A rapid protein structure alignment algorithm based on a text modeling technique

Structural alignment of proteins is widely used in various fields of structural biology. In order to further improve the quality of alignment, we describe an algorithm for structural alignment based on text modelling techniques. The technique firstly superimposes secondary structure elements of two proteins and then, models the 3D-structure of the protein in a sequence of alphabets. These sequences are utilized by a step-by-step sequence alignment procedure to align two protein structures. A benchmark test was organized on a set of 200 non-homologous proteins to evaluate the program and compare it to state of the art programs, e.g. CE, SAL, TM-align and 3D-BLAST. On average, the results of all-against-all structure comparison by the program have a competitive accuracy with CE and TM-align where the algorithm has a high running speed like 3D-BLAST.     

How to manage rheumatoid arthritis according to classic biomarkers and polymorphisms?

Objectives

Single nucleotide polymorphisms (SNPs), genetic background, and epigenetics play important roles in rheumatoid arthritis (RA). These factors can be useful in RA diagnosis, prognosis, and treatment response evaluation, particularly with the growing trends in personalized medicine. Therefore, categorizing classic genes and SNPs in RA can present an appropriate guideline for RA management.

Discussion

Prognostic and diagnostic biomarkers play important roles in RA diagnosis and treatment. Categorizing SNPs is not an easy process yet, but selecting classic SNPs can be useful worldwide, according to basic similarities that exist in genomes. In this review, we compiled some of these RA-associated SNPs and biomarkers in a table, according to newly identified factors. The role of epigenetics in RA is undeniable; using epigenetic biomarkers like histone deacetylase (HDACs) can be useful in RA diagnosis and treatment. miRs such as miR-146a, miR-155, and miR-222 are useful in diagnosis and can be used in treatment by interfering with other factors’ functions. Interleukins (ILs) seem to be good prognostic and diagnostic markers and can be targeted in RA treatment.

Conclusion

Using multiple types of biomarkers, such as genes, SNPs, and epigenetic biomarkers like HDACs can be useful in RA management and treatment. PTPN22, HLA-DR polymorphisms, miRs, and HDACs are considerable in RA susceptibility; hence, they can be valuable biomarkers in future studies. This article gathered separate information from approximately 100 articles to present useful biomarkers and polymorphisms in one review.

     

The value of using polymorphisms in anti-platelet therapy

Objectives and Backgrounds

Cardiovascular events occure as a result of various risk factors, such as uric acid (UA), inflammation, hormones and other materials that induce C- reactive protein (CRP) expression. These factors lead to complement activation, and endothelial damages. Damaged endothelial cells release heparan sulfate which inhibits tissue factor activity and von Willed brand factor (VWVF) and causes aggregation. Finally this cascade of events cause platelets aggregation and leads to heart ischemia and cardiovascular events.

Discussion

Anti-platelet therapy is an interesting premise. Anti-platelet resistance patients and bleeding as a result of using ticagrelor and prasugrel should be considered in this treatment methods. Anti-platelet drugs such as clopidogrel are prescribed in cardiovascular events. Platelets have VWF receptors and P2Y12 receptors on their surface, and thus, targeting these receptors can be useful in treatment. The active metabolites of clopidogrel bind to P2Y12R and inhibit ADP binding; thus, clopidogrel inhibits aggregation by interfering in several events as a result of the inhibition of ADP attachment to P2Y12R of the platelet. However, the polymorphisms of P2Y12 and other genes mentioned in Table 1 showed treatment resistance in anti-platelet therapy, highlighting that these SNPs can be helpful in anti-platelet therapy.

Conclusion

The knowledge of these SNPs may decrease the number of unwanted effects that endanger patients with cardiovascular diseases and avoids ineffective anti-platelet therapy in several patients. Clopidogrel, ticagrelor, prasugrel, and aspirin and CYP2C19 and their SNPs are very important subjects in anti-platelet therapy. To present the importance of using pharmacogenetics in anti-platelet therapy, we discuss here the association between these drugs and the SNPs for therapeutic resistance.

