Post-translational modifications are changes that are made to proteins after synthesis, typically mediated by enzymes. The human proteome is extremely diverse. Whilst the genome is essentially constant across different cell populations in the human body bar a few exceptions , in order for individual cells to perform their individual functions, and respond to environmental stimuli, a variety of different proteins must be expressed at different timepoints across cells, tissues and organs.
The diversity of the proteome is achieved by several different mechanisms; post-translational modifications are one example. The process by which DNA is converted to proteins, and how post-translational modifications add to the complexity of the proteome.
The key steps in protein phosphorylation summarized. The key steps involved in protein ubiquitination. This process generally culminates with an isopeptide bond forming between ubiquitin and the lysine residue of the protein substrate.
Ubiquitination serves several functions, the most common being to flag proteins for degradation by the proteasome, but there are others including: immune and inflammatory response, organelle biogenesis and signaling roles in DNA repair. As such, it is often studied in the context of cancer where the regulation of cellular processes and cell cycle regulation can go awry. An image detailing the acetylation of a lysine side chain.
Figure 3. The analysis of proteins and their PTMs is particularly important for the study of heart disease, cancer, neurodegenerative diseases, and diabetes 7. The main challenges in studying post-translationally modified proteins are the development of specific detection and purification methods.
Fortunately, these technical obstacles are being overcome with a variety of new and refined proteomics technologies.
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Glyceraldehyde 3-phosphate dehydrogenase GAPDH possesses several phosphorylation sites, but as the sites differ significantly between different organisms, it is plausible that phosphorylation is not a major determinant of GAPDH activity in chloroplasts Baginsky, Although Ser is conserved in higher plants, Ser has been found phosphorylated only in Arabidopsis plants Hou et al.
These examples indicate that further studies are urgently needed in order to fully understand the dynamic regulation of Calvin cycle enzymes and to pinpoint the responsible enzymes involved Friso and van Wijk, ; Baginsky, Furthermore, a number of other enzymes involved in carbon assimilation have been shown to be post-translationally modified. For instance, fructose 1,6-bisphosphate aldolase FBA is trimethylated at a conserved Lys residue close to the C-terminus of the protein, however, without any effect on catalytic activity or the oligomeric state of the enzyme Mininno et al.
Starch synthesis and degradation occur in a coordinated manner on a diurnal basis. Reversible protein phosphorylation plays an important role also in the regulation of starch metabolism Tetlow et al. Interestingly, starch synthase has been reported to be phosphorylated in a light dependent manner, i. Analyses of amyloplasts and chloroplasts from Triticum aestivum wheat have shown that some isoforms of starch-branching enzymes SBE are catalytically activated by phosphorylation and deactivated by dephosphorylation of one or more of their Ser residues Tetlow et al.
Additionally, phosphorylation is apparently involved in the formation of protein complexes composed of starch synthase, SBE isoforms as well as other enzymes with undefined role s Tetlow et al. Recently developed new experimental tools, i. Detailed knowledge about the effects of protein phosphorylation and redox regulation on the photosynthetic reactions already exists, but the regulation of most metabolic pathways in the chloroplast is poorly understood.
Because a specific amino acid residue may be targeted by different PTM types e. Future studies are likely to reveal novel modification types as well as molecular mechanisms of PTM-dependent regulation of various metabolic pathways in chloroplasts. PM, MG, and MK have made substantial intellectual contribution to the work, participated in writing and revised the paper.
MK and MG have drawn the figures. All authors have approved the paper for publication. The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
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Plant Cell 25, — Currently, PTS is observed mainly in secreted and transmembrane proteins in multicellular eukaryotes and have not yet been observed in nucleic and cytoplasmic proteins TPSTs govern the transfer of an activated sulfate from 3-phospho adenosine 5-phosphosulfate to tyrosine residues within acidic motifs of polypeptides Figure 3K Recently, it has been observed that PTS has vital roles in many biological processes like protein—protein interactions, leukocyte rolling on endothelial cells, visual functions and viral entry into cells PTMs have a vital role in almost all biological processes and fine-tune numerous molecular functions.
Therefore, the footprints of disruption in PTMs can be seen in many diseases. This network contains 97 diseases and biological processes. Involvement of PTMs in diseases and biological processes. D Involvement of PTMs in disease and biological processes. Besides, one can see that cancer is also one of the most affected diseases. Consistently with this observation, the biological processes related to cancer are among the high-degree nodes signaling, DNA repair, control of replication and apoptosis.
