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

SPOT: A machine learning model that predicts specific substrates for transport proteins

Transport proteins play a crucial role in cellular metabolism and are central to many aspects of molecular biology and medicine. Determining the function of transport proteins experimentally is challenging, as they become unstable when isolated from cell membranes. Machine learning-based predictions could provide an efficient alternative. However, existing methods are limited to predicting a small number of specific substrates or broad transporter classes. These limitations stem partly from using small data sets for model training and a choice of input features that lack sufficient information about the prediction problem. Here, we present SPOT, the first general machine learning model that can successfully predict specific substrates for arbitrary transport proteins, achieving an accuracy above 92% on independent and diverse test data covering widely different transporters and a broad range of metabolites. SPOT uses Transformer Networks to represent transporters and substrates numerically. To overcome the problem of missing negative data for training, it augments a large data set of known transporter-substrate pairs with carefully sampled random molecules as non-substrates. SPOT not only predicts specific transporter-substrate pairs, but also outperforms previously published models designed to predict broad substrate classes for individual transport proteins. We provide a web server and Python function that allows users to explore the substrate scope of arbitrary transporters.

PortPred: Exploiting deep learning embeddings of amino acid sequences for the identification of transporter proteins and their substrates

The physiology of every living cell is regulated at some level by transporter proteins which constitute a relevant portion of membrane-bound proteins and are involved in the movement of ions, small and macromolecules across bio-membranes. The importance of transporter proteins is unquestionable. The prediction and study of previously unknown transporters can lead to the discovery of new biological pathways, drugs and treatments. Here we present PortPred, a tool to accurately identify transporter proteins and their substrate starting from the protein amino acid sequence. PortPred successfully combines pre-trained deep learning-based protein embeddings and machine learning classification approaches and outperforms other state-of-the-art methods. In addition, we present a comparison of the most promising protein sequence embeddings (Unirep, SeqVec, ProteinBERT, ESM-1b) and their performances for this specific task.

TranSyT, an innovative framework for identifying transport systems

MOTIVATION

The importance and rate of development of genome-scale metabolic models have been growing for the last few years, increasing the demand for software solutions that automate several steps of this process. However, since TRIAGE's release, software development for the automatic integration of transport reactions into models has stalled.

RESULTS

Here, we present the Transport Systems Tracker (TranSyT). Unlike other transport systems annotation software, TranSyT does not rely on manual curation to expand its internal database, which is derived from highly curated records retrieved from the Transporters Classification Database and complemented with information from other data sources. TranSyT compiles information regarding transporter families and proteins, and derives reactions into its internal database, making it available for rapid annotation of complete genomes. All transport reactions have GPR associations and can be exported with identifiers from four different metabolite databases. TranSyT is currently available as a plugin for merlin v4.0 and an app for KBase.

AVAILABILITY AND IMPLEMENTATION

TranSyT web service: https://transyt.bio.di.uminho.pt/; GitHub for the tool: https://github.com/BioSystemsUM/transyt; GitHub with examples and instructions to run TranSyT: https://github.com/ecunha1996/transyt_paper.

Enhanced identification of membrane transport proteins: a hybrid approach combining ProtBERT-BFD and convolutional neural networks

Transmembrane transport proteins (transporters) play a crucial role in the fundamental cellular processes of all organisms by facilitating the transport of hydrophilic substrates across hydrophobic membranes. Despite the availability of numerous membrane protein sequences, their structures and functions remain largely elusive. Recently, natural language processing (NLP) techniques have shown promise in the analysis of protein sequences. Bidirectional Encoder Representations from Transformers (BERT) is an NLP technique adapted for proteins to learn contextual embeddings of individual amino acids within a protein sequence. Our previous strategy, TooT-BERT-T, differentiated transporters from non-transporters by employing a logistic regression classifier with fine-tuned representations from ProtBERT-BFD. In this study, we expand upon this approach by utilizing representations from ProtBERT, ProtBERT-BFD, and MembraneBERT in combination with classical classifiers. Additionally, we introduce TooT-BERT-CNN-T, a novel method that fine-tunes ProtBERT-BFD and discriminates transporters using a Convolutional Neural Network (CNN). Our experimental results reveal that CNN surpasses traditional classifiers in discriminating transporters from non-transporters, achieving an MCC of 0.89 and an accuracy of 95.1 % on the independent test set. This represents an improvement of 0.03 and 1.11 percentage points compared to TooT-BERT-T, respectively.

In-silico functional analysis of hypothetical proteins from Lactiplantibacillus plantarum plasmids reveals enrichment of cell envelope proteins

Lactiplantibacillus plantarum is one of the important species of lactic acid bacterium (LAB) found in diverse environments, with many strains exhibiting probiotic properties. In our previous study, 41.6% of protein families (PFs) encoded by 395 plasmids from several L. plantarum strains were found to be hypothetical proteins with no predicted function. This study aimed at predicting the functions of these 647 hypothetical proteins using 21 different bioinformatics methods. As a result, 160 PFs could be newly annotated. A lower proportion of plasmid-specific functions was annotated as compared to the functions shared between plasmids and chromosomes. Also, hypothetical proteins were less conserved than the annotated proteins across L.plantarum plasmids. Based on the subcellular localization, cell envelope proteins represented the biggest category in the newly annotated proteins. Transporters (112 PFs) which was a part of cell envelop proteins represented the largest functional group. Additionally, 40 and 25 other PFs were predicted to contain signal peptides and transmembrane helices, respectively. We speculate that such hypothetical proteins might be involved in the transport of various chemicals and environmental interactions in L. plantarum. In the future, functional characterization of these proteins through wet-lab experimental approach can provide novel insights into their contribution to the physiology, probiotic properties, and industrial utility of these bacteria.

Prediction of transport proteins from sequence information with the deep learning approach

Transport proteins (TPs) are vital to the growth and life of all living things, especially in fields of microbial pathogenesis and drug resistance of tumor cells. Accurately identifying potential TPs remains an important challenge for the advancement of functional genomics. This study aimed to develop a tool for predicting TPs using the deep learning approach. Here, we proposed DeepTP, a convolutional neural network model that uses parallel subnetworks to extract features from protein sequences and uses fully connected layers for TP classification. To train and evaluate the performance of the developed model, datasets were collected from the UniProtKB/Swiss-Prot database. The test results revealed that the proposed model could successfully identify TPs with the AUCROC, accuracy, F-value, and Matthews correlation coefficient of 0.9719, 0.9513, 0.8982, and 0.8679, respectively. By further comparison, DeepTP achieved better performance than other commonly used methods. Analysis of the gradients of prediction score concerning input suggested that DeepTP makes predictions by recognizing the functional domains of TPs. We anticipate that DeepTP will serve as a useful tool for predicting TPs in large-scale genome projects, which will facilitate the discovery of novel TPs.

