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

Inferring interaction type in gene regulatory networks using co-expression data

BACKGROUND: Knowledge of interaction types in biological networks is important for understanding the functional organization of the cell. Currently information-based approaches are widely used for inferring gene regulatory interactions from genomics data, such as gene expression profiles; however, these approaches do not provide evidence about the regulation type (positive or negative sign) of the interaction. RESULTS: This paper describes a novel algorithm, “Signing of Regulatory Networks” (SIREN), which can infer the regulatory type of interactions in a known gene regulatory network (GRN) given corresponding genome-wide gene expression data. To assess our new approach, we applied it to three different benchmark gene regulatory networks, including Escherichia coli, prostate cancer, and an in silico constructed network. Our new method has approximately 68, 70, and 100 percent accuracy, respectively, for these networks. To showcase the utility of SIREN algorithm, we used it to predict previously unknown regulation types for 454 interactions related to the prostate cancer GRN. CONCLUSIONS: SIREN is an efficient algorithm with low computational complexity; hence, it is applicable to large biological networks. It can serve as a complementary approach for a wide range of network reconstruction methods that do not provide information about the interaction type. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s13015-015-0054-4) contains supplementary material, which is available to authorized users

A novel pattern matching algorithm for genomic patterns related to protein motifs

Background: Patterns on proteins and genomic sequences are vastly analyzed, extracted and collected in databases. Although protein patterns originate from genomic coding regions, very few works have directly or indirectly dealt with coding region patterns induced from protein patterns. Results: In this paper, we have defined a new genomic pattern structure suitable for representing induced patterns from proteins. The provided pattern structure, which is called “Consecutive Positions Scoring Matrix (CPSSM)”, is a replacement for protein patterns and profiles in the genomic context. CPSSMs can be identified, discovered, and searched in genomes. Then, we have presented a novel pattern matching algorithm between the defined genomic pattern and genomic sequences based on dynamic programming. In addition, we have modified the provided algorithm to support intronic gaps and huge sequences. We have implemented and tested the provided algorithm on real data. The results on Saccharomyces cerevisiae’s genome show 132% more true positives and no false negatives and the results on human genome show no false negatives and 10 times as many true positives as those in previous works. Conclusion: CPSSM and provided methods could be used for open reading frame detection and gene finding. The application is available with source codes to run and download at http://app.foroughmand.ir/cpssm/ . </jats:p

Exceptional pairs of amino acid neighbors in α-helices

AbstractAmino acids seem to have specific preferences for various locations in α-helices. These specific preferences, called singlet local propensity (SLP), have been determined by calculating the preference of occurrence of each amino acid in different positions of the α-helix. We have studied the occurrence of amino acids, single or pairs, in different positions, singlet or doublet, of α-helices in a database of 343 non-homologous proteins representing a unique superfamily from the SCOP database with a resolution better than 2.5 Å from the Protein Data Bank. The preference of single amino acids for various locations of the helix was shown by the relative entropy of each amino acid with respect to the background. Based on the total relative entropy of all amino acids occurring in a single position, the Ncap position was found to be the most selective position in the α-helix. A rigorous statistical analysis of amino acid pair occurrences showed that there are exceptional pairs for which, the observed frequency of occurrence in various doublet positions of the α-helix is significantly different from the expected frequency of occurrence in that position. The doublet local propensity (DLP) was defined as the preference of occurrences of amino acid pairs in different doublet positions of the α-helix. For most amino acid pairs, the observed DLP (DLPO) was nearly equal to the expected DLP (DLPE), which is the product of the related SLPs. However, for exceptional pairs of amino acids identified above, the DLPO and DLPE values were significantly different. Based on the relative values of DLPO and DLPE, exceptional amino acid pairs were divided into two categories. Those, for which the DLPO values are higher than DLPE, should have a strong tendency to pair together in the specified position. For those pairs which the DLPO values are less than DLPE, there exists a hindrance in neighboring of the two amino acids in that specific position of the α-helix. These cases have been identified and listed in various tables in this paper. The amount of mutual information carried by the exceptional pairs of amino acids was significantly higher than the average mutual information carried by other amino acid pairs. The average mutual information conveyed by amino acid pairs in each doublet position was found to be very small but non-zero