In Silico Identification of Protein S-Palmitoylation Sites and Their Involvement in Human Inherited Disease

Recognition and remodeling of endosomal zones by sorting nexins

The proteolipid code determines how cytosolic proteins find and remodel membrane surfaces. Here, we investigate how this process works with sorting nexins Snx1 and Snx3. Both proteins form sorting machines by recognizing membrane zones enriched in phosphatidylinositol 3-phosphate (PI3P), phosphatidylserine (PS) and cholesterol. This co-localized combination forms a unique "lipid codon" or lipidon that we propose is responsible for endosomal targeting, as revealed by structures and interactions of their PX domain-based readers. We outline a membrane recognition and remodeling mechanism for Snx1 and Snx3 involving this code element alongside transmembrane pH gradients, dipole moment-guided docking and specific protein-protein interactions. This generates an initial membrane-protein assembly (memtein) that then recruits retromer and additional PX proteins to recruit cell surface receptors for sorting to the trans-Golgi network (TGN), lysosome and plasma membranes. Post-translational modification (PTM) networks appear to regulate how the sorting machines form and operate at each level. The commonalities and differences between these sorting nexins show how the proteolipid code orchestrates parallel flows of molecular information from ribosome emergence to organelle genesis, and illuminates a universally applicable model of the membrane.

Sequence-based machine learning method for predicting the effects of phosphorylation on protein-protein interactions

Protein phosphorylation, catalyzed by kinases, is an important biochemical process, which plays an essential role in multiple cell signaling pathways. Meanwhile, protein-protein interactions (PPI) constitute the signaling pathways. Abnormal phosphorylation status on protein can regulate protein functions through PPI to evoke severe diseases, such as Cancer and Alzheimer's disease. Due to the limited experimental evidence and high costs to experimentally identify novel evidence of phosphorylation regulation on PPI, it is necessary to develop a high-accuracy and user-friendly artificial intelligence method to predict phosphorylation effect on PPI. Here, we proposed a novel sequence-based machine learning method named PhosPPI, which achieved better identification performance (Accuracy and AUC) than other competing predictive methods of Betts, HawkDock and FoldX. PhosPPI is now freely available in web server (https://phosppi.sjtu.edu.cn/). This tool can help the user to identify functional phosphorylation sites affecting PPI and explore phosphorylation-associated disease mechanism and drug development.

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.

Posttranslational modifications in proteins: resources, tools and prediction methods

Posttranslational modifications (PTMs) refer to amino acid side chain modification in some proteins after their biosynthesis. There are more than 400 different types of PTMs affecting many aspects of protein functions. Such modifications happen as crucial molecular regulatory mechanisms to regulate diverse cellular processes. These processes have a significant impact on the structure and function of proteins. Disruption in PTMs can lead to the dysfunction of vital biological processes and hence to various diseases. High-throughput experimental methods for discovery of PTMs are very laborious and time-consuming. Therefore, there is an urgent need for computational methods and powerful tools to predict PTMs. There are vast amounts of PTMs data, which are publicly accessible through many online databases. In this survey, we comprehensively reviewed the major online databases and related tools. The current challenges of computational methods were reviewed in detail as well.

Akt Is S-Palmitoylated: A New Layer of Regulation for Akt

The protein kinase Akt/PKB participates in a great variety of processes, including translation, cell proliferation and survival, as well as malignant transformation and viral infection. In the last few years, novel Akt posttranslational modifications have been found. However, how these modification patterns affect Akt subcellular localization, target specificity and, in general, function is not thoroughly understood. Here, we postulate and experimentally demonstrate by acyl-biotin exchange (ABE) assay and ³H-palmitate metabolic labeling that Akt is S-palmitoylated, a modification related to protein sorting throughout subcellular membranes. Mutating cysteine 344 into serine blocked Akt S-palmitoylation and diminished its phosphorylation at two key sites, T308 and T450. Particularly, we show that palmitoylation-deficient Akt increases its recruitment to cytoplasmic structures that colocalize with lysosomes, a process stimulated during autophagy. Finally, we found that cysteine 344 in Akt1 is important for proper its function, since Akt1-C344S was unable to support adipocyte cell differentiation in vitro. These results add an unexpected new layer to the already complex Akt molecular code, improving our understanding of cell decision-making mechanisms such as cell survival, differentiation and death.

