GPS: a novel group-based phosphorylation predicting and scoring method

A modular DNA origami nanocompartment for engineering a cell-free, protein unfolding and degradation pathway

Within the cell, chemical reactions are often confined and organized through a modular architecture. This facilitates the targeted localization of molecular species and their efficient translocation to subsequent sites. Here we present a cell-free nanoscale model that exploits compartmentalization strategies to carry out regulated protein unfolding and degradation. Our synthetic model comprises two connected DNA origami nanocompartments (each measuring 25 nm × 41 nm × 53 nm): one containing the protein unfolding machine, p97, and the other housing the protease chymotrypsin. We achieve the unidirectional immobilization of p97 within the first compartment, establishing a gateway mechanism that controls substrate recruitment, translocation and processing within the second compartment. Our data show that, whereas spatial confinement increases the rate of the individual reactions by up to tenfold, the physical connection of the compartmentalized enzymes into a chimera efficiently couples the two reactions and reduces off-target proteolysis by almost sixfold. Hence, our modular approach may serve as a blueprint for engineering artificial nanofactories with reshaped catalytic performance and functionalities beyond those observed in natural systems.

A Review of Machine Learning and Algorithmic Methods for Protein Phosphorylation Site Prediction

Post-translational modifications (PTMs) have key roles in extending the functional diversity of proteins and, as a result, regulating diverse cellular processes in prokaryotic and eukaryotic organisms. Phosphorylation modification is a vital PTM that occurs in most proteins and plays a significant role in many biological processes. Disorders in the phosphorylation process lead to multiple diseases, including neurological disorders and cancers. The purpose of this review is to organize this body of knowledge associated with phosphorylation site (p-site) prediction to facilitate future research in this field. At first, we comprehensively review all related databases and introduce all steps regarding dataset creation, data preprocessing, and method evaluation in p-site prediction. Next, we investigate p-site prediction methods, which are divided into two computational groups: algorithmic and machine learning (ML). Additionally, it is shown that there are basically two main approaches for p-site prediction by ML: conventional and end-to-end deep learning methods, both of which are given an overview. Moreover, this review introduces the most important feature extraction techniques, which have mostly been used in p-site prediction. Finally, we create three test sets from new proteins related to the released version of the database of protein post-translational modifications (dbPTM) in 2022 based on general and human species. Evaluating online p-site prediction tools on newly added proteins introduced in the dbPTM 2022 release, distinct from those in the dbPTM 2019 release, reveals their limitations. In other words, the actual performance of these online p-site prediction tools on unseen proteins is notably lower than the results reported in their respective research papers.

Enhancing Sumoylation Site Prediction: A Deep Neural Network with Discriminative Features

Sumoylation is a post-translation modification (PTM) mechanism that involves many critical biological processes, such as gene expression, localizing and stabilizing proteins, and replicating the genome. Moreover, sumoylation sites are associated with different diseases, including Parkinson's and Alzheimer's. Due to its vital role in the biological process, identifying sumoylation sites in proteins is significant for monitoring protein functions and discovering multiple diseases. Therefore, in the literature, several computational models utilizing conventional ML methods have been introduced to classify sumoylation sites. However, these models cannot accurately classify the sumoylation sites due to intrinsic limitations associated with the conventional learning methods. This paper proposes a robust computational model (called Deep-Sumo) for predicting sumoylation sites based on a deep-learning algorithm with efficient feature representation methods. The proposed model employs a half-sphere exposure method to represent protein sequences in a feature vector. Principal Component Analysis is applied to extract discriminative features by eliminating noisy and redundant features. The discriminant features are given to a multilayer Deep Neural Network (DNN) model to predict sumoylation sites accurately. The performance of the proposed model is extensively evaluated using a 10-fold cross-validation test by considering various statistical-based performance measurement metrics. Initially, the proposed DNN is compared with the traditional learning algorithm, and subsequently, the performance of the Deep-Sumo is compared with the existing models. The validation results show that the proposed model reports an average accuracy of 96.47%, with improvement compared with the existing models. It is anticipated that the proposed model can be used as an effective tool for drug discovery and the diagnosis of multiple diseases.

Phosformer: an explainable transformer model for protein kinase-specific phosphorylation predictions

MOTIVATION

The human genome encodes over 500 distinct protein kinases which regulate nearly all cellular processes by the specific phosphorylation of protein substrates. While advances in mass spectrometry and proteomics studies have identified thousands of phosphorylation sites across species, information on the specific kinases that phosphorylate these sites is currently lacking for the vast majority of phosphosites. Recently, there has been a major focus on the development of computational models for predicting kinase-substrate associations. However, most current models only allow predictions on a subset of well-studied kinases. Furthermore, the utilization of hand-curated features and imbalances in training and testing datasets pose unique challenges in the development of accurate predictive models for kinase-specific phosphorylation prediction. Motivated by the recent development of universal protein language models which automatically generate context-aware features from primary sequence information, we sought to develop a unified framework for kinase-specific phosphosite prediction, allowing for greater investigative utility and enabling substrate predictions at the whole kinome level.

RESULTS

We present a deep learning model for kinase-specific phosphosite prediction, termed Phosformer, which predicts the probability of phosphorylation given an arbitrary pair of unaligned kinase and substrate peptide sequences. We demonstrate that Phosformer implicitly learns evolutionary and functional features during training, removing the need for feature curation and engineering. Further analyses reveal that Phosformer also learns substrate specificity motifs and is able to distinguish between functionally distinct kinase families. Benchmarks indicate that Phosformer exhibits significant improvements compared to the state-of-the-art models, while also presenting a more generalized, unified, and interpretable predictive framework.

AVAILABILITY AND IMPLEMENTATION

Code and data are available at https://github.com/esbgkannan/phosformer.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Elucidating the role of the L-type calcium channel in excitability and energetics in the heart: The ISHR 2020 Research Achievement Award Lecture

Cardiovascular disease continues to be the leading health burden worldwide and with the rising rates in obesity and type II diabetes and ongoing effects of long COVID, it is anticipated that the burden of cardiovascular morbidity and mortality will increase. Calcium is essential to cardiac excitation and contraction. The main route for Ca²⁺ influx is the L-type Ca²⁺ channel (Ca_v1.2) and embryos that are homozygous null for the Ca_v1.2 gene are lethal at day 14 postcoitum. Acute changes in Ca²⁺ influx through the channel contribute to arrhythmia and sudden death, and chronic increases in intracellular Ca²⁺ contribute to pathological hypertrophy and heart failure. We use a multidisciplinary approach to study the regulation of the channel from the molecular level through to in vivo CRISPR mutant animal models. Here we describe some examples of our work from over 2 decades studying the role of the channel under physiological and pathological conditions. Our single channel analysis of purified human Ca_v1.2 protein in proteoliposomes has contributed to understanding direct molecular regulation of the channel including identifying the critical serine involved in the "fight or flight" response. Using the same approach we identified the cysteine responsible for altered function during oxidative stress. Chronic activation of the L-type Ca²⁺ channel during oxidative stress occurs as a result of persistent glutathionylation of the channel that contributes to the development of hypertrophy. We describe for the first time that activation of the channel alters mitochondrial function (and energetics) on a beat-to-beat basis via movement of cytoskeletal proteins. In translational studies we have used this response to "report" mitochondrial function in models of cardiomyopathy and to test efficacy of novel therapies to prevent cardiomyopathy.

