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. 2021 Apr 7:2021:baab012.
doi: 10.1093/database/baab012.

Posttranslational modifications in proteins: resources, tools and prediction methods

Shahin Ramazi  1 , Javad Zahiri  1   2   3
Affiliations

Posttranslational modifications in proteins: resources, tools and prediction methods

Shahin Ramazi et al. Database (Oxford). .

Abstract

Posttranslational modifications (PTMs) refer to amino acid side chain modification in some proteins after their biosynthesis. There are more than 400 different types of PTMs affecting many aspects of protein functions. Such modifications happen as crucial molecular regulatory mechanisms to regulate diverse cellular processes. These processes have a significant impact on the structure and function of proteins. Disruption in PTMs can lead to the dysfunction of vital biological processes and hence to various diseases. High-throughput experimental methods for discovery of PTMs are very laborious and time-consuming. Therefore, there is an urgent need for computational methods and powerful tools to predict PTMs. There are vast amounts of PTMs data, which are publicly accessible through many online databases. In this survey, we comprehensively reviewed the major online databases and related tools. The current challenges of computational methods were reviewed in detail as well.

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Figures

Figure 1.
Figure 1.
Summarized information of major PTMs (24 PTMs with more than 80 experimentally verified reported modified sites) according to the dbPTM databank (October 2020). All frequencies are shown in log scale. (A) Clustergram indicating the frequency of each PTM on different amino acids. (B) Frequency of major PTMs. (C) Frequency of each amino acid that was reported as a modified site.
Figure 2.
Figure 2.
Schematic PTM discovery timeline for 10 major PTMs: phosphorylation (28), methylation (29), sulfation (30), acetylation (31), ubiquitylation (32), prenylation (33), myristoylation (34), SUMOylation (35), palmitoylation (36), different types of glycosylation (N-glycosylation (37), O-glycosylation (38), C-glycosylation (39) and S-glycosylation (40)), phosphoglycosylation (41) and glycosylphosphatidylinositol (GPI anchored) (42). For each PTM, target residue(s) and the organism in which the related PTM was discovered for the first time are shown.
Figure 3.
Figure 3.
Schematic illustration of the 10 most studied PTMs including Phosphorylation (A), Acetylation (B), Ubiquitylation (C), Methylation (D), N-glycosylation (E), O-glycosylation (F), SUMOylation (G), S-palmitoylation (H), N-myristoylation (I), Prenylation (J), and Sulfation (k).
Figure 4.
Figure 4.
Involvement of PTMs in diseases and biological processes. (A). Tripartite network of PTM involvement in diseases and biological processes for the 10 major PTMs. (B) The degree of the biological processes with degree ≥3 in the tripartite network. (C) The degree of the diseases with degree ≥2 in the tripartite network. (D) Involvement of PTMs in disease and biological processes.
Figure 5.
Figure 5.
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.
Figure 6.
Figure 6.
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.
Figure 7.
Figure 7.
Online PTM prediction tools. The values of five important performance assessment measures have been extracted from the related publications: specificity (SP), sensitivity (SN), accuracy (ACC), Matthews’s correlation coefficient (MCC) and area under the ROC curve (AUC).
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