Survey of Natural Language Processing Techniques in Bioinformatics <s> Predicting Protein Function. <s> Motivation: An ambitious goal of proteomics is to elucidate the structure, interactions and functions of all proteins within cells and organisms. The expectation is that this will provide a fuller appreciation of cellular processes and networks at the protein level, ultimately leading to a better understanding of disease mechanisms and suggesting new means for intervention. This paper addresses the question: can protein–protein interactions be predicted directly from primary structure and associated data? Using a diverse database of known protein interactions, a Support Vector Machine (SVM) learning system was trained to recognize and predict interactions based solely on primary structure and associated physicochemical properties. Results: Inductive accuracy of the trained system, defined here as the percentage of correct protein interaction predictions for previously unseen test sets, averaged 80% for the ensemble of statistical experiments. Future proteomics studies may benefit from this research by proceeding directly from the automated identification of a cell’s gene products to prediction of protein interaction pairs. <s> BIB001 </s> Survey of Natural Language Processing Techniques in Bioinformatics <s> Predicting Protein Function. <s> In this paper we address the problem of extracting features relevant for predicting protein--protein interaction sites from the three-dimensional structures of protein complexes. Our approach is based on information about evolutionary conservation and surface disposition. We implement a neural network based system, which uses a cross validation procedure and allows the correct detection of 73% of the residues involved in protein interactions in a selected database comprising 226 heterodimers. Our analysis confirms that the chemico-physical properties of interacting surfaces are difficult to distinguish from those of the whole protein surface. However neural networks trained with a reduced representation of the interacting patch and sequence profile are sufficient to generalize over the different features of the contact patches and to predict whether a residue in the protein surface is or is not in contact. By using a blind test, we report the prediction of the surface interacting sites of three structural components of the Dnak molecular chaperone system, and find close agreement with previously published experimental results. We propose that the predictor can significantly complement results from structural and functional proteomics. <s> BIB002 </s> Survey of Natural Language Processing Techniques in Bioinformatics <s> Predicting Protein Function. <s> BackgroundIdentifying all protein-protein interactions in an organism is a major objective of proteomics. A related goal is to know which protein pairs are present in the same protein complex. High-throughput methods such as yeast two-hybrid (Y2H) and affinity purification coupled with mass spectrometry (APMS) have been used to detect interacting proteins on a genomic scale. However, both Y2H and APMS methods have substantial false-positive rates. Aside from high-throughput interaction screens, other gene- or protein-pair characteristics may also be informative of physical interaction. Therefore it is desirable to integrate multiple datasets and utilize their different predictive value for more accurate prediction of co-complexed relationship.ResultsUsing a supervised machine learning approach – probabilistic decision tree, we integrated high-throughput protein interaction datasets and other gene- and protein-pair characteristics to predict co-complexed pairs (CCP) of proteins. Our predictions proved more sensitive and specific than predictions based on Y2H or APMS methods alone or in combination. Among the top predictions not annotated as CCPs in our reference set (obtained from the MIPS complex catalogue), a significant fraction was found to physically interact according to a separate database (YPD, Yeast Proteome Database), and the remaining predictions may potentially represent unknown CCPs.ConclusionsWe demonstrated that the probabilistic decision tree approach can be successfully used to predict co-complexed protein (CCP) pairs from other characteristics. Our top-scoring CCP predictions provide testable hypotheses for experimental validation. <s> BIB003 </s> Survey of Natural Language Processing Techniques in Bioinformatics <s> Predicting Protein Function. <s> In order to understand the molecular machinery of the cell, we need to know about the multitude of protein–protein interactions that allow the cell to function. High-throughput technologies provide some data about these interactions, but so far that data is fairly noisy. Therefore, computational techniques for predicting protein–protein interactions could be of significant value. One approach to predicting interactions in silico is to produce from first principles a detailed model of a candidate interaction. We take an alternative approach, employing a relatively simple model that learns dynamically from a large collection of data. In this work, we describe an attraction– repulsion model, in which