32P labeling of protein phosphorylation and metabolite association in the mitochondria matrix

Res-GCN: Identification of protein phosphorylation sites using graph convolutional network and residual network

An essential post-translational modification, phosphorylation is intimately related with a wide range of biological activities. The advancement of effective computational methods for correctly recognizing phosphorylation sites is important for in-depth understanding of various physiological phenomena. However, the traditional method of identifying phosphorylation sites experimentally is time-consuming and laborious, which makes it difficult to meet the processing demands of today's big data. This research proposes the use of a novel model, Res-GCN, to recognize the phosphorylation sites of SARS-CoV-2. Firstly, eight feature extraction strategies are utilized to digitize the protein sequence from multiple viewpoints, including amino acid property encodings (AAindex), pseudo-amino acid composition (PseAAC), adapted normal distribution bi-profile Bayes (ANBPB), dipeptide composition (DC), binary encoding (BE), enhanced amino acid composition (EAAC), Word2Vec, and BLOSUM62 matrices. Secondly, elastic net is utilized to eliminate redundant data in the fused matrix. Finally, a combination of graph convolutional network (GCN) and residual network (ResNet) is used to classify the phosphorylated sites and output predictions using a fully connected layer (FC). The performance of Res-GCN is tested by 5-fold cross-validation and independent testing, and excellent results are obtained on S/T and Y datasets. This demonstrates that the Res-GCN model exhibits exceptional predictive performance and generalizability.

Phosphopeptide-bridged NH2-TiO2-mediated carbon dots self-enhancing and electrochemiluminescence microsensors for label-free protein kinase A detection

A novel electrochemiluminescence (ECL) method was developed for determination of protein kinase A (PKA) ultra-sensitively based on amidated nano-titanium (NH2-TiO2) embellished carbon dots (Mg@N-CDs) fluorescent probe, which integrated the target recognition and ECL signal enhancement. The Cys-labeled kemptides were employed to build a serine-rich synthetic substrate-heptapeptide (Cys-kemptide) on the Au-electrode surface. Then, the PKA-induced biosensor was triggered as a signal switch to introduce the large amounts of TiO2 decorated Mg@N-CD nanohybrid (Ti@NMg-CDs) into AuE/Cys-phosphopeptides for signal output. In particular, the presence of PKA could induce the formation of Cys-phosphopeptides by the catalytic reaction between specific substrate (kemptide) and PKA, which acts as an initiator to link the Ti@NMg-CDs according to the bridge interactions Ti-O-P. In this way, multiple Cys-phosphopeptides were adsorbed onto a single Ti@NMg-CDs, and the Ti@NMg-CDs not only provided high specific selectivity but also large surface area, as well as unprecedented high ECL efficiency. Using this PKA-induced enhanced sensor, the limit of detection of the PKA was 4.89 × 10-4 U/mL (S/N = 3). The proposed ECL biosensor was also universally applicable for the screening of PKA inhibitors and determining of other kinases activity. Our sensing system has excellent performance of specificity and the screening of kinase inhibitors, as well as it will inspire future effort in clinical diagnostics and new drug discovery.

Nphos: Database and Predictor of Protein N-phosphorylation

Protein N-phosphorylation is widely present in nature and participates in various biological processes. However, current knowledge on N-phosphorylation is extremely limited compared to that on O-phosphorylation. In this study, we collected 11,710 experimentally verified N-phosphosites of 7344 proteins from 39 species and subsequently constructed the database Nphos to share up-to-date information on protein N-phosphorylation. Upon these substantial data, we characterized the sequential and structural features of protein N-phosphorylation. Moreover, after comparing hundreds of learning models, we chose and optimized gradient boosting decision tree (GBDT) models to predict three types of human N-phosphorylation, achieving mean area under the receiver operating characteristic curve (AUC) values of 90.56%, 91.24%, and 92.01% for pHis, pLys, and pArg, respectively. Meanwhile, we discovered 488,825 distinct N-phosphosites in the human proteome. The models were also deployed in Nphos for interactive N-phosphosite prediction. In summary, this work provides new insights and points for both flexible and focused investigations of N-phosphorylation. It will also facilitate a deeper and more systematic understanding of protein N-phosphorylation modification by providing a data and technical foundation. Nphos is freely available at http://www.bio-add.org/Nphos/ and http://ppodd.org.cn/Nphos/.

DeepPhoPred: Accurate Deep Learning Model to Predict Microbial Phosphorylation

Phosphorylation is a substantial posttranslational modification of proteins that refers to adding a phosphate group to the amino acid side chain after translation process in the ribosome. It is vital to coordinate cellular functions, such as regulating metabolism, proliferation, apoptosis, subcellular trafficking, and other crucial physiological processes. Phosphorylation prediction in a microbial organism can assist in understanding pathogenesis and host-pathogen interaction, drug and antibody design, and antimicrobial agent development. Experimental methods for predicting phosphorylation sites are costly, slow, and tedious. Hence low-cost and high-speed computational approaches are highly desirable. This paper presents a new deep learning tool called DeepPhoPred for predicting microbial phospho-serine (pS), phospho-threonine (pT), and phospho-tyrosine (pY) sites. DeepPhoPred incorporates a two-headed convolutional neural network architecture with the squeeze and excitation blocks followed by fully connected layers that jointly learn significant features from the peptide's structural and evolutionary information to predict phosphorylation sites. Our empirical results demonstrate that DeepPhoPred significantly outperforms the existing microbial phosphorylation site predictors with its highly efficient deep-learning architecture. DeepPhoPred as a standalone predictor, all its source codes, and our employed datasets are publicly available at https://github.com/faisalahm3d/DeepPhoPred.

