Identification of retinoid acid induced 16 as a novel androgen receptor target in prostate cancer cells

BACKGROUND

Retinoid acid induced 16 (RAI16) was reported to enhance tumorigenesis in hepatocellular carcinoma (HCC). The androgen receptor (AR) is a nuclear hormone receptor that functions as a critical oncogene in several cancer progressions. However, whether RAI16 is a candidate AR target gene that may involve in prostate cancer progression was unclear.

MATERIALS & METHODS

RAI16 expression was detected in prostate cancer cells with or without the AR agonist R1881 treatment by quantitative RT-PCR and Western blot. Direct AR binding to the RAI16 promoter was tested using AR chromatin immunoprecipitation (ChIP) and luciferase assay. Cell viability and colony formation assays in response to R1881 were analyzed in cells with RAI16 knockdown by specific siRNA.

RESULTS

The expression of RAI16 was high in LNCaP(AI), LNCaP(AD), C4-2 expressing AR, but low in Du145 and Pc-3 cells without AR expressing. In addition, the expression of RAI16 could be induced by 10 nM R1881 treatment LNCaP(AD) and C4-2 cells, but inhibited by AR specific siRNA treatment. Furthermore, AR binds directly to ARE3 (-2003~-1982bp) of RAI16 promoter region by ChIP and luciferase assay. RAI16 knockdown inhibited the enhancement of cell viability and colony formation of AR stimulation.

CONCLUSIONS

We demonstrate for the first time that RAI16 is a direct target gene of AR. RAI16 may involved in cell growth of prostate cancer cells in response to AR signaling.

Novel mutations in SPEF2 causing different defects between flagella and cilia bridge: the phenotypic link between MMAF and PCD

Severe asthenozoospermia is a common cause of male infertility. Recent studies have revealed that SPEF2 mutations lead to multiple morphological abnormalities of the sperm flagella (MMAF) without primary ciliary dyskinesia (PCD) symptoms in males, but PCD phenotype was also found in one female individual. Therefore, whether there is a phenotypic continuum ranging from infertile patients with PCD to MMAF patients with no or low noise PCD manifestations remains elusive. Here, we performed whole-exome sequencing in 47 patients with severe asthenozoospermia from 45 unrelated Chinese families. We identified four novel biallelic mutations in SPEF2 (8.9%, 4/45) in six affected individuals (12.8%, 6/47), while no deleterious biallelic variants in SPEF2 were detected in 637 controls, including 219 with oligoasthenospermia, 195 with non-obstructive azoospermia, and 223 fertile controls. Notably, all six patients exhibited PCD-like symptoms, including recurrent airway infections, bronchitis, and rhinosinusitis. Ultrastructural analysis revealed normal 9 + 2 axonemes of respiratory cilia but consistently abnormal 9 + 0 axoneme or disordered accessory structures of sperm flagella, indicating different roles of SPEF2 in sperm flagella and respiratory cilia. Subsequently, a Spef2 knockout mouse model was used to validate the PCD-like phenotype and male infertility, where the subfertility of female Spef2^-/- mice was found unexpectedly. Overall, our data bridge the link between MMAF and PCD based on the association of SPEF2 mutations with both infertility and PCD in males and provide basis for further exploring the molecular mechanism of SPEF2 during spermiogenesis and ciliogenesis.

Fat-specific protein 27α inhibits autophagy-dependent lipid droplet breakdown in white adipocytes

AIMS/INTRODUCTION

Fat-specific protein 27 (FSP27) α is the major isoform of FSP27 in white adipose tissue (WAT), and is essential for large unilocular lipid droplet (LD) formation in white adipocytes. In contrast, FSP27β is abundantly expressed in brown adipose tissue (BAT), and plays an important role in small multilocular LD formation. In FSP27 KO mice in which FSP27α and β are both depleted, WAT is characterized by multilocular LD formation, and by increased mitochondrial abundance and energy expenditure, whereas BAT conversely manifests large oligolocular LDs and reduced energy expenditure.

MATERIALS AND METHODS

We investigated the effects of autophagy in WAT and BAT of wild type (WT) and FSP27 knockout (KO) mice. In addition, we examined the effects of FSP27α and FSP27β to the induction of autophagy in COS cells.

