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Computational Modeling of Lysine Post-Translational Modification: An Overview

Living organisms have a magnificent ordered and complex structure. In regulating the cellular functions, post-translational modifications (PTMs) are critical molecular measures. They alter protein conformation, modulating their activity, stability and localization. Up to date, more than 300 types of PTMs are experimentally discovered in vivo and in vitro pathways [1,2]. Major and common PTMs are methylation, ubiquitination, succinylation, phosphorylation, glycosylation, acetylation, and sumoylation.

The PTMs are categorized into several axes. The first one is grouped by the residue side-chains of modification sites. In this category, almost 15 of the 20 types of amino acid side-chains can undergo the modifications (Table 1) [6,7]. The second one is a fragment of coenzyme or co-substrate coupled to the protein and concomitant modification by chemical nature, including S-adenosylmethionine dependent methylation, acetyl CoA dependent acetylation, NAD-dependent ADP ribosylation, CoASH-dependent phosphopantetheinylation, ATPdependent phosphorylation, and phosphoadenosinephosphosulfate (PAPS) -dependent sulfurylation. The third categorization of PTM is grouped by the hydrophobic residues for membrane localization. It has acquired various lipid modifications [prenylation, glycosyl phosphatidylinositol (GPI), palmitoylation anchor attachment, glypiation, farnesylation, geranylgeranylation]. However, many PTMs have biased and overlapped with the arrangement of other PTMs in surrounding amino acid sequences. This tendency is often embodied within a sequence motif. For example, it has been observed that nearly 60% class PTMs of protein succinylation sites are surrounded or overlapped with protein acetylation sites [8]. These PTMs can also affect the physicochemical properties of proteins, which can provide a mechanism for the dynamic regulation of molecular, self-assembly [9]. The PTMs have been found in the all types of proteins such as the structural proteins, plasma membrane receptors and nuclear transcription factors (Figure 1).
Lysine is one of the most frequently occurred PTM sites, which has important regulatory and functional consequences. In 1964, Allfrey et al. [10] observed that the gene expression can be regulated by covalently introducing methyl and acetyl groups on lysine residues in histones. Recently, some studies have discovered that lysine acts as a hot spot for PTMs, and a number of protein lysine modifications could occur in both histone and non-histone proteins [11,12]. For instance, lysine methylation in non-histone proteins can regulate the protein activity and protein structure stability [13]. In 2004, the Nobel Prize in Chemistry was awarded jointly to Aaron Ciechanover, Avram Hershko and Irwin Rose for the discovery of lysine ubiquitin-mediated protein degradation [14].
Moreover, in biological process, lysine can be modified by the primary glycolytic intermediate 1,3-biophosphoglycerate (1,3-BPG) through 3-phosphoglyceryl-lysine protein [15], whereas in glycolytic processes lysine glycation is involved [16]. A rapid progress in proteomic technologies have greatly accelerated the identification of lysine modifications proteins and the discovery of new lysine PTMs [11,12,17,18]. Therefore, it is urgently needed to know the function of these lysine PTMs, since the number of lysine PTMs have been greatly expanded to the research community. Moreover, it is also essential to create lysine PTM databases for researchers to store, query and manage the lysine PTM data.

