Unraveling the transcriptional complexity of compactness in sistan grape cluster

© 2018 Elsevier. This manuscript version is made available under the CC-BY-NC-ND 4.0 license:http://creativecommons.org/licenses/by-nc-nd/4.0/ This author accepted manuscript is made available following 24 month embargo from date of publication (Feb 2018) in accordance with the publisher’s archiving policyYaghooti grape of Sistan is the earliest ripening grape in Iran, harvested every May annually. It is adapted to dry conditions in Sistan region and its water requirement is less than the other grape cultivars. The transcriptional complexity of this grape was studied in three stages of cluster development. Totally, 24121 genes were expressed in different cluster development steps (step 1: cluster formation, step 2: berry formation, step 3: final size of cluster) of which 3040 genes in the first stage, 2381 genes in the second stage and 2400 genes in the third stage showed a significant increase in expression. GO analysis showed that when the clusters are ripening, the activity of the nucleus, cytoplasmic, cytosol, membrane and chloroplast genes in the cluster architecture cells decreases. In contrast, the activity of the endoplasmic reticulum, vacuole and extracellular region genes enhances. When Yaghooti grape is growing and developing, some of metabolic pathways were activated in the response to biotic and abiotic stresses. Gene co-expression network reconstruction showed that AGAMOUS is a key gene in compactness of Sistan grape cluster, because it influences on expression of GA gene which leads to increase cluster length and berries size

A study of the interaction space of two lactate dehydrogenase isoforms (LDHA and LDHB) and some of their inhibitors using proteochemometrics modeling

Abstract Lactate dehydrogenase (LDH) is a tetramer enzyme that converts pyruvate to lactate reversibly. This enzyme becomes important because it is associated with diseases such as cancers, heart disease, liver problems, and most importantly, corona disease. As a system-based method, proteochemometrics does not require knowledge of the protein's three-dimensional structure, but rather depends on the amino acid sequence and protein descriptors. Here, we applied this methodology to model a set of LDHA and LDHB isoenzyme inhibitors. To implement the proteochemetrics method, the camb package in the R Studio Server programming environment was used. The activity of 312 compounds of LDHA and LDHB isoenzyme inhibitors from the valid Binding DB database was retrieved. The proteochemometrics method was applied to three machine learning algorithms gradient amplification model, random forest, and support vector machine as regression methods to find the best model. Through the combination of different models into an ensemble (greedy and stacking optimization), we explored the possibility of improving the performance of models. For the RF best ensemble model of inhibitors of LDHA and LDHB isoenzymes, and were 0.66 and 0.62, respectively. LDH inhibitory activation is influenced by Morgan fingerprints and topological structure descriptors

afpCOOL: A tool for antifreeze protein prediction

Various cold-adapted organisms produce antifreeze proteins (AFPs), which prevent the freezing of cell fluids by inhibiting the growth of ice crystals. AFPs are currently being recognized in various organisms, living in extremely low temperatures. AFPs have several important applications in increasing freeze tolerance of plants, maintaining the tissue in frozen conditions and producing cold-hardy plants by applying transgenic technology. Substantial differences in the sequence and structure of the AFPs, pose a challenge for researchers to identify these proteins. In this paper, we proposed a novel method to identify AFPs, using supportive vector machine (SVM) by incorporating 4 types of features. Results of the two used benchmark datasets, revealed the strength of the proposed method in AFP prediction. According to the results of an independent test setup, our method outperformed the current state-of-the-art methods. In addition, the comparison results of the discrimination power of different feature types revealed that physicochemical descriptors are the most contributing features in AFP detection. This method has been implemented as a stand-alone tool, named afpCOOL, for various operating systems to predict AFPs with a user friendly graphical interface