Prediction Of Kinase-specific Phosphorylation Sites Research Articles

GPS 6.0: an updated server for prediction of kinase-specific phosphorylation sites in proteins.

Protein phosphorylation, catalyzed by protein kinases (PKs), is one of the most important post-translational modifications (PTMs), and involved in regulating almost all of biological processes. Here, we report an updated server, Group-based Prediction System (GPS) 6.0, for prediction of PK-specific phosphorylation sites (p-sites) in eukaryotes. First, we pre-trained a general model using penalized logistic regression (PLR), deep neural network (DNN), and Light Gradient Boosting Machine (LightGMB) on 490762 non-redundant p-sites in 71407 proteins. Then, transfer learning was conducted to obtain 577 PK-specific predictors at the group, family and single PK levels, using a well-curated data set of 30043 known site-specific kinase-substrate relations in 7041 proteins. Together with the evolutionary information, GPS 6.0 could hierarchically predict PK-specific p-sites for 44046 PKs in 185 species. Besides the basic statistics, we also offered the knowledge from 22 public resources to annotate the prediction results, including the experimental evidence, physical interactions, sequence logos, and p-sites in sequences and 3D structures. The GPS 6.0 server is freely available at https://gps.biocuckoo.cn. We believe that GPS 6.0 could be a highly useful service for further analysis of phosphorylation.

GPS 6.0: an updated server for prediction of kinase-specific phosphorylation sites in proteins

GPS 6.0: an updated server for prediction of kinase-specific phosphorylation sites in proteins

KinasePhos 3.0: Redesign and Expansion of the Prediction on Kinase-specific Phosphorylation Sites

The purpose of this work is to enhance KinasePhos, a machine learning-based kinase-specific phosphorylation site prediction tool. Experimentally verified kinase-specific phosphorylation data were collected from PhosphoSitePlus, UniProtKB, the GPS 5.0, and Phospho.ELM. In total, 41,421 experimentally verified kinase-specific phosphorylation sites were identified. A total of 1380 unique kinases were identified, including 753 with existing classification information from KinBase and the remaining 627 annotated by building a phylogenetic tree. Based on this kinase classification, a total of 771 predictive models were built at the individual, family, and group levels, using at least 15 experimentally verified substrate sites in positive training datasets. The improved models demonstrated their effectiveness compared with other prediction tools. For example, the prediction of sites phosphorylated by the protein kinase B, casein kinase 2, and protein kinase A families had accuracies of 94.5%, 92.5%, and 90.0%, respectively. The average prediction accuracy for all 771 models was 87.2%. For enhancing interpretability, the SHapley Additive exPlanations (SHAP) method was employed to assess feature importance. The web interface of KinasePhos 3.0 has been redesigned to provide comprehensive annotations of kinase-specific phosphorylation sites on multiple proteins. Additionally, considering the large scale of phosphoproteomic data, a downloadable prediction tool is available at https://awi.cuhk.edu.cn/KinasePhos/download.html or https://github.com/tom-209/KinasePhos-3.0-executable-file.

A Pretrained ELECTRA Model for Kinase-Specific Phosphorylation Site Prediction.

Phosphorylation plays a vital role in signal transduction and cell cycle. Identifying and understanding phosphorylation through machine-learning methods has a long history. However, existing methods only learn representations of a protein sequence segment from a labeled dataset itself, which could result in biased or incomplete features, especially for kinase-specific phosphorylation site prediction in which training data are typically sparse. To learn a comprehensive contextual representation of a protein sequence segment for kinase-specific phosphorylation site prediction, we pretrained our model from over 24 million unlabeled sequence fragments using ELECTRA (Efficiently Learning an Encoder that Classifies Token Replacements Accurately). The pretrained model was applied to kinase-specific site prediction of kinases CDK, PKA, CK2, MAPK, and PKC. The pretrained ELECTRA model achieves 9.02% improvement over BERT and 11.10% improvement over MusiteDeep in the area under the precision-recall curve on the benchmark data.

PhosIDN: an integrated deep neural network for improving protein phosphorylation site prediction by combining sequence and protein-protein interaction information.

