Combinatorial Chemistry & High Throughput Screening - Volume 22, Issue 10, 2019

Background: Human rhinovirus has different identified serotypes and is the most common cause of cold in humans. To date, many genes have been discovered to be related to rhinovirus infection. However, the pathogenic mechanism of rhinovirus is difficult to elucidate through experimental approaches due to the high cost and consuming time. Methods and Results: In this study, we presented a novel approach that relies on machine-learning algorithms and identified two genes OTOF and SOCS1. The expression levels of these genes in the blood samples can be used to accurately distinguish virus-infected and non-infected individuals. Conclusion: Our findings suggest the crucial roles of these two genes in rhinovirus infection and the robustness of the computational tool in dissecting pathogenic mechanisms.

Background and Objective: Blue rubber bleb nevus syndrome (BRBN) or Bean syndrome is a rare Venous Malformation (VM)-associated disorder, which mostly affects the skin and gastrointestinal tract in early childhood. Somatic mutations in TEK have been identified from BRBN patients; however, the etiology of TEK mutation-negative patients of BRBN need further investigation. Methods: Two unrelated sporadic BRBNs and one sporadic VM were firstly screened for any rare nonsilent mutation in TEK by Sanger sequencing and subsequently applied to whole-exome sequencing to identify underlying disease causative variants. Overexpression assay and immunoblotting were used to evaluate the functional effect of the candidate disease causative variants. Results: In the VM case, we identified the known causative somatic mutation in the TEK gene c.2740C>T (p.Leu914Phe). In the BRBN patients, we identified two rare germline variants in GLMN gene c.761C>G (p.Pro254Arg) and c.1630G>T(p.Glu544*). The GLMN-P254R-expressing and GLMN-E544X-expressing HUVECs exhibited increased phosphorylation of mTOR-Ser-2448 in comparison with GLMN-WTexpressing HUVECs in vitro. Conclusion: Our results demonstrated that rare germline variants in GLMN might contribute to the pathogenesis of BRBN. Moreover, abnormal mTOR signaling might be the pathogenesis mechanism underlying the dysfunction of GLMN protein.

Background: Genes are known as functional patterns in the genome and are presumed to have biological significance. They can indicate binding sites for transcription factors and they encode certain proteins. Finding genes from biological sequences is a major task in computational biology for unraveling the mechanisms of gene expression. Objective: Planted motif finding problems are a class of mathematical models abstracted from the process of detecting genes from genome, in which a specific gene with a number of mutations is planted into a randomly generated background sequence, and then gene finding algorithms can be tested to check if the planted gene can be found in feasible time. Methods: In this work, a spectral rotation method based on triplet periodicity property is proposed to solve planted motif finding problems. Results: The proposed method gives significant tolerance of base mutations in genes. Specifically, genes having a number of substitutions can be detected from randomly generated background sequences. Experimental results on genomic data set from Saccharomyces cerevisiae reveal that genes can be visually distinguished. It is proposed that genes with about 50% mutations can be detected from randomly generated background sequences. Conclusion: It is found that with about 5 insertions or deletions, this method fails in finding the planted genes. For a particular case, if the deletion of bases is located at the beginning of the gene, that is, bases are not randomly deleted, then the tolerance of the method for base deletion is increased.

Aims and Objective: Cancer is one of the deadliest diseases, taking the lives of millions every year. Traditional methods of treating cancer are expensive and toxic to normal cells. Fortunately, anti-cancer peptides (ACPs) can eliminate this side effect. However, the identification and development of new anti-cancer peptides through experiments take a lot of time and money, therefore, it is necessary to develop a fast and accurate calculation model to identify the anti-cancer peptide. Machine learning algorithms are a good choice. Materials and Methods: In our study, a multi-classifier system was used, combined with multiple machine learning models, to predict anti-cancer peptides. These individual learners are composed of different feature information and algorithms, and form a multi-classifier system by voting. Results and Conclusion: The experiments show that the overall prediction rate of each individual learner is above 80% and the overall accuracy of multi-classifier system for anti-cancer peptides prediction can reach 95.93%, which is better than the existing prediction model.

