Prediction of posttranslational modification of proteins from their amino acid sequence

Computational systems approach towards phosphoproteomics and their downstream regulation

Protein phosphorylation plays an essential role in modulating cell signalling and its downstream transcriptional and translational regulations. Until recently, protein phosphorylation has been studied mostly using low-throughput biochemical assays. The advancement of mass spectrometry (MS)-based phosphoproteomics transformed the field by enabling measurement of proteome-wide phosphorylation events, where tens of thousands of phosphosites are routinely identified and quantified in an experiment. This has brought a significant challenge in analysing large-scale phosphoproteomic data, making computational methods and systems approaches integral parts of phosphoproteomics. Previous works have primarily focused on reviewing the experimental techniques in MS-based phosphoproteomics, yet a systematic survey of the computational landscape in this field is still missing. Here, we review computational methods and tools, and systems approaches that have been developed for phosphoproteomics data analysis. We categorise them into four aspects including data processing, functional analysis, phosphoproteome annotation and their integration with other omics, and in each aspect, we discuss the key methods and example studies. Lastly, we highlight some of the potential research directions on which future work would make a significant contribution to this fast-growing field. We hope this review provides a useful snapshot of the field of computational systems phosphoproteomics and stimulates new research that drives future development.

Deep Learning-Based Advances In Protein Posttranslational Modification Site and Protein Cleavage Prediction

Posttranslational modification (PTM ) is a ubiquitous phenomenon in both eukaryotes and prokaryotes which gives rise to enormous proteomic diversity. PTM mostly comes in two flavors: covalent modification to polypeptide chain and proteolytic cleavage. Understanding and characterization of PTM is a fundamental step toward understanding the underpinning of biology. Recent advances in experimental approaches, mainly mass-spectrometry-based approaches, have immensely helped in obtaining and characterizing PTMs. However, experimental approaches are not enough to understand and characterize more than 450 different types of PTMs and complementary computational approaches are becoming popular. Recently, due to the various advancements in the field of Deep Learning (DL), along with the explosion of applications of DL to various fields, the field of computational prediction of PTM has also witnessed the development of a plethora of deep learning (DL)-based approaches. In this book chapter, we first review some recent DL-based approaches in the field of PTM site prediction. In addition, we also review the recent advances in the not-so-studied PTM , that is, proteolytic cleavage predictions. We describe advances in PTM prediction by highlighting the Deep learning architecture, feature encoding, novelty of the approaches, and availability of the tools/approaches. Finally, we provide an outlook and possible future research directions for DL-based approaches for PTM prediction.

Current status of PTMs structural databases: applications, limitations and prospects

Protein 3D structures, determined by their amino acid sequences, are the support of major crucial biological functions. Post-translational modifications (PTMs) play an essential role in regulating these functions by altering the physicochemical properties of proteins. By virtue of their importance, several PTM databases have been developed and released in decades, but very few of these databases incorporate real 3D structural data. Since PTMs influence the function of the protein and their aberrant states are frequently implicated in human diseases, providing structural insights to understand the influence and dynamics of PTMs is crucial for unraveling the underlying processes. This review is dedicated to the current status of databases providing 3D structural data on PTM sites in proteins. Some of these databases are general, covering multiple types of PTMs in different organisms, while others are specific to one particular type of PTM, class of proteins or organism. The importance of these databases is illustrated with two major types of in silico applications: predicting PTM sites in proteins using machine learning approaches and investigating protein structure-function relationships involving PTMs. Finally, these databases suffer from multiple problems and care must be taken when analyzing the PTMs data.

iProtGly-SS: A Tool to Accurately Predict Protein Glycation Site Using Structural-Based Features

Posttranslational modification (PTM) is an important biological mechanism to promote functional diversity among the proteins. So far, a wide range of PTMs has been identified. Among them, glycation is considered as one of the most important PTMs. Glycation is associated with different neurological disorders including Parkinson and Alzheimer. It is also shown to be responsible for different diseases, including vascular complications of diabetes mellitus. Despite all the efforts have been made so far, the prediction performance of glycation sites using computational methods remains limited. Here we present a newly developed machine learning tool called iProtGly-SS that utilizes sequential and structural information as well as Support Vector Machine (SVM) classifier to enhance lysine glycation site prediction accuracy. The performance of iProtGly-SS was investigated using the three most popular benchmarks used for this task. Our results demonstrate that iProtGly-SS is able to achieve 81.61%, 93.62%, and 92.95% prediction accuracies on these benchmarks, which are significantly better than those results reported in the previous studies. iProtGly-SS is implemented as a web-based tool which is publicly available at http://brl.uiu.ac.bd/iprotgly-ss/ .

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

Motivation

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

Results

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

Availability and implementation

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

Supplementary information

Supplementary data are available at Bioinformatics online.

FINDER: an automated software package to annotate eukaryotic genes from RNA-Seq data and associated protein sequences

BACKGROUND

Gene annotation in eukaryotes is a non-trivial task that requires meticulous analysis of accumulated transcript data. Challenges include transcriptionally active regions of the genome that contain overlapping genes, genes that produce numerous transcripts, transposable elements and numerous diverse sequence repeats. Currently available gene annotation software applications depend on pre-constructed full-length gene sequence assemblies which are not guaranteed to be error-free. The origins of these sequences are often uncertain, making it difficult to identify and rectify errors in them. This hinders the creation of an accurate and holistic representation of the transcriptomic landscape across multiple tissue types and experimental conditions. Therefore, to gauge the extent of diversity in gene structures, a comprehensive analysis of genome-wide expression data is imperative.

