Histone Deacetylase 6, belong to class II HDAC family with unique structural characteristics since it possesses two deacetylase catalytic domains (DD1 and DD2), which are interconnected by a linker region. HDAC6 deacetylate non-histone substrate such as cortactin, α-tubulin, P53, tau protein, among others. The malfunction or deregulation of this protein has been involved in several diseases including cancer, Alzheimer´s diseases, and Parkinson´s diseases. Thus, it has emerged as a pharmacological target for the treatment of these diseases. In order to design new drugs to target HDAC6 selectively, it is important to have structural knowledge of this enzyme. Thus, in the present study, we built a three-dimensional (3D) model of DD1 and DD2 bound by the linker region, in order to identify the interactions established selectively by HDAC6 inhibitors and elucidate the participation of both catalytic domains in the recognition we used molecular docking and molecular dynamic simulation tools. As results, we found that the catalytic tunnel of DD1 is wider and shallower than DD2 with different residue composition that could be exploited to achieve ligand selectivity design. By MD simulation and docking it was possible to determine the residues that contribute favorably to the ligand-HDAC6 recognition, in DD1 the residues were F105, S173, H215, G224, Y225, H255, W284, K353, and R383 and in DD2 were: H500, P501, S568, P608, H610, H611, H619, F620, H651, F680, P748, L749, E779, E780 and Y782. Finally, MD simulation reveals some structural difference in the terminal loops, a linker region, and loops adjacent to the catalytic site between the apo-HDAC6 model and the ligand-HDAC6. 

Immunoinformatics has come by leaps and bounds to finding potent vaccine candidates against various pathogens. In the current study, a combination of different T (CD4+ and CD8+) and B cell epitope prediction tools were applied to find peptides containing multiple epitopes against Ebola nucleoprotein (NP) and the presentation of peptides to human leukocyte antigen (HLA) molecules was analyzed by prediction, docking, and population coverage tools. Further, ELISA was carried out to measure IFN-γ in peptide-stimulated peripheral blood mononuclear cells isolated from the blood of healthy volunteers. Six peptides containing multiple T and B cell epitopes were obtained after prediction studies and eliminating the peptides liable to generate an autoimmune and allergic response. All peptides displayed 100% conservancy in Zaire Ebola virus. Prediction tools, Auto dock Vina and CABS-dock results confirmed the ability of predicted peptides to bind with diverse HLA alleles. Population coverage analysis predicted high coverage (> 85%) for the expected immune response in four continents (Africa, America, Asia and Europe). Peptide-stimulated cells showed enhanced IFN-γ secretion as compared to unstimulated cells. Therefore, the identified NP peptides can be considered as potential synthetic vaccine candidates against the Ebola virus.

DNA methylation plays a crucial role in the regulation of gene expression, including the expression of viral genes. Viral genome uses host machinery to replicate and therefore, methylation at CpG islands of viral DNA can help in regulating the replication of the dreadful pathogens. In the present study, comparative genomics was performed for Dengue virus which a life-threatening virus is causing millions of deaths every year. So far, very little is known about the effects of mutations in DENV serotypes. This study included more than 3000 genome sequences of four serotypes of Dengue virus. These sequences were evaluated and interpreted. On analysis of complete sequences of DENV serotype 1, three CpG islands were found. Likewise, for DENV-2, DENV-3, and DENV-4; two, four and three CpG islands were found, respectively. On comparing CpG sequences of DENV serotype 1 and DENV serotype 2, no major identity pattern was recorded as such. But, on blasting CpG sequences of serotype 1 and serotype 3, >90% query coverage and >90% identity on average was recorded. In case of CpG blast of serotype 1 and serotype 4, although the query coverage was low (<50% on average), the identity was high (>90% on average) and in many cases was completely identical i.e. 100%. The results exhibited that the site of DNA methylation is preserved in DENV-1, 3 and 4 even after having evident disparities among all serotypes. This can be exploited for methylation-induced gene silencing for aiming a mutual vaccine or drug. In the field of comparative genomics, our study can direct to a novel perceptiveness of epigenetics.