Processes related to apoptosis, protein—protein interaction, signaling, cell cycle control, chromatin assembly, organization and stability, DNA repair, protein degradation, protein trafficking and targeting, regulation of gene expression and transcription control are the other high-degree biological processes. Moreover, we can say that ubiquitylation, prenylation, glycosylation, S-palmitoylation and SUMOylation have the most involvement in diseases.
On the other hand, the PTMs with the highest number of interactions with biological processes are phosphorylation, ubiquitylation, methylation, acetylation and SUMOylation. Putting all together, we can conclude that the disruption in the pathways of these five PTMs has a great impact on the normal functioning of the cell and, as the result, on the organisms. Due to the considerable cost and difficulties of experimental methods for identifying PTMs, recently many computational methods have been developed for predicting PTMs Almost all of these methods need a set of experimentally validated PTMs to build a prediction model.
Therefore, the availability of valid public databases of PTMs is the first step toward this end. There are a variety of such public databases that could be utilized easily by the scientific community for developing computational methods 17 , According to the scope and diversity of the covered PTMs, these databanks can be classified into two main groups: general databases and specific databases. The general databases contain different types of PTMs, regardless of target residue and organisms.
These databases provide a broad scope of information for various PTMs. The current public PTM databases are greatly different in the number of stored modified proteins, the number of modified sites and the number of covered PTM types. Figure 5 shows a bubble chart of main PTM databases according to these three parameters.
As it is evident from the figure, due to the extensive number of studies on phosphorylation, the specific databases are mainly focused on phosphorylation. From this point of view, glycosylation is the second most interested PTM. In the following, the five largest databases are described briefly. Also, Table 1 summarizes the current main public PTM databases. A database was considered as secondary if it was an integration of some other databases.
Bubble chart for PTM databases. The chart was drawn based on three parameters for the databases: the number of stored modified proteins, the number of modified sites and the number of covered PTM types. This database is the largest database in terms of the number of recorded proteins and also in terms of the number of stored PTM types Figure 5.
However, the major amount of its data are extracted from human, mouse and rat Generally speaking, any computational method for predicting a specific type of PTM has four main steps: data gathering, feature extraction, learning the predictor and performance assessment. These steps have been schematically shown in Figure 6. In the following, these steps are described in detail. Also, the related challenges and problems in each step are discussed as well. A schematic flowchart to show how a predictor works for PTM prediction.
A Data collection and dataset creation. B Feature selection. C Creating training and testing models. D Evaluation of the performance of the models. The first step of a PTM prediction method is gathering the data of proteins that undergo the PTM of interest, in order to assemble a valid dataset Figure 6A. The final dataset must include both positive polypeptide sequences having a target residue that has undergone PTM and negative polypeptide sequences having a target residue that has not been affected by PTM samples in order to enable us to train a machine learning algorithm for predicting PTMs.
Positive data selection: almost all studies use the aforementioned databases such as dbPTM or Uniprot to gather the positive samples. Negative data selection: selecting the negative dataset is the most challenging part of the data gathering step. There are three main strategies for selecting the negative dataset. A random set of proteins with an equal number of the positive set is selected. Then, those occurrences of the target residue that did not undergo the PTM are considered as the negative samples.
The second strategy works like the first, but only those proteins are considered, to construct the negative dataset, that none of their target residues have undergone that specific PTM based on experimental evidences.
The third strategy examines only the proteins that are included in the positive dataset. In this case, those occurrences of the target residue that have not undergone PTM are considered as the negative samples. This step varies from study to study. CDhit is used as the major tool to detect similar samples sequences. Regardless of the strategy that is used for the negative data selection, in almost all cases, filtered datasets are imbalanced, and size of the negative dataset is greater than that of positive dataset in various extent sometimes the negative dataset is greater by some order of magnitude.
Due to the biases that can be introduced by the imbalanced datasets in the learning phase when a very specialized learning method is not used, which usually is the case , prior to the feature extraction and learning a classification model, a dataset balancing step is required. In this step, the positive or negative samples protein sequences , according to the various biological properties, are coded into numerical feature vectors to be used to learn the final predictor classifier.
For this encoding, firstly, using a sliding window, all proteins are partitioned into polypeptides with length W , in such a way that the target residue according to the PTM of interest is placed at the center of the polypeptides Figure 6B.
