The prediction of palmitoylation site locations using a multiple feature extraction method

Machine Learning Methods in Prediction of Protein Palmitoylation Sites: A Brief Review

Protein palmitoylation is a fundamental and reversible post-translational lipid modification that involves a series of biological processes. Although a large number of experimental studies have explored the molecular mechanism behind the palmitoylation process, the computational methods has attracted much attention for its good performance in predicting palmitoylation sites compared with expensive and time-consuming biochemical experiments. The prediction of protein palmitoylation sites is helpful to reveal its biological mechanism. Therefore, the research on the application of machine learning methods to predict palmitoylation sites has become a hot topic in bioinformatics and promoted the development in the related fields. In this review, we briefly introduced the recent development in predicting protein palmitoylation sites by using machine learningbased methods and discussed their benefits and drawbacks. The perspective of machine learning-based methods in predicting palmitoylation sites was also provided. We hope the review could provide a guide in related fields.

A Eukaryote-Wide Perspective on the Diversity and Evolution of the ARF GTPase Protein Family

The evolution of eukaryotic cellular complexity is interwoven with the extensive diversification of many protein families. One key family is the ARF GTPases that act in eukaryote-specific processes, including membrane traffic, tubulin assembly, actin dynamics, and cilia-related functions. Unfortunately, our understanding of the evolution of this family is limited. Sampling an extensive set of available genome and transcriptome sequences, we have assembled a data set of over 2,000 manually curated ARF family genes from 114 eukaryotic species, including many deeply diverged protist lineages, and carried out comprehensive molecular phylogenetic analyses. These reconstructed as many as 16 ARF family members present in the last eukaryotic common ancestor, nearly doubling the previously inferred ancient system complexity. Evidence for the wide occurrence and ancestral origin of Arf6, Arl13, and Arl16 is presented for the first time. Moreover, Arl17, Arl18, and SarB, newly described here, are absent from well-studied model organisms and as a result their function(s) remain unknown. Analyses of our data set revealed a previously unsuspected diversity of membrane association modes and domain architectures within the ARF family. We detail the step-wise expansion of the ARF family in the metazoan lineage, including discovery of several new animal-specific family members. Delving back to its earliest evolution in eukaryotes, the resolved relationship observed between the ARF family paralogs sets boundaries for scenarios of vesicle coat origins during eukaryogenesis. Altogether, our work fundamentally broadens the understanding of the diversity and evolution of a protein family underpinning the structural and functional complexity of the eukaryote cells.

NPalmitoylDeep-PseAAC: A Predictor of N-Palmitoylation Sites in Proteins Using Deep Representations of Proteins and PseAAC via Modified 5-Steps Rule

Background:

Among all the major Post-translational modification, lipid modifications possess special significance due to their widespread functional importance in eukaryotic cells. There exist multiple types of lipid modifications and Palmitoylation, among them, is one of the broader types of modification, having three different types. The N-Palmitoylation is carried out by attachment of palmitic acid to an N-terminal cysteine. Due to the association of N-Palmitoylation with various biological functions and diseases such as Alzheimer’s and other neurodegenerative diseases, its identification is very important.

Objective:

The in vitro, ex vivo and in vivo identification of Palmitoylation is laborious, time-taking and costly. There is a dire need for an efficient and accurate computational model to help researchers and biologists identify these sites, in an easy manner. Herein, we propose a novel prediction model for the identification of N-Palmitoylation sites in proteins.

Method:

The proposed prediction model is developed by combining the Chou’s Pseudo Amino Acid Composition (PseAAC) with deep neural networks. We used well-known deep neural networks (DNNs) for both the tasks of learning a feature representation of peptide sequences and developing a prediction model to perform classification.

Results:

Among different DNNs, Gated Recurrent Unit (GRU) based RNN model showed the highest scores in terms of accuracy, and all other computed measures, and outperforms all the previously reported predictors.

Conclusion:

The proposed GRU based RNN model can help to identify N-Palmitoylation in a very efficient and accurate manner which can help scientists understand the mechanism of this modification in proteins.