RESULTS

Food deprivation induced autophagy in BAT of WT mice, as well as in WAT of FSP27 KO mice, suggesting that enhanced autophagy is characteristic of adipocytes with small multilocular LDs. Pharmacological inhibition of autophagy attenuated the fasting-induced loss of LD area in adipocytes with small multilocular LDs (BAT of WT mice and WAT of FSP27 KO mice), without affecting that in adipocytes with large unilocular or oligolocular LDs (WAT of WT mice or in BAT of FSP27 KO mice). Overexpression of FSP27α inhibited autophagy induction by serum deprivation in COS cells, whereas that of FSP27β had no such effect.

CONCLUSIONS

The present results thus showed that FSP27α inhibits autophagy and might thereby contribute to the energy-storage function of WAT.

AMC-Net: Asymmetric and multi-scale convolutional neural network for multi-label HPA classification

BACKGROUND AND OBJECTIVES

The multi-label Human Protein Atlas (HPA) classification can yield a better understanding of human diseases and help doctors to enhance the automatic analysis of biomedical images. The existing automatic protein recognition methods have been limited to single pattern. Therefore, an automatic multi-label human protein atlas recognition system with satisfactory performance should be conducted. This work aims to build an automatic recognition system for multi-label human protein atlas classification based on deep learning.

METHODS

In this work, an automatic feature extraction and multi-label classification framework is proposed. Specifically, an asymmetric and multi-scale convolutional neural network is designed for HPA classification. Furthermore, this work introduces a combined loss that consists of the binary cross-entropy and F1-score losses to improve identification performance.

RESULTS

Rigorous experiments are conducted to estimate the proposed system. In particular, unlike the current automatic identification systems, which focus on a limited number of patterns, the proposed method is capable of classifying mixed patterns of proteins in microscope images and can handle the subcellular multi-label protein classification task including 28 subcellular localization patterns. The proposed framework based on deep convolutional neural network outperformed the existing approaches with a F1-score of 0.823, which illustrates the robustness and effectiveness of the proposed system.

CONCLUSION

This study proposed a high-performance recognition system for protein atlas classification based on deep learning, and it achieved an automatic multi-label human protein atlas identification framework with superior performance than previous studies.

Multitask Protein Function Prediction through Task Dissimilarity

Automated protein function prediction is a challenging problem with distinctive features, such as the hierarchical organization of protein functions and the scarcity of annotated proteins for most biological functions. We propose a multitask learning algorithm addressing both issues. Unlike standard multitask algorithms, which use task (protein functions) similarity information as a bias to speed up learning, we show that dissimilarity information enforces separation of rare class labels from frequent class labels, and for this reason is better suited for solving unbalanced protein function prediction problems. We support our claim by showing that a multitask extension of the label propagation algorithm empirically works best when the task relatedness information is represented using a dissimilarity matrix as opposed to a similarity matrix. Moreover, the experimental comparison carried out on three model organism shows that our method has a more stable performance in both "protein-centric" and "function-centric" evaluation settings.

Importance of collision cross section measurements by ion mobility mass spectrometry in structural biology

The field of ion mobility mass spectrometry (IM-MS) has developed rapidly in recent decades, with new fundamental advances underpinning innovative applications. This has been particularly noticeable in the field of biomacromolecular structure determination and structural biology, with pioneering studies revealing new structural insight for complex protein assemblies which control biological function. This perspective offers a review of recent developments in IM-MS which have enabled expanding applications in protein structural biology, principally focusing on the quantitative measurement of collision cross sections and their interpretation to describe higher order protein structures.

Genome-wide analyses as part of the international FTLD-TDP whole-genome sequencing consortium reveals novel disease risk factors and increases support for immune dysfunction in FTLD

