Porter: a new, accurate server for protein secondary structure prediction

SUMMARY: Porter is a new system for protein secondary structure prediction in three classes. Porter relies on bidirectional recurrent neural networks with shortcut connections, accurate coding of input profiles obtained from multiple sequence alignments, second stage filtering by recurrent neural networks, incorporation of long range information and large-scale ensembles of predictors. Porter's accuracy, tested by rigorous 5-fold cross-validation on a large set of proteins, exceeds 79%, significantly above a copy of the state-of-the-art SSpro server, better than any system published to date. AVAILABILITY: Porter is available as a public web server at http://distill.ucd.ie/porter/ CONTACT: gianluca.pollastri@ucd.ie.     

Protein structural motif prediction in multidimensional phi-psi space leads to improved secondary structure prediction.

A significant step towards establishing the structure and function of a protein is the prediction of the local conformation of the polypeptide chain. In this article, we present systems for the prediction of three new alphabets of local structural motifs. The motifs are built by applying multidimensional scaling (MDS) and clustering to pair-wise angular distances for multiple phi-psi angle values collected from high-resolution protein structures. The predictive systems, based on ensembles of bidirectional recurrent neural network architectures, and trained on a large non-redundant set of protein structures, achieve 72%, 66%, and 60% correct motif prediction on an independent test set for di-peptides (six classes), tri-peptides (eight classes) and tetra-peptides (14 classes), respectively, 28-30% above baseline statistical predictors. We then build a further system, based on ensembles of two-layered bidirectional recurrent neural networks, to map structural motif predictions into a traditional 3-class (helix, strand, coil) secondary structure. This system achieves 79.5% correct prediction using the "hard" CASP 3-class assignment, and 81.4% with a more lenient assignment, outperforming a sophisticated state-of-the-art predictor (Porter) trained in the same experimental conditions. The structural motif predictor is publicly available at: http://distill.ucd.ie/porter+/.     

Ab initio and template-based prediction of multi-class distance maps by two-dimensional recursive neural networks

Background

Prediction of protein structures from their sequences is still one of the open grand challenges of computational biology. Some approaches to protein structure prediction, especially ab initio ones, rely to some extent on the prediction of residue contact maps. Residue contact map predictions have been assessed at the CASP competition for several years now. Although it has been shown that exact contact maps generally yield correct three-dimensional structures, this is true only at a relatively low resolution (3–4 Å from the native structure). Another known weakness of contact maps is that they are generally predicted ab initio, that is not exploiting information about potential homologues of known structure.

Results

We introduce a new class of distance restraints for protein structures: multi-class distance maps. We show that C _α trace reconstructions based on 4-class native maps are significantly better than those from residue contact maps. We then build two predictors of 4-class maps based on recursive neural networks: one ab initio, or relying on the sequence and on evolutionary information; one template-based, or in which homology information to known structures is provided as a further input. We show that virtually any level of sequence similarity to structural templates (down to less than 10%) yields more accurate 4-class maps than the ab initio predictor. We show that template-based predictions by recursive neural networks are consistently better than the best template and than a number of combinations of the best available templates. We also extract binary residue contact maps at an 8 Å threshold (as per CASP assessment) from the 4-class predictors and show that the template-based version is also more accurate than the best template and consistently better than the ab initio one, down to very low levels of sequence identity to structural templates. Furthermore, we test both ab-initio and template-based 8 Å predictions on the CASP7 targets using a pre-CASP7 PDB, and find that both predictors are state-of-the-art, with the template-based one far outperforming the best CASP7 systems if templates with sequence identity to the query of 10% or better are available. Although this is not the main focus of this paper we also report on reconstructions of C _α traces based on both ab initio and template-based 4-class map predictions, showing that the latter are generally more accurate even when homology is dubious.

Conclusion

Accurate predictions of multi-class maps may provide valuable constraints for improved ab initio and template-based prediction of protein structures, naturally incorporate multiple templates, and yield state-of-the-art binary maps. Predictions of protein structures and 8 Å contact maps based on the multi-class distance map predictors described in this paper are freely available to academic users at the url http://distill.ucd.ie/.     