Computational prediction of lysine PTM sites
Some kinds of PTMs, such as succinylation, ubiquitination, acylation, methylation, sumoylation and deamination, occurred on lysine residues. For the last several decades remarkable progresses have been carried in the identification and functional analysis of lysine PTMs in proteins. Lysine PTMs play a vital role in protein folding, protein function, and interactions with other proteins [19,20]. Due to the important biological functions of protein lysine PTMs, it is very important to analyze and understand the function of lysine PTMs.
The lysine PTMs of proteins have been identified by a variety of experimental techniques including the mass spectrometry (MS) [21,22], chromatin immunoprecipitation (ChIP) [23], liquid chromatography [24], radioactive chemical method [25], western blotting [26], and eastern blotting [24]. The MS technique is one of the mainstay routes in detecting PTMs in a high-throughput manner. The new MS and capillary liquid chromatography instrumentation have made revolutionary advance in enrichment strategies in our growing understanding of many PTMs [27]. A similar strategy of fragmentation for PTM identification is the beam-type collision induced dissociation, also called higher energy collisional dissociation [28]. These types of fragmentation are characterized by higher activation energy. Most of the fragmentation methods of precursor ions are based on the radical anions or thermal electrons [29]. In general, the experimental analysis of PTMs often requires labor intensive sample preparations and hazardous or expensive chemical reagents. The substrate is separated from nonradioactive ATP by the kinase assay and generates radioactive waste. Since most of the radioactive substances show a short half-life, the fresh reagent must be frequently acquired. And sometimes, the substrate concentration of assay is often much higher than the expected substrate concentrations [30]. In the above discussion we can summarize that, the identification of PTMs by the experimental techniques is laborious, time-consuming and usually expensive.
In contrast with the traditional experimental methods, computational analysis of lysine PTMs has also been an attractive and alternative approach due to its accuracy, cost-effective and high-speed [31,32]. The computational methods are more efficient for identifying large-scale novel lysine PTM substrates. A summary of the prediction pipeline of lysine PTM is shown in Figure 2. The computational tools can narrow down the number of potentially candidates and rapidly generate useful information for investigating further experimental approach. So far the computational prediction of protein lysine PTMs has been an important research topic in the field of protein bioinformatics. Although the great progress has been made by employing various statistical learning approaches with numerous feature vectors, a problem is to obtain more accurate prediction. It needs rigorous features encoding methods, machine learning, and statistical analysis to predict lysine PTMs. Indeed, computational method development of lysine PTM site prediction has initiated since 2008 [33]. In the next section, we will introduce some existing databases for lysine PTMs.

Residues Reactions Example
Asp (D) phosphorylation isomerization to isoaspartyl Protein tyrosine phosphatases; response regulators in two-component systems

Databases of lysine PTM sites
Recently, rapid progresses in proteomic technologies have greatly accelerated the identification of well-characterized lysine PTM sites. Determinations of lysine PTM data with experimental technologies are also greatly extended. How to organize, store and update these data becomes an important issue. Up to now, a number of experimentally verified lysine PTM databases have been constructed (Table 2). For instance, the CPLM is a lysine PTM database that integrates abundant protein annotations [34]. In total, the CPLM database contained 45,748 lysine modification proteins with 189,919 experimentally verified lysine modification sites for 122 species (CPLM 1.0). It is expected that huge data will be generated from lysine PTMs in the future. Therefore, the diversity of protein lysine PTMs requires specialized databases to store them. Based on lysine PTM databases, many bioinformatics methods have been developed for analyzing the internal motif [35,36]. It is becoming a hot topic in the study of protein bioinformatics.

Feature for the computational prediction of lysine PTM sites
Feature mining is one of the most important steps for predicting lysine PTM sites. Appropriate features in the prediction model enable the accurate prediction of protein lysine PTMs. In general, the feature vectors refer to the characterization of the sequences and local structures around the protein functional sites. Ideally, the features can clearly distinguish PTM sites from other random sites. In the real world, however, the feature of protein functional sites can also exist on the non-functional sites of proteins. In the prediction PTM sites, this specific problem is particularly prominent due to the sequence diversity. For instance, some sequence motifs are very weak and not available with the sequence evolutionary information [37][38][39][40][41][42]. To address this problem, we can search PSI-BLAST [32,43,44] against the NCBI NR database to generate a profile (i.e., position-specific scoring matrix [45][46][47][48][49][50]. Such a sequence profile reflects the conservation and variation between protein sequences through the evolutionary information [37][38][39]. Moreover, to isolate the weak motifs from protein sequences, Hidden Markov models (HMMs) have been extensively used [51,52]. It can examine the unaligned sequences or a common motif within a set of unaligned sequences. HMM profiles can be automatically trained or estimated, from unaligned protein sequences [51].
In the prediction of lysine PTMs, researchers have made plenty of efforts for mining the protein lysine PTM characteristics. These characteristics might be suitable for a particular protein lysine PTM classification problem, thus mining new features is always an important task for lysine PTM prediction. The features are mostly encoded by three ways, namely based on the protein sequence, evolutionary, and structural information (Figure 2). In most cases, the features are extracted from protein sequences because the protein sequence data is more enthusiastically available than the protein structure data. In addition, the features based on protein sequences are often straightforward and the simplest features. For instance, the linear arrangement of residues directly depicts the flanking sequences of lysine PTM sites. In the linear arrangement of residues, the physicochemical amino acid index properties have also been widely used in the prediction of protein lysine PTM sites [53,54].