Membrane binding properties of the C-terminal segment of retinol dehydrogenase 8

Light absorption by rhodopsin leads to the release of all-trans retinal (ATRal) in the lipid phase of photoreceptor disc membranes. Retinol dehydrogenase 8 (RDH8) then reduces ATRal into all-trans retinol, which is the first step of the visual cycle. The membrane binding of RDH8 has been postulated to be mediated by one or more palmitoylated cysteines located in its C-terminus. Different peptide variants of the C-terminus of RDH8 were thus used to obtain information on the mechanism of membrane binding of this enzyme. Steady-state and time-resolved fluorescence measurements were performed using short and long C-terminal segments of bovine RDH8, comprising one or two tryptophan residues. The data demonstrate that the amphipathic alpha helical structure of the first portion of the C-terminus of RDH8 strongly contributes to its membrane binding, which is also favored by palmitoylation of at least one of the cysteines located in the last portion of the C-terminus.

Mal-Light: Enhancing Lysine Malonylation Sites Prediction Problem Using Evolutionary-based Features

Post Translational Modification (PTM) is considered an important biological process with a tremendous impact on the function of proteins in both eukaryotes, and prokaryotes cells. During the past decades, a wide range of PTMs has been identified. Among them, malonylation is a recently identified PTM which plays a vital role in a wide range of biological interactions. Notwithstanding, this modification plays a potential role in energy metabolism in different species including Homo Sapiens. The identification of PTM sites using experimental methods is time-consuming and costly. Hence, there is a demand for introducing fast and cost-effective computational methods. In this study, we propose a new machine learning method, called Mal-Light, to address this problem. To build this model, we extract local evolutionary-based information according to the interaction of neighboring amino acids using a bi-peptide based method. We then use Light Gradient Boosting (LightGBM) as our classifier to predict malonylation sites. Our results demonstrate that Mal-Light is able to significantly improve malonylation site prediction performance compared to previous studies found in the literature. Using Mal-Light we achieve Matthew's correlation coefficient (MCC) of 0.74 and 0.60, Accuracy of 86.66% and 79.51%, Sensitivity of 78.26% and 67.27%, and Specificity of 95.05% and 91.75%, for Homo Sapiens and Mus Musculus proteins, respectively. Mal-Light is implemented as an online predictor which is publicly available at: (http://brl.uiu.ac.bd/MalLight/).

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/.

Methodology for Detecting Protein Palmitoylation

It is well established that palmitoylation plays a key role in the regulation of immune checkpoints, but the technical challenges in detecting protein palmitoylation have significantly prohibited further researches in this field. Till now, different approaches have been proposed, such as mutagenesis, antibody-based methods, bioinformatic prediction, "palmitate-centric" approaches, and "cysteine-centric" approaches. Of specific importance, high-throughput methods that allow the unbiased discovery of palmitoylation in the whole proteome should be further improved and employed. This chapter will summarize the methodological progresses for detecting protein palmitoylation, aiming to facilitate future researches in the lipid modification of immune checkpoint proteins.

Palmitoylation of Hepatitis C Virus NS2 Regulates Its Subcellular Localization and NS2-NS3 Autocleavage

Chronic infection with hepatitis C virus (HCV) is a major cause of severe liver diseases responsible for nearly 400,000 deaths per year. HCV NS2 protein is a multifunctional regulator of HCV replication involved in both viral-genome replication and infectious-virus assembly. However, the underlying mechanism that enables the protein to participate in multiple steps of HCV replication remains unknown. In this study, we discovered that NS2 palmitoylation is the master regulator of its multiple functions, including NS2-mediated self-cleavage and HCV envelope protein recruitment to the virus assembly sites, which in turn promote HCV RNA replication and infectious-particle assembly, respectively. This newly revealed information suggests that NS2 palmitoylation could serve as a promising target to inhibit both HCV RNA replication and virus assembly, representing a new avenue for host-targeting strategies against HCV infection.