Frontotemporal lobar degeneration with neuronal inclusions of the TAR DNA-binding protein 43 (FTLD-TDP) represents the most common pathological subtype of FTLD. We established the international FTLD-TDP whole-genome sequencing consortium to thoroughly characterize the known genetic causes of FTLD-TDP and identify novel genetic risk factors. Through the study of 1131 unrelated Caucasian patients, we estimated that C9orf72 repeat expansions and GRN loss-of-function mutations account for 25.5% and 13.9% of FTLD-TDP patients, respectively. Mutations in TBK1 (1.5%) and other known FTLD genes (1.4%) were rare, and the disease in 57.7% of FTLD-TDP patients was unexplained by the known FTLD genes. To unravel the contribution of common genetic factors to the FTLD-TDP etiology in these patients, we conducted a two-stage association study comprising the analysis of whole-genome sequencing data from 517 FTLD-TDP patients and 838 controls, followed by targeted genotyping of the most associated genomic loci in 119 additional FTLD-TDP patients and 1653 controls. We identified three genome-wide significant FTLD-TDP risk loci: one new locus at chromosome 7q36 within the DPP6 gene led by rs118113626 (p value = 4.82e - 08, OR = 2.12), and two known loci: UNC13A, led by rs1297319 (p value = 1.27e - 08, OR = 1.50) and HLA-DQA2 led by rs17219281 (p value = 3.22e - 08, OR = 1.98). While HLA represents a locus previously implicated in clinical FTLD and related neurodegenerative disorders, the association signal in our study is independent from previously reported associations. Through inspection of our whole-genome sequence data for genes with an excess of rare loss-of-function variants in FTLD-TDP patients (n ≥ 3) as compared to controls (n = 0), we further discovered a possible role for genes functioning within the TBK1-related immune pathway (e.g., DHX58, TRIM21, IRF7) in the genetic etiology of FTLD-TDP. Together, our study based on the largest cohort of unrelated FTLD-TDP patients assembled to date provides a comprehensive view of the genetic landscape of FTLD-TDP, nominates novel FTLD-TDP risk loci, and strongly implicates the immune pathway in FTLD-TDP pathogenesis.

Revealing evolutionary constraints on proteins through sequence analysis

Statistical analysis of alignments of large numbers of protein sequences has revealed "sectors" of collectively coevolving amino acids in several protein families. Here, we show that selection acting on any functional property of a protein, represented by an additive trait, can give rise to such a sector. As an illustration of a selected trait, we consider the elastic energy of an important conformational change within an elastic network model, and we show that selection acting on this energy leads to correlations among residues. For this concrete example and more generally, we demonstrate that the main signature of functional sectors lies in the small-eigenvalue modes of the covariance matrix of the selected sequences. However, secondary signatures of these functional sectors also exist in the extensively-studied large-eigenvalue modes. Our simple, general model leads us to propose a principled method to identify functional sectors, along with the magnitudes of mutational effects, from sequence data. We further demonstrate the robustness of these functional sectors to various forms of selection, and the robustness of our approach to the identification of multiple selected traits.

Rapid interpretation of small-angle X-ray scattering data

The fundamental aim of structural analyses in biophysics is to reveal a mutual relation between a molecule’s dynamic structure and its physiological function. Small-angle X-ray scattering (SAXS) is an experimental technique for structural characterization of macromolecules in solution and enables time-resolved analysis of conformational changes under physiological conditions. As such experiments measure spatially averaged low-resolution scattering intensities only, the sparse information obtained is not sufficient to uniquely reconstruct a three-dimensional atomistic model. Here, we integrate the information from SAXS into molecular dynamics simulations using computationally efficient native structure-based models. Dynamically fitting an initial structure towards a scattering intensity, such simulations produce atomistic models in agreement with the target data. In this way, SAXS data can be rapidly interpreted while retaining physico-chemical knowledge and sampling power of the underlying force field. We demonstrate our method’s performance using the example of three protein systems. Simulations are faster than full molecular dynamics approaches by more than two orders of magnitude and consistently achieve comparable accuracy. Computational demands are reduced sufficiently to run the simulations on commodity desktop computers instead of high-performance computing systems. These results underline that scattering-guided structure-based simulations provide a suitable framework for rapid early-stage refinement of structures towards SAXS data with particular focus on minimal computational resources and time.

Proteins are the molecular nanomachines in biological cells and thus vital to any known form of life. From the evolutionary perspective, viable protein structure emerges on the basis of a ‘form-follows-function’ principle. A protein’s designated function is inextricably linked to dynamic conformational changes, which can be observed by small-angle X-ray scattering. Intensities from SAXS contain low-resolution information on the protein’s shape at different steps of its functional cycle. We are interested in directly getting an atomistic model of this encoded structure. One powerful approach is to include the experimental data into computational simulations of the protein’s function-related physical motions. We combine scattering intensities with coarse-grained native structure-based models. These models are computationally highly efficient yet describe the system’s dynamics realistically. Here, we present our method for rapid interpretation of scattering intensities from SAXS to derive structural models, using minimal computational resources and time.