iCysMod: an integrative database for protein cysteine modifications in eukaryotes

As important post-translational modifications, protein cysteine modifications (PCMs) occurring at cysteine thiol group play critical roles in the regulation of various biological processes in eukaryotes. Due to the rapid advancement of high-throughput proteomics technologies, a large number of PCM events have been identified but remain to be curated. Thus, an integrated resource of eukaryotic PCMs will be useful for the research community. In this work, we developed an integrative database for protein cysteine modifications in eukaryotes (iCysMod), which curated and hosted 108 030 PCM events for 85 747 experimentally identified sites on 31 483 proteins from 48 eukaryotes for 8 types of PCMs, including oxidation, S-nitrosylation (-SNO), S-glutathionylation (-SSG), disulfide formation (-SSR), S-sulfhydration (-SSH), S-sulfenylation (-SOH), S-sulfinylation (-SO2H) and S-palmitoylation (-S-palm). Then, browse and search options were provided for accessing the dataset, while various detailed information about the PCM events was well organized for visualization. With human dataset in iCysMod, the sequence features around the cysteine modification sites for each PCM type were analyzed, and the results indicated that various types of PCMs presented distinct sequence recognition preferences. Moreover, different PCMs can crosstalk with each other to synergistically orchestrate specific biological processes, and 37 841 PCM events involved in 119 types of PCM co-occurrences at the same cysteine residues were finally obtained. Taken together, we anticipate that the database of iCysMod would provide a useful resource for eukaryotic PCMs to facilitate related researches, while the online service is freely available at http://icysmod.omicsbio.info.

Computational advances of tumor marker selection and sample classification in cancer proteomics

Cancer proteomics has become a powerful technique for characterizing the protein markers driving transformation of malignancy, tracing proteome variation triggered by therapeutics, and discovering the novel targets and drugs for the treatment of oncologic diseases. To facilitate cancer diagnosis/prognosis and accelerate drug target discovery, a variety of methods for tumor marker identification and sample classification have been developed and successfully applied to cancer proteomic studies. This review article describes the most recent advances in those various approaches together with their current applications in cancer-related studies. Firstly, a number of popular feature selection methods are overviewed with objective evaluation on their advantages and disadvantages. Secondly, these methods are grouped into three major classes based on their underlying algorithms. Finally, a variety of sample separation algorithms are discussed. This review provides a comprehensive overview of the advances on tumor maker identification and patients/samples/tissues separations, which could be guidance to the researches in cancer proteomics.

A critical assessment of the feature selection methods used for biomarker discovery in current metaproteomics studies

Microbial community (MC) has great impact on mediating complex disease indications, biogeochemical cycling and agricultural productivities, which makes metaproteomics powerful technique for quantifying diverse and dynamic composition of proteins or peptides. The key role of biostatistical strategies in MC study is reported to be underestimated, especially the appropriate application of feature selection method (FSM) is largely ignored. Although extensive efforts have been devoted to assessing the performance of FSMs, previous studies focused only on their classification accuracy without considering their ability to correctly and comprehensively identify the spiked proteins. In this study, the performances of 14 FSMs were comprehensively assessed based on two key criteria (both sample classification and spiked protein discovery) using a variety of metaproteomics benchmarks. First, the classification accuracies of those 14 FSMs were evaluated. Then, their abilities in identifying the proteins of different spiked concentrations were assessed. Finally, seven FSMs (FC, LMEB, OPLS-DA, PLS-DA, SAM, SVM-RFE and T-Test) were identified as performing consistently superior or good under both criteria with the PLS-DA performing consistently superior. In summary, this study served as comprehensive analysis on the performances of current FSMs and could provide a valuable guideline for researchers in metaproteomics.

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/.