BaCelLo: a balanced subcellular localization predictor

MOTIVATION: The knowledge of the subcellular localization of a protein is fundamental for elucidating its function. It is difficult to determine the subcellular location for eukaryotic cells with experimental high-throughput procedures. Computational procedures are then needed for annotating the subcellular location of proteins in large scale genomic projects. RESULTS: BaCelLo is a predictor for five classes of subcellular localization (secretory pathway, cytoplasm, nucleus, mitochondrion and chloroplast) and it is based on different SVMs organized in a decision tree. The system exploits the information derived from the residue sequence and from the evolutionary information contained in alignment profiles. It analyzes the whole sequence composition and the compositions of both the N- and C-termini. The training set is curated in order to avoid redundancy. For the first time a balancing procedure is introduced in order to mitigate the effect of biased training sets. Three kingdom-specific predictors are implemented: for animals, plants and fungi, respectively. When distributing the proteins from animals and fungi into four classes, accuracy of BaCelLo reach 74% and 76%, respectively; a score of 67% is obtained when proteins from plants are distributed into five classes. BaCelLo outperforms the other presently available methods for the same task and gives more balanced accuracy and coverage values for each class. We also predict the subcellular localization of five whole proteomes, Homo sapiens, Mus musculus, Caenorhabditis elegans, Saccharomyces cerevisiae and Arabidopsis thaliana, comparing the protein content in each different compartment. AVAILABILITY: BaCelLo can be accessed at http://www.biocomp.unibo.it/bacello/.     

Support vector machine approach for protein subcellular localization prediction

MOTIVATION: Subcellular localization is a key functional characteristic of proteins. A fully automatic and reliable prediction system for protein subcellular localization is needed, especially for the analysis of large-scale genome sequences. RESULTS: In this paper, Support Vector Machine has been introduced to predict the subcellular localization of proteins from their amino acid compositions. The total prediction accuracies reach 91.4% for three subcellular locations in prokaryotic organisms and 79.4% for four locations in eukaryotic organisms. Predictions by our approach are robust to errors in the protein N-terminal sequences. This new approach provides superior prediction performance compared with existing algorithms based on amino acid composition and can be a complementary method to other existing methods based on sorting signals. AVAILABILITY: A web server implementing the prediction method is available at http://www.bioinfo.tsinghua.edu.cn/SubLoc/. SUPPLEMENTARY INFORMATION: Supplementary material is available at http://www.bioinfo.tsinghua.edu.cn/SubLoc/.     

pLoc_bal-mGpos: Predict subcellular localization of Gram-positive bacterial proteins by quasi-balancing training dataset and PseAAC

Knowledge of protein subcellular localization is vitally important for both basic research and drug development. With the avalanche of protein sequences emerging in the post-genomic age, it is highly desired to develop computational tools for timely and effectively identifying their subcellular localization purely based on the sequence information alone. Recently, a predictor called “pLoc-mGpos” was developed for identifying the subcellular localization of Gram-positive bacterial proteins. Its performance is overwhelmingly better than that of the other predictors for the same purpose, particularly in dealing with multi-label systems in which some proteins, called “multiplex proteins”, may simultaneously occur in two or more subcellular locations. Although it is indeed a very powerful predictor, more efforts are definitely needed to further improve it. This is because pLoc-mGpos was trained by an extremely skewed dataset in which some subset (subcellular location) was over 11 times the size of the other subsets. Accordingly, it cannot avoid the bias consequence caused by such an uneven training dataset. To alleviate such bias consequence, we have developed a new and bias-reducing predictor called pLoc_bal-mGpos by quasi-balancing the training dataset. Rigorous target jackknife tests on exactly the same experiment-confirmed dataset have indicated that the proposed new predictor is remarkably superior to pLoc-mGpos, the existing state-of-the-art predictor in identifying the subcellular localization of Gram-positive bacterial proteins. To maximize the convenience for most experimental scientists, a user-friendly web-server for the new predictor has been established at http://www.jci-bioinfo.cn/pLoc_bal-mGpos/, by which users can easily get their desired results without the need to go through the detailed mathematics.     