the interaction between a pair of proteins is represented as the sum of attractive and repulsive forces associated with small, domain- or motif-sized features along the length of each protein. The model is discriminative, learning simultaneously from known interactions and from pairs of proteins that are known (or suspected) not to interact. The model is efficient to compute and scales well to very large collections of data. In a cross-validated comparison using known yeast interactions, the attraction–repulsion method performs better than several competing techniques. <s> BIB004 </s> Survey of Natural Language Processing Techniques in Bioinformatics <s> Predicting Protein Function. <s> Motivation: Structural genomics projects are beginning to produce protein structures with unknown function, therefore, accurate, automated predictors of protein function are required if all these structures are to be properly annotated in reasonable time. Identifying the interface between two interacting proteins provides important clues to the function of a protein and can reduce the search space required by docking algorithms to predict the structures of complexes. ::: ::: Results: We have combined a support vector machine (SVM) approach with surface patch analysis to predict protein--protein binding sites. Using a leave-one-out cross-validation procedure, we were able to successfully predict the location of the binding site on 76% of our dataset made up of proteins with both transient and obligate interfaces. With heterogeneous cross-validation, where we trained the SVM on transient complexes to predict on obligate complexes (and vice versa), we still achieved comparable success rates to the leave-one-out cross-validation suggesting that sufficient properties are shared between transient and obligate interfaces. ::: ::: Availability: A web application based on the method can be found at http://www.bioinformatics.leeds.ac.uk/ppi_pred. The dataset of 180 proteins used in this study is also available via the same web site. ::: ::: Contact: westhead@bmb.leeds.ac.uk ::: ::: Supplementary information: http://www.bioinformatics.leeds.ac.uk/ppi-pred/supp-material <s> BIB005 </s> Survey of Natural Language Processing Techniques in Bioinformatics <s> Predicting Protein Function. <s> Motivation: Despite advances in high-throughput methods for discovering protein--protein interactions, the interaction networks of even well-studied model organisms are sketchy at best, highlighting the continued need for computational methods to help direct experimentalists in the search for novel interactions. ::: ::: Results: We present a kernel method for predicting protein--protein interactions using a combination of data sources, including protein sequences, Gene Ontology annotations, local properties of the network, and homologous interactions in other species. Whereas protein kernels proposed in the literature provide a similarity between single proteins, prediction of interactions requires a kernel between pairs of proteins. We propose a pairwise kernel that converts a kernel between single proteins into a kernel between pairs of proteins, and we illustrate the kernel's effectiveness in conjunction with a support vector machine classifier. Furthermore, we obtain improved performance by combining several sequence-based kernels based on k-mer frequency, motif and domain content and by further augmenting the pairwise sequence kernel with features that are based on other sources of data. ::: ::: We apply our method to predict physical interactions in yeast using data from the BIND database. At a false positive rate of 1% the classifier retrieves close to 80% of a set of trusted interactions. We thus demonstrate the ability of our method to make accurate predictions despite the sizeable fraction of false positives that are known to exist in interaction databases. ::: ::: Availability: The classification experiments were performed using PyML available at http://pyml.sourceforge.net. Data are available at: http://noble.gs.washington.edu/proj/sppi ::: ::: Contact: asa@gs.washington.edu <s> BIB006 </s> Survey of Natural Language Processing Techniques in Bioinformatics <s> Predicting Protein Function. <s> Protein-protein interactions play a key role in many biological systems. High-through- put methods can directly detect the set of interact- ing proteins in yeast, but the results are often incomplete and exhibit high false-positive and false- negative rates. Recently, many different research groups independently suggested using supervised learning methods to integrate direct and indirect biological data sources for the protein interaction prediction task. However, the data sources, ap- proaches, and implementations varied. Further- more, the protein interaction prediction task itself can be subdivided into prediction of (1) physical interaction, (2) co-complex relationship, and (3) path- way co-membership. To investigate systematically the utility of different data sources and the way the data is encoded as features for predicting each of these types of protein interactions, we assembled a large set of biological features and varied their encoding for use in each of the three prediction