Protein-Protein Interaction Identification Using a Similarity-Constrained Graph Model

Protein-protein interaction (PPI) identification is an important task in text mining. Most PPI detection systems make predictions solely based on evidence within a single sentence and often suffer from the heavy burden of manual annotation. This paper approaches PPI detection task from a different paradigm by investigating the context of protein pairs collected from a large corpus and their relations. First, crucial cues in the context are exploited to make initial predictions. Then, relational similarity between protein pairs is calculated. Finally, evidence from the two views is integrated in the framework of minimum cuts algorithm. Experimental results show that the graph model achieves better performance than standard supervised approaches. Using 20 percent data as the training set, our algorithm achieves higher accuracy than support vector machine (SVM) using 80 percent data as training data. Moreover, the semi-supervised settings reveal promising directions for PPI identification exploiting unlabeled data.

Unsupervised Discovery of Geometrically Common Structural Motifs and Long-Range Contacts in Protein 3D Structures

The essential role of small evolutionarily conserved structural units in proteins has been extensively researched and validated. A popular example are serine proteases, where the peptide cleavage reaction is realized by a configuration of only three residues. Brought to spatial proximity during the protein folding process, such structural motifs are often long-range contacts and usually hard to detect at sequence level. Due to the constantly increasing resource of protein 3D structure data, the computational identification of structural motifs can contribute significantly to the understanding of protein fold and function. Thus, we propose a method to discover structural motifs of high geometrical similarity and desired sequence separation in protein 3D structure data. By utilizing methods originated from data mining, no a priori knowledge is required. The applicability of the method is demonstrated by the identification of the catalytic unit of serine proteases and the ion-coordination center of cupredoxins. Furthermore, large-scale analysis of the entire Protein Data Bank points towards the presence of ubiquitous structural motifs, independent of any specific fold or function. We envision that our method is suitable to uncover functional mechanisms and to derive fingerprint libraries of structural motifs, which could be used to assess protein family association.

Tissue-specific expression and post-transcriptional regulation of the ATPase inhibitory factor 1 (IF1) in human and mouse tissues

The ATPase inhibitory factor 1 (IF1) is an intrinsically disordered protein that regulates the activity of the mitochondrial ATP synthase. Phosphorylation of S39 in IF1 prevents it from binding to the enzyme and thus abolishes its inhibitory activity. Dysregulation of IF1 is linked to different human diseases, providing a relevant biomarker of cancer progression. However, the tissue content of IF1 relative to the abundance of the ATP synthase is unknown. In this study, we characterized the tissue-specific expression of IF1 in human and mouse tissues and quantitated the content of IF1 and of ATP synthase. We found relevant differences in IF1 expression between human and mouse tissues and found that in high-energy-demanding tissues, the molar content of IF1 exceeds that of the ATP synthase. In these tissues, a fraction of IF1 is bound to the enzyme, and the other fraction is phosphorylated and hence is unable to bind the enzyme. Post-transcriptional control accounts for most of the regulated expression of IF1, especially in mouse heart, where IF1 mRNA translation is repressed by the leucine-rich pentatricopeptide repeat containing protein. Overall, these findings enlighten the cellular biology of IF1 and pave the way to development of additional models that address its role in pathophysiology.-Esparza-Moltó, P. B., Nuevo-Tapioles, C., Chamorro, M., Nájera, L., Torresano, L., Santacatterina, F., Cuezva, J. M. Tissue-specific expression and post-transcriptional regulation of the ATPase inhibitory factor 1 (IF1) in human and mouse tissues.

CIDEC Is Involved in LPS-Induced Inflammation and Apoptosis in Renal Tubular Epithelial Cells

Adenosine 5'-monophosphate-activated protein kinase (AMPK) has been shown to have anti-inflammatory effect by inhibition of the nuclear factor κB (NF-κB) pathway and is involved in lipopolysaccharide (LPS)-induced inflammation. Cell-death-inducing DFF45-like effector C (CIDEC) can directly down-regulate AMPK activity through interacting with AMPKα subunit. However, whether the AMPK or CIDEC is involved in LPS-induced inflammation in renal tubular epithelial cells is still unknown. Therefore, we studied the role of AMPK and CIDEC in LPS-treated NRK-52E cells. Our results showed that LPS could up-regulate the expression of CIDEC in vitro and in vivo. Silencing CIDEC by CIDEC-siRNA could restore expression of phosphorylated-AMPKα which was decreased by LPS, suppress LPS-induced NF-κB pathway activation, and TNF-α, IL-6, and IL-1β production in NRK-52E cells. Furthermore, silencing CIDEC also partially alleviated LPS-induced epithelial cells apoptosis. In conclusion, the results demonstrated that CIDEC/AMPK signaling pathway played an important role in LPS-induced inflammation and epithelial cells apoptosis.

