Prediction of membrane transport proteins and their substrate specificities using primary sequence information

Effective prediction of bacterial type IV secreted effectors by combined features of both C-termini and N-termini

Various bacterial pathogens can deliver their secreted substrates also called as effectors through type IV secretion systems (T4SSs) into host cells and cause diseases. Since T4SS secreted effectors (T4SEs) play important roles in pathogen-host interactions, identifying them is crucial to our understanding of the pathogenic mechanisms of T4SSs. A few computational methods using machine learning algorithms for T4SEs prediction have been developed by using features of C-terminal residues. However, recent studies have shown that targeting information can also be encoded in the N-terminal region of at least some T4SEs. In this study, we present an effective method for T4SEs prediction by novelly integrating both N-terminal and C-terminal sequence information. First, we collected a comprehensive dataset across multiple bacterial species of known T4SEs and non-T4SEs from literatures. Then, three types of distinctive features, namely amino acid composition, composition, transition and distribution and position-specific scoring matrices were calculated for 50 N-terminal and 100 C-terminal residues. After that, we employed information gain represent to rank the importance score of the 150 different position residues for T4SE secretion signaling. At last, 125 distinctive position residues were singled out for the prediction model to classify T4SEs and non-T4SEs. The support vector machine model yields a high receiver operating curve of 0.916 in the fivefold cross-validation and an accuracy of 85.29% for the independent test set.

MLACP: machine-learning-based prediction of anticancer peptides

Cancer is the second leading cause of death globally, and use of therapeutic peptides to target and kill cancer cells has received considerable attention in recent years. Identification of anticancer peptides (ACPs) through wet-lab experimentation is expensive and often time consuming; therefore, development of an efficient computational method is essential to identify potential ACP candidates prior to in vitro experimentation. In this study, we developed support vector machine- and random forest-based machine-learning methods for the prediction of ACPs using the features calculated from the amino acid sequence, including amino acid composition, dipeptide composition, atomic composition, and physicochemical properties. We trained our methods using the Tyagi-B dataset and determined the machine parameters by 10-fold cross-validation. Furthermore, we evaluated the performance of our methods on two benchmarking datasets, with our results showing that the random forest-based method outperformed the existing methods with an average accuracy and Matthews correlation coefficient value of 88.7% and 0.78, respectively. To assist the scientific community, we also developed a publicly accessible web server at www.thegleelab.org/MLACP.html.

Advances in methods for identification and characterization of plant transporter function

Transport proteins are crucial for cellular function at all levels. Numerous importers and exporters facilitate transport of a diverse array of metabolites and ions intra- and intercellularly. Identification of transporter function is essential for understanding biological processes at both the cellular and organismal level. Assignment of a functional role to individual transporter proteins or to identify a transporter with a given substrate specificity has notoriously been challenging. Recently, major advances have been achieved in function-driven screens, phenotype-driven screens, and in silico-based approaches. In this review, we highlight examples that illustrate how new technology and tools have advanced identification and characterization of plant transporter functions.

Efflux proteins at the blood-brain barrier: review and bioinformatics analysis

1. Efflux proteins at the blood-brain barrier provide a mechanism for export of waste products of normal metabolism from the brain and help to maintain brain homeostasis. They also prevent entry into the brain of a wide range of potentially harmful compounds such as drugs and xenobiotics. 2. Conversely, efflux proteins also hinder delivery of therapeutic drugs to the brain and central nervous system used to treat brain tumours and neurological disorders. For bypassing efflux proteins, a comprehensive understanding of their structures, functions and molecular mechanisms is necessary, along with new strategies and technologies for delivery of drugs across the blood-brain barrier. 3. We review efflux proteins at the blood-brain barrier, classified as either ATP-binding cassette (ABC) transporters (P-gp, BCRP, MRPs) or solute carrier (SLC) transporters (OATP1A2, OATP1A4, OATP1C1, OATP2B1, OAT3, EAATs, PMAT/hENT4 and MATE1). 4. This includes information about substrate and inhibitor specificity, structural organisation and mechanism, membrane localisation, regulation of expression and activity, effects of diseases and conditions and the principal technique used for in vivo analysis of efflux protein activity: positron emission tomography (PET). 5. We also performed analyses of evolutionary relationships, membrane topologies and amino acid compositions of the proteins, and linked these to structure and function.