RESULTS

We present FINDER, a fully automated computational tool that optimizes the entire process of annotating genes and transcript structures. Unlike current state-of-the-art pipelines, FINDER automates the RNA-Seq pre-processing step by working directly with raw sequence reads and optimizes gene prediction from BRAKER2 by supplementing these reads with associated proteins. The FINDER pipeline (1) reports transcripts and recognizes genes that are expressed under specific conditions, (2) generates all possible alternatively spliced transcripts from expressed RNA-Seq data, (3) analyzes read coverage patterns to modify existing transcript models and create new ones, and (4) scores genes as high- or low-confidence based on the available evidence across multiple datasets. We demonstrate the ability of FINDER to automatically annotate a diverse pool of genomes from eight species.

CONCLUSIONS

FINDER takes a completely automated approach to annotate genes directly from raw expression data. It is capable of processing eukaryotic genomes of all sizes and requires no manual supervision-ideal for bench researchers with limited experience in handling computational tools.

Comprehensive Quantification of Carboxymethyllysine-Modified Peptides in Human Plasma

A prolonged hyperglycemic condition in diabetes mellitus results in glycation of plasma proteins. N(ε)-Carboxymethyllysine (CML) is a well-known protein advanced glycation end product, and one of its mechanisms of formation is through further oxidation of Amadori compound modified lysine (AML). Unlike enrichment of AML peptides using boronate affinity, biochemical enrichment methods are scarce for comprehensive profiling of CML-modified peptides. To address this problem, we used AML peptide sequence and site of modification as template library to identify and quantify CML peptides. In this study, a parallel reaction monitoring workflow was developed to comprehensively quantify CML modified peptides in Type 1 diabetic subjects' plasma with good and poor glycemic control (n = 20 each). A total of 58 CML modified peptides were quantified, which represented 57 CML modification sites in 19 different proteins. Out of the 58 peptides, five were significantly higher in poor glycemic control samples with the area under the receiver operating characteristic curve ≥0.83. These peptides could serve as promising indicators of glycemic control in Type 1 diabetes management.

Phosphorylation of the N-terminal domain of ribosomal P-stalk protein uL10 governs its association with the ribosome

The uL10 protein is the main constituent of the ribosomal P-stalk, anchoring the whole stalk to the ribosome through interactions with rRNA. The P-stalk is the core of the GTPase-associated center (GAC), a critical element for ribosome biogenesis and ribosome translational activity. All P-stalk proteins (uL10, P1, and P2) undergo phosphorylation within their C termini. Here, we show that uL10 has multiple phosphorylation sites, mapped also within the N-terminal rRNA-binding domain. Our results reveal that the introduction of a negative charge within the N terminus of uL10 impairs its association with the ribosome. These findings demonstrate that uL10 N-terminal phosphorylation has regulatory potential governing the uL10 interaction with the ribosome and may control the activity of GAC.

Assessing predictors for new post translational modification sites: A case study on hydroxylation

Post-translational modification (PTM) sites have become popular for predictor development. However, with the exception of phosphorylation and a handful of other examples, PTMs suffer from a limited number of available training examples and sparsity in protein sequences. Here, proline hydroxylation is taken as an example to compare different methods and evaluate their performance on new experimentally determined sites. As a guide for effective experimental design, predictors require both high specificity and sensitivity. However, the self-reported performance may often not be indicative of prediction quality and detection of new sites is not guaranteed. We have benchmarked seven published hydroxylation site predictors on two newly constructed independent datasets. The self-reported performance is found to widely overestimate the real accuracy measured on independent datasets. No predictor performs better than random on new examples, indicating the refined models do not sufficiently generalize to detect new sites. The number of false positives is high and precision low, in particular for non-collagen proteins whose motifs are not conserved. As hydroxylation site predictors do not generalize for new data, caution is advised when using PTM predictors in the absence of independent evaluations, in particular for highly specific sites involved in signalling.

Machine learning methods are extensively used by biologists to design and interpret experiments. Predictors which take the only sequence as input are of particular interest due to the large amount of available sequence data and high self-reported performance. In this work, we evaluated post-translational modification (PTM) predictors for hydroxylation sites and found that they perform no better than random, in strong contrast to performances reported in their original publications. PTMs are chemical amino acid alterations providing the cell with conditional mechanisms to fine tune protein function, regulating complex biological processes such as signalling and cell cycle. Hydroxylation sites are a good PTM test case due to the availability of a range of predictors and an abundance of newly experimentally detected modification sites. Poor performances in our results highlight the overlooked problem of predicting PTMs when best practices are not followed and training data are likely incomplete. Experimentalists should be careful when using PTM predictors blindly and more independent assessments are needed to establish their usefulness in practice.

Bioinformatics analysis of capsid protein of different subtypes rabbit hemorrhagic disease virus

Background

Rabbit Hemorrhagic Disease Virus (RHDV) belongs to the Caliciviridae family, is a highly lethal pathogen to rabbits. Increasing numbers of studies have demonstrated the existence of antigenic variation in RHDV, leading to the emergence of a new RHDV isolate (RHDVb). However, the underlying factors determining the emergence of the new RHDV and its unpredictable epidemiology remain unclear. To investigate these issues, we selected more than 184 partial and/or complete genome sequences of RHDV from GenBank and analyzed their phylogenetic relationships, divergence, and predicted protein modification sites.

