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APOBEC3-related somatic mutations are predominant in biliary tract cancers (BTCs). We aimed to elucidate the roles of APOBEC3A/3B functional polymorphisms and their influencing factors on the development of cholangiocarcinoma (CCA) and gallbladder cancer (GBC). Polymorphisms at the promoter regions of APOBEC3A and APOBEC3B were genotyped in 3231 participants using quantitative PCR. Dual-luciferase reporter assay was applied to investigate the promoter activity. The difference in gene accessibility between CCA cells and GBC cells was analyzed through single-cell transposase accessible chromatin sequencing. The effect of APOBEC3A on apoptosis was examined by cytometry. It is found that rs2267401-G at the APOBEC3B promoter decreases CCA risk (age-, gender-adjusted odds ratio [AOR], 0.69; 95% confidence interval [CI], 0.51-0.94) but increases GBC risk (AOR, 2.04; 95% CI, 1.35-3.10). rs2267401-G confers a decreased APOBEC3B promoter activity in CCA cells but an increased activity in GBC cells, possibly because the transcriptional repressor TFAP2A is over-expressed in CCA. Tumor necrosis factor-α (TNF-α) increases the level of APOBEC3B via inhibiting TFAP2A expression rather than directly increasing the accessibility of APOBEC3B promoter. APOBEC3A promoter rs12157810-C decreased the risks of CCA and GBC, with an AOR (95% CI) of 0.80 (0.66-0.97) and 0.75 (0.59-0.95), respectively. rs12157810-C upregulated the promoter activity in both CCA and GBC cells. TNF-α upregulated the activity of the APOBEC3A promoter with rs12157810-C via increasing the accessibility of Ets-1 p68. APOBEC3A overexpression attenuates cancer evolution by causing apoptosis, in contrast to APOBEC3B. The heterogeneity in the transcriptional regulation of APOBEC3B affects the evolutionary potential of cancer cells in the inflammatory microenvironment.

Protein is very important for almost all living creatures because it participates in most complicated and essential biological processes. Determining the functions of given proteins is one of the most essential problems in protein science. Such determination can be conducted through traditional experiments. However, the experimental methods are always time-consuming and of high costs. In recent years, computational methods give useful aids for identification of protein functions. This study presented a new multi-label classifier for identifying functions of mouse proteins. Due to the number of functional types, which were termed as labels in the classification procedure, a label space partition method was employed to divide labels into some partitions. On each partition, a multi-label classifier was constructed. The classifiers based on all partitions were integrated in the proposed classifier. The cross-validation results proved that the proposed classifier was of good performance. Classifiers with label partition were superior to those without label partition or with random label partition.

Subcellular localization attempts to assign proteins to one of the cell compartments that performs specific biological functions. Finding the link between proteins, biological functions, and subcellular localization is an effective way to investigate the general organization of living cells in a systematic manner. However, determining the subcellular localization of proteins by traditional experimental approaches is difficult. Here, protein–protein interaction networks, functional enrichment on gene ontology and pathway, and a set of proteins having confirmed subcellular localization were applied to build prediction models for human protein subcellular localizations. To build an effective predictive model, we employed a variety of robust machine learning algorithms, including Boruta feature selection, minimum redundancy maximum relevance, Monte Carlo feature selection, and LightGBM. Then, the incremental feature selection method with random forest and support vector machine was used to discover the essential features. Furthermore, 38 key features were determined by integrating results of different feature selection methods, which may provide critical insights into the subcellular location of proteins. Their biological functions of subcellular localizations were discussed according to recent publications. In summary, our computational framework can help advance the understanding of subcellular localization prediction techniques and provide a new perspective to investigate the patterns of protein subcellular localization and their biological importance.

Identifying protein subcellular locations is an important topic in protein function prediction. Interacting proteins may share similar locations. Thus, it is imperative to infer protein subcellular locations by taking protein-protein interactions (PPIs)into account. In this study, we present a network embedding-based method, node2loc, to identify protein subcellular locations. node2loc first learns distributed embeddings of proteins in a protein-protein interaction (PPI)network using node2vec. Then the learned embeddings are further fed into a recurrent neural network (RNN). To resolve the severe class imbalance of different subcellular locations, Synthetic Minority Over-sampling Technique (SMOTE)is applied to artificially synthesize proteins for minority classes. node2loc is evaluated on our constructed human benchmark dataset with 16 subcellular locations and yields a Matthews correlation coefficient (MCC)value of 0.800, which is superior to baseline methods. In addition, node2loc yields a better performance on a Yeast benchmark dataset with 17 locations. The results demonstrate that the learned representations from a PPI network have certain discriminative ability for classifying protein subcellular locations. However, node2loc is a transductive method, it only works for proteins connected in a PPI network, and it needs to be retrained for new proteins. In addition, the PPI network needs be annotated to some extent with location information. node2loc is freely available at https://github.com/xypan1232/node2loc.

The bromodomain and extra-terminal domain (BET) family of proteins, especially bromodomain-containing protein 4 (BRD4), has emerged as exciting anti-tumor targets due to their important roles in epigenetic regulation. Therefore, the discovery of BET inhibitors with promising anti-tumor efficacy will provide a novel approach to epigenetic anticancer therapy. Recently, we discovered the new BET inhibitor compound 171, which is derived from a polo-like kinase 1 (PLK1)-BRD4 dual inhibitor based on our previous research. Compound 171 was found to maintain BET inhibition ability without PLK1 inhibition, and there was no selectivity among BET family members. The in vitro and in vivo results both indicated that the overall anti-tumor activity of compound 171 was improved compared with the (+)-JQ-1 or OTX-015 BET inhibitors. Furthermore, we found that compound 171 could regulate the expression of cell cycle-regulating proteins including c-Myc and p21 and induce cell cycle arrest in the G0/G1 phase. However, compound 171 only has a quite limited effect on apoptosis, in considering that apoptosis was only observed at doses greater than 50 μM. To determine the mechanisms underlying cell death, proliferation activity assay was conducted. The results showed that compound 171 induced clear anti-proliferative effects at doses that no obvious apoptosis was induced, which indicated that the cell cycle arresting effect contributed mostly to its anti-tumor activity. The result of this study revealed the anti-tumor mechanism of compound 171, and laid a foundation for the combination therapy in clinical practice, if compound 171 or its series compounds become drug candidates in the future.

Sirt6 is an NADH (NAD(+))-dependent deacetylase with a critical role in hepatic lipid metabolism. Ketogenesis is controlled by a signaling network of hepatic lipid metabolism. However, how Sirt6 functions in ketogenesis remains unclear. Here, we demonstrated that Sirt6 functions as a mediator of ketogenesis in response to a fasting and ketogenic diet (KD). The KD-fed hepatocyte-specific Sirt6 deficiency (HKO) mice exhibited impaired ketogenesis, which was due to enhanced Fsp27 (fat-specific induction of protein 27), a protein known to regulate lipid metabolism. In contrast, overexpression of Sirt6 in mouse primary hepatocytes promoted ketogenesis. Mechanistically, Sirt6 repressed Fsp27β expression by interacting with Crebh (cAMP response element–binding protein H) and preventing its recruitment to the Fsp27β gene promoter. The KD-fed HKO mice also showed exacerbated hepatic steatosis and inflammation. Finally, Fsp27 silencing rescued hypoketonemia and other metabolic phenotypes in KD-fed HKO mice. Our data suggest that the Sirt6–Crebh–Fsp27 axis is pivotal for hepatic lipid metabolism and inflammation. Sirt6 may be a pharmacological target to remedy metabolic diseases.