Overexpression of a Plasma Membrane-Localized SbSRP-Like Protein Enhances Salinity and Osmotic Stress Tolerance in Transgenic Tobacco

An obligate halophyte, Salicornia brachiata grows in salt marshes and is considered to be a potential resource of salt- and drought-responsive genes. It is important to develop an understanding of the mechanisms behind enhanced salt tolerance. To increase this understanding, a novel SbSRP gene was cloned, characterized, over-expressed, and functionally validated in the model plant Nicotiana tabacum. The genome of the halophyte S. brachiata contains two homologs of an intronless SbSRP gene of 1,262 bp in length that encodes for a stress-related protein. An in vivo localization study confirmed that SbSRP is localized on the plasma membrane. Transgenic tobacco plants (T1) that constitutively over-express the SbSRP gene showed improved salinity and osmotic stress tolerance. In comparison to Wild Type (WT) and Vector Control (VC) plants, transgenic lines showed elevated relative water and chlorophyll content, lower malondialdehyde content, lower electrolyte leakage and higher accumulation of proline, free amino acids, sugars, polyphenols, and starch under abiotic stress treatments. Furthermore, a lower build-up of H2O2 content and superoxide-radicals was found in transgenic lines compared to WT and VC plants under stress conditions. Transcript expression of Nt-APX (ascorbate peroxidase), Nt-CAT (catalase), Nt-SOD (superoxide dismutase), Nt-DREB (dehydration responsive element binding factor), and Nt-AP2 (apetala2) genes was higher in transgenic lines under stress compared to WT and VC plants. The results suggested that overexpression of membrane-localized SbSRP mitigates salt and osmotic stress in the transgenic tobacco plant. It was hypothesized that SbSRP can be a transporter protein to transmit the environmental stimuli downward through the plasma membrane. However, a detailed study is required to ascertain its exact role in the abiotic stress tolerance mechanism. Overall, SbSRP is a potential candidate to be used for engineering salt and osmotic tolerance in crops.

Comprehensive analysis of the numbers, lengths and amino acid compositions of transmembrane helices in prokaryotic, eukaryotic and viral integral membrane proteins of high-resolution structure

We report a comprehensive analysis of the numbers, lengths and amino acid compositions of transmembrane helices in 235 high-resolution structures of integral membrane proteins. The properties of 1551 transmembrane helices in the structures were compared with those obtained by analysis of the same amino acid sequences using topology prediction tools. Explanations for the 81 (5.2%) missing or additional transmembrane helices in the prediction results were identified. Main reasons for missing transmembrane helices were mis-identification of N-terminal signal peptides, breaks in α-helix conformation or charged residues in the middle of transmembrane helices and transmembrane helices with unusual amino acid composition. The main reason for additional transmembrane helices was mis-identification of amphipathic helices, extramembrane helices or hairpin re-entrant loops. Transmembrane helix length had an overall median of 24 residues and an average of 24.9 ± 7.0 residues and the most common length was 23 residues. The overall content of residues in transmembrane helices as a percentage of the full proteins had a median of 56.8% and an average of 55.7 ± 16.0%. Amino acid composition was analysed for the full proteins, transmembrane helices and extramembrane regions. Individual proteins or types of proteins with transmembrane helices containing extremes in contents of individual amino acids or combinations of amino acids with similar physicochemical properties were identified and linked to structure and/or function. In addition to overall median and average values, all results were analysed for proteins originating from different types of organism (prokaryotic, eukaryotic, viral) and for subgroups of receptors, channels, transporters and others.

