms has attracted much attention for its critical roles in various cellular processes such as cell growth and cell division. Here a novel machine learning predictor, MPSite (Microbial Phosphorylation Site predictor), was developed to identify microbial phosphorylation sites using the enhanced characteristics of sequence features. The final feature vectors optimized via a Wilcoxon rank sum test. A random forest classifier was then trained using the optimum features to build the predictor. Benchmarking investigation using the 5-fold cross-validation and independent datasets test showed that the MPSite is able to achieve robust performance on the S- and T-phosphorylation site prediction. It also outperformed other existing methods on the comprehensive independent datasets. We anticipate that the MPSite is a powerful tool for proteome-wide prediction of microbial phosphorylation sites and facilitates hypothesis-driven functional interrogation of phosphorylation proteins. A web application with the curated datasets is freely available at http://kurata14.bio.kyutech.ac.jp/MPSite/.

Integration of A Deep Learning Classifier with A Random Forest Approach for Predicting Malonylation Sites

As a newly-identified protein post-translational modification, malonylation is involved in a variety of biological functions. Recognizing malonylation sites in substrates represents an initial but crucial step in elucidating the molecular mechanisms underlying protein malonylation. In this study, we constructed a deep learning (DL) network classifier based on long short-term memory (LSTM) with word embedding (LSTM_WE) for the prediction of mammalian malonylation sites. LSTM_WE performs better than traditional classifiers developed with common pre-defined feature encodings or a DL classifier based on LSTM with a one-hot vector. The performance of LSTM_WE is sensitive to the size of the training set, but this limitation can be overcome by integration with a traditional machine learning (ML) classifier. Accordingly, an integrated approach called LEMP was developed, which includes LSTM_WE and the random forest classifier with a novel encoding of enhanced amino acid content. LEMP performs not only better than the individual classifiers but also superior to the currently-available malonylation predictors. Additionally, it demonstrates a promising performance with a low false positive rate, which is highly useful in the prediction application. Overall, LEMP is a useful tool for easily identifying malonylation sites with high confidence. LEMP is available at http://www.bioinfogo.org/lemp.

Prediction of S-nitrosylation sites by integrating support vector machines and random forest

Cysteine S-nitrosylation is a type of reversible post-translational modification of proteins, which controls diverse biological processes.