Dataset Information

Predicting Amyloidogenic Proteins in the Proteomes of Plants.

ABSTRACT: Amyloids are protein fibrils with characteristic spatial structure. Though amyloids were long perceived to be pathogens that cause dozens of incurable pathologies in humans and mammals, it is currently clear that amyloids also represent a functionally important form of protein structure implicated in a variety of biological processes in organisms ranging from archaea and bacteria to fungi and animals. Despite their social significance, plants remain the most poorly studied group of organisms in the field of amyloid biology. To date, amyloid properties have only been demonstrated in vitro or in heterologous systems for a small number of plant proteins. Here, for the first time, we performed a comprehensive analysis of the distribution of potentially amyloidogenic proteins in the proteomes of approximately 70 species of land plants using the Waltz and SARP (Sequence Analysis based on the Ranking of Probabilities) bioinformatic algorithms. We analyzed more than 2.9 million protein sequences and found that potentially amyloidogenic proteins are abundant in plant proteomes. We found that such proteins are overrepresented among membrane as well as DNA- and RNA-binding proteins of plants. Moreover, seed storage and defense proteins of most plant species are rich in amyloidogenic regions. Taken together, our data demonstrate the diversity of potentially amyloidogenic proteins in plant proteomes and suggest biological processes where formation of amyloids might be functionally important.

SUBMITTER: Antonets KS

PROVIDER: S-EPMC5666836 | biostudies-literature | 2017 Oct

REPOSITORIES: biostudies-literature

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Predicting Amyloidogenic Proteins in the Proteomes of Plants.

Antonets Kirill S KS Nizhnikov Anton A AA

International journal of molecular sciences 20171016 10

Amyloids are protein fibrils with characteristic spatial structure. Though amyloids were long perceived to be pathogens that cause dozens of incurable pathologies in humans and mammals, it is currently clear that amyloids also represent a functionally important form of protein structure implicated in a variety of biological processes in organisms ranging from archaea and bacteria to fungi and animals. Despite their social significance, plants remain the most poorly studied group of organisms in ...[more]

PMID: 29035294

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Project description:The advancement in site-directed spin labeling of proteins has enabled EPR studies to expand into newer research areas within the umbrella of protein-membrane interactions. Recently, membrane remodeling by amyloidogenic and non-amyloidogenic proteins has gained a substantial interest in relation to driving and controlling vital cellular processes such as endocytosis, exocytosis, shaping of organelles like endoplasmic reticulum, Golgi and mitochondria, intracellular vesicular trafficking, formation of filopedia and multivesicular bodies, mitochondrial fusion and fission, and synaptic vesicle fusion and recycling in neurotransmission. Misregulation in any of these processes due to an aberrant protein (mutation or misfolding) or alteration of lipid metabolism can be detrimental to the cell and cause disease. Dissection of the structural basis of membrane remodeling by proteins is thus quite necessary for an understanding of the underlying mechanisms, but it remains a formidable task due to the difficulties of various common biophysical tools in monitoring the dynamic process of membrane binding and bending by proteins. This is largely since membranes generally complicate protein structure analysis and this problem is amplified for structural analysis in the presence of different types of membrane curvatures. Recent EPR studies on membrane remodeling by proteins show that a significant structural information can be generated to delineate the role of different protein modules, domains and individual amino acids in the generation of membrane curvature. These studies also show how EPR can complement the data obtained by high resolution techniques such as X-ray and NMR. This perspective covers the application of EPR in recent studies for understanding membrane remodeling by amyloidogenic and non-amyloidogenic proteins that is useful for researchers interested in using or complimenting EPR to gain better understanding of membrane remodeling. We also discuss how a single protein can generate different type of membrane curvatures using specific conformations for specific membrane structures and how EPR is a versatile tool well-suited to analyze subtle alterations in structures under such modifying conditions which otherwise would have been difficult using other biophysical tools.

