Lung cancer is an inflammation-associated epithelial carcinoma. A highly active interleukin 6 (IL-6)/glycoprotein 130 (gp130)/signal transducer and activator of transcription 3 (STAT3) pathway has been identified in a subset of primary lung cancer and closely correlated with tumor progression and poor prognosis. In a previous study, the frequent occurrence of somatic gain-of-function mutations was observed in the gp130-encoding IL6ST gene in exon 6 in 60% of inflammatory hepatocellular adenomas. Prompted by this finding, we assessed 110 Chinese lung carcinomas using PCR and direct DNA sequencing but found no somatic mutation of IL6ST in exon 6. However, one new potential germline missense mutation c.599C>G was identified in one adenocarcinoma that harbors wild-type epidermal growth factor receptor and KRAS. Protein modeling analysis showed that this mutation might not affect the gp130 protein conformation. Moreover, activated STAT3 was observed in most of the lung tumor tissues at a higher level than that in matched normal lung tissues. In conclusion, the c.599C>G mutation may be a new single nucleotide polymorphism of IL6ST, but mutations in exon 6 of this gene are not apparently common genetic variations occurring and leading to constitutive activation of STAT3 in lung cancer.
Identification of epitopes which invoke strong humoral responses is an essential issue in the field of immunology. Localizing epitopes by experimental methods is expensive in terms of time, cost, and effort; therefore, computational methods feature for its low cost and high speed was employed to predict B-cell epitopes. In this paper, we review the recent advance of bioinformatics resources and tools in conformational B-cell epitope prediction, including databases, algorithms, web servers, and their applications in solving problems in related areas. To stimulate the development of better tools, some promising directions are also extensively discussed.
Epitope prediction based on random peptide library screening has become a focus as a promising method in immunoinformatics research. Some novel software and web-based servers have been proposed in recent years and have succeeded in given test cases. However, since the number of available mimotopes with the relevant structure of template-target complex is limited, a systematic evaluation of these methods is still absent. In this study, a new benchmark dataset was defined. Using this benchmark dataset and a representative dataset, five examples of the most popular epitope prediction software products which are based on random peptide library screening have been evaluated. Using the benchmark dataset, in no method did performance exceed a 0.42 precision and 0.37 sensitivity, and the MCC scores suggest that the epitope prediction results of these software programs are greater than random prediction about 0.09-0.13; while using the representative dataset, most of the values of these performance measures are slightly improved, but the overall performance is still not satisfactory. Many test cases in the benchmark dataset cannot be applied to these pieces of software due to software limitations. Moreover chances are that these software products are overfitted to the small dataset and will fail in other cases. Therefore finding the correlation between mimotopes and genuine epitope residues is still far from resolved and much larger dataset for mimotope-based epitope prediction is desirable.
Epitope mapping from affinity-selected peptides has become popular in epitope prediction, and correspondingly many Web-based tools have been developed in recent years. However, the performance of these tools varies in different circumstances. To address this problem, we employed an ensemble approach to incorporate two popular Web tools, MimoPro and Pep-3D-Search, together for taking advantages offered by both methods so as to give users more options for their specific purposes of epitope-peptide mapping. The combined operation of Union finds as many associated peptides as possible from both methods, which increases sensitivity in finding potential epitopic regions on a given antigen surface. The combined operation of Intersection achieves to some extent the mutual verification by the two methods and hence increases the likelihood of locating the genuine epitopic region on a given antigen in relation to the interacting peptides. The Consistency between Intersection and Union is an indirect sufficient condition to assess the likelihood of successful peptide-epitope mapping. On average from 27 tests, the combined operations of PepMapper outperformed either MimoPro or Pep-3D-Search alone. Therefore, PepMapper is another multipurpose mapping tool for epitope prediction from affinity-selected peptides. The Web server can be freely accessed at: http://informatics.nenu.edu.cn/PepMapper/
The continued spread of highly pathogenic avian influenza (HPAI) H5N1 virus underscores the importance of effective antiviral approaches. AVFluIgG01 is a potent and broad-reactive H5N1-neutralizing human monoclonal antibody (mAb) showing great potential for use either for therapeutic purposes or as a basis of vaccine development, but its antigenic epitope and neutralization mechanism have not been finely characterized. In this study, we first demonstrated that AVFluIgG01 targets a novel conformation-dependent epitope in the globular head region of H5N1 hemagglutinin (HA). By selecting mimotopes from a random peptide library in combination with computational algorithms and site-directed mutagenesis, the epitope was mapped to three conserved discontinuous sites (I-III) that are located closely at the three-dimensional structure of HA. Further, we found that this HA1-specific human mAb can efficiently block both virus-receptor binding and post-attachment steps, while its Fab fragment exerts the post-attachment inhibition only. Consistently, AVFluIgG01 could inhibit HA-mediated cell-cell membrane fusion at a dose-dependent manner and block the acquisition of pH-induced protease sensitivity. These results suggest a neutralization mechanism of AVFluIgG01 by simultaneously blocking viral attachment to the receptors on host cells and interfering with HA conformational rearrangements associated with membrane fusion. The presented data provide critical information for developing novel antiviral therapeutics and vaccines against HPAI H5N1 virus.
Bioluminescent proteins are important for various cellular processes, such as gene expression analysis, drug discovery, bioluminescent imaging, toxicity determination, and DNA sequencing studies. Hence, the correct identification of bioluminescent proteins is of great importance both for helping genome annotation and providing a supplementary role to experimental research to obtain insight into bioluminescent proteins functions. However, few computational methods are available for identifying bioluminescent proteins. Therefore, in this paper we develop a new method to predict bioluminescent proteins using a model based on position specific scoring matrix and auto covariance. Tested by 10-fold cross-validation and independent test, the accuracy of the proposed model reaches 85.17% for the training dataset and 90.71% for the testing dataset respectively. These results indicate that our predictor is a useful tool to predict bioluminescent proteins. This is the first study in which evolutionary information and local sequence environment information have been successfully integrated for predicting bioluminescent proteins. A web server (BLPre) that implements the proposed predictor is freely available.
Related JoVE Video
Journal of Visualized Experiments
What is Visualize?
JoVE Visualize is a tool created to match the last 5 years of PubMed publications to methods in JoVE's video library.
How does it work?
We use abstracts found on PubMed and match them to JoVE videos to create a list of 10 to 30 related methods videos.
Video X seems to be unrelated to Abstract Y...
In developing our video relationships, we compare around 5 million PubMed articles to our library of over 4,500 methods videos. In some cases the language used in the PubMed abstracts makes matching that content to a JoVE video difficult. In other cases, there happens not to be any content in our video library that is relevant to the topic of a given abstract. In these cases, our algorithms are trying their best to display videos with relevant content, which can sometimes result in matched videos with only a slight relation.