Incorporating significant amino acid pairs to identify O-linked glycosylation sites on transmembrane proteins and non-transmembrane proteins

TRP-BERT: Discrimination of transient receptor potential (TRP) channels using contextual representations from deep bidirectional transformer based on BERT

Transient receptor potential (TRP) channels are non-selective cation channels that act as ion channels and are primarily found on the plasma membrane of numerous animal cells. These channels are involved in the physiology and pathophysiology of a wide variety of biological processes, including inhibition and progression of cancer, pain initiation, inflammation, regulation of pressure, thermoregulation, secretion of salivary fluid, and homeostasis of Ca²⁺ and Mg²⁺. Increasing evidences indicate that mutations in the gene encoding TRP channels play an essential role in a broad array of diseases. Therefore, these channels are becoming popular as potential drug targets for several diseases. The diversified role of these channels demands a prediction model to classify TRP channels from other channel proteins (non-TRP channels). Therefore, we presented an approach based on the Support Vector Machine (SVM) classifier and contextualized word embeddings from Bidirectional Encoder Representations from Transformers (BERT) to represent protein sequences. BERT is a deeply bidirectional language model and a neural network approach to Natural Language Processing (NLP) that achieves outstanding performance on various NLP tasks. We apply BERT to generate contextualized representations for every single amino acid in a protein sequence. Interestingly, these representations are context-sensitive and vary for the same amino acid appearing in different positions in the sequence. Our proposed method showed 80.00% sensitivity, 96.03% specificity, 95.47% accuracy, and a 0.56 Matthews correlation coefficient (MCC) for an independent test set. We suggest that our proposed method could effectively classify TRP channels from non-TRP channels and assist biologists in identifying new potential TRP channels.

Incorporating support vector machine with sequential minimal optimization to identify anticancer peptides

BACKGROUND

Cancer is one of the major causes of death worldwide. To treat cancer, the use of anticancer peptides (ACPs) has attracted increased attention in recent years. ACPs are a unique group of small molecules that can target and kill cancer cells fast and directly. However, identifying ACPs by wet-lab experiments is time-consuming and labor-intensive. Therefore, it is significant to develop computational tools for ACPs prediction. Though some ACP prediction tools have been developed recently, their performances are not well enough and most of them do not offer a function to distinguish ACPs from antimicrobial peptides (AMPs). Considering the fact that a growing number of studies have shown that some AMPs exhibit anticancer function, this work tries to build a model for distinguishing AMPs from ACPs in addition to a model that predicts ACPs from whole peptides.

RESULTS

This study chooses amino acid composition, N5C5, k-space, position-specific scoring matrix (PSSM) as features, and analyzes them by machine learning methods, including support vector machine (SVM) and sequential minimal optimization (SMO) to build a model (model 2) for distinguishing ACPs from whole peptides. Another model (model 1) that distinguishes ACPs from AMPs is also developed. Comparing to previous models, models developed in this research show better performance (accuracy: 85.5% for model 1 and 95.2% for model 2).

CONCLUSIONS

This work utilizes a new feature, PSSM, which contributes to better performance than other features. In addition to SVM, SMO is used in this research for optimizing SVM and the SMO-optimized models show better performance than non-optimized models. Last but not least, this work provides two different functions, including distinguishing ACPs from AMPs and distinguishing ACPs from all peptides. The second SMO-optimized model, which utilizes PSSM as a feature, performs better than all other existing tools.

Large-scale mass spectrometry data combined with demographics analysis rapidly predicts methicillin resistance in Staphylococcus aureus

BACKGROUND

A mass spectrometry-based assessment of methicillin resistance in Staphylococcus aureus would have huge potential in addressing fast and effective prediction of antibiotic resistance. Since delays in the traditional antibiotic susceptibility testing, methicillin-resistant S. aureus remains a serious threat to human health.

RESULTS

Here, linking a 7 years of longitudinal study from two cohorts in the Taiwan area of over 20 000 individually resolved methicillin susceptibility testing results, we identify associations of methicillin resistance with the demographics and mass spectrometry data. When combined together, these connections allow for machine-learning-based predictions of methicillin resistance, with an area under the receiver operating characteristic curve of >0.85 in both the discovery [95% confidence interval (CI) 0.88-0.90] and replication (95% CI 0.84-0.86) populations.