Structure and Protein Interaction-Based Gene Ontology Annotations Reveal Likely Functions of Uncharacterized Proteins on Human Chromosome 17

Understanding the function of human proteins is essential to decipher the molecular mechanisms of human diseases and phenotypes. Of the 17 470 human protein coding genes in the neXtProt 2018-01-17 database with unequivocal protein existence evidence (PE1), 1260 proteins do not have characterized functions. To reveal the function of poorly annotated human proteins, we developed a hybrid pipeline that creates protein structure prediction using I-TASSER and infers functional insights for the target protein from the functional templates recognized by COFACTOR. As a case study, the pipeline was applied to all 66 PE1 proteins with unknown or insufficiently specific function (uPE1) on human chromosome 17 as of neXtProt 2017-07-01. Benchmark testing on a control set of 100 well-characterized proteins randomly selected from the same chromosome shows high Gene Ontology (GO) term prediction accuracies of 0.69, 0.57, and 0.67 for molecular function (MF), biological process (BP), and cellular component (CC), respectively. Three pipelines of function annotations (homology detection, protein-protein interaction network inference, and structure template identification) have been exploited by COFACTOR. Detailed analyses show that structure template detection based on low-resolution protein structure prediction made the major contribution to the enhancement of the sensitivity and precision of the annotation predictions, especially for cases that do not have sequence-level homologous templates. For the chromosome 17 uPE1 proteins, the I-TASSER/COFACTOR pipeline confidently assigned MF, BP, and CC for 13, 33, and 49 proteins, respectively, with predicted functions ranging from sphingosine N-acyltransferase activity and sugar transmembrane transporter to cytoskeleton constitution. We highlight the 13 proteins with confident MF predictions; 11 of these are among the 33 proteins with confident BP predictions and 12 are among the 49 proteins with confident CC. This study demonstrates a novel computational approach to systematically annotate protein function in the human proteome and provides useful insights to guide experimental design and follow-up validation studies of these uncharacterized proteins.

Necroptosis inhibition as a therapy for Niemann-Pick disease, type C1: Inhibition of RIP kinases and combination therapy with 2-hydroxypropyl-β-cyclodextrin

Niemann-Pick disease, type C1 (NPC1) is an inborn error of metabolism that results in endolysosomal accumulation of unesterified cholesterol. Clinically, NPC1 manifests as cholestatic liver disease in the newborn or as a progressive neurogenerative condition characterized by cerebellar ataxia and cognitive decline. Currently there are no FDA approved therapies for NPC1. Thus, understanding the pathological processes that contribute to neurodegeneration will be important in both developing and testing potential therapeutic interventions. Neuroinflammation and necroptosis contribute to the NPC1 pathological cascade. Receptor Interacting Protein Kinase 1 and 3 (RIPK1 and RIPK3), are protein kinases that play a central role in mediating neuronal necroptosis. Our prior work suggested that pharmacological inhibition of RIPK1 had a significant but modest beneficial effect; however, the inhibitors used in that study had suboptimal pharmacokinetic properties. In this work we evaluated both pharmacological and genetic inhibition of RIPK1 kinase activity. Lifespan in both Npc1^-/- mice treated with GSK'547, a RIPK1 inhibitor with better pharmacokinetic properties, and Npc1^-/-:Ripk1^kd/kd double mutant mice was significantly increased. In both cases the increase in lifespan was modest, suggesting that the therapeutic potential of RIPK1 inhibition, as a monotherapy, is limited. We thus investigated the potential of combining RIPK1 inhibition with 2-hydroxypropyl-β-cyclodextrin (HPβCD) therapy HPβCD has been shown to slow neurological disease progression in NPC1 mice, cats and patients. HPβCD appeared to have an additive positive effect on the pathology and survival of Npc1^-/-:Ripk1^kd/kd mice. RIPK1 and RIPK3 are both critical components of the necrosome, thus we were surprised to observe no increase survival in Npc1^-/-;Ripk3^-/- mice compared to Npc1^-/- mice. These data suggest that although necroptosis is occurring in NPC1, the observed effects of RIPK1 inhibition may be related to its RIPK3-independent role in neuroinflammation and cytokine production.