Disto-TRP: An approach for identifying transient receptor potential (TRP) channels using structural information generated by AlphaFold

The ability to predict 3D protein structures computationally has significantly advanced biological research. The AlphaFold protein structure database, developed by DeepMind, has provided a wealth of predicted protein structures and has the potential to bring about revolutionary changes in the field of life sciences. However, directly determining the function of proteins from their structures remains a challenging task. The Distogram from AlphaFold is used in this study as a novel feature set to identify transient receptor potential (TRP) channels. Distograms feature vectors and pre-trained language model (BERT) features were combined to improve prediction performance for transient receptor potential (TRP) channels. The method proposed in this study demonstrated promising performance on many evaluation metrics. For five-fold cross-validation, the method achieved a Sensitivity (SN) of 87.00%, Specificity (SP) of 93.61%, Accuracy (ACC) of 93.39%, and a Matthews correlation coefficient (MCC) of 0.52. Additionally, on an independent dataset, the method obtained 100.00% SN, 95.54% SP, 95.73% ACC, and an MCC of 0.69. The results demonstrate the potential for using structural information to predict protein function. In the future, it is hoped that such structural information will be incorporated into artificial intelligence networks to explore more useful and valuable functional information in the biological field.

Identification of conserved genomic signatures specific to Bifidobacterium species colonising the human gut

Bifidobacterium species are known for their ability to inhabit various habitats and are often regarded as the first colonisers of the human gut. In the present work, we have used comparative genomics to identify conserved genomic signatures specific to Bifidobacterium species associated with the human gut. Our approach discovered five genomic signatures with varying lengths and confidence. Among the predicted five signatures, a 1790 bp multi-drug resistance (MDR) signature was found to be remarkably specific to only those species that can colonise the human gut. The signature codes for a membrane transport protein belonging to the major facilitator superfamily (MFS) generally involved in MDR. Phylogenetic analyses of the MDR signature suggest a lineage-specific evolution of the MDR signature in bifidobacteria colonising the human gut. Functional annotation led to the discovery of two conserved domains in the protein; a catalytic MFS domain involved in the efflux of drugs and toxins, and a regulatory cystathionine-β-synthase (CBS) domain that can interact with adenosyl-carriers. Molecular docking simulation performed with the modelled tertiary structure of the MDR signature revealed the putative functional role of the covalently linked domains. The MFS domain displayed a high affinity towards various protein synthesis inhibitor antibiotics and human bile acids, whereas the C-terminally linked CBS domain exhibited favourable binding with molecular structures of ATP and AMP. Therefore, we believe that the predicted signature represents a niche-specific survival trait involved in bile and antibiotic resistance, imparting an adaptive advantage to the Bifidobacterium species colonising the human gut.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13205-023-03492-4.

Phylogenetic, structural, functional characterisation and effect of exogenous spermidine on rice (Oryza sativa) HAK transporters under salt stress

The HAK (High-affinity K+ ) family members mediate K+ transport that confers normal plant growth and resistance against unfavourable environmental conditions. Rice (Oryza sativa L.) HAK transporters have been extensively investigated for phylogenetic analyses with other plants species with very few of them functionally characterised. But very little information is known about their evolutionary aspects, overall structural, functional characterisation, and global expression pattern of the complete HAK family members in response to salt stress. In this study, 27 rice transporters were phylogenetically clustered with different dicot and monocot family members. Subsequently, the exon-intron structural patterns, conserved motif analyses, evolutionary divergence based different substitution matrix, orthologous-paralogous relationships were studied elaborately. Structural characterisations included a comparative study of secondary and tertiary structure, post-translational modifications, correspondence analyses, normal mode analyses, K+ /Na+ binding affinities of each of the OsHAK gene members. Global expression profile under salt stress showed clade-specific expression pattern of the proteins. Additionally, five OsHAK genes were chosen for further expression analyses in root and shoot tissues of two rice varieties during short-term salinity in the presence and absence of exogenous spermidine. All the information can be used as first-hand data for dissecting the administrative role of rice HAK transporters under various abiotic stresses.

Quantitative proteomics reveals key pathways in the symbiotic interface and the likely extracellular property of soybean symbiosome

An effective symbiosis between legumes and rhizobia relies largely on diverse proteins at the plant-rhizobium interface for material transportation and signal transduction during symbiotic nitrogen fixation. Here, we report a comprehensive proteome atlas of the soybean symbiosome membrane (SM), peribacteroid space (PBS), and root microsomal fraction (RMF) using state-of-the-art label-free quantitative proteomic technology. In total, 1759 soybean proteins with diverse functions are detected in the SM, and 1476 soybean proteins and 369 rhizobial proteins are detected in the PBS. The diversity of SM proteins detected suggests multiple origins of the SM. Quantitative comparative analysis highlights amino acid metabolism and nutrient uptake in the SM, indicative of the key pathways in nitrogen assimilation. The detection of soybean secretory proteins in the PBS and receptor-like kinases in the SM provides evidence for the likely extracellular property of the symbiosome and the potential signaling communication between both symbionts at the symbiotic interface. Our proteomic data provide clues for how some of the sophisticated regulation between soybean and rhizobium at the symbiotic interface is achieved, and suggest approaches for symbiosis engineering.

TransAAP: an automated annotation pipeline for membrane transporter prediction in bacterial genomes

Membrane transporters are a large group of proteins that span cell membranes and contribute to critical cell processes, including delivery of essential nutrients, ejection of waste products, and assisting the cell in sensing environmental conditions. Obtaining an accurate and specific annotation of the transporter proteins encoded by a micro-organism can provide details of its likely nutritional preferences and environmental niche(s), and identify novel transporters that could be utilized in small molecule production in industrial biotechnology. The Transporter Automated Annotation Pipeline (TransAAP) (http://www.membranetransport.org/transportDB2/TransAAP_login.html) is a fully automated web service for the prediction and annotation of membrane transport proteins in an organism from its genome sequence, by using comparisons with both curated databases such as the TCDB (Transporter Classification Database) and TDB, as well as selected Pfams and TIGRFAMs of transporter families and other methodologies. TransAAP was used to annotate transporter genes in the prokaryotic genomes in the National Center for Biotechnology Information (NCBI) RefSeq; these are presented in the transporter database TransportDB (http://www.membranetransport.org) website, which has a suite of data visualization and analysis tools. Creation and maintenance of a bioinformatic database specific for transporters in all genomic datasets is essential for microbiology research groups and the general research/biotechnology community to obtain a detailed picture of membrane transporter systems in various environments, as well as comprehensive information on specific membrane transport proteins.