pLoc-mEuk: Predict subcellular localization of multi-label eukaryotic proteins by extracting the key GO information into general PseAAC

Many efforts have been made in predicting the subcellular localization of eukaryotic proteins, but most of the existing methods have the following two limitations: (1) their coverage scope is less than ten locations and hence many organelles in an eukaryotic cell cannot be covered, and (2) they can only be used to deal with single-label systems in which each of the constituent proteins has one and only one location. Actually, proteins with multiple locations are particularly interesting since they may have some exceptional functions very important for in-depth understanding the biological process in a cell and for selecting drug target as well. Although several predictors (such as “Euk-mPLoc”, “Euk-PLoc 2.0” and “iLoc-Euk”) can cover up to 22 different location sites, and they also have the function to treat multi-labeled proteins, further efforts are needed to improve their prediction quality, particularly in enhancing the absolute true rate and in reducing the absolute false rate. Here we propose a new predictor called “pLoc-mEuk” by extracting the key GO (Gene Ontology) information into the general PseAAC (Pseudo Amino Acid Composition). Rigorous cross-validations on a high-quality and stringent benchmark dataset have indicated that the proposed pLoc-mEuk predictor is remarkably superior to iLoc-Euk, the best of the aforementioned three predictors. To maximize the convenience of most experimental scientists, a user-friendly web-server for the new predictor has been established at http://www.jci-bioinfo.cn/pLoc-mEuk/, by which users can easily get their desired results without the need to go through the complicated mathematics involved.     

Predicting subcellular localization with AdaBoost Learner

Protein subcellular localization, which tells where a protein resides in a cell, is an important characteristic of a protein, and relates closely to the function of proteins. The prediction of their subcellular localization plays an important role in the prediction of protein function, genome annotation and drug design. Therefore, it is an important and challenging role to predict subcellular localization using bio-informatics approach. In this paper, a robust predictor, AdaBoost Learner is introduced to predict protein subcellular localization based on its amino acid composition. Jackknife cross-validation and independent dataset test were used to demonstrate that Adaboost is a robust and efficient model in predicting protein subcellular localization. As a result, the correct prediction rates were 74.98% and 80.12% for the Jackknife test and independent dataset test respectively, which are higher than using other existing predictors. An online server for predicting subcellular localization of proteins based on AdaBoost classifier was available on http://chemdata.shu. edu.cn/sl12.     

Plant-mPLoc: A Top-Down Strategy to Augment the Power for Predicting Plant Protein Subcellular Localization

One of the fundamental goals in proteomics and cell biology is to identify the functions of proteins in various cellular organelles and pathways. Information of subcellular locations of proteins can provide useful insights for revealing their functions and understanding how they interact with each other in cellular network systems. Most of the existing methods in predicting plant protein subcellular localization can only cover three or four location sites, and none of them can be used to deal with multiplex plant proteins that can simultaneously exist at two, or move between, two or more different location sites. Actually, such multiplex proteins might have special biological functions worthy of particular notice. The present study was devoted to improve the existing plant protein subcellular location predictors from the aforementioned two aspects. A new predictor called “Plant-mPLoc” is developed by integrating the gene ontology information, functional domain information, and sequential evolutionary information through three different modes of pseudo amino acid composition. It can be used to identify plant proteins among the following 12 location sites: (1) cell membrane, (2) cell wall, (3) chloroplast, (4) cytoplasm, (5) endoplasmic reticulum, (6) extracellular, (7) Golgi apparatus, (8) mitochondrion, (9) nucleus, (10) peroxisome, (11) plastid, and (12) vacuole. Compared with the existing methods for predicting plant protein subcellular localization, the new predictor is much more powerful and flexible. Particularly, it also has the capacity to deal with multiple-location proteins, which is beyond the reach of any existing predictors specialized for identifying plant protein subcellular localization. As a user-friendly web-server, Plant-mPLoc is freely accessible at http://www.csbio.sjtu.edu.cn/bioinf/plant-multi/. Moreover, for the convenience of the vast majority of experimental scientists, a step-by-step guide is provided on how to use the web-server to get the desired results. It is anticipated that the Plant-mPLoc predictor as presented in this paper will become a very useful tool in plant science as well as all the relevant areas.     