Hepatitis C virus (HCV) nonstructural protein 2 (NS2) is a multifunctional protein implicated in both HCV RNA replication and virus particle assembly. NS2-encoded cysteine protease is responsible for autoprocessing of NS2-NS3 precursor, an essential step in HCV RNA replication. NS2 also promotes HCV particle assembly by recruiting envelope protein 2 (E2) to the virus assembly sites located at the detergent-resistant membranes (DRM). However, the fundamental mechanism regulating multiple functions of NS2 remains unclear. In this study, we discovered that NS2 is palmitoylated at the position 113 cysteine residue (NS2/C113) when expressed by itself in cells and during infectious-HCV replication. Blocking NS2 palmitoylation by introducing an NS2/C113S mutation reduced NS2-NS3 autoprocessing and impaired HCV RNA replication. Replication of the NS2/C113S mutant was restored by inserting an encephalomyocarditis virus (EMCV) internal ribosome entry site (IRES) between NS2 and NS3 to separate the two proteins independently of NS2-mediated autoprocessing. These results suggest that NS2 palmitoylation is critical for HCV RNA replication by promoting NS2-NS3 autoprocessing. The NS2/C113S mutation also impaired infectious-HCV assembly, DRM localization of NS2 and E2, and colocalization of NS2 with Core and endoplasmic reticulum lipid raft-associated protein 2 (Erlin-2). In conclusion, our study revealed that two major functions of NS2 involved in HCV RNA replication and virus assembly, i.e., NS2-NS3 autoprocessing and E2 recruitment to the DRM, are regulated by palmitoylation at NS2/C113. Since S-palmitoylation is reversible, NS2 palmitoylation likely allows NS2 to fine tune both HCV RNA replication and infectious-particle assembly.

IMPORTANCE Chronic infection with hepatitis C virus (HCV) is a major cause of severe liver diseases responsible for nearly 400,000 deaths per year. HCV NS2 protein is a multifunctional regulator of HCV replication involved in both viral-genome replication and infectious-virus assembly. However, the underlying mechanism that enables the protein to participate in multiple steps of HCV replication remains unknown. In this study, we discovered that NS2 palmitoylation is the master regulator of its multiple functions, including NS2-mediated self-cleavage and HCV envelope protein recruitment to the virus assembly sites, which in turn promote HCV RNA replication and infectious-particle assembly, respectively. This newly revealed information suggests that NS2 palmitoylation could serve as a promising target to inhibit both HCV RNA replication and virus assembly, representing a new avenue for host-targeting strategies against HCV infection.

Differential S-Acylation of Enveloped Viruses

Post-translational modifications often regulate protein functioning. Covalent attachment of long chain fatty acids to cysteine residues via a thioester linkage (known as protein palmitoylation or S-acylation) affects protein trafficking, protein-protein and protein-membrane interactions. This post-translational modification is coupled to membrane fusion or virus assembly and may affect viral replication in vitro and thus also virus pathogenesis in vivo. In this review we outline modern methods to study S-acylation of viral proteins and to characterize palmitoylproteomes of virus infected cells. The palmitoylation site predictor CSS-palm is critically tested against the Class I enveloped virus proteins. We further focus on identifying the S-acylation sites directly within acyl-peptides and the specific fatty acid (e.g, palmitate, stearate) bound to them using MALDI-TOF MS-based approaches. The fatty acid heterogeneity/ selectivity issue attracts now more attention since the recently published 3D-structures of two DHHC-acyl-transferases gave a hint how this might be achieved.

Protein palmitoylation and cancer

Protein S-palmitoylation is a reversible post-translational modification that alters the localization, stability, and function of hundreds of proteins in the cell. S-palmitoylation is essential for the function of both oncogenes (e.g., NRAS and EGFR) and tumor suppressors (e.g., SCRIB, melanocortin 1 receptor). In mammalian cells, the thioesterification of palmitate to internal cysteine residues is catalyzed by 23 Asp-His-His-Cys (DHHC)-family palmitoyl S-acyltransferases while the removal of palmitate is catalyzed by serine hydrolases, including acyl-protein thioesterases (APTs). These enzymes modulate the function of important oncogenes and tumor suppressors and often display altered expression patterns in cancer. Targeting S-palmitoylation or the enzymes responsible for palmitoylation dynamics may therefore represent a candidate therapeutic strategy for certain cancers.

Proteomic analysis and prediction of amino acid variations that influence protein posttranslational modifications

Accumulative studies have indicated that amino acid variations through changing the type of residues of the target sites or key flanking residues could directly or indirectly influence protein posttranslational modifications (PTMs) and bring about a detrimental effect on protein function. Computational mutation analysis can greatly narrow down the efforts on experimental work. To increase the utilization of current computational resources, we first provide an overview of computational prediction of amino acid variations that influence protein PTMs and their functional analysis. We also discuss the challenges that are faced while developing novel in silico approaches in the future. The development of better methods for mutation analysis-related protein PTMs will help to facilitate the development of personalized precision medicine.