Optimized Data Set and Feature Construction for Substrate Prediction of Membrane Transporters

α-Helical transmembrane proteins termed membrane transporters mediate the passage of small hydrophilic substrate molecules across biological lipid bilayer membranes. Annotating the specific substrates of the dozens to hundreds of individual transporters of an organism is an important task. In the past, machine learning classifiers have been successfully trained on pan-organism data sets to predict putative substrates of transporters. Here, we critically examine the selection of an optimal data set of protein sequence features for the classification task. We focus on membrane transporters of the three model organisms Escherichia coli, Arabidopsis thaliana, and Saccharomyces cerevisiae, as well as human. We show that organism-specific classifiers can be robustly trained if at least 20 samples are available for each substrate class. If information from position-specific scoring matrices is included, such classifiers have F1 scores between 0.85 and 1.00. For the largest data set (A. thaliana), a 4-class classifier yielded an F-score of 0.97. On a pan-organism data set composed of transporters of all four organisms, amino acid and sugar transporters were predicted with an F1 score of 0.91.

ISTRF: Identification of sucrose transporter using random forest

Sucrose transporter (SUT) is a type of transmembrane protein that exists widely in plants and plays a significant role in the transportation of sucrose and the specific signal sensing process of sucrose. Therefore, identifying sucrose transporter is significant to the study of seed development and plant flowering and growth. In this study, a random forest-based model named ISTRF was proposed to identify sucrose transporter. First, a database containing 382 SUT proteins and 911 non-SUT proteins was constructed based on the UniProt and PFAM databases. Second, k-separated-bigrams-PSSM was exploited to represent protein sequence. Third, to overcome the influence of imbalance of samples on identification performance, the Borderline-SMOTE algorithm was used to overcome the shortcoming of imbalance training data. Finally, the random forest algorithm was used to train the identification model. It was proved by 10-fold cross-validation results that k-separated-bigrams-PSSM was the most distinguishable feature for identifying sucrose transporters. The Borderline-SMOTE algorithm can improve the performance of the identification model. Furthermore, random forest was superior to other classifiers on almost all indicators. Compared with other identification models, ISTRF has the best general performance and makes great improvements in identifying sucrose transporter proteins.

Use Chou's 5-Steps Rule With Different Word Embedding Types to Boost Performance of Electron Transport Protein Prediction Model

Living organisms receive necessary energy substances directly from cellular respiration. The completion of electron storage and transportation requires the process of cellular respiration with the aid of electron transport chains. Therefore, the work of deciphering electron transport proteins is inevitably needed. The identification of these proteins with high performance has a prompt dependence on the choice of methods for feature extraction and machine learning algorithm. In this study, protein sequences served as natural language sentences comprising words. The nominated word embedding-based feature sets, hinged on the word embedding modulation and protein motif frequencies, were useful for feature choosing. Five word embedding types and a variety of conjoint features were examined for such feature selection. The support vector machine algorithm consequentially was employed to perform classification. The performance statistics within the 5-fold cross-validation including average accuracy, specificity, sensitivity, as well as MCC rates surpass 0.95. Such metrics in the independent test are 96.82, 97.16, 95.76 percent, and 0.9, respectively. Compared to state-of-the-art predictors, the proposed method can generate more preferable performance above all metrics indicating the effectiveness of the proposed method in determining electron transport proteins. Furthermore, this study reveals insights about the applicability of various word embeddings for understanding surveyed sequences.

Nitrogen source as a modulator of the metabolic activity of Pedobacter lusitanus NL19: a transcriptomic approach

Secondary metabolites (SMs) are compounds with relevant biological activities. Their production under laboratory conditions, especially in broth, is still challenging. An example is the pedopeptins, which are nonribosomal peptides active against some bacteria listed by the WHO for which new antibiotics are urgently needed. Their biosynthesis is inhibited by high concentrations of peptone from casein (PC) in tryptic soy broth (TSB), and we applied a RNA-seq approach to identify Pedobacter lusitanus NL19 cellular pathways modulated by this condition. Results were validated by qPCR and revealed 261 differentially expressed genes (DEGs), 46.3% of them with a predicted biological function. Specifically, high concentration of PC significantly repressed the de novo biosynthesis of biotin (- 60X) and the production of nonribosomal peptide synthetases (NRPS) of pedopeptins (about - 14X), but no effect was observed on the expression of other NRPS. Transcription of a L-Dap synthesis operon that includes a protein with a σ⁷⁰-like domain was also reduced (about - 7X). High concentrations of PC led to a significant overexpression of MFS and RND efflux pumps and a ferrous iron uptake system, suggesting the redirection of cell machinery to export compounds such as amino acids, sugars and metal divalent cations, alongside with a slight increase of iron import. KEY POINTS: • Higher concentrations of phosphate sources highly repress many operons • High concentrations of peptone from casein (PC) cause biotin's operon repression • High concentrations of PC downregulate the production of peptides of unknown function.

Uncovering Novel Plasma Membrane Carboxylate Transporters in the Yeast Cyberlindnera jadinii

The yeast Cyberlindnera jadinii has great potential in the biotechnology industry due to its ability to produce a variety of compounds of interest, including carboxylic acids. In this work, we identified genes encoding carboxylate transporters from this yeast species. The functional characterization of sixteen plasma membrane carboxylate transporters belonging to the AceTr, SHS, TDT, MCT, SSS, and DASS families was performed by heterologous expression in Saccharomyces cerevisiae. The newly identified C. jadinii transporters present specificity for mono-, di-, and tricarboxylates. The transporters CjAto5, CjJen6, CjSlc5, and CjSlc13-1 display the broadest substrate specificity; CjAto2 accepts mono- and dicarboxylates; and CjAto1,3,4, CjJen1-5, CjSlc16, and CjSlc13-2 are specific for monocarboxylic acids. A detailed characterization of these transporters, including phylogenetic reconstruction, 3D structure prediction, and molecular docking analysis is presented here. The properties presented by these transporters make them interesting targets to be explored as organic acid exporters in microbial cell factories.