PhosAF: An integrated deep learning architecture for predicting protein phosphorylation sites with AlphaFold2 predicted structures

Phosphorylation is indispensable in comprehending biological processes, while biological experimental methods for identifying phosphorylation sites are tedious and arduous. With the rapid growth of biotechnology, deep learning methods have made significant progress in site prediction tasks. Nevertheless, most existing predictors only consider protein sequence information, that limits the capture of protein spatial information. Building upon the latest advancement in protein structure prediction by AlphaFold2, a novel integrated deep learning architecture PhosAF is developed to predict phosphorylation sites in human proteins by integrating CMA-Net and MFC-Net, which considers sequence and structure information predicted by AlphaFold2. Here, CMA-Net module is composed of multiple convolutional neural network layers and multi-head attention is appended to obtaining the local and long-term dependencies of sequence features. Meanwhile, the MFC-Net module composed of deep neural network layers is used to capture the complex representations of evolutionary and structure features. Furthermore, different features are combined to predict the final phosphorylation sites. In addition, we put forward a new strategy to construct reliable negative samples via protein secondary structures. Experimental results on independent test data and case study indicate that our model PhosAF surpasses the current most advanced methods in phosphorylation site prediction.

Attenphos: General Phosphorylation Site Prediction Model Based on Attention Mechanism

Phosphorylation site prediction has important application value in the field of bioinformatics. It can act as an important reference and help with protein function research, protein structure research, and drug discovery. So, it is of great significance to propose scientific and effective calculation methods to accurately predict phosphorylation sites. In this study, we propose a new method, Attenphos, based on the self-attention mechanism for predicting general phosphorylation sites in proteins. The method not only captures the long-range dependence information of proteins but also better represents the correlation between amino acids through feature vector encoding transformation. Attenphos takes advantage of the one-dimensional convolutional layer to reduce the number of model parameters, improve model efficiency and prediction accuracy, and enhance model generalization. Comparisons between our method and existing state-of-the-art prediction tools were made using balanced datasets from human proteins and unbalanced datasets from mouse proteins. We performed prediction comparisons using independent test sets. The results showed that Attenphos demonstrated the best overall performance in the prediction of Serine (S), Threonine (T), and Tyrosine (Y) sites on both balanced and unbalanced datasets. Compared to current state-of-the-art methods, Attenphos has significantly higher prediction accuracy. This proves the potential of Attenphos in accelerating the identification and functional analysis of protein phosphorylation sites and provides new tools and ideas for biological research and drug discovery.

Unveiling OSCP as the potential therapeutic target for mitochondrial dysfunction-related diseases

Mitochondria are important organelles in cells responsible for energy production and regulation. Mitochondrial dysfunction has been implicated in the pathogenesis of many diseases. Oligomycin sensitivity-conferring protein (OSCP), a component of the inner mitochondrial membrane, has been studied for a long time. OSCP is a component of the F1Fo-ATP synthase in mitochondria and is closely related to the regulation of the mitochondrial permeability transition pore (mPTP). Studies have shown that OSCP plays an important role in cardiovascular disease, neurological disorders, and tumor development. This review summarizes the localization, structure, function, and regulatory mechanisms of OSCP and outlines its role in cardiovascular disease, neurological disease, and tumor development. In addition, this article reviews the research on the interaction between OSCP and mPTP. Finally, the article suggests future research directions, including further exploration of the mechanism of action of OSCP, the interaction between OSCP and other proteins and signaling pathways, and the development of new treatment strategies for mitochondrial dysfunction. In conclusion, in-depth research on OSCP will help to elucidate its importance in cell function and disease and provide new ideas for the treatment and prevention of related diseases.

LMPhosSite: A Deep Learning-Based Approach for General Protein Phosphorylation Site Prediction Using Embeddings from the Local Window Sequence and Pretrained Protein Language Model

Phosphorylation is one of the most important post-translational modifications and plays a pivotal role in various cellular processes. Although there exist several computational tools to predict phosphorylation sites, existing tools have not yet harnessed the knowledge distilled by pretrained protein language models. Herein, we present a novel deep learning-based approach called LMPhosSite for the general phosphorylation site prediction that integrates embeddings from the local window sequence and the contextualized embedding obtained using global (overall) protein sequence from a pretrained protein language model to improve the prediction performance. Thus, the LMPhosSite consists of two base-models: one for capturing effective local representation and the other for capturing global per-residue contextualized embedding from a pretrained protein language model. The output of these base-models is integrated using a score-level fusion approach. LMPhosSite achieves a precision, recall, Matthew's correlation coefficient, and F1-score of 38.78%, 67.12%, 0.390, and 49.15%, for the combined serine and threonine independent test data set and 34.90%, 62.03%, 0.298, and 44.67%, respectively, for the tyrosine independent test data set, which is better than the compared approaches. These results demonstrate that LMPhosSite is a robust computational tool for the prediction of the general phosphorylation sites in proteins.

TransPhos: A Deep-Learning Model for General Phosphorylation Site Prediction Based on Transformer-Encoder Architecture

Protein phosphorylation is one of the most critical post-translational modifications of proteins in eukaryotes, which is essential for a variety of biological processes. Plenty of attempts have been made to improve the performance of computational predictors for phosphorylation site prediction. However, most of them are based on extra domain knowledge or feature selection. In this article, we present a novel deep learning-based predictor, named TransPhos, which is constructed using a transformer encoder and densely connected convolutional neural network blocks, for predicting phosphorylation sites. Data experiments are conducted on the datasets of PPA (version 3.0) and Phospho. ELM. The experimental results show that our TransPhos performs better than several deep learning models, including Convolutional Neural Networks (CNN), Long-term and short-term memory networks (LSTM), Recurrent neural networks (RNN) and Fully connected neural networks (FCNN), and some state-of-the-art deep learning-based prediction tools, including GPS2.1, NetPhos, PPRED, Musite, PhosphoSVM, SKIPHOS, and DeepPhos. Our model achieves a good performance on the training datasets of Serine (S), Threonine (T), and Tyrosine (Y), with AUC values of 0.8579, 0.8335, and 0.6953 using 10-fold cross-validation tests, respectively, and demonstrates that the presented TransPhos tool considerably outperforms competing predictors in general protein phosphorylation site prediction.