Results

Phylogenetic analysis showed that classic RHDV isolates, RHDVa, and RHDVb formed different clades. It’s interesting to note that RHDVa being more closely related to classic RHDV than RHDVb, while RHDVb had a closer genetic relationship to Rabbit Calicivirus (RCV) than to classic RHDV isolates. Moreover, divergence analysis suggested that the accumulation of amino acid (aa) changes might be a consequence of adaptive diversification of capsid protein (VP60) during the division between classical RHDV, RHDVa, RHDVb, and RCV. Notably, the prediction of N-glycosylation sites suggested that RHDVb subtypes had two unique N-glycosylation sites (aa 301, 362) but lacked three other N-glycosylation sites (aa 45, 308, 474) displayed in classic RHDV and RHDVa VP60 implying this divergence of N-glycosylation sites in RHDV might affect viral virulence. Analysis of phosphorylation sites also indicated that some phosphorylation sites in RHDVa and RHDVb differed from those in classic RHDV, potentially related to antigenic variation in RHDV.

Conclusion

The genetic relationship between RHDVb and RCV was closer than classic RHDV isolates. Moreover, compared to RHDV and RHDVa, RHDVb had two unique N-glycosylation sites but lacked three sites, which might affect the virulence of RHDV. These results may provide new clues for further investigations of the origin of new types of RHDV and the mechanisms of genetic variation in RHDV.

Characterization and prediction of positional 4-hydroxyproline and sulfotyrosine, two post-translational modifications that can occur at substantial levels in CHO cells-expressed biotherapeutics

Biotherapeutics may contain a multitude of different post-translational modifications (PTMs) that need to be assessed and possibly monitored and controlled to ensure reproducible product quality. During early development of biotherapeutics, unexpected PTMs might be prevented by in silico identification and characterization together with further molecular engineering. Mass determinations of a human IgG1 (mAb1) and a bispecific IgG-ligand fusion protein (BsAbA) demonstrated the presence of unusual PTMs resulting in major +80 Da, and +16/+32 Da chain variants, respectively. For mAb1, analytical cation exchange chromatography demonstrated the presence of an acidic peak accounting for 20%. A + 79.957 Da modification was localized within the light chain complementarity-determining region-2 and identified as a sulfation based on accurate mass, isotopic distribution, and a complete neutral loss reaction upon collision-induced dissociation. Top-down ultrahigh resolution MALDI-ISD FT-ICR MS of modified and unmodified Fabs allowed the allocation of the sulfation to a specific Tyr residue. An aspartate in amino-terminal position-3 relative to the affected Tyr was found to play a key role in determining the sulfation. For BsAbA, a + 15.995 Da modification was observed and localized to three specific Pro residues explaining the +16 Da chain A, and +16 Da and +32 Da chain B variants. The BsAbA modifications were verified as 4-hydroxyproline and not 3-hydroxyproline in a tryptic peptide map via co-chromatography with synthetic peptides containing the two isomeric forms. Finally, our approach for an alert system based on in-house in silico predictors is presented. This system is designed to prevent these PTMs by molecular design and engineering during early biotherapeutic development.

SPECTRUM - A MATLAB Toolbox for Proteoform Identification from Top-Down Proteomics Data

Top-Down Proteomics (TDP) is an emerging proteomics protocol that involves identification, characterization, and quantitation of intact proteins using high-resolution mass spectrometry. TDP has an edge over other proteomics protocols in that it allows for: (i) accurate measurement of intact protein mass, (ii) high sequence coverage, and (iii) enhanced identification of post-translational modifications (PTMs). However, the complexity of TDP spectra poses a significant impediment to protein search and PTM characterization. Furthermore, limited software support is currently available in the form of search algorithms and pipelines. To address this need, we propose 'SPECTRUM', an open-architecture and open-source toolbox for TDP data analysis. Its salient features include: (i) MS2-based intact protein mass tuning, (ii) de novo peptide sequence tag analysis, (iii) propensity-driven PTM characterization, (iv) blind PTM search, (v) spectral comparison, (vi) identification of truncated proteins, (vii) multifactorial coefficient-weighted scoring, and (viii) intuitive graphical user interfaces to access the aforementioned functionalities and visualization of results. We have validated SPECTRUM using published datasets and benchmarked it against salient TDP tools. SPECTRUM provides significantly enhanced protein identification rates (91% to 177%) over its contemporaries. SPECTRUM has been implemented in MATLAB, and is freely available along with its source code and documentation at https://github.com/BIRL/SPECTRUM/.

PTMselect: optimization of protein modifications discovery by mass spectrometry

Discovery of protein modification sites relies on protein digestion by proteases and mass spectrometry (MS) identification of the modified peptides. Depending on proteases used and target protein sequence, this method yields highly variable coverage of modification sites. We introduce PTMselect, a digestion-simulating software which tailors the optimal set of proteases for discovery of global or targeted modification from any single or multiple proteins.