Fast H-DROP: A thirty times accelerated version of H-DROP for interactive SVM-based prediction of helical domain linkers

Efficient and rapid prediction of domain regions from amino acid sequence information alone is often required for swift structural and functional characterization of large multi-domain proteins. Here we introduce Fast H-DROP, a thirty times accelerated version of our previously reported H-DROP (Helical Domain linker pRediction using OPtimal features), which is unique in specifically predicting helical domain linkers (boundaries). Fast H-DROP, analogously to H-DROP, uses optimum features selected from a set of 3000 ones by combining a random forest and a stepwise feature selection protocol. We reduced the computational time from 8.5 min per sequence in H-DROP to 14 s per sequence in Fast H-DROP on an 8 Xeon processor Linux server by using SWISS-PROT instead of Genbank non-redundant (nr) database for generating the PSSMs. The sensitivity and precision of Fast H-DROP assessed by cross-validation were 33.7 and 36.2%, which were merely ~2% lower than that of H-DROP. The reduced computational time of Fast H-DROP, without affecting prediction performances, makes it more interactive and user-friendly. Fast H-DROP and H-DROP are freely available from http://domserv.lab.tuat.ac.jp/ .

Mapping the membrane proteome of anaerobic gut fungi identifies a wealth of carbohydrate binding proteins and transporters

BACKGROUND

Engineered cell factories that convert biomass into value-added compounds are emerging as a timely alternative to petroleum-based industries. Although often overlooked, integral membrane proteins such as solute transporters are pivotal for engineering efficient microbial chassis. Anaerobic gut fungi, adapted to degrade raw plant biomass in the intestines of herbivores, are a potential source of valuable transporters for biotechnology, yet very little is known about the membrane constituents of these non-conventional organisms. Here, we mined the transcriptome of three recently isolated strains of anaerobic fungi to identify membrane proteins responsible for sensing and transporting biomass hydrolysates within a competitive and rather extreme environment.

RESULTS

Using sequence analyses and homology, we identified membrane protein-coding sequences from assembled transcriptomes from three strains of anaerobic gut fungi: Neocallimastix californiae, Anaeromyces robustus, and Piromyces finnis. We identified nearly 2000 transporter components: about half of these are involved in the general secretory pathway and intracellular sorting of proteins; the rest are predicted to be small-solute transporters. Unexpectedly, we found a number of putative sugar binding proteins that are associated with prokaryotic uptake systems; and approximately 100 class C G-protein coupled receptors (GPCRs) with non-canonical putative sugar binding domains.

CONCLUSIONS

We report the first comprehensive characterization of the membrane protein machinery of biotechnologically relevant anaerobic gut fungi. Apart from identifying conserved machinery for protein sorting and secretion, we identify a large number of putative solute transporters that are of interest for biotechnological applications. Notably, our data suggests that the fungi display a plethora of carbohydrate binding domains at their surface, perhaps as a means to sense and sequester some of the sugars that their biomass degrading, extracellular enzymes produce.

Identification of a Novel L-rhamnose Uptake Transporter in the Filamentous Fungus Aspergillus niger