Mn2+ -Phos-Tag Polyacrylamide for the Quantification of Protein Phosphorylation Levels

This paper provides a guideline for optimizing and utilizing Mn²⁺ Phos-tag gel technology to separate phosphorylated proteins from their unphosphorylated counterparts. It provides key insights into methods for careful sample preparation and experimental directions for determining the appropriate Phos-tag gel compositions and electrophoresis and western blotting conditions. This protocol has been used to successfully resolve proteins extracted from cardiac and skeletal muscles. The guidelines can be extended for optimizing protocols to resolve proteins from other cells or tissue sources. With this, phosphoproteomics and the elucidation of underlying mechanisms of disease progression can be accelerated. © 2021 The Authors. Current Protocols published by Wiley Periodicals LLC.

PhosIDN: an integrated deep neural network for improving protein phosphorylation site prediction by combining sequence and protein-protein interaction information

Motivation

Phosphorylation is one of the most studied post-translational modifications, which plays a pivotal role in various cellular processes. Recently, deep learning methods have achieved great success in prediction of phosphorylation sites, but most of them are based on convolutional neural network that may not capture enough information about long-range dependencies between residues in a protein sequence. In addition, existing deep learning methods only make use of sequence information for predicting phosphorylation sites, and it is highly desirable to develop a deep learning architecture that can combine heterogeneous sequence and protein–protein interaction (PPI) information for more accurate phosphorylation site prediction.

Results

We present a novel integrated deep neural network named PhosIDN, for phosphorylation site prediction by extracting and combining sequence and PPI information. In PhosIDN, a sequence feature encoding sub-network is proposed to capture not only local patterns but also long-range dependencies from protein sequences. Meanwhile, useful PPI features are also extracted in PhosIDN by a PPI feature encoding sub-network adopting a multi-layer deep neural network. Moreover, to effectively combine sequence and PPI information, a heterogeneous feature combination sub-network is introduced to fully exploit the complex associations between sequence and PPI features, and their combined features are used for final prediction. Comprehensive experiment results demonstrate that the proposed PhosIDN significantly improves the prediction performance of phosphorylation sites and compares favorably with existing general and kinase-specific phosphorylation site prediction methods.

Availability and implementation

PhosIDN is freely available at https://github.com/ustchangyuanyang/PhosIDN.

Supplementary information

Supplementary data are available at Bioinformatics online.

The extensive and functionally uncharacterized mitochondrial phosphoproteome

More than half a century ago, reversible protein phosphorylation was linked to mitochondrial metabolism through the regulation of pyruvate dehydrogenase. Since this discovery, the number of identified mitochondrial protein phosphorylation sites has increased by orders of magnitude, driven largely by technological advances in mass spectrometry-based phosphoproteomics. However, the majority of these modifications remain uncharacterized, rendering their function and relevance unclear. Nonetheless, recent studies have shown that disruption of resident mitochondrial protein phosphatases causes substantial metabolic dysfunction across organisms, suggesting that proper management of mitochondrial phosphorylation is vital for organellar and organismal homeostasis. While these data suggest that phosphorylation within mitochondria is of critical importance, significant gaps remain in our knowledge of how these modifications influence organellar function. Here, we curate publicly available datasets to map the extent of protein phosphorylation within mammalian mitochondria and to highlight the known functions of mitochondrial-resident phosphatases. We further propose models by which phosphorylation may affect mitochondrial enzyme activities, protein import and processing, and overall organellar homeostasis.

Metabolomics bridging proteomics along metabolites/oncometabolites and protein modifications: Paving the way toward integrative multiomics

Systems biology adopted functional and integrative multiomics approaches enable to discover the whole set of interacting regulatory components such as genes, transcripts, proteins, metabolites, and metabolite dependent protein modifications. This interactome build up the midpoint of protein-protein/PTM, protein-DNA/RNA, and protein-metabolite network in a cell. As the key drivers in cellular metabolism, metabolites are precursors and regulators of protein post-translational modifications [PTMs] that affect protein diversity and functionality. The precisely orchestrated core pattern of metabolic networks refer to paradigm 'metabolites regulate PTMs, PTMs regulate enzymes, and enzymes modulate metabolites' through a multitude of feedback and feed-forward pathway loops. The concept represents a flawless PTM-metabolite-enzyme(protein) regulomics underlined in reprogramming cancer metabolism. Immense interconnectivity of those biomolecules in their spectacular network of intertwined metabolic pathways makes integrated proteomics and metabolomics an excellent opportunity, and the central component of integrative multiomics framework. It will therefore be of significant interest to integrate global proteome and PTM-based proteomics with metabolomics to achieve disease related altered levels of those molecules. Thereby, present update aims to highlight role and analysis of interacting metabolites/oncometabolites, and metabolite-regulated PTMs loop which may function as translational monitoring biomarkers along the reprogramming continuum of oncometabolism.

DeepPPSite: A deep learning-based model for analysis and prediction of phosphorylation sites using efficient sequence information

Phosphorylation is a ubiquitous type of post-translational modification (PTM) that occurs in both eukaryotic and prokaryotic cells where in a phosphate group binds with amino acid residues. These specific residues, i.e., serine (S), threonine (T), and tyrosine (Y), exhibit diverse functions at the molecular level. Recent studies have determined that some diseases such as cancer, diabetes, and neurodegenerative diseases are caused by abnormal phosphorylation. Based on its potential applications in biological research and drug development, the large-scale identification of phosphorylation sites has attracted interest. Existing wet-lab technologies for targeting phosphorylation sites are overpriced and time consuming. Thus, computational algorithms that can efficiently accelerate the annotation of phosphorylation sites from massive protein sequences are needed. Numerous machine learning-based methods have been implemented for phosphorylation sites prediction. However, despite extensive efforts, existing computational approaches continue to have inadequate performance, particularly in terms of overall ACC, MCC, and AUC. In this paper, we report a novel deep learning-based predictor to overcome these performance hurdles, DeepPPSite, which was constructed using a stacked long short-term memory recurrent network for predicting phosphorylation sites. The proposed technique expediently learns the protein representations from conjoint protein descriptors. The experimental results indicated that our model achieved superior performance on the training dataset for S, T and Y, with MCC values of 0.608, 0.602, and 0.558, respectively, using a 10-fold cross-validation test. We further determined the generalization efficacy of the proposed predictor DeepPPSite by conducting a rigorous independent test. The predictive MCC values were 0.358, 0.356, and 0.350 for the S, T, and Y phosphorylation sites, respectively. Rigorous cross-validation and independent validation tests for the three types of phosphorylation sites demonstrated that the designed DeepPPSite tool significantly outperforms state-of-the-art methods.