Purpose To analyse the factors predicting uncontrolled seizures in epilepsy with auditory features (EAF). Methods We analysed individual data from EAF patients who were previously reported. Two authors independently reviewed the titles and abstracts identified and extracted data from each eligible study using a standardized form. The outcome measure was uncontrolled seizures. The odds ratio (OR) and 95% confidence interval (CI) were used. Results A total of 27 studies including 181 patients with familial and sporadic EAF met our inclusion criteria. None of the clinical factors appeared to affect seizure outcomes significantly except that treatment with carbamazepine was a protective factor against uncontrolled seizures (OR = 0.399, 95% CI: 0.195–0.820, p = 0.012), and polytherapy was associated with uncontrolled seizures. Treatment with carbamazepine was also a protective factor against uncontrolled seizures for families with LGI1 mutations (OR = 0.248, 95% CI: 0.085–0.724, p = 0.011). Carbamazepine might have a better efficacy in patients with frequent seizures (p = 0.041). Low-dose carbamazepine might completely control seizures in some EAF patients, although other effective doses of antiepileptic drugs might not. Patients without carbamazepine treatment were more likely to use new antiepileptic drugs, which might be due to the higher rate of uncontrolled seizures. Conclusions Carbamazepine treatment is a protective factor against uncontrolled seizures for EAF. However, this evidence is not strong enough to state that carbamazepine is the first choice drug for EAF.

Phenotype is one of the most significant concepts in genetics, which is used to describe all the characteristics of a research object that can be observed. Considering that phenotype reflects the integrated features of genotype and environment factors, it is hard to define phenotype characteristics, even difficult to predict unknown phenotypes. Restricted by current biological techniques, it is still quite expensive and time-consuming to obtain sufficient structural information of large-scale phenotype-associated genes/proteins. Various bioinformatics methods have been presented to solve such problem, and researchers have confirmed the efficacy and prediction accuracy of functional network-based prediction. But general functional descriptions have highly complicated inner structures for phenotype prediction. To further address this issue and improve the efficacy of phenotype prediction on more than ten kinds of phenotypes, we first extract functional enrichment features from GO and KEGG, and then use node2vec to learn functional embedding features of genes from a gene-gene network. All these features are analyzed by some feature selection methods (Boruta, minimum redundancy maximum relevance) to generate a feature list. Such list is fed into the incremental feature selection, incorporating some multi-label classifiers built by RAkEL and some classic base classifiers, to build an optimum multi-label multi-class classification model for phenotype prediction. According to recent researches, our method has indeed identified many literature-supported genes/proteins and their associated phenotypes, and even some candidate genes with re-assigned new phenotypes, which provide a new computational tool for the accurate and effective phenotypic prediction.

Host proteins incorporated into virus particles have been reported to contribute to infectivity and tissue-tropism. This incorporation of host proteins is expected to be variable among viral particles, however, protein analysis at single-virus levels has been challenging. We have developed a method to detect host proteins incorporated on the surface of virions using the in situ proximity ligation assay (isPLA) with rolling circle amplification (RCA), employing oligonucleotide-conjugated antibody pairs. The technique allows highly selective and sensitive antibody-based detection of viral and host proteins on the surface of individual virions. We detected recombinant noninfectious sub-viral particles (SVPs) of tick-borne encephalitis virus (TBEV) immobilized in microtiter wells as fluorescent particles detected by regular fluorescence microscopy. Counting the particles in the images enabled us to estimate individual TBEVSVP counts in different samples. Using isPLA we detected individual calnexin-, CD9-, CD81-, CD29-and CD59-positive SVPs among the viral particles. Our data suggests that a diversity of host proteins may be incorporated into TEBV, illustrating that isPLA with digital counting enables single-virus analysis of host protein incorporation. (C) 2020 The Authors. Published by Elsevier Inc.

The toxicity of doxorubicin to the cardiovascular system often limits its benefits and widespread use as chemotherapy. The mechanisms involved in doxorubicin-induced cardiovascular damage and possible protective interventions are not well-explored. Using human aortic endothelial cells, we show vitamin D3 strongly attenuates doxorubicin-induced senescence and cell cycle arrest. We further show the protective effects of vitamin D3 are mediated by the upregulation of IL-10 and FOXO3a expression through fine modulation of pAMPKα/SIRT1/FOXO3a complex activity. These results have great significance in finding a target for mitigating doxorubicin-induced cardiovascular toxicity.

Protein-protein interactions (PPIs) describe the direct physical contact of two proteins that usually results in specific biological functions or regulatory processes. The characterization and study of PPIs through the investigation of their pattern and principle have remained a question in biological studies. Various experimental and computational methods have been used for PPI studies, but most of them are based on the sequence similarity with current validated PPI participators or cellular localization patterns. Most methods ignore the fact that PPIs are defined by their specific biological functions. In this study, we constructed a novel rule-based computational method using gene ontology and KEGG pathway annotation of PPI participators that correspond to the complicated biological effects of PPIs. Our newly presented computational method identified a group of biological functions that are tightly associated with PPIs and provided a new function-based tool for PPI studies in a rule manner.

The secreted mucilage trails of the diatom Navicula sp. in the process of motility were studied by scanning electron microscopy (SEM), transmission electron microscope (TEM), atomic force microscopy (AFM) and Raman spectra etc. Contrary to previous studies, force measurement was taken directly on the mucilage trails of live cells using the method of in situ force mapping by AFM. The retraction force curve presented an increased tip-substrate peak and a small saw-tooth pattern tip-mucilage peak. Especially, same measurements on various substrates with different surface energy revealed that the mucilage trails actually functioned as a medium increasing the adhesive force between the diatom and substrates, which is crucial to diatom’s adhesion and locomotion. In addition, the mechanical properties of mucilage trails were quite different from mucilage strands in the maximum adhesive force and the maximum polymer extension length. Raman spectra indicated the difference in compositions that both of the two kinds of mucilages had proteins and polysaccharide, but the mucilage strands contained some other components with C=O, —CH2— and —CH3 asymmetric and symmetric stretches. This research hammers out more precise information about mucilage trails which would be useful in terms of diatom motility and biofouling prevention.