CONCLUSIONS

Our predictive model facilitates early detection for methicillin resistance of patients with S. aureus infection. The large-scale antibiotic resistance study has unbiasedly highlighted putative candidates that could improve trials of treatment efficiency and inform on prescriptions.

dbPTM in 2019: exploring disease association and cross-talk of post-translational modifications

The dbPTM (http://dbPTM.mbc.nctu.edu.tw/) has been maintained for over 10 years with the aim to provide functional and structural analyses for post-translational modifications (PTMs). In this update, dbPTM not only integrates more experimentally validated PTMs from available databases and through manual curation of literature but also provides PTM-disease associations based on non-synonymous single nucleotide polymorphisms (nsSNPs). The high-throughput deep sequencing technology has led to a surge in the data generated through analysis of association between SNPs and diseases, both in terms of growth amount and scope. This update thus integrated disease-associated nsSNPs from dbSNP based on genome-wide association studies. The PTM substrate sites located at a specified distance in terms of the amino acids encoded from nsSNPs were deemed to have an association with the involved diseases. In recent years, increasing evidence for crosstalk between PTMs has been reported. Although mass spectrometry-based proteomics has substantially improved our knowledge about substrate site specificity of single PTMs, the fact that the crosstalk of combinatorial PTMs may act in concert with the regulation of protein function and activity is neglected. Because of the relatively limited information about concurrent frequency and functional relevance of PTM crosstalk, in this update, the PTM sites neighboring other PTM sites in a specified window length were subjected to motif discovery and functional enrichment analysis. This update highlights the current challenges in PTM crosstalk investigation and breaks the bottleneck of how proteomics may contribute to understanding PTM codes, revealing the next level of data complexity and proteomic limitation in prospective PTM research.

CRISPR/Cas9-Mediated Vitellogenin Receptor Knockout Leads to Functional Deficiency in the Reproductive Development of Plutella xylostella

The vitellogenin receptor (VgR) belongs to the low-density lipoprotein receptor (LDLR) gene superfamily and plays an indispensable role in Vg transport, yolk deposition, and oocyte development. For this reason, it has become a promising target for pest control. The involvement of VgR in Vg transport and reproductive functions remains unclear in diamondback moths, Plutella xylostella (L.), a destructive pest of cruciferous crops. Here, we cloned and identified the complete cDNA sequence of P. xylostella VgR, which encoded 1805 amino acid residues and contained four conserved domains of LDLR superfamily. PxVgR was mainly expressed in female adults, more specifically in the ovary. PxVgR protein also showed the similar expression profile with the PxVgR transcript. CRISPR/Cas9-mediated PxVgR knockout created a homozygous mutant of P. xylostella with 5-bp-nucleotide deletion in the PxVgR. The expression deficiency of PxVgR protein was detected in the ovaries and eggs of mutant individuals. Vg protein was still detected in the eggs of the mutant individuals, but with a decreased expression level. However, PxVg transcripts were not significantly affected by the PxVgR knockout. Knockout of PxVgR resulted in shorter ovarioles of newly emerged females. No significant difference was detected between wild and mutant individuals in terms of the number of eggs laid in the first 3 days after mating. The loss of PxVgR gene resulted in smaller and whiter eggs and lower egg hatching rate. This study represents the first report on the functions of VgR in Vg transport, ovary development, oviposition, and embryonic development of P. xylostella using CRISPR/Cas9 technology. This study lays the foundation for understanding molecular mechanisms of P. xylostella reproduction, and for making use of VgR as a potential genetic-based molecular target for better control of the P. xylostella.

Characterization of a Vitellogenin Receptor in the Bumblebee, Bombus lantschouensis (Hymenoptera, Apidae)

The vitellogenin receptor (VgR) belongs to the low-density lipoprotein receptor (LDLR) family, responsible for mediating the endocytosis of vitellogenin (Vg) into the ovaries to promote ovarian growth and oviposition. Here, we cloned and measured VgR gene expression characteristics in the bumblebee Bombus lantschouensis. RNA interference was used to validate VgR function. The results showed that the full length of the BLVgR cDNA was 5519 bp, which included a 5280 bp open reading frame encoding 1759 amino acids (AAs). Sequence alignment revealed that the protein contained 12 LDLa, 5 EGF, 2 EGF-CA and 10 LY domains. Phylogenetic analysis showed that BLVgR and the VgR of Bombus terrestris clustered together and the tree of bumblebees (Bombus) appeared as one clade next to honeybees (Apis). Transcript expression analysis showed that BLVgR was expressed in all tested tissues and showed the highest abundance in the ovaries. BLVgR expression was present in all developmental stages. However, the expression level in larvae was extremely low. In addition, the expression of BLVgR was significantly upregulated after egg laying in both workers and queens. In new emerging workers injected with 5 µg of VgR dsRNA, the expression level of BLVgR was 4-fold lower than that in the GFP dsRNA-injected group after 72 h. Furthermore, BLVgR silencing significantly reduced the number of eggs laid (3.67 ± 1.96 eggs) and delayed the first egg-laying time (16.31 ± 2.07 days) in worker microcolonies when compared to dsGFP (37.31 ± 4.09 eggs, 8.15 ± 0.22 days) and DEPC-treated water injected controls (16.42 ± 2.24 eggs, 10.00 ± 0.37 days). In conclusion, the BLVgR gene and its reproductive function were explored in the bumblebee B. lantschouensis. This gene plays an important role in egg laying time and egg number.