Biophysical Inference of Epistasis and the Effects of Mutations on Protein Stability and Function

Understanding the relationship between protein sequence, function, and stability is a fundamental problem in biology. The essential function of many proteins that fold into a specific structure is their ability to bind to a ligand, which can be assayed for thousands of mutated variants. However, binding assays do not distinguish whether mutations affect the stability of the binding interface or the overall fold. Here, we introduce a statistical method to infer a detailed energy landscape of how a protein folds and binds to a ligand by combining information from many mutated variants. We fit a thermodynamic model describing the bound, unbound, and unfolded states to high quality data of protein G domain B1 binding to IgG-Fc. We infer distinct folding and binding energies for each mutation providing a detailed view of how mutations affect binding and stability across the protein. We accurately infer the folding energy of each variant in physical units, validated by independent data, whereas previous high-throughput methods could only measure indirect changes in stability. While we assume an additive sequence-energy relationship, the binding fraction is epistatic due its nonlinear relation to energy. Despite having no epistasis in energy, our model explains much of the observed epistasis in binding fraction, with the remaining epistasis identifying conformationally dynamic regions.

Inactivation of Magel2 suppresses oxytocin neurons through synaptic excitation-inhibition imbalance

Prader-Willi and the related Schaaf-Yang Syndromes (PWS/SYS) are rare neurodevelopmental disorders characterized by overlapping phenotypes of high incidence of autism spectrum disorders (ASD) and neonatal feeding difficulties. Based on clinical and basic studies, oxytocin pathway defects are suggested to contribute disease pathogenesis but the mechanism has been poorly understood. Specifically, whether the impairment in oxytocin system is limited to neuropeptide levels and how the functional properties of broader oxytocin neuron circuits affected in PWS/SYS have not been addressed. Using cell type specific electrophysiology, we investigated basic synaptic and cell autonomous properties of oxytocin neurons in the absence of MAGEL2; a hypothalamus enriched ubiquitin ligase regulator that is inactivated in both syndromes. We observed significant suppression of overall ex vivo oxytocin neuron activity, which was largely contributed by altered synaptic input profile; with reduced excitatory and increased inhibitory currents. Our results suggest that dysregulation of oxytocin system goes beyond altered neuropeptide expression and synaptic excitation inhibition imbalance impairs overall oxytocin pathway function.

Learned protein embeddings for machine learning

Motivation

Machine-learning models trained on protein sequences and their measured functions can infer biological properties of unseen sequences without requiring an understanding of the underlying physical or biological mechanisms. Such models enable the prediction and discovery of sequences with optimal properties. Machine-learning models generally require that their inputs be vectors, and the conversion from a protein sequence to a vector representation affects the model's ability to learn. We propose to learn embedded representations of protein sequences that take advantage of the vast quantity of unmeasured protein sequence data available. These embeddings are low-dimensional and can greatly simplify downstream modeling.

Results

The predictive power of Gaussian process models trained using embeddings is comparable to those trained on existing representations, which suggests that embeddings enable accurate predictions despite having orders of magnitude fewer dimensions. Moreover, embeddings are simpler to obtain because they do not require alignments, structural data, or selection of informative amino-acid properties. Visualizing the embedding vectors shows meaningful relationships between the embedded proteins are captured.

Availability and implementation

The embedding vectors and code to reproduce the results are available at https://github.com/fhalab/embeddings_reproduction/.

Supplementary information

Supplementary data are available at Bioinformatics online.

iFeature: a Python package and web server for features extraction and selection from protein and peptide sequences

Summary

Structural and physiochemical descriptors extracted from sequence data have been widely used to represent sequences and predict structural, functional, expression and interaction profiles of proteins and peptides as well as DNAs/RNAs. Here, we present iFeature, a versatile Python-based toolkit for generating various numerical feature representation schemes for both protein and peptide sequences. iFeature is capable of calculating and extracting a comprehensive spectrum of 18 major sequence encoding schemes that encompass 53 different types of feature descriptors. It also allows users to extract specific amino acid properties from the AAindex database. Furthermore, iFeature integrates 12 different types of commonly used feature clustering, selection and dimensionality reduction algorithms, greatly facilitating training, analysis and benchmarking of machine-learning models. The functionality of iFeature is made freely available via an online web server and a stand-alone toolkit.

Availability and implementation

http://iFeature.erc.monash.edu/; https://github.com/Superzchen/iFeature/.

Supplementary information

Supplementary data are available at Bioinformatics online.