tasks. Six different classifiers were used to assess the accuracy in predicting interactions, Random Forest (RF), RF similarity-based k-Nearest-Neigh- bor, Naive Bayes, Decision Tree, Logistic Regres- sion, and Support Vector Machine. For all classifi- ers, the three prediction tasks had different success rates, and co-complex prediction appears to be an easier task than the other two. Independently of prediction task, however, the RF classifier consis- tently ranked as one of the top two classifiers for all combinations of feature sets. Therefore, we used this classifier to study the importance of different biological datasets. First, we used the splitting func- tion of the RF tree structure, the Gini index, to estimate feature importance. Second, we deter- mined classification accuracy when only the top- ranking features were used as an input in the classifier. We find that the importance of different features depends on the specific prediction task and the way they are encoded. Strikingly, gene expres- sion is consistently the most important feature for all three prediction tasks, while the protein interac- tions identified using the yeast-2-hybrid system were not among the top-ranking features under any con- <s> BIB007 </s> Survey of Natural Language Processing Techniques in Bioinformatics <s> Predicting Protein Function. <s> Motivation: Due to the growing number of completely sequenced genomes, functional annotation of proteins becomes a more and more important issue. Here, we describe a method for the prediction of sites within protein domains, which are part of protein--ligand interactions. As recently demonstrated, these sites are not trivial to detect because of a varying degree of conservation of their location and type within a domain family. ::: ::: Results: The developed method for the prediction of protein--ligand interaction sites is based on a newly defined interaction profile hidden Markov model (ipHMM) topology that takes structural and sequence data into account. It is based on a homology search via a posterior decoding algorithm that yields probabilities for interacting sequence positions and inherits the efficiency and the power of the profile hidden Markov model (pHMM) methodology. The algorithm enhances the quality of interaction site predictions and is a suitable tool for large scale studies, which was already demonstrated for pHMMs. ::: ::: Availability: The MATLAB-files are available on request from the first author. ::: ::: Contact: tobias.mueller@biozentrum.uni-wuerzburg.de ::: ::: Supplementary information: http://domains.bioapps.biozentrum.uni-wuerzburg.de/ <s> BIB008 </s> Survey of Natural Language Processing Techniques in Bioinformatics <s> Predicting Protein Function. <s> Identifying the interface between two interacting proteins provides important clues to the function of a protein, and is becoming increasing relevant to drug discovery. Here, surface patch analysis was combined with a Bayesian network to predict protein-protein binding sites with a success rate of 82% on a benchmark dataset of 180 proteins, improving by 6% on previous work and well above the 36% that would be achieved by a random method. A comparable success rate was achieved even when evolutionary information was missing, a further improvement on our previous method which was unable to handle incomplete data automatically. In a case study of the Mog1p family, we showed that our Bayesian network method can aid the prediction of previously uncharacterised binding sites and provide important clues to protein function. On Mog1p itself a putative binding site involved in the SLN1-SKN7 signal transduction pathway was detected, as was a Ran binding site, previously characterized solely by conservation studies, even though our automated method operated without using homologous proteins. On the remaining members of the family (two structural genomics targets, and a protein involved in the photosystem II complex in higher plants) we identified novel binding sites with little correspondence to those on Mog1p. These results suggest that members of the Mog1p family bind to different proteins and probably have different functions despite sharing the same overall fold. We also demonstrated the applicability of our method to drug discovery efforts by successfully locating a number of binding sites involved in the protein-protein interaction network of papilloma virus infection. In a separate study, we attempted to distinguish between the two types of binding site, obligate and non-obligate, within our dataset using a second Bayesian network. This proved difficult although some separation was achieved on the basis of patch size, electrostatic potential and conservation. Such was the similarity between the two interacting patch types, we were able to use obligate binding site properties to predict the location of non-obligate binding sites and vice versa. <s> BIB009 </s> Survey of Natural Language Processing Techniques in Bioinformatics <s> Predicting Protein Function. <s> Protein–protein interactions can be altered by mutating one or more “hot spots,” the subset of residues that account for most of the interface's binding free energy. The identification of hot