Accurate, high-coverage assignment of in vivo protein kinases to phosphosites from in vitro phosphoproteomic specificity data

Phosphoproteomic experiments routinely observe thousands of phosphorylation sites. To understand the intracellular signaling processes that generated this data, one or more causal protein kinases must be assigned to each phosphosite. However, limited knowledge of kinase specificity typically restricts assignments to a small subset of a kinome. Starting from a statistical model of a high-throughput, in vitro kinase-substrate assay, I have developed an approach to high-coverage, multi-label kinase-substrate assignment called IV-KAPhE (“In vivo-Kinase Assignment for Phosphorylation Evidence”). Tested on human data, IV-KAPhE outperforms other methods of similar scope. Such computational methods generally predict a densely connected kinase-substrate network, with most sites targeted by multiple kinases, pointing either to unaccounted-for biochemical constraints or significant cross-talk and signaling redundancy. I show that such predictions can potentially identify biased kinase-site misannotations within families of closely related kinase isozymes and they provide a robust basis for kinase activity analysis.

Proteins can pass around information inside cells about changes in the environment. This process, called intracellular signaling, helps to trigger appropriate cellular responses to environmental changes. One of the main ways information is passed to proteins is through chemical “tagging,” called phosphorylation, by enzymes called protein kinases. We can measure the phosphorylation state of practically all proteins in a cell at any moment. Starting from known cases of phosphorylation by a kinase, many computational methods have been developed to predict if the kinase might tag a certain spot on another protein or if an observed tag was attached by the kinase, with different models for each kinase. I have developed a new method that instead uses a single model to assign one or more kinases to each observed tag, built from the latest large-scale experimental data. This change in focus and unbiased training data allows my method to be significantly more accurate than past methods. I also explored useful applications for my method. For example, I used it to show that much of our knowledge about which kinase is responsible for each tag is probably inaccurately biased towards the commonly studied ones.

p21-Activated Kinase 1 Promotes Breast Tumorigenesis via Phosphorylation and Activation of the Calcium/Calmodulin-Dependent Protein Kinase II

p21-Activated kinase-1 (Pak1) is frequently overexpressed and/or amplified in human breast cancer and is necessary for transformation of mammary epithelial cells. Here, we show that Pak1 interacts with and phosphorylates the Calcium/Calmodulin-dependent Protein Kinase II (CaMKII), and that pharmacological inhibition or depletion of Pak1 leads to diminished activity of CaMKII. We found a strong correlation between Pak1 and CaMKII expression in human breast cancer samples, and combined inhibition of Pak1 and CaMKII with small-molecule inhibitors was synergistic and induced apoptosis more potently in Her2 positive and triple negative breast cancer (TNBC) cells. Co-adminstration of Pak and CaMKII small-molecule inhibitors resulted in a dramatic reduction of proliferation and an increase in apoptosis in a 3D cell culture setting, as well as an impairment in migration and invasion of TNBC cells. Finally, mice bearing xenografts of TNBC cells showed a significant delay in tumor growth when treated with small-molecule inhibitors of Pak and CaMKII. These data delineate a signaling pathway from Pak1 to CaMKII that is required for efficient proliferation, migration and invasion of mammary epithelial cells, and suggest new therapeutic strategies in breast cancer.

PKSPS: a novel method for predicting kinase of specific phosphorylation sites based on maximum weighted bipartite matching algorithm and phosphorylation sequence enrichment analysis

With the development of biotechnology, a large number of phosphorylation sites have been experimentally confirmed and collected, but only a few of them have kinase annotations. Since experimental methods to detect kinases at specific phosphorylation sites are expensive and accidental, some computational methods have been proposed to predict the kinase of these sites, but most methods only consider single sequence information or single functional network information. In this study, a new method Predicting Kinase of Specific Phosphorylation Sites (PKSPS) is developed to predict kinases of specific phosphorylation sites in human proteins by combining PKSPS-Net with PKSPS-Seq, which considers protein-protein interaction (PPI) network information and sequence information. For PKSPS-Net, kinase-kinase and substrate-substrate similarity are quantified based on the topological similarity of proteins in the PPI network, and maximum weighted bipartite matching algorithm is proposed to predict kinase-substrate relationship. In PKSPS-Seq, phosphorylation sequence enrichment analysis is used to analyze the similarity of local sequences around phosphorylation sites and predict the kinase of specific phosphorylation sites (KSP). PKSPS has been proved to be more effective than the PKSPS-Net or PKSPS-Seq on different sets of kinases. Further comparison results show that the PKSPS method performs better than existing methods. Finally, the case study demonstrates the effectiveness of the PKSPS in predicting kinases of specific phosphorylation sites. The open source code and data of the PKSPS can be obtained from https://github.com/guoxinyunncu/PKSPS.

Model-based analysis uncovers mutations altering autophagy selectivity in human cancer

Autophagy can selectively target protein aggregates, pathogens, and dysfunctional organelles for the lysosomal degradation. Aberrant regulation of autophagy promotes tumorigenesis, while it is far less clear whether and how tumor-specific alterations result in autophagic aberrance. To form a link between aberrant autophagy selectivity and human cancer, we establish a computational pipeline and prioritize 222 potential LIR (LC3-interacting region) motif-associated mutations (LAMs) in 148 proteins. We validate LAMs in multiple proteins including ATG4B, STBD1, EHMT2 and BRAF that impair their interactions with LC3 and autophagy activities. Using a combination of transcriptomic, metabolomic and additional experimental assays, we show that STBD1, a poorly-characterized protein, inhibits tumor growth via modulating glycogen autophagy, while a patient-derived W203C mutation on LIR abolishes its cancer inhibitory function. This work suggests that altered autophagy selectivity is a frequently-used mechanism by cancer cells to survive during various stresses, and provides a framework to discover additional autophagy-related pathways that influence carcinogenesis.

Although autophagy has been linked to tumourigenesis, it is unclear how genomic alterations affect autophagy selectivity in tumours. Here, the authors establish a pipeline that integrates computational and experimental approaches to show that altered autophagy selectivity is frequent in cancer cells and link glycogen autophagy with tumourigenesis.

Application of Bayesian phylogenetic inference modelling for evolutionary genetic analysis and dynamic changes in 2019-nCoV

The novel coronavirus (2019-nCoV) has recently caused a large-scale outbreak of viral pneumonia both in China and worldwide. In this study, we obtained the entire genome sequence of 777 new coronavirus strains as of 29 February 2020 from a public gene bank. Bioinformatics analysis of these strains indicated that the mutation rate of these new coronaviruses is not high at present, similar to the mutation rate of the severe acute respiratory syndrome (SARS) virus. The similarities of 2019-nCoV and SARS virus suggested that the S and ORF6 proteins shared a low similarity, while the E protein shared the higher similarity. The 2019-nCoV sequence has similar potential phosphorylation sites and glycosylation sites on the surface protein and the ORF1ab polyprotein as the SARS virus; however, there are differences in potential modification sites between the Chinese strain and some American strains. At the same time, we proposed two possible recombination sites for 2019-nCoV. Based on the results of the skyline, we speculate that the activity of the gene population of 2019-nCoV may be before the end of 2019. As the scope of the 2019-nCoV infection further expands, it may produce different adaptive evolutions due to different environments. Finally, evolutionary genetic analysis can be a useful resource for studying the spread and virulence of 2019-nCoV, which are essential aspects of preventive and precise medicine.