IAS: Interaction Specific GO Term Associations for Predicting Protein-Protein Interaction Networks

Proteins carry out their function in a cell through interactions with other proteins. A large scale protein-protein interaction (PPI) network of an organism provides static yet an essential structure of interactions, which is valuable clue for understanding the functions of proteins and pathways. PPIs are determined primarily by experimental methods; however, computational PPI prediction methods can supplement or verify PPIs identified by experiment. Here, we developed a novel scoring method for predicting PPIs from Gene Ontology (GO) annotations of proteins. Unlike existing methods that consider functional similarity as an indication of interaction between proteins, the new score, named the protein-protein Interaction Association Score (IAS), was computed from GO term associations of known interacting protein pairs in 49 organisms. IAS was evaluated on PPI data of six organisms and found to outperform existing GO term-based scoring methods. Moreover, consensus scoring methods that combine different scores further improved performance of PPI prediction.

Towards Extracting Supporting Information About Predicted Protein-Protein Interactions

One of the goals of relation extraction is to identify protein-protein interactions (PPIs) in biomedical literature. Current systems are capturing binary relations and also the direction and type of an interaction. Besides assisting in the curation PPIs into databases, there has been little real-world application of these algorithms. We describe UPSITE, a text mining tool for extracting evidence in support of a hypothesized interaction. Given a predicted PPI, UPSITE uses a binary relation detector to check whether a PPI is found in abstracts in PubMed. If it is not found, UPSITE retrieves documents relevant to each of the two proteins separately, and extracts contextual information about biological events surrounding each protein, and calculates semantic similarity of the two proteins to provide evidential support for the predicted PPI. In evaluations, relation extraction achieved an Fscore of 0.88 on the HPRD50 corpus, and semantic similarity measured with angular distance was found to be statistically significant. With the development of PPI prediction algorithms, the burden of interpreting the validity and relevance of novel PPIs is on biologists. We suggest that presenting annotations of the two proteins in a PPI side-by-side and a score that quantifies their similarity lessens this burden to some extent.

The Frequency Response of Outer Hair Cell Voltage-Dependent Motility Is Limited by Kinetics of Prestin

The voltage-dependent protein SLC26a5 (prestin) underlies outer hair cell electromotility (eM), which is responsible for cochlear amplification in mammals. The electrical signature of eM is a bell-shaped nonlinear capacitance (NLC), deriving from prestin sensor-charge (Qp) movements, which peaks at the membrane voltage, Vh, where charge is distributed equally on either side of the membrane. Voltage dependencies of NLC and eM differ depending on interrogation frequency and intracellular chloride, revealing slow intermediate conformational transitions between anion binding and voltage-driven Qp movements. Consequently, NLC exhibits low-pass characteristics, substantially below prevailing estimates of eM frequency response. Here we study in guinea pig and mouse of either sex synchronous prestin electrical (NLC, Qp) and mechanical (eM) activity across frequencies under voltage clamp (whole cell and microchamber). We find that eM and Qp magnitude and phase correspond, indicating tight piezoelectric coupling. Electromechanical measures (both NLC and eM) show dual-Lorentzian, low-pass behavior, with a limiting (τ2) time constant at Vh of 32.6 and 24.8 μs, respectively. As expected for voltage-dependent kinetics, voltage excitation away from Vh has a faster, flatter frequency response, with our fastest measured τ2 for eM of 18.2 μs. Previous observations of ultrafast eM (τ ≈ 2 μs) were obtained at offsets far removed from Vh We hypothesize that trade-offs in eM gain-bandwith arising from voltage excitation at membrane potentials offset from Vh influence the effectiveness of cochlear amplification across frequencies.SIGNIFICANCE STATEMENT Of two types of hair cells within the organ of Corti, inner hair cells and outer hair cells, the latter evolved to boost sensitivity to sounds. Damage results in hearing loss of 40-60 dB, revealing amplification gains of 100-1000× that arise from voltage-dependent mechanical responses [electromotility (eM)]. eM, driven by the membrane protein prestin, may work beyond 70 kHz. However, this speed exceeds, by over an order of magnitude, kinetics of typical voltage-dependent membrane proteins. We find eM is actually low pass in nature, indicating that prestin bears kinetics typical of other membrane proteins. These observations highlight potential difficulties in providing sufficient amplification beyond a cutoff frequency near 20 kHz. Nevertheless, observed trade-offs in eM gain-bandwith may sustain cochlear amplification across frequency.