The study of plant biomass utilization by fungi is a research field of great interest due to its many implications in ecology, agriculture and biotechnology. Most of the efforts done to increase the understanding of the use of plant cell walls by fungi have been focused on the degradation of cellulose and hemicellulose, and transport and metabolism of their constituent monosaccharides. Pectin is another important constituent of plant cell walls, but has received less attention. In relation to the uptake of pectic building blocks, fungal transporters for the uptake of galacturonic acid recently have been reported in Aspergillus niger and Neurospora crassa. However, not a single L-rhamnose (6-deoxy-L-mannose) transporter has been identified yet in fungi or in other eukaryotic organisms. L-rhamnose is a deoxy-sugar present in plant cell wall pectic polysaccharides (mainly rhamnogalacturonan I and rhamnogalacturonan II), but is also found in diverse plant secondary metabolites (e.g. anthocyanins, flavonoids and triterpenoids), in the green seaweed sulfated polysaccharide ulvan, and in glycan structures from viruses and bacteria. Here, a comparative plasmalemma proteomic analysis was used to identify candidate L-rhamnose transporters in A. niger. Further analysis was focused on protein ID 1119135 (RhtA) (JGI A. niger ATCC 1015 genome database). RhtA was classified as a Family 7 Fucose: H+ Symporter (FHS) within the Major Facilitator Superfamily. Family 7 currently includes exclusively bacterial transporters able to use different sugars. Strong indications for its role in L-rhamnose transport were obtained by functional complementation of the Saccharomyces cerevisiae EBY.VW.4000 strain in growth studies with a range of potential substrates. Biochemical analysis using L-[³H(G)]-rhamnose confirmed that RhtA is a L-rhamnose transporter. The RhtA gene is located in tandem with a hypothetical alpha-L-rhamnosidase gene (rhaB). Transcriptional analysis of rhtA and rhaB confirmed that both genes have a coordinated expression, being strongly and specifically induced by L-rhamnose, and controlled by RhaR, a transcriptional regulator involved in the release and catabolism of the methyl-pentose. RhtA is the first eukaryotic L-rhamnose transporter identified and functionally validated to date.

The growth of filamentous fungi on plant biomass, which occurs through the utilization of its components (e.g. D-glucose, D-xylose, L-arabinose, L-rhamnose) as carbon sources, is a highly regulated event. L-rhamnose (6-deoxy-L-mannose) is a deoxy-sugar present in plant cell wall pectic polysaccharides (mainly rhamnogalacturonan I and rhamnogalacturonan II), but also in diverse plant secondary metabolites, ulvan from green seaweeds and glycan structures from virus and bacteria. The utilization, transformation or detoxification of this monosaccharide by fungi involves a first step of chemical hydrolysis, performed by alpha-L-rhamnosidases, and a second step of transport into the cell, prior to its metabolization. While many rhamnosidases have been identified, not a single eukaryotic plasma membrane L-rhamnose transporter is known to date. In this study we identified and characterized, for the first time, a fungal L-rhamnose transporter (RhtA), from the industrial workhorse Aspergillus niger. We also found that RhtA putative orthologs are conserved throughout different fungal orders, opening the possibility of identifying new transporters of its kind.

A Method for the Annotation of Functional Similarities of Coding DNA Sequences: the Case of a Populated Cluster of Transmembrane Proteins

The analysis of a large number of human and mouse genes codifying for a populated cluster of transmembrane proteins revealed that some of the genes significantly vary in their primary nucleotide sequence inter-species and also intra-species. In spite of that divergence and of the fact that all these genes share a common parental function we asked the question of whether at DNA level they have some kind of common compositional structure, not evident from the analysis of their primary nucleotide sequence. To reveal the existence of gene clusters not based on primary sequence relationships we have analyzed 13574 human and 14047 mouse genes by the composon-clustering methodology. The data presented show that most of the genes from each one of the samples are distributed in 18 clusters sharing the common compositional features between the particular human and mouse clusters. It was observed, in addition, that between particular human and mouse clusters having similar composon-profiles large variations in gene population were detected as an indication that a significant amount of orthologs between both species differs in compositional features. A gene cluster containing exclusively genes codifying for transmembrane proteins, an important fraction of which belongs to the Rhodopsin G-protein coupled receptor superfamily, was also detected. This indicates that even though some of them display low sequence similarity, all of them, in both species, participate with similar compositional features in terms of composons. We conclude that in this family of transmembrane proteins in general and in the Rhodopsin G-protein coupled receptor in particular, the composon-clustering reveals the existence of a type of common compositional structure underlying the primary nucleotide sequence closely correlated to function.