A label-free electrochemical immunosensor for quantitative detection of human epididymis specific protein 4 antigen (HE4 Ag) was developed by a novel multi-amplification signal system. The multi-amplification signal system was formed by loading bimetallic Au@Pd holothurian-shaped nanoparticles (Au@Pd HSs) on titanium oxide nanoclusters functionalized nitrogen-doped reduced graphene oxide (TiO

Compound-protein interactions play important roles in every cell via the recognition and regulation of specific functional proteins. The correct identification of compound-protein interactions can lead to a good comprehension of this complicated system and provide useful input for the investigation of various attributes of compounds and proteins. In this study, we attempted to understand this system by extracting properties from both proteins and compounds, in which proteins were represented by gene ontology and KEGG pathway enrichment scores and compounds were represented by molecular fragments. Advanced feature selection methods, including minimum redundancy maximum relevance, incremental feature selection, and the basic machine learning algorithm random forest, were used to analyze these properties and extract core factors for the determination of actual compound-protein interactions. Compound-protein interactions reported in The Binding Databases were used as positive samples. To improve the reliability of the results, the analytic procedure was executed five times using different negative samples. Simultaneously, five optimal prediction methods based on a random forest and yielding maximum MCCs of approximately 77.55 % were constructed and may be useful tools for the prediction of compound-protein interactions. This work provides new clues to understanding the system of compound-protein interactions by analyzing extracted core features. Our results indicate that compound-protein interactions are related to biological processes involving immune, developmental and hormone-associated pathways.

This article outlines the recent progress in surface enhanced Raman spectroscopy (SERS) based proteins applications. SERS is a specific Raman spectroscopic technique that provides enhanced Raman signals (several orders of magnitude greater than normal) for numerous Raman-active analyte molecules adsorbed onto rough metal surfaces. SERS is a sensitive, selective, and versatile technique that lends to fast data acquisition in actual time. Therefore, SERS has undergone rapid development because of its technical advantages in instrumentation, data analysis methods and its multiple biological applications. This article highlights several representative areas in proteins where SERS could be employed. Some of the proteins applications of SERS are more maturely developed, whereas others are in their initial stages of development (in laboratories). This article discusses the recent developments in SERS based quantitative analysis: directly with different substrates (e.g. biomolecules on electrodes, colloidal particles, and periodic pattern structure and tip based substrates). Furthermore, SERS based techniques are advantageous for obtaining valuable information on protein-protein, protein-ligand, and protein-drug recognitions via spectral differences among molecular bridges. SERS based techniques show considerable promise for qualitative and/or quantitative analyses of biological systems.

AIM AND OBJECTIVE Correct prediction of protein structural class is beneficial to investigation on protein functions, regulations and interactions. In recent years, several computational methods have been proposed in this regard. However, based on various features, it is still a great challenge to select proper classification algorithm and extract essential features to participate in classification. MATERIAL AND METHODS In this study, a feature and algorithm selection method was presented for improving the accuracy of protein structural class prediction. The amino acid compositions and physiochemical features were adopted to represent features and thirty-eight machine learning algorithms collected in Weka were employed. All features were first analyzed by a feature selection method, minimum redundancy maximum relevance (mRMR), producing a feature list. Then, several feature sets were constructed by adding features in the list one by one. For each feature set, thirtyeight algorithms were executed on a dataset, in which proteins were represented by features in the set. The predicted classes yielded by these algorithms and true class of each protein were collected to construct a dataset, which were analyzed by mRMR method, yielding an algorithm list. From the algorithm list, the algorithm was taken one by one to build an ensemble prediction model. Finally, we selected the ensemble prediction model with the best performance as the optimal ensemble prediction model. RESULTS Experimental results indicate that the constructed model is much superior to models using single algorithm and other models that only adopt feature selection procedure or algorithm selection procedure. CONCLUSION The feature selection procedure or algorithm selection procedure are really helpful for building an ensemble prediction model that can yield a better performance.

Fluorous chemistry has brought many applications from catalysis to separation science, from supramolecular materials to analytical chemistry. However, fluorous-based Quartz Crystal Microbalance (QCM) has not been reported so far. In the current paper, fluorous interaction has been firstly utilized in QCM, and carbohydrate–protein interaction and carbohydrate–carbohydrate interaction have been detected afterward. Download : Download full-size image

Porcine epidemic diarrhea (PED) re-emerged in China in late 2010 and has now become widespread. Accumulated evidence indicates that this large-scale outbreak of diarrhea was caused by variants of the highly virulent porcine epidemic diarrhea virus (PEDV). A pandemic PEDV YC2014 strain (YC2014) was isolated from clinical samples. An iTRAQ-based comparative quantitative proteomic study of IPEC-J2 cells infected with YC2014 and a classical CV777 strain (CV777) was performed to determine the differences between pandemic and classical PEDV strain infection. Totals of 353 and 299 differentially expressed proteins were identified upon YC2014 and CV777 infection, respectively. The canonical pathways and functional networks involved in both PEDV infections were analyzed. The results indicated that the PEDV suppressed protein synthesis of IPEC-J2 cells through down-regulation of the PI3K-AKT/mTOR signaling pathways. Infection with YC2014 could activate the JAK-STAT signaling pathway and the NF-κB pathway more intensively than CV777. YC2014 could activate NF-κB pathway more intensively than CV777. On the basis of differentially expressed proteins, we propose that PEDV might disrupt apoptosis and may elicit stronger inflammatory cascades as well. This study might contribute to an understanding of the pathogenesis of PEDV infection and aid in the development of effective preventive and control vaccines.

Cancer is a significant public health problem worldwide. Complete identification of genes related to one type of cancer facilitates earlier diagnosis and effective treatments. In this study, two widely used algorithms, the random walk with restart algorithm and the shortest path algorithm, were adopted to construct two parameterized computational methods, namely, an RWR-based method and an SP-based method; based on these methods, an integrated method was constructed for identifying novel disease genes. To validate the utility of the integrated method, data for oral cancer were used, on which the RWR-based and SP-based methods were trained, thereby building two optimal methods. The integrated method combining these optimal methods was further adopted to identify the novel genes of oral cancer. As a result, 85 novel genes were inferred, among which eleven genes (e.g., MYD88, FGFR2, NF-κBIA) were identified by both the RWR-based and SP-based methods, 70 genes (e.g., BMP4, IFNG, KITLG) were discovered only by the RWR-based method and four genes (L1R1, MCM6, NOG and CXCR3) were predicted only by the SP-based method. Extensive analyses indicate that several novel genes have strong associations with cancers, indicating the effectiveness of the integrated method for identifying disease genes.

Background Milu, also known as Père David's deer (Elaphurus davidianus), was widely distributed in East Asia but recently experienced a severe bottleneck. Only 18 survived by the end of the 19th century, and the current population of 4500 individuals was propagated from just 11 kept by the 11th British Duke of Bedford. This species is known for its distinguishable appearance, the driving force behind which is still a mystery. To aid efforts to explore these phenomena, we constructed a draft genome of the species. Findings In total, we generated 321.86 gigabases (Gb) of raw DNA sequence from whole-genome sequencing of a male milu deer using an Illumina HiSeq 2000 platform. Assembly yielded a final genome with a scaffold N50 size of 3.03 megabases (Mb) and a total length of 2.52 Gb. Moreover, we identified 20 125 protein-coding genes and 988.1 Mb of repetitive sequences. In addition, homology-based searches detected 280 rRNA, 1335 miRNA, 1441 snRNA, and 893 tRNA sequences in the milu genome. The divergence time between E. davidianus and Bos taurus was estimated to be about 28.20 million years ago (Mya). We identified 167 species-specific genes and 293 expanded gene families in the milu lineage. Conclusions We report the first reference genome of milu, which will provide a valuable resource for studying the species’ demographic history of severe bottleneck and the genetic mechanism(s) of special phenotypic evolution.