MSLoc-DT: A new method for predicting the protein subcellular location of multispecies based on decision templates

Revealing the subcellular location of newly discovered protein sequences can bring insight to their function and guide research at the cellular level. The rapidly increasing number of sequences entering the genome databanks has called for the development of automated analysis methods. Currently, most existing methods used to predict protein subcellular locations cover only one, or a very limited number of species. Therefore, it is necessary to develop reliable and effective computational approaches to further improve the performance of protein subcellular prediction and, at the same time, cover more species. The current study reports the development of a novel predictor called MSLoc-DT to predict the protein subcellular locations of human, animal, plant, bacteria, virus, fungi, and archaea by introducing a novel feature extraction approach termed Amino Acid Index Distribution (AAID) and then fusing gene ontology information, sequential evolutionary information, and sequence statistical information through four different modes of pseudo amino acid composition (PseAAC) with a decision template rule. Using the jackknife test, MSLoc-DT can achieve 86.5, 98.3, 90.3, 98.5, 95.9, 98.1, and 99.3% overall accuracy for human, animal, plant, bacteria, virus, fungi, and archaea, respectively, on seven stringent benchmark datasets. Compared with other predictors (e.g., Gpos-PLoc, Gneg-PLoc, Virus-PLoc, Plant-PLoc, Plant-mPLoc, ProLoc-Go, Hum-PLoc, GOASVM) on the gram-positive, gram-negative, virus, plant, eukaryotic, and human datasets, the new MSLoc-DT predictor is much more effective and robust. Although the MSLoc-DT predictor is designed to predict the single location of proteins, our method can be extended to multiple locations of proteins by introducing multilabel machine learning approaches, such as the support vector machine and deep learning, as substitutes for the K-nearest neighbor (KNN) method. As a user-friendly web server, MSLoc-DT is freely accessible at http://bioinfo.ibp.ac.cn/MSLOC_DT/index.html.     

Large-scale predictions of gram-negative bacterial protein subcellular locations

Many species of Gram-negative bacteria are pathogenic bacteria that can cause disease in a host organism. This pathogenic capability is usually associated with certain components in Gram-negative cells. Therefore, developing an automated method for fast and reliable prediction of Gram-negative protein subcellular location will allow us to not only timely annotate gene products, but also screen candidates for drug discovery. However, protein subcellular location prediction is a very difficult problem, particularly when more location sites need to be involved and when unknown query proteins do not have significant homology to proteins of known subcellular locations. PSORT-B, a recently updated version of PSORT, widely used for predicting Gram-negative protein subcellular location, only covers five location sites. Also, the data set used to train PSORT-B contains many proteins with high degrees of sequence identity in a same location group and, hence, may bear a strong homology bias. To overcome these problems, a new predictor, called "Gneg-PLoc", is developed. Featured by fusing many basic classifiers each being trained with a stringent data set containing proteins with strictly less than 25% sequence identity to one another in a same location group, the new predictor can cover eight subcellular locations; that is, cytoplasm, extracellular space, fimbrium, flagellum, inner membrane, nucleoid, outer membrane, and periplasm. In comparison with PSORT-B, the new predictor not only covers more subcellular locations, but also yields remarkably higher success rates. Gneg-PLoc is available as a Web server at http://202.120.37.186/bioinf/Gneg. To support the demand of people working in the relevant areas, a downloadable file is provided at the same Web site to list the results identified by Gneg-PLoc for 49 907 Gram-negative protein entries in the Swiss-Prot database that have no subcellular location annotations or are annotated with uncertain terms. The large-scale results will be updated twice a year to cover the new entries of Gram-negative bacterial proteins and reflect the new development of Gneg-PLoc.     

Secretome Prediction and Analysis in Sacred Lotus (Nelumbo nucifera Gaertn.)