Transporter occluded-state conformation-induced endocytosis: Amino acid transporter ATB0,+-mediated tumor targeting of liposomes for docetaxel delivery for hepatocarcinoma therapy

Rapidly proliferating tumor cells upregulate specific amino acid transporters, which hold great potential for tumor-selective drug delivery. Published reports have focused primarily on blocking these transporters as a means of starving the tumor cells of amino acids, but their potential in drug delivery remains understudied. In the present study, we developed liposomes functionalized with lysine and polyoxyethylene stearate conjugate (LPS) to interact with ATB^0,+, an amino acid transporter overexpressed in hepatocarcinoma and the liver cancer cell line HepG2. The LPS modified liposomes (LPS-Lips) were ~100nm in size and exhibited high drug encapsulation efficiency as 94.7%. The uptake of LPS-Lips in HepG2 cells was dependent on Na⁺ and Cl^-. Molecular dynamic simulation showed that a sustained occluded state of the transporter upon binding to co-transported ions was formed and LPS-Lips triggered the cellular internalization of liposomes. We loaded these LPS-Lips with docetaxel and evaluated the potential of ATB^0,+-mediated endocytosis of the drug-loaded LPS-Lips in HepG2 cells in vitro and in syngeneic mouse transplants in vivo. Compared with unmodified liposomes, which did not interact with ATB^0,+, LPS-Lips exhibited the ability to deliver docetaxel more efficiently into tumor cells with consequent greater antitumor efficacy and less systemic toxicity. These studies provide first evidences that ATB^0,+ can be used as a novel and effective target for drug delivery system in tumor cells using chemically modified liposomes for loading with chemotherapeutics and targeting them for the transporter-mediated endocytosis. As ATB^0,+ is highly upregulated in several cancers, this approach holds potential for tumor-selective delivery of drugs to treat these cancer types.

Classify vertebrate hemoglobin proteins by incorporating the evolutionary information into the general PseAAC with the hybrid approach

Hemoglobin is an oxygen-binding protein widely present in all kingdoms of life from prokaryotic to eukaryotic, but well established in the vertebrate system. An attempt was made to determine the Vertebrate hemoglobin (VerHb) protein on their animal classifications, based on general pseudo amino acid composition (PseAAC)'s evolutionary profiles and hybrid approach. The support vector machine (SVM) has been applied to develop all models, the prediction results further compared according to their animal classification. The performance of the approaches estimated using five-fold cross-validation techniques. The prediction performance was further investigated by receiver operating characteristic (ROC) and prediction score graphs. The prediction accuracy (ACC), sensitivity (SN) and specificity (SP) were examined to find the accurate predictions on the threshold level. Based on the approach, a web-tool has been developed for identifying the VerHb proteins.

Amino acid composition analysis of human secondary transport proteins and implications for reliable membrane topology prediction

Secondary transporters in humans are a large group of proteins that transport a wide range of ions, metals, organic and inorganic solutes involved in energy transduction, control of membrane potential and osmotic balance, metabolic processes and in the absorption or efflux of drugs and xenobiotics. They are also emerging as important targets for development of new drugs and as target sites for drug delivery to specific organs or tissues. We have performed amino acid composition (AAC) and phylogenetic analyses and membrane topology predictions for 336 human secondary transport proteins and used the results to confirm protein classification and to look for trends and correlations with structural domains and specific substrates and/or function. Some proteins showed statistically high contents of individual amino acids or of groups of amino acids with similar physicochemical properties. One recurring trend was a correlation between high contents of charged and/or polar residues with misleading results in predictions of membrane topology, which was especially prevalent in Mitochondrial Carrier family proteins. We demonstrate how charged or polar residues located in the middle of transmembrane helices can interfere with their identification by membrane topology tools resulting in missed helices in the prediction. Comparison of AAC in the human proteins with that in 235 secondary transport proteins from Escherichia coli revealed similar overall trends along with differences in average contents for some individual amino acids and groups of similar amino acids that are presumed to result from a greater number of functions and complexity in the higher organism.