spots requires a significant experimental effort, highlighting the practical value of hot spot predictions. We present two knowledge-based models that improve the ability to predict hot spots: K-FADE uses shape specificity features calculated by the Fast Atomic Density Evaluation (FADE) program, and K-CON uses biochemical contact features. The combined K-FADE/CON (KFC) model displays better overall predictive accuracy than computational alanine scanning (Robetta–Ala). In addition, because these methods predict different subsets of known hot spots, a large and significant increase in accuracy is achieved by combining KFC and Robetta–Ala. The KFC analysis is applied to the calmodulin (CaM)/smooth muscle myosin light chain kinase (smMLCK) interface, and to the bone morphogenetic protein-2 (BMP-2)/BMP receptor-type I (BMPR-IA) interface. The results indicate a strong correlation between KFC hot spot predictions and mutations that significantly reduce the binding affinity of the interface. Proteins 2007. © 2007 Wiley-Liss, Inc. <s> BIB010 </s> Survey of Natural Language Processing Techniques in Bioinformatics <s> Predicting Protein Function. <s> Recent advances in structural proteomics call for development of fast and reliable automatic methods for prediction of functional surfaces of proteins with known three-dimensional structure, including binding sites for known and unknown protein partners as well as oligomerization interfaces. Despite significant progress the problem is still far from being solved. Most existing methods rely, at least partially, on evolutionary information from multiple sequence alignments projected on protein surface. The common drawback of such methods is their limited applicability to the proteins with a sparse set of sequential homologs, as well as inability to detect interfaces in evolutionary variable regions. In this study, the authors developed an improved method for predicting interfaces from a single protein structure, which is based on local statistical properties of the protein surface derived at the level of atomic groups. The proposed Protein IntErface Recognition (PIER) method achieved the overall precision of 60% at the recall threshold of 50% at the residue level on a diverse benchmark of 490 homodimeric, 62 heterodimeric, and 196 transient interfaces (compared with 25% precision at 50% recall expected from random residue function assignment). For 70% of proteins in the benchmark, the binding patch residues were successfully detected with precision exceeding 50% at 50% recall. The calculation only took seconds for an average 300-residue protein. The authors demonstrated that adding the evolutionary conservation signal only marginally influenced the overall prediction performance on the benchmark; moreover, for certain classes of proteins, using this signal actually resulted in a deteriorated prediction. Thorough benchmarking using other datasets from literature showed that PIER yielded improved performance as compared with several alignment-free or alignment-dependent predictions. The accuracy, efficiency, and dependence on structure alone make PIER a suitable tool for automated high-throughput annotation of protein structures emerging from structural proteomics projects. <s> BIB011 </s> Survey of Natural Language Processing Techniques in Bioinformatics <s> Predicting Protein Function. <s> The successful prediction of protein subcellular localization directly from protein primary sequence is useful to protein function prediction and drug discovery. In this paper, by using the concept of pseudo amino acid composition (PseAAC), the mycobacterial proteins are studied and predicted by support vector machine (SVM) and increment of diversity combined with modified Mahalanobis Discriminant (IDQD). The results of jackknife cross-validation for 450 non-redundant proteins show that the overall predicted successful rates of SVM and IDQD are 82.2% and 79.1%, respectively. Compared with other existing methods, SVM combined with PseAAC display higher accuracies. <s> BIB012 </s> Survey of Natural Language Processing Techniques in Bioinformatics <s> Predicting Protein Function. <s> The outer membrane proteins (OMPs) are β-barrel membrane proteins that performed lots of biology functions. The discriminating OMPs from other non-OMPs is a very important task for understanding some biochemical process. In this study, a method that combines increment of diversity with modified Mahalanobis Discriminant, called IDQD, is presented to predict 208 OMPs, 206 transmembrane helical proteins (TMHPs) and 673 globular proteins (GPs) by using Chou's pseudo amino acid compositions as parameters. The overall accuracy of jackknife cross-validation is 93.2% and 96.1%, respectively, for three datasets (OMPs, TMHPs and GPs) and two datasets (OMPs and non-OMPs). These predicted results suggest that the method can be effectively applied to discriminate OMPs, TMHPs and GPs. And it also indicates that the pseudo amino acid composition can better reflect the core feature of membrane proteins than the classical amino acid composition. <s> BIB013 </s> Survey of Natural Language Processing Techniques in Bioinformatics <s> Predicting Protein Function. <s> Predicting