MotivationPhosphorylation is one of the most studied post-translational modifications, which plays a pivotal role in various cellular processes. Recently, deep learning methods have achieved great success in prediction of phosphorylation sites, but most of them are based on convolutional neural network that may not capture enough information about long-range dependencies between residues in a protein sequence. In addition, existing deep learning methods only make use of sequence information for predicting phosphorylation sites, and it is highly desirable to develop a deep learning architecture that can combine heterogeneous sequence and protein–protein interaction (PPI) information for more accurate phosphorylation site prediction. ResultsWe present a novel integrated deep neural network named PhosIDN, for phosphorylation site prediction by extracting and combining sequence and PPI information. In PhosIDN, a sequence feature encoding sub-network is proposed to capture not only local patterns but also long-range dependencies from protein sequences. Meanwhile, useful PPI features are also extracted in PhosIDN by a PPI feature encoding sub-network adopting a multi-layer deep neural network. Moreover, to effectively combine sequence and PPI information, a heterogeneous feature combination sub-network is introduced to fully exploit the complex associations between sequence and PPI features, and their combined features are used for final prediction. Comprehensive experiment results demonstrate that the proposed PhosIDN significantly improves the prediction performance of phosphorylation sites and compares favorably with existing general and kinase-specific phosphorylation site prediction methods.Availability and implementationPhosIDN is freely available at https://github.com/ustchangyuanyang/PhosIDN.Supplementary information Supplementary data are available at Bioinformatics online.

GPS 5.0: An Update on the Prediction of Kinase-specific Phosphorylation Sites in Proteins

In eukaryotes, protein phosphorylation is specifically catalyzed by numerous protein kinases (PKs), faithfully orchestrates various biological processes, and reversibly determines cellular dynamics and plasticity. Here we report an updated algorithm of Group-based Prediction System (GPS) 5.0 to improve the performance for predicting kinase-specific phosphorylation sites (p-sites). Two novel methods, position weight determination (PWD) and scoring matrix optimization (SMO), were developed. Compared with other existing tools, GPS 5.0 exhibits a highly competitive accuracy. Besides serine/threonine or tyrosine kinases, GPS 5.0 also supports the prediction of dual-specificity kinase-specific p-sites. In the classical module of GPS 5.0, 617 individual predictors were constructed for predicting p-sites of 479 human PKs. To extend the application of GPS 5.0, a species-specific module was implemented to predict kinase-specific p-sites for 44,795 PKs in 161 eukaryotes. The online service and local packages of GPS 5.0 are freely available for academic research at http://gps.biocuckoo.cn.

Methylation determines the extracellular calcium sensitivity of the leak channel NALCN in hippocampal dentate granule cells

The sodium leak channel NALCN is a key player in establishing the resting membrane potential (RMP) in neurons and transduces changes in extracellular Ca2+ concentration ([Ca2+]e) into increased neuronal excitability as the downstream effector of calcium-sensing receptor (CaSR). Gain-of-function mutations in the human NALCN gene cause encephalopathy and severe intellectual disability. Thus, understanding the regulatory mechanisms of NALCN is important for both basic and translational research. This study reveals a novel mechanism for NALCN regulation by arginine methylation. Hippocampal dentate granule cells in protein arginine methyltransferase 7 (PRMT7)-deficient mice display a depolarization of the RMP, decreased threshold currents, and increased excitability compared to wild-type neurons. Electrophysiological studies combined with molecular analysis indicate that enhanced NALCN activities contribute to hyperexcitability in PRMT7−/− neurons. PRMT7 depletion in HEK293T cells increases NALCN activity by shifting the dose-response curve of NALCN inhibition by [Ca2+]e without affecting NALCN protein levels. In vitro methylation studies show that PRMT7 methylates a highly conserved Arg1653 of the NALCN gene located in the carboxy-terminal region that is implicated in CaSR-mediated regulation. A kinase-specific phosphorylation site prediction program shows that the adjacent Ser1652 is a potential phosphorylation site. Consistently, our data from site-specific mutants and PKC inhibitors suggest that Arg1653 methylation might modulate Ser1652 phosphorylation mediated by CaSR/PKC-delta, leading to [Ca2+]e-mediated NALCN suppression. Collectively, these data suggest that PRMT7 deficiency decreases NALCN methylation at Arg1653, which, in turn, decreases CaSR/PKC-mediated Ser1652 phosphorylation, lifting NALCN inhibition, thereby enhancing neuronal excitability. Thus, PRMT7-mediated NALCN inhibition provides a potential target for the development of therapeutic tools for neurological diseases.

DeepPhos: prediction of protein phosphorylation sites with deep learning.