Drug-target interaction is a key research topic in drug discovery since correct identification of target proteins of drug candidates can help screen out those with unacceptable toxicities, thereby saving expense. In this study, we developed a novel computational approach to predict drug target groups that may reduce the number of candidate target proteins associated with a query drug. A benchmark dataset, consisting of 3028 drugs assigned within nine categories, was constructed by collecting data from KEGG. The nine categories are (1) G protein-coupled receptors, (2) cytokine receptors, (3) nuclear receptors, (4) ion channels, (5) transporters, (6) enzymes, (7) protein kinases, (8) cellular antigens and (9) pathogens. The proposed method combines the data gleaned from chemical-chemical similarities, chemical-chemical connections and chemical-protein connections to allocate drugs to each of the nine target groups. A jackknife test applied to the training dataset that was constructed from the benchmark dataset, provided an overall correct prediction rate of 87.45%, as compared to 87.79% for the test dataset that was constructed by randomly selecting 10% of samples from the benchmark dataset. These prediction rates are much higher than the 11.11% achieved by random guesswork. These promising results suggest that the proposed method can become a useful tool in identifying drug target groups. This article is part of a Special Issue entitled: Computational Proteomics, Systems Biology & Clinical Implications. Guest Editor: Yudong Cai.

A drug side effect is an undesirable effect which occurs in addition to the intended therapeutic effect of the drug. The unexpected side effects that many patients suffer from are the major causes of large-scale drug withdrawal. To address the problem, it is highly demanded by pharmaceutical industries to develop computational methods for predicting the side effects of drugs. In this study, a novel computational method was developed to predict the side effects of drug compounds by hybridizing the chemical-chemical and protein-chemical interactions. Compared to most of the previous works, our method can rank the potential side effects for any query drug according to their predicted level of risk. A training dataset and test datasets were constructed from the benchmark dataset that contains 835 drug compounds to evaluate the method. By a jackknife test on the training dataset, the 1st order prediction accuracy was 86.30%, while it was 89.16% on the test dataset. It is expected that the new method may become a useful tool for drug design, and that the findings obtained by hybridizing various interactions in a network system may provide useful insights for conducting in-depth pharmacological research as well, particularly at the level of systems biomedicine.

Drug combinatorial therapy could be more effective in treating some complex diseases than single agents due to better efficacy and reduced side effects. Although some drug combinations are being used, their underlying molecular mechanisms are still poorly understood. Therefore, it is of great interest to deduce a novel drug combination by their molecular mechanisms in a robust and rigorous way. This paper attempts to predict effective drug combinations by a combined consideration of: (1) chemical interaction between drugs, (2) protein interactions between drugs’ targets, and (3) target enrichment of KEGG pathways. A benchmark dataset was constructed, consisting of 121 confirmed effective combinations and 605 random combinations. Each drug combination was represented by 465 features derived from the aforementioned three properties. Some feature selection techniques, including Minimum Redundancy Maximum Relevance and Incremental Feature Selection, were adopted to extract the key features. Random forest model was built with its performance evaluated by 5-fold cross-validation. As a result, 55 key features providing the best prediction result were selected. These important features may help to gain insights into the mechanisms of drug combinations, and the proposed prediction model could become a useful tool for screening possible drug combinations.

A drug's biological half-life is defined as the time required for the human body to metabolize or eliminate 50% of the initial drug dosage. Correctly measuring the half-life of a given drug is helpful for the safe and accurate usage of the drug. In this study, we investigated which gene ontology (GO) terms and biological pathways were highly related to the determination of drug half-life. The investigated drugs, with known half-lives, were analyzed based on their enrichment scores for associated GO terms and KEGG pathways. These scores indicate which GO terms or KEGG pathways the drug targets. The feature selection method, minimum redundancy maximum relevance, was used to analyze these GO terms and KEGG pathways and to identify important GO terms and pathways, such as sodium-independent organic anion transmembrane transporter activity (GO:0015347), monoamine transmembrane transporter activity (GO:0008504), negative regulation of synaptic transmission (GO:0050805), neuroactive ligand-receptor interaction (hsa04080), serotonergic synapse (hsa04726), and linoleic acid metabolism (hsa00591), among others. This analysis confirmed our results and may show evidence for a new method in studying drug half-lives and building effective computational methods for the prediction of drug half-lives.

The human brain is one of the most mysterious tissues in the body. Our knowledge of the human brain is limited due to the complexity of its structure and the microscopic nature of connections between brain regions and other tissues in the body. In this study, we analyzed the gene expression profiles of three brain regions—the brain stem, cerebellum and cerebral cortex—to identify genes that are differentially expressed among these different brain regions in humans and to obtain a list of robust, region-specific, differentially expressed genes by comparing the expression signatures from different individuals. Feature selection methods, specifically minimum redundancy maximum relevance and incremental feature selection, were employed to analyze the gene expression profiles. Sequential minimal optimization, a machine-learning algorithm, was employed to examine the utility of selected genes. We also performed a literature search, and we discuss the experimental evidence for the important physiological functions of several highly ranked genes, including NR2E1, DAO, and LRRC7, and we give our analyses on a gene (TFAP2B) that have not been investigated or experimentally validated. As a whole, the results of our study will improve our ability to predict and understand genes related to brain regionalization and function.

Phytophthora capsici is an important oomycete plant pathogen that causes significant losses worldwide. The carboxylic acid amide fungicide flumorph has shown excellent activity against oomycete plant pathogens. Despite its potential, there remains concern that the sexual reproduction of oomycete pathogens, which results in genetic recombination, could result in the rapid development of resistance to flumorph. The current study utilized an iTRAQ (isobaric tags for relative and absolute quantitation) based method to compare differences between the proteome of the parental P. capsici isolate PCAS1 and its sexual progeny S2-838, which exhibits significant resistance to flumorph. A total of 2396 individual proteins were identified, of these, 181 were considered to be associated with the adaptive response of P. capsici to flumorph. The subsequent bioinformatic analysis revealed that the adaptive response of P. capsici to flumorph was complex and regulated by multiple mechanisms, including utilising carbohydrate from the host environment to compensate for the cell wall stress induced by flumorph, a shift in energy generation, decreased amino acids biosynthesis, and elevated levels of proteins associated with the pathogen’s response to stimulus and transmembrane transport. Moreover, the results of the study provided crucial data that could provide the basis for early monitoring of flumorph resistance in field populations of P. capsici.