Histone deacetylases (HDACs) are a family of proteins whose main function is the removal of acetyl groups from lysine residues located on histone and non-histone substrates, which regulates gene transcription and other activities in cells. HDACs are composed of 18 isoforms grouped into four classes, three classes are zinc-dependent, class I (HDAC1, 2, 3 and 8), II (HDAC4, 5, 6, 7, 9 and 1) and IV (HDAC11); and the other is NAD+-dependent (class III), so-called “sirtuins”1. HDAC1 has been involved in cancer development, thus its inhibition has emerged as a promissory therapeutic strategy, and the acquisition of the structural knowledge could help in the rational drug design in order to achieve selective inhibitors2. HDAC1, possess two additional conserved metal binding sites (Site 1 and Site 2), also described in other isoforms, suggesting a possible structural role that affects HDAC catalytical activity. In this work, we performed molecular dynamics (MD) simulation of HDAC1 in order to reveal structural details about these sites and how it affects the interactions with reported HDAC inhibitors. Our results suggest that these sites play an important structural role, since it impacts in the structural flexibility of HDCA1 reflected in parameters like RMSD and RMSF, it also affects the molecular recognition between the inhibitor and HDAC1. 

The high-throughput and increasingly affordable nature of Next-Generation Sequencing (NGS) has greatly improved our ability to detect genomic variants. Although reliable results and rapid processing are critical in biomedical research and clinical applications, the accuracy of variants detection is the core concern of researchers. Characteristics of data are the decisive factor in algorithm and parameter selection, however currently popular and widely used pipelines often lack detailed parameters, such as GATK best practices, Genome VIP and integration software may SpeedSeq, which will mislead inexperienced analyst. In addition, quality management throughout the pipeline is severely overlooked, and quality control is often only found during the raw sequencing data. Therefore, a detailed and comprehensive analysis pipeline and quality management in each step are of significant importance. In this study, we discussed and summarized how to make a choice on software and parameters depending on the data characteristics. We also clarify the current quality management strategies for Illumina technology-based sequencing data at different stages of analysis based on an idea of the gain pattern of information. Which means new information is emerging and new detail and delicate metrics should be defined to ensure accurate results as the analysis goes on.

Along with the emergence of the biotechnology practices in the research and development sector within the past decade, proteomics has played a major role in determination of the behavior of protein and its analysis in the organism’s genetic makeup and expression in several species of organisms which helped the researchers and biotechnologists to identify and quantify the protein based on their amino acid sequences. It also helped to understand post-translational modifications, alterations and deletions in proteins through various in vivo techniques such as real-time imaging systems, vascular proteomic mapping and various tools of system biology and bioinformatics. These techniques lead to the advancement in the current scenario of proteomics and also help to extrapolate the discoveries and researches like the characterization of human plasma proteome, protein identification leading to mosquito birth control, and pancreatic cancer research. This article retrospects and introspects upon the methodologies and the techniques used to predict the protein structure in order to bring about the advances in the proteomics research and bioinformatics such as in the field of medicinal biotechnology, drug design (CADD), homology modeling, ab initio structure prediction, nano-LC, etc.

Gene prediction is an important approach to improve the annotation of metagenomic genes. A variety of gene prediction models based on different principles had been implemented, with emphasis on statistical models, Markov or improved Markov models, deep learning models, and so on. The current gene prediction algorithms, such as FragGeneScan, Prodigal, MetaGeneAnnotator, Orphelia, Glimmer3, GeneMarkS-2, were specially designed for short fragments or whole genomes; however, the former will result in the identified genes being incomplete and the latter is not suitable for unknown species. Meanwhile, according to our previous benchmark results of these algorithms, the prediction error rate was relatively high (27.10%~54.70%), especially for datasets with low coverage (staggered dataset). In this study, we proposed an algorithm based on feature selection of ORFs named as Consensus, which combined the ORFs generated from known models, extracted the ORFs’ feature matrix and the corresponding label matrix.  Finally, the optimal solution was obtained by the least square’s solution to the feature and label matrixes. The overall indicator of gene prediction via Consensus was better than that of single software (F-score was 82.94% on the staggered dataset). Even more remarkably, we compared the results of models using two longer assembled scaffolds datasets of the real mock metagenomic samples containing 20 bacterial strains from NCBI (National Center for Biotechnology Information) instead of simulated reads, which would truly reflect the predictive power of the models. We believe our findings will improve the study of novel genes and annotation pipelines in unknown metagenomic species.