Prediction of functional phosphorylation sites by incorporating evolutionary information

Protein phosphorylation is a ubiquitous protein post-translational modification, which plays an important role in cellular signaling systems underlying various physiological and pathological processes. Current in silico methods mainly focused on the prediction of phosphorylation sites, but rare methods considered whether a phosphorylation site is functional or not. Since functional phosphorylation sites are more valuable for further experimental research and a proportion of phosphorylation sites have no direct functional effects, the prediction of functional phosphorylation sites is quite necessary for this research area. Previous studies have shown that functional phosphorylation sites are more conserved than non-functional phosphorylation sites in evolution. Thus, in our method, we developed a web server by integrating existing phosphorylation site prediction methods, as well as both absolute and relative evolutionary conservation scores to predict the most likely functional phosphorylation sites. Using our method, we predicted the most likely functional sites of the human, rat and mouse proteomes and built a database for the predicted sites. By the analysis of overall prediction results, we demonstrated that protein phosphorylation plays an important role in all the enriched KEGG pathways. By the analysis of protein-specific prediction results, we demonstrated the usefulness of our method for individual protein studies. Our method would help to characterize the most likely functional phosphorylation sites for further studies in this research area.     

Implicit motif distribution based hybrid computational kernel for sequence classification

MOTIVATION: We designed a general computational kernel for classification problems that require specific motif extraction and search from sequences. Instead of searching for explicit motifs, our approach finds the distribution of implicit motifs and uses as a feature for classification. Implicit motif distribution approach may be used as modus operandi for bioinformatics problems that require specific motif extraction and search, which is otherwise computationally prohibitive. RESULTS: A system named P2SL that infer protein subcellular targeting was developed through this computational kernel. Targeting-signal was modeled by the distribution of subsequence occurrences (implicit motifs) using self-organizing maps. The boundaries among the classes were then determined with a set of support vector machines. P2SL hybrid computational system achieved approximately 81% of prediction accuracy rate over ER targeted, cytosolic, mitochondrial and nuclear protein localization classes. P2SL additionally offers the distribution potential of proteins among localization classes, which is particularly important for proteins, shuttle between nucleus and cytosol. AVAILABILITY: http://staff.vbi.vt.edu/volkan/p2sl and http://www.i-cancer.fen.bilkent.edu.tr/p2sl CONTACT: rengul@bilkent.edu.tr.     

A framework for incorporating functional interrelationships into protein function prediction algorithms

The functional annotation of proteins is one of the most important tasks in the post-genomic era. Although many computational approaches have been developed in recent years to predict protein function, most of these traditional algorithms do not take interrelationships among functional terms into account, such as different GO terms usually coannotate with some common proteins. In this study, we propose a new functional similarity measure in the form of Jaccard coefficient to quantify these interrelationships and also develop a framework for incorporating GO term similarity into protein function prediction process. The experimental results of cross-validation on S. cerevisiae and Homo sapiens data sets demonstrate that our method is able to improve the performance of protein function prediction. In addition, we find that small size terms associated with a few of proteins obtain more benefit than the large size ones when considering functional interrelationships. We also compare our similarity measure with other two widely used measures, and results indicate that when incorporated into function prediction algorithms, our proposed measure is more effective. Experiment results also illustrate that our algorithms outperform two previous competing algorithms, which also take functional interrelationships into account, in prediction accuracy. Finally, we show that our method is robust to annotations in the database which are not complete at present. These results give new insights about the importance of functional interrelationships in protein function prediction.     