Machine Learning-driven Protein Library Design: A Path Toward Smarter Libraries

Proteins are small yet valuable biomolecules that play a versatile role in therapeutics and diagnostics. The intricate sequence-structure-function paradigm in the realm of proteins opens the possibility for directly mapping amino acid sequence to function. However, the rugged nature of the protein fitness landscape and an astronomical number of possible mutations even for small proteins make navigating this system a daunting task. Moreover, the scarcity of functional proteins and the ease with which deleterious mutations are introduced, due to complex epistatic relationships, compound the existing challenges. This highlights the need for auxiliary tools in current techniques such as rational design and directed evolution. To that end, the state-of-the-art machine learning can offer time and cost efficiency in finding high fitness proteins, circumventing unnecessary wet-lab experiments. In the context of improving library design, machine learning provides valuable insights via its unique features such as high adaptation to complex systems, multi-tasking, and parallelism, and the ability to capture hidden trends in input data. Finally, both the advancements in computational resources and the rapidly increasing number of sequences in protein databases will allow more promising and detailed insights delivered from machine learning to protein library design. In this chapter, fundamental concepts and a method for machine learning-driven library design leveraging deep sequencing datasets will be discussed. We elaborate on (1) basic knowledge about machine learning algorithms, (2) the benefit of machine learning in library design, and (3) methodology for implementing machine learning in library design.

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.

Protein-Protein Interface Topology as a Predictor of Secondary Structure and Molecular Function Using Convolutional Deep Learning

To power the specific recognition and binding of protein partners into functional complexes, a wealth of information about the structure and function of the partners is necessarily encoded into the global shape of protein-protein interfaces and their local topological features. To identify whether this is the case, this study uses convolutional deep learning methods (typically leveraged for 2D image recognition) on 3D voxel representations of protein-protein interfaces colored by burial depth. A novel two-stage network fed with voxelizations of each interface at two distinct resolutions achieves balance between performance and computational cost. From the shape of the interfaces, the network tries to predict the presence of secondary structure motifs at the interface and the molecular function of the corresponding complex. Secondary structure and certain classes of function are found to be very well predicted, validating the hypothesis that interface shape is a conveyor of higher-level information. Interface patterns triggering the recognition of specific classes are also identified and described.

ActTRANS: Functional classification in active transport proteins based on transfer learning and contextual representations

MOTIVATION

Primary and secondary active transport are two types of active transport that involve using energy to move the substances. Active transport mechanisms do use proteins to assist in transport and play essential roles to regulate the traffic of ions or small molecules across a cell membrane against the concentration gradient. In this study, the two main types of proteins involved in such transport are classified from transmembrane transport proteins. We propose a Support Vector Machine (SVM) with contextualized word embeddings from Bidirectional Encoder Representations from Transformers (BERT) to represent protein sequences. BERT is a powerful model in transfer learning, a deep learning language representation model developed by Google and one of the highest performing pre-trained model for Natural Language Processing (NLP) tasks. The idea of transfer learning with pre-trained model from BERT is applied to extract fixed feature vectors from the hidden layers and learn contextual relations between amino acids in the protein sequence. Therefore, the contextualized word representations of proteins are introduced to effectively model complex structures of amino acids in the sequence and the variations of these amino acids in the context. By generating context information, we capture multiple meanings for the same amino acid to reveal the importance of specific residues in the protein sequence.

RESULTS

The performance of the proposed method is evaluated using five-fold cross-validation and independent test. The proposed method achieves an accuracy of 85.44 %, 88.74 % and 92.84 % for Class-1, Class-2, and Class-3, respectively. Experimental results show that this approach can outperform from other feature extraction methods using context information, effectively classify two types of active transport and improve the overall performance.

DeepSIRT: A deep neural network for identification of sirtuin targets and their subcellular localizations

Sirtuins are a family of proteins that play a key role in regulating a wide range of cellular processes including DNA regulation, metabolism, aging/longevity, cell survival, apoptosis, and stress resistance. Sirtuins are protein deacetylases and include in the class III family of histone deacetylase enzymes (HDACs). The class III HDACs contains seven members of the sirtuin family from SIRT1 to SIRT7. The seven members of the sirtuin family have various substrates and are present in nearly all subcellular localizations including the nucleus, cytoplasm, and mitochondria. In this study, a deep neural network approach using one-dimensional Convolutional Neural Networks (CNN) was proposed to build a prediction model that can accurately identify the outcome of the sirtuin protein by targeting their subcellular localizations. Therefore, the function and localization of sirtuin targets were analyzed and annotated to compartmentalize into distinct subcellular localizations. We further reduced the sequence similarity between protein sequences and three feature extraction methods were applied in datasets. Finally, the proposed method has been tested and compared with various machine-learning algorithms. The proposed method is validated on two independent datasets and showed an average of up to 85.77 % sensitivity, 97.32 % specificity, and 0.82 MCC for seven members of the sirtuin family of proteins.

Molecular characterization of Gyps africanus (African white-backed vulture) organic anion transporter 1 and 2 expressed in the kidney

Gyps species have been previously shown to be highly sensitive to the toxic effects of diclofenac, when present in their food sources as drug residues following use as a veterinary medicine. Vultures exposed to diclofenac soon become depressed and die with signs of severe visceral gout and renal damage on necropsy. The molecular mechanism behind toxicity and renal excretion of uric acid is still poorly understood. With the clinical pictures suggesting renal uric acid excretion as the target site for toxicity, as a first step the following study was undertaken to determine the uric acid excretory pathways present in the African white-backed vulture (Gyps africanus) (AWB), one of the species susceptible to toxicity. Using transcriptome analysis, immunohistochemistry and functional predictions, we demonstrated that AWB makes use of the organic anion transporter 2 (OAT2) for their uric acid excretion. RT-qPCR analysis subsequently demonstrated relatively similar expression of the OAT2 transporter in the vulture and chicken. Lastly docking analysis, predicted that the non-steroidal drugs induce their toxicity through an allosteric binding.

Physiological Functions of Bacterial "Multidrug" Efflux Pumps

Bacterial multidrug efflux pumps have come to prominence in human and veterinary pathogenesis because they help bacteria protect themselves against the antimicrobials used to overcome their infections. However, it is increasingly realized that many, probably most, such pumps have physiological roles that are distinct from protection of bacteria against antimicrobials administered by humans. Here we undertake a broad survey of the proteins involved, allied to detailed examples of their evolution, energetics, structures, chemical recognition, and molecular mechanisms, together with the experimental strategies that enable rapid and economical progress in understanding their true physiological roles. Once these roles are established, the knowledge can be harnessed to design more effective drugs, improve existing microbial production of drugs for clinical practice and of feedstocks for commercial exploitation, and even develop more sustainable biological processes that avoid, for example, utilization of petroleum.