PIRSitePredict for protein functional site prediction using position-specific rules

Methods focused on predicting 'global' annotations for proteins (such as molecular function, biological process and presence of domains or membership in a family) have reached a relatively mature stage. Methods to provide fine-grained 'local' annotation of functional sites (at the level of individual amino acid) are now coming to the forefront, especially in light of the rapid accumulation of genetic variant data. We have developed a computational method and workflow that predicts functional sites within proteins using position-specific conditional template annotation rules (namely PIR Site Rules or PIRSRs for short). Such rules are curated through review of known protein structural and other experimental data by structural biologists and are used to generate high-quality annotations for the UniProt Knowledgebase (UniProtKB) unreviewed section. To share the PIRSR functional site prediction method with the broader scientific community, we have streamlined our workflow and developed a stand-alone Java software package named PIRSitePredict. We demonstrate the use of PIRSitePredict for functional annotation of de novo assembled genome/transcriptome by annotating uncharacterized proteins from Trinity RNA-seq assembly of embryonic transcriptomes of the following three cartilaginous fishes: Leucoraja erinacea (Little Skate), Scyliorhinus canicula (Small-spotted Catshark) and Callorhinchus milii (Elephant Shark). On average about 1200 lines of annotations were predicted for each species.

PTM-ssMP: A Web Server for Predicting Different Types of Post-translational Modification Sites Using Novel Site-specific Modification Profile

Protein post-translational modifications (PTMs) are chemical modifications of a protein after its translation. Owing to its play an important role in deep understanding of various biological processes and the development of effective drugs, PTM site prediction have become a hot topic in bioinformatics. Recently, many online tools are developed to prediction various types of PTM sites, most of which are based on local sequence and some biological information. However, few of existing tools consider the relations between different PTMs for their prediction task. Here, we develop a web server called PTM-ssMP to predict PTM site, which adopts site-specific modification profile (ssMP) to efficiently extract and encode the information of both proximal PTMs and local sequence simultaneously. In PTM-ssMP we provide efficient prediction of multiple types of PTM site including phosphorylation, lysine acetylation, ubiquitination, sumoylation, methylation, O-GalNAc, O-GlcNAc, sulfation and proteolytic cleavage. To assess the performance of PTM-ssMP, a large number of experimentally verified PTM sites are collected from several sources and used to train and test the prediction models. Our results suggest that ssMP consistently contributes to remarkable improvement of prediction performance. In addition, results of independent tests demonstrate that PTM-ssMP compares favorably with other existing tools for different PTM types. PTM-ssMP is implemented as an online web server with user-friendly interface, which is freely available at http://bioinformatics.ustc.edu.cn/PTM-ssMP/index/.

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.

iProtGly-SS: Identifying protein glycation sites using sequence and structure based features

Glycation is chemical reaction by which sugar molecule bonds with a protein without the help of enzymes. This is often cause to many diseases and therefore the knowledge about glycation is very important. In this paper, we present iProtGly-SS, a protein lysine glycation site identification method based on features extracted from sequence and secondary structural information. In the experiments, we found the best feature groups combination: Amino Acid Composition, Secondary Structure Motifs, and Polarity. We used support vector machine classifier to train our model and used an optimal set of features using a group based forward feature selection technique. On standard benchmark datasets, our method is able to significantly outperform existing methods for glycation prediction. A web server for iProtGly-SS is implemented and publicly available to use: http://brl.uiu.ac.bd/iprotgly-ss/.

[Application of molecular descriptors for recognition of phosphorylation sites in amino acid sequences]

Recognition of the phosphorylation sites in proteins is required for reconstruction of regulatory processes in living systems. This task is complicated because the phosphorylation motifs in amino acid sequences are considerably degenerated. To improve the prediction efficacy researchers often use additional descriptors, which should reflect physicochemical features of site-surrounding regions. We have evaluated the reasonability of this approach by applying molecular descriptors (MNA) for structural presentation of the peptide segments. Comparative testing was performed using the prognostic method PASS and two input data types: sets of the MNA descriptors represented peptides as chemical structures and amino acid sequences written using a one-letter code. Training sets were classified in accordance with the established types of the enzymes (protein kinases), modifying corresponding phosphorylation sites. The accuracy estimates obtained by prognosis validation for various classes of substrates were significantly different with both the letters and molecular descriptors. In case of the letter description, the prognosis accuracy demonstrated less dependence on the length of peptides in the training set, while in the case of structural descriptors the accuracy level was determined by the peptide size and descriptor characteristics (MNA levels). The maximal prognosis accuracy related to various kinase families was achieved at different sizes of molecular fragments covered by the MNA descriptors of corresponding levels. This obviously reflected structural differences in surroundings of phosphorylation sites modified by various protein kinases. The use of molecular descriptors provided the prognostic results comparable with the results obtained using traditional letter representation. The prognosis accuracy demonstrated less dependence on the method describing site-surrounding peptides at higher accuracy rates. Applying the MNA descriptors it is possible to achieve better accuracy in the cases when the letter description cannot provide acceptable accuracy.

Prediction of post-translational modification sites using multiple kernel support vector machine

Protein post-translational modification (PTM) is an important mechanism that is involved in the regulation of protein function. Considering the high-cost and labor-intensive of experimental identification, many computational prediction methods are currently available for the prediction of PTM sites by using protein local sequence information in the context of conserved motif. Here we proposed a novel computational method by using the combination of multiple kernel support vector machines (SVM) for predicting PTM sites including phosphorylation, O-linked glycosylation, acetylation, sulfation and nitration. To largely make use of local sequence information and site-modification relationships, we developed a local sequence kernel and Gaussian interaction profile kernel, respectively. Multiple kernels were further combined to train SVM for efficiently leveraging kernel information to boost predictive performance. We compared the proposed method with existing PTM prediction methods. The experimental results revealed that the proposed method performed comparable or better performance than the existing prediction methods, suggesting the feasibility of the developed kernels and the usefulness of the proposed method in PTM sites prediction.