SPR immunosensor combined with Ti4+@TiP nanoparticles for the evaluation of phosphorylated alpha-synuclein level

A highly sensitive and specific surface plasmon resonance (SPR) method using one anti-alpha-synuclein antibody (anti-αS) and titanium phosphate nanoparticles (Ti⁴⁺@TiP) was developed for quantitative evaluation of phosphorylated αS level which was defined by the ratio of p-αS to total alpha-synuclein (t-αS) (p-αS/t-αS). The close affinities of anti-αS to αS (0.975 pM^-1) and p-αS (0.938 pM^-1) were obtained. Based on this fact , both αS forms were simultaneously captured and the t-αS was quantified using the anti-αS immobilized Au chip. With the selective recognition of Ti⁴⁺@TiP nanoparticles, the p-αS was quantified. The dynamic ranges of our method were 1.0~20.0 pg mL^-1 for the detection of t-αS and 0.1~10.0 pg mL^-1 for that of p-αS. The analysis of αS- and p-αS-spiked artificial cerebrospinal fluid samples revealed the high accuracy of the method. Furthermore, the concentrations of αS and p-αS in clinical CSF samples collected from three healthy donors were determined and displayed a high correlation with the results from a commercial ELISA kit, confirming the viability and of the proposed method. The method is convenient, economical, and practical for the evaluation of phosphorylated αS level with high sensitivity and selectivity. It is of great significance for the early diagnosis of PD and the evaluation of PD progression.Graphical abstract.

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.

Identifying Interactions Between Kinases and Substrates Based on Protein-Protein Interaction Network

Protein phosphorylation is a kind of important post-translational modification of protein, which plays a critical role in many biological processes of eukaryote. Identifying kinase-substrate interactions is helpful to understand the mechanism of many diseases. Many computational algorithms for kinase-substrate interactions identification have been proposed. However, most of those methods are mainly focused on utilizing protein local sequence information. In this article, we propose a new computational method to predict kinase-substrate interactions based on protein-protein interaction (PPI) network. Different from existing methods, the PPI network is utilized to measure the similarities of kinase-kinase and substrate-substrate, respectively. Then, the pairwise similarities of kinase-kinase and substrate-substrate are adjusted based on the assumption that the similarities of kinase-kinase and substrate-substrate are more reliable if they are in the same cluster. Finally, the bi-random walk is used to predict potential kinase-substrate interactions. The experimental results show that our method outperforms other state-of-the-art algorithms in performance. Furthermore, the case study demonstrates that it is effective in predicting potential kinase-substrate interactions.

Mitochondrial phosphoproteomics of mammalian tissues

Mitochondria are essential for several biological processes including energy metabolism and cell survival. Accordingly, impaired mitochondrial function is involved in a wide range of human pathologies including diabetes, cancer, cardiovascular, and neurodegenerative diseases. Within the past decade a growing body of evidence indicates that reversible phosphorylation plays an important role in the regulation of a variety of mitochondrial processes as well as tissue-specific mitochondrial functions in mammals. The rapidly increasing number of mitochondrial phosphorylation sites and phosphoproteins identified is largely ascribed to recent advances in phosphoproteomic technologies such as fractionation, phosphopeptide enrichment, and high-sensitivity mass spectrometry. However, the functional importance and the specific kinases and phosphatases involved have yet to be determined for the majority of these mitochondrial phosphorylation sites. This review summarizes the progress in establishing the mammalian mitochondrial phosphoproteome and the technical challenges encountered while characterizing it, with a particular focus on large-scale phosphoproteomic studies of mitochondria from human skeletal muscle.

A novel method for predicting post-translational modifications on serine and threonine sites by using site-modification network profiles

Post-translational modifications (PTMs) regulate many aspects of biological behaviours including protein-protein interactions and cellular processes. Identification of PTM sites is helpful for understanding the PTM regulatory mechanisms. The PTMs on serine and threonine sites include phosphorylation, O-linked glycosylation and acetylation. Although a lot of computational approaches have been developed for PTM site prediction, currently most of them generate the predictive models by employing only local sequence information and few of them consider the relationship between different PTMs. In this paper, by adopting the site-modification network (SMNet) profiles that efficiently incorporate in situ PTM information, we develop a novel method to predict PTM sites on serine and threonine. PTM data are collected from various PTM databases and the SMNet is built to reflect the relationship between multiple PTMs, from which SMNet profiles are extracted to train predictive models based on SVM. Performance analysis of the SVM models shows that the SMNet profiles play an important role in accurately predicting PTM sites on serine and threonine. Furthermore, the proposed method is compared with existing PTM prediction approaches. The results from 10-fold cross-validation demonstrate that the proposed method with SMNet profiles performs remarkably better than existing methods, suggesting the power of SMNet profiles in identifying PTM sites.

A novel network-based computational method to predict protein phosphorylation on tyrosine sites

Phosphorylation plays a great role in regulating a variety of cellular processes and the identification of tyrosine phosphorylation sites is fundamental for understanding the post-translational modification (PTM) regulation processes. Although a lot of computational methods have been developed, most of them only concern local sequence information and few studies focus on the tyrosine sites with in situ PTM information, which refers to different types of PTM occurring on the same modification site. In this study, by constructing the site-modification network that efficiently incorporates in situ PTM information, we introduce a novel network-based computational method, site-modification network-based inference (SMNBI) to predict tyrosine phosphorylation. In order to verify the effectiveness of the proposed method, we compare it with other network-based computational methods. The results clearly show the superior performance of SMNBI. Besides, we extensively compare SMNBI with other sequence-based methods including SVM and Bayesian decision theory. The evaluation demonstrates the power of site-modification network in predicting tyrosine phosphorylation. The proposed method is freely available at http://bioinformatics.ustc.edu.cn/smnbi/ .