Incorporating a transfer learning technique with amino acid embeddings to efficiently predict N-linked glycosylation sites in ion channels

Glycosylation is a dynamic enzymatic process that attaches glycan to proteins or other organic molecules such as lipoproteins. Research has shown that such a process in ion channel proteins plays a fundamental role in modulating ion channel functions. This study used a computational method to predict N-linked glycosylation sites, the most common type, in ion channel proteins. From segments of ion channel proteins centered around N-linked glycosylation sites, the amino acid embedding vectors of each residue were concatenated to create features for prediction. We experimented with two different models for converting amino acids to their corresponding embeddings: one was fed with ion channel sequences and the other with a large dataset composed of more than one million protein sequences. The latter model stemmed from the idea of transfer learning technique and emerged as a more efficient feature extractor. Our best model was obtained from this transfer learning approach and a hyperparameter tuning process with a random search on 5-fold cross-validation data. It achieved an accuracy, specificity, sensitivity, and Matthews correlation coefficient of 93.4%, 92.8%, 98.6%, and 0.726, respectively. Corresponding scores on an independent test were 92.9%, 92.2%, 99%, and 0.717. These results outperform the position-specific scoring matrix features that are predominantly employed in post-translational modification site predictions. Furthermore, compared to N-GlyDE, GlycoEP, SPRINT-Gly, the most recent N-linked glycosylation site predictors, our model yields higher scores on the above 4 metrics, thus further demonstrating the efficiency of our approach.

Quorum sensing regulates 'swim-or-stick' lifestyle in the phycosphere

Interactions between phytoplankton and bacteria play major roles in global biogeochemical cycles and oceanic nutrient fluxes. These interactions occur in the microenvironment surrounding phytoplankton cells, known as the phycosphere. Bacteria in the phycosphere use either chemotaxis or attachment to benefit from algal excretions. Both processes are regulated by quorum sensing (QS), a cell–cell signalling mechanism that uses small infochemicals to coordinate bacterial gene expression. However, the role of QS in regulating bacterial attachment in the phycosphere is not clear. Here, we isolated a Sulfitobacter pseudonitzschiae F5 and a Phaeobacter sp. F10 belonging to the marine Roseobacter group and an Alteromonas macleodii F12 belonging to Alteromonadaceae, from the microbial community of the ubiquitous diatom Asterionellopsis glacialis. We show that only the Roseobacter group isolates (diatom symbionts) can attach to diatom transparent exopolymeric particles. Despite all three bacteria possessing genes involved in motility, chemotaxis, and attachment, only S. pseudonitzschiae F5 and Phaeobacter sp. F10 possessed complete QS systems and could synthesize QS signals. Using UHPLC–MS/MS, we identified three QS molecules produced by both bacteria of which only 3‐oxo‐C_16:1‐HSL strongly inhibited bacterial motility and stimulated attachment in the phycosphere. These findings suggest that QS signals enable colonization of the phycosphere by algal symbionts.

Prediction of Ion Channels and their Types from Protein Sequences: Comprehensive Review and Comparative Assessment

BACKGROUND

Ion channels are a large and growing protein family. Many of them are associated with diseases, and consequently, they are targets for over 700 drugs. Discovery of new ion channels is facilitated with computational methods that predict ion channels and their types from protein sequences. However, these methods were never comprehensively compared and evaluated.

OBJECTIVE

We offer first-of-its-kind comprehensive survey of the sequence-based predictors of ion channels. We describe eight predictors that include five methods that predict ion channels, their types, and four classes of the voltage-gated channels. We also develop and use a new benchmark dataset to perform comparative empirical analysis of the three currently available predictors.

RESULTS

While several methods that rely on different designs were published, only a few of them are currently available and offer a broad scope of predictions. Support and availability after publication should be required when new methods are considered for publication. Empirical analysis shows strong performance for the prediction of ion channels and modest performance for the prediction of ion channel types and voltage-gated channel classes. We identify a substantial weakness of current methods that cannot accurately predict ion channels that are categorized into multiple classes/types.

CONCLUSION

Several predictors of ion channels are available to the end users. They offer practical levels of predictive quality. Methods that rely on a larger and more diverse set of predictive inputs (such as PSIONplus) are more accurate. New tools that address multi-label prediction of ion channels should be developed.

A representation transfer learning approach for enhanced prediction of growth hormone binding proteins

Growth hormone binding proteins (GHBPs) are soluble proteins that play an important role in the modulation of signaling pathways pertaining to growth hormones. GHBPs are selective and bind non-covalently with growth hormones, but their functions are still not fully understood. Identification and characterization of GHBPs are the preliminary steps for understanding their roles in various cellular processes. As wet lab based experimental methods involve high cost and labor, computational methods can facilitate in narrowing down the search space of putative GHBPs. Performance of machine learning algorithms largely depends on the quality of features that it feeds on. Informative and non-redundant features generally result in enhanced performance and for this purpose feature selection algorithms are commonly used. In the present work, a novel representation transfer learning approach is presented for prediction of GHBPs. For their accurate prediction, deep autoencoder based features were extracted and subsequently SMO-PolyK classifier is trained. The prediction model is evaluated by both leave one out cross validation (LOOCV) and hold out independent testing set. On LOOCV, the prediction model achieved 89.8%% accuracy, with 89.4% sensitivity and 90.2% specificity and accuracy of 93.5%, sensitivity of 90.2% and specificity of 96.8% is attained on the hold out testing set. Further a comparison was made between the full set of sequence-based features, top performing sequence features extracted using feature selection algorithm, deep autoencoder based features and generalized low rank model based features on the prediction accuracy. Principal component analysis of the representative features along with t-sne visualization demonstrated the effectiveness of deep features in prediction of GHBPs. The present method is robust and accurate and may complement other wet lab based methods for identification of novel GHBPs.

Integrated Analysis of mRNA and microRNA Elucidates the Regulation of Glycyrrhizic Acid Biosynthesis in Glycyrrhiza uralensis Fisch

Licorice (Glycyrrhiza) is a staple Chinese herbal medicine in which the primary bioactive compound is glycyrrhizic acid (GA), which has important pharmacological functions. To date, the structural genes involved in GA biosynthesis have been identified. However, the regulation of these genes in G. uralensis has not been elucidated. In this study, we performed a comprehensive analysis based on the transcriptome and small RNAome by high-throughput sequencing. In total, we identified 18 structural GA genes and 3924 transporter genes. We identified genes encoding 2374 transporters, 1040 transcription factors (TFs), 262 transcriptional regulators (TRs) and 689 protein kinases (PKs), which were coexpressed with at least one structural gene. We also identified 50,970 alternative splicing (AS) events, in which 17 structural genes exhibited AS. Finally, we also determined that miRNAs potentially targeted 4 structural genes, and 318, 8, and 218 miRNAs potentially regulated 150 TFs, 34 TRs, and 88 PKs, respectively, related to GA. Overall, the results of this study helped to elucidate the gene expression and regulation of GA biosynthesis in G. uralensis, provided a theoretical basis for the synthesis of GA via synthetic biology, and laid a foundation for the cultivation of new varieties of licorice with high GA content.

