Bevacizumab and trastuzumab are two antibody based antiangiogenic drugs that are in clinical practice for the treatment of different cancers. Presently applications of these drugs are based on the empirical choice of clinical experts that follow towards population based clinical trials and, hence, their molecular efficacies in terms of quantitative estimates are not being explored. Moreover, different clinical trials with these drugs showed different toxicity symptoms in patients. Here, using molecular docking study, we made an attempt to reveal the molecular rationale regarding their efficacy and off-target toxicity. Though our study reinforces their antiangiogenic potentiality and, among the two, trastuzumab has much higher efficacy; however, this study also reveals that compared to bevacizumab, trastuzumab has higher toxicity effect, specially on the cardiovascular system. This study also reveals the molecular rationale of ocular dysfunction by antiangiogenic drugs. The molecular rationale of toxicity as revealed in this study may help in the judicious choice as well as therapeutic scheduling of these drugs in different cancers.

Ebola virus (EBOV) is a deadly virus that has caused several fatal outbreaks. Recently it caused another outbreak and resulted in thousands afflicted cases. Effective and approved vaccine or therapeutic treatment against this virus is still absent. In this study, we aimed to predict B-cell epitopes from several EBOV encoded proteins which may aid in developing new antibody-based therapeutics or viral antigen detection method against this virus. Multiple sequence alignment (MSA) was performed for the identification of conserved region among glycoprotein (GP), nucleoprotein (NP), and viral structural proteins (VP40, VP35, and VP24) of EBOV. Next, different consensus immunogenic and conserved sites were predicted from the conserved region(s) using various computational tools which are available in Immune Epitope Database (IEDB). Among GP, NP, VP40, VP35, and VP30 protein, only NP gave a 100% conserved GEQYQQLR B-cell epitope that fulfills the ideal features of an effective B-cell epitope and could lead a way in the milieu of Ebola treatment. However, successful in vivo and in vitro studies are prerequisite to determine the actual potency of our predicted epitope and establishing it as a preventing medication against all the fatal strains of EBOV.

The T118M mutation in PMP22 gene is associated with Charcot Marie Tooth, type 1A (CMT1A). CMT1A is a form of Charcot-Marie-Tooth disease, the most common inherited disorder of the peripheral nervous system. Mutations in CMT related disorder are seen to increase the stability of the protein resulting in the diseased state. We performed SNP analysis for all the nsSNPs of PMP22 protein and carried out molecular dynamics simulation for T118M mutation to compare the stability difference between the wild type protein structure and the mutant protein structure. The mutation T118M resulted in the overall increase in the stability of the mutant protein. The superimposed structure shows marked structural variation between the wild type and the mutant protein structures.

Histone deacetylase (HDAC) has a critical function in regulating gene expression. The inhibition of HDAC has developed as an interesting anticancer research area that targets biological processes such as cell cycle, apoptosis, and cell differentiation. In this study, an HDAC inhibitor that is available commercially, suberoyl anilide hydroxamic acid (SAHA), has been modified to improve its efficacy and reduce the side effects of the compound. Hydrophobic cap and zinc-binding group of these compounds were substituted with boron-based compounds, whereas the linker region was substituted with p-aminobenzoic acid. The molecular docking analysis resulted in 8 ligands with ΔG binding value more negative than the standards, SAHA and trichostatin A (TSA). That ligands were analyzed based on the nature of QSAR, pharmacological properties, and ADME-Tox. It is conducted to obtain a potent inhibitor of HDAC class II Homo sapiens. The screening process result gave one best ligand, Nova2 (513246-99-6), which was then further studied by molecular dynamics simulations.