Observation selection bias in contact prediction and its implications for structural bioinformatics

Next Generation Sequencing is dramatically increasing the number of known protein sequences, with related experimentally determined protein structures lagging behind. Structural bioinformatics is attempting to close this gap by developing approaches that predict structure-level characteristics for uncharacterized protein sequences, with most of the developed methods relying heavily on evolutionary information collected from homologous sequences. Here we show that there is a substantial observational selection bias in this approach: the predictions are validated on proteins with known structures from the PDB, but exactly for those proteins significantly more homologs are available compared to less studied sequences randomly extracted from Uniprot. Structural bioinformatics methods that were developed this way are thus likely to have over-estimated performances; we demonstrate this for two contact prediction methods, where performances drop up to 60% when taking into account a more realistic amount of evolutionary information. We provide a bias-free dataset for the validation for contact prediction methods called NOUMENON.

Single-residue posttranslational modification sites at the N-terminus, C-terminus or in-between: To be or not to be exposed for enzyme access

Many protein posttranslational modifications (PTMs) are the result of an enzymatic reaction. The modifying enzyme has to recognize the substrate protein's sequence motif containing the residue(s) to be modified; thus, the enzyme's catalytic cleft engulfs these residue(s) and the respective sequence environment. This residue accessibility condition principally limits the range where enzymatic PTMs can occur in the protein sequence. Non‐globular, flexible, intrinsically disordered segments or large loops/accessible long side chains should be preferred whereas residues buried in the core of structures should be void of what we call canonical, enzyme‐generated PTMs. We investigate whether PTM sites annotated in UniProtKB (with MOD_RES/LIPID keys) are situated within sequence ranges that can be mapped to known 3D structures. We find that N‐ or C‐termini harbor essentially exclusively canonical PTMs. We also find that the overwhelming majority of all other PTMs are also canonical though, later in the protein's life cycle, the PTM sites can become buried due to complex formation. Among the remaining cases, some can be explained (i) with autocatalysis, (ii) with modification before folding or after temporary unfolding, or (iii) as products of interaction with small, diffusible reactants. Others require further research how these PTMs are mechanistically generated in vivo.

dictyBase 2015: Expanding data and annotations in a new software environment

dictyBase is the model organism database for the social amoeba Dictyostelium discoideum and related species. The primary mission of dictyBase is to provide the biomedical research community with well-integrated high quality data, and tools that enable original research. Data presented at dictyBase is obtained from sequencing centers, groups performing high throughput experiments such as large-scale mutagenesis studies, and RNAseq data, as well as a growing number of manually added functional gene annotations from the published literature, including Gene Ontology, strain, and phenotype annotations. Through the Dicty Stock Center we provide the community with an impressive amount of annotated strains and plasmids. Recently, dictyBase accomplished a major overhaul to adapt an outdated infrastructure to the current technological advances, thus facilitating the implementation of innovative tools and comparative genomics. It also provides new strategies for high quality annotations that enable bench researchers to benefit from the rapidly increasing volume of available data. dictyBase is highly responsive to its users needs, building a successful relationship that capitalizes on the vast efforts of the Dictyostelium research community. dictyBase has become the trusted data resource for Dictyostelium investigators, other investigators or organizations seeking information about Dictyostelium, as well as educators who use this model system.

Structural Analysis of PTM Hotspots (SAPH-ire)--A Quantitative Informatics Method Enabling the Discovery of Novel Regulatory Elements in Protein Families

Predicting the biological function potential of post-translational modifications (PTMs) is becoming increasingly important in light of the exponential increase in available PTM data from high-throughput proteomics. We developed structural analysis of PTM hotspots (SAPH-ire)—a quantitative PTM ranking method that integrates experimental PTM observations, sequence conservation, protein structure, and interaction data to allow rank order comparisons within or between protein families. Here, we applied SAPH-ire to the study of PTMs in diverse G protein families, a conserved and ubiquitous class of proteins essential for maintenance of intracellular structure (tubulins) and signal transduction (large and small Ras-like G proteins). A total of 1728 experimentally verified PTMs from eight unique G protein families were clustered into 451 unique hotspots, 51 of which have a known and cited biological function or response. Using customized software, the hotspots were analyzed in the context of 598 unique protein structures. By comparing distributions of hotspots with known versus unknown function, we show that SAPH-ire analysis is predictive for PTM biological function. Notably, SAPH-ire revealed high-ranking hotspots for which a functional impact has not yet been determined, including phosphorylation hotspots in the N-terminal tails of G protein gamma subunits—conserved protein structures never before reported as regulators of G protein coupled receptor signaling. To validate this prediction we used the yeast model system for G protein coupled receptor signaling, revealing that gamma subunit–N-terminal tail phosphorylation is activated in response to G protein coupled receptor stimulation and regulates protein stability in vivo. These results demonstrate the utility of integrating protein structural and sequence features into PTM prioritization schemes that can improve the analysis and functional power of modification-specific proteomics data.

What's that gene (or protein)? Online resources for exploring functions of genes, transcripts, and proteins

The genomic era has enabled research projects that use approaches including genome-scale screens, microarray analysis, next-generation sequencing, and mass spectrometry-based proteomics to discover genes and proteins involved in biological processes. Such methods generate data sets of gene, transcript, or protein hits that researchers wish to explore to understand their properties and functions and thus their possible roles in biological systems of interest. Recent years have seen a profusion of Internet-based resources to aid this process. This review takes the viewpoint of the curious biologist wishing to explore the properties of protein-coding genes and their products, identified using genome-based technologies. Ten key questions are asked about each hit, addressing functions, phenotypes, expression, evolutionary conservation, disease association, protein structure, interactors, posttranslational modifications, and inhibitors. Answers are provided by presenting the latest publicly available resources, together with methods for hit-specific and data set-wide information retrieval, suited to any genome-based analytical technique and experimental species. The utility of these resources is demonstrated for 20 factors regulating cell proliferation. Results obtained using some of these are discussed in more depth using the p53 tumor suppressor as an example. This flexible and universally applicable approach for characterizing experimental hits helps researchers to maximize the potential of their projects for biological discovery.