Peste des Petits Ruminants (PPR) is an acute, highly contagious, and febrile viral disease that affects both domestic and wild small ruminants. The disease has become a major obstacle to the development of sustainable Agriculture. Hemagglutinin (H), the envelope glycoprotein of Peste des Petits Ruminants Virus (PPRV), plays a crucial role in regulating viral adsorption and entry, thus determining pathogenicity, and release of newly produced viral particles. In order to accurately understand the epidemic of the disease and the interactions between the virus and host, we launch the work. Here, we examined H gene from all four lineages of the PPRV to investigate evolutionary and epidemiologic dynamics of PPRV by the Bayesian method. In addition, we predicted positive selection sites due to selective pressures. Finally, we studied the interaction between H protein and SLAM receptor based on homology model of the complex. Phylogenetic analysis suggested that H gene can also be used to investigate evolutionary and epidemiologic dynamics of PPRV. Positive selection analysis identified four positive selection sites in H gene, in which only one common site (aa246) was detected by two methods, suggesting strong operation structural and/or functional constraint of changes on the H protein. This target site may be of interest for future mutagenesis studies. The results of homology modeling showed PPRVHv-shSLAM binding interface and MVH-maSLAM binding interface were consistent, wherein the groove in the B4 blade and B5 of the head domain of PPRVHv bound to the AGFCC' β-sheets of the membrane-distal ectodomain of shSLAM. The binding regions could provide insight on the nature of the protein for epitope vaccine design, novel drug discovery, and rational drug design against PPRV.

Lung cancer, characterized by uncontrolled cell growth in the lung tissue, is the leading cause of global cancer deaths. Until now, effective treatment of this disease is limited. Many synthetic compounds have emerged with the advancement of combinatorial chemistry. Identification of effective lung cancer candidate drug compounds among them is a great challenge. Thus, it is necessary to build effective computational methods that can assist us in selecting for potential lung cancer drug compounds. In this study, a computational method was proposed to tackle this problem. The chemical-chemical interactions and chemical-protein interactions were utilized to select candidate drug compounds that have close associations with approved lung cancer drugs and lung cancer-related genes. A permutation test and K-means clustering algorithm were employed to exclude candidate drugs with low possibilities to treat lung cancer. The final analysis suggests that the remaining drug compounds have potential anti-lung cancer activities and most of them have structural dissimilarity with approved drugs for lung cancer.

The Asian citrus psyllid, Diaphorina citri is the principal vector of the highly destructive citrus disease called Huanglongbing (HLB) or citrus greening, which is a major threat to citrus cultivation worldwide. More effective pest control strategies against this pest entail the identification of potential chemosensory proteins that could be used in the development of attractants or repellents. However, the molecular basis of olfaction in the Asian citrus psyllid is not completely understood. Therefore, we performed this study to analyze the antennal and abdominal transcriptome of the Asian citrus psyllid. We identified a large number of transcripts belonging to nine chemoreception-related gene families and compared their expression in male and female adult antennae and terminal abdomen. In total, 9 odorant binding proteins (OBPs), 12 chemosensory proteins (CSPs), 46 odorant receptors (ORs), 20 gustatory receptors (GRs), 35 ionotropic receptors (IRs), 4 sensory neuron membrane proteins (SNMPs) and 4 different gene families encoding odorant-degrading enzymes (ODEs): 80 cytochrome P450s (CYPs), 12 esterase (ESTs), and 5 aldehyde dehydrogenases (ADE) were annotated in the D. citri antennal and abdominal transcriptomes. Our results revealed that a large proportion of chemosensory genes exhibited no distinct differences in their expression patterns in the antennae and terminal abdominal tissues. Notably, RNA sequencing (RNA-seq) data and quantitative real time-PCR (qPCR) analyses showed that 4 DictOBPs, 4 DictCSPs, 4 DictIRs, 1 DictSNMP, and 2 DictCYPs were upregulated in the antennae relative to that in terminal abdominal tissues. Furthermore, 2 DictOBPs (DictOBP8 and DictOBP9), 2 DictCSPs (DictOBP8 and DictOBP12), 4 DictIRs (DictIR3, DictIR6, DictIR10, and DictIR35), and 1 DictCYP (DictCYP57) were expressed at higher levels in the male antennae than in the female antennae. Our study provides the first insights into the molecular basis of chemoreception in this insect pest. Further studies on the identified differentially expressed genes would facilitate the understanding of insect olfaction and their role in the interactions between olfactory system and biological processes.

MicroRNAs (miRNAs) are small, non-coding RNAs that affect the post-transcriptional regulation of various biological pathways. To date, it is not fully understood how miRNAs regulate mitochondrial biogenesis. This study aimed at the identification of the role of miRNA-27b in mitochondria biogenesis. The mitochondria content in C2C12 cells was significantly increased during myogenic differentiation and accompanied by a marked decrease of miRNA-27b expression. Furthermore, the expression of the predicted target gene of miRNA-27b, forkhead box j3 (Foxj3), was also increased during myogenic differentiation. Luciferase activity assays confirmed that miRNA-27b directly targets the 3'-untranslated region (3'-UTR) of Foxj3. Overexpression of miRNA-27b provoked a decrease of mitochondria content and diminished expression of related mitochondrial genes and Foxj3 both at mRNA and protein levels. The expression levels of downstream genes of Foxj3, such as Mef2c, PGC1α, NRF1 and mtTFA, were also decreased in C2C12 cells upon overexpression of miRNA-27b. These results suggested that miRNA-27b may affect mitochondria biogenesis by down-regulation of Foxj3 during myocyte differentiation.

A simple and effective surface-enhanced Raman scattering (SERS)-based protocol for the detection of protein–small molecule interactions has been developed. We employed silver-coated magnetic particles (AgMNPs), which can provide high SERS activity as a protein carrier to capture a small molecule. Combining magnetic separation and the SERS method for protein detection, highly reproducible SERS spectra of a protein–small molecule complex can be obtained with high sensitivity. This time-saving method employs an external magnetic field to induce the AgMNPs to aggregate to increase the amount of atto610-biotin/avidin complex in a unit area with the SERS enhancement. Because of the contribution of the AgMNP aggregation to the SERS, this protocol has great potential for practical high-throughput detection of the protein–small molecule complex and the antigen–antibody immunocomplex.

Protein’s subcellular location, which indicates where a protein resides in a cell, is an important characteristic of protein. Correctly assigning proteins to their subcellular locations would be of great help to the prediction of proteins’ function, genome annotation, and drug design. Yet, in spite of great technical advance in the past decades, it is still time-consuming and laborious to experimentally determine protein subcellular locations on a high throughput scale. Hence, four integrated-algorithm methods were developed to fulfill such high throughput prediction in this article. Two data sets taken from the literature (Chou and Elrod, Protein Eng 12:107–118, 1999) were used as training set and test set, which consisted of 2,391 and 2,598 proteins, respectively. Amino acid composition was applied to represent the protein sequences. The jackknife cross-validation was used to test the training set. The final best integrated-algorithm predictor was constructed by integrating 10 algorithms in Weka (a software tool for tackling data mining tasks, http://www.cs.waikato.ac.nz/ml/weka/ ) based on an mRMR (Minimum Redundancy Maximum Relevance, http://research.janelia.org/peng/proj/mRMR/ ) method. It can achieve correct rate of 77.83 and 80.56% for the training set and test set, respectively, which is better than all of the 60 algorithms collected in Weka. This predicting software is available upon request.