The preliminary study presented within this paper shows a comparative study of various texture features extracted from liver ultrasonic images by employing Multilayer Perceptron (MLP), a type of artificial neural network, to study the presence of disease conditions. An ultrasound (US) image shows echo-texture patterns, which defines the organ characteristics. Ultrasound images of liver disease conditions such as “fatty liver,” “cirrhosis,” and “hepatomegaly” produce distinctive echo patterns. However, various ultrasound imaging artifacts and speckle noise make these echo-texture patterns difficult to identify and often hard to distinguish visually. Here, based on the extracted features from the ultrasonic images, we employed an artificial neural network for the diagnosis of disease conditions in liver and finding of the best classifier that distinguishes between abnormal and normal conditions of the liver. Comparison of the overall performance of all the feature classifiers concluded that “mixed feature set” is the best feature set. It showed an excellent rate of accuracy for the training data set. The gray level run length matrix (GLRLM) feature shows better results when the network was tested against unknown data.

The AUTO-MUTE 2.0 stand-alone software package includes a collection of programs for predicting functional changes to proteins upon single residue substitutions, developed by combining structure-based features with trained statistical learning models. Three of the predictors evaluate changes to protein stability upon mutation, each complementing a distinct experimental approach. Two additional classifiers are available, one for predicting activity changes due to residue replacements and the other for determining the disease potential of mutations associated with nonsynonymous single nucleotide polymorphisms (nsSNPs) in human proteins. These five command-line driven tools, as well as all the supporting programs, complement those that run our AUTO-MUTE web-based server. Nevertheless, all the codes have been rewritten and substantially altered for the new portable software, and they incorporate several new features based on user feedback. Included among these upgrades is the ability to perform three highly requested tasks: to run “big data” batch jobs; to generate predictions using modified protein data bank (PDB) structures, and unpublished personal models prepared using standard PDB file formatting; and to utilize NMR structure files that contain multiple models.

This paper presents a hybrid method to extract endocardial contour of the right ventricular (RV) in 4-slices from 3D echocardiography dataset. The overall framework comprises four processing phases. In Phase I, the region of interest (ROI) is identified by estimating the cavity boundary. Speckle noise reduction and contrast enhancement were implemented in Phase II as preprocessing tasks. In Phase III, the RV cavity region was segmented by generating intensity threshold which was used for once for all frames. Finally, Phase IV is proposed to extract the RV endocardial contour in a complete cardiac cycle using a combination of shape-based contour detection and improved radial search algorithm. The proposed method was applied to 16 datasets of 3D echocardiography encompassing the RV in long-axis view. The accuracy of experimental results obtained by the proposed method was evaluated qualitatively and quantitatively. It has been done by comparing the segmentation results of RV cavity based on endocardial contour extraction with the ground truth. The comparative analysis results show that the proposed method performs efficiently in all datasets with overall performance of 95% and the root mean square distances (RMSD) measure in terms of mean ± SD was found to be 2.21 ± 0.35 mm for RV endocardial contours.

Quantitative proteomics applications in mass spectrometry depend on the knowledge of the mass-to-charge ratio (m/z) values of proteotypic peptides for the proteins under study and their product ions. MRMPath and MRMutation, web-based bioinformatics software that are platform independent, facilitate the recovery of this information by biologists. MRMPath utilizes publicly available information related to biological pathways in the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. All the proteins involved in pathways of interest are recovered and processed in silico to extract information relevant to quantitative mass spectrometry analysis. Peptides may also be subjected to automated BLAST analysis to determine whether they are proteotypic. MRMutation catalogs and makes available, following processing, known (mutant) variants of proteins from the current UniProtKB database. All these results, available via the web from well-maintained, public databases, are written to an Excel spreadsheet, which the user can download and save. MRMPath and MRMutation can be freely accessed. As a system that seeks to allow two or more resources to interoperate, MRMPath represents an advance in bioinformatics tool development. As a practical matter, the MRMPath automated approach represents significant time savings to researchers.