Prediction of kinase–substrate relations based on heterogeneous networks

Protein phosphorylation catalyzed by kinases plays essential roles in various intracellular processes. With an increasing number of phosphorylation sites verified experimentally by high-throughput technologies and assigned as substrates of specific kinases, prediction of potential kinase–substrate relations (KSRs) attracts increasing attention. Although a large number of computational methods have been designed, most of them only focus on local protein sequence information. A few KSR prediction approaches integrate protein–protein interaction and protein sequence information into existing machine learning algorithms at the cost of high feature dimensions or reduced sensitivity. In this work, we introduce two novel heterogeneous networks, HetNet-PPI and HetNet-SEQ, by incorporating PPI and similarity of protein sequences into the kinase–substrate heterogeneous networks, respectively. Based on these two heterogeneous networks, we further propose two new KSR prediction methods, HeteSim-PPI and HeteSim-SEQ, by adopting the HeteSim algorithm, which is recently proposed for relevance measure in heterogeneous information networks. Comprehensive evaluation results of the two methods show that similarity of protein sequences is more effective in improving KSR prediction performance as HeteSim-SEQ outperforms HeteSim-PPI in most cases. Further comparison results demonstrate that HeteSim-SEQ is superior to existing methods including BDT, SVM and iGPS, suggesting the effectiveness of the proposed network-based method in predicting potential KSRs.

In vivo microscopy reveals extensive embedding of capillaries within the sarcolemma of skeletal muscle fibers

OBJECTIVE

To provide insight into mitochondrial function in vivo, we evaluated the 3D spatial relationship between capillaries, mitochondria, and muscle fibers in live mice.

METHODS

3D volumes of in vivo murine TA muscles were imaged by MPM. Muscle fiber type, mitochondrial distribution, number of capillaries, and capillary-to-fiber contact were assessed. The role of Mb-facilitated diffusion was examined in Mb KO mice. Distribution of GLUT4 was also evaluated in the context of the capillary and mitochondrial network.

RESULTS

MPM revealed that 43.6 ± 3.3% of oxidative fiber capillaries had ≥50% of their circumference embedded in a groove in the sarcolemma, in vivo. Embedded capillaries were tightly associated with dense mitochondrial populations lateral to capillary grooves and nearly absent below the groove. Mitochondrial distribution, number of embedded capillaries, and capillary-to-fiber contact were proportional to fiber oxidative capacity and unaffected by Mb KO. GLUT4 did not preferentially localize to embedded capillaries.

CONCLUSIONS

Embedding capillaries in the sarcolemma may provide a regulatory mechanism to optimize delivery of oxygen to heterogeneous groups of muscle fibers. We hypothesize that mitochondria locate to PV regions due to myofibril voids created by embedded capillaries, not to enhance the delivery of oxygen to the mitochondria.

Stimulation of oxidative phosphorylation by calcium in cardiac mitochondria is not influenced by cAMP and PKA activity

Cardiac oxidative ATP generation is finely tuned to match several-fold increases in energy demand. Calcium has been proposed to play a role in the activation of ATP production via PKA phosphorylation in response to intramitochondrial cAMP generation. We evaluated the effect of cAMP, its membrane permeable analogs (dibutyryl-cAMP, 8-bromo-cAMP), and the PKA inhibitor H89 on respiration of isolated pig heart mitochondria. cAMP analogs did not stimulate State 3 respiration of Ca2 +-depleted mitochondria (82.2 ± 3.6% of control), in contrast to the 2-fold activation induced by 0.95 μM free Ca2 +, which was unaffected by H89. Using fluorescence and integrating sphere spectroscopy, we determined that Ca2 + increased the reduction of NADH (8%), and of cytochromes bH (3%), c1 (3%), c (4%), and a (2%), together with a doubling of conductances for Complex I + III and Complex IV. None of these changes were induced by cAMP analogs nor abolished by H89. In Ca2 +-undepleted mitochondria, we observed only slight changes in State 3 respiration rates upon addition of 50 μM cAMP (85 ± 9.9%), dibutyryl-cAMP (80.1 ± 5.2%), 8-bromo-cAMP (88.6 ± 3.3%), or 1 μM H89 (89.7 ± 19.9%) with respect to controls. Similar results were obtained when measuring respiration in heart homogenates. Addition of exogenous PKA with dibutyryl-cAMP or the constitutively active catalytic subunit of PKA to isolated mitochondria decreased State 3 respiration by only 5–15%. These functional studies suggest that alterations in mitochondrial cAMP and PKA activity do not contribute significantly to the acute Ca2 + stimulation of oxidative phosphorylation

The oligomycin-sensitivity conferring protein of mitochondrial ATP synthase: emerging new roles in mitochondrial pathophysiology

The oligomycin-sensitivity conferring protein (OSCP) of the mitochondrial F(O)F1 ATP synthase has long been recognized to be essential for the coupling of proton transport to ATP synthesis. Located on top of the catalytic F1 sector, it makes stable contacts with both F1 and the peripheral stalk, ensuring the structural and functional coupling between F(O) and F1, which is disrupted by the antibiotic, oligomycin. Recent data have established that OSCP is the binding target of cyclophilin (CyP) D, a well-characterized inducer of the mitochondrial permeability transition pore (PTP), whose opening can precipitate cell death. CyPD binding affects ATP synthase activity, and most importantly, it decreases the threshold matrix Ca²⁺ required for PTP opening, in striking analogy with benzodiazepine 423, an apoptosis-inducing agent that also binds OSCP. These findings are consistent with the demonstration that dimers of ATP synthase generate Ca²⁺-dependent currents with features indistinguishable from those of the PTP and suggest that ATP synthase is directly involved in PTP formation, although the underlying mechanism remains to be established. In this scenario, OSCP appears to play a fundamental role, sensing the signal(s) that switches the enzyme of life in a channel able to precipitate cell death.