Accelerated formulation development of monoclonal antibodies (mAbs) and mAb-based modalities: review of methods and tools

More therapeutic monoclonal antibodies and antibody-based modalities are in development today than ever before, and a faster and more accurate drug discovery process will ensure that the number of candidates coming to the biopharmaceutical pipeline will increase in the future. The process of drug product development and, specifically, formulation development is a critical bottleneck on the way from candidate selection to fully commercialized medicines. This article reviews the latest advances in methods of formulation screening, which allow not only the high-throughput selection of the most suitable formulation but also the prediction of stability properties under manufacturing and long-term storage conditions. We describe how the combination of automation technologies and high-throughput assays creates the opportunity to streamline the formulation development process starting from early preformulation screening through to commercial formulation development. The application of quality by design (QbD) concepts and modern statistical tools are also shown here to be very effective in accelerated formulation development of both typical antibodies and complex modalities derived from them.

Progress and challenges in predicting protein methylation sites

We review the progress in the prediction of protein methylation sites in the past 10 years and discuss the challenges that are faced while developing novel predictors in the future.

The structural and functional signatures of proteins that undergo multiple events of post-translational modification

The structural, functional, and mechanistic characterization of several types of post-translational modifications (PTMs) is well-documented. PTMs, however, may interact or interfere with one another when regulating protein function. Yet, characterization of the structural and functional signatures of their crosstalk has been hindered by the scarcity of data. To this end, we developed a unified sequence-based predictor of 23 types of PTM sites that, we believe, is a useful tool in guiding biological experiments and data interpretation. We then used experimentally determined and predicted PTM sites to investigate two particular cases of potential PTM crosstalk in eukaryotes. First, we identified proteins statistically enriched in multiple types of PTM sites and found that they show preferences toward intrinsically disordered regions as well as functional roles in transcriptional, posttranscriptional, and developmental processes. Second, we observed that target sites modified by more than one type of PTM, referred to as shared PTM sites, show even stronger preferences toward disordered regions than their single-PTM counterparts; we explain this by the need for these regions to accommodate multiple partners. Finally, we investigated the influence of single and shared PTMs on differential regulation of protein-protein interactions. We provide evidence that molecular recognition features (MoRFs) show significant preferences for PTM sites, particularly shared PTM sites, implicating PTMs in the modulation of this specific type of macromolecular recognition. We conclude that intrinsic disorder is a strong structural prerequisite for complex PTM-based regulation, particularly in context-dependent protein-protein interactions related to transcriptional and developmental processes.

AVAILABILITY

www.modpred.org.

Transamidase subunit GAA1/GPAA1 is a M28 family metallo-peptide-synthetase that catalyzes the peptide bond formation between the substrate protein's omega-site and the GPI lipid anchor's phosphoethanolamine

The transamidase subunit GAA1/GPAA1 is predicted to be the enzyme that catalyzes the attachment of the glycosylphosphatidyl (GPI) lipid anchor to the carbonyl intermediate of the substrate protein at the ω-site. Its ~300-amino acid residue lumenal domain is a M28 family metallo-peptide-synthetase with an α/β hydrolase fold, including a central 8-strand β-sheet and a single metal (most likely zinc) ion coordinated by 3 conserved polar residues. Phosphoethanolamine is used as an adaptor to make the non-peptide GPI lipid anchor look chemically similar to the N terminus of a peptide.

Motifs tree: a new method for predicting post-translational modifications

MOTIVATION

Post-translational modifications (PTMs) are important steps in the maturation of proteins. Several models exist to predict specific PTMs, from manually detected patterns to machine learning methods. On one hand, the manual detection of patterns does not provide the most efficient classifiers and requires an important workload, and on the other hand, models built by machine learning methods are hard to interpret and do not increase biological knowledge. Therefore, we developed a novel method based on patterns discovery and decision trees to predict PTMs. The proposed algorithm builds a decision tree, by coupling the C4.5 algorithm with genetic algorithms, producing high-performance white box classifiers. Our method was tested on the initiator methionine cleavage (IMC) and N(α)-terminal acetylation (N-Ac), two of the most common PTMs.

RESULTS

The resulting classifiers perform well when compared with existing models. On a set of eukaryotic proteins, they display a cross-validated Matthews correlation coefficient of 0.83 (IMC) and 0.65 (N-Ac). When used to predict potential substrates of N-terminal acetyltransferaseB and N-terminal acetyltransferaseC, our classifiers display better performance than the state of the art. Moreover, we present an analysis of the model predicting IMC for Homo sapiens proteins and demonstrate that we are able to extract experimentally known facts without prior knowledge. Those results validate the fact that our method produces white box models.

AVAILABILITY AND IMPLEMENTATION

Predictors for IMC and N-Ac and all datasets are freely available at http://terminus.unige.ch/.