Sacred lotus, Nelumbo nucifera (Gaertn.), is a basal eudicot with agricultural and medicinal importance. The secretome and proteins in some other subcellular locations including endoplasmic reticulum (ER), mitochondrion, chloroplast, and membrane of sacred lotus were predicted using a set of computational tools. The distribution of proteins in each subcellular location in sacred lotus was compared with proteins in five other plant species. Plant proteomes contained approximately 6–9 % of secreted proteins, 13–15 % membrane proteins, 12–20 % mitochondrial or chloroplast proteins, respectively. Plant secreted proteins consist of a large number of hydrolases and peroxidases which may contribute to cell wall formation, rhizome development and seed germination regulation. The information of secretome and other protein subcellular locations in sacred lotus and other species can be accessed at the PlantSecKB website (http://proteomics.ysu.edu/secretomes/plant.php).     

Two‐dimensional reference map for the basic proteome of the human dorsolateral prefrontal cortex (dlPFC) of the prefrontal lobe region of the brain

We describe a 2‐DE proteomic reference map containing 227 basic proteins in the dorsolateral prefrontal cortex region of the human brain. Proteins were separated in the first dimension on pH 6–11 IPG strips using paper‐bridge loading and on 12% SDS‐PAGE in the second dimension. Proteins were subsequently identified by MS and spectra were analyzed using an in‐house proteomics data analysis platform, Proline. The 2‐DE reference map is available via the UCD 2‐DE Proteome Database ( http://proteomics‐portal.ucd.ie:8082 ) and can also be accessed via the WORLD‐2DPAGE Portal ( http://www.expasy.ch/world‐2dpage/ ). The associated protein identification data have been submitted to the PRIDE database (accession numbers 10018–10033). Separation of proteins in the basic region resolves more membrane associated proteins relevant to the synaptic pathology central to many neurological disorders. The 2‐DE reference map will aid with further characterisation of neurological disorders such as bipolar and schizophrenia.     

Multi Label Learning for Prediction of Human Protein Subcellular Localizations

Predicting protein subcellular locations has attracted much attention in the past decade. However, one of the most challenging problems is that many proteins were found simultaneously existing in, or moving between, two or more different cell components in a eukaryotic cell. Seldom previous predictors were able to deal with such multiplex proteins although they have extremely important implications in future drug discovery in terms of their specific subcellular targeting. Approximately 20% of the human proteome consists of such multiplex proteins with multiple sample labels. In order to efficiently handle such multiplex human proteins, we have developed a novel multi-label (ML) learning and prediction framework called ML-PLoc, which decomposes the multi-label prediction problem into multiple independent binary classification problems. ML-PLoc is constructed based on support vector machine (SVM) and sequential evolution information. Experimental results show that ML-PLoc can achieve an overall accuracy 64.6% and recall ratio 67.2% on a benchmark dataset consisting of 14 human subcellular locations, and is very powerful for dealing with multiplex proteins. The current approach represents a new strategy to deal with the multi-label biological problems. ML-PLoc software is freely available for academic use at: http://www.csbio.sjtu.edu.cn/bioinf/ML-PLoc.     

Euk-mPLoc: a fusion classifier for large-scale eukaryotic protein subcellular location prediction by incorporating multiple sites

One of the critical challenges in predicting protein subcellular localization is how to deal with the case of multiple location sites. Unfortunately, so far, no efforts have been made in this regard except for the one focused on the proteins in budding yeast only. For most existing predictors, the multiple-site proteins are either excluded from consideration or assumed even not existing. Actually, proteins may simultaneously exist at, or move between, two or more different subcellular locations. For instance, according to the Swiss-Prot database (version 50.7, released 19-Sept-2006), among the 33,925 eukaryotic protein entries that have experimentally observed subcellular location annotations, 2715 have multiple location sites, meaning about 8% bearing the multiplex feature. Proteins with multiple locations or dynamic feature of this kind are particularly interesting because they may have some very special biological functions intriguing to investigators in both basic research and drug discovery. Meanwhile, according to the same Swiss-Prot database, the number of total eukaryotic protein entries (except those annotated with "fragment" or those with less than 50 amino acids) is 90,909, meaning a gap of (90,909-33,925) = 56,984 entries for which no knowledge is available about their subcellular locations. Although one can use the computational approach to predict the desired information for the blank, so far, all the existing methods for predicting eukaryotic protein subcellular localization are limited in the case of single location site only. To overcome such a barrier, a new ensemble classifier, named Euk-mPLoc, was developed that can be used to deal with the case of multiple location sites as well. Euk-mPLoc is freely accessible to the public as a Web server at http://202.120.37.186/bioinf/euk-multi. Meanwhile, to support the people working in the relevant areas, Euk-mPLoc has been used to identify all eukaryotic protein entries in the Swiss-Prot database that do not have subcellular location annotations or are annotated as being uncertain. The large-scale results thus obtained have been deposited at the same Web site via a downloadable file prepared with Microsoft Excel and named "Tab_Euk-mPLoc.xls". Furthermore, to include new entries of eukaryotic proteins and reflect the continuous development of Euk-mPLoc in both the coverage scope and prediction accuracy, we will timely update the downloadable file as well as the predictor, and keep users informed by publishing a short note in the Journal and making an announcement in the Web Page.     