     

A hybrid of bees algorithm and flux balance analysis with OptKnock as a platform for in silico optimization of microbial strains

Microbial strain optimization focuses on improving technological properties of the strain of microorganisms. However, the complexities of the metabolic networks, which lead to data ambiguity, often cause genetic modification on the desirable phenotypes difficult to predict. Furthermore, vast number of reactions in cellular metabolism lead to the combinatorial problem in obtaining optimal gene deletion strategy. Consequently, the computation time increases exponentially with the increase in the size of the problem. Hence, we propose an extension of a hybrid of Bees Algorithm and Flux Balance Analysis (BAFBA) by integrating OptKnock into BAFBA to validate the result. This paper presents a number of computational experiments to test on the performance and capability of BAFBA. Escherichia coli, Bacillus subtilis and Clostridium thermocellum are the model organisms in this paper. Also included is the identification of potential reactions to improve the production of succinic acid, lactic acid and ethanol, plus the discussion on the changes in the flux distribution of the predicted mutants. BAFBA shows potential in suggesting the non-intuitive gene knockout strategies and a low variability among the several runs. The results show that BAFBA is suitable, reliable and applicable in predicting optimal gene knockout strategy.     

Database and tools for metabolic network analysis

Metabolic network analysis has attracted much attention in the area of systems biology. It has a profound role in understanding the key features of organism metabolic networks and has been successfully applied in several fields of systems biology, including in silico gene knockouts, production yield improvement using engineered microbial strains, drug target identification, and phenotype prediction. A variety of metabolic network databases and tools have been developed in order to assist research in these fields. Databases that comprise biochemical data are normally integrated with the use of metabolic network analysis tools in order to give a more comprehensive result. This paper reviews and compares eight databases as well as twenty one recent tools. The aim of this review is to study the different types of tools in terms of the features and usability, as well as the databases in terms of the scope and data provided. These tools can be categorised into three main types: standalone tools; toolbox-based tools; and web-based tools. Furthermore, comparisons of the databases as well as the tools are also provided to help software developers and users gain a clearer insight and a better understanding of metabolic network analysis. Additionally, this review also helps to provide useful information that can be used as guidance in choosing tools and databases for a particular research interest.     

In-Vitro Activity of Doxycycline and β-Lactam Combinations Against Different Strains of <Emphasis Type="Italic">Burkholderia pseudomallei</Emphasis>

Although doxycycline is active against Burkholderia pseudomallei and has been used in the eradication stage of melioidosis therapy, it is not regularly used during the initial intensive phase. In order to assess its potential use in intensive phase therapy, we investigated in vitro pharmacodynamic activity of doxycycline and β-lactams alone and in combination against four Malaysian strains of B. pseudomallei. Using a checkerboard assay, the combinations of doxycycline and imipenem, doxycycline and ceftazidime, and doxycycline and amoxicillin–clavulanate tested against four strains showed indifferent effects with summation fractional inhibitory concentration values ranging from 0.62 to 2.12. Time-kill experiments also indicated that the combinations of doxycycline/β-lactam antibiotics against four tested strains did not fulfil synergy criteria, in which all combinations showed indifferent effects with ? 1.36 to 1.26-log CFU/mL compared to the most active monotherapy regimen in each combination. No re-growth of bacteria was detected after the early killing in doxycycline/β-lactam combination regimens compared to β-lactam monotherapy regimens, in which 9 out of 10 were associated with re-growth of bacteria. As no synergistic activity was observed, this in vitro study showed that doxycycline offers no additional benefit to be used in combination with β-lactams in the intensive phase of therapy.     

An Evolutionary Firefly Algorithm for the Estimation of Nonlinear Biological Model Parameters

The development of accurate computational models of biological processes is fundamental to computational systems biology. These models are usually represented by mathematical expressions that rely heavily on the system parameters. The measurement of these parameters is often difficult. Therefore, they are commonly estimated by fitting the predicted model to the experimental data using optimization methods. The complexity and nonlinearity of the biological processes pose a significant challenge, however, to the development of accurate and fast optimization methods. We introduce a new hybrid optimization method incorporating the Firefly Algorithm and the evolutionary operation of the Differential Evolution method. The proposed method improves solutions by neighbourhood search using evolutionary procedures. Testing our method on models for the arginine catabolism and the negative feedback loop of the p53 signalling pathway, we found that it estimated the parameters with high accuracy and within a reasonable computation time compared to well-known approaches, including Particle Swarm Optimization, Nelder-Mead, and Firefly Algorithm. We have also verified the reliability of the parameters estimated by the method using an a posteriori practical identifiability test.