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 functional annotation of simultaneously improved stability, accuracy and false discovery rate achieved by a sequence-based deep learning

Functional annotation of protein sequence with high accuracy has become one of the most important issues in modern biomedical studies, and computational approaches of significantly accelerated analysis process and enhanced accuracy are greatly desired. Although a variety of methods have been developed to elevate protein annotation accuracy, their ability in controlling false annotation rates remains either limited or not systematically evaluated. In this study, a protein encoding strategy, together with a deep learning algorithm, was proposed to control the false discovery rate in protein function annotation, and its performances were systematically compared with that of the traditional similarity-based and de novo approaches. Based on a comprehensive assessment from multiple perspectives, the proposed strategy and algorithm were found to perform better in both prediction stability and annotation accuracy compared with other de novo methods. Moreover, an in-depth assessment revealed that it possessed an improved capacity of controlling the false discovery rate compared with traditional methods. All in all, this study not only provided a comprehensive analysis on the performances of the newly proposed strategy but also provided a tool for the researcher in the fields of protein function annotation.

Palmitoylation is a post-translational modification of Alix regulating the membrane organization of exosome-like small extracellular vesicles

BACKGROUND

Virtually all cell types have the capacity to secrete nanometer-sized extracellular vesicles, which have emerged in recent years as potent signal transducers and cell-cell communicators. The multifunctional protein Alix is a bona fide exosomal regulator and skeletal muscle cells can release Alix-positive nano-sized extracellular vesicles, offering a new paradigm for understanding how myofibers communicate within skeletal muscle and with other organs. S-palmitoylation is a reversible lipid post-translational modification, involved in different biological processes, such as the trafficking of membrane proteins, achievement of stable protein conformations, and stabilization of protein interactions.

METHODS

Here, we have used an integrated biochemical-biophysical approach to determine whether S-palmitoylation contributes to the regulation of extracellular vesicle production in skeletal muscle cells.

RESULTS

We ascertained that Alix is S-palmitoylated and that this post-translational modification influences its protein-protein interaction with CD9, a member of the tetraspanin protein family. Furthermore, we showed that the structural organization of the lipid bilayer of the small (nano-sized) extracellular vesicle membrane with altered palmitoylation is qualitatively different compared to mock control vesicles.

CONCLUSIONS

We propose that S-palmitoylation regulates the function of Alix in facilitating the interactions among extracellular vesicle-specific regulators and maintains the proper structural organization of exosome-like extracellular vesicle membranes.

GENERAL SIGNIFICANCE

Beyond its biological relevance, our study also provides the means for a comprehensive structural characterization of EVs.

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.

Features of reactive cysteines discovered through computation: from kinase inhibition to enrichment around protein degrons

Large-scale characterisation of cysteine modification is enabling study of the physicochemical determinants of reactivity. We find that location of cysteine at the amino terminus of an α-helix, associated with activity in thioredoxins, is under-represented in human protein structures, perhaps indicative of selection against background reactivity. An amino-terminal helix location underpins the covalent linkage for one class of kinase inhibitors. Cysteine targets for S-palmitoylation, S-glutathionylation, and S-nitrosylation show little correlation with pKa values predicted from structures, although flanking sequences of S-palmitoylated sites are enriched in positively-charged amino acids, which could facilitate palmitoyl group transfer to substrate cysteine. A surprisingly large fraction of modified sites, across the three modifications, would be buried in native protein structure. Furthermore, modified cysteines are (on average) closer to lysine ubiquitinations than are unmodified cysteines, indicating that cysteine redox biology could be associated with protein degradation and degron recognition.

Prediction of phosphorylation sites based on Krawtchouk image moments

Protein phosphorylation is one of the most pervasive post-translational modifications and regulates diverse cellular processes in organisms. Under the catalysis of protein kinases, protein phosphorylation usually occurred in the residues serine (S), threonine (T), or tyrosine (Y). In this contribution, we proposed a novel scheme (named KMPhos) for the theoretical prediction of protein phosphorylation sites. First, the numerical matrix was obtained from a protein sequence fragment by replacing the characters of the residues with the chemical descriptors of amino acid molecules to approximately describe the chemical environment of the protein fragment, which was turned to the grayscale image. Then the Krawtchouk image moments were calculated and used to establish the support vector machine models. The accuracies of 10-fold cross validation for the obtained models on the training set are up to 89.7%, 88.6%, and 90.1% for the residues S, Y, and T, respectively. For the independent test set, the prediction accuracies are up to 90.7% (S), 87.8% (T), and 89.3% (Y). The results of ROC and other evaluations are also satisfactory. Compared with several specialized prediction tools, KMPhos provided the higher accuracy and reliability. An available KMPhos package is provided and can be used directly for phosphorylation sites prediction.