Modular DAG-RNN architectures for assembling coarse protein structures.

We develop and test machine learning methods for the prediction of coarse 3D protein structures, where a protein is represented by a set of rigid rods associated with its secondary structure elements (alpha-helices and beta-strands). First, we employ cascades of recursive neural networks derived from graphical models to predict the relative placements of segments. These are represented as discretized distance and angle maps, and the discretization levels are statistically inferred from a large and curated dataset. Coarse 3D folds of proteins are then assembled starting from topological information predicted in the first stage. Reconstruction is carried out by minimizing a cost function taking the form of a purely geometrical potential. We show that the proposed architecture outperforms simpler alternatives and can accurately predict binary and multiclass coarse maps. The reconstruction procedure proves to be fast and often leads to topologically correct coarse structures that could be exploited as a starting point for various protein modeling strategies. The fully integrated rod-shaped protein builder (predictor of contact maps + reconstruction algorithm) can be accessed at http://distill.ucd.ie/.     

iLoc-Euk: a multi-label classifier for predicting the subcellular localization of singleplex and multiplex eukaryotic proteins

Predicting protein subcellular localization is an important and difficult problem, particularly when query proteins may have the multiplex character, i.e., simultaneously exist at, or move between, two or more different subcellular location sites. Most of the existing protein subcellular location predictor can only be used to deal with the single-location or "singleplex" proteins. Actually, multiple-location or "multiplex" proteins should not be ignored because they usually posses some unique biological functions worthy of our special notice. By introducing the "multi-labeled learning" and "accumulation-layer scale", a new predictor, called iLoc-Euk, has been developed that can be used to deal with the systems containing both singleplex and multiplex proteins. As a demonstration, the jackknife cross-validation was performed with iLoc-Euk on a benchmark dataset of eukaryotic proteins classified into the following 22 location sites: (1) acrosome, (2) cell membrane, (3) cell wall, (4) centriole, (5) chloroplast, (6) cyanelle, (7) cytoplasm, (8) cytoskeleton, (9) endoplasmic reticulum, (10) endosome, (11) extracellular, (12) Golgi apparatus, (13) hydrogenosome, (14) lysosome, (15) melanosome, (16) microsome (17) mitochondrion, (18) nucleus, (19) peroxisome, (20) spindle pole body, (21) synapse, and (22) vacuole, where none of proteins included has ≥25% pairwise sequence identity to any other in a same subset. The overall success rate thus obtained by iLoc-Euk was 79%, which is significantly higher than that by any of the existing predictors that also have the capacity to deal with such a complicated and stringent system. As a user-friendly web-server, iLoc-Euk is freely accessible to the public at the web-site http://icpr.jci.edu.cn/bioinfo/iLoc-Euk. It is anticipated that iLoc-Euk may become a useful bioinformatics tool for Molecular Cell Biology, Proteomics, System Biology, and Drug Development Also, its novel approach will further stimulate the development of predicting other protein attributes.     