Molecular Characterization and Expression of Vitellogenin and Vitellogenin Receptor of Thitarodes pui (Lepidoptera: Hepialidae), an Insect on the Tibetan Plateau

Vitellogenin (Vg) and vitellogenin receptor (VgR) play important roles in the vitellogenesis of insects. In this study, we cloned and characterized the two corresponding genes (TpVg and TpVgR) in an economically important insect, Thitarodes pui (Lepidoptera: Hepialidae), from the Tibetan plateau. The full length of TpVg is 5566 bp with a 5373 bp open reading frame (ORF) encoding 1,790 amino acids. Sequence alignment revealed that TpVg has three conserved domains: a Vitellogenin_N domain, a DUF1943 domain, and a von Willebrand factor type D domain (VWD). The full length of TpVgR is 5732 bp, with a 5397 bp ORF encoding 1798 amino acids. BLASTP showed that TpVgR belongs to the low-density lipoprotein receptor (LDLR) gene superfamily. Structural analysis revealed that TpVgR has a group of four structural domains: a ligand-binding domain (LBD), an epidermal growth factor (EGF)-precursor homology domain, a transmembrane (TM) domain, and a cytoplasmic domain. In addition, TpVgR has four cysteine-rich LDL repeats in the first ligand-binding site and seven in the second. Quantitative real-time polymerase chain reaction analysis revealed that the expression levels of TpVg and TpVgR are much higher in later pupa than in either the larval or adult stage, implying that the synthesis and uptake of Vg in T. pui occurs in the later pupal stage. These results will help us to understand the molecular mechanism of the reproductive capacity and will provide new insight into the mass rearing and utilization of T. pui.

Characterization of human enterovirus71 virus-like particles used for vaccine antigens

Human enterovirus 71 (EV71) is a major causative pathogen of hand, foot and mouth disease (HFMD) and has caused outbreaks with significant mortality among young children in the Asia-Pacific region in recent years. Towards developing a vaccine for this disease, we have expressed and purified EV71 virus-like particles (VLPs), which resemble the authentic virus in appearance, capsid structure and protein sequence, from insect cells (Sf9) using a multistep chromatography process. We demonstrated intracellular localization of the VLPs in host cells by in situ immunogold detection, electron microscopy and immunofluorescence. Characteristics of these EV71 VLPs were studied using a variety of immunological and physicochemical techniques, which aimed to reveal that the purified EV71 VLPs have good morphology and structure consistent with natural EV71 empty capsids. Results of the amino acid analysis, SDS-PAGE, Western blotting and high-performance liquid chromatography confirmed the high purity of the EV71 VLPs. However the sedimentation coefficient of the VLPs showed that they were smaller than that of secreted EV71 VLPs purified by discontinuous cesium chloride density gradients, they were similar to the empty capsids of natural EV71 virions reported previously. Combined with the previous study that EV71 VLPs purified by a multistep chromatography process were able to elicit strong humoral immune responses in mice, our results further supported the conclusion that our EV71 VLPs had well-preserved molecular and structural characteristics. The EV71 VLPs produced from the baculovirus expression system and purified by a multistep chromatography process displayed key structural and immunological features, which would contribute to their efficacy as a HFMD vaccine.

Incorporating efficient radial basis function networks and significant amino acid pairs for predicting GTP binding sites in transport proteins

Background

Guanonine-protein (G-protein) is known as molecular switches inside cells, and is very important in signals transmission from outside to inside cell. Especially in transport protein, most of G-proteins play an important role in membrane trafficking; necessary for transferring proteins and other molecules to a variety of destinations outside and inside of the cell. The function of membrane trafficking is controlled by G-proteins via Guanosine triphosphate (GTP) binding sites. The GTP binding sites active G-proteins initiated to membrane vesicles by interacting with specific effector proteins. Without the interaction from GTP binding sites, G-proteins could not be active in membrane trafficking and consequently cause many diseases, i.e., cancer, Parkinson… Thus it is very important to identify GTP binding sites in membrane trafficking, in particular, and in transport protein, in general.

Results

We developed the proposed model with a cross-validation and examined with an independent dataset. We achieved an accuracy of 95.6% for evaluating with cross-validation and 98.7% for examining the performance with the independent data set. For newly discovered transport protein sequences, our approach performed remarkably better than similar methods such as GTPBinder, NsitePred and TargetSOS. Moreover, a friendly web server was developed for identifying GTP binding sites in transport proteins available for all users.

Conclusions

We approached a computational technique using PSSM profiles and SAAPs for identifying GTP binding residues in transport proteins. When we included SAAPs into PSSM profiles, the predictive performance achieved a significant improvement in all measurement metrics. Furthermore, the proposed method could be a power tool for determining new proteins that belongs into GTP binding sites in transport proteins and can provide useful information for biologists.

Prediction of FAD binding sites in electron transport proteins according to efficient radial basis function networks and significant amino acid pairs

BACKGROUND

Cellular respiration is a catabolic pathway for producing adenosine triphosphate (ATP) and is the most efficient process through which cells harvest energy from consumed food. When cells undergo cellular respiration, they require a pathway to keep and transfer electrons (i.e., the electron transport chain). Due to oxidation-reduction reactions, the electron transport chain produces a transmembrane proton electrochemical gradient. In case protons flow back through this membrane, this mechanical energy is converted into chemical energy by ATP synthase. The convert process is involved in producing ATP which provides energy in a lot of cellular processes. In the electron transport chain process, flavin adenine dinucleotide (FAD) is one of the most vital molecules for carrying and transferring electrons. Therefore, predicting FAD binding sites in the electron transport chain is vital for helping biologists understand the electron transport chain process and energy production in cells.