Protein kinase Cα regulates the nucleocytoplasmic shuttling of KRIT1

KRIT1 is a scaffolding protein that regulates multiple molecular mechanisms, including cell-cell and cell-matrix adhesion, and redox homeostasis and signaling. However, rather little is known about how KRIT1 is itself regulated. KRIT1 is found in both the cytoplasm and the nucleus, yet the upstream signaling proteins and mechanisms that regulate KRIT1 nucleocytoplasmic shuttling are not well understood. Here, we identify a key role for protein kinase C (PKC) in this process. In particular, we found that PKC activation promotes the redox-dependent cytoplasmic localization of KRIT1, whereas inhibition of PKC or treatment with the antioxidant N-acetylcysteine leads to KRIT1 nuclear accumulation. Moreover, we demonstrated that the N-terminal region of KRIT1 is crucial for the ability of PKC to regulate KRIT1 nucleocytoplasmic shuttling, and may be a target for PKC-dependent regulatory phosphorylation events. Finally, we found that silencing of PKCα, but not PKCδ, inhibits phorbol 12-myristate 13-acetate (PMA)-induced cytoplasmic enrichment of KRIT1, suggesting a major role for PKCα in regulating KRIT1 nucleocytoplasmic shuttling. Overall, our findings identify PKCα as a novel regulator of KRIT1 subcellular compartmentalization, thus shedding new light on the physiopathological functions of this protein.

Phosphorylation of RIAM by src promotes integrin activation by unmasking the PH domain of RIAM

Integrin activation controls cell adhesion, migration, invasion, and extracellular matrix remodeling. RIAM (RAP1-GTP-interacting adaptor molecule) is recruited by activated RAP1 to the plasma membrane (PM) to mediate integrin activation via an inside-out signaling pathway. This process requires the association of the pleckstrin homology (PH) domain of RIAM with the membrane PIP2. We identify a conserved intermolecular interface that masks the PIP2-binding site in the PH domains of RIAM. Our data indicate that phosphorylation of RIAM by Src family kinases disrupts this PH-mediated interface, unmasks the membrane PIP2-binding site, and promotes integrin activation. We further demonstrate that this process requires phosphorylation of Tyr267 and Tyr427 in the RIAM PH domain by Src. Our data reveal an unorthodox regulatory mechanism of small GTPase effector proteins by phosphorylation-dependent PM association of the PH domain and provide new insights into the link between Src kinases and integrin signaling.

Bioinformatics toolbox for exploring protein phosphorylation network

A clear systematic delineation of the interactions between phosphorylation sites on substrates and their effector kinases plays a fundamental role in revealing cellular activities, understanding signaling modulation mechanisms and proposing novel hypotheses. The emergence of bioinformatics tools contributes to studying phosphorylation network. Some of them feature the visualization of network, enabling more effective trace of the underlying biological problems in a clear and succinct way. In this review, we aimed to provide a toolbox for exploring phosphorylation network. We first systematically surveyed 19 tools that are available for exploring phosphorylation networks, and subsequently comparatively analyzed and summarized these tools to guide tool selection in terms of functionality, data sources, performance, network visualization and implementation, and finally briefly discussed the application cases of these tools. In different scenarios, the conclusion on the suitability of a tool for a specific user may vary. Nevertheless, easily accessible bioinformatics tools are proved to facilitate biological findings. Hopefully, this work might also assist non-specialists, students, as well as computational scientists who aim at developing novel tools in the field of phosphorylation modification.

GPS-Palm: a deep learning-based graphic presentation system for the prediction of S-palmitoylation sites in proteins

As an important reversible lipid modification, S-palmitoylation mainly occurs at specific cysteine residues in proteins, participates in regulating various biological processes and is associated with human diseases. Besides experimental assays, computational prediction of S-palmitoylation sites can efficiently generate helpful candidates for further experimental consideration. Here, we reviewed the current progress in the development of S-palmitoylation site predictors, as well as training data sets, informative features and algorithms used in these tools. Then, we compiled a benchmark data set containing 3098 known S-palmitoylation sites identified from small- or large-scale experiments, and developed a new method named data quality discrimination (DQD) to distinguish data quality weights (DQWs) between the two types of the sites. Besides DQD and our previous methods, we encoded sequence similarity values into images, constructed a deep learning framework of convolutional neural networks (CNNs) and developed a novel algorithm of graphic presentation system (GPS) 6.0. We further integrated nine additional types of sequence-based and structural features, implemented parallel CNNs (pCNNs) and designed a new predictor called GPS-Palm. Compared with other existing tools, GPS-Palm showed a >31.3% improvement of the area under the curve (AUC) value (0.855 versus 0.651) for general prediction of S-palmitoylation sites. We also produced two species-specific predictors, with corresponding AUC values of 0.900 and 0.897 for predicting human- and mouse-specific sites, respectively. GPS-Palm is free for academic research at http://gpspalm.biocuckoo.cn/.

GPS 5.0: An Update on the Prediction of Kinase-specific Phosphorylation Sites in Proteins

In eukaryotes, protein phosphorylation is specifically catalyzed by numerous protein kinases (PKs), faithfully orchestrates various biological processes, and reversibly determines cellular dynamics and plasticity. Here we report an updated algorithm of Group-based Prediction System (GPS) 5.0 to improve the performance for predicting kinase-specific phosphorylation sites (p-sites). Two novel methods, position weight determination (PWD) and scoring matrix optimization (SMO), were developed. Compared with other existing tools, GPS 5.0 exhibits a highly competitive accuracy. Besides serine/threonine or tyrosine kinases, GPS 5.0 also supports the prediction of dual-specificity kinase-specific p-sites. In the classical module of GPS 5.0, 617 individual predictors were constructed for predicting p-sites of 479 human PKs. To extend the application of GPS 5.0, a species-specific module was implemented to predict kinase-specific p-sites for 44,795 PKs in 161 eukaryotes. The online service and local packages of GPS 5.0 are freely available for academic research at http://gps.biocuckoo.cn.

An Information Entropy-Based Approach for Computationally Identifying Histone Lysine Butyrylation

Butyrylation plays a crucial role in the cellular processes. Due to limit of techniques, it is a challenging task to identify histone butyrylation sites on a large scale. To fill the gap, we propose an approach based on information entropy and machine learning for computationally identifying histone butyrylation sites. The proposed method achieves 0.92 of area under the receiver operating characteristic (ROC) curve over the training set by 3-fold cross validation and 0.80 over the testing set by independent test. Feature analysis implies that amino acid residues in the down/upstream of butyrylation sites would exhibit specific sequence motif to a certain extent. Functional analysis suggests that histone butyrylation was most possibly associated with four pathways (systemic lupus erythematosus, alcoholism, viral carcinogenesis and transcriptional misregulation in cancer), was involved in binding with other molecules, processes of biosynthesis, assembly, arrangement or disassembly and was located in such complex as consists of DNA, RNA, protein, etc. The proposed method is useful to predict histone butyrylation sites. Analysis of feature and function improves understanding of histone butyrylation and increases knowledge of functions of butyrylated histones.

