Breast cancer vaccination comes to age: impacts of bioinformatics

Introduction: Breast cancer, as one of the major causes of cancer death among women, is the central focus of this study. The recent advances in the development and application of computational tools and bioinformatics in the field of immunotherapy of malignancies such as breast cancer have emerged the new dominion of immunoinformatics, and therefore, next generation of immunomedicines. Methods: Having reviewed the most recent works on the applications of computational tools, we provide comprehensive insights into the breast cancer incidence and its leading causes as well as immunotherapy approaches and the future trends. Furthermore, we discuss the impacts of bioinformatics on different stages of vaccine design for the breast cancer, which can be used to produce much more efficient vaccines through a rationalized time- and cost-effective in silico approaches prior to conducting costly experiments. Results: The tools can be significantly used for designing the immune system-modulating drugs and vaccines based on in silico approaches prior to in vitro and in vivo experimental evaluations. Application of immunoinformatics in the cancer immunotherapy has shown its success in the pre-clinical models. This success returns back to the impacts of several powerful computational approaches developed during the last decade. Conclusion: Despite the invention of a number of vaccines for the cancer immunotherapy, more computational and clinical trials are required to design much more efficient vaccines against various malignancies, including breast cancer

Structural prediction of a novel laminarinase from the psychrophilic glaciozyma antarctica PI12 and its temperature adaptation analysis

Here, we present a novel psychrophilic ß-glucanase from Glaciozyma antarctica PI12 yeast that has been structurally modeled and analyzed in detail. To our knowledge, this is the first attempt to model a psychrophilic laminarinase from yeast. Because of the low sequence identity (<40 %), a threading method was applied to predict a 3D structure of the enzyme using the MODELLER9v12 program. The results of a comparative study using other mesophilic, thermophilic, and hyperthermophilic laminarinases indicated several amino acid substitutions on the surface of psychrophilic laminarinase that totally increased the flexibility of its structure for efficient catalytic reactions at low temperatures. Whereas several structural factors in the overall structure can explain the weak thermal stability, this research suggests that the psychrophilic adaptation and catalytic activity at low temperatures were achieved through existence of longer loops and shorter or broken helices and strands, an increase in the number of aromatic and hydrophobic residues, a reduction in the number of hydrogen bonds and salt bridges, a higher total solvent accessible surface area, and an increase in the exposure of the hydrophobic side chains to the solvent. The results of comparative molecular dynamics simulation and principal component analysis confirmed the above strategies adopted by psychrophilic laminarinase to increase its catalytic efficiency and structural flexibility to be active at cold temperature

Cancer treatment comes to age: from one-size-fits-all to next-generation sequencing (NGS) technologies

Cancer is one of the leading causes of death worldwide and one of the greatest challenges in extending life expectancy. The paradigm of one-size-fits-all medicine has already given way to the stratification of patients by disease subtypes, clinical characteristics, and biomarkers (stratified medicine). The introduction of next-generation sequencing (NGS) in clinical oncology has made it possible to tailor cancer patient therapy to their molecular profiles. NGS is expected to lead the transition to precision medicine (PM), where the right therapeutic approach is chosen for each patient based on their characteristics and mutations. Here, we highlight how the NGS technology facilitates cancer treatment. In this regard, first, precision medicine and NGS technology are reviewed, and then, the NGS revolution in precision medicine is described. In the sequel, the role of NGS in oncology and the existing limitations are discussed. The available databases and bioinformatics tools and online servers used in NGS data analysis are also reviewed. The review ends with concluding remarks

TS-AMIR: a topology string alignment method for intensive rapid protein structure comparison

<p>Abstract</p> <p>Background</p> <p>In structural biology, similarity analysis of protein structure is a crucial step in studying the relationship between proteins. Despite the considerable number of techniques that have been explored within the past two decades, the development of new alternative methods is still an active research area due to the need for high performance tools.</p> <p>Results</p> <p>In this paper, we present TS-AMIR, a Topology String Alignment Method for Intensive Rapid comparison of protein structures. The proposed method works in two stages: In the first stage, the method generates a topology string based on the geometric details of secondary structure elements, and then, utilizes an n-gram modelling technique over entropy concept to capture similarities in these strings. This initial correspondence map between secondary structure elements is submitted to the second stage in order to obtain the alignment at the residue level. Applying the Kabsch method, a heuristic step-by-step algorithm is adopted in the second stage to align the residues, resulting in an optimal rotation matrix and minimized RMSD. The performance of the method was assessed in different information retrieval tests and the results were compared with those of CE and TM-align, representing two geometrical tools, and YAKUSA, 3D-BLAST and SARST as three representatives of linear encoding schemes. It is shown that the method obtains a high running speed similar to that of the linear encoding schemes. In addition, the method runs about 800 and 7200 times faster than TM-align and CE respectively, while maintaining a competitive accuracy with TM-align and CE.</p> <p>Conclusions</p> <p>The experimental results demonstrate that linear encoding techniques are capable of reaching the same high degree of accuracy as that achieved by geometrical methods, while generally running hundreds of times faster than conventional programs.</p