RESULTS

We used an independent data set to evaluate the performance of the proposed method, which had an accuracy of 69.84 %. We compared the performance of the proposed method in analyzing two newly discovered electron transport protein sequences with that of the general FAD binding predictor presented by Mishra and Raghava and determined that the accuracy of the proposed method improved by 9-45 % and its Matthew's correlation coefficient was 0.14-0.5. Furthermore, the proposed method enabled reducing the number of false positives significantly and can provide useful information for biologists.

CONCLUSIONS

We developed a method that is based on PSSM profiles and SAAPs for identifying FAD binding sites in newly discovered electron transport protein sequences. This approach achieved a significant improvement after we added SAAPs to PSSM features to analyze FAD binding proteins in the electron transport chain. The proposed method can serve as an effective tool for predicting FAD binding sites in electron transport proteins and can help biologists understand the functions of the electron transport chain, particularly those of FAD binding sites. We also developed a web server which identifies FAD binding sites in electron transporters available for academics.

Molecular Characterization and Function Analysis of the Vitellogenin Receptor from the Cotton Bollworm, Helicoverpa armigera (Hübner) (Lepidoptera, Noctuidae)

Developing oocytes accumulate plentiful yolk protein during oogenesis through receptor-mediated endocytosis. The vitellogenin receptor (VgR), belonging to the low-density lipoprotein receptor (LDLR) family, regulates the absorption of yolk protein. In this work, the full-length vitellogenin receptor (HaVgR) in the cotton bollworm Helicoverpa armigera was identified, encoding a 1817 residue protein. Sequence alignment revealed that the sequence of HaVgR contained all of the conservative structural motifs of LDLR family members, and phylogenetic analysis indicated that HaVgR had a high identity among Lepidoptera and was distinct from that of other insects. Consistent with other insects, HaVgR was specifically expressed in ovarian tissue. The developmental expression pattern showed that HaVgR was first transcribed in the newly metamorphosed female adults, reached a peak in 2-day-old adults and then declined. Western blot analysis also revealed an ovarian-specific and developing expression pattern, which was consistent with the HaVgR mRNA transcription. Moreover, RNAi-mediated HaVgR knockdown strongly reduced the VgR expression in both the mRNA and protein levels, which inhibited the yolk protein deposition in the ovaries, led to the dramatic accumulation of vitellogenin and the up-regulation of HaVg expression in hemolymph, and eventually resulted in a declined fecundity. Together, all of these findings demonstrate that HaVgR is a specific receptor in uptake and transportation of yolk protein for the maturation of oocytes and that it plays a critical role in female reproduction.

A two-layered machine learning method to identify protein O-GlcNAcylation sites with O-GlcNAc transferase substrate motifs

Protein O-GlcNAcylation, involving the β-attachment of single N-acetylglucosamine (GlcNAc) to the hydroxyl group of serine or threonine residues, is an O-linked glycosylation catalyzed by O-GlcNAc transferase (OGT). Molecular level investigation of the basis for OGT's substrate specificity should aid understanding how O-GlcNAc contributes to diverse cellular processes. Due to an increasing number of O-GlcNAcylated peptides with site-specific information identified by mass spectrometry (MS)-based proteomics, we were motivated to characterize substrate site motifs of O-GlcNAc transferases. In this investigation, a non-redundant dataset of 410 experimentally verified O-GlcNAcylation sites were manually extracted from dbOGAP, OGlycBase and UniProtKB. After detection of conserved motifs by using maximal dependence decomposition, profile hidden Markov model (profile HMM) was adopted to learn a first-layered model for each identified OGT substrate motif. Support Vector Machine (SVM) was then used to generate a second-layered model learned from the output values of profile HMMs in first layer. The two-layered predictive model was evaluated using a five-fold cross validation which yielded a sensitivity of 85.4%, a specificity of 84.1%, and an accuracy of 84.7%. Additionally, an independent testing set from PhosphoSitePlus, which was really non-homologous to the training data of predictive model, was used to demonstrate that the proposed method could provide a promising accuracy (84.05%) and outperform other O-GlcNAcylation site prediction tools. A case study indicated that the proposed method could be a feasible means of conducting preliminary analyses of protein O-GlcNAcylation and has been implemented as a web-based system, OGTSite, which is now freely available at http://csb.cse.yzu.edu.tw/OGTSite/.

dbPTM 2016: 10-year anniversary of a resource for post-translational modification of proteins