MotivationPhosphorylation is the most studied post-translational modification, which is crucial for multiple biological processes. Recently, many efforts have been taken to develop computational predictors for phosphorylation site prediction, but most of them are based on feature selection and discriminative classification. Thus, it is useful to develop a novel and highly accurate predictor that can unveil intricate patterns automatically for protein phosphorylation sites.ResultsIn this study we present DeepPhos, a novel deep learning architecture for prediction of protein phosphorylation. Unlike multi-layer convolutional neural networks, DeepPhos consists of densely connected convolutional neuron network blocks which can capture multiple representations of sequences to make final phosphorylation prediction by intra block concatenation layers and inter block concatenation layers. DeepPhos can also be used for kinase-specific prediction varying from group, family, subfamily and individual kinase level. The experimental results demonstrated that DeepPhos outperforms competitive predictors in general and kinase-specific phosphorylation site prediction.Availability and implementationThe source code of DeepPhos is publicly deposited at https://github.com/USTCHIlab/DeepPhos.Supplementary information Supplementary data are available at Bioinformatics online.

PhosContext2vec: a distributed representation of residue-level sequence contexts and its application to general and kinase-specific phosphorylation site prediction

Phosphorylation is the most important type of protein post-translational modification. Accordingly, reliable identification of kinase-mediated phosphorylation has important implications for functional annotation of phosphorylated substrates and characterization of cellular signalling pathways. The local sequence context surrounding potential phosphorylation sites is considered to harbour the most relevant information for phosphorylation site prediction models. However, currently there is a lack of condensed vector representation for this important contextual information, despite the presence of varying residue-level features that can be constructed from sequence homology profiles, structural information, and physicochemical properties. To address this issue, we present PhosContext2vec which is a distributed representation of residue-level sequence contexts for potential phosphorylation sites and demonstrate its application in both general and kinase-specific phosphorylation site predictions. Benchmarking experiments indicate that PhosContext2vec could achieve promising predictive performance compared with several other existing methods for phosphorylation site prediction. We envisage that PhosContext2vec, as a new sequence context representation, can be used in combination with other informative residue-level features to improve the classification performance in a number of related bioinformatics tasks that require appropriate residue-level feature vector representation and extraction. The web server of PhosContext2vec is publicly available at http://phoscontext2vec.erc.monash.edu/.

MusiteDeep: a deep-learning framework for general and kinase-specific phosphorylation site prediction.

Computational methods for phosphorylation site prediction play important roles in protein function studies and experimental design. Most existing methods are based on feature extraction, which may result in incomplete or biased features. Deep learning as the cutting-edge machine learning method has the ability to automatically discover complex representations of phosphorylation patterns from the raw sequences, and hence it provides a powerful tool for improvement of phosphorylation site prediction. We present MusiteDeep, the first deep-learning framework for predicting general and kinase-specific phosphorylation sites. MusiteDeep takes raw sequence data as input and uses convolutional neural networks with a novel two-dimensional attention mechanism. It achieves over a 50% relative improvement in the area under the precision-recall curve in general phosphorylation site prediction and obtains competitive results in kinase-specific prediction compared to other well-known tools on the benchmark data. MusiteDeep is provided as an open-source tool available at https://github.com/duolinwang/MusiteDeep. xudong@missouri.edu. Supplementary data are available at Bioinformatics online.

Prediction of kinase-specific phosphorylation sites through an integrative model of protein context and sequence

Identifying kinase substrates and the specific phosphorylation sites they regulate is an important factor in understanding protein function regulation and signalling pathways. Computational prediction of kinase targets – assigning kinases to putative substrates, and selecting from protein sequence the sites that kinases can phosphorylate – requires the consideration of both the cellular context that kinases operate in, as well as their binding affinity. This consideration enables investigation of how phosphorylation influences a range of biological processes.We report here a novel probabilistic model for classifying kinase-specific phosphorylation sites from sequence across three model organisms: human, mouse and yeast. The model incorporates position-specific amino acid frequencies, and counts of co-occurring amino acids from kinase binding sites. We show how this model can be seamlessly integrated with protein interactions and cell-cycle abundance profiles. When evaluating the prediction accuracy of our method, PhosphoPICK, on an independent hold-out set of kinase-specific phosphorylation sites, it achieved an average specificity of 97%, with 32% sensitivity. We compared PhosphoPICK's ability, through cross-validation, to predict kinase-specific phosphorylation sites with alternative methods, and show that at high levels of specificity PhosphoPICK obtains greater sensitivity for most comparisons made.We investigated the relationship between kinase-specific phosphorylation sites and nuclear localisation signals. We show that kinases PKA, Akt1 and AurB have an over-representation of predicted binding sites at particular positions downstream from predicted nuclear localisation signals, demonstrating an important role for these kinases in regulating the nuclear import of proteins.PhosphoPICK is freely available as a web-service at http://bioinf.scmb.uq.edu.au/phosphopick.