the binding sites between two interacting proteins provides important clues to the function of a protein. In this study, we present a building block of proteins called order profiles to use the evolutionary information of the protein sequence frequency profiles and apply this building block to produce a class of propensities called order profile interface propensities. For comparisons, we revisit the usage of residue interface propensities and binary profile interface propensities for protein binding site prediction. Each kind of propensities combined with sequence profiles and accessible surface areas are inputted into SVM. When tested on four types of complexes (hetero-permanent complexes, hetero-transient complexes, homo-permanent complexes and homo-transient complexes), experimental results show that the order profile interface propensities are better than residue interface propensities and binary profile interface propensities. Therefore, order profile is a suitable profile-level building block of the protein sequences and can be widely used in many tasks of computational biology, such as the sequence alignment, the prediction of domain boundary, the designation of knowledge-based potentials and the protein remote homology detection. <s> BIB014 </s> Survey of Natural Language Processing Techniques in Bioinformatics <s> Predicting Protein Function. <s> Mycobacterium tuberculosis is the primary pathogen causing tuberculosis, which is one of the most prevalent infectious diseases. The subcellular location of mycobacterial proteins can provide essential clues for proteins function research and drug discovery. Therefore, it is highly desirable to develop a computational method for fast and reliable prediction of subcellular location of mycobacterial proteins. In this study, we developed a support vector machine (SVM) based method to predict subcellular location of mycobacterial proteins. A total of 444 non-redundant mycobacterial proteins were used to train and test proposed model by using jackknife cross validation. By selecting traditional pseudo amino acid composition (PseAAC) as parameters, the overall accuracy of 83.3% was achieved. Moreover, a feature selection technique was developed to find out an optimal amount of PseAAC for improving predictive performance. The optimal amount of PseAAC improved overall accuracy from 83.3 to 87.2%. In addition, the reduced amino acids in N-terminus and non N-terminus of proteins were combined in models for further improving predictive successful rate. As a result, the maximum overall accuracy of 91.2% was achieved with average accuracy of 79.7%. The proposed model provides highly useful information for further experimental research. The prediction model can be accessed free of charge at http://cobi.uestc.edu.cn/cobi/people/hlin/webserver. <s> BIB015 </s> Survey of Natural Language Processing Techniques in Bioinformatics <s> Predicting Protein Function. <s> Apoptosis proteins play an essential role in regulating a balance between cell proliferation and death. The successful prediction of subcellular localization of apoptosis proteins directly from primary sequence is much benefited to understand programmed cell death and drug discovery. In this paper, by use of Chou's pseudo amino acid composition (PseAAC), a total of 317 apoptosis proteins are predicted by support vector machine (SVM). The jackknife cross-validation is applied to test predictive capability of proposed method. The predictive results show that overall prediction accuracy is 91.1% which is higher than previous methods. Furthermore, another dataset containing 98 apoptosis proteins is examined by proposed method. The overall predicted successful rate is 92.9%. <s> BIB016 </s> Survey of Natural Language Processing Techniques in Bioinformatics <s> Predicting Protein Function. <s> Identifying interaction sites in proteins provides important clues to the function of a protein and is becoming increasingly relevant in topics such as systems biology and drug discovery. Although there are numerous papers on the prediction of interaction sites using information derived from structure, there are only a few case reports on the prediction of interaction residues based solely on protein sequence. Here, a sliding window approach is combined with the Random Forests method to predict protein interaction sites using (i) a combination of sequence- and structure-derived parameters and (ii) sequence information alone. For sequence-based prediction we achieved a precision of 84% with a 26% recall and an F-measure of 40%. When combined with structural information, the prediction performance increases to a precision of 76% and a recall of 38% with an F-measure of 51%. We also present an attempt to rationalize the sliding window size and demonstrate that a nine-residue window is the most suitable for predictor construction. Finally, we demonstrate the applicability of our prediction methods by modeling the Ras-Raf complex using predicted interaction sites as target binding interfaces. Our results suggest that it is possible to predict protein interaction sites with quite a high accuracy using only sequence information. <s> BIB017 </s> Survey of Natural Language