Owing to the importance of the post-translational modifications (PTMs) of proteins in regulating biological processes, the dbPTM (http://dbPTM.mbc.nctu.edu.tw/) was developed as a comprehensive database of experimentally verified PTMs from several databases with annotations of potential PTMs for all UniProtKB protein entries. For this 10th anniversary of dbPTM, the updated resource provides not only a comprehensive dataset of experimentally verified PTMs, supported by the literature, but also an integrative interface for accessing all available databases and tools that are associated with PTM analysis. As well as collecting experimental PTM data from 14 public databases, this update manually curates over 12 000 modified peptides, including the emerging S-nitrosylation, S-glutathionylation and succinylation, from approximately 500 research articles, which were retrieved by text mining. As the number of available PTM prediction methods increases, this work compiles a non-homologous benchmark dataset to evaluate the predictive power of online PTM prediction tools. An increasing interest in the structural investigation of PTM substrate sites motivated the mapping of all experimental PTM peptides to protein entries of Protein Data Bank (PDB) based on database identifier and sequence identity, which enables users to examine spatially neighboring amino acids, solvent-accessible surface area and side-chain orientations for PTM substrate sites on tertiary structures. Since drug binding in PDB is annotated, this update identified over 1100 PTM sites that are associated with drug binding. The update also integrates metabolic pathways and protein-protein interactions to support the PTM network analysis for a group of proteins. Finally, the web interface is redesigned and enhanced to facilitate access to this resource.

Eukaryotic glycosylation: online methods for site prediction on protein sequences

This chapter runs through several online predictors enabling prediction of glycosylation sites on protein sequences. Most online methods provide in place documentation and examples, but this chapter provides a general overview and workflow for each method.

Incorporating significant amino acid pairs and protein domains to predict RNA splicing-related proteins with functional roles

Machinery of pre-mRNA splicing is carried out through the interaction of RNA sequence elements and a variety of RNA splicing-related proteins (SRPs) (e.g. spliceosome and splicing factors). Alternative splicing, which is an important post-transcriptional regulation in eukaryotes, gives rise to multiple mature mRNA isoforms, which encodes proteins with functional diversities. However, the regulation of RNA splicing is not yet fully elucidated, partly because SRPs have not yet been exhaustively identified and the experimental identification is labor-intensive. Therefore, we are motivated to design a new method for identifying SRPs with their functional roles in the regulation of RNA splicing. The experimentally verified SRPs were manually curated from research articles. According to the functional annotation of Splicing Related Gene Database, the collected SRPs were further categorized into four functional groups including small nuclear Ribonucleoprotein, Splicing Factor, Splicing Regulation Factor and Novel Spliceosome Protein. The composition of amino acid pairs indicates that there are remarkable differences among four functional groups of SRPs. Then, support vector machines (SVMs) were utilized to learn the predictive models for identifying SRPs as well as their functional roles. The cross-validation evaluation presents that the SVM models trained with significant amino acid pairs and functional domains could provide a better predictive performance. In addition, the independent testing demonstrates that the proposed method could accurately identify SRPs in mammals/plants as well as effectively distinguish between SRPs and RNA-binding proteins. This investigation provides a practical means to identifying potential SRPs and a perspective for exploring the regulation of RNA splicing.

topPTM: a new module of dbPTM for identifying functional post-translational modifications in transmembrane proteins

Transmembrane (TM) proteins have crucial roles in various cellular processes. The location of post-translational modifications (PTMs) on TM proteins is associated with their functional roles in various cellular processes. Given the importance of PTMs in the functioning of TM proteins, this study developed topPTM (available online at http://topPTM.cse.yzu.edu.tw), a new dbPTM module that provides a public resource for identifying the functional PTM sites on TM proteins with structural topology. Experimentally verified TM topology data were integrated from TMPad, TOPDB, PDBTM and OPM. In addition to the PTMs obtained from dbPTM, experimentally verified PTM sites were manually extracted from research articles by text mining. In an attempt to provide a full investigation of PTM sites on TM proteins, all UniProtKB protein entries containing annotations related to membrane localization and TM topology were considered potential TM proteins. Two effective tools were then used to annotate the structural topology of the potential TM proteins. The TM topology of TM proteins is represented by graphical visualization, as well as by the PTM sites. To delineate the structural correlation between the PTM sites and TM topologies, the tertiary structure of PTM sites on TM proteins was visualized by Jmol program. Given the support of research articles by manual curation and the investigation of domain-domain interactions in Protein Data Bank, 1347 PTM substrate sites are associated with protein-protein interactions for 773 TM proteins. The database content is regularly updated on publication of new data by continuous surveys of research articles and available resources.

Molecular characterization of the vitellogenin receptor from the tick, Amblyomma hebraeum (Acari: Ixodidae)

We have identified the full-length cDNA encoding a vitellogenin receptor (VgR) from the African bont tick Amblyomma hebraeum Koch (1844). VgRs are members of the low-density lipoprotein receptor superfamily that promote the uptake of the yolk protein vitellogenin (Vg), from the haemolymph. The AhVgR (GenBank accession No. JX846592) is 5703 bp, and encodes an 1801 aa protein with a 196.5 kDa molecular mass following cleavage of a 22 aa signal peptide. Phylogenetic analysis indicates that AhVgR is highly similar to other tick VgRs. AhVgR is expressed in only the ovary of mated, engorged females, and is absent in all other female tissues and in both fed and unfed males. Unfed, adult females injected with a VgR-dsRNA probe to knock-down VgR expression experienced a significant delay in ovary development and started oviposition significantly later than controls. These results indicate that the expression of AhVgR is important for the uptake of Vg and subsequent maturation of the oocytes.