PhosTransfer: A Deep Transfer Learning Framework for Kinase-Specific Phosphorylation Site Prediction in Hierarchy

Machine learning algorithms have been widely used for predicting kinase-specific phosphorylation sites. However, the scarcity of training data for specific kinases makes it difficult to train effective models for predicting their phosphorylation sites. In this paper, we propose a deep transfer learning framework, PhosTransfer, for improving kinase-specific phosphorylation site prediction. It banks on the hierarchical information encoded in the kinase classification tree (KCT) which involves four levels: kinase groups, families, subfamilies and protein kinases (PKs). With PhosTransfer, predictive models associated with tree nodes at higher levels, which are trained with more sufficient training data, can be transferred and reused as feature extractors for predictive models of tree nodes at a lower level. Out results indicate that models with deep transfer learning out-performed those without transfer learning for 73 out of 79 tested PKs. The positive effect of deep transfer learning is better demonstrated in the prediction of phosphosites for kinase nodes with less training data. These improved performances are further validated and explained by the visualisation of vector representations generated from hidden layers pre-trained at different KCT levels.

Mechanism of adrenergic CaV1.2 stimulation revealed by proximity proteomics

Increased cardiac contractility during the fight-or-flight response is caused by β-adrenergic augmentation of CaV1.2 voltage-gated calcium channels1-4. However, this augmentation persists in transgenic murine hearts expressing mutant CaV1.2 α1C and β subunits that can no longer be phosphorylated by protein kinase A-an essential downstream mediator of β-adrenergic signalling-suggesting that non-channel factors are also required. Here we identify the mechanism by which β-adrenergic agonists stimulate voltage-gated calcium channels. We express α1C or β2B subunits conjugated to ascorbate peroxidase5 in mouse hearts, and use multiplexed quantitative proteomics6,7 to track hundreds of proteins in the proximity of CaV1.2. We observe that the calcium-channel inhibitor Rad8,9, a monomeric G protein, is enriched in the CaV1.2 microenvironment but is depleted during β-adrenergic stimulation. Phosphorylation by protein kinase A of specific serine residues on Rad decreases its affinity for β subunits and relieves constitutive inhibition of CaV1.2, observed as an increase in channel open probability. Expression of Rad or its homologue Rem in HEK293T cells also imparts stimulation of CaV1.3 and CaV2.2 by protein kinase A, revealing an evolutionarily conserved mechanism that confers adrenergic modulation upon voltage-gated calcium channels.

In Silico Tools and Phosphoproteomic Software Exclusives

Proteomics and phosphoproteomics have been emerging as new dimensions of omics. Phosphorylation has a profound impact on the biological functions and applications of proteins. It influences everything from intrinsic activity and extrinsic executions to cellular localization. This post-translational modification has been subjected to detailed study and has been an object of analytical curiosity with the advent of faster instrumentation. The major strength of phosphoproteomic research lies in the fact that it gives an overall picture of the workforce of the cell. Phosphoproteomics gives deeper insights into understanding the mechanism behind development and progression of a disease. This review for the first time consolidates the list of existing bioinformatics tools developed for phosphoproteomics. The gap between development of bioinformatics tools and their implementation in clinical research is highlighted. The challenge facing progress is ideally believed to be the interdisciplinary arena this field of research is associated with. For meaningful solutions and deliverables, these tools need to be implemented in clinical studies for obtaining answers to pharmacodynamic questions, saving time, costs and energy. This review hopes to invoke some thought in this direction.

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.

Molecular basis for autoinhibition of RIAM regulated by FAK in integrin activation

RAP1-interacting adapter molecule (RIAM) mediates RAP1-induced integrin activation. The RAS-association (RA) segment of the RA-PH module of RIAM interacts with GTP-bound RAP1 and phosphoinositol 4,5 bisphosphate but this interaction is inhibited by the N-terminal segment of RIAM. Here we report the structural basis for the autoinhibition of RIAM by an intramolecular interaction between the IN region (aa 27-93) and the RA-PH module. We solved the crystal structure of IN-RA-PH to a resolution of 2.4-Å. The structure reveals that the IN segment associates with the RA segment and thereby suppresses RIAM:RAP1 association. This autoinhibitory configuration of RIAM can be released by phosphorylation at Tyr45 in the IN segment. Specific inhibitors of focal adhesion kinase (FAK) blocked phosphorylation of Tyr45, inhibited stimulated translocation of RIAM to the plasma membrane, and inhibited integrin-mediated cell adhesion in a Tyr45-dependent fashion. Our results reveal an unusual regulatory mechanism in small GTPase signaling by which the effector molecule is autoinhibited for GTPase interaction, and a modality of integrin activation at the level of RIAM through a FAK-mediated feedforward mechanism that involves reversal of autoinhibition by a tyrosine kinase associated with integrin signaling.

KSIMC: Predicting Kinase⁻Substrate Interactions Based on Matrix Completion

Protein phosphorylation is an important chemical modification catalyzed by kinases. It plays important roles in many cellular processes. Predicting kinase–substrate interactions is vital to understanding the mechanism of many diseases. Many computational methods have been proposed to identify kinase–substrate interactions. However, the prediction accuracy still needs to be improved. Therefore, it is necessary to develop an efficient computational method to predict kinase–substrate interactions. In this paper, we propose a novel computational approach, KSIMC, to identify kinase–substrate interactions based on matrix completion. Firstly, the kinase similarity and substrate similarity are calculated by aligning sequence of kinase–kinase and substrate–substrate, respectively. Then, the original association network is adjusted based on the similarities. Finally, the matrix completion is used to predict potential kinase–substrate interactions. The experiment results show that our method outperforms other state-of-the-art algorithms in performance. Furthermore, the relevant databases and scientific literature verify the effectiveness of our algorithm for new kinase–substrate interaction identification.

Cardiac CaV1.2 channels require β subunits for β-adrenergic-mediated modulation but not trafficking

Ca2+ channel β-subunit interactions with pore-forming α-subunits are long-thought to be obligatory for channel trafficking to the cell surface and for tuning of basal biophysical properties in many tissues. Unexpectedly, we demonstrate that transgenic expression of mutant α1C subunits lacking capacity to bind CaVβ can traffic to the sarcolemma in adult cardiomyocytes in vivo and sustain normal excitation-contraction coupling. However, these β-less Ca2+ channels cannot be stimulated by β-adrenergic pathway agonists, and thus adrenergic augmentation of contractility is markedly impaired in isolated cardiomyocytes and in hearts. Similarly, viral-mediated expression of a β-subunit-sequestering peptide sharply curtailed β-adrenergic stimulation of WT Ca2+ channels, identifying an approach to specifically modulate β-adrenergic regulation of cardiac contractility. Our data demonstrate that β subunits are required for β-adrenergic regulation of CaV1.2 channels and positive inotropy in the heart, but are dispensable for CaV1.2 trafficking to the adult cardiomyocyte cell surface, and for basal function and excitation-contraction coupling.

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Biomarker detection is one of the more important biomedical questions for high-throughput 'omics' researchers, and almost all existing biomarker detection algorithms generate one biomarker subset with the optimized performance measurement for a given dataset. However, a recent study demonstrated the existence of multiple biomarker subsets with similarly effective or even identical classification performances. This protocol presents a simple and straightforward methodology for detecting biomarker subsets with binary classification performances, better than a user-defined cutoff. The protocol consists of data preparation and loading, baseline information summarization, parameter tuning, biomarker screening, result visualization and interpretation, biomarker gene annotations, and result and visualization exportation at publication quality. The proposed biomarker screening strategy is intuitive and demonstrates a general rule for developing biomarker detection algorithms. A user-friendly graphical user interface (GUI) was developed using the programming language Python, allowing biomedical researchers to have direct access to their results. The source code and manual of kSolutionVis can be downloaded from http://www.healthinformaticslab.org/supp/resources.php.

