Subcellular Localization of Microalgal Proteins

Introduction

With the advent of high-throughput genomics, the determination of protein function is increasingly dependent on adequate sequence analysis. Functional annotation of genomes requires correlating an increasing amount of experimental evidence with an exponentially increasing sequence similarity data. However, this is a daunting task and the quality of the results depends on the adequacy of the sequence analysis procedures used. In addition, eukaryotic cells add another layer of complexity due to multiple membrane-bound intracellular compartments. Microalgae are eukaryotic microorganism widely used as a primary model for deciphering the processes occurring in the intracellular compartment of photosynthetic cells. Organelle-specific proteomics studies have begun to map its various subproteomes, and the function of proteins depends largely on their subcellular localization in the cell. Therefore, sequence-based prediction software is necessary to assign protein subcellular localization on a genome-wide scale.

Fig 1. Workflow for collecting green algae proteins with a known targeting cleavage site. (Mukherjee A, et al., 2019)

Customized Solutions

There are at least ten major subcellular localizations in eukaryotic microalgae. Traditional tools can incorrectly predict the localization of nuclear-encoded algal proteins, predicting many chloroplast proteins as mitochondrial targets. At Lifeasible, we provide specialized services for the subcellular localization of microalgal proteins to predict the intracellular localization of these proteins in one of three intracellular compartments in green algae: mitochondria, chloroplasts, and the secretory pathway.

The function of a protein is closely related to its subcellular location in the cell. An essential step in microalgal proteomics is determining each proteins subcellular localization. Over the years, our skilled scientists have used various experimental methods for identifying the location of microalgal proteins and computational methods for predicting the subcellular location of microalgal proteins.

• In situ hybridization.
• Prediction of overall protein amino acid composition.
• Prediction of known target sequences.
• Prediction of sequence homology and/or motifs.
• Combination of the above three prediction methods.

Our goal is to help you predict the subcellular location of microalgal proteins based on machine learning to integrate protein interaction and functional information. The currently available prediction methods differ in three main areas of critical importance to the user:

• The underlying biological model.
• Positioning coverage.
• Prediction accuracy.

Advantages of Our Solutions

• Advanced protein prediction computational tools.
• Efficient and reliable prediction results.
• Accurate identification of microalgal proteins at subcellular locations.
• Complete data set with a high level of sequence homology and only small deviations.
• Provides detailed information about potentially detected motifs and target sequences.
• Prediction results are in excellent agreement with results from microalgal proteomics studies.

We provide a suitable intracellular targeting prediction tool for your microalgal studies to efficiently identify microalgal proteins localized in chloroplasts and mitochondria. If you are interested in our solutions for the subcellular localization of microalgal proteins, please contact us directly.