HMMpTM: improving transmembrane protein topology prediction using phosphorylation and glycosylation site prediction

During the last two decades a large number of computational methods have been developed for predicting transmembrane protein topology. Current predictors rely on topogenic signals in the protein sequence, such as the distribution of positively charged residues in extra-membrane loops and the existence of N-terminal signals. However, phosphorylation and glycosylation are post-translational modifications (PTMs) that occur in a compartment-specific manner and therefore the presence of a phosphorylation or glycosylation site in a transmembrane protein provides topological information. We examine the combination of phosphorylation and glycosylation site prediction with transmembrane protein topology prediction. We report the development of a Hidden Markov Model based method, capable of predicting the topology of transmembrane proteins and the existence of kinase specific phosphorylation and N/O-linked glycosylation sites along the protein sequence. Our method integrates a novel feature in transmembrane protein topology prediction, which results in improved performance for topology prediction and reliable prediction of phosphorylation and glycosylation sites. The method is freely available at http://bioinformatics.biol.uoa.gr/HMMpTM.

Incorporating substrate sequence motifs and spatial amino acid composition to identify kinase-specific phosphorylation sites on protein three-dimensional structures

BACKGROUND

Protein phosphorylation catalyzed by kinases plays crucial regulatory roles in cellular processes. Given the high-throughput mass spectrometry-based experiments, the desire to annotate the catalytic kinases for in vivo phosphorylation sites has motivated. Thus, a variety of computational methods have been developed for performing a large-scale prediction of kinase-specific phosphorylation sites. However, most of the proposed methods solely rely on the local amino acid sequences surrounding the phosphorylation sites. An increasing number of three-dimensional structures make it possible to physically investigate the structural environment of phosphorylation sites.

RESULTS

In this work, all of the experimental phosphorylation sites are mapped to the protein entries of Protein Data Bank by sequence identity. It resulted in a total of 4508 phosphorylation sites containing the protein three-dimensional (3D) structures. To identify phosphorylation sites on protein 3D structures, this work incorporates support vector machines (SVMs) with the information of linear motifs and spatial amino acid composition, which is determined for each kinase group by calculating the relative frequencies of 20 amino acid types within a specific radial distance from central phosphorylated amino acid residue. After the cross-validation evaluation, most of the kinase-specific models trained with the consideration of structural information outperform the models considering only the sequence information. Furthermore, the independent testing set which is not included in training set has demonstrated that the proposed method could provide a comparable performance to other popular tools.

CONCLUSION

The proposed method is shown to be capable of predicting kinase-specific phosphorylation sites on 3D structures and has been implemented as a web server which is freely accessible at http://csb.cse.yzu.edu.tw/PhosK3D/. Due to the difficulty of identifying the kinase-specific phosphorylation sites with similar sequenced motifs, this work also integrates the 3D structural information to improve the cross classifying specificity.

Use of baculovirus expression system for generation of virus-like particles: successes and challenges

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A brief overview of principles and applications of BES.

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Generation of VLPs using BES.

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Major properties of BES: promoting generation of VLPs.

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Bioprocess considerations for generation of VLPs.

The baculovirus expression system (BES) has been one of the versatile platforms for the production of recombinant proteins requiring multiple post-translational modifications, such as folding, oligomerization, phosphorylation, glycosylation, acylation, disulfide bond formation and proteolytic cleavage. Advances in recombinant DNA technology have facilitated application of the BES, and made it possible to express multiple proteins simultaneously in a single infection and to produce multimeric proteins sharing functional similarity with their natural analogs. Therefore, the BES has been used for the production of recombinant proteins and the construction of virus-like particles (VLPs), as well as for the development of subunit vaccines, including VLP-based vaccines. The VLP, which consists of one or more structural proteins but no viral genome, resembles the authentic virion but cannot replicate in cells. The high-quality recombinant protein expression and post-translational modifications obtained with the BES, along with its capacity to produce multiple proteins, imply that it is ideally suited to VLP production. In this article, we critically review the pros and cons of using the BES as a platform to produce both enveloped and non-enveloped VLPs.

ETMB-RBF: discrimination of metal-binding sites in electron transporters based on RBF networks with PSSM profiles and significant amino acid pairs

Background

Cellular respiration is the process by which cells obtain energy from glucose and is a very important biological process in living cell. As cells do cellular respiration, they need a pathway to store and transport electrons, the electron transport chain. The function of the electron transport chain is to produce a trans-membrane proton electrochemical gradient as a result of oxidation–reduction reactions. In these oxidation–reduction reactions in electron transport chains, metal ions play very important role as electron donor and acceptor. For example, Fe ions are in complex I and complex II, and Cu ions are in complex IV. Therefore, to identify metal-binding sites in electron transporters is an important issue in helping biologists better understand the workings of the electron transport chain.

