PhosPred-RF: A Novel Sequence-Based Predictor for Phosphorylation Sites Using Sequential Information Only

Wei, Leyi; Xing, Pengwei; Tang, Jijun; Zou, Quan

doi:10.1109/tnb.2017.2661756

Cited by 114 publications

(64 citation statements)

References 41 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Although several dozen prediction methods exist (16), six available tools were compared with PHOSforUS to assess the algorithm's real-world performance (10,11,(13)(14)(15)(16). These methods were chosen because they were freely accessible and could handle the large datasets used for testing (see Methods).…”

Section: An Improved Phosphorylation Site Predictor Resulting From Comentioning

confidence: 99%

“…Using the presence or absence of +1 Pro to separate phosphorylated and non-phosphorylated sequences into four subclasses reveals substantial differences in amino acid conservation patterns. Many classic examples of vertical information used in phosphorylation site prediction have been previously reported (10,13,14,16), but we focus here on the special case of Ser and Thr sites with +1 Pro (noting Tyr shows no +1 sites), and demonstrate that this sequence motif, although not diagnostic by itself, is particularly useful in site prediction. Testing several residue types and locations in the neighborhood of known phosphorylation sites, the presence of +1 Pro is the single most informative position in differentiating subgroups from the complete dataset (Supplementary Figure S2B).…”

Section: Vertical Sequence Information From Eukaryotic Phosphorylatiomentioning

confidence: 98%

See 1 more Smart Citation

Hidden dynamic signatures drive substrate selectivity in the disordered phosphoproteome

Cho

Wrabl

Taylor

et al. 2019

Preprint

View full text Add to dashboard Cite

Keywords conformational equilibrium, intrinsic disorder, cellular signaling, protein ensemble, local unfolding Author Contributions MHC, JOW, JT, VJH designed research; MHC performed research; MHC contributed new analytic tools; MHC, JOW, JT, VJH analyzed data; MHC, JOW, JT, VJH wrote the paper. AbstractPhosphorylation sites are hyper-abundant in the disordered proteins of eukaryotes, suggesting that conformational dynamics (or heterogeneity) may play a major role in determining to what extent a kinase interacts with a particular substrate. In biophysical terms, substrate selectivity may be determined not just by the structural and chemical complementarity between the kinase and its protein substrates, but also by the free energy difference between the conformational ensembles that are recognized by the kinase and those that are not. To test this hypothesis, we developed an informatics framework based on statistical thermodynamics, which allows us to probe for dynamic contributions to phosphorylation, as evaluated by the ability to predict Ser/Thr/ Tyr phosphorylation sites in the disordered proteome. Essential to this framework is a decomposition of substrate sequence information into two types: vertical information encoding conserved kinase specificity motifs and horizontal (distributed) information encoding substrate conformational dynamics that are embedded, but often not apparent, within position specific conservation patterns. We find not only that conformational dynamics play a major role, but that they are the dominant contribution to substrate selectivity.In fact, the main substrate classifier distinguishing selectivity is the magnitude of change in compaction of the disordered chain upon phosphorylation. Thus, in addition to providing fundamental insights into the underlying mechanistic consequences of phosphorylation across the entire proteome, our approach provides a novel statistical thermodynamic strategy for partitioning any sequence-based search into contributions from direct chemical and structural complementarity and those from changes in conformational dynamics. Using this framework, we developed a high-performance open-source phosphorylation site predictor, PHOSforUS, which is freely available at https://github.com/bxlab/PHOSforUS. Furthermore, our results indicate that the sequence neighborhoods of many Serine (Ser) and Threonine (Thr) phosphorylation sites, specifically those containing Pro immediately C-terminal to the phosphorylated site (i.e. the +1 Pro sequence motif ), are "energetically poised" to undergo a phosphorylation-induced change in the dimensions of the disordered ensemble, suggestive of a direct link between the conformational dimensions of the disordered substrate and its ability to be recognized and phosphorylated.

show abstract

Section: An Improved Phosphorylation Site Predictor Resulting From Comentioning

confidence: 99%

Section: Vertical Sequence Information From Eukaryotic Phosphorylatiomentioning

confidence: 98%

Hidden dynamic signatures drive substrate selectivity in the disordered phosphoproteome