Prediction of protein kinase-specific phosphorylation sites in hierarchical structure using functional information and random forest

Reversible protein phosphorylation is one of the most important post-translational modifications, which regulates various biological cellular processes. Identification of the kinase-specific phosphorylation sites is helpful for understanding the phosphorylation mechanism and regulation processes. Although a number of computational approaches have been developed, currently few studies are concerned about hierarchical structures of kinases, and most of the existing tools use only local sequence information to construct predictive models. In this work, we conduct a systematic and hierarchy-specific investigation of protein phosphorylation site prediction in which protein kinases are clustered into hierarchical structures with four levels including kinase, subfamily, family and group. To enhance phosphorylation site prediction at all hierarchical levels, functional information of proteins, including gene ontology (GO) and protein-protein interaction (PPI), is adopted in addition to primary sequence to construct prediction models based on random forest. Analysis of selected GO and PPI features shows that functional information is critical in determining protein phosphorylation sites for every hierarchical level. Furthermore, the prediction results of Phospho.ELM and additional testing dataset demonstrate that the proposed method remarkably outperforms existing phosphorylation prediction methods at all hierarchical levels. The proposed method is freely available at http://bioinformatics.ustc.edu.cn/phos_pred/.

PKIS: computational identification of protein kinases for experimentally discovered protein phosphorylation sites

Background

Dynamic protein phosphorylation is an essential regulatory mechanism in various organisms. In this capacity, it is involved in a multitude of signal transduction pathways. Kinase-specific phosphorylation data lay the foundation for reconstruction of signal transduction networks. For this reason, precise annotation of phosphorylated proteins is the first step toward simulating cell signaling pathways. However, the vast majority of kinase-specific phosphorylation data remain undiscovered and existing experimental methods and computational phosphorylation site (P-site) prediction tools have various limitations with respect to addressing this problem.

Results

To address this issue, a novel protein kinase identification web server, PKIS, is here presented for the identification of the protein kinases responsible for experimentally verified P-sites at high specificity, which incorporates the composition of monomer spectrum (CMS) encoding strategy and support vector machines (SVMs). Compared to widely used P-site prediction tools including KinasePhos 2.0, Musite, and GPS2.1, PKIS largely outperformed these tools in identifying protein kinases associated with known P-sites. In addition, PKIS was used on all the P-sites in Phospho.ELM that currently lack kinase information. It successfully identified 14 potential SYK substrates with 36 known P-sites. Further literature search showed that 5 of them were indeed phosphorylated by SYK. Finally, an enrichment analysis was performed and 6 significant SYK-related signal pathways were identified.

Conclusions

In general, PKIS can identify protein kinases for experimental phosphorylation sites efficiently. It is a valuable bioinformatics tool suitable for the study of protein phosphorylation. The PKIS web server is freely available at http://bioinformatics.ustc.edu.cn/pkis.

Continuous monitoring of enzymatic activity within native electrophoresis gels: application to mitochondrial oxidative phosphorylation complexes

Native gel electrophoresis allows the separation of very small amounts of protein complexes while retaining aspects of their activity. In-gel enzymatic assays are usually performed by using reaction-dependent deposition of chromophores or light-scattering precipitates quantified at fixed time points after gel removal and fixation, limiting the ability to analyze the enzyme reaction kinetics. Herein, we describe a custom reaction chamber with reaction medium recirculation and filtering and an imaging system that permits the continuous monitoring of in-gel enzymatic activity even in the presence of turbidity. Images were continuously collected using time-lapse high-resolution digital imaging, and processing routines were developed to obtain kinetic traces of the in-gel activities and analyze reaction time courses. This system also permitted the evaluation of enzymatic activity topology within the protein bands of the gel. This approach was used to analyze the reaction kinetics of two mitochondrial complexes in native gels. Complex IV kinetics showed a short initial linear phase in which catalytic rates could be calculated, whereas Complex V activity revealed a significant lag phase followed by two linear phases. The utility of monitoring the entire kinetic behavior of these reactions in native gels, as well as the general application of this approach, is discussed.

Cardiac mitochondrial matrix and respiratory complex protein phosphorylation

It has become appreciated over the last several years that protein phosphorylation within the cardiac mitochondrial matrix and respiratory complexes is extensive. Given the importance of oxidative phosphorylation and the balance of energy metabolism in the heart, the potential regulatory effect of these classical signaling events on mitochondrial function is of interest. However, the functional impact of protein phosphorylation and the kinase/phosphatase system responsible for it are relatively unknown. Exceptions include the well-characterized pyruvate dehydrogenase and branched chain α-ketoacid dehydrogenase regulatory system. The first task of this review is to update the current status of protein phosphorylation detection primarily in the matrix and evaluate evidence linking these events with enzymatic function or protein processing. To manage the scope of this effort, we have focused on the pathways involved in energy metabolism. The high sensitivity of modern methods of detecting protein phosphorylation and the low specificity of many kinases suggests that detection of protein phosphorylation sites without information on the mole fraction of phosphorylation is difficult to interpret, especially in metabolic enzymes, and is likely irrelevant to function. However, several systems including protein translocation, adenine nucleotide translocase, cytochrome c, and complex IV protein phosphorylation have been well correlated with enzymatic function along with the classical dehydrogenase systems. The second task is to review the current understanding of the kinase/phosphatase system within the matrix. Though it is clear that protein phosphorylation occurs within the matrix, based on (32)P incorporation and quantitative mass spectrometry measures, the kinase/phosphatase system responsible for this process is ill-defined. An argument is presented that remnants of the much more labile bacterial protein phosphoryl transfer system may be present in the matrix and that the evaluation of this possibility will require the application of approaches developed for bacterial cell signaling to the mitochondria.