Probing an optimal class distribution for enhancing prediction and feature characterization of plant virus-encoded RNA-silencing suppressors

To counter the host RNA silencing defense mechanism, many plant viruses encode RNA silencing suppressor proteins. These groups of proteins share very low sequence and structural similarities among them, which consequently hamper their annotation using sequence similarity-based search methods. Alternatively the machine learning-based methods can become a suitable choice, but the optimal performance through machine learning-based methods is being affected by various factors such as class imbalance, incomplete learning, selection of inappropriate features, etc. In this paper, we have proposed a novel approach to deal with the class imbalance problem by finding the optimal class distribution for enhancing the prediction accuracy for the RNA silencing suppressors. The optimal class distribution was obtained using different resampling techniques with varying degrees of class distribution starting from natural distribution to ideal distribution, i.e., equal distribution. The experimental results support the fact that optimal class distribution plays an important role to achieve near perfect learning. The best prediction results are obtained with Sequential Minimal Optimization (SMO) learning algorithm. We could achieve a sensitivity of 98.5 %, specificity of 92.6 % with an overall accuracy of 95.3 % on a tenfold cross validation and is further validated using leave one out cross validation test. It was also observed that the machine learning models trained on oversampled training sets using synthetic minority oversampling technique (SMOTE) have relatively performed better than on both randomly undersampled and imbalanced training data sets. Further, we have characterized the important discriminatory sequence features of RNA-silencing suppressors which distinguish these groups of proteins from other protein families.

Amino Acid Substitutions of CrrB Responsible for Resistance to Colistin through CrrC in Klebsiella pneumoniae

Colistin is a last-resort antibiotic for treatment of carbapenem-resistant Klebsiella pneumoniae A recent study indicated that missense mutations in the CrrB protein contribute to colistin resistance. In our previous study, mechanisms of colistin resistance were defined in 17 of 26 colistin-resistant K. pneumoniae clinical isolates. Of the remaining nine strains, eight were highly resistant to colistin. In the present study, crrAB sequences were determined for these eight strains. Six separate amino acid substitutions in CrrB (Q10L, Y31H, W140R, N141I, P151S, and S195N) were detected. Site-directed mutagenesis was used to generate crrB loci harboring individual missense mutations; introduction of the mutated genes into a susceptible strain, A4528, resulted in 64- to 1,024-fold increases in colistin MICs. These crrB mutants showed increased accumulation of H239_3062, H239_3059, pmrA, pmrC, and pmrH transcripts by quantitative reverse transcription (qRT)-PCR. Deletion of H239_3062 (but not that of H239_3059) in the A4528 crrB(N141I) strain attenuated resistance to colistin, and H239_3062 was accordingly named crrC Similarly, accumulation of pmrA, pmrC, and pmrH transcripts induced by crrB(N141I) was significantly attenuated upon deletion of crrC Complementation of crrC restored resistance to colistin and accumulation of pmrA, pmrC, and pmrH transcripts in a crrB(N141I) ΔcrrC strain. In conclusion, novel individual CrrB amino acid substitutions (Y31H, W140R, N141I, P151S, and S195N) were shown to be responsible for colistin resistance. We hypothesize that CrrB mutations induce CrrC expression, thereby inducing elevated expression of the pmrHFIJKLM operon and pmrC (an effect mediated via the PmrAB two-component system) and yielding increased colistin resistance.