TooT-T: discrimination of transport proteins from non-transport proteins

Background

Membrane transport proteins (transporters) play an essential role in every living cell by transporting hydrophilic molecules across the hydrophobic membranes. While the sequences of many membrane proteins are known, their structure and function is still not well characterized and understood, owing to the immense effort needed to characterize them. Therefore, there is a need for advanced computational techniques takes sequence information alone to distinguish membrane transporter proteins; this can then be used to direct new experiments and give a hint about the function of a protein.

Results

This work proposes an ensemble classifier TooT-T that is trained to optimally combine the predictions from homology annotation transfer and machine-learning methods to determine the final prediction. Experimental results obtained by cross-validation and independent testing show that combining the two approaches is more beneficial than employing only one.

Conclusion

The proposed model outperforms all of the state-of-the-art methods that rely on the protein sequence alone, with respect to accuracy and MCC. TooT-T achieved an overall accuracy of 90.07% and 92.22% and an MCC 0.80 and 0.82 with the training and independent datasets, respectively.

TranCEP: Predicting the substrate class of transmembrane transport proteins using compositional, evolutionary, and positional information

Transporters mediate the movement of compounds across the membranes that separate the cell from its environment and across the inner membranes surrounding cellular compartments. It is estimated that one third of a proteome consists of membrane proteins, and many of these are transport proteins. Given the increase in the number of genomes being sequenced, there is a need for computational tools that predict the substrates that are transported by the transmembrane transport proteins. In this paper, we present TranCEP, a predictor of the type of substrate transported by a transmembrane transport protein. TranCEP combines the traditional use of the amino acid composition of the protein, with evolutionary information captured in a multiple sequence alignment (MSA), and restriction to important positions of the alignment that play a role in determining the specificity of the protein. Our experimental results show that TranCEP significantly outperforms the state-of-the-art predictors. The results quantify the contribution made by each type of information used.

Understanding high ε-poly-l-lysine production by Streptomyces albulus using pH shock strategy in the level of transcriptomics

ε-Poly-l-lysine (ε-PL) is a natural food preservative, which exhibits antimicrobial activity against a wide spectra of microorganisms. The production of ε-PL was significantly enhanced by pH shock in our previous study, but the underlying mechanism is poorly understood. According to transcriptional and physiological analyses in this study, the mprA/B and pepD signal transduction system was first proved to be presented and activated in Streptomyces albulus M-Z18 by pH shock, which positively regulated the transcription of ε-PL synthetase (Pls) gene and enhanced the Pls activity during fermentation. Furthermore, pH shock changed the ratio of unsaturation to saturation fatty acid in the membrane through up-regulating the transcription of fatty acid desaturase genes (SAZ_RS14940, SAZ_RS14945). In addition, pH shock also enhanced the transcription of cytochrome c oxidase (SAZ_RS15070, SAZ_RS15075), ferredoxin reductase (SAZ_RS34975) and iron sulfur protein (SAZ_RS31410) genes, and finally resulted in the improvement of cell respiratory activity. As a result, pH shock was considered to influence a wide range of proteins including regulators, fatty acid desaturase, respiratory chain component, and ATP-binding cassette transporter during fermentation. These combined influences might contribute to enhanced ε-PL productivity with pH shock.

Unusual Metabolism and Hypervariation in the Genome of a Gracilibacterium (BD1-5) from an Oil-Degrading Community

CPR bacteria are generally predicted to be symbionts due to their extensive biosynthetic deficits. Although monophyletic, they are not monolithic in terms of their lifestyles. The organism described here appears to have evolved an unusual metabolic platform not reliant on glucose or pentose sugars. Its biology appears to be centered around bacterial host-derived compounds and/or cell detritus. Amino acids likely provide building blocks for nucleic acids, peptidoglycan, and protein synthesis. We resolved an unusual repeat region that would be invisible without genome curation. The nucleotide sequence is apparently under strong diversifying selection, but the amino acid sequence is under stabilizing selection. The amino acid repeat also occurs in a surface protein of a coexisting bacterium, suggesting colocation and possibly interdependence.

The candidate phyla radiation (CPR) comprises a large monophyletic group of bacterial lineages known almost exclusively based on genomes obtained using cultivation-independent methods. Within the CPR, Gracilibacteria (BD1-5) are particularly poorly understood due to undersampling and the inherent fragmented nature of available genomes. Here, we report the first closed, curated genome of a gracilibacterium from an enrichment experiment inoculated from the Gulf of Mexico and designed to investigate hydrocarbon degradation. The gracilibacterium rose in abundance after the community switched to dominance by Colwellia. Notably, we predict that this gracilibacterium completely lacks glycolysis, the pentose phosphate and Entner-Doudoroff pathways. It appears to acquire pyruvate, acetyl coenzyme A (acetyl-CoA), and oxaloacetate via degradation of externally derived citrate, malate, and amino acids and may use compound interconversion and oxidoreductases to generate and recycle reductive power. The initial genome assembly was fragmented in an unusual gene that is hypervariable within a repeat region. Such extreme local variation is rare but characteristic of genes that confer traits under pressure to diversify within a population. Notably, the four major repeated 9-mer nucleotide sequences all generate a proline-threonine-aspartic acid (PTD) repeat. The genome of an abundant Colwellia psychrerythraea population has a large extracellular protein that also contains the repeated PTD motif. Although we do not know the host for the BD1-5 cell, the high relative abundance of the C. psychrerythraea population and the shared surface protein repeat may indicate an association between these bacteria.

Environmental conditions shape the nature of a minimal bacterial genome

Of the 473 genes in the genome of the bacterium with the smallest genome generated to date, 149 genes have unknown function, emphasising a universal problem; less than 1% of proteins have experimentally determined annotations. Here, we combine the results from state-of-the-art in silico methods for functional annotation and assign functions to 66 of the 149 proteins. Proteins that are still not annotated lack orthologues, lack protein domains, and/ or are membrane proteins. Twenty-four likely transporter proteins are identified indicating the importance of nutrient uptake into and waste disposal out of the minimal bacterial cell in a nutrient-rich environment after removal of metabolic enzymes. Hence, the environment shapes the nature of a minimal genome. Our findings also show that the combination of multiple different state-of-the-art in silico methods for annotating proteins is able to predict functions, even for difficult to characterise proteins and identify crucial gaps for further development.