Role of mitochondrial Ca2+ in the regulation of cellular energetics

Calcium is an important signaling molecule involved in the regulation of many cellular functions. The large free energy in the Ca(2+) ion membrane gradients makes Ca(2+) signaling inherently sensitive to the available cellular free energy, primarily in the form of ATP. In addition, Ca(2+) regulates many cellular ATP-consuming reactions such as muscle contraction, exocytosis, biosynthesis, and neuronal signaling. Thus, Ca(2+) becomes a logical candidate as a signaling molecule for modulating ATP hydrolysis and synthesis during changes in numerous forms of cellular work. Mitochondria are the primary source of aerobic energy production in mammalian cells and also maintain a large Ca(2+) gradient across their inner membrane, providing a signaling potential for this molecule. The demonstrated link between cytosolic and mitochondrial Ca(2+) concentrations, identification of transport mechanisms, and the proximity of mitochondria to Ca(2+) release sites further supports the notion that Ca(2+) can be an important signaling molecule in the energy metabolism interplay of the cytosol with the mitochondria. Here we review sites within the mitochondria where Ca(2+) plays a role in the regulation of ATP generation and potentially contributes to the orchestration of cellular metabolic homeostasis. Early work on isolated enzymes pointed to several matrix dehydrogenases that are stimulated by Ca(2+), which were confirmed in the intact mitochondrion as well as cellular and in vivo systems. However, studies in these intact systems suggested a more expansive influence of Ca(2+) on mitochondrial energy conversion. Numerous noninvasive approaches monitoring NADH, mitochondrial membrane potential, oxygen consumption, and workloads suggest significant effects of Ca(2+) on other elements of NADH generation as well as downstream elements of oxidative phosphorylation, including the F(1)F(O)-ATPase and the cytochrome chain. These other potential elements of Ca(2+) modification of mitochondrial energy conversion will be the focus of this review. Though most specific molecular mechanisms have yet to be elucidated, it is clear that Ca(2+) provides a balanced activation of mitochondrial energy metabolism that exceeds the alteration of dehydrogenases alone.

Regulation of mammalian mitochondrial translation by post-translational modifications

Mitochondria are responsible for the production of over 90% of the energy in eukaryotes through oxidative phosphorylation performed by electron transfer and ATP synthase complexes. Mitochondrial translation machinery is responsible for the synthesis of 13 essential proteins of these complexes encoded by the mitochondrial genome. Emerging data suggest that acetyl-CoA, NAD(+), and ATP are involved in regulation of this machinery through post-translational modifications of its protein components. Recent high-throughput proteomics analyses and mapping studies have provided further evidence for phosphorylation and acetylation of ribosomal proteins and translation factors. Here, we will review our current knowledge related to these modifications and their possible role(s) in the regulation of mitochondrial protein synthesis using the homology between mitochondrial and bacterial translation machineries. However, we have yet to determine the effects of phosphorylation and acetylation of translation components in mammalian mitochondrial biogenesis. This article is part of a Special Issue entitled: Mitochondrial Gene Expression.

Regulation of oxidative phosphorylation complex activity: effects of tissue-specific metabolic stress within an allometric series and acute changes in workload

The concentration of mitochondrial oxidative phosphorylation complexes (MOPCs) is tuned to the maximum energy conversion requirements of a given tissue; however, whether the activity of MOPCs is altered in response to acute changes in energy conversion demand is unclear. We hypothesized that MOPCs activity is modulated by tissue metabolic stress to maintain the energy-metabolism homeostasis. Metabolic stress was defined as the observed energy conversion rate/maximum energy conversion rate. The maximum energy conversion rate was assumed to be proportional to the concentration of MOPCs, as determined with optical spectroscopy, gel electrophoresis, and mass spectrometry. The resting metabolic stress of the heart and liver across the range of resting metabolic rates within an allometric series (mouse, rabbit, and pig) was determined from MPOCs content and literature respiratory values. The metabolic stress of the liver was high and nearly constant across the allometric series due to the proportional increase in MOPCs content with resting metabolic rate. In contrast, the MOPCs content of the heart was essentially constant in the allometric series, resulting in an increasing metabolic stress with decreasing animal size. The MOPCs activity was determined in native gels, with an emphasis on Complex V. Extracted MOPCs enzyme activity was proportional to resting metabolic stress across tissues and species. Complex V activity was also shown to be acutely modulated by changes in metabolic stress in the heart, in vivo and in vitro. The modulation of extracted MOPCs activity suggests that persistent posttranslational modifications (PTMs) alter MOPCs activity both chronically and acutely, specifically in the heart. Protein phosphorylation of Complex V was correlated with activity inhibition under several conditions, suggesting that protein phosphorylation may contribute to activity modulation with energy metabolic stress. These data are consistent with the notion that metabolic stress modulates MOPCs activity in the heart.

Mitochondrial protein phosphorylation as a regulatory modality: implications for mitochondrial dysfunction in heart failure

Phosphorylation of mitochondrial proteins has been recognized for decades, and the regulation of pyruvate- and branched-chain α-ketoacid dehydrogenases by an atypical kinase/phosphatase cascade is well established. More recently, the development of new mass spectrometry-based technologies has led to the discovery of many novel phosphorylation sites on a variety of mitochondrial targets. The evidence suggests that the major classes of kinase and several phosphatases may be present at the mitochondrial outer membrane, intermembrane space, inner membrane, and matrix, but many questions remain to be answered as to the location, timing, and reversibility of these phosphorylation events and whether they are functionally relevant. The authors review phosphorylation as a mitochondrial regulatory strategy and highlight its possible role in the pathophysiology of cardiac hypertrophy and failure.

Intrinsic protein kinase activity in mitochondrial oxidative phosphorylation complexes

Mitochondrial protein phosphorylation is a well-recognized metabolic control mechanism, with the classical example of pyruvate dehydrogenase (PDH) regulation by specific kinases and phosphatases of bacterial origin. However, despite the growing number of reported mitochondrial phosphoproteins, the identity of the protein kinases mediating these phosphorylation events remains largely unknown. The detection of mitochondrial protein kinases is complicated by the low concentration of kinase relative to that of the target protein, the lack of specific antibodies, and contamination from associated, but nonmatrix, proteins. In this study, we use blue native gel electrophoresis (BN-PAGE) to isolate rat and porcine heart mitochondrial complexes for screening of protein kinase activity. To detect kinase activity, one-dimensional BN-PAGE gels were exposed to [γ-(32)P]ATP and then followed by sodium dodecyl sulfate gel electrophoresis. Dozens of mitochondrial proteins were labeled with (32)P in this setting, including all five complexes of oxidative phosphorylation and several citric acid cycle enzymes. The nearly ubiquitous (32)P protein labeling demonstrates protein kinase activity within each mitochondrial protein complex. The validity of this two-dimensional BN-PAGE method was demonstrated by detecting the known PDH kinases and phosphatases within the PDH complex band using Western blots and mass spectrometry. Surprisingly, these same approaches detected only a few additional conventional protein kinases, suggesting a major role for autophosphorylation in mitochondrial proteins. Studies on purified Complex V and creatine kinase confirmed that these proteins undergo autophosphorylation and, to a lesser degree, tenacious (32)P-metabolite association. In-gel Complex IV activity was shown to be inhibited by ATP, and partially reversed by phosphatase activity, consistent with an inhibitory role for protein phosphorylation in this complex. Collectively, this study proposes that many of the mitochondrial complexes contain an autophosphorylation mechanism, which may play a functional role in the regulation of these multiprotein units.