Methods

We propose a method based on Position Specific Scoring Matrix (PSSM) profiles and significant amino acid pairs to identify metal-binding residues in electron transport proteins.

Results

We have selected a non-redundant set of 55 metal-binding electron transport proteins as our dataset. The proposed method can predict metal-binding sites in electron transport proteins with an average 10-fold cross-validation accuracy of 93.2% and 93.1% for metal-binding cysteine and histidine, respectively. Compared with the general metal-binding predictor from A. Passerini et al., the proposed method can improve over 9% of sensitivity, and 14% specificity on the independent dataset in identifying metal-binding cysteines. The proposed method can also improve almost 76% sensitivity with same specificity in metal-binding histidine, and MCC is also improved from 0.28 to 0.88.

Conclusions

We have developed a novel approach based on PSSM profiles and significant amino acid pairs for identifying metal-binding sites from electron transport proteins. The proposed approach achieved a significant improvement with independent test set of metal-binding electron transport proteins.

DbPTM 3.0: an informative resource for investigating substrate site specificity and functional association of protein post-translational modifications

Protein modification is an extremely important post-translational regulation that adjusts the physical and chemical properties, conformation, stability and activity of a protein; thus altering protein function. Due to the high throughput of mass spectrometry (MS)-based methods in identifying site-specific post-translational modifications (PTMs), dbPTM (http://dbPTM.mbc.nctu.edu.tw/) is updated to integrate experimental PTMs obtained from public resources as well as manually curated MS/MS peptides associated with PTMs from research articles. Version 3.0 of dbPTM aims to be an informative resource for investigating the substrate specificity of PTM sites and functional association of PTMs between substrates and their interacting proteins. In order to investigate the substrate specificity for modification sites, a newly developed statistical method has been applied to identify the significant substrate motifs for each type of PTMs containing sufficient experimental data. According to the data statistics in dbPTM, >60% of PTM sites are located in the functional domains of proteins. It is known that most PTMs can create binding sites for specific protein-interaction domains that work together for cellular function. Thus, this update integrates protein–protein interaction and domain–domain interaction to determine the functional association of PTM sites located in protein-interacting domains. Additionally, the information of structural topologies on transmembrane (TM) proteins is integrated in dbPTM in order to delineate the structural correlation between the reported PTM sites and TM topologies. To facilitate the investigation of PTMs on TM proteins, the PTM substrate sites and the structural topology are graphically represented. Also, literature information related to PTMs, orthologous conservations and substrate motifs of PTMs are also provided in the resource. Finally, this version features an improved web interface to facilitate convenient access to the resource.

ProDis-ContSHC: learning protein dissimilarity measures and hierarchical context coherently for protein-protein comparison in protein database retrieval

BACKGROUND

The need to retrieve or classify protein molecules using structure or sequence-based similarity measures underlies a wide range of biomedical applications. Traditional protein search methods rely on a pairwise dissimilarity/similarity measure for comparing a pair of proteins. This kind of pairwise measures suffer from the limitation of neglecting the distribution of other proteins and thus cannot satisfy the need for high accuracy of the retrieval systems. Recent work in the machine learning community has shown that exploiting the global structure of the database and learning the contextual dissimilarity/similarity measures can improve the retrieval performance significantly. However, most existing contextual dissimilarity/similarity learning algorithms work in an unsupervised manner, which does not utilize the information of the known class labels of proteins in the database.

RESULTS

In this paper, we propose a novel protein-protein dissimilarity learning algorithm, ProDis-ContSHC. ProDis-ContSHC regularizes an existing dissimilarity measure dij by considering the contextual information of the proteins. The context of a protein is defined by its neighboring proteins. The basic idea is, for a pair of proteins (i, j), if their context N(i) and N(j) is similar to each other, the two proteins should also have a high similarity. We implement this idea by regularizing dij by a factor learned from the context N(i) and N(j).Moreover, we divide the context to hierarchial sub-context and get the contextual dissimilarity vector for each protein pair. Using the class label information of the proteins, we select the relevant (a pair of proteins that has the same class labels) and irrelevant (with different labels) protein pairs, and train an SVM model to distinguish between their contextual dissimilarity vectors. The SVM model is further used to learn a supervised regularizing factor. Finally, with the new Supervised learned Dissimilarity measure, we update the Protein Hierarchial Context Coherently in an iterative algorithm--ProDis-ContSHC.We test the performance of ProDis-ContSHC on two benchmark sets, i.e., the ASTRAL 1.73 database and the FSSP/DALI database. Experimental results demonstrate that plugging our supervised contextual dissimilarity measures into the retrieval systems significantly outperforms the context-free dissimilarity/similarity measures and other unsupervised contextual dissimilarity measures that do not use the class label information.