Extensive molecular tinkering in the evolution of the membrane attachment mode of the Rheb GTPase

Rheb is a conserved and widespread Ras-like GTPase involved in cell growth regulation mediated by the (m)TORC1 kinase complex and implicated in tumourigenesis in humans. Rheb function depends on its association with membranes via prenylated C-terminus, a mechanism shared with many other eukaryotic GTPases. Strikingly, our analysis of a phylogenetically rich sample of Rheb sequences revealed that in multiple lineages this canonical and ancestral membrane attachment mode has been variously altered. The modifications include: (1) accretion to the N-terminus of two different phosphatidylinositol 3-phosphate-binding domains, PX in Cryptista (the fusion being the first proposed synapomorphy of this clade), and FYVE in Euglenozoa and the related undescribed flagellate SRT308; (2) acquisition of lipidic modifications of the N-terminal region, namely myristoylation and/or S-palmitoylation in seven different protist lineages; (3) acquisition of S-palmitoylation in the hypervariable C-terminal region of Rheb in apusomonads, convergently to some other Ras family proteins; (4) replacement of the C-terminal prenylation motif with four transmembrane segments in a novel Rheb paralog in the SAR clade; (5) loss of an evident C-terminal membrane attachment mechanism in Tremellomycetes and some Rheb paralogs of Euglenozoa. Rheb evolution is thus surprisingly dynamic and presents a spectacular example of molecular tinkering.

MDD-Palm: Identification of protein S-palmitoylation sites with substrate motifs based on maximal dependence decomposition

S-palmitoylation, the covalent attachment of 16-carbon palmitic acids to a cysteine residue via a thioester linkage, is an important reversible lipid modification that plays a regulatory role in a variety of physiological and biological processes. As the number of experimentally identified S-palmitoylated peptides increases, it is imperative to investigate substrate motifs to facilitate the study of protein S-palmitoylation. Based on 710 non-homologous S-palmitoylation sites obtained from published databases and the literature, we carried out a bioinformatics investigation of S-palmitoylation sites based on amino acid composition. Two Sample Logo indicates that positively charged and polar amino acids surrounding S-palmitoylated sites may be associated with the substrate site specificity of protein S-palmitoylation. Additionally, maximal dependence decomposition (MDD) was applied to explore the motif signatures of S-palmitoylation sites by categorizing a large-scale dataset into subgroups with statistically significant conservation of amino acids. Single features such as amino acid composition (AAC), amino acid pair composition (AAPC), position specific scoring matrix (PSSM), position weight matrix (PWM), amino acid substitution matrix (BLOSUM62), and accessible surface area (ASA) were considered, along with the effectiveness of incorporating MDD-identified substrate motifs into a two-layered prediction model. Evaluation by five-fold cross-validation showed that a hybrid of AAC and PSSM performs best at discriminating between S-palmitoylation and non-S-palmitoylation sites, according to the support vector machine (SVM). The two-layered SVM model integrating MDD-identified substrate motifs performed well, with a sensitivity of 0.79, specificity of 0.80, accuracy of 0.80, and Matthews Correlation Coefficient (MCC) value of 0.45. Using an independent testing dataset (613 S-palmitoylated and 5412 non-S-palmitoylated sites) obtained from the literature, we demonstrated that the two-layered SVM model could outperform other prediction tools, yielding a balanced sensitivity and specificity of 0.690 and 0.694, respectively. This two-layered SVM model has been implemented as a web-based system (MDD-Palm), which is now freely available at http://csb.cse.yzu.edu.tw/MDDPalm/.

Progress toward Understanding Protein S-acylation: Prospective in Plants

S-acylation, also known as S-palmitoylation or palmitoylation, is a reversible post-translational lipid modification in which long chain fatty acid, usually the 16-carbon palmitate, covalently attaches to a cysteine residue(s) throughout the protein via a thioester bond. It is involved in an array of important biological processes during growth and development, reproduction and stress responses in plant. S-acylation is a ubiquitous mechanism in eukaryotes catalyzed by a family of enzymes called Protein S-Acyl Transferases (PATs). Since the discovery of the first PAT in yeast in 2002 research in S-acylation has accelerated in the mammalian system and followed by in plant. However, it is still a difficult field to study due to the large number of PATs and even larger number of putative S-acylated substrate proteins they modify in each genome. This is coupled with drawbacks in the techniques used to study S-acylation, leading to the slower progress in this field compared to protein phosphorylation, for example. In this review we will summarize the discoveries made so far based on knowledge learnt from the characterization of protein S-acyltransferases and the S-acylated proteins, the interaction mechanisms between PAT and its specific substrate protein(s) in yeast and mammals. Research in protein S-acylation and PATs in plants will also be covered although this area is currently less well studied in yeast and mammalian systems.