BacHbpred: Support Vector Machine Methods for the Prediction of Bacterial Hemoglobin-Like Proteins

The recent upsurge in microbial genome data has revealed that hemoglobin-like (HbL) proteins may be widely distributed among bacteria and that some organisms may carry more than one HbL encoding gene. However, the discovery of HbL proteins has been limited to a small number of bacteria only. This study describes the prediction of HbL proteins and their domain classification using a machine learning approach. Support vector machine (SVM) models were developed for predicting HbL proteins based upon amino acid composition (AC), dipeptide composition (DC), hybrid method (AC + DC), and position specific scoring matrix (PSSM). In addition, we introduce for the first time a new prediction method based on max to min amino acid residue (MM) profiles. The average accuracy, standard deviation (SD), false positive rate (FPR), confusion matrix, and receiver operating characteristic (ROC) were analyzed. We also compared the performance of our proposed models in homology detection databases. The performance of the different approaches was estimated using fivefold cross-validation techniques. Prediction accuracy was further investigated through confusion matrix and ROC curve analysis. All experimental results indicate that the proposed BacHbpred can be a perspective predictor for determination of HbL related proteins. BacHbpred, a web tool, has been developed for HbL prediction.

Quantitative structure-activity relationship model for the fetal-maternal blood concentration ratio of chemicals in humans

A quantitative structure-activity relationship (QSAR) model of the fetal-maternal blood concentration ratio (F/M ratio) of chemicals was developed to predict the placental transfer in humans. Data on F/M ratio of 55 compounds found in the literature were separated into training (75%, 41 compounds) and testing sets (25%, 14 compounds). The training sets were then subjected to multiple linear regression analysis using the descriptors of molecular weight (MW), topological polar surface area (TopoPSA), and maximum E-state of hydrogen atom (Hmax). Multiple linear regression analysis and a cross-validation showed a relatively high adjusted coefficient of determination (Ra(2)) (0.73) and cross-validated coefficient of determination (Q(2)) (0.71), after removing three outliers. In the external validation, R(2) for external validation (R(2)pred) was calculated to be 0.51. These results suggested that the QSAR model developed in this study can be considered reliable in terms of its robustness and predictive performance. Since it is difficult to examine the F/M ratio in humans experimentally, this QSAR model for prediction of the placental transfer of chemicals in humans could be useful in risk assessment of chemicals in humans.

HRGRN: A Graph Search-Empowered Integrative Database of Arabidopsis Signaling Transduction, Metabolism and Gene Regulation Networks

The biological networks controlling plant signal transduction, metabolism and gene regulation are composed of not only tens of thousands of genes, compounds, proteins and RNAs but also the complicated interactions and co-ordination among them. These networks play critical roles in many fundamental mechanisms, such as plant growth, development and environmental response. Although much is known about these complex interactions, the knowledge and data are currently scattered throughout the published literature, publicly available high-throughput data sets and third-party databases. Many 'unknown' yet important interactions among genes need to be mined and established through extensive computational analysis. However, exploring these complex biological interactions at the network level from existing heterogeneous resources remains challenging and time-consuming for biologists. Here, we introduce HRGRN, a graph search-empowered integrative database of Arabidopsis signal transduction, metabolism and gene regulatory networks. HRGRN utilizes Neo4j, which is a highly scalable graph database management system, to host large-scale biological interactions among genes, proteins, compounds and small RNAs that were either validated experimentally or predicted computationally. The associated biological pathway information was also specially marked for the interactions that are involved in the pathway to facilitate the investigation of cross-talk between pathways. Furthermore, HRGRN integrates a series of graph path search algorithms to discover novel relationships among genes, compounds, RNAs and even pathways from heterogeneous biological interaction data that could be missed by traditional SQL database search methods. Users can also build subnetworks based on known interactions. The outcomes are visualized with rich text, figures and interactive network graphs on web pages. The HRGRN database is freely available at http://plantgrn.noble.org/hrgrn/.

SCMMTP: identifying and characterizing membrane transport proteins using propensity scores of dipeptides

BACKGROUND

Identifying putative membrane transport proteins (MTPs) and understanding the transport mechanisms involved remain important challenges for the advancement of structural and functional genomics. However, the transporter characters are mainly acquired from MTP crystal structures which are hard to crystalize. Therefore, it is desirable to develop bioinformatics tools for the effective large-scale analysis of available sequences to identify novel transporters and characterize such transporters.