Euk-PLoc: an ensemble classifier for large-scale eukaryotic protein subcellular location prediction

With the avalanche of newly-found protein sequences emerging in the post genomic era, it is highly desirable to develop an automated method for fast and reliably identifying their subcellular locations because knowledge thus obtained can provide key clues for revealing their functions and understanding how they interact with each other in cellular networking. However, predicting subcellular location of eukaryotic proteins is a challenging problem, particularly when unknown query proteins do not have significant homology to proteins of known subcellular locations and when more locations need to be covered. To cope with the challenge, protein samples are formulated by hybridizing the information derived from the gene ontology database and amphiphilic pseudo amino acid composition. Based on such a representation, a novel ensemble hybridization classifier was developed by fusing many basic individual classifiers through a voting system. Each of these basic classifiers was engineered by the KNN (K-Nearest Neighbor) principle. As a demonstration, a new benchmark dataset was constructed that covers the following 18 localizations: (1) cell wall, (2) centriole, (3) chloroplast, (4) cyanelle, (5) cytoplasm, (6) cytoskeleton, (7) endoplasmic reticulum, (8) extracell, (9) Golgi apparatus, (10) hydrogenosome, (11) lysosome, (12) mitochondria, (13) nucleus, (14) peroxisome, (15) plasma membrane, (16) plastid, (17) spindle pole body, and (18) vacuole. To avoid the homology bias, none of the proteins included has > or =25% sequence identity to any other in a same subcellular location. The overall success rates thus obtained via the 5-fold and jackknife cross-validation tests were 81.6 and 80.3%, respectively, which were 40-50% higher than those performed by the other existing methods on the same strict dataset. The powerful predictor, named "Euk-PLoc", is available as a web-server at http://202.120.37.186/bioinf/euk . Furthermore, to support the need of people working in the relevant areas, a downloadable file will be provided at the same website to list the results predicted by Euk-PLoc for all eukaryotic protein entries (excluding fragments) in Swiss-Prot database that do not have subcellular location annotations or are annotated as being uncertain. The large-scale results will be updated twice a year to include the new entries of eukaryotic proteins and reflect the continuous development of Euk-PLoc.     

PSI: A Comprehensive and Integrative Approach for Accurate Plant Subcellular Localization Prediction

Predicting the subcellular localization of proteins conquers the major drawbacks of high-throughput localization experiments that are costly and time-consuming. However, current subcellular localization predictors are limited in scope and accuracy. In particular, most predictors perform well on certain locations or with certain data sets while poorly on others. Here, we present PSI, a novel high accuracy web server for plant subcellular localization prediction. PSI derives the wisdom of multiple specialized predictors via a joint-approach of group decision making strategy and machine learning methods to give an integrated best result. The overall accuracy obtained (up to 93.4%) was higher than best individual (CELLO) by ∼10.7%. The precision of each predicable subcellular location (more than 80%) far exceeds that of the individual predictors. It can also deal with multi-localization proteins. PSI is expected to be a powerful tool in protein location engineering as well as in plant sciences, while the strategy employed could be applied to other integrative problems. A user-friendly web server, PSI, has been developed for free access at http://bis.zju.edu.cn/psi/.     

Using protein-protein interaction network information to predict the subcellular locations of proteins in budding yeast

The information of protein subcellular localization is vitally important for in-depth understanding the intricate pathways that regulate biological processes at the cellular level. With the rapidly increasing number of newly found protein sequence in the Post-Genomic Age, many automated methods have been developed attempting to help annotate their subcellular locations in a timely manner. However, very few of them were developed using the protein-protein interaction (PPI) network information. In this paper, we have introduced a new concept called "tethering potential" by which the PPI information can be effectively fused into the formulation for protein samples. Based on such a network frame, a new predictor called Yeast-PLoc has been developed for identifying budding yeast proteins among their 19 subcellular location sites. Meanwhile, a purely sequence-based approach, called the "hybrid-property" method, is integrated into Yeast-PLoc as a fall-back to deal with those proteins without sufficient PPI information. The overall success rate by the jackknife test on the 4,683 yeast proteins in the training dataset was 70.25%. Furthermore, it was shown that the success rate by Yeast- PLoc on an independent dataset was remarkably higher than those by some other existing predictors, indicating that the current approach by incorporating the PPI information is quite promising. As a user-friendly web-server, Yeast-PLoc is freely accessible at http://yeastloc.biosino.org/.