Coupling dynamics and evolutionary information with structure to identify protein regulatory and functional binding sites

Binding sites in proteins can be either specifically functional binding sites (active sites) that bind specific substrates with high affinity or regulatory binding sites (allosteric sites), that modulate the activity of functional binding sites through effector molecules. Owing to their significance in determining protein function, the identification of protein functional and regulatory binding sites is widely acknowledged as an important biological problem. In this work, we present a novel binding site prediction method, Active and Regulatory site Prediction (AR-Pred), which supplements protein geometry, evolutionary, and physicochemical features with information about protein dynamics to predict putative active and allosteric site residues. As the intrinsic dynamics of globular proteins plays an essential role in controlling binding events, we find it to be an important feature for the identification of protein binding sites. We train and validate our predictive models on multiple balanced training and validation sets with random forest machine learning and obtain an ensemble of discrete models for each prediction type. Our models for active site prediction yield a median area under the curve (AUC) of 91% and Matthews correlation coefficient (MCC) of 0.68, whereas the less well-defined allosteric sites are predicted at a lower level with a median AUC of 80% and MCC of 0.48. When tested on an independent set of proteins, our models for active site prediction show comparable performance to two existing methods and gains compared to two others, while the allosteric site models show gains when tested against three existing prediction methods. AR-Pred is available as a free downloadable package at https://github.com/sambitmishra0628/AR-PRED_source .     

Spliced leader RNA of trypanosomes: in vivo mutational analysis reveals extensive and distinct requirements for trans splicing and cap4 formation.

In trypanosomes mRNAs are generated through trans splicing. The spliced leader (SL) RNA, which donates the 5'-terminal mini-exon to each of the protein coding exons, plays a central role in the trans splicing process. We have established in vivo assays to study in detail trans splicing, cap4 modification, and RNP assembly of the SL RNA in the trypanosomatid species Leptomonas seymouri. First, we found that extensive sequences within the mini-exon are required for SL RNA function in vivo, although a conserved length of 39 nt is not essential. In contrast, the intron sequence appears to be surprisingly tolerant to mutation; only the stem-loop II structure is indispensable. The asymmetry of the sequence requirements in the stem I region suggests that this domain may exist in different functional conformations. Second, distinct mini-exon sequences outside the modification site are important for efficient cap4 formation. Third, all SL RNA mutations tested allowed core RNP assembly, suggesting flexible requirements for core protein binding. In sum, the results of our mutational analysis provide evidence for a discrete domain structure of the SL RNA and help to explain the strong phylogenetic conservation of the mini-exon sequence and of the overall SL RNA secondary structure; they also suggest that there may be certain differences between trans splicing in nematodes and trypanosomes. This approach provides a basis for studying RNA-RNA interactions in the trans spliceosome.     

Predicting protein functions from PPI networks using functional aggregation

Predicting protein functions computationally from massive protein-protein interaction (PPI) data generated by high-throughput technology is one of the challenges and fundamental problems in the post-genomic era. Although there have been many approaches developed for computationally predicting protein functions, the mutual correlations among proteins in terms of protein functions have not been thoroughly investigated and incorporated into existing prediction methods, especially in voting based prediction methods. In this paper, we propose an innovative method to predict protein functions from PPI data by aggregating the functional correlations among relevant proteins using the Choquet-Integral in fuzzy theory. This functional aggregation measures the real impact of each relevant protein function on the final prediction results, and reduces the impact of repeated functional information on the prediction. Accordingly, a new protein similarity and a new iterative prediction algorithm are proposed in this paper. The experimental evaluations on real PPI datasets demonstrate the effectiveness of our method.     

Diversity of 7 SL RNA from the signal recognition particle of maize endosperm.

An 11 S ribonucleoprotein particle was isolated from maize endosperm and shown to be functionally and structurally equivalent to the mammalian signal recognition particle. However, unlike animal cells which apparently contain a single 7 SL RNA species, maize endosperm contains a heterogeneous population of 7 SL RNA. To investigate this diversity, we have cloned and sequenced a number of the maize endosperm 7 SL RNAs isolated from functionally active SRP preparations. Some maize 7 SL RNAs are strikingly similar, differing by single base changes or short deletions; surprisingly, others share less than 70 percent sequence homology. Despite differences in primary sequence, nearly identical secondary structures can be suggested for all maize 7 SL RNAs, consistent with a proposed functional role in protein translocation for each of these RNAs. The amount of new available sequence data enabled us to define two conserved regions of presumed functional importance: A conserved sequence -G-N-A-R- in the center of a variable region which forms a well defined stem-loop and possibly is involved in an interaction with the 19 kDa protein of the SRP. Secondly, three short nucleotide stretches located in the central domain of 7 SL RNA may form part of a dynamic RNA-switch structure.     