Processing Techniques in Bioinformatics <s> Predicting Protein Function. <s> Motivation: A number of methods have been reported that predict protein–protein interactions (PPIs) with high accuracy using only simple sequence-based features such as amino acid 3mer content. This is surprising, given that many protein interactions have high specificity that depends on detailed atomic recognition between physiochemically complementary surfaces. Are the reported high accuracies realistic? ::: ::: Results: We find that the reported accuracies of the predictions are significantly over-estimated, and strongly dependent on the structure of the training and testing datasets used. The choice of which protein pairs are deemed as non-interactions in the training data has a variable impact on the accuracy estimates, and the accuracies can be artificially inflated by a bias towards dominant samples in the positive data which result from the presence of hub proteins in the protein interaction network. To address this bias, we propose a positive set-specific method to create a ‘balanced’ negative set maintaining the degree distribution for each protein, leading to the conclusion that simple sequence-based features contain insufficient information to be useful for predicting PPIs, but that protein domain-based features have some predictive value. ::: ::: Availability: Our method, named ‘BRS-nonint’, is available at http://www.bioinformatics.leeds.ac.uk/BRS-nonint/. All the datasets used in this study are derived from publicly available data, and are available at http://www.bioinformatics.leeds.ac.uk/BRS-nonint/PPI_RandomBalance.html ::: ::: Contact:maozuguo@hit.edu.cn; d.r.westhead@leeds.ac.uk <s> BIB018 </s> Survey of Natural Language Processing Techniques in Bioinformatics <s> Predicting Protein Function. <s> The Golgi apparatus is an important eukaryotic organelle. Successful prediction of Golgi protein types can provide valuable information for elucidating protein functions involved in various biological processes. In this work, a method is proposed by combining a special mode of pseudo amino acid composition (increment of diversity) with the modified Mahalanobis discriminant for predicting Golgi protein types. The benchmark dataset used to train the predictor thus formed contains 95 Golgi proteins in which none of proteins included has ≥ 40% pairwise sequence identity to any other. The accuracy obtained by the jackknife test was 74.7%, with the ROC curve of 0.772 in identifying cis-Golgi proteins and trans-Golgi proteins. Subsequently, the method was extended to discriminate cis-Golgi network proteins from cis-Golgi network membrane proteins and trans-Golgi network proteins from trans-Golgi network membrane proteins, respectively. The accuracies thus obtained were 76.1% and 83.7%, respectively. These results indicate that our method may become a useful tool in the relevant areas. As a user-friendly web-server, the predictor is freely accessible at http://immunet.cn/SubGolgi/. <s> BIB019 </s> Survey of Natural Language Processing Techniques in Bioinformatics <s> Predicting Protein Function. <s> Ion channels are integral membrane proteins that control movement of ions into or out of cells. They are key components in a wide range of biological processes. Different types of ion channels have different biological functions. With the appearance of vast proteomic data, it is highly desirable for both basic research and drug-target discovery to develop a computational method for the reliable prediction of ion channels and their types. In this study, we developed a support vector machine-based method to predict ion channels and their types using primary sequence information. A feature selection technique, analysis of variance (ANOVA), was introduced to remove feature redundancy and find out an optimized feature set for improving predictive performance. Jackknife cross-validated results show that the proposed method can discriminate ion channels from non-ion channels with an overall accuracy of 86.6%, classify voltage-gated ion channels and ligand-gated ion channels with an overall accuracy of 92.6% and predict four types (potassium, sodium, calcium and anion) of voltage-gated ion channels with an overall accuracy of 87.8%, respectively. These results indicate that the proposed method can correctly identify ion channels and provide important instructions for drug-target discovery. The predictor can be freely downloaded from http://cobi.uestc.edu.cn/people/hlin/tools/IonchanPred/. <s> BIB020 </s> Survey of Natural Language Processing Techniques in Bioinformatics <s> Predicting Protein Function. <s> Abstract Mycobacterium can cause many serious diseases, such as tuberculosis and leprosy. Its membrane proteins play a critical role for multidrug-resistance and its tenacious survival ability. Knowing the types of membrane proteins will provide novel insights into understanding their functions and facilitate drug target discovery. In this study, a novel method was developed for predicting mycobacterial membrane protein and their types by using over-represented tripeptides. A total of 295 non-membrane proteins and 274 membrane proteins were collected to evaluate the