Research and development in the area of biomedical literature analysis aims at providing life science researchers with effective means to access and exploit knowledge contained in scientific publications. Virtually all journal publications and many conference proceedings are nowadays readily available in an electronic form—for instance, as abstracts through the MEDLINE citation index or as full-text articles through PubMed Central. Nevertheless, keeping up to date with and searching for recent findings in a research domain remains a tedious task hampered by inefficient and ineffective means for access and exploitation. Biomedical text analysis aims to improve access to unstructured knowledge by alleviating searches, providing auto generated summaries of documents and topics, linking and integrating publications with structured resources, visualizing content for better understanding, and guiding researchers to novel hypotheses and into knowledge discovery. Focused research over recent years has improved fundamental solutions for biomedical text mining, such as document retrieval, named entity recognition, normalization and grounding, and extraction of relationships, with levels of accuracy that reach human annotators when considering inter annotator agreement. Consequently, more and more integrative analysis tools were put forward by the text mining community targeting a broad audience of end users: generic and task-specific search engines for life science researchers, interfaces for networks synthesis based on textual evidences, or more specialized tools searching for transcription factors, or primer sequences. This special issue of Advances in Bioinformatics presents overviews and examples of end-user-oriented biomedical text mining tools for bioinformaticians, molecular biologists, biochemists, clinicians, pharmacologists, and other researchers in life sciences. We start with A. Manconi et al. survey on “Literature retrieval and mining in bioinformatics: state of the art and challenges.” The authors introduce the major concepts that life science researchers should be familiar with getting the best out of existing text mining solutions, and survey key tools and research. In a dedicated second part of their survey, the authors address the major challenges both life science researchers and solution developers are facing at this point. The reader will find plenty of references to existing search tools, resources, and research papers. A. E. Thessen et al. focus on a particular domain, presenting an overview of “Applications of natural language processing in biodiversity science.” The authors review the application of natural processing and machine learning for biological information extraction regarding cellular processes, taxonomic names, and morphological characters. You will find detailed examples, a summary of all steps involved in information extraction, and lots of references to existing tools and resources. S. T. Ahmed et al. introduce their semantic faceted search engine, BioEve, in “A novel framework to facilitate interactive literature search.” They couple an automated extraction system with a cognitive search and navigation service, to alleviate the process of searching and browsing huge amounts of literature such as provided/delivered by MEDLINE. BioEve enables interactive query refinement and suggests concepts and entities (like genes, drugs, and diseases) to quickly filter and modify search directions, thereby achieving semantic enrichment that improves insight gains while searching literature. S. V. Landeghem et al. present their EVEX resource in “Exploring biomolecular literature with EVEX: connecting genes through events, homology, and indirect associations.” The authors extracted more than 20 million biomolecular events involving genes and proteins, such as phosphorylation and gene regulation, from MEDLINE. The online tool generates a summary on the searched gene denoting all regulated genes, binding partners, subcellular locations, and other related data linked to the searched gene. We conclude this special issue with the paper by A. Divoli et al., discussing whether “Do Peers see more in a paper than its authors.” In a meta-analysis using automatic text analysis, they address questions such as how informative an abstract is compared to the full text; and how peers and authors might view the major contributions of a paper differently. Their analyses are comparing the information content of an abstract, as written by the paper's authors, to sentences that mention the paper as a reference, written by peers. Using this strategy, A. Divoli et al. found, for example, that citing sentences contain 20% additional concepts (likely important contributions) that were not mentioned in the abstract of the paper referred to, but maybe should have been to help attract even more peers.

Background. Oligonucleotide microarrays allow for high-throughput gene expression profiling assays. The technology relies on the fundamental assumption that observed hybridization signal intensities (HSIs) for each intended target, on average, correlate with their target's true concentration in the sample. However, systematic, nonbiological variation from several sources undermines this hypothesis. Background hybridization signal has been previously identified as one such important source, one manifestation of which appears in the form of spatial autocorrelation. Results. We propose an algorithm, pyn, for the elimination of spatial autocorrelation in HSIs, exploiting the duality of desirable mutual information shared by probes in a common probe set and undesirable mutual information shared by spatially proximate probes. We show that this correction procedure reduces spatial autocorrelation in HSIs; increases HSI reproducibility across replicate arrays; increases differentially expressed gene detection power; and performs better than previously published methods. Conclusions. The proposed algorithm increases both precision and accuracy, while requiring virtually no changes to users' current analysis pipelines: the correction consists merely of a transformation of raw HSIs (e.g., CEL files for Affymetrix arrays). A free, open-source implementation is provided as an R package, compatible with standard Bioconductor tools. The approach may also be tailored to other platform types and other sources of bias.