Short-chain acyl-CoA dehydrogenase deficiency: from gene to cell pathology and possible disease mechanisms

Short-chain acyl-CoA dehydrogenase deficiency (SCADD) is an inherited disorder of mitochondrial fatty acid oxidation that is characterized by the presence of increased butyrylcarnitine and ethylmalonic acid (EMA) concentrations in plasma and urine. Individuals with symptomatic SCADD may show relatively severe phenotype, while the majority of those who are diagnosed through newborn screening by tandem mass spectrometry may remain asymptomatic. As such, the associated clinical symptoms are very diverse, ranging from severe metabolic or neuromuscular disabilities to asymptomatic. Molecular analysis of affected individuals has identified rare gene variants along with two common gene variants, c.511C > T and c.625G > A. In vitro studies have demonstrated that the common variants as well as the great majority of rare variants, which are missense variants, impair folding, that may lead to toxic accumulation of the encoded protein, and/or metabolites, and initiate excessive production of ROS and chronic oxidative stress. It has been suggested that this cell toxicity in combination with yet unknown factors can trigger disease development. This association and the full implications of SCADD are not commonly appreciated. Accordingly, there is a worldwide discussion of the relationship of clinical manifestation to SCADD, and whether SCAD gene variants are disease associated at all. Therefore, SCADD is not part of the newborn screening programs in most countries, and consequently many patients with SCAD gene variants do not get a diagnosis and the possibilities to be followed up during development.

The in silico identification of human bile salt export pump (ABCB11) inhibitors associated with cholestatic drug-induced liver injury

Drug-induced liver injury (DILI) is one of the major causes of drug attrition and failure. Currently, there is increasing evidence that direct inhibition of the human bile salt export pump (BSEP/ABCB11) by drugs and/or metabolites is one of the most important mechanisms of cholestatic DILI. In the present study, we employ two in silico methods, random forest (RF) and the pharmacophore method, to recognize potential BSEP inhibitors that could cause cholestatic DILI, with the aim of mitigating the risk of cholestatic DILI to some extent. The RF model achieved the best prediction performance, producing AUC (area under receiver operating characteristic curve) values of 0.901, 0.929 and 0.996 for leave-one-out cross-validation, the test set and the external test set, respectively, indicating that the built RF model has a satisfactory identification ability. As a complement to the RF model, the pharmacophore model was also built and was proved to be reliable with good predictive performance based on the internal and external validation results. Further analysis indicates that hydrophobicity, molecular size and polarity are important factors that influence the inhibitory activity of BSEP. Furthermore, the two models are applied to screen FDA-approved small molecule drugs, among which the drugs with the potential risk of cholestatic DILI are reported. In conclusion, the RF and pharmacophore models that we present can be considered as integrated screening tools to indicate the potential risk of cholestatic DILI by inhibition of BSEP.

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/.

An amphipathic α-helix directs palmitoylation of the large intracellular loop of the sodium/calcium exchanger

The electrogenic sodium/calcium exchanger (NCX) mediates bidirectional calcium transport controlled by the transmembrane sodium gradient. NCX inactivation occurs in the absence of phosphatidylinositol 4,5-bisphosphate and is facilitated by palmitoylation of a single cysteine at position 739 within the large intracellular loop of NCX. The aim of this investigation was to identify the structural determinants of NCX1 palmitoylation. Full-length NCX1 (FL-NCX1) and a YFP fusion protein of the NCX1 large intracellular loop (YFP-NCX1) were expressed in HEK cells. Single amino acid changes around Cys-739 in FL-NCX1 and deletions on the N-terminal side of Cys-739 in YFP-NCX1 did not affect NCX1 palmitoylation, with the exception of the rare human polymorphism S738F, which enhanced FL-NCX1 palmitoylation, and D741A, which modestly reduced it. In contrast, deletion of a 21-amino acid segment enriched in aromatic amino acids on the C-terminal side of Cys-739 abolished YFP-NCX1 palmitoylation. We hypothesized that this segment forms an amphipathic α-helix whose properties facilitate Cys-739 palmitoylation. Introduction of negatively charged amino acids to the hydrophobic face or of helix-breaking prolines impaired palmitoylation of both YFP-NCX1 and FL-NCX1. Alanine mutations on the hydrophilic face of the helix significantly reduced FL-NCX1 palmitoylation. Of note, when the helix-containing segment was introduced adjacent to cysteines that are not normally palmitoylated, they became palmitoylation sites. In conclusion, we have identified an amphipathic α-helix in the NCX1 large intracellular loop that controls NCX1 palmitoylation. NCX1 palmitoylation is governed by a distal secondary structure element rather than by local primary sequence.