PhosphoPICK: modelling cellular context to map kinase-substrate phosphorylation events.

The determinants of kinase-substrate phosphorylation can be found both in the substrate sequence and the surrounding cellular context. Cell cycle progression, interactions with mediating proteins and even prior phosphorylation events are necessary for kinases to maintain substrate specificity. While much work has focussed on the use of sequence-based methods to predict phosphorylation sites, there has been very little work invested into the application of systems biology to understand phosphorylation. Lack of specificity in many kinase substrate binding motifs means that sequence methods for predicting kinase binding sites are susceptible to high false-positive rates. We present here a model that takes into account protein-protein interaction information, and protein abundance data across the cell cycle to predict kinase substrates for 59 human kinases that are representative of important biological pathways. The model shows high accuracy for substrate prediction (with an average AUC of 0.86) across the 59 kinases tested. When using the model to complement sequence-based kinase-specific phosphorylation site prediction, we found that the additional information increased prediction performance for most comparisons made, particularly on kinases from the CMGC family. We then used our model to identify functional overlaps between predicted CDK2 substrates and targets from the E2F family of transcription factors. Our results demonstrate that a model harnessing context data can account for the short-falls in sequence information and provide a robust description of the cellular events that regulate protein phosphorylation. The method is freely available online as a web server at the website http://bioinf.scmb.uq.edu.au/phosphopick. m.boden@uq.edu.au Supplementary data are available at Bioinformatics online.

Incorporating substrate sequence motifs and spatial amino acid composition to identify kinase-specific phosphorylation sites on protein three-dimensional structures

BackgroundProtein phosphorylation catalyzed by kinases plays crucial regulatory roles in cellular processes. Given the high-throughput mass spectrometry-based experiments, the desire to annotate the catalytic kinases for in vivo phosphorylation sites has motivated. Thus, a variety of computational methods have been developed for performing a large-scale prediction of kinase-specific phosphorylation sites. However, most of the proposed methods solely rely on the local amino acid sequences surrounding the phosphorylation sites. An increasing number of three-dimensional structures make it possible to physically investigate the structural environment of phosphorylation sites.ResultsIn this work, all of the experimental phosphorylation sites are mapped to the protein entries of Protein Data Bank by sequence identity. It resulted in a total of 4508 phosphorylation sites containing the protein three-dimensional (3D) structures. To identify phosphorylation sites on protein 3D structures, this work incorporates support vector machines (SVMs) with the information of linear motifs and spatial amino acid composition, which is determined for each kinase group by calculating the relative frequencies of 20 amino acid types within a specific radial distance from central phosphorylated amino acid residue. After the cross-validation evaluation, most of the kinase-specific models trained with the consideration of structural information outperform the models considering only the sequence information. Furthermore, the independent testing set which is not included in training set has demonstrated that the proposed method could provide a comparable performance to other popular tools.ConclusionThe proposed method is shown to be capable of predicting kinase-specific phosphorylation sites on 3D structures and has been implemented as a web server which is freely accessible at http://csb.cse.yzu.edu.tw/PhosK3D/. Due to the difficulty of identifying the kinase-specific phosphorylation sites with similar sequenced motifs, this work also integrates the 3D structural information to improve the cross classifying specificity.

Identifying Protein Phosphorylation Sites with Kinase Substrate Specificity on Human Viruses

Viruses infect humans and progress inside the body leading to various diseases and complications. The phosphorylation of viral proteins catalyzed by host kinases plays crucial regulatory roles in enhancing replication and inhibition of normal host-cell functions. Due to its biological importance, there is a desire to identify the protein phosphorylation sites on human viruses. However, the use of mass spectrometry-based experiments is proven to be expensive and labor-intensive. Furthermore, previous studies which have identified phosphorylation sites in human viruses do not include the investigation of the responsible kinases. Thus, we are motivated to propose a new method to identify protein phosphorylation sites with its kinase substrate specificity on human viruses. The experimentally verified phosphorylation data were extracted from virPTM – a database containing 301 experimentally verified phosphorylation data on 104 human kinase-phosphorylated virus proteins. In an attempt to investigate kinase substrate specificities in viral protein phosphorylation sites, maximal dependence decomposition (MDD) is employed to cluster a large set of phosphorylation data into subgroups containing significantly conserved motifs. The experimental human phosphorylation sites are collected from Phospho.ELM, grouped according to its kinase annotation, and compared with the virus MDD clusters. This investigation identifies human kinases such as CK2, PKB, CDK, and MAPK as potential kinases for catalyzing virus protein substrates as confirmed by published literature. Profile hidden Markov model is then applied to learn a predictive model for each subgroup. A five-fold cross validation evaluation on the MDD-clustered HMMs yields an average accuracy of 84.93% for Serine, and 78.05% for Threonine. Furthermore, an independent testing data collected from UniProtKB and Phospho.ELM is used to make a comparison of predictive performance on three popular kinase-specific phosphorylation site prediction tools. In the independent testing, the high sensitivity and specificity of the proposed method demonstrate the predictive effectiveness of the identified substrate motifs and the importance of investigating potential kinases for viral protein phosphorylation sites.