performance of proposed method. The results of jackknife cross-validation test show that our method achieves an overall accuracy of 93.0% in discriminating between mycobacterial membrane proteins and mycobacterial non-membrane proteins and an overall accuracy of 93.1% in classifying mycobacterial membrane protein types. By comparing with other methods, the proposed method showed excellent predictive performance. Based on the proposed method, we built a predictor, called MycoMemSVM, which is freely available at http://lin.uestc.edu.cn/server/MycoMemSVM . It is anticipated that MycoMemSVM will become a useful tool for the annotation of mycobacterial membrane proteins and the development of anti-mycobacterium drug design. <s> BIB021 </s> Survey of Natural Language Processing Techniques in Bioinformatics <s> Predicting Protein Function. <s> Protein remote homology detection is one of the most important problems in bioinformatics. Discriminative methods such as support vector machines (SVM) have shown superior performance. However, the performance of SVM-based methods depends on the vector representations of the protein sequences. Prior works have demonstrated that sequence-order effects are relevant for discrimination, but little work has explored how to incorporate the sequence-order information along with the amino acid physicochemical properties into the prediction. In order to incorporate the sequence-order effects into the protein remote homology detection, the physicochemical distance transformation (PDT) method is proposed. Each protein sequence is converted into a series of numbers by using the physicochemical property scores in the amino acid index (AAIndex), and then the sequence is converted into a fixed length vector by PDT. The sequence-order information can be efficiently included into the feature vector with little computational cost by this approach. Finally, the feature vectors are input into a support vector machine classifier to detect the protein remote homologies. Our experiments on a well-known benchmark show the proposed method SVM-PDT achieves superior or comparable performance with current state-of-the-art methods and its computational cost is considerably superior to those of other methods. When the evolutionary information extracted from the frequency profiles is combined with the PDT method, the profile-based PDT approach can improve the performance by 3.4% and 11.4% in terms of ROC score and ROC50 score respectively. The local sequence-order information of the protein can be efficiently captured by the proposed PDT and the physicochemical properties extracted from the amino acid index are incorporated into the prediction. The physicochemical distance transformation provides a general framework, which would be a valuable tool for protein-level study. <s> BIB022 </s> Survey of Natural Language Processing Techniques in Bioinformatics <s> Predicting Protein Function. <s> Multi-functional enzymes are enzymes that perform multiple physiological functions. Characterization and identification of multi-functional enzymes are critical for communication and cooperation between different functions and pathways within a complex cellular system or between cells. In present study, we collected literature-reported 6,799 multi-functional enzymes and systematically characterized them in structural, functional, and evolutionary aspects. It was found that four physiochemical properties, that is, charge, polarizability, hydrophobicity, and solvent accessibility, are important for characterization of multi-functional enzymes. Accordingly, a combinational model of support vector machine and random forest model was constructed, based on which 6,956 potential novel multi-functional enzymes were successfully identified from the ENZYME database. Moreover, it was observed that multi-functional enzymes are non-evenly distributed in species, and that Bacteria have relatively more multi-functional enzymes than Archaebacteria and Eukaryota. Comparative analysis indicated that the multi-functional enzymes experienced a fluctuation of gene gain and loss during the evolution from S. cerevisiae to H. sapiens. Further pathway analyses indicated that a majority of multi-functional enzymes were well preserved in catalyzing several essential cellular processes, for example, metabolisms of carbohydrates, nucleotides, and amino acids. What's more, a database of known multi-functional enzymes and a server for novel multi-functional enzyme prediction were also constructed for free access at http://bioinf.xmu.edu.cn/databases/MFEs/index.htm. <s> BIB023 </s> Survey of Natural Language Processing Techniques in Bioinformatics <s> Predicting Protein Function. <s> The mitochondrion is a key organelle of eukaryotic cell that provides the energy for cellular activities. Correctly identifying submitochondria locations of proteins can provide plentiful information for understanding their functions. However, using web-experimental methods to recognize submitochondria locations of proteins are time-consuming and costly. Thus, it is highly desired to develop a bioinformatics method to predict the submitochondria locations of mitochondrion proteins. In this work, a novel method based