Cytosolic events involved in chloroplast protein targeting.

Chloroplasts are unique organelles that are responsible for photosynthesis. Although chloroplasts contain their own genome, the majority of chloroplast proteins are encoded by the nuclear genome. These proteins are transported to the chloroplasts after translation in the cytosol. Chloroplasts contain three membrane systems (outer/inner envelope and thylakoid membranes) that subdivide the interior into three soluble compartments known as the intermembrane space, stroma, and thylakoid lumen. Several targeting mechanisms are required to deliver proteins to the correct chloroplast membrane or soluble compartment. These mechanisms have been extensively studied using purified chloroplasts in vitro. Prior to targeting these proteins to the various compartments of the chloroplast, they must be correctly sorted in the cytosol. To date, it is not clear how these proteins are sorted in the cytosol and then targeted to the chloroplasts. Recently, the cytosolic carrier protein AKR2 and its associated cofactor Hsp17.8 for outer envelope membrane proteins of chloroplasts were identified. Additionally, a mechanism for controlling unimported plastid precursors in the cytosol has been discovered. This review will mainly focus on recent findings concerning the possible cytosolic events that occur prior to protein targeting to the chloroplasts. This article is part of a Special Issue entitled: Protein Import and Quality Control in Mitochondria and Plastids.

Identification of sorting motifs of AtßFruct4 for trafficking from the ER to the vacuole through the Golgi and PVC.

Although much is known about the molecular mechanisms involved in transporting soluble proteins to the central vacuole, the mechanisms governing the trafficking of membrane proteins remain largely unknown. In this study, we investigated the mechanism involved in targeting the membrane protein, AtßFructosidase 4 (AtßFruct4), to the central vacuole in protoplasts. AtßFruct4 as a green fluorescent protein (GFP) fusion protein was transported as a membrane protein during transit from the endoplasmic reticulum (ER) through the Golgi apparatus and the prevacuolar compartment (PVC). The N-terminal cytosolic domain of AtßFruct4 was sufficient for transport from the ER to the central vacuole and contained sequence motifs required for trafficking. The sequence motifs, LL and PI, were found to be critical for ER exit, while the EEE and LCPYTRL sequence motifs played roles in trafficking primarily from the trans Golgi network (TGN) to the PVC and from the PVC to the central vacuole, respectively. In addition, actin filaments and AtRabF2a, a Rab GTPase, played critical roles in vacuolar trafficking at the TGN and PVC, respectively. On the basis of these results, we propose that the vacuolar trafficking of AtßFruct4 depends on multiple sequence motifs located at the N-terminal cytoplasmic domain that function as exit and/or sorting signals in different stages during the trafficking process.

An Arabidopsis prenylated Rab acceptor 1 isoform, AtPRA1.B6, displays differential inhibitory effects on anterograde trafficking of proteins at the endoplasmic reticulum.

Prenylated Rab acceptors (PRAs), members of the Ypt-interacting protein family of small membrane proteins, are thought to aid the targeting of prenylated Rabs to their respective endomembrane compartments. In plants, the Arabidopsis (Arabidopsis thaliana) PRA1 family contains 19 members that display varying degrees of sequence homology to animal PRA1 and localize to the endoplasmic reticulum (ER) and/or endosomes. However, the exact role of these proteins remains to be fully characterized. In this study, the effect of AtPRA1.B6, a member of the AtPRA1 family, on the anterograde trafficking of proteins targeted to various endomembrane compartments was investigated. High levels of AtPRA1.B6 resulted in differential inhibition of coat protein complex II vesicle-mediated anterograde trafficking. The trafficking of the vacuolar proteins sporamin:GFP (for green fluorescent protein) and AALP:GFP, the secretory protein invertase:GFP, and the plasma membrane proteins PMP:GFP and H+-ATPase:GFP was inhibited in a dose-dependent manner, while the trafficking of the Golgi-localized proteins ST:GFP and KAM1(ΔC):mRFP was not affected. Conversely, in RNA interference plants displaying lower levels of AtPRA1.B6 transcripts, the trafficking efficiency of sporamin:GFP and AALP:GFP to the vacuole was increased. Localization and N-glycan pattern analyses of cargo proteins revealed that AtPRA1.B6-mediated inhibition of anterograde trafficking occurs at the ER. In addition, AtPRA1.B6 levels were controlled by cellular processes, including 26S proteasome-mediated proteolysis. Based on these results, we propose that AtPRA1.B6 is a negative regulator of coat protein complex II vesicle-mediated anterograde trafficking for a subset of proteins at the ER.

In silico methods and tools for drug discovery.

In the past, conventional drug discovery strategies have been successfully employed to develop new drugs, but the process from lead identification to clinical trials takes more than 12 years and costs approximately $1.8 billion USD on average. Recently, in silico approaches have been attracting considerable interest because of their potential to accelerate drug discovery in terms of time, labor, and costs. Many new drug compounds have been successfully developed using computational methods. In this review, we briefly introduce computational drug discovery strategies and outline up-to-date tools to perform the strategies as well as available knowledge bases for those who develop their own computational models. Finally, we introduce successful examples of anti-bacterial, anti-viral, and anti-cancer drug discoveries that were made using computational methods.

User guide for the discovery of potential drugs via protein structure prediction and ligand docking simulation.

Due to accumulating protein structure information and advances in computational methodologies, it has now become possible to predict protein-compound interactions. In biology, the classic strategy for drug discovery has been to manually screen multiple compounds (small scale) to identify potential drug compounds. Recent strategies have utilized computational drug discovery methods that involve predicting target protein structures, identifying active sites, and finding potential inhibitor compounds at large scale. In this protocol article, we introduce an in silico drug discovery protocol. Since multi-drug resistance of pathogenic bacteria remains a challenging problem to address, UDP-N-acetylmuramate-L-alanine ligase (murC) of Acinetobacter baumannii was used as an example, which causes nosocomial infection in hospital setups and is responsible for high mortality worldwide. This protocol should help microbiologists to expand their knowledge and research scope.

In silico approaches and tools for the prediction of drug metabolism and fate: A review.

The fate of administered drugs is largely influenced by their metabolism. For example, endogenous enzyme-catalyzed conversion of drugs may result in therapeutic inactivation or activation or may transform the drugs into toxic chemical compounds. This highlights the importance of drug metabolism in drug discovery and development, and accounts for the wide variety of experimental technologies that provide insights into the fate of drugs. In view of the high cost of traditional drug development, a number of computational approaches have been developed for predicting the metabolic fate of drug candidates, allowing for screening of large numbers of chemical compounds and then identifying a small number of promising candidates. In this review, we introduce in silico approaches and tools that have been developed to predict drug metabolism and fate, and assess their potential to facilitate the virtual discovery of promising drug candidates. We also provide a brief description of various recent models for predicting different aspects of enzyme-drug reactions and provide a list of recent in silico tools used for drug metabolism prediction.