ET-GRU: using multi-layer gated recurrent units to identify electron transport proteins

BACKGROUND

Electron transport chain is a series of protein complexes embedded in the process of cellular respiration, which is an important process to transfer electrons and other macromolecules throughout the cell. It is also the major process to extract energy via redox reactions in the case of oxidation of sugars. Many studies have determined that the electron transport protein has been implicated in a variety of human diseases, i.e. diabetes, Parkinson, Alzheimer's disease and so on. Few bioinformatics studies have been conducted to identify the electron transport proteins with high accuracy, however, their performance results require a lot of improvements. Here, we present a novel deep neural network architecture to address this problem.

RESULTS

Most of the previous studies could not use the original position specific scoring matrix (PSSM) profiles to feed into neural networks, leading to a lack of information and the neural networks consequently could not achieve the best results. In this paper, we present a novel approach by using deep gated recurrent units (GRU) on full PSSMs to resolve this problem. Our approach can precisely predict the electron transporters with the cross-validation and independent test accuracy of 93.5 and 92.3%, respectively. Our approach demonstrates superior performance to all of the state-of-the-art predictors on electron transport proteins.

CONCLUSIONS

Through the proposed study, we provide ET-GRU, a web server for discriminating electron transport proteins in particular and other protein functions in general. Also, our achievement could promote the use of GRU in computational biology, especially in protein function prediction.

Using word embedding technique to efficiently represent protein sequences for identifying substrate specificities of transporters

Membrane transport proteins and their substrate specificities play crucial roles in various cellular functions. Identifying the substrate specificities of membrane transport proteins is closely related to protein-target interaction prediction, drug design, membrane recruitment, and dysregulation analysis, thus being an important problem for bioinformatics researchers. In this study, we applied word embedding approach, the main cause for natural language processing breakout in recent years, to protein sequences of transporters. We defined each protein sequence based on the word embeddings and frequencies of its biological words. The protein features were then fed into machine learning models for prediction. We also varied the lengths of protein sequence's constituent biological words to find the optimal length which generated the most discriminative feature set. Compared to four other feature types created from protein sequences, our proposed features can help prediction models yield superior performance. Our best models reach an average area under the curve of 0.96 and 0.99, respectively on the 5-fold cross validation and the independent test. With this result, our study can help biologists identify transporters based on substrate specificities as well as provides a basis for further research that enriches a field of applying natural language processing techniques in bioinformatics.

Capturing the applicability of in vitro-in silico membrane transporter data in chemical risk assessment and biomedical research

Costs, scientific and ethical concerns related to animal tests for regulatory decision-making have stimulated the development of alternative methods. When applying alternative approaches, kinetics have been identified as a key element to consider. Membrane transporters affect the kinetic processes of absorption, distribution, metabolism and excretion (ADME) of various compounds, such as drugs or environmental chemicals. Therefore, pharmaceutical scientists have intensively studied transporters impacting drug efficacy and safety. Besides pharmacokinetics, transporters are considered as major determinant of toxicokinetics, potentially representing an essential piece of information in chemical risk assessment. To capture the applicability of transporter data for kinetic-based risk assessment in non-pharmaceutical sectors, the EU Reference Laboratory for Alternatives to Animal Testing (EURL ECVAM) created a survey with a view of identifying the improvements needed when using in vitro and in silico methods. Seventy-three participants, from different sectors and with various kinds of expertise, completed the survey. The results revealed that transporters are investigated mainly during drug development, but also for risk assessment purposes of food and feed contaminants, industrial chemicals, cosmetics, nanomaterials and in the context of environmental toxicology, by applying both in vitro and in silico tools. However, to rely only on alternative methods for chemical risk assessment, it is critical that the data generated by in vitro and in silico methods are scientific integer, reproducible and of high quality so that they are trusted by decision makers and used by industry. In line, the respondents identified various challenges related to the interpretation and use of transporter data from non-animal methods. Overall, it was determined that a combined mechanistically-anchored in vitro-in silico approach, validated against available human data, would gain confidence in using transporter data within an animal-free risk assessment paradigm. Finally, respondents involved primarily in fundamental research expressed lower confidence in non-animal studies to unravel complex transporter mechanisms.

A Genome-Scale Metabolic Model for Methylococcus capsulatus (Bath) Suggests Reduced Efficiency Electron Transfer to the Particulate Methane Monooxygenase

Background: Genome-scale metabolic models allow researchers to calculate yields, to predict consumption and production rates, and to study the effect of genetic modifications in silico, without running resource-intensive experiments. While these models have become an invaluable tool for optimizing industrial production hosts like Escherichia coli and S. cerevisiae, few such models exist for one-carbon (C1) metabolizers.

Results: Here, we present a genome-scale metabolic model for Methylococcus capsulatus (Bath), a well-studied obligate methanotroph, which has been used as a production strain of single cell protein (SCP). The model was manually curated, and spans a total of 879 metabolites connected via 913 reactions. The inclusion of 730 genes and comprehensive annotations, make this model not only a useful tool for modeling metabolic physiology, but also a centralized knowledge base for M. capsulatus (Bath). With it, we determined that oxidation of methane by the particulate methane monooxygenase could be driven both through direct coupling or uphill electron transfer, both operating at reduced efficiency, as either scenario matches well with experimental data and observations from literature.

Conclusion: The metabolic model will serve the ongoing fundamental research of C1 metabolism, and pave the way for rational strain design strategies toward improved SCP production processes in M. capsulatus.

Bioinformatics Analysis and Functional Prediction of Transmembrane Proteins in Entamoeba histolytica

Entamoeba histolytica is an invasive, pathogenic parasite causing amoebiasis. Given that proteins involved in transmembrane (TM) transport are crucial for the adherence, invasion, and nutrition of the parasite, we conducted a genome-wide bioinformatics analysis of encoding proteins to functionally classify and characterize all the TM proteins in E. histolytica. In the present study, 692 TM proteins have been identified, of which 546 are TM transporters. For the first time, we report a set of 141 uncharacterized proteins predicted as TM transporters. The percentage of TM proteins was found to be lower in comparison to the free-living eukaryotes, due to the extracellular nature and functional diversification of the TM proteins. The number of multi-pass proteins is larger than the single-pass proteins; though both have their own significance in parasitism, multi-pass proteins are more extensively required as these are involved in acquiring nutrition and for ion transport, while single-pass proteins are only required at the time of inciting infection. Overall, this intestinal parasite implements multiple mechanisms for establishing infection, obtaining nutrition, and adapting itself to the new host environment. A classification of the repertoire of TM transporters in the present study augments several hints on potential methods of targeting the parasite for therapeutic benefits.