Cardioprotective role of IGF-1 in the hypertrophied myocardium of the spontaneously hypertensive rats: A key effect on NHE-1 activity

AIM

Myocardial Na⁺/H⁺ exchanger-1 (NHE-1) hyperactivity and oxidative stress are interrelated phenomena playing pivotal roles in the development of pathological cardiac hypertrophy and heart failure. Exercise training is effective to convert pathological into physiological hypertrophy in the spontaneously hypertensive rats (SHR), and IGF-1-key humoral mediator of exercise training-inhibits myocardial NHE-1, at least in normotensive rats. Therefore, we hypothesize that IGF-1 by hampering NHE-1 hyperactivity and oxidative stress should exert a cardioprotective effect in the SHR.

METHODS

NHE-1 activity [proton efflux ( J H + ) mmol L^-1 min^-1], expression and phosphorylation; H₂O₂ production; superoxide dismutase (SOD) activity; contractility and calcium transients were measured in SHR hearts in the presence/absence of IGF-1.

RESULTS

IGF-1 significantly decreased NHE-1 activity ( J H + at pH_i 6.95: 1.39 ± 0.32, n = 9 vs C 3.27 ± 0.3, n = 20, P < .05); effect prevented by AG1024, an antagonist of IGF-1 receptor (2.7 ± 0.4, n = 7); by the PI3K inhibitor wortmannin (3.14 ± 0.41, n = 7); and the AKT inhibitor MK2206 (3.37 ± 0.43, n = 14). Moreover, IGF-1 exerted an antioxidant effect revealed by a significant reduction in H₂O₂ production accompanied by an increase in SOD activity. In addition, IGF-1 improved cardiomyocyte contractility as evidenced by an increase in sarcomere shortening and a decrease in the relaxation constant, underlined by an increase in the amplitude and rate of decay of the calcium transients.

CONCLUSION

IGF-1 exerts a cardioprotective role on the hypertrophied hearts of the SHR, in which the inhibition of NHE-1 hyperactivity, as well as the positive inotropic and antioxidant effects, emerges as key players.

Capture of Dense Core Vesicles at Synapses by JNK-Dependent Phosphorylation of Synaptotagmin-4

Delivery of neurotrophins and neuropeptides via long-range trafficking of dense core vesicles (DCVs) from the cell soma to nerve terminals is essential for synapse modulation and circuit function. But the mechanism by which transiting DCVs are captured at specific sites is unknown. Here, we discovered that Synaptotagmin-4 (Syt4) regulates the capture and spatial distribution of DCVs in hippocampal neurons. We found that DCVs are highly mobile and undergo long-range translocation but switch directions only at the distal ends of axons, revealing a circular trafficking pattern. Phosphorylation of serine 135 of Syt4 by JNK steers DCV trafficking by destabilizing Syt4-Kif1A interaction, leading to a transition from microtubule-dependent DCV trafficking to capture at en passant presynaptic boutons by actin. Furthermore, neuronal activity increased DCV capture via JNK-dependent phosphorylation of the S135 site of Syt4. Our data reveal a mechanism that ensures rapid, site-specific delivery of DCVs to synapses.

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Syt4-bearing dense core vesicles in axons traffic continually in a circular pattern

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Phosphorylation of S135 of Syt4 by JNK destabilizes Syt4-Kif1A binding

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Destabilized Syt4-Kif1A binding promotes capture of vesicles at synapses by actin

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Neuronal activity increases vesicle capture via S135-dependent JNK phosphorylation

PhosContext2vec: a distributed representation of residue-level sequence contexts and its application to general and kinase-specific phosphorylation site prediction

Phosphorylation is the most important type of protein post-translational modification. Accordingly, reliable identification of kinase-mediated phosphorylation has important implications for functional annotation of phosphorylated substrates and characterization of cellular signalling pathways. The local sequence context surrounding potential phosphorylation sites is considered to harbour the most relevant information for phosphorylation site prediction models. However, currently there is a lack of condensed vector representation for this important contextual information, despite the presence of varying residue-level features that can be constructed from sequence homology profiles, structural information, and physicochemical properties. To address this issue, we present PhosContext2vec which is a distributed representation of residue-level sequence contexts for potential phosphorylation sites and demonstrate its application in both general and kinase-specific phosphorylation site predictions. Benchmarking experiments indicate that PhosContext2vec could achieve promising predictive performance compared with several other existing methods for phosphorylation site prediction. We envisage that PhosContext2vec, as a new sequence context representation, can be used in combination with other informative residue-level features to improve the classification performance in a number of related bioinformatics tasks that require appropriate residue-level feature vector representation and extraction. The web server of PhosContext2vec is publicly available at http://phoscontext2vec.erc.monash.edu/.

ksrMKL: a novel method for identification of kinase-substrate relationships using multiple kernel learning

Phosphorylation exerts a crucial role in multiple biological cellular processes which is catalyzed by protein kinases and closely related to many diseases. Identification of kinase-substrate relationships is important for understanding phosphorylation and provides a fundamental basis for further disease-related research and drug design. In this study, we develop a novel computational method to identify kinase-substrate relationships based on multiple kernel learning. The comparative analysis is based on a 10-fold cross-validation process and the dataset collected from the Phospho.ELM database. The results show that ksrMKL is greatly improved in various measures when compared with the single kernel support vector machine. Furthermore, with an independent test dataset extracted from the PhosphoSitePlus database, we compare ksrMKL with two existing kinase-substrate relationship prediction tools, namely iGPS and PKIS. The experimental results show that ksrMKL has better prediction performance than these existing tools.

Identification of a novel cAMP dependent protein kinase A phosphorylation site on the human cardiac calcium channel

The "Fight or Flight" response is elicited by extrinsic stress and is necessary in many species for survival. The response involves activation of the β-adrenergic signalling pathway. Surprisingly the mechanisms have remained unresolved. Calcium influx through the cardiac L-type Ca²⁺ channel (Ca_v1.2) is absolutely required. Here we identify the functionally relevant site for PKA phosphorylation on the human cardiac L-type Ca²⁺ channel pore forming α1 subunit using a novel approach. We used a cell free system where we could assess direct effects of PKA on human purified channel protein function reconstituted in proteoliposomes. In addition to assessing open probability of channel protein we used semi-quantitative fluorescent phosphoprotein detection and MS/MS mass spectrometry analysis to demonstrate the PKA specificity of the site. Robust increases in frequency of channel openings were recorded after phosphorylation of the long and short N terminal isoforms and the channel protein with C terminus truncated at aa1504. A protein kinase A anchoring protein (AKAP) was not required. We find the novel PKA phosphorylation site at Ser1458 is in close proximity to the Repeat IV S6 region and induces a conformational change in the channel protein that is necessary and sufficient for increased calcium influx through the channel.