Characterization and Prediction of Lysine (K)-Acetyl-Transferase Specific Acetylation Sites

Lysine acetylation is a well-studied post-translational modification on both histone and nonhistone proteins. More than 2000 acetylated proteins and 4000 lysine acetylation sites have been identified by large scale mass spectrometry or traditional experimental methods. Although over 20 lysine (K)-acetyl-transferases (KATs) have been characterized, which KAT is responsible for a given protein or lysine site acetylation is mostly unknown. In this work, we collected KAT-specific acetylation sites manually and analyzed sequence features surrounding the acetylated lysine of substrates from three main KAT families (CBP/p300, GCN5/PCAF, and the MYST family). We found that each of the three KAT families acetylates lysines with different sequence features. Based on these differences, we developed a computer program, Acetylation Set Enrichment Based method to predict which KAT-families are responsible for acetylation of a given protein or lysine site. Finally, we evaluated the efficiency of our method, and experimentally detected four proteins that were predicted to be acetylated by two KAT families when one representative member of the KAT family is over expressed. We conclude that our approach, combined with more traditional experimental methods, may be useful for identifying KAT families responsible for acetylated substrates proteome-wide.

Restricted growth of U-type infectious haematopoietic necrosis virus (IHNV) in rainbow trout cells may be linked to casein kinase II activity

Previously, we demonstrated that a representative M genogroup type strain of infectious haematopoietic necrosis virus (IHNV) from rainbow trout grows well in rainbow trout‐derived RTG‐2 cells, but a U genogroup type strain from sockeye salmon has restricted growth, associated with reduced genome replication and mRNA transcription. Here, we analysed further the mechanisms for this growth restriction of U‐type IHNV in RTG‐2 cells, using strategies that assessed differences in viral genes, host immune regulation and phosphorylation. To determine whether the viral glycoprotein (G) or non‐virion (NV) protein was responsible for the growth restriction, four recombinant IHNV viruses were generated in which the G gene of an infectious IHNV clone was replaced by the G gene of U‐ or M‐type IHNV and the NV gene was replaced by NV of U‐ or M‐type IHNV. There was no significant difference in the growth of these recombinants in RTG‐2 cells, indicating that G and NV proteins are not major factors responsible for the differential growth of the U‐ and M‐type strains. Poly I:C pretreatment of RTG‐2 cells suppressed the growth of both U‐ and M‐type IHNV, although the M virus continued to replicate at a reduced level. Both viruses induced type 1 interferon (IFN1) and the IFN1 stimulated gene Mx1, but the expression levels in M‐infected cells were significantly higher than in U‐infected cells and an inhibitor of the IFN1‐inducible protein kinase PKR, 2‐aminopurine (2‐AP), did not affect the growth of U‐ or M‐type IHNV in RTG‐2 cells. These data did not indicate a role for the IFN1 system in the restricted growth of U‐type IHNV in RTG‐2 cells. Prediction of kinase‐specific phosphorylation sites in the viral phosphoprotein (P) using the NetPhosK program revealed differences between U‐ and M‐type P genes at five phosphorylation sites. Pretreatment of RTG‐2 cells with a PKC inhibitor or a p38MAPK inhibitor did not affect the growth of the U‐ and M‐type viruses. However, 100 μm of the casein kinase II (CKII) inhibitor, 5,6‐dichloro‐1‐β‐d‐ribofuranosylbenzimidazole (DRB), reduced the titre of the U type 8.3‐fold at 24 h post‐infection. In contrast, 100 μm of the CKII inhibitor reduced the titre of the M type only 1.3‐fold at 48 h post‐infection. Our data suggest that the different growth of U‐ and M‐type IHNV in RTG‐2 cells may be linked to a differential requirement for cellular protein kinases such as CKII for their growth.