Characterization of protein species and weighted protein co-expression network regulation of Escherichia coli in response to serum killing using a 2-DE based proteomics approach

Posttranslational modifications, providing covalent alterations to extend their functions, show protein species on 2-DE gels, but our knowledge on protein species is still limited. In the present study, characteristics of protein species are determined in Escherichia coli using 2-DE based proteomics. In the E. coli proteome, 691 unique proteins (representing 1096 protein spots) accounting for 15.37% of gene-coding proteins of the bacterium are identified. Out of them, 191 have 596 protein species. Proteins with higher abundance, a higher proportion of Glu, Gly, Lys, and higher pI are more likely to have protein species. Further investigation on bacterial serum resistance indicates that more proteins with protein species are found in the bacterium in response to serum stress. A weighted protein co-expression network shows that protein species are related to topological connection as a result of protein regulation. The node protein IleS is demonstrated to contribute to serum resistance using a gene-deleted mutant. These results have revealed general characteristic features of bacterial species, and also provided novel insights into the biological significance of bacterial protein species, particularly the role in serum resistance.

In vitro biological characterization of DCUN1D5 in DNA damage response

BACKGROUND

Novel prognostic biomarkers or therapeutic molecular targets for laryngeal squamous cell carcinoma (LSCC) are an urgent priority. We here sought to identify multiple novel LSCC-associated genes.

METHODS

Using high-density microarray expression profiling, we identified multiple genes that were significantly altered between human LSCCs and paired normal tissues. Potential oncogenic functions of one such gene, DCUN1D5, were further characterized in vitro.

RESULTS

Our results demonstrated that DCUN1D5 was highly expressed in LSCCs. Overexpression of DCUN1D5 in vitro resulted in 2.7-fold increased cellular migration, 67.5% increased invasive capacity, and 2.6-fold increased proliferation. Endogenous DCUN1D5 expression was decreased in a time-dependent manner after genotoxic stress, and silencing of DCUN1D5 by siRNA decreased the number of cells in the S phase by 10.2% and increased apoptosis by 11.7%.

CONCLUSION

Our data suggest that DCUN1D5 in vitro might have vital roles in DNA damage response, but further studies are warranted to assess its significance in vivo.

Low-resolution structure of the soluble domain GPAA1 (yGPAA170-247) of the glycosylphosphatidylinositol transamidase subunit GPAA1 from Saccharomyces cerevisiae

The GPI (glycosylphosphatidylinositol) transamidase complex catalyses the attachment of GPI anchors to eukaryotic proteins in the lumen of ER (endoplasmic reticulum). The Saccharomyces cerevisiae GPI transamidase complex consists of the subunits yPIG-K (Gpi8p), yPIG-S (Gpi17p), yPIG-T (Gpi16p), yPIG-U (CDC91/GAB1) and yGPAA1. We present the production of the two recombinant proteins yGPAA1^70–247 and yGPAA1^70–339 of the luminal domain of S. cerevisiae GPAA1, covering the amino acids 70–247 and 70–339 respectively. The secondary structural content of the stable and monodisperse yGPAA1^70–247 has been determined to be 28% α-helix and 27% β-sheet. SAXS (small-angle X-ray scattering) data showed that yGPAA1^70–247 has an R_g (radius of gyration) of 2.72±0.025 nm and D_max (maximum dimension) of 9.14 nm. These data enabled the determination of the two domain low-resolution solution structure of yGPAA1^70–247. The large elliptical shape of yGPAA1^70–247 is connected via a short stalk to the smaller hook-like domain of 0.8 nm in length and 3.5 nm in width. The topological arrangement of yGPAA1^70–247 will be discussed together with the recently determined low-resolution structures of yPIG-K^24–337 and yPIG-S^38–467 from S. cerevisiae in the GPI transamidase complex.

Identification of Peptide lv, a novel putative neuropeptide that regulates the expression of L-type voltage-gated calcium channels in photoreceptors

Neuropeptides are small protein-like signaling molecules with diverse roles in regulating neural functions such as sleep/wake cycles, pain modulation, synaptic plasticity, and learning and memory. Numerous drugs designed to target neuropeptides, their receptors, or relevant pathways have been developed in the past few decades. Hence, the discovery and characterization of new neuropeptides and their functions have received considerable attention from scientific research. Computational bioinformatics coupled with functional assays are powerful tools to address the difficulties in discovering new bioactive peptides. In this study, a new bioinformatic strategy was designed to screen full length human and mouse cDNA databases to search for novel peptides. One was discovered and named peptide Lv because of its ability to enhance L-type voltage-gated calcium channel (L-VGCC) currents in retinal photoreceptors. Using matrix-assisted laser desorption/ionization-time of flight mass spectrometry (MALDI-TOF MS), peptide Lv was detected in the culture media, which indicated that it was secreted from 661W cells transfected with the gene. In vitro treatments with either glutathione S-transferase (GST) fusion peptide Lv or synthesized peptide Lv enhanced L-VGCC channel activities in cone photoreceptors. At the molecular level, peptide Lv stimulated cAMP production, enhanced phosphorylation of extracellular signal-regulated kinase (ERK), and increased the protein expression of L-VGCCα1 subunits in cone photoreceptors. Therefore, the biological activities of peptide Lv may be very important in the modulation of L-VGCC dependent neural plasticity.