A Novel Phosphorylation Site-Kinase Network-Based Method for the Accurate Prediction of Kinase-Substrate Relationships

Protein phosphorylation is catalyzed by kinases which regulate many aspects that control death, movement, and cell growth. Identification of the phosphorylation site-specific kinase-substrate relationships (ssKSRs) is important for understanding cellular dynamics and provides a fundamental basis for further disease-related research and drug design. Although several computational methods have been developed, most of these methods mainly use local sequence of phosphorylation sites and protein-protein interactions (PPIs) to construct the prediction model. While phosphorylation presents very complicated processes and is usually involved in various biological mechanisms, the aforementioned information is not sufficient for accurate prediction. In this study, we propose a new and powerful computational approach named KSRPred for ssKSRs prediction, by introducing a novel phosphorylation site-kinase network (pSKN) profiles that can efficiently incorporate the relationships between various protein kinases and phosphorylation sites. The experimental results show that the pSKN profiles can efficiently improve the prediction performance in collaboration with local sequence and PPI information. Furthermore, we compare our method with the existing ssKSRs prediction tools and the results demonstrate that KSRPred can significantly improve the prediction performance compared with existing tools.

Crk Tyrosine Phosphorylation Regulates PDGF-BB-inducible Src Activation and Breast Tumorigenicity and Metastasis

The activity of Src family kinases (Src being the prototypical member) is tightly regulated by differential phosphorylation on Tyr416 (positive) and Tyr527 (negative), a duet that reciprocally regulates kinase activity. The latter negative regulation of Src on Tyr527 is mediated by C-terminal Src kinase (CSK) that phosphorylates Tyr527 and maintains Src in a clamped negative regulated state by promoting an intramolecular association. Here it is demonstrated that the SH2- and SH3-domain containing adaptor protein CrkII, by virtue of its phosphorylation on Tyr239, regulates the Csk/Src signaling axis to control Src activation. Once phosphorylated, the motif (PIpYARVIQ) forms a consensus sequence for the SH2 domain of CSK to form a pTyr239-CSK complex. Functionally, when expressed in Crk^-/- MEFs or in Crk^+/+ HS683 cells, Crk Y239F delayed PDGF-BB-inducible Src Tyr416 phosphorylation. Moreover, expression of Crk Y239F in HS683 cells delayed Src kinase activation and suppressed the cell-invasive and -transforming phenotypes. Finally, through loss-of-function and epistasis experiments using CRISPR-Cas9-engineered 4T1 murine breast cancer cells, Crk Tyr239 is implicated in breast cancer tumor growth and metastasis in orthotopic immunocompetent 4T1 mice model of breast adenocarcinoma. These findings delineate a novel role for Crk Tyr239 phosphorylation in the regulation of Src kinases, as well as a potential molecular explanation for a long-standing question as to how Crk regulates the activation of Src kinases.Implications: These findings provide new perspectives on the versatility of Crk in cancer by demonstrating how Crk mechanistically drives, through a tyrosine phosphorylation-dependent manner, tumor growth, and metastasis. Mol Cancer Res; 16(1); 173-83. ©2017 AACR.

Proteolytic cleavage and PKA phosphorylation of α1C subunit are not required for adrenergic regulation of CaV1.2 in the heart

Calcium influx through the voltage-dependent L-type calcium channel (Ca_V1.2) rapidly increases in the heart during "fight or flight" through activation of the β-adrenergic and protein kinase A (PKA) signaling pathway. The precise molecular mechanisms of β-adrenergic activation of cardiac Ca_V1.2, however, are incompletely known, but are presumed to require phosphorylation of residues in α_1C and C-terminal proteolytic cleavage of the α_1C subunit. We generated transgenic mice expressing an α_1C with alanine substitutions of all conserved serine or threonine, which is predicted to be a potential PKA phosphorylation site by at least one prediction tool, while sparing the residues previously shown to be phosphorylated but shown individually not to be required for β-adrenergic regulation of Ca_V1.2 current (17-mutant). A second line included these 17 putative sites plus the five previously identified phosphoregulatory sites (22-mutant), thus allowing us to query whether regulation requires their contribution in combination. We determined that acute β-adrenergic regulation does not require any combination of potential PKA phosphorylation sites conserved in human, guinea pig, rabbit, rat, and mouse α_1C subunits. We separately generated transgenic mice with inducible expression of proteolytic-resistant α_1C Prevention of C-terminal cleavage did not alter β-adrenergic stimulation of Ca_V1.2 in the heart. These studies definitively rule out a role for all conserved consensus PKA phosphorylation sites in α_1C in β-adrenergic stimulation of Ca_V1.2, and show that phosphoregulatory sites on α_1C are not redundant and do not each fractionally contribute to the net stimulatory effect of β-adrenergic stimulation. Further, proteolytic cleavage of α_1C is not required for β-adrenergic stimulation of Ca_V1.2.

Sensitization of TRPA1 by Protein Kinase A

The TRPA1 ion channel is expressed in nociceptive (pain-sensitive) somatosensory neurons and is activated by a wide variety of chemical irritants, such as acrolein in smoke or isothiocyanates in mustard. Here, we investigate the enhancement of TRPA1 function caused by inflammatory mediators, which is thought to be important in lung conditions such as asthma and COPD. Protein kinase A is an important kinase acting downstream of inflammatory mediators to cause sensitization of TRPA1. By using site-directed mutagenesis, patch-clamp electrophysiology and calcium imaging we identify four amino acid residues, S86, S317, S428, and S972, as the principal targets of PKA-mediated phosphorylation and sensitization of TRPA1.

Unraveling Kinase Activation Dynamics Using Kinase-Substrate Relationships from Temporal Large-Scale Phosphoproteomics Studies

In response to stimuli, biological processes are tightly controlled by dynamic cellular signaling mechanisms. Reversible protein phosphorylation occurs on rapid time-scales (milliseconds to seconds), making it an ideal carrier of these signals. Advances in mass spectrometry-based proteomics have led to the identification of many tens of thousands of phosphorylation sites, yet for the majority of these the kinase is unknown and the underlying network topology of signaling networks therefore remains obscured. Identifying kinase substrate relationships (KSRs) is therefore an important goal in cell signaling research. Existing consensus sequence motif based prediction algorithms do not consider the biological context of KSRs, and are therefore insensitive to many other mechanisms guiding kinase-substrate recognition in cellular contexts. Here, we use temporal information to identify biologically relevant KSRs from Large-scale In Vivo Experiments (KSR-LIVE) in a data-dependent and automated fashion. First, we used available phosphorylation databases to construct a repository of existing experimentally-predicted KSRs. For each kinase in this database, we used time-resolved phosphoproteomics data to examine how its substrates changed in phosphorylation over time. Although substrates for a particular kinase clustered together, they often exhibited a different temporal pattern to the phosphorylation of the kinase. Therefore, although phosphorylation regulates kinase activity, our findings imply that substrate phosphorylation likely serve as a better proxy for kinase activity than kinase phosphorylation. KSR-LIVE can thereby infer which kinases are regulated within a biological context. Moreover, KSR-LIVE can also be used to automatically generate positive training sets for the subsequent prediction of novel KSRs using machine learning approaches. We demonstrate that this approach can distinguish between Akt and Rps6kb1, two kinases that share the same linear consensus motif, and provide evidence suggesting IRS-1 S265 as a novel Akt site. KSR-LIVE is an open-access algorithm that allows users to dissect phosphorylation signaling within a specific biological context, with the potential to be included in the standard analysis workflow for studying temporal high-throughput signal transduction data.