Genomic evidence for genes encoding leucine-rich repeat receptors linked to resistance against the eukaryotic extra- and intracellular Brassica napus pathogens Leptosphaeria maculans and Plasmodiophora brassicae

Genes coding for nucleotide-binding leucine-rich repeat (LRR) receptors (NLRs) control resistance against intracellular (cell-penetrating) pathogens. However, evidence for a role of genes coding for proteins with LRR domains in resistance against extracellular (apoplastic) fungal pathogens is limited. Here, the distribution of genes coding for proteins with eLRR domains but lacking kinase domains was determined for the Brassica napus genome. Predictions of signal peptide and transmembrane regions divided these genes into 184 coding for receptor-like proteins (RLPs) and 121 coding for secreted proteins (SPs). Together with previously annotated NLRs, a total of 720 LRR genes were found. Leptosphaeria maculans-induced expression during a compatible interaction with cultivar Topas differed between RLP, SP and NLR gene families; NLR genes were induced relatively late, during the necrotrophic phase of pathogen colonization. Seven RLP, one SP and two NLR genes were found in Rlm1 and Rlm3/Rlm4/Rlm7/Rlm9 loci for resistance against L. maculans on chromosome A07 of B. napus. One NLR gene at the Rlm9 locus was positively selected, as was the RLP gene on chromosome A10 with LepR3 and Rlm2 alleles conferring resistance against L. maculans races with corresponding effectors AvrLm1 and AvrLm2, respectively. Known loci for resistance against L. maculans (extracellular hemi-biotrophic fungus), Sclerotinia sclerotiorum (necrotrophic fungus) and Plasmodiophora brassicae (intracellular, obligate biotrophic protist) were examined for presence of RLPs, SPs and NLRs in these regions. Whereas loci for resistance against P. brassicae were enriched for NLRs, no such signature was observed for the other pathogens. These findings demonstrate involvement of (i) NLR genes in resistance against the intracellular pathogen P. brassicae and a putative NLR gene in Rlm9-mediated resistance against the extracellular pathogen L. maculans.

Moonlighting Activity of Secreted Inflammation-Regulatory Proteins

Moonlighting proteins exhibit multiple activities in different cellular compartments, and their abnormal regulation could play an important role in many diseases. To date, many proteins have been identified with moonlighting activity, and more such proteins are being gradually identified. Among the proteins that possess moonlighting activity, several secreted proteins exhibit multiple activities in different cellular locations, such as the extracellular matrix, nucleus, and cytoplasm. While acute inflammation starts rapidly and generally disappears in a few days, chronic inflammation can last for months or years. This is generally because of the failure to eliminate the cause of inflammation, along with repeated exposure to the inflammatory agent. Chronic inflammation is now considered as an overwhelming burden to the general wellbeing of patients and noted as an underlying cause of several diseases. Moonlighting proteins can contribute to the process of chronic inflammation; therefore, it is imperative to overview some proteins that exhibit multiple functions in inflammatory diseases. In this review, we will focus on inflammation, particularly unravelling several well-known secreted proteins with multiple functions in different cellular locations.

The E3 ubiquitin ligase SP1-like 1 plays a positive role in peroxisome biogenesis in Arabidopsis

Peroxisomes are dynamic organelles crucial for a variety of metabolic processes during the development of eukaryotic organisms, and are functionally linked to other subcellular organelles, such as mitochondria and chloroplasts. Peroxisomal matrix proteins are imported by peroxins (PEX proteins), yet the modulation of peroxin functions is poorly understood. We previously reported that, besides its known function in chloroplast protein import, the Arabidopsis E3 ubiquitin ligase SP1 (suppressor of ppi1 locus1) also targets to peroxisomes and mitochondria, and promotes the destabilization of the peroxisomal receptor-cargo docking complex components PEX13 and PEX14. Here we present evidence that in Arabidopsis, SP1's closest homolog SP1-like 1 (SPL1) plays an opposite role to SP1 in peroxisomes. In contrast to sp1, loss-of-function of SPL1 led to reduced peroxisomal β-oxidation activity, and enhanced the physiological and growth defects of pex14 and pex13 mutants. Transient co-expression of SPL1 and SP1 promoted each other's destabilization. SPL1 reduced the ability of SP1 to induce PEX13 turnover, and it is the N-terminus of SP1 and SPL1 that determines whether the protein is able to promote PEX13 turnover. Finally, SPL1 showed prevalent targeting to mitochondria, but rather weak and partial localization to peroxisomes. Our data suggest that these two members of the same E3 protein family utilize distinct mechanisms to modulate peroxisome biogenesis, where SPL1 reduces the function of SP1. Plants and possibly other higher eukaryotes may employ this small family of E3 enzymes to differentially modulate the dynamics of several organelles essential to energy metabolism via the ubiquitin-proteasome system.