In this study, a new method for the quantitative evaluation of proteins is developed using competitive resonance Raman spectroscopy. A chelation reaction between bathocuproine disulfonate (BCS) and Cu+ which is reduced by proteins in an alkaline environment, is utilized to create a BCS–Cu+ complex that has strong resonance Raman activity. As a result, the method presented here enables protein detection over a much wider concentration range than conventional methods. Furthermore, the resonance Raman-based method can provide an improved lower limit of detection (500 pg/mL) compared to that obtained by colorimetric and ultraviolet- (UV-) based methods. Additionally, protein-to-protein variation can be determined using a standard curve created from known concentrations of bovine serum albumin (BSA), and excellent protein recovery is observed. More importantly, the proposed method was employing to the real sample (fetal bovine serum [FBS]). Based on the calibration curve from this method, it is extremely accurate for the determination of total protein concentrations in FBS. Results of this study demonstrate that the proposed method provides a novel and highly sensitive protocol for reagent-based protein assays.

Studies of protein phenotypes represent a central challenge of modern genetics in the post-genome era because effective and accurate investigation of protein phenotypes is one of the most critical procedures to identify functional biological processes in microscale, which involves the analysis of multifactorial traits and has greatly contributed to the development of modern biology in the post genome era. Therefore, we have developed a novel computational method that identifies novel proteins associated with certain phenotypes in yeast based on the protein-protein interaction network. Unlike some existing network-based computational methods that identify the phenotype of a query protein based on its direct neighbors in the local network, the proposed method identifies novel candidate proteins for a certain phenotype by considering all annotated proteins with this phenotype on the global network using a shortest path (SP) algorithm. The identified proteins are further filtered using both a permutation test and their interactions and sequence similarities to annotated proteins. We compared our method with another widely used method called random walk with restart (RWR). The biological functions of proteins for each phenotype identified by our SP method and the RWR method were analyzed and compared. The results confirmed a large proportion of our novel protein phenotype annotation, and the RWR method showed a higher false positive rate than the SP method. Our method is equally effective for the prediction of proteins involving in all the eleven clustered yeast phenotypes with a quite low false positive rate. Considering the universality and generalizability of our supporting materials and computing strategies, our method can further be applied to study other organisms and the new functions we predicted can provide pertinent instructions for the further experimental verifications.

Gathering vast data sets of cancer genomes requires more efficient and autonomous procedures to classify cancer types and to discover a few essential genes to distinguish different cancers. Because protein expression is more stable than gene expression, we chose reverse phase protein array (RPPA) data, a powerful and robust antibody-based high-throughput approach for targeted proteomics, to perform our research. In this study, we proposed a computational framework to classify the patient samples into ten major cancer types based on the RPPA data using the SMO (Sequential minimal optimization) method. A careful feature selection procedure was employed to select 23 important proteins from the total of 187 proteins by mRMR (minimum Redundancy Maximum Relevance Feature Selection) and IFS (Incremental Feature Selection) on the training set. By using the 23 proteins, we successfully classified the ten cancer types with an MCC (Matthews Correlation Coefficient) of 0.904 on the training set, evaluated by 10-fold cross-validation, and an MCC of 0.936 on an independent test set. Further analysis of these 23 proteins was performed. Most of these proteins can present the hallmarks of cancer; Chk2, for example, plays an important role in the proliferation of cancer cells. Our analysis of these 23 proteins lends credence to the importance of these genes as indicators of cancer classification. We also believe our methods and findings may shed light on the discoveries of specific biomarkers of different types of cancers.

Procaspase-3 (p32) is processed by upstream caspases to p12 and p20 subunits, which heterodimerize. Concomitant with formation of the active heterotetramer, p20 is autoprocessed to p17. Treatment of HL-60 cells with lactacystin, a selective inhibitor of the proteasome, exponentially increased caspase-3-like hydrolytic activity and induced apoptosis but had little or no effect on the activity of upstream caspase-8, caspase-9, or granzyme B. Lactacystin treatment decreased the p32 zymogen and evoked the accumulation of the p17 and p12 subunits. Treatment of transfected human retinoblast 911 cells with a proteasome inhibitor evoked the accumulation of epitope-tagged p12, p17, and p20 but had no effect on p32 zymogen. This result suggests that caspase-3 subunits, in contrast to the zymogen, are unstable because of degradation by the ubiquitin-proteasome system. Ubiquitin conjugates of p12 and p17 accumulated in cells that were cotransfected with p12 and a caspase inactive mutant of p17. Substitution of arginine for all eight lysines of p12 almost abolished its ubiquitination. Any single lysine or lysine pair was sufficient for p12 ubiquitination. Lactacystin treatment of HL-60 cells induced proteolytic processing of the X-linked inhibitor of apoptosis (XIAP) and decreased full-length XIAP, which is known to have ubiquitin-protein ligase activity for active caspase-3. These findings indicate that caspase-3 subunits can be degraded by the ubiquitin-proteasome system and suggest that lactacystin induces apoptosis in part by disabling the ubiquitin-protein ligase function of XIAP and by stabilizing active caspase-3 subunits.

Nuclear factor kappaB-inducing kinase (NIK) is a member of the MAP kinase kinase kinase family that was first identified as a component of the TNF-R1-induced NF-kappaB activation pathway (TNF, tumor necrosis factor; nuclear factor kappaB, NF-kappaB). Gene knockout study, however, suggests that NIK is dispensable for TNF-R1- but required for lymphotoxin-beta receptor-induced NF-kappaB activation. A NIK kinase inactive mutant is a potent inhibitor of NF-kappaB activation triggered by various stimuli, suggesting that NIK is involved in a broad range of NF-kappaB activation pathways. To unambiguously identify signaling pathways that NIK participates in, we screened antibody arrays for proteins that are associated with NIK. This effort identified ErbB4, one of the EGF/heregulin receptors, and Grb7, an adapter protein associated with ErbB4 (ErbB, epidermal growth factor receptor family protein; EGF, epidermal growth factor; Grb, growth factor receptor bound). Coimmunoprecipitation experiments demonstrated that NIK interacted with Grb7, as well as Grb10 and Grb14, but not Grb2. Domain mapping experiments indicated that the central GM domain of Grb7 was sufficient for its interaction with NIK. Coimmunoprecipitation experiments also indicated that Grb7 and NIK could be simultaneously recruited into signaling complexes of all known EGF/heregulin receptors, including EGFR, ErbB2, ErbB3, and ErbB4. In reporter gene assays, NIK could potentiate Grb7, ErbB2/ErbB4, and EGF-induced NF-kappaB activation. A NIK kinase inactive mutant could block ErbB2/ErbB4 and EGF-induced NF-kappaB activation. Moreover, EGF/heregulin receptors activated NF-kappaB in wild-type, but not NIK-/- embryonic fibroblasts. Our findings suggest that NIK is a component of the EGF/heregulin receptor signaling complexes and involved in NF-kappaB activation triggered by these receptors.