DephosSite: a machine learning approach for discovering phosphotase-specific dephosphorylation sites

Protein dephosphorylation, which is an inverse process of phosphorylation, plays a crucial role in a myriad of cellular processes, including mitotic cycle, proliferation, differentiation, and cell growth. Compared with tyrosine kinase substrate and phosphorylation site prediction, there is a paucity of studies focusing on computational methods of predicting protein tyrosine phosphatase substrates and dephosphorylation sites. In this work, we developed two elegant models for predicting the substrate dephosphorylation sites of three specific phosphatases, namely, PTP1B, SHP-1, and SHP-2. The first predictor is called MGPS-DEPHOS, which is modified from the GPS (Group-based Prediction System) algorithm with an interpretable capability. The second predictor is called CKSAAP-DEPHOS, which is built through the combination of support vector machine (SVM) and the composition of k-spaced amino acid pairs (CKSAAP) encoding scheme. Benchmarking experiments using jackknife cross validation and 30 repeats of 5-fold cross validation tests show that MGPS-DEPHOS and CKSAAP-DEPHOS achieved AUC values of 0.921, 0.914 and 0.912, for predicting dephosphorylation sites of the three phosphatases PTP1B, SHP-1, and SHP-2, respectively. Both methods outperformed the previously developed kNN-DEPHOS algorithm. In addition, a web server implementing our algorithms is publicly available at http://genomics.fzu.edu.cn/dephossite/ for the research community.

jEcho: an Evolved weight vector to CHaracterize the protein's posttranslational modification mOtifs

Protein’s posttranslational modification (PTM) represents a major dynamic regulation of protein functions after the translation of polypeptide chains from mRNA molecule. Compared with the costly and labor-intensive wet laboratory characterization of PTMs, the computer-based detection of PTM residues has been a major complementary technique in recent years. Previous studies demonstrated that the PTM-flanking positions convey different contributions to the computational detection of PTM residue, but did not directly translate this observation into the in silico PTM prediction. We propose a weight vector to represent the variant contributions of the PTM-flanking positions and use an evolutionary algorithm to optimize the vector. Even a simple nearest neighbor algorithm with the incorporated optimal weight vector outperforms the currently available algorithms. The algorithm is implemented as an easy-to-use computer program, jEcho version 1.0. The implementation language, Java, makes jEcho platform-independent and visually interactive. The predicted results may be directly exported as publication-quality images or text files. jEcho may be downloaded from http://www.healthinformaticslab.org/supp/.

The KLP-7 Residue S546 Is a Putative Aurora Kinase Site Required for Microtubule Regulation at the Centrosome in C. elegans

Regulation of microtubule dynamics is essential for many cellular processes, including proper assembly and function of the mitotic spindle. The kinesin-13 microtubule-depolymerizing enzymes provide one mechanism to regulate microtubule behaviour temporally and spatially. Vertebrate MCAK locates to chromatin, kinetochores, spindle poles, microtubule tips, and the cytoplasm, implying that the regulation of kinesin-13 activity and subcellular targeting is complex. Phosphorylation of kinesin-13 by Aurora kinase inhibits microtubule depolymerization activity and some Aurora phosphorylation sites on kinesin-13 are required for subcellular localization. Herein, we determine that a C. elegans deletion mutant klp-7(tm2143) causes meiotic and mitotic defects that are consistent with an increase in the amount of microtubules in the cytoplasmic and spindle regions of meiotic embryos, and an increase in microtubules emanating from centrosomes. We show that KLP-7 is phosphorylated by Aurora A and Aurora B kinases in vitro, and that the phosphorylation by Aurora A is stimulated by TPXL-1. Using a structure-function approach, we establish that one putative Aurora kinase site, S546, within the C-terminal part of the core domain is required for the function, but not subcellular localization, of KLP-7 in vivo. Furthermore, FRAP analysis reveals microtubule-dependent differences in the turnover of KLP-7(S546A) and KLP-7(S546E) mutant proteins at the centrosome, suggesting a possible mechanism for the regulation of KLP-7 by Aurora kinase.

Computational identification of protein kinases and kinase-specific substrates in plants

The protein phosphorylation catalyzed by protein kinases (PKs) plays an essential role in almost all biological progresses in plants. Thus, the identification of PKs and kinase-specific substrates is fundamental for understanding the regulatory mechanisms of protein phosphorylation especially in controlling plant growth and development. In this chapter, we describe the computational methods and protocols for the identification of PKs and kinase-specific substrates in plants, by using Vitis vinifera as an example. First, the proteome sequences and experimentally identified phosphorylation sites (p-sites) in Vitis vinifera were downloaded. The potential PKs were computationally identified based on preconstructed Hidden Markov Model (HMM) profiles and ortholog searches, whereas the kinase-specific p-sites, or site-specific kinase-substrate relations (ssKSRs) were initially predicted by the software package of Group-based Prediction System (GPS) and further processed by the iGPS algorithm (in vivo GPS) to filter potentially false positive hits. All primary data sets and prediction results of Vitis vinifera are available at: http://ekpd.biocuckoo.org/protocol.php.

Regulation of translesion DNA synthesis: Posttranslational modification of lysine residues in key proteins

Posttranslational modification of proteins often controls various aspects of their cellular function. Indeed, over the past decade or so, it has been discovered that posttranslational modification of lysine residues plays a major role in regulating translesion DNA synthesis (TLS) and perhaps the most appreciated lysine modification is that of ubiquitination. Much of the recent interest in ubiquitination stems from the fact that proliferating cell nuclear antigen (PCNA) was previously shown to be specifically ubiquitinated at K164 and that such ubiquitination plays a key role in regulating TLS. In addition, TLS polymerases themselves are now known to be ubiquitinated. In the case of human polymerase η, ubiquitination at four lysine residues in its C-terminus appears to regulate its ability to interact with PCNA and modulate TLS. Within the past few years, advances in global proteomic research have revealed that many proteins involved in TLS are, in fact, subject to a previously underappreciated number of lysine modifications. In this review, we will summarize the known lysine modifications of several key proteins involved in TLS; PCNA and Y-family polymerases η, ι, κ and Rev1 and we will discuss the potential regulatory effects of such modification in controlling TLS in vivo.

A grammar inference approach for predicting kinase specific phosphorylation sites

Kinase mediated phosphorylation site detection is the key mechanism of post translational mechanism that plays an important role in regulating various cellular processes and phenotypes. Many diseases, like cancer are related with the signaling defects which are associated with protein phosphorylation. Characterizing the protein kinases and their substrates enhances our ability to understand the mechanism of protein phosphorylation and extends our knowledge of signaling network; thereby helping us to treat such diseases. Experimental methods for predicting phosphorylation sites are labour intensive and expensive. Also, manifold increase of protein sequences in the databanks over the years necessitates the improvement of high speed and accurate computational methods for predicting phosphorylation sites in protein sequences. Till date, a number of computational methods have been proposed by various researchers in predicting phosphorylation sites, but there remains much scope of improvement. In this communication, we present a simple and novel method based on Grammatical Inference (GI) approach to automate the prediction of kinase specific phosphorylation sites. In this regard, we have used a popular GI algorithm Alergia to infer Deterministic Stochastic Finite State Automata (DSFA) which equally represents the regular grammar corresponding to the phosphorylation sites. Extensive experiments on several datasets generated by us reveal that, our inferred grammar successfully predicts phosphorylation sites in a kinase specific manner. It performs significantly better when compared with the other existing phosphorylation site prediction methods. We have also compared our inferred DSFA with two other GI inference algorithms. The DSFA generated by our method performs superior which indicates that our method is robust and has a potential for predicting the phosphorylation sites in a kinase specific manner.

jEcho: an evolved weight vector to characterize the protein's post-translational modification motifs

Protein's post-translational modification (PTM) represents a major dynamic regulation of protein functions after the translation of polypeptide chains from mRNA molecule. Compared with the costly and labor intensive wet lab characterization of PTMs, the computer-based detection of PTM residues has been a major complementary technique in recent years. Previous studies demonstrated that the PTM-flanking positions convey different contributions to the computational detection of PTM residue, but did not directly translate this observation into the in silico PTM prediction. We propose a weight vector to represent the variant contributions of the PTM flanking positions, and use an evolutionary algorithm to optimize the vector. Even a simple nearest neighbor algorithm with the incorporated optimal weight vector outperforms the currently available algorithms. The algorithm is implemented as an easy-to-use computer program, jEcho version 1.0. The implementation language, Java, makes jEcho platform-independent and visually interactive. The predicted results may be directly exported as publication-quality images or text files. jEcho may be downloaded from http://www.healthinformaticslab.org/supp/ .