Background: Citrullination, an important post-translational modification of proteins, alters the molecular weight and electrostatic charge of the protein side chains. Citrulline, in protein sequences, is catalyzed by a class of Peptidyl Arginine Deiminases (PADs). Dependent on Ca2+, PADs include five isozymes: PAD 1, 2, 3, 4/5, and 6. Citrullinated proteins have been identified in many biological and pathological processes. Among them, abnormal protein citrullination modification can lead to serious human diseases, including multiple sclerosis and rheumatoid arthritis. Objective: It is important to identify the citrullination sites in protein sequences. The accurate identification of citrullination sites may contribute to the studies on the molecular functions and pathological mechanisms of related diseases. Methods and Results: In this study, after an encoded training set (containing 116 positive and 348 negative samples) into the feature matrix, the mRMR method was used to analyze the 941- dimensional features which were sorted on the basis of their importance. Then, a predictive model based on a self-normalizing neural network (SNN) was proposed to predict the citrullination sites in protein sequences. Incremental Feature Selection (IFS) and 10-fold cross-validation were used as the model evaluation method. Three classical machine learning models, namely random forest, support vector machine, and k-nearest neighbor algorithm, were selected and compared with the SNN prediction model using the same evaluation methods. SNN may be the best tool for citrullination site prediction. The maximum value of the Matthews Correlation Coefficient (MCC) reached 0.672404 on the basis of the optimal classifier of SNN. Conclusion: The results showed that the SNN-based prediction methods performed better when evaluated by some common metrics, such as MCC, accuracy, and F1-Measure. SNN prediction model also achieved a better balance in the classification and recognition of positive and negative samples from datasets compared with the other three models.

Aims and Objective: The magic scaffolds rhodanine and thiazolidine are very important heterocyclic compounds in drug design and discovery. Those are important heterocyclic compounds that have attracted a great deal of attention due to the fact that they exhibit a variety of bioactivities including antibacterial, antifungal, antiviral, antimalarial, and anti-inflammatory activities. These agents often exhibit selective toxicity. The goal of this study was molecular docking, green and solvent-free efficient synthesis of a new series of hetero/aromatic substituted rhodanine and thiazolidine analogues and then investigation of their antimicrobial activity. Materials and Methods: To a mixture of TZD or rhodanine (1 mmol) in the presence of ionic liquid ChCl/urea, various aldehyde (1 mmol) was added. After completion of the reaction, obtained crude compound was collected by filtration and products were recrystallized from ethanol. The binding-free energy between all synthesized compounds with 3EEJ protein (C. glabrata enzyme) were obtained by molecular docking studies. These compounds were evaluated using microdilution method against (ATCC 6538) and (ATCC 12228) Gram-negative, (ATCC 8739) and (ATCC 9027) as Gram-positive and (ATCC 1012), (ATCC 339), C. (ATCC 1057), (ATCC 503), (ATCC 340) and (ATCC 194) as fungi. Results: All of the acceptable products were determined by 1H NMR, 13C NMR, Mas and FT-IR spectroscopy. The binding-free energy between compounds 10a and 10b with 3EEJ protein were found to be -8.08 kcal/mol and -8.15 kcal/mol, respectively. These compounds having a heteroaromatic ring attached to the TZD or rhodanine core showed excellent antimicrobial activity with MIC values of 0.25-8 μg/mL (compound 10a) and 0.5-16 μg/mL (compound 10b) against the most tested fungi strains, Gram-positive and Gram-negative bacteria. Conclusion: A convenient and rapid method has been developed for the synthesis of rhodanine and thiazolidine-2,4-dione (TZD) derivatives as efficient antimicrobial agents using a Deep Eutectic Ionic Liquids (DEILs) choline chloride urea under solvent-free condition. Among the newly synthesized compounds, (Z)-5-((quinoxalin-3-yl) methylene) thiazolidine-2, 4-dione (10a) and (Z)- 5- ((quinoxalin-3-yl) methylene)-2-thioxothiazolidin-one (10b) exerted the promising effect and these compounds can be considered to be further probed as inhibitors of cgDHFR enzyme.

Objective: KF/Clinoptilolite nanoparticles are employed as a heterogeneous catalyst for the preparation of pyrido[2,1-a]isoquinoline derivatives through a four-component reaction of isoquinoline, two different alkyl bromides and an electron deficient internal alkynes at ambient temperature in water as green solvent. Methods: In this research, (2,2-Diphenyl-1-picrylhydrazyl) radical trapping and reducing potential of ferric ion experiments was used for determining antioxidant activity of some newly synthesized compounds such as 5a, 5c, 5f and 5g and comparing results with synthetic antioxidants (TBHQ and BHT). Results: Compounds 5a, 5c, 5f and 5g display trace DPPH radical trapping and excellent reducing power of ferric ion. Furthermore, the power of some prepared compounds against Gram-positive and Gram-negative bacteria was proved by employing the disk dispersion experiment. Conclusion: The obtained results of disk diffusion test showed that compounds 5a, 5d and 5e prevented the bacterial growth. The reported procedure shows the advantages of clean reaction, high yield and simple purification.