Widely predicting specific protein functions based on protein-protein interaction data and gene expression profile

GESTs (gene expression similarity and taxonomy similarity), a gene functional prediction approach previously proposed by us, is based on gene expression similarity and concept similarity of functional classes defined in Gene Ontology (GO). In this paper, we extend this method to protein-protein interaction data by introducing several methods to filter the neighbors in protein interaction networks for a protein of unknown function(s). Unlike other conventional methods, the proposed approach automatically selects the most appropriate functional classes as specific as possible during the learning process, and calls on genes annotated to nearby classes to support the predictions to some small-sized specific classes in GO. Based on the yeast protein-protein interaction information from MIPS and a dataset of gene expression profiles, we assess the performances of our approach for predicting protein functions to “biology process” by three measures particularly designed for functional classes organized in GO. Results show that our method is powerful for widely predicting gene functions with very specific functional terms. Based on the GO database published in December 2004, we predict some proteins whose functions were unknown at that time, and some of the predictions have been confirmed by the new SGD annotation data published in April, 2006.     

Widely predicting specific protein functions based on protein-protein interaction data and gene expression profile

GESTs (gene expression similarity and taxonomy similarity), a gene functional prediction approach previously proposed by us, is based on gene expression similarity and concept similarity of functional classes defined in Gene Ontology (GO). In this paper, we extend this method to protein-protein interac-tion data by introducing several methods to filter the neighbors in protein interaction networks for a protein of unknown function(s). Unlike other conventional methods, the proposed approach automati-cally selects the most appropriate functional classes as specific as possible during the learning proc-ess, and calls on genes annotated to nearby classes to support the predictions to some small-sized specific classes in GO. Based on the yeast protein-protein interaction information from MIPS and a dataset of gene expression profiles, we assess the performances of our approach for predicting protein functions to “biology process” by three measures particularly designed for functional classes organ-ized in GO. Results show that our method is powerful for widely predicting gene functions with very specific functional terms. Based on the GO database published in December 2004, we predict some proteins whose functions were unknown at that time, and some of the predictions have been confirmed by the new SGD annotation data published in April, 2006.     

An uncapped RNA suggests a model for Caenorhabditis elegans polycistronic pre-mRNA processing

Polycistronic pre-mRNAs from Caenohabditis elegans operons are processed by internal cleavage and polyadenylation to create 3' ends of mature mRNAs. This is accompanied by trans-splicing with SL2 approximately 100 nucleotides downstream of the 3' end formation sites to create the 5' ends of downstream mRNAs. SL2 trans-splicing depends on a U-rich element (Ur), located approximately 70 nucleotides upstream of the trans-splice site in the intercistronic region (ICR), as well as a functional 3' end formation signal. Here we report the existence of a novel gene-length RNA, the Ur-RNA, starting just upstream of the Ur element. The expression of Ur-RNA is dependent on 3' end formation as well as on the presence of the Ur element, but does not require a trans-splice site. The Ur-RNA is not capped, and alteration of the location of the Ur element in either the 5' or 3' direction alters the location of the 5' end of the Ur-RNA. We propose that a 5' to 3' exonuclease degrades the precursor RNA following cleavage at the poly(A) site, stopping when it reaches the Ur element, presumably attributable to a bound protein. Part of the function of this protein can be performed by the MS2 coat protein. Recruitment of coat protein to the ICR in the absence of the Ur element results in accumulation of an RNA equivalent to Ur-RNA, and restores trans-splicing. Only SL1, however, is used. Therefore, coat protein is sufficient for blocking the exonuclease and thereby allowing formation of a substrate for trans-splicing, but it lacks the ability to recruit the SL2 snRNP. Our results also demonstrate that MS2 coat protein can be used as an in vivo block to an exonuclease, which should have utility in mRNA stability studies.     