Physicochemical sequence characteristics that influence S-palmitoylation propensity

Over the past 30 years, several hundred eukaryotic proteins spanning from yeast to man have been shown to be S-palmitoylated. This post-translational modification involves the reversible addition of a 16-carbon saturated fatty acyl chain onto the cysteine residue of a protein where it regulates protein membrane association and distribution, conformation, and stability. However, the large-scale proteome-wide discovery of new palmitoylated proteins has been hindered by the difficulty of identifying a palmitoylation consensus sequence. Using a bioinformatics approach, we show that the enrichment of hydrophobic and basic residues, the cellular context of the protein, and the structural features of the residues surrounding the palmitoylated cysteine all influence the likelihood of palmitoylation. We developed a new palmitoylation predictor that incorporates these identified features, and this predictor achieves a Matthews Correlation Coefficient of .74 using 10-fold cross validation, and significantly outperforms existing predictors on unbiased testing sets. This demonstrates that palmitoylation sites can be predicted with accuracy by taking into account not only physiochemical properties of the modified cysteine and its surrounding residues, but also structural parameters and the subcellular localization of the modified cysteine. This will allow for improved predictions of palmitoylated residues in uncharacterized proteins. A web-based version of this predictor is currently under development.

A machine-learning approach for predicting palmitoylation sites from integrated sequence-based features

Palmitoylation is the covalent attachment of lipids to amino acid residues in proteins. As an important form of protein posttranslational modification, it increases the hydrophobicity of proteins, which contributes to the protein transportation, organelle localization, and functions, therefore plays an important role in a variety of cell biological processes. Identification of palmitoylation sites is necessary for understanding protein-protein interaction, protein stability, and activity. Since conventional experimental techniques to determine palmitoylation sites in proteins are both labor intensive and costly, a fast and accurate computational approach to predict palmitoylation sites from protein sequences is in urgent need. In this study, a support vector machine (SVM)-based method was proposed through integrating PSI-BLAST profile, physicochemical properties, [Formula: see text]-mer amino acid compositions (AACs), and [Formula: see text]-mer pseudo AACs into the principal feature vector. A recursive feature selection scheme was subsequently implemented to single out the most discriminative features. Finally, an SVM method was implemented to predict palmitoylation sites in proteins based on the optimal features. The proposed method achieved an accuracy of 99.41% and Matthews Correlation Coefficient of 0.9773 for a benchmark dataset. The result indicates the efficiency and accuracy of our method in prediction of palmitoylation sites based on protein sequences.

Palmitoylation controls DLK localization, interactions and activity to ensure effective axonal injury signaling

Dual leucine-zipper kinase (DLK) is critical for axon-to-soma retrograde signaling following nerve injury. However, it is unknown how DLK, a predicted soluble kinase, conveys long-distance signals and why homologous kinases cannot compensate for loss of DLK. Here, we report that DLK, but not homologous kinases, is palmitoylated at a conserved site adjacent to its kinase domain. Using short-hairpin RNA knockdown/rescue, we find that palmitoylation is critical for DLK-dependent retrograde signaling in sensory axons. This functional importance is because of three novel cellular and molecular roles of palmitoylation, which targets DLK to trafficking vesicles, is required to assemble DLK signaling complexes and, unexpectedly, is essential for DLK's kinase activity. By simultaneously controlling DLK localization, interactions, and activity, palmitoylation ensures that only vesicle-bound DLK is active in neurons. These findings explain how DLK specifically mediates nerve injury responses and reveal a novel cellular mechanism that ensures the specificity of neuronal kinase signaling.