Alterations in the Urinary Microbiota Are Associated With Cesarean Delivery

Similar to the gut, the bladder contains urinary microbiota, and its bacterial composition and structure are determined by the individual’s health status. Cesarean section is a traumatic event for women and it is correlated with postpartum complications. To better understand the urinary microbiota alterations caused by cesarean section, 16S rDNA sequencing was used to assess urine specimens collected by transurethral catheterization from 30 healthy women undergoing cesarean section pre-delivery (PreD) and post-delivery (PostD). A significant increase in bacterial diversity and more detectable bacteria at the phylum, family, and genus levels was observed in the PostD group compared to the PreD group, indicating that cesarean delivery (a process that includes surgery and delivery) altered the bacterial community. Specifically, the phylum Firmicutes and its affiliated family Lactobacillaceae and genus Lactobacillus dramatically decreased in the PostD group, suggesting that beneficial bacteria decreased after cesarean section, and clinicians should be aware that this might increase the risk of complications. Concurrently, the phylum Proteobacteria and its affiliated bacteria Pseudomonadaceae and Pseudomonas increased in the PostD group compared to the PreD group. This indicates that pathogen growth increases after cesarean section, making it important for clinicians to combat these changes to protect women from infectious diseases. Interestingly, several metabolic pathways, such as metabolism of energy, cofactors and vitamins were strengthened in the PostD group, whereas membrane transport was lessened in this group. This suggests that women’s metabolic disorders might be cured by balancing urinary microbiota. In conclusion, the altered urinary microbiota between the PreD and PostD periods appears to provide insight into how to prevent postpartum metabolic disorders.

Harnessing the evolutionary information on oxygen binding proteins through Support Vector Machines based modules

Objectives

The arrival of free oxygen on the globe, aerobic life is becoming possible. However, it has become very clear that the oxygen binding proteins are widespread in the biosphere and are found in all groups of organisms, including prokaryotes, eukaryotes as well as in fungi, plants, and animals. The exponential growth and availability of fresh annotated protein sequences in the databases motivated us to develop an improved version of “Oxypred” for identifying oxygen-binding proteins.

Results

In this study, we have proposed a method for identifying oxy-proteins with two different sequence similarity cutoffs 50 and 90%. A different amino acid composition based Support Vector Machines models was developed, including the evolutionary profiles in the form position-specific scoring matrix (PSSM). The fivefold cross-validation techniques were applied to evaluate the prediction performance. Also, we compared with existing methods, which shows nearly 97% recognition, but, our newly developed models were able to recognize almost 99.99 and 100% in both oxy-50 and 90% similarity models respectively. Our result shows that our approaches are faster and achieve a better prediction performance over the existing methods. The web-server Oxypred2 was developed for an alternative method for identifying oxy-proteins with more additional modules including PSSM, available at http://bioinfo.imtech.res.in/servers/muthu/oxypred2/home.html.

Electronic supplementary material

The online version of this article (10.1186/s13104-018-3383-9) contains supplementary material, which is available to authorized users.

MFS Transporters and GABA Metabolism Are Involved in the Self-Defense Against DON in Fusarium graminearum

Trichothecene mycotoxins, such as deoxynivalenol (DON) produced by the fungal pathogen, Fusarium graminearum, are not only important for plant infection but are also harmful to human and animal health. Trichothecene targets the ribosomal protein Rpl3 that is conserved in eukaryotes. Hence, a self-defense mechanism must exist in DON-producing fungi. It is reported that TRI (trichothecene biosynthesis) 101 and TRI12 are two genes responsible for self-defense against trichothecene toxins in Fusarium. In this study, however, we found that simultaneous disruption of TRI101 and TRI12 has no obvious influence on DON resistance upon exogenous DON treatment in F. graminearum, suggesting that other mechanisms may be involved in self-defense. By using RNA-seq, we identified 253 genes specifically induced in DON-treated cultures compared with samples from cultures treated or untreated with cycloheximide, a commonly used inhibitor of eukaryotic protein synthesis. We found that transporter genes are significantly enriched in this group of DON-induced genes. Of those genes, 15 encode major facilitator superfamily transporters likely involved in mycotoxin efflux. Significantly, we found that genes involved in the metabolism of gamma-aminobutyric acid (GABA), a known inducer of DON production in F. graminearum, are significantly enriched among the DON-induced genes. The GABA biosynthesis gene PROLINE UTILIZATION 2-2 (PUT2-2) is downregulated, while GABA degradation genes are upregulated at least twofold upon treatment with DON, resulting in decreased levels of GABA. Taken together, our results suggest that transporters influencing DON efflux are important for self-defense and that GABA mediates the balance of DON production and self-defense in F. graminearum.

Comparative proteome analysis reveals pathogen specific outer membrane proteins of Leptospira

Proteomes of pathogenic Leptospira interrogans and L. borgpetersenii and the saprophytic L. biflexa were filtered through computational tools to identify Outer Membrane Proteins (OMPs) that satisfy the required biophysical parameters for their presence on the outer membrane. A total of 133, 130, and 144 OMPs were identified in L. interrogans, L. borgpetersenii, and L. biflexa, respectively, which forms approximately 4% of proteomes. A holistic analysis of transporting and pathogenic characteristics of OMPs together with Clusters of Orthologous Groups (COGs) among the OMPs and their distribution across 3 species was made and put forward a set of 21 candidate OMPs specific to pathogenic leptospires. It is also found that proteins homologous to the candidate OMPs were also present in other pathogenic species of leptospires. Six OMPs from L. interrogans and 2 from L. borgpetersenii observed to have similar COGs while those were not found in any intermediate or saprophytic forms. These OMPs appears to have role in infection and pathogenesis and useful for anti-leptospiral strategies.

Proteomic Profiling of Leishmania donovani Promastigote Subcellular Organelles

To facilitate a greater understanding of the biological processes in the medically important Leishmania donovani parasite, a combination of differential and density-gradient ultracentrifugation techniques were used to achieve a comprehensive subcellular fractionation of the promastigote stage. An in-depth label-free proteomic LC-MS/MS analysis of the density gradients resulted in the identification of ∼50% of the Leishmania proteome (3883 proteins detected), which included ∼645 integral membrane proteins and 1737 uncharacterized proteins. Clustering and subcellular localization of proteins was based on a subset of training Leishmania proteins with known subcellular localizations that had been determined using biochemical, confocal microscopy, or immunoelectron microscopy approaches. This subcellular map will be a valuable resource that will help dissect the cell biology and metabolic processes associated with specific organelles of Leishmania and related kinetoplastids.