Algorithm of lysine PTM sites prediction
After determining the appropriate features, the next job is to select an appropriate machine learning algorithm to integrate these features for the prediction of protein lysine PTM sites. Generally, machine learning model used for building the trained model to test the novel dataset. It will improve the accuracy of the prediction if the prediction algorithm is appropriate. These prediction algorithms of lysine PTM sites can be classified into two categories, i.e., statistical probabilistic algorithms and machine learning algorithms. In the next sections, we will discuss some of the probabilistic and machine learning classifiers for lysine PTM prediction. PupDB is a most popular database for protein pupylation sites, which was constructed by collecting the experimentally identified pupylated proteins and pupylation sites from the published studies. Until now, it is an updated database for lysine pupylation sites. PhosphoSitePlus [72] http://www.phosphosite.org Hornbeck et al. also created a PhosphoSitePlus database from experimentally indentified PTM in human and mouse proteins. It has included phosphorylation, acetylation, ubiquitination, and methylation sites.
UbiProt [73] http://ubiprot.org.ru/ The lysine ubiquitin-modified site Database (UbiProt) was integrated thousands of high-confidence in vivo identified lysine ubiqutination on the basis of mass spectrometry. It has also included the specific information of proteins, including the nature of protein, species (mostly yeast and humans), ubiquitin-modified feature, references and related links.

Figure 2:
A flow-chart for computational prediction of protein lysine PTMs. Initially, the dataset was collected from public database. Then need to preprocess the collected datasets for making proper positive and negative samples. The encoded feature vectors were independently put into the probabilistic/machine learning models to produce independent prediction scores. Eventually, optimum performance scores were calculated by using cross-validation and parameter optimization, a confident cutoff was considered to identify the lysine PTM site.

Naïve Bayes
Naïve Bayes (NB) is a statistical probabilistic algorithm based on the statistical learning theory of Bayesian theorem [55]. The advantages of NB algorithm are very straightforward and high speed. In NB theorem, the posterior probability of a random event is the conditional probability, which is assigned after the relevant evidence is taken into account. The majority of biologists think that, for analyzing the biological data NB is an essential algorithm for analyzing biological [56]. Although, the NB models are much outlier affected and do not handle the noise datasets [57]. In lysine PTM prediction, the NB algorithm has been widely used [41].

Random forest
The random forest (RF) algorithm is a machine learning algorithm developed by Leo Breiman [58]. This model developed by using an ensemble of classification trees. RF has been widely used in lysine PTM prediction [31,32]. It was implemented as the RF package in R at https://cran.r-project.org/web/packages/randomForest/. RF is one of the most influential machine learning algorithm [59].

Support vector machine
Graft is an efficient machine learning algorithm, support vector machine (SVM) has been widely used in lysine PTM prediction [32]. In particular, the kernel radial basis function (RBF) with LIBSVM package (http://www.csie.ntu.edu.tw/~cjlin/libsvm/) was used to train the classifiers [60]. For a given training vector x i ∈R n , if the corresponding class label is y i ∈{-1, 1}, i=1, 2, … , L, then the optimized SVM model is given by: Under the required condition, where , w is the weight vector to the hyperplane, C is the cost parameter, and i ζ is the slack variable. The RBF kernel can be easily transformed to liner separation from high dimensional features. The commonly used RBF function can be defined as: where γ is the kernel parameter and γ>0, which determines how the samples are transformed to a high-dimensional space. The tuning parameters C and γ were maximized based on the training dataset by performing grid search.
In this study, we have shown an overview of lysine PTM site prediction. The application and development for predicting lysine PTM sites is emerging as a promising field in protein bioinformatics research. Fundamentally, high-throughput omics techniques require rigorous computational analysis for more accurate prediction. Combining experimental and computational technologies for analyzing lysine PTMs dataset will certainly enhance our knowledge.