Protein composition and function of red and white skeletal muscle mitochondria

Red and white muscles are faced with very different energetic demands. However, it is unclear whether relative mitochondrial protein expression is different between muscle types. Mitochondria from red and white porcine skeletal muscle were isolated with a Percoll gradient. Differences in protein composition were determined using blue native (BN)-PAGE, two-dimensional differential in gel electrophoresis (2D DIGE), optical spectroscopy, and isobaric tag for relative and absolute quantitation (iTRAQ). Complex IV and V activities were compared using BN-PAGE in-gel activity assays, and maximal mitochondrial respiration rates were assessed using pyruvate (P) + malate (M), glutamate (G) + M, and palmitoyl-carnitine (PC) + M. Without the Percoll step, major cytosolic protein contamination was noted for white mitochondria. Upon removal of contamination, very few protein differences were observed between red and white mitochondria. BN-PAGE showed no differences in the subunit composition of Complexes I-V or the activities of Complexes IV and V. iTRAQ analysis detected 358 mitochondrial proteins, 69 statistically different. Physiological significance may be lower: at a 25% difference, 48 proteins were detected; at 50%, 14 proteins were detected; and 3 proteins were detected at a 100%. Thus any changes could be argued to be physiologically modest. One area of difference was fat metabolism where four β-oxidation enzymes were ∼25% higher in red mitochondria. This was correlated with a 40% higher rate of PC+M oxidation in red mitochondria compared with white mitochondria with no differences in P+M and G+M oxidation. These data suggest that metabolic demand differences between red and white muscle fibers are primarily matched by the number of mitochondria and not by significant alterations in the mitochondria themselves.

The mitochondrial proteome and human disease

For nearly three decades, the sequence of the human mitochondrial genome (mtDNA) has provided a molecular framework for understanding maternally inherited diseases. However, the vast majority of human mitochondrial disorders are caused by nuclear genome defects, which is not surprising since the mtDNA encodes only 13 proteins. Advances in genomics, mass spectrometry, and computation have only recently made it possible to systematically identify the complement of over 1,000 proteins that comprise the mammalian mitochondrial proteome. Here, we review recent progress in characterizing the mitochondrial proteome and highlight insights into its complexity, tissue heterogeneity, evolutionary origins, and biochemical versatility. We then discuss how this proteome is being used to discover the genetic basis of respiratory chain disorders as well as to expand our definition of mitochondrial disease. Finally, we explore future prospects and challenges for using the mitochondrial proteome as a foundation for systems analysis of the organelle.

The mitochondrial proteome: a dynamic functional program in tissues and disease states

The nuclear DNA transcriptional programming of the mitochondria proteome varies dramatically between tissues depending on its functional requirements. This programming generally regulates all of the proteins associated with a metabolic or biosynthetic pathway associated with a given function, essentially regulating the maximum rate of the pathway while keeping the enzymes at the same molar ratio. This may permit the same regulatory mechanisms to function at low- and high-flux capacity situations. This alteration in total protein content results in rather dramatic changes in the mitochondria proteome between tissues. A tissues mitochondria proteome also changes with disease state, in Type 1 diabetes the liver mitochondrial proteome shifts to support ATP production, urea synthesis, and fatty acid oxidation. Acute flux regulation is modulated by numerous posttranslational events that also are highly variable between tissues. The most studied posttranslational modification is protein phosphorylation, which is found all of the complexes of oxidative phosphorylation and most of the major metabolic pathways. The functional significance of these modifications is currently a major area of research along with the kinase and phosphatase regulatory network. This near ubiquitous presence of protein phosphorylations, and other posttranslational events, in the matrix suggest that not all posttranslational events have functional significance. Screening methods are being introduced to detect the active or dynamic posttranslational sites to focus attention on sites that might provide insight into regulatory mechanisms.

Succinyl-CoA synthetase is a phosphate target for the activation of mitochondrial metabolism

Succinyl-CoA synthetase (SCS) is the only mitochondrial enzyme capable of ATP production via substrate level phosphorylation in the absence of oxygen, but it also plays a key role in the citric acid cycle, ketone metabolism, and heme synthesis. Inorganic phosphate (P(i)) is a signaling molecule capable of activating oxidative phosphorylation at several sites, including NADH generation and as a substrate for ATP formation. In this study, it was shown that P(i) binds the porcine heart SCS alpha-subunit (SCSalpha) in a noncovalent manner and enhances its enzymatic activity, thereby providing a new target for P(i) activation in mitochondria. Coupling 32P labeling of intact mitochondria with SDS gel electrophoresis revealed that 32P labeling of SCSalpha was enhanced in substrate-depleted mitochondria. Using mitochondrial extracts and purified bacterial SCS (BSCS), we showed that this enhanced 32P labeling resulted from a simple binding of 32P, not covalent protein phosphorylation. The ability of SCSalpha to retain its 32P throughout the SDS denaturing gel process was unique over the entire mitochondrial proteome. In vitro studies also revealed a P(i)-induced activation of SCS activity by more than 2-fold when mitochondrial extracts and purified BSCS were incubated with millimolar concentrations of P(i). Since the level of 32P binding to SCSalpha was increased in substrate-depleted mitochondria, where the matrix P(i) concentration is increased, we conclude that SCS activation by P(i) binding represents another mitochondrial target for the P(i)-induced activation of oxidative phosphorylation and anaerobic ATP production in energy-limited mitochondria.