Project description:BackgroundAmyloids are insoluble fibrillar aggregates that are highly associated with complex human diseases, such as Alzheimer's disease, Parkinson's disease, and type II diabetes. Recently, many studies reported that some specific regions of amino acid sequences may be responsible for the amyloidosis of proteins. It has become very important for elucidating the mechanism of amyloids that identifying the amyloidogenic regions. Accordingly, several computational methods have been put forward to discover amyloidogenic regions. The majority of these methods predicted amyloidogenic regions based on the physicochemical properties of amino acids. In fact, position, order, and correlation of amino acids may also influence the amyloidosis of proteins, which should be also considered in detecting amyloidogenic regions.ResultsTo address this problem, we proposed a novel machine-learning approach for predicting amyloidogenic regions, called ReRF-Pred. Firstly, the pseudo amino acid composition (PseAAC) was exploited to characterize physicochemical properties and correlation of amino acids. Secondly, tripeptides composition (TPC) was employed to represent the order and position of amino acids. To improve the distinguishability of TPC, all possible tripeptides were analyzed by the binomial distribution method, and only those which have significantly different distribution between positive and negative samples remained. Finally, all samples were characterized by PseAAC and TPC of their amino acid sequence, and a random forest-based amyloidogenic regions predictor was trained on these samples. It was proved by validation experiments that the feature set consisted of PseAAC and TPC is the most distinguishable one for detecting amyloidosis. Meanwhile, random forest is superior to other concerned classifiers on almost all metrics. To validate the effectiveness of our model, ReRF-Pred is compared with a series of gold-standard methods on two datasets: Pep-251 and Reg33. The results suggested our method has the best overall performance and makes significant improvements in discovering amyloidogenic regions.ConclusionsThe advantages of our method are mainly attributed to that PseAAC and TPC can describe the differences between amyloids and other proteins successfully. The ReRF-Pred server can be accessed at http://106.12.83.135:8080/ReRF-Pred/.

Project description:BackgroundThe structural and functional features associated with Simple Sequence Proteins (SSPs) are non-globularity, disease states, signaling and post-translational modification. SSPs are also an important source of genetic and possibly phenotypic variation. Analysis of 249 prokaryotic proteomes offers a new opportunity to examine the genomic properties of SSPs.ResultsSSPs are a minority but they grow with proteome size. This relationship is exhibited across species varying in genomic GC, mutational bias, life style, and pathogenicity. Their proportion in each proteome is strongly influenced by genomic base compositional bias. In most species simple duplications is favoured, but in a few cases such as Mycobacteria, large families of duplications occur. Amino acid preference in SSPs exhibits a trend towards low cost of biosynthesis. In SSPs and in non-SSPs, Alanine, Glycine, Leucine, and Valine are abundant in species widely varying in genomic GC whereas Isoleucine and Lysine are rich only in organisms with low genomic GC. Arginine is abundant in SSPs of two species and in the non-SSPs of Xanthomonas oryzae. Asparagine is abundant only in SSPs of low GC species. Aspartic acid is abundant only in the non-SSPs of Halobacterium sp NRC1. The abundance of Serine in SSPs of 62 species extends over a broader range compared to that of non-SSPs. Threonine(T) is abundant only in SSPs of a couple of species. SSPs exhibit preferential association with Cell surface, Cell membrane and Transport functions and a negative association with Metabolism. Mesophiles and Thermophiles display similar ranges in the content of SSPs.ConclusionAlthough SSPs are a minority, the genomic forces of base compositional bias and duplications influence their growth and pattern in each species. The preferences and abundance of amino acids are governed by low biosynthetic cost, evolutionary age and base composition of codons. Abundance of charged amino acids Arginine and Aspartic acid is severely restricted. SSPs preferentially associate with cell surface and interface functions as opposed to metabolism, wherein proteins of high sequence complexity with globular structures are preferred. Mesophiles and Thermophiles are similar with respect to the content of SSPs. Our analysis serves to expand the commonly held views on SSPs.

Dataset Information

Predicting Amyloidogenic Proteins in the Proteomes of Plants.

Publications

Predicting Amyloidogenic Proteins in the Proteomes of Plants.

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