Exploring the transportome of the biosurfactant producing yeast Starmerella bombicola

Starmerella bombicola is a non-conventional yeast mainly known for its capacity to produce high amounts of the glycolipids ‘sophorolipids’. Although its product has been used as biological detergent for a couple of decades, the genetics of S. bombicola are still largely unknown. Computational analysis of the yeast’s genome enabled us to identify 254 putative transporter genes that make up the entire transportome. For each of them, a potential substrate was predicted using homology analysis, subcellular localization prediction and RNA sequencing in different stages of growth. One transporter family is of exceptional importance to this yeast: the ATP Binding Cassette (ABC) transporter Superfamily, because it harbors the main driver behind the highly efficient sophorolipid export. Furthermore, members of this superfamily translocate a variety of compounds ranging from antibiotics to hydrophobic molecules. We conducted an analysis of this family by creating deletion mutants to understand their role in the export of hydrophobic compounds, antibiotics and sophorolipids. Doing this, we could experimentally confirm the transporters participating in the efflux of medium chain fatty alcohols, particularly decanol and undecanol, and identify a second sophorolipid transporter that is located outside the sophorolipid biosynthetic gene cluster.

Identification And Expression Analysis of FORMIN Gene Family In Rice (Oryza Sativa)

Background: FORMIN proteins, which are composed of proteins containing FH1 and FH2 domains, play crucial roles in the growth and development of organisms. However, the functions of FORMINs in rice (Oryza sativa) remain largely unclear. Results: In this study, a total of 17 FORMIN genes were identified in rice, OsFH17 was the first time identified in this study. In addition, the distribution on chromosomes, gene structure, as well as conserved motifs of rice FORMINs was investigated. According to their protein structural and phylogenetic features, these 17 rice FORMIN genes were classified into two distinct subfamilies. Subcellular localization prediction showed that rice FORMINs were located in cytosol, golgi, endoplasmic reticulum, extracellular, and vacuole. Protein protein interaction (PPI) prediction results shown that FORMIN protein might answer hormone signals and be involved in cytoskeleton dynamics regulation and cell wall morphology regulation. The results of silico analysis and qRT-PCR confirmation of the gene expression showed that the expression of rice FORMINs were related to their tissue distribution. Moreover, OsFH3, OsFH5 and OsFH7 were upregulated under phytohormone treatments. Conclusions: Overall, our research may shed light on the understanding and further investigation of the biological functions of rice FORMINs.

Exploring the Transportome of the Biosurfactant Producing Yeast Starmerella Bombicola

Starmerella bombicola is a non-conventional yeast mainly known for its capacity to produce high amounts of the glycolipids ‘sophorolipids’. Although its product has been used as biological detergent for a couple of decades, the genetics of S. bombicola are still largely unknown. Computational analysis of the yeast’s genome enabled us to identify 254 putative transporter genes that make up the entire transportome. For each of them, a potential substrate was predicted using homology analysis, subcellular localization prediction and RNA sequencing in different stages of growth. One transporter family is of exceptional importance to this yeast: the ATP Binding Cassette (ABC) transporter Superfamily, because it harbors the main driver behind the highly efficient sophorolipid export. Furthermore, members of this superfamily translocate a variety of compounds ranging from antibiotics to hydrophobic molecules. We conducted an analysis of this family by creating deletion mutants to understand their role in the export of hydrophobic compounds, antibiotics and sophorolipids. Doing this, we could experimentally confirm the transporters participating in the efflux of medium chain fatty alcohols, particularly decanol and undecanol, and identify a second sophorolipid transporter that is located outside the sophorolipid biosynthetic gene cluster.

HumDLoc: Human Protein Subcellular Localization Prediction Using Deep Neural Network

Aims: To develop a tool that can annotate subcellular localization of human proteins. Background: With the progression of high throughput human proteomics projects, an enormous amount of protein sequence data has been discovered in the recent past. All these raw sequence data require precise mapping and annotation for their respective biological role and functional attributes. The functional characteristics of protein molecules are highly dependent on the subcellular localization/ compartment. Therefore, a fully automated and reliable protein subcellular localization prediction system would be very useful for current proteomic research. Objective: To develop a machine learning-based predictive model that can annotate the subcellular localization of human proteins with high accuracy and precision. Methods: In this study, we used the PSI-CD-HIT homology criterion and utilized the sequence-based features of protein sequences to develop a powerful subcellular localization predictive model. The dataset used to train the HumDLoc model was extracted from a reliable data source, Uniprot knowledge base, which helps the model to generalize on the unseen dataset. Result : The proposed model, HumDLoc, was compared with two of the most widely used techniques: CELLO and DeepLoc, and other machine learning-based tools. The result demonstrated promising predictive performance of HumDLoc model based on various machine learning parameters such as accuracy (≥97.00%), precision (≥0.86), recall (≥0.89), MCC score (≥0.86), ROC curve (0.98 square unit), and precision-recall curve (0.93 square unit). Conclusion: In conclusion, HumDLoc was able to outperform several alternative tools for correctly predicting subcellular localization of human proteins. The HumDLoc has been hosted as a web-based tool at https://bioserver.iiita.ac.in/HumDLoc/.

Protein sequence information extraction and subcellular localization prediction with gapped k-Mer method

Background Subcellular localization prediction of protein is an important component of bioinformatics, which has great importance for drug design and other applications. A multitude of computational tools for proteins subcellular location have been developed in the recent decades, however, existing methods differ in the protein sequence representation techniques and classification algorithms adopted. Results In this paper, we firstly introduce two kinds of protein sequences encoding schemes: dipeptide information with space and Gapped k-mer information. Then, the Gapped k-mer calculation method which is based on quad-tree is also introduced. Conclusions >From the prediction results, this method not only reduces the dimension, but also improves the prediction precision of protein subcellular localization.