Lung cancer causes a large number of deaths per year. Until now, a cure for this disease has not been found or developed. Finding an effective drug through traditional experimental methods invariably costs millions of dollars and takes several years. It is imperative that computational methods be developed to integrate several types of existing information to identify candidate drugs for further study, which could reduce the cost and time of development. In this study, we tried to advance this effort by proposing a computational method to identify candidate drugs for non-small cell lung cancer (NSCLC), a major type of lung cancer. The method used three steps: (1) preliminary screening, (2) screening compounds by an association test and a permutation test, (3) screening compounds using an EM clustering algorithm. In the first step, based on the chemical-chemical interaction information reported in STITCH, a well-known database that reports interactions between chemicals and proteins, and approved NSCLC drugs, compounds that can interact with at least one approved NSCLC drug were picked. In the second step, the association test selected compounds that can interact with at least one NSCLC-related chemical and at least one NSCLC-related gene, and subsequently, the permutation test was used to discard nonspecific compounds from the remaining compounds. In the final step, core compounds were selected using a powerful clustering algorithm, the EM algorithm. Six putative compounds, protoporphyrin IX, hematoporphyrin, canertinib, lapatinib, pelitinib, and dacomitinib, were identified by this method. Previously published data show that all of the selected compounds have been reported to possess anti-NSCLC activity, indicating high probabilities of these compounds being novel candidate drugs for NSCLC.

Gametogenesis is a complex biological process of producing cells for sexual reproduction. Xlr super family members containing a conserved COR1 domain play essential roles in gametogenesis. In the present study, we identified that Slx2, a novel member of Xlr super family, is specifically expressed in the meiotic oocytes, which is demonstrated by western blotting and immunohistochemistry studies. In the first meiotic prophase, SLX2 is unevenly distributed in the nuclei of oocytes, during which phase SLX2 is partly co-localized with SYCP3 in synaptonemal complex and γH2AX in the nucleus of oocytes. Interestingly, the localization of SLX2 was found to be switched into the cytoplasm of oocytes after prometaphase I during oocyte maturation. Furthermore, yeast two-hybrid and coimmunoprecipitation studies demonstrated that SLX2 interacts with BLOS2, which is a novel centrosome-associated protein, and co-localized with γ-Tubulin, which is a protein marker of chromosome segregation in meiosis. These results indicated that SLX2 might get involved in chromosomes segregation during meiosis by interaction with BLOS2. In conclusion, SLX2 might be a novel gametogenesis-related protein that could play multiple roles in regulation of meiotic processes including synaptonemal complex assembly and chromosome segregation.

A cosmid contig was constructed from a YAC clone with a 220-kb insert that spans the centromeric side of the human MHC class II region, corresponding to the mouse t complex. The gene order was identified to be HSET–HKE1.5–HKE2–HKE3–RING1–HKE6–HKE 4 (RING5). The genomic sequence of a 42,801-bp long region encoded by one cosmid clone in the RING1, HKE6, and HKE4 subregions was determined by the shotgun method. The exon–intron organization of these three genes, RING1 (Ring finger protein), HKE6 (steroid dehydrogenase-like protein), and HKE4 (transmembrane protein with histidine-rich charge clusters), was determined. The previously reported RING2 gene was revealed to be identical to HKE6. Transcripts from HKE4 were detected in the placenta, lung, kidney, and pancreas. Those of HKE6 were found in the liver and pancreas. The 25-kb region proximal to the RING1 gene includes an extensive dense cluster of Alu repeats (about 1.2 Alu per kb), and no gene has been identified in this so far. The region is equivalent to part of the mouse t complex and could be of relevance to human development.

Lead (Pb) and decabromodiphenyl ether (BDE209) are the main contaminants at e-waste recycling sites, and their potential toxicological effects on terrestrial organisms have received extensive attention. However, the impact on earthworms of exposure to the two chemicals remains almost unknown. Therefore, indoor incubation tests were performed on control and contaminated soil samples to determine the Pb accumulations and toxic effects by earthworms in the presence of BDE209 for the first time. The results have demonstrated that BDE209 presence can affect Pb bioaccumulation efficiency compared with exposure to Pb alone. The Pb contents in earthworms had a highly positive correlation with the Pb concentrations in soils. For different Pb doses, almost contrary response trends were found for Pb uptake examined separately on day 7 or 28, and dose-effect relationships were clearly observed in the presence of BDE209. After 7 days of exposure, the earthworm bodies receiving 1-mg kg(-1) BDE209 dose showed significantly lower Pb contents (average = 175.85 mg kg(-1)) and bioaccumulation factor (average = 0.574) than those receiving non-BDE209 treatments (217.39 mg kg(-1) and 1.209, respectively). As the incubation time extended, the influence of BDE209 presence on Pb uptake gradually declined. Additionally, either single or combined exposure to both chemicals can affect the protein synthesis in earthworms (p 0.01), while different levels of BDE209 addition barely caused visible differences. The results of these observations have provided a basic understanding on the potential toxicological effects of joint Pb and BDE209 exposure on terrestrial invertebrates.

Determination of drug's target protein is very important for studying drug-target interaction network, while drug-target interaction network is a key area in the drug discovery pipeline. Thus correct prediction of drug's target protein is very helpful to promote the development of drug discovery. In this study, we developed a two-step similarity-based method to predict drug's target group. In each step, a similarity score (obtained by graph representation in the first step, and chemical functional group representation in the second step) was employed to make prediction. Since some drugs can target proteins distributing in more than one group of proteins, the method provided a series of candidate target groups for each drug. As a result, the first-order prediction accuracy on training set and test set were 79.01% and 76.43%, respectively, which were much higher than the success rate of a random guess. The results show that using graph representation to encode drug is a good choice in this area. We expect that this contribution will provide some help to understand drug-target interaction network.

Mitochondrial genome has proven to be a powerful tool for phylogenetic inference, phylogeography, and molecular evolution. In this study, we determined the complete mitochondrial genome of Emberiza tristrami (Passeriformes: Emberizidae) for use in future phylogenetic analyses. This circular mitochondrial genome is 16,789 bp in length and composed of 13 typical protein-coding genes, 22 transfer RNA genes, 2 ribosomal RNA genes, and 1 putative control region (CR). One extra nucleotide “C” of nad3 is not detected in the mitogenome of E. tristrami. The CR of E. tristrami can be divided into three domains: ETAS (extended termination-associated sequence) domain I (nt 1–431), central conserved domain II (nt 432–847), and CSB (conserved sequence block) domain III (nt 848–1217). Six conserved sequence boxes in the central conserved domain II were identified as boxes F, E, D, C, b, and B.

Most drugs have beneficial as well as adverse effects and exert their biological functions by adjusting and altering the functions of their target proteins. Thus, knowledge of drugs target proteins is essential for the improvement of therapeutic effects and mitigation of undesirable side effects. In the study, we proposed a novel prediction method based on drug/compound ontology information extracted from ChEBI to identify drugs target groups from which the kind of functions of a drug may be deduced. By collecting data in KEGG, a benchmark dataset consisting of 876 drugs, categorized into four target groups, was constructed. To evaluate the method more thoroughly, the benchmark dataset was divided into a training dataset and an independent test dataset. It is observed by jackknife test that the overall prediction accuracy on the training dataset was 83.12%, while it was 87.50% on the test dataset—the predictor exhibited an excellent generalization. The good performance of the method indicates that the ontology information of the drugs contains rich information about their target groups, and the study may become an inspiration to solve the problems of this sort and bridge the gap between ChEBI ontology and drugs target groups.