Cold adaptations study of glycosyl hydrolase enzymes via computational methods

Psychrophiles are cold loving organisms that have adapted to live in permanently cold environments. These microorganisms synthesize psychrophilic enzymes with high catalytic efficiencies at cold temperaturesranging from -20°C to +10°C. This research intends to perform an in silico analysis of the cold adaptation of Glycosyl hydrolase enzymes isolated from psychrophilic yeast Glaciozyma antarctica. Two enzyme were selected; ß-mannanase (PMAN) and ß-glucanase (PLAM) from two different glycosyl hydrolase families with different domains. A 3D model was predicted for both genes using a fold recognition method. The proteins were comparatively studied against their mesophilic, thermophilic, and hyperthermophilic counterparts. The study of these enzymes illustrates that they mostly use similar strategies for cold adaptation.The structure of PLAM and PMAN consist of longer loops in three different positions. Their structure also has several amino acids substitution including increased number of alanine, glycine, and polar residues and decreased number of proline, arginine, and hydrophobic residues. The PLAM and PMAN structure showed longer motions around the entrance region to active site. A lower number of salt bridges and H-bonds have been observed in the PLAM and PMAN structure. PLAM consists of 5 salt bridges while its homologous proteins have 9, 7, and 18 salt bridges, respectively. Also, the number of H-bonds per residue is 0.54 where it is 0.62, 0.63, and 0.70 for its homologous counterparts. Furthermore, PMAN includes 5 salt bridges in its structure while its homologous counterparts have 10, 14, and 21 salt bridges, respectively. The number of H-bonds per residue for PMAN is 0.62 while it is 0.71, 0.73 and 0.78 for its homologous counterparts. The PLAM structure has 41% of secondary structure, while its homologous counterparts have 54%, 58%, and 60% of secondary structure. Also, this percentage is 47% for PMAN, and 48%, 50%, and 53% for its homologous proteins. Additionally, they also use different strategies related to the role of salt bridges in their structure. The PLAM structure contains alternative salt bridges connecting inner and outer leaflets, while the PMAN structure includes weakly linked salt bridges between residues located on a loop instead of ß-sheet. In conclusion, in silico analysis of two psychrophilic proteins revealed novel characteristics of these cold adapted enzymes. The analysis showed the adopted strategies by these two proteins in contributing to the general and local flexibility of their structure and increase capability of the enzymes to be active at cold temperatures. The presented findings in this research will assist future attempts in the rational design of enzymes with enhanced enzymatic capabilities

Structural and Functional Analysis of Mutated Human Pyrin B30.2 Domain

Background: Familial Mediterranean Fever (FMF) is a prototypical hereditary autoinflammatory disease affecting principally Mediterranean populations and characterized by recurrent frequent fever and inflammation. The disease is essentially caused by inherited mutations in the MEFV gene which encodes pyrin protein. The reported mutations are mostly located on the B30.2 domain in the C-terminal end of the protein. Objective: The present study reports a structural comparison of the five most common mutated structures including M694V, V726A, M694I, R761H, and M680I. The aim of this study was to determine the structural and functional disorders caused by the mutations in the human pyrin protein. Results: The comparison revealed that all mutations make overall changes in the structure of the domain. Further, the effects of these mutations on structural and molecular behavior of the B30.2 domain were compared with the native structure using MD simulation by GROMACS software. The results revealed that all the studied mutants have a destabilizing effect on the protein structure. Additionally, analyzing the projection of the motions of the proteins in phase space demonstrates high rigidity of the mutated structures in comparison with the native protein. Conclusion: The results of simulations elucidate how the mutations affect the physiological functioning of the pyrin B30.2 domain and cause the occurrence of the FMF disease. </jats:sec

Temperature adaptation analysis of a psychrophilic mannanase through structural, functional and molecular dynamics simulation

The present paper reports structure prediction and analysis of a psychrophilic β-mannanase from Glaciozyma antarctica PI12 yeast. A threading method was used for 3D structure prediction of the enzyme using the MODELLER 9v12 program regarding its low sequence identity (<30%). The constructed model has been used in a comparative study to analyse its cold adaptation mechanism using other mesophilic, thermophilic, and hyperthermophilic mannanases. The structural and molecular dynamics analysis suggests that flexibility of the enzyme is increased through different structural characteristics, and therefore, the possibility of efficient catalytic reactions is provided at cold environment. These characteristics are the presence of longer loops, broken or shorter strands and helices, a lower number of salt bridges and hydrogen bonds, a higher exposure of the hydrophobic side chains to the solvent and an increased total solvent accessible surface area. Furthermore, the high catalytic efficiency and structural flexibility of the psychrophilic mannanase was supported by the results of principal component analysis

A novel efficient drug repurposing framework through drug-disease association data integration using convolutional neural networks

Abstract Drug repurposing is an exciting field of research toward recognizing a new FDA-approved drug target for the treatment of a specific disease. It has received extensive attention regarding the tedious, time-consuming, and highly expensive procedure with a high risk of failure of new drug discovery. Data-driven approaches are an important class of methods that have been introduced for identifying a candidate drug against a target disease. In the present study, a model is proposed illustrating the integration of drug-disease association data for drug repurposing using a deep neural network. The model, so-called IDDI-DNN, primarily constructs similarity matrices for drug-related properties (three matrices), disease-related properties (two matrices), and drug-disease associations (one matrix). Then, these matrices are integrated into a unique matrix through a two-step procedure benefiting from the similarity network fusion method. The model uses a constructed matrix for the prediction of novel and unknown drug-disease associations through a convolutional neural network. The proposed model was evaluated comparatively using two different datasets including the gold standard dataset and DNdataset. Comparing the results of evaluations indicates that IDDI-DNN outperforms other state-of-the-art methods concerning prediction accuracy