Accurate in silico identification of protein succinylation sites using an iterative semi-supervised learning technique

As a widespread type of protein post-translational modifications (PTMs), succinylation plays an important role in regulating protein conformation, function and physicochemical properties. Compared with the labor-intensive and time-consuming experimental approaches, computational predictions of succinylation sites are much desirable due to their convenient and fast speed. Currently, numerous computational models have been developed to identify PTMs sites through various types of two-class machine learning algorithms. These methods require both positive and negative samples for training. However, designation of the negative samples of PTMs was difficult and if it is not properly done can affect the performance of computational models dramatically. So that in this work, we implemented the first application of positive samples only learning (PSoL) algorithm to succinylation sites prediction problem, which was a special class of semi-supervised machine learning that used positive samples and unlabeled samples to train the model. Meanwhile, we proposed a novel succinylation sites computational predictor called SucPred (succinylation site predictor) by using multiple feature encoding schemes. Promising results were obtained by the SucPred predictor with an accuracy of 88.65% using 5-fold cross validation on the training dataset and an accuracy of 84.40% on the independent testing dataset, which demonstrated that the positive samples only learning algorithm presented here was particularly useful for identification of protein succinylation sites. Besides, the positive samples only learning algorithm can be applied to build predictors for other types of PTMs sites with ease. A web server for predicting succinylation sites was developed and was freely accessible at http://59.73.198.144:8088/SucPred/.

PGluS: prediction of protein S-glutathionylation sites with multiple features and analysis

S-Glutathionylation is a reversible protein post-translational modification, which generates mixed disulfides between glutathione (GSH) and cysteine residues, playing an important role in regulating protein stability, activity, and redox regulation. To fully understand S-glutathionylation mechanisms, identification of substrates and specific S-glutathionylated sites is crucial. Compared with the labor-intensive and time-consuming experimental approaches, computational predictions of S-glutathionylated sites are very desirable due to their convenience and high speed. Therefore, in this study, a new bioinformatics tool named PGluS was developed to predict S-glutathionylated sites based on multiple features and support vector machines. The performance of PGluS was measured with an accuracy of 71.41% and a MCC of 0.431 using the 5-fold cross-validation on the training dataset. Additionally, PGluS was evaluated using an independent testing dataset resulting in an accuracy of 71.25%, which demonstrated that PGluS was very promising for predicting S-glutathionylated sites. Furthermore, feature analysis was performed and it was shown that all features adopted in this method contributed to the S-glutathionylation process. A site-specific analysis showed that S-glutathionylation was intimately correlated with the features derived from its surrounding sites. The conclusions derived from this study might help to understand more of the S-glutathionylation mechanism and guide the related experimental validation. For public access, PGluS is freely accessible at .

PalmPred: an SVM based palmitoylation prediction method using sequence profile information

Protein palmitoylation is the covalent attachment of the 16-carbon fatty acid palmitate to a cysteine residue. It is the most common acylation of protein and occurs only in eukaryotes. Palmitoylation plays an important role in the regulation of protein subcellular localization, stability, translocation to lipid rafts and many other protein functions. Hence, the accurate prediction of palmitoylation site(s) can help in understanding the molecular mechanism of palmitoylation and also in designing various related experiments. Here we present a novel in silico predictor called ‘PalmPred’ to identify palmitoylation sites from protein sequence information using a support vector machine model. The best performance of PalmPred was obtained by incorporating sequence conservation features of peptide of window size 11 using a leave-one-out approach. It helped in achieving an accuracy of 91.98%, sensitivity of 79.23%, specificity of 94.30%, and Matthews Correlation Coefficient of 0.71. PalmPred outperformed existing palmitoylation site prediction methods – IFS-Palm and WAP-Palm on an independent dataset. Based on these measures it can be anticipated that PalmPred will be helpful in identifying candidate palmitoylation sites. All the source datasets, standalone and web-server are available at http://14.139.227.92/mkumar/palmpred/.

A functional genomic analysis of Arabidopsis thaliana PP2C clade D

In the reference dicot plant Arabidopsis thaliana, the PP2C family of P-protein phosphatases includes the products of 80 genes that have been separated into ten multi-protein clades plus six singletons. Clade D includes the products of nine genes distributed among three chromosomes (APD1, At3g12620; APD2, At3g17090; APD3, At3g51370; APD4, At3g55050; APD5, At4g33920; APD6, At4g38520; APD7, At5g02760; APD8, At5g06750; and APD9, At5g66080). As part of a functional genomics analysis of protein phosphorylation, we retrieved expression data from public databases and determined the subcellular protein localization of the members of clade D. While the nine proteins have been grouped together based upon primary sequence alignments, we observed no obvious common patterns in expression or localization. We found chimera with the GFP associated with the nucleus, plasma membrane, the endomembrane system, and mitochondria in transgenic plants.