Using indirect protein-protein interactions for protein complex prediction

Protein complexes are fundamental for understanding principles of cellular organizations. As the sizes of protein-protein interaction (PPI) networks are increasing, accurate and fast protein complex prediction from these PPI networks can serve as a guide for biological experiments to discover novel protein complexes. However, it is not easy to predict protein complexes from PPI networks, especially in situations where the PPI network is noisy and still incomplete. Here, we study the use of indirect interactions between level-2 neighbors (level-2 interactions) for protein complex prediction. We know from previous work that proteins which do not interact but share interaction partners (level-2 neighbors) often share biological functions. We have proposed a method in which all direct and indirect interactions are first weighted using topological weight (FS-Weight), which estimates the strength of functional association. Interactions with low weight are removed from the network, while level-2 interactions with high weight are introduced into the interaction network. Existing clustering algorithms can then be applied to this modified network. We have also proposed a novel algorithm that searches for cliques in the modified network, and merge cliques to form clusters using a "partial clique merging" method. Experiments show that (1) the use of indirect interactions and topological weight to augment protein-protein interactions can be used to improve the precision of clusters predicted by various existing clustering algorithms; and (2) our complex-finding algorithm performs very well on interaction networks modified in this way. Since no other information except the original PPI network is used, our approach would be very useful for protein complex prediction, especially for prediction of novel protein complexes.     

Globally predicting protein functions based on co-expressed protein-protein interaction networks and ontology taxonomy similarities

Determining protein functions is an important task in the post-genomic era. Most of the current methods work on some large-sized functional classes selected from functional categorization systems prior to the prediction processes. GESTs, a prediction approach previously proposed by us, is based on gene expression similarity and taxonomy similarity of the functional classes. Unlike many conventional methods, it does not require pre-selecting the functional classes and can predict specific functions for genes according to the functional annotations of their co-expressed genes. In this paper, we extend this method for analyzing protein-protein interaction data. We introduce gene expression data to filter the interacting neighbors of a protein in order to enhance the degree of functional consensus among the neighbors. Using the taxonomy similarity of protein functional classes, the proposed approach can call on the interacting neighbor proteins annotated to nearby classes to support the predictions for an uncharacterized protein, and automatically select the most appropriate small-sized specific functional classes in Gene Ontology (GO) during the learning process. By three measures particularly designed for the functional classes organized in GO, we evaluate the effects of using different taxonomy similarity scores on the prediction performance. Based on the yeast protein-protein interaction data from MIPS and a dataset of gene expression profiles, we show that this method is powerful for predicting protein function to very specific terms. Compared with the other two taxonomy similarity measures used in this study, if we want to achieve higher prediction accuracy with an acceptable specific level (predicted depth), SB-TS measure proposed by us is a reasonable choice for ontology-based functional predictions.     

Probabilistic protein function prediction from heterogeneous genome-wide data

Dramatic improvements in high throughput sequencing technologies have led to a staggering growth in the number of predicted genes. However, a large fraction of these newly discovered genes do not have a functional assignment. Fortunately, a variety of novel high-throughput genome-wide functional screening technologies provide important clues that shed light on gene function. The integration of heterogeneous data to predict protein function has been shown to improve the accuracy of automated gene annotation systems. In this paper, we propose and evaluate a probabilistic approach for protein function prediction that integrates protein-protein interaction (PPI) data, gene expression data, protein motif information, mutant phenotype data, and protein localization data. First, functional linkage graphs are constructed from PPI data and gene expression data, in which an edge between nodes (proteins) represents evidence for functional similarity. The assumption here is that graph neighbors are more likely to share protein function, compared to proteins that are not neighbors. The functional linkage graph model is then used in concert with protein domain, mutant phenotype and protein localization data to produce a functional prediction. Our method is applied to the functional prediction of Saccharomyces cerevisiae genes, using Gene Ontology (GO) terms as the basis of our annotation. In a cross validation study we show that the integrated model increases recall by 18%, compared to using PPI data alone at the 50% precision. We also show that the integrated predictor is significantly better than each individual predictor. However, the observed improvement vs. PPI depends on both the new source of data and the functional category to be predicted. Surprisingly, in some contexts integration hurts overall prediction accuracy. Lastly, we provide a comprehensive assignment of putative GO terms to 463 proteins that currently have no assigned function.     