GPS 2.1: enhanced prediction of kinase-specific phosphorylation sites with an algorithm of motif length selection

As the most important post-translational modification of proteins, phosphorylation plays essential roles in all aspects of biological processes. Besides experimental approaches, computational prediction of phosphorylated proteins with their kinase-specific phosphorylation sites has also emerged as a popular strategy, for its low-cost, fast-speed and convenience. In this work, we developed a kinase-specific phosphorylation sites predictor of GPS 2.1 (Group-based Prediction System), with a novel but simple approach of motif length selection (MLS). By this approach, the robustness of the prediction system was greatly improved. All algorithms in GPS old versions were also reserved and integrated in GPS 2.1. The online service and local packages of GPS 2.1 were implemented in JAVA 1.5 (J2SE 5.0) and freely available for academic researches at: http://gps.biocuckoo.org.

RegPhos: a system to explore the protein kinase–substrate phosphorylation network in humans

Protein phosphorylation catalyzed by kinases plays crucial regulatory roles in intracellular signal transduction. With the increasing number of experimental phosphorylation sites that has been identified by mass spectrometry-based proteomics, the desire to explore the networks of protein kinases and substrates is motivated. Manning et al. have identified 518 human kinase genes, which provide a starting point for comprehensive analysis of protein phosphorylation networks. In this study, a knowledgebase is developed to integrate experimentally verified protein phosphorylation data and protein–protein interaction data for constructing the protein kinase–substrate phosphorylation networks in human. A total of 21 110 experimental verified phosphorylation sites within 5092 human proteins are collected. However, only 4138 phosphorylation sites (∼20%) have the annotation of catalytic kinases from public domain. In order to fully investigate how protein kinases regulate the intracellular processes, a published kinase-specific phosphorylation site prediction tool, named KinasePhos is incorporated for assigning the potential kinase. The web-based system, RegPhos, can let users input a group of human proteins; consequently, the phosphorylation network associated with the protein subcellular localization can be explored. Additionally, time-coursed microarray expression data is subsequently used to represent the degree of similarity in the expression profiles of network members. A case study demonstrates that the proposed scheme not only identify the correct network of insulin signaling but also detect a novel signaling pathway that may cross-talk with insulin signaling network. This effective system is now freely available at http://RegPhos.mbc.nctu.edu.tw.

A Summary of Computational Resources for Protein Phosphorylation

Protein phosphorylation is the most ubiquitous post-translational modification (PTM), and plays important roles in most of biological processes. Identification of site-specific phosphorylated substrates is fundamental for understanding the molecular mechanisms of phosphorylation. Besides experimental approaches, prediction of potential candidates with computational methods has also attracted great attention for its convenience, fast-speed and low-cost. In this review, we present a comprehensive but brief summarization of computational resources of protein phosphorylation, including phosphorylation databases, prediction of non-specific or organism-specific phosphorylation sites, prediction of kinase-specific phosphorylation sites or phospho-binding motifs, and other tools. The latest compendium of computational resources for protein phosphorylation is available at: http://gps.biocuckoo.org/links.php.

PostMod: sequence based prediction of kinase-specific phosphorylation sites with indirect relationship

BackgroundPost-translational modifications (PTMs) have a key role in regulating cell functions. Consequently, identification of PTM sites has a significant impact on understanding protein function and revealing cellular signal transductions. Especially, phosphorylation is a ubiquitous process with a large portion of proteins undergoing this modification. Experimental methods to identify phosphorylation sites are labor-intensive and of high-cost. With the exponentially growing protein sequence data, development of computational approaches to predict phosphorylation sites is highly desirable.ResultsHere, we present a simple and effective method to recognize phosphorylation sites by combining sequence patterns and evolutionary information and by applying a novel noise-reducing algorithm. We suggested that considering long-range region surrounding a phosphorylation site is important for recognizing phosphorylation peptides. Also, from compared results to AutoMotif in 36 different kinase families, new method outperforms AutoMotif. The mean accuracy, precision, and recall of our method are 0.93, 0.67, and 0.40, respectively, whereas those of AutoMotif with a polynomial kernel are 0.91, 0.47, and 0.17, respectively. Also our method shows better or comparable performance in four main kinase groups, CDK, CK2, PKA, and PKC compared to six existing predictors.ConclusionOur method is remarkable in that it is powerful and intuitive approach without need of a sophisticated training algorithm. Moreover, our method is generally applicable to other types of PTMs.