Cho

Wrabl

Taylor

et al. 2019

Preprint

View full text Add to dashboard Cite

show abstract

“…Thus, these two methods were trained on the validated OC-related gene set S oc , through which the optimal parameters can be determined. We used the jackknife test [46, 47], which is one of the classic cross-validation methods [48, 49], to evaluate the performance of these two methods, i . e ., each OC-related gene in S oc was singled out sequentially, and the remaining genes in S oc were used to generate predictions under various combinations of parameters.…”

Section: Methodsmentioning

confidence: 99%

An integrated method for the identification of novel genes related to oral cancer

Lei¹,

Yang²,

Xing³

et al. 2017

PLoS ONE

View full text Add to dashboard Cite

Cancer is a significant public health problem worldwide. Complete identification of genes related to one type of cancer facilitates earlier diagnosis and effective treatments. In this study, two widely used algorithms, the random walk with restart algorithm and the shortest path algorithm, were adopted to construct two parameterized computational methods, namely, an RWR-based method and an SP-based method; based on these methods, an integrated method was constructed for identifying novel disease genes. To validate the utility of the integrated method, data for oral cancer were used, on which the RWR-based and SP-based methods were trained, thereby building two optimal methods. The integrated method combining these optimal methods was further adopted to identify the novel genes of oral cancer. As a result, 85 novel genes were inferred, among which eleven genes (e.g., MYD88, FGFR2, NF-κBIA) were identified by both the RWR-based and SP-based methods, 70 genes (e.g., BMP4, IFNG, KITLG) were discovered only by the RWR-based method and four genes (L1R1, MCM6, NOG and CXCR3) were predicted only by the SP-based method. Extensive analyses indicate that several novel genes have strong associations with cancers, indicating the effectiveness of the integrated method for identifying disease genes.

show abstract

“…ncbi.nlm.nih.gov/pubmed), and Gene Ontology (Ashburner et al, 2000) term annotation. The Human Protein Reference Database (HPRD) (Prasad et al, 2009) is a protein database for experimentally derived information about human proteomics, including protein and protein interactions (Ding et al, 2016;Wei et al, 2017a), post-translational modifications (PTMs) (Wei et al, 2017b) and other information. We download all human PPIs from this database, containing 15,231 proteins and 38,167 interactions.…”

Section: Analysis Of Disease Characteristics Of Hccmentioning

confidence: 99%

Predict New Therapeutic Drugs for Hepatocellular Carcinoma Based on Gene Mutation and Expression

Gao

2020

Front. Bioeng. Biotechnol.

View full text Add to dashboard Cite

Hepatocellular carcinoma (HCC) is the fourth most common primary liver tumor and is an important medical problem worldwide. However, the use of current therapies for HCC is no possible to be cured, and despite numerous attempts and clinical trials, there are not so many approved targeted treatments for HCC. So, it is necessary to identify additional treatment strategies to prevent the growth of HCC tumors. We are looking for a systematic drug repositioning bioinformatics method to identify new drug candidates for the treatment of HCC, which considers not only aberrant genomic information, but also the changes of transcriptional landscapes. First, we screen the collection of HCC feature genes, i.e., kernel genes, which frequently mutated in most samples of HCC based on human mutation data. Then, the gene expression data of HCC in TCGA are combined to classify the kernel genes of HCC. Finally, the therapeutic score (TS) of each drug is calculated based on the kolmogorov-smirnov statistical method. Using this strategy, we identify five drugs that associated with HCC, including three drugs that could treat HCC and two drugs that might have side-effect on HCC. In addition, we also make Connectivity Map (CMap) profiles similarity analysis and KEGG enrichment analysis on drug targets. All these findings suggest that our approach is effective for accurate predicting novel therapeutic options for HCC and easily to be extended to other tumors.

show abstract

PhosPred-RF: A Novel Sequence-Based Predictor for Phosphorylation Sites Using Sequential Information Only

Cited by 114 publications

References 41 publications

Hidden dynamic signatures drive substrate selectivity in the disordered phosphoproteome

Hidden dynamic signatures drive substrate selectivity in the disordered phosphoproteome

An integrated method for the identification of novel genes related to oral cancer

Predict New Therapeutic Drugs for Hepatocellular Carcinoma Based on Gene Mutation and Expression

Contact Info

Product

Resources

About