There is no agreement on the size of W , and various sizes have been used in different studies. Roughly speaking, W varies from 11 to Some studies select an optimized size for W through a try-and-error approach Finally, according to the appropriate biological descriptors such as amino acid composition, di-peptide composition, similarity score to the known motifs and physicochemical properties, each polypeptide of length W is encoded as a numerical feature vector.
After feature extraction, data are ready to train a classifier model for predicting the PTM, given a protein of interest Figure 6C. There are a variety of classifiers that can be trained.
At this step, based on the performance of different classifiers and knowledge of the experts that are involved in the study, a suitable classifier is selected. After parameter optimization, the classifier is trained on a subset of the assembled dataset that is called the training dataset , and then, the predictor is ready to be assessed and compared with the current state-of-the-art methods. In some studies, an additional process, named feature selection, is done prior to building the final predictor.
A standard and widely used procedure for assessing the performance of a given classifier is k-fold cross validation Figure 6D. In this process, the available dataset is randomly partitioned into k equal-sized disjoint subsets. This process is repeated k times in such a way that every subset is used exactly once as the test set. Finally, the average performance over all k test sets is reported.
The most common values for k are 5 and 10 in the PTM prediction studies. Despite the fact that some studies have used a large value for k , the large values lead to less accurate estimates of the generalization power of the classifier and test error rate All of these measures can be calculated based on the four basic elements of the confusion matrix Table 2.
For definition of these performance, refer to Refs. In addition to the aforementioned measures, ROC and area under the ROC curve are also two major performance evaluation measures There are some important flaws in performance comparison based on k -fold cross validation, which can lead to a biased conclusion.
As mentioned above, the data are randomly portioned into k distinct folds subsets in a k -fold CV procedure. Therefore, if only the train and test data of all the k folds are identical for two methods, the results of those methods are comparable.
However, many studies compare their k -fold CV results without satisfying this condition. Another common flaw is using the same data for parameter tuning and feature selection and for performance evaluation. In such situation, the performance of the predictor is overestimated, and the classifier will perform poorly on the unseen samples.
In the presence of enough data for the PTMs, which usually are available except for newly discovered PTMs, some studies carry out an independent test experiment. In this experiment, a dataset of positive and negative samples is assembled or a benchmark dataset may be used as an independent test data, which have not been used in any of the previous steps, and the performance of the classifier is evaluated again using this dataset.
Usually, the performance on an independent test set is lower than that of k -fold CV and is a better estimation of the real-world performance of a method. To show the strength of the proposed methods in real-world biological problems, some studies use their trained models on a set of biologically important proteins, which have recently been studied, to indicate that their method can effectively detect the newly reported and experimentally validated PTMs.
Considering the high cost of experimental identification of PTMs, in recent years, many computational methods have been proposed for the prediction of PTMs. Many of these methods have been introduced as publicly accessible tools. Figure 7 provides a comprehensive list of these tools. In addition to the PTM prediction tools, Nickchi et al. In this case, PEIMAN gives two distinct lists of proteins and then integrates the enrichment results and provides a list of highly enriched terms of both protein sets.
Online PTM prediction tools. PTMs are the chemical modification of a protein after translation and have a wide range of effects on the function and structure of the target proteins. These processes occur on almost all proteins, and many domains within proteins are modified on multiple amino acids by diverse modifications. The function of a modified protein is often strongly affected by these modifications that play important roles in a myriad of cellular processes. There is strong evidence that shows that disruptions in PTMs can lead to various diseases.
Hence, increased knowledge about the potential PTMs of a target protein may increase our understanding of the molecular processes in which it takes part. High-throughput experimental methods for the discovery of PTMs are very labor-intensive and time-consuming.
Thus, there is an urgent need for prediction methods and powerful tools to predict PTMs. There is a considerable amount of PTM data available from various publicly accessible databanks, which are valuable resources for mining patterns to train new models for PTM prediction.
In recent years, many computational methods have been developed for this purpose. However, there are some common weaknesses in assessing these methods, and so it seems that such methods should be evaluated more critically.
Considering the diversity of PTMs and new PTMs that are reported every couple of years on one hand, and the advancement of machine learning algorithms on the other hand, we can conclude that this field will attract more attention in the future.
The authors would like to thanks Mohammad Hossein Afsharinia for his help with preparing the graphics and Saber Mohammadi for his help with editing the manuscript. Also, the authors appreciate the anonymous reviewers for their very constructive comments.
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