Evolutionary optimization of transcription factor binding motif detection

All the cell types are under strict control of how their genes are transcribed into expressed transcripts by the temporally dynamic orchestration of the transcription factor binding activities. Given a set of known binding sites (BSs) of a given transcription factor (TF), computational TFBS screening technique represents a cost efficient and large scale strategy to complement the experimental ones. There are two major classes of computational TFBS prediction algorithms based on the tertiary and primary structures, respectively. A tertiary structure based algorithm tries to calculate the binding affinity between a query DNA fragment and the tertiary structure of the given TF. Due to the limited number of available TF tertiary structures, primary structure based TFBS prediction algorithm is a necessary complementary technique for large scale TFBS screening. This study proposes a novel evolutionary algorithm to randomly mutate the weights of different positions in the binding motif of a TF, so that the overall TFBS prediction accuracy is optimized. The comparison with the most widely used algorithm, Position Weight Matrix (PWM), suggests that our algorithm performs better or the same level in all the performance measurements, including sensitivity, specificity, accuracy and Matthews correlation coefficient. Our data also suggests that it is necessary to remove the widely used assumption of independence between motif positions. The supplementary material may be found at: http://www.healthinformaticslab.org/supp/ .

Computational phosphorylation network reconstruction: methods and resources

The succession of protein activation and deactivation mediated by phosphorylation and dephosphorylation events constitutes a key mechanism of molecular information transfer in cellular systems. To deduce the details of those molecular information cascades and networks has been a central goal pursued by both experimental and computational approaches. Many computational network reconstruction methods employing an array of different statistical learning methods have been developed to infer phosphorylation networks based on different types of molecular data sets such as protein sequence, protein structure, or phosphoproteomics data. In this chapter, different computational network inference methods and resources for biological network reconstruction with a particular focus on phosphorylation networks are surveyed.

Centrosome/Cell cycle uncoupling and elimination in the endoreduplicating intestinal cells of C. elegans

The centrosome cycle is most often coordinated with mitotic cell division through the activity of various essential cell cycle regulators, consequently ensuring that the centriole is duplicated once, and only once, per cell cycle. However, this coupling can be altered in specific developmental contexts; for example, multi-ciliated cells generate hundreds of centrioles without any S-phase requirement for their biogenesis, while Drosophila follicle cells eliminate their centrosomes as they begin to endoreduplicate. In order to better understand how the centrosome cycle and the cell cycle are coordinated in a developmental context we use the endoreduplicating intestinal cell lineage of C. elegans to address how novel variations of the cell cycle impact this important process. In C. elegans, the larval intestinal cells undergo one nuclear division without subsequent cytokinesis, followed by four endocycles that are characterized by successive rounds of S-phase. We monitored the levels of centriolar/centrosomal markers and found that centrosomes lose their pericentriolar material following the nuclear division that occurs during the L1 stage and is thereafter never re-gained. The centrioles then become refractory to S phase regulators that would normally promote duplication during the first endocycle, after which they are eliminated during the L2 stage. Furthermore, we show that SPD-2 plays a central role in the numeral regulation of centrioles as a potential target of CDK activity. On the other hand, the phosphorylation on SPD-2 by Polo-like kinase, the transcriptional regulation of genes that affect centriole biogenesis, and the ubiquitin/proteasome degradation pathway, contribute collectively to the final elimination of the centrioles during the L2 stage.

Exploiting holistic approaches to model specificity in protein phosphorylation

Phosphate plays a chemically unique role in shaping cellular signaling of all current living systems, especially eukaryotes. Protein phosphorylation has been studied at several levels, from the near-site context, both in sequence and structure, to the crowded cellular environment, and ultimately to the systems-level perspective. Despite the tremendous advances in mass spectrometry and efforts dedicated to the development of ad hoc highly sophisticated methods, phosphorylation site inference and associated kinase identification are still unresolved problems in kinome biology. The sequence and structure of the substrate near-site context are not sufficient alone to model the in vivo phosphorylation rules, and they should be integrated with orthogonal information in all possible applications. Here we provide an overview of the different contexts that contribute to protein phosphorylation, discussing their potential impact in phosphorylation site annotation and in predicting kinase-substrate specificity.

The evolutionary strata of DARPP-32 tail implicates hierarchical functional expansion in higher vertebrates

DARPP-32 (dopamine and adenosine 3', 5'-monophosphate-regulated phosphoprotein of 32 kDa), which belongs to PPP1R1 gene family, is known to act as an important integrator in dopamine-mediated neurotransmission via the inhibition of protein phosphatase-1 (PP1). Besides its neuronal roles, this protein also behaves as a key player in pathological and pharmacological aspects. Use of bioinformatics and phylogenetics approaches to further characterize the molecular features of DARPP-32 can guide future works. Predicted phosphorylation sites on DARPP-32 show conservation across vertebrates. Phylogenetics analysis indicates evolutionary strata of phosphorylation site acquisition at the C-terminus, suggesting functional expansion of DARPP-32, where more diverse signalling cues may involve in regulating DARPP-32 in inhibiting PP1 activity. Moreover, both phylogenetics and synteny analyses suggest de novo origination of PPP1R1 gene family via chromosomal rearrangement and exonization.

An ensemble method approach to investigate kinase-specific phosphorylation sites

Protein phosphorylation is one of the most significant and well-studied post-translational modifications, and it plays an important role in various cellular processes. It has made a considerable impact in understanding the protein functions which are involved in revealing signal transductions and various diseases. The identification of kinase-specific phosphorylation sites has an important role in elucidating the mechanism of phosphorylation; however, experimental techniques for identifying phosphorylation sites are labor intensive and expensive. An exponentially increasing number of protein sequences generated by various laboratories across the globe require computer-aided procedures for reliably and quickly identifying the phosphorylation sites, opening a new horizon for in silico analysis. In this regard, we have introduced a novel ensemble method where we have selected three classifiers (least square support vector machine, multilayer perceptron, and k-Nearest Neighbor) and three different feature encoding parameters (dipeptide composition, physicochemical properties of amino acids, and protein–protein similarity score). Each of these classifiers is trained on each of the three different parameter systems. The final results of the ensemble method are obtained by fusing the results of all the classifiers by a weighted voting algorithm. Extensive experiments reveal that our proposed method can successfully predict phosphorylation sites in a kinase-specific manner and performs significantly better when compared with other existing phosphorylation site prediction methods.