Prediction of protein function using protein-protein interaction data.

Assigning functions to novel proteins is one of the most important problems in the postgenomic era. Several approaches have been applied to this problem, including the analysis of gene expression patterns, phylogenetic profiles, protein fusions, and protein-protein interactions. In this paper, we develop a novel approach that employs the theory of Markov random fields to infer a protein's functions using protein-protein interaction data and the functional annotations of protein's interaction partners. For each function of interest and protein, we predict the probability that the protein has such function using Bayesian approaches. Unlike other available approaches for protein annotation in which a protein has or does not have a function of interest, we give a probability for having the function. This probability indicates how confident we are about the prediction. We employ our method to predict protein functions based on "biochemical function," "subcellular location," and "cellular role" for yeast proteins defined in the Yeast Proteome Database (YPD, www.incyte.com), using the protein-protein interaction data from the Munich Information Center for Protein Sequences (MIPS, mips.gsf.de). We show that our approach outperforms other available methods for function prediction based on protein interaction data. The supplementary data is available at www-hto.usc.edu/~msms/ProteinFunction.     

Most mRNAs in the nematode Ascaris lumbricoides are trans-spliced: a role for spliced leader addition in translational efficiency.

Some pre-mRNAs in nematodes are processed by trans-splicing. In this reaction, a 22-nt 5' terminal exon (the spliced leader, SL) and its associated 2,2,7-trimethylguanosine cap are acquired from a specialized Sm snRNP, the SL RNP. Although it has been evident for many years that not all nematode mRNAs contain the SL sequence, the prevalence of trans-spliced mRNAs has, with the exception of Caenorhabditis elegans, not been determined. To address this question in an organism amenable to biochemical analysis, we have prepared a message-dependent protein synthesis system from developing embryos of the parasitic nematode, Ascaris lumbricoides. Using this system, we have used both hybrid-arrest and hybrid-selection approaches to show that the vast majority (80-90%) of A. lumbricoides mRNAs contain the SL sequence and therefore are processed by trans-splicing. Furthermore, to examine the effect of SL addition on translation, we have measured levels of protein synthesis in extracts programmed with a variety of synthetic mRNAs. We find that the SL sequence itself and its associated hypermethylated cap functionally collaborate to enhance translational efficiency, presumably at the level of initiation of protein synthesis. These results indicate that trans-splicing plays a larger role in nematode gene expression than previously suspected.     

Interaction with 7SL RNA but not with HIV-1 genomic RNA or P bodies is required for APOBEC3F virion packaging

Human cytidine deaminase apolipoprotein B mRNA-editing catalytic polypeptide-like 3F (APOBEC3F, or A3F), like APOBEC3G, has broad antiviral activity against diverse retroelements, including Vif-deficient human immunodeficiency virus (HIV)-1. Its antiviral functions are known to rely on its virion encapsidation and be suppressed by HIV-1 Vif, which recruits Cullin5-based E3 ubiquitin ligases. However, the factors that mediate A3F virion packaging have not yet been identified. In this study, we demonstrate that A3F specifically interacts with cellular signal recognition particle (SRP) RNA (7SL RNA), which is selectively packaged into HIV-1 virions. Efficient packaging of 7SL RNA as well as A3F was mediated by the RNA-binding nucleocapsid domain of HIV-1 Gag. Reducing 7SL RNA packaging by overexpression of SRP19 protein inhibited A3F virion packaging. Although A3F has been shown to interact with P bodies and viral genomic RNA, our data indicated that P bodies and HIV-1 genomic RNA were not required for A3F packaging. Thus, in addition to its well-known function in SRPs, 7SL RNA, which is encapsidated into diverse retroviruses, also participates in the innate antiviral function of host cytidine deaminases.