A rapid and highly sensitive bicinchoninic acid (BCA) reagent-based protein quantitation tool was developed using competitive resonance Raman (RR) and surface-enhanced resonance Raman scattering (SERRS) methods. A chelation reaction between BCA and Cu(+), which is reduced by protein in an alkaline environment, is exploited to create a BCA-Cu(+) complex that has strong RR and SERRS activities. Using these methods, protein concentrations in solutions can be quantitatively measured at concentrations as low as 50 μg mL(-1) and 10 pg mL(-1). There are many advantages of using RR and SERRS-based assays. These assays exhibit a much wider linear concentration range and provide an additional one (RR method) to four (SERRS method) orders of magnitude increase in detection limits relative to UV-based methods. Protein-to-protein variation is determined using a reference to a standard curve at concentrations of BSA that exhibits excellent recoveries. These novel methods are extremely accurate in detecting total protein concentrations in solution. This improvement in protein detection sensitivity could yield advances in the biological sciences and medical diagnostic field and extend the applications of reagent-based protein assay techniques.

Respiratory syncytial virus (RSV) is a significant cause of severe respiratory illness worldwide, particularly in infants, young children, and the elderly. Although no licensed vaccine is currently available, an engineered version of the metastable RSV fusion (F) surface glycoprotein—stabilized in the pre-fusion (pre-F) conformation by “DS-Cav1” mutations—elicits high titer RSV-neutralizing responses. Moreover, pre-F-specific antibodies, often against the neutralization-sensitive antigenic site Ø in the membrane-distal head region of trimeric F glycoprotein, comprise a substantial portion of the human response to natural RSV infection. To focus the vaccine-elicited response to antigenic site Ø, we designed a series of RSV F immunogens that comprised the membrane-distal head of the F glycoprotein in its pre-F conformation. These “head-only” immunogens formed monomers, dimers, and trimers. Antigenic analysis revealed that a majority of the 70 engineered head-only immunogens displayed reactivity to site Ø-targeting antibodies, which was similar to that of the parent RSV F DS-Cav1 trimers, often with increased thermostability. We evaluated four of these head-only immunogens in detail, probing their recognition by antibodies, their physical stability, structure, and immunogenicity. When tested in naïve mice, a head-only trimer, half the size of the parent RSV F trimer, induced RSV titers, which were statistically comparable to those induced by DS-Cav1. When used to boost DS-Cav1-primed mice, two head-only RSV F immunogens, a dimer and a trimer, boosted RSV-neutralizing titers to levels that were comparable to those boosted by DS-Cav1, although with higher site Ø-directed responses. Our results provide proof-of-concept for the ability of the smaller head-only RSV F immunogens to focus the vaccine-elicited response to antigenic site Ø. Decent primary immunogenicity, enhanced physical stability, potential ease of manufacture, and potent immunogenicity upon boosting suggest these head-only RSV F immunogens, engineered to retain the pre-fusion conformation, may have advantages as candidate RSV vaccines.

The domains are the structural and functional units of proteins. With the avalanche of protein sequences generated in the postgenomic age, it is highly desired to develop effective methods for predicting the protein domains according to the sequences information alone, so as to facilitate the structure prediction of proteins and speed up their functional annotation. However, although many efforts have been made in this regard, prediction of protein domains from the sequence information still remains a challenging and elusive problem. Here, a new method was developed by combing the techniques of RF (random forest), mRMR (maximum relevance minimum redundancy), and IFS (incremental feature selection), as well as by incorporating the features of physicochemical and biochemical properties, sequence conservation, residual disorder, secondary structure, and solvent accessibility. The overall success rate achieved by the new method on an independent dataset was around 73%, which was about 28-40% higher than those by the existing method on the same benchmark dataset. Furthermore, it was revealed by an in-depth analysis that the features of evolution, codon diversity, electrostatic charge, and disorder played more important roles than the others in predicting protein domains, quite consistent with experimental observations. It is anticipated that the new method may become a high-throughput tool in annotating protein domains, or may, at the very least, play a complementary role to the existing domain prediction methods, and that the findings about the key features with high impacts to the domain prediction might provide useful insights or clues for further experimental investigations in this area. Finally, it has not escaped our notice that the current approach can also be utilized to study protein signal peptides, B-cell epitopes, HIV protease cleavage sites, among many other important topics in protein science and biomedicine.

Many of the neutralising antibodies, isolated to date, display limited activities against the globally most prevalent HIV-1 subtypes A and C. Therefore, those subtypes are considered to be an important target for antibody-based therapy. Variable domains of llama heavy chain antibodies (VHH) have some superior properties compared with classical antibodies. Therefore we describe the application of trimeric forms of envelope proteins (Env), derived from HIV-1 of subtype A and B/C, for a prolonged immunization of two llamas. A panel of VHH, which interfere with CD4 binding to HIV-1 Env were selected with use of panning. The results of binding and competition assays to various Env, including a variant with a stabilized CD4-binding state (gp120(Ds2)), cross-competition experiments, maturation analysis and neutralisation assays, enabled us to classify the selected VHH into three groups. The VHH of group I were efficient mainly against viruses of subtype A, C and B'/C. The VHH of group II resemble the broadly neutralising antibody (bnmAb) b12, neutralizing mainly subtype B and C viruses, however some had a broader neutralisation profile. A representative of the third group, 2E7, had an even higher neutralization breadth, neutralizing 21 out of the 26 tested strains belonging to the A, A/G, B, B/C and C subtypes. To evaluate the contribution of certain amino acids to the potency of the VHH a small set of the mutants were constructed. Surprisingly this yielded one mutant with slightly improved neutralisation potency against 92UG37.A9 (subtype A) and 96ZM651.02 (subtype C). These findings and the well-known stability of VHH indicate the potential application of these VHH as anti-HIV-1 microbicides.

The Anatomical Therapeutic Chemical (ATC) classification system, recommended by the World Health Organization, categories drugs into different classes according to their therapeutic and chemical characteristics. For a set of query compounds, how can we identify which ATC-class (or classes) they belong to? It is an important and challenging problem because the information thus obtained would be quite useful for drug development and utilization. By hybridizing the informations of chemical-chemical interactions and chemical-chemical similarities, a novel method was developed for such purpose. It was observed by the jackknife test on a benchmark dataset of 3,883 drug compounds that the overall success rate achieved by the prediction method was about 73% in identifying the drugs among the following 14 main ATC-classes: (1) alimentary tract and metabolism; (2) blood and blood forming organs; (3) cardiovascular system; (4) dermatologicals; (5) genitourinary system and sex hormones; (6) systemic hormonal preparations, excluding sex hormones and insulins; (7) anti-infectives for systemic use; (8) antineoplastic and immunomodulating agents; (9) musculoskeletal system; (10) nervous system; (11) antiparasitic products, insecticides and repellents; (12) respiratory system; (13) sensory organs; (14) various. Such a success rate is substantially higher than 7% by the random guess. It has not escaped our notice that the current method can be straightforwardly extended to identify the drugs for their 2(nd)-level, 3(rd)-level, 4(th)-level, and 5(th)-level ATC-classifications once the statistically significant benchmark data are available for these lower levels.