New scoring schema for finding motifs in DNA Sequences

<p>Abstract</p> <p>Background</p> <p>Pattern discovery in DNA sequences is one of the most fundamental problems in molecular biology with important applications in finding regulatory signals and transcription factor binding sites. An important task in this problem is to search (or predict) known binding sites in a new DNA sequence. For this reason, all subsequences of the given DNA sequence are scored based on an scoring function and the prediction is done by selecting the best score. By assuming no dependency between binding site base positions, most of the available tools for known binding site prediction are designed. Recently Tomovic and Oakeley investigated the statistical basis for either a claim of dependence or independence, to determine whether such a claim is generally true, and they presented a scoring function for binding site prediction based on the dependency between binding site base positions. Our primary objective is to investigate the scoring functions which can be used in known binding site prediction based on the assumption of dependency or independency in binding site base positions.</p> <p>Results</p> <p>We propose a new scoring function based on the dependency between all positions in biding site base positions. This scoring function uses joint information content and mutual information as a measure of dependency between positions in transcription factor binding site. Our method for modeling dependencies is simply an extension of position independency methods. We evaluate our new scoring function on the real data sets extracted from JASPAR and TRANSFAC data bases, and compare the obtained results with two other well known scoring functions.</p> <p>Conclusion</p> <p>The results demonstrate that the new approach improves known binding site discovery and show that the joint information content and mutual information provide a better and more general criterion to investigate the relationships between positions in the TFBS. Our scoring function is formulated by simple mathematical calculations. By implementing our method on several biological data sets, it can be induced that this method performs better than methods that do not consider dependencies.</p

Optogenetic Stimulation of Primary Cardiomyocytes Expressing ChR2

Introduction: Non-clinical cardiovascular drug safety assessment is the main step in the progress of new pharmaceutical products. Cardiac drug safety testing focuses on a delayed rectifier potassium channel block and QT interval prolongation, whereas optogenetics is a powerful technology for modulating the electrophysiological properties of excitable cells.Methods: For this purpose, the blue light-gated ion channel, channelrhodopsin-2 (ChR2), has been introduced into isolated primary neonatal cardiomyocytes via a lentiviral vector. After being subjected to optical stimulation, transmembrane potential and intracellular calcium were assessed.Results: Here, we generated cardiomyocytes expressing ChR2 (light-sensitive protein), that upon optical stimulation, the cardiomyocytes depolarized result from alterations of membrane voltage and intracellular calcium.Conclusion: This cell model was easily adapted to a cell culture system in a laboratory, making this method very attractive for therapeutic research on cardiac optogenetics. DOI: 10.34172/jlms.2021.3

Thermal Unfolding Pathway of PHD2 Catalytic Domain in Three Different PHD2 Species: Computational Approaches

Prolyl hydroxylase domain 2 containing protein (PHD2) is a key protein in regulation of angiogenesis and metastasis. In normoxic condition, PHD2 triggers the degradation of hypoxia-inducible factor 1 (HIF-1α) that induces the expression of hypoxia response genes. Therefore the correct function of PHD2 would inhibit angiogenesis and consequent metastasis of tumor cells in normoxic condition. PHD2 mutations were reported in some common cancers. However, high levels of HIF-1α protein were observed even in normoxic metastatic tumors with normal expression of wild type PHD2. PHD2 malfunctions due to protein misfolding may be the underlying reason of metastasis and invasion in such cases. In this study, we scrutinize the unfolding pathways of the PHD2 catalytic domain’s possible species and demonstrate the properties of their unfolding states by computational approaches. Our study introduces the possibility of aggregation disaster for the prominent species of PHD2 during its partial unfolding. This may justify PHD2 inability to regulate HIF-1α level in some normoxic tumor types

Protein Complex Discovery by Interaction Filtering from Protein Interaction Networks Using Mutual Rank Coexpression and Sequence Similarity

The evaluation of the biological networks is considered the essential key to understanding the complex biological systems. Meanwhile, the graph clustering algorithms are mostly used in the protein-protein interaction (PPI) network analysis. The complexes introduced by the clustering algorithms include noise proteins. The error rate of the noise proteins in the PPI network researches is about 40–90%. However, only 30–40% of the existing interactions in the PPI databases depend on the specific biological function. It is essential to eliminate the noise proteins and the interactions from the complexes created via clustering methods. We have introduced new methods of weighting interactions in protein clusters and the splicing of noise interactions and proteins-based interactions on their weights. The coexpression and the sequence similarity of each pair of proteins are considered the edge weight of the proteins in the network. The results showed that the edge filtering based on the amount of coexpression acts similar to the node filtering via graph-based characteristics. Regarding the removal of the noise edges, the edge filtering has a significant advantage over the graph-based method. The edge filtering based on the amount of sequence similarity has the ability to remove the noise proteins and the noise interactions

Correction: Classification of Lung Cancer Tumors Based on Structural and Physicochemical Properties of Proteins by Bioinformatics Models.

[This corrects the article DOI: 10.1371/journal.pone.0040017.]