RESULTS

This work proposes a novel method (SCMMTP) based on the scoring card method (SCM) using dipeptide composition to identify and characterize MTPs from an existing dataset containing 900 MTPs and 660 non-MTPs which are separated into a training dataset consisting 1,380 proteins and an independent dataset consisting 180 proteins. The SCMMTP produced estimating propensity scores for amino acids and dipeptides as MTPs. The SCMMTP training and test accuracy levels respectively reached 83.81% and 76.11%. The test accuracy of support vector machine (SVM) using a complicated classification method with a low possibility for biological interpretation and position-specific substitution matrix (PSSM) as a protein feature is 80.56%, thus SCMMTP is comparable to SVM-PSSM. To identify MTPs, SCMMTP is applied to three datasets including: 1) human transmembrane proteins, 2) a photosynthetic protein dataset, and 3) a human protein database. MTPs showing α-helix rich structure is agreed with previous studies. The MTPs used residues with low hydration energy. It is hypothesized that, after filtering substrates, the hydrated water molecules need to be released from the pore regions.

CONCLUSIONS

SCMMTP yields estimating propensity scores for amino acids and dipeptides as MTPs, which can be used to identify novel MTPs and characterize transport mechanisms for use in further experiments.

AVAILABILITY

http://iclab.life.nctu.edu.tw/iclab_webtools/SCMMTP/.

Insights into the molecular basis of piezophilic adaptation: Extraction of piezophilic signatures

Piezophiles are the organisms which can successfully survive at extreme pressure conditions. However, the molecular basis of piezophilic adaptation is still poorly understood. Analysis of the protein sequence adjustments that had taken place during evolution can help to reveal the sequence adaptation parameters responsible for protein functional and structural adaptation at such high pressure conditions. In this current work we have used SVM classifier for filtering strong instances and generated human interpretable rules from these strong instances by using the PART algorithm. These generated rules were analyzed for getting insights into the molecular signature patterns present in the piezophilic proteins. The experiments were performed on three different temperature ranges piezophilic groups, namely psychrophilic-piezophilic, mesophilic-piezophilic, and thermophilic-piezophilic for the detailed comparative study. The best classification results were obtained as we move up the temperature range from psychrophilic-piezophilic to thermophilic-piezophilic. Based on the physicochemical classification of amino acids and using feature ranking algorithms, hydrophilic and polar amino acid groups have higher discriminative ability for psychrophilic-piezophilic and mesophilic-piezophilic groups along with hydrophobic and nonpolar amino acids for the thermophilic-piezophilic groups. We also observed an overrepresentation of polar, hydrophilic and small amino acid groups in the discriminatory rules of all the three temperature range piezophiles along with aliphatic, nonpolar and hydrophobic groups in the mesophilic-piezophilic and thermophilic-piezophilic groups.

Discrimination of membrane transporter protein types using K-nearest neighbor method derived from the similarity distance of total diversity measure

Membrane transporters play crucial roles in the fundamental cellular processes of living organisms. Computational techniques are very necessary to annotate the transporter functions. In this study, a multi-class K nearest neighbor classifier based on the increment of diversity (KNN-ID) was developed to discriminate the membrane transporter types when the increment of diversity (ID) was introduced as one of the novel similarity distances. Comparisons with multiple recently published methods showed that the proposed KNN-ID method outperformed the other methods, obtaining more than 20% improvement for overall accuracy. The overall prediction accuracy reached was 83.1%, when the K was selected as 2. The prediction sensitivity achieved 76.7%, 89.1%, 80.1% for channels/pores, electrochemical potential-driven transporters, primary active transporters, respectively. Discrimination and comparison between any two different classes of transporters further demonstrated that the proposed method is a potential classifier and will play a complementary role for facilitating the functional assignment of transporters.

A consensus subunit-specific model for annotation of substrate specificity for ABC transporters

A consensus classification model was built by considering three subunit proteins individually to predict the substrate specificity of ABC transporters.