Transcript expression analysis of putative Trypanosoma brucei GPI-anchored surface proteins during development in the tsetse and mammalian hosts

Human African Trypanosomiasis is a devastating disease caused by the parasite Trypanosoma brucei. Trypanosomes live extracellularly in both the tsetse fly and the mammal. Trypanosome surface proteins can directly interact with the host environment, allowing parasites to effectively establish and maintain infections. Glycosylphosphatidylinositol (GPI) anchoring is a common posttranslational modification associated with eukaryotic surface proteins. In T. brucei, three GPI-anchored major surface proteins have been identified: variant surface glycoproteins (VSGs), procyclic acidic repetitive protein (PARP or procyclins), and brucei alanine rich proteins (BARP). The objective of this study was to select genes encoding predicted GPI-anchored proteins with unknown function(s) from the T. brucei genome and characterize the expression profile of a subset during cyclical development in the tsetse and mammalian hosts. An initial in silico screen of putative T. brucei proteins by Big PI algorithm identified 163 predicted GPI-anchored proteins, 106 of which had no known functions. Application of a second GPI-anchor prediction algorithm (FragAnchor), signal peptide and trans-membrane domain prediction software resulted in the identification of 25 putative hypothetical proteins. Eighty-one gene products with hypothetical functions were analyzed for stage-regulated expression using semi-quantitative RT-PCR. The expression of most of these genes were found to be upregulated in trypanosomes infecting tsetse salivary gland and proventriculus tissues, and 38% were specifically expressed only by parasites infecting salivary gland tissues. Transcripts for all of the genes specifically expressed in salivary glands were also detected in mammalian infective metacyclic trypomastigotes, suggesting a possible role for these putative proteins in invasion and/or establishment processes in the mammalian host. These results represent the first large-scale report of the differential expression of unknown genes encoding predicted T. brucei surface proteins during the complete developmental cycle. This knowledge may form the foundation for the development of future novel transmission blocking strategies against metacyclic parasites.

Human African Trypanosomiasis (HAT) is a fatal disease caused by African trypanosomes and transmitted by an infected tsetse fly. Presently, there are no vaccines to prevent mammalian infections. Proteins expressed on the trypanosome surface can influence the host environment and allow for their transmission. Potentially accessible to the adaptive immune systems of vertebrate hosts, these proteins could serve as future vaccine targets. Identification and characterization of these currently unknown proteins can help us develop strategies to alter the host environment, making it inhospitable for the parasite, thereby reducing disease transmission. While there is extensive knowledge about trypanosome development in the mammalian host, less is known about the molecular events in the tsetse fly, particularly the salivary gland stages. We used an in silico approach to identify putative surface proteins from the known genome sequence of Trypanosoma brucei, and we describe the stage specific expression of these genes during development in the tsetse fly and mammalian host. Our findings show that a majority of unknown transcripts encoding predicted surface proteins are expressed by the parasites infecting tsetse salivary glands. These data will help focus future investigations into transmission-blocking approaches targeting the expressed antigens of trypanosomes infecting tsetse salivary glands.

Targeted proteome investigation via selected reaction monitoring mass spectrometry

Due to the enormous complexity of proteomes which constitute the entirety of protein species expressed by a certain cell or tissue, proteome-wide studies performed in discovery mode are still limited in their ability to reproducibly identify and quantify all proteins present in complex biological samples. Therefore, the targeted analysis of informative subsets of the proteome has been beneficial to generate reproducible data sets across multiple samples. Here we review the repertoire of antibody- and mass spectrometry (MS) -based analytical tools which is currently available for the directed analysis of predefined sets of proteins. The topics of emphasis for this review are Selected Reaction Monitoring (SRM) mass spectrometry, emerging tools to control error rates in targeted proteomic experiments, and some representative examples of applications. The ability to cost- and time-efficiently generate specific and quantitative assays for large numbers of proteins and posttranslational modifications has the potential to greatly expand the range of targeted proteomic coverage in biological studies. This article is part of a Special Section entitled: Understanding genome regulation and genetic diversity by mass spectrometry.

An algorithm for identifying multiply modified endogenous proteins using both full-scan and high-resolution tandem mass spectrometric data

Mass spectrometry based proteomic experiments have advanced considerably over the past decade with high-resolution and mass accuracy tandem mass spectrometry (MS/MS) capabilities now allowing routine interrogation of large peptides and proteins. Often a major bottleneck to 'top-down' proteomics, however, is the ability to identify and characterize the complex peptides or proteins based on the acquired high-resolution MS/MS spectra. For biological samples containing proteins with multiple unpredicted processing events, unsupervised identifications can be particularly challenging. Described here is a newly created search algorithm (MAR) designed for the identification of experimentally detected peptides or proteins. This algorithm relies only on predefined list of 'differential' modifications (e.g. phosphorylation) and a FASTA-formatted protein database, and is not constrained to full-length proteins for identification. The algorithm is further powered by the ability to leverage identified mass differences between chromatographically separated ions within full-scan MS spectra to automatically generate a list of likely 'differential' modifications to be searched. The utility of the algorithm is demonstrated with the identification of 54 unique polypeptides from human apolipoprotein enriched from the high-density lipoprotein particle (HDL), and searching time benchmarks demonstrate scalability (12 high-resolution MS/MS scans searched per minute with modifications considered). This parallelizable algorithm provides an additional solution for converting high-quality MS/MS data of multiply processed proteins into reliable identifications.

Mathematical descriptions of biochemical networks: stability, stochasticity, evolution

In this paper, we review some fundamental aspects, as well as some new developments, in the emerging field of network biology. The focus of attention is placed on mathematical approaches to conceptual modeling of biomolecular networks with special emphasis on dynamic stability, stochasticity and evolution.