Protein post-translational modifications: In silico prediction tools and molecular modeling

Post-translational modifications (PTMs) occur in almost all proteins and play an important role in numerous biological processes by significantly affecting proteins' structure and dynamics. Several computational approaches have been developed to study PTMs (e.g., phosphorylation, sumoylation or palmitoylation) showing the importance of these techniques in predicting modified sites that can be further investigated with experimental approaches. In this review, we summarize some of the available online platforms and their contribution in the study of PTMs. Moreover, we discuss the emerging capabilities of molecular modeling and simulation that are able to complement these bioinformatics methods, providing deeper molecular insights into the biological function of post-translational modified proteins.

Palmitoylation of caspase-6 by HIP14 regulates its activation

Caspase-6 (CASP6) has an important role in axonal degeneration during neuronal apoptosis and in the neurodegenerative diseases Alzheimer and Huntington disease. Decreasing CASP6 activity may help to restore neuronal function in these and other diseases such as stroke and ischemia, where increased CASP6 activity has been implicated. The key to finding approaches to decrease CASP6 activity is a deeper understanding of the mechanisms regulating CASP6 activation. We show that CASP6 is posttranslationally palmitoylated by the palmitoyl acyltransferase HIP14 and that the palmitoylation of CASP6 inhibits its activation. Palmitoylation of CASP6 is decreased both in Hip14-/- mice, where HIP14 is absent, and in YAC128 mice, a model of Huntington disease, where HIP14 is dysfunctional and where CASP6 activity is increased. Molecular modeling suggests that palmitoylation of CASP6 may inhibit its activation via steric blockage of the substrate-binding groove and inhibition of CASP6 dimerization, both essential for CASP6 function. Our studies identify palmitoylation as a novel CASP6 modification and as a key regulator of CASP6 activity.

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.

Clinical relevance of short-chain acyl-CoA dehydrogenase (SCAD) deficiency: Exploring the role of new variants including the first SCAD-disease-causing allele carrying a synonymous mutation

Short-chain acyl-coA dehydrogenase deficiency (SCADD) is an autosomal recessive inborn error of mitochondrial fatty acid oxidation caused by ACADS gene alterations. SCADD is a heterogeneous condition, sometimes considered to be solely a biochemical condition given that it has been associated with variable clinical phenotypes ranging from no symptoms or signs to metabolic decompensation occurring early in life.

A reason for this variability is due to SCAD alterations, such as the common p.Gly209Ser, that confer a disease susceptibility state but require a complex multifactorial/polygenic condition to manifest clinically.

Our study focuses on 12 SCADD patients carrying 11 new ACADS variants, with the purpose of defining genotype–phenotype correlations based on clinical data, metabolite evaluation, molecular analyses, and in silico functional analyses.

Interestingly, we identified a synonymous variant, c.765G > T (p.Gly255Gly) that influences ACADS mRNA splicing accuracy. mRNA characterisation demonstrated that this variant leads to an aberrant splicing product, harbouring a premature stop codon.

Molecular analysis and in silico tools are able to characterise ACADS variants, identifying the severe mutations and consequently indicating which patients could benefit from a long term follow- up. We also emphasise that synonymous mutations can be relevant features and potentially associated with SCADD.

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Molecular and functional analysis can identify severe genotypes

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Patients with severe genotype should receive a long term follow-up.

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We identified the first SCAD synonymous variant.

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The first SCAD silent variant was proven to achieve into a disease causing allele.