CONCLUSIONS

Using the contextual proteins with their class labels in the database, we can improve the accuracy of the pairwise dissimilarity/similarity measures dramatically for the protein retrieval tasks. In this work, for the first time, we propose the idea of supervised contextual dissimilarity learning, resulting in the ProDis-ContSHC algorithm. Among different contextual dissimilarity learning approaches that can be used to compare a pair of proteins, ProDis-ContSHC provides the highest accuracy. Finally, ProDis-ContSHC compares favorably with other methods reported in the recent literature.

Purification, cDNA structure and biological significance of a single insulin-like growth factor-binding domain protein (SIBD-1) identified in the hemocytes of the spider Cupiennius salei

Cupiennius salei single insulin-like growth factor-binding domain protein (SIBD-1), which exhibits an IGFBP N-terminal domain-like profile, was identified in the hemocytes of the spider C. salei. SIBD-1 was purified by RP-HPLC and the sequence determined by a combination of Edman degradation and 5'-3'- RACE PCR. The peptide (8676.08 Da) is composed of 78 amino acids, contains six intrachain disulphide bridges and carries a modified Thr residue at position 2. SIBD-1 mRNA expression was detected by quantitative real-time PCR mainly in hemocytes, but also in the subesophageal nerve mass and muscle. After infection, the SIBD-1 content in the hemocytes decreases and, simultaneously, the temporal SIBD-1 expression seems to be down-regulated. Two further peptides, SIBD-2 and IGFBP-rP1, also exhibiting IGFBP N-terminal domain variants with unknown functions, were identified on cDNA level in spider hemocytes and venom glands. We conclude that SIBD-1 may play an important role in the immune system of spiders.

Carboxylator: incorporating solvent-accessible surface area for identifying protein carboxylation sites

In proteins, glutamate (Glu) residues are transformed into γ-carboxyglutamate (Gla) residues in a process called carboxylation. The process of protein carboxylation catalyzed by γ-glutamyl carboxylase is deemed to be important due to its involvement in biological processes such as blood clotting cascade and bone growth. There is an increasing interest within the scientific community to identify protein carboxylation sites. However, experimental identification of carboxylation sites via mass spectrometry-based methods is observed to be expensive, time-consuming, and labor-intensive. Thus, we were motivated to design a computational method for identifying protein carboxylation sites. This work aims to investigate the protein carboxylation by considering the composition of amino acids that surround modification sites. With the implication of a modified residue prefers to be accessible on the surface of a protein, the solvent-accessible surface area (ASA) around carboxylation sites is also investigated. Radial basis function network is then employed to build a predictive model using various features for identifying carboxylation sites. Based on a five-fold cross-validation evaluation, a predictive model trained using the combined features of amino acid sequence (AA20D), amino acid composition, and ASA, yields the highest accuracy at 0.874. Furthermore, an independent test done involving data not included in the cross-validation process indicates that in silico identification is a feasible means of preliminary analysis. Additionally, the predictive method presented in this work is implemented as Carboxylator ( http://csb.cse.yzu.edu.tw/Carboxylator/ ), a web-based tool for identifying carboxylated proteins with modification sites in order to help users in investigating γ-glutamyl carboxylation.

Incorporating distant sequence features and radial basis function networks to identify ubiquitin conjugation sites

Ubiquitin (Ub) is a small protein that consists of 76 amino acids about 8.5 kDa. In ubiquitin conjugation, the ubiquitin is majorly conjugated on the lysine residue of protein by Ub-ligating (E3) enzymes. Three major enzymes participate in ubiquitin conjugation. They are E1, E2 and E3 which are responsible for activating, conjugating and ligating ubiquitin, respectively. Ubiquitin conjugation in eukaryotes is an important mechanism of the proteasome-mediated degradation of a protein and regulating the activity of transcription factors. Motivated by the importance of ubiquitin conjugation in biological processes, this investigation develops a method, UbSite, which uses utilizes an efficient radial basis function (RBF) network to identify protein ubiquitin conjugation (ubiquitylation) sites. This work not only investigates the amino acid composition but also the structural characteristics, physicochemical properties, and evolutionary information of amino acids around ubiquitylation (Ub) sites. With reference to the pathway of ubiquitin conjugation, the substrate sites for E3 recognition, which are distant from ubiquitylation sites, are investigated. The measurement of F-score in a large window size (-20∼+20) revealed a statistically significant amino acid composition and position-specific scoring matrix (evolutionary information), which are mainly located distant from Ub sites. The distant information can be used effectively to differentiate Ub sites from non-Ub sites. As determined by five-fold cross-validation, the model that was trained using the combination of amino acid composition and evolutionary information performs best in identifying ubiquitin conjugation sites. The prediction sensitivity, specificity, and accuracy are 65.5%, 74.8%, and 74.5%, respectively. Although the amino acid sequences around the ubiquitin conjugation sites do not contain conserved motifs, the cross-validation result indicates that the integration of distant sequence features of Ub sites can improve predictive performance. Additionally, the independent test demonstrates that the proposed method can outperform other ubiquitylation prediction tools.