on support vector machine was developed to predict the submitochondria locations of mitochondrion proteins by using over-represented tetrapeptides selected by using binomial distribution. A reliable and rigorous benchmark dataset including 495 mitochondrion proteins with sequence identity ≤25% was constructed for testing and evaluating the proposed model. Jackknife cross-validated results showed that the 91.1% of the 495 mitochondrion proteins can be correctly predicted. Subsequently, our model was estimated by three existing benchmark datasets. The overall accuracies are 94.0, 94.7 and 93.4%, respectively, suggesting that the proposed model is potentially useful in the realm of mitochondrion proteome research. Based on this model, we built a predictor called TetraMito which is freely available at http://lin.uestc.edu.cn/server/TetraMito. <s> BIB024 </s> Survey of Natural Language Processing Techniques in Bioinformatics <s> Predicting Protein Function. <s> Abstract The functions of Golgi apparatus are to store, package and distribute proteins. Knowing the type of a Golgi-resident protein will provide in-depth insight into its function. In this study, we developed a support vector machine-based method to identify the types of Golgi-resident proteins by using only amino acid sequence information. A strictly and objective dataset including 137 proteins with the sequence identity http://lin.uestc.edu.cn/server/subGolgi2 . <s> BIB025 </s> Survey of Natural Language Processing Techniques in Bioinformatics <s> Predicting Protein Function. <s> Chloroplasts are organelles found in plant cells that conduct photosynthesis. The subchloroplast locations of proteins are correlated with their functions. With the availability of a great number of protein data, it is highly desired to develop a computational method to predict the subchloroplast locations of chloroplast proteins. In this study, we proposed a novel method to predict subchloroplast locations of proteins using tripeptide compositions. It first used the binomial distribution to optimize the feature sets. Then the support vector machine was selected to perform the prediction of subchloroplast locations of proteins. The proposed method was tested on a reliable and rigorous dataset including 259 chloroplast proteins with sequence identity ≤ 25%. In the jack-knife cross-validation, 92.21% envelope proteins, 93.20% thylakoid membrane, 52.63% thylakoid lumen and 85.00% stroma can be correctly identified. The overall accuracy achieves 88.03% which is higher than that of other models. Based on this method, a predictor called ChloPred has been built and can be freely available from http://cobi.uestc.edu.cn/people/hlin/tools/ChloPred/. The predictor will provide important information for theoretical and experimental research of chloroplast proteins. <s> BIB026 </s> Survey of Natural Language Processing Techniques in Bioinformatics <s> Predicting Protein Function. <s> Protein remote homology detection is a key problem in bioinformatics. Currently the discriminative methods, such as Support Vector Machine (SVM) can achieve the best performance. The most efficient approach to improve the performance of SVM-based methods is to find a general protein representation method that is able to convert proteins with different lengths into fixed length vectors and captures the different properties of the proteins for the discrimination. The bottleneck of designing the protein representation method is that native proteins have different lengths. Motivated by the success of the pseudo amino acid composition (PseAAC) proposed by Chou, we applied this approach for protein remote homology detection. Some new indices derived from the amino acid index (AAIndex) database are incorporated into the PseAAC to improve the generalization ability of this method. Finally, the performance is further improved by combining the modified PseAAC with profile-based protein representation containing the evolutionary information extracted from the frequency profiles. Our experiments on a well-known benchmark show this method achieves superior or comparable performance with current state-of-the-art methods. <s> BIB027 </s> Survey of Natural Language Processing Techniques in Bioinformatics <s> Predicting Protein Function. <s> Biology is meaningful and important to identify cytokines and investigate their various functions and biochemical mechanisms. However, several issues remain, including the large scale of benchmark datasets, serious imbalance of data, and discovery of new gene families. In this paper, we employ the machine learning approach based on a novel ensemble classifier to predict cytokines. We directly selected amino acids sequences as research objects. First, we pretreated the benchmark data accurately. Next, we analyzed the physicochemical properties and distribution of whole amino acids and then extracted a group of 120-dimensional (120D) valid features to represent sequences. Third, in the view of the serious imbalance in benchmark datasets, we utilized a sampling approach based on the synthetic minority oversampling technique algorithm and K-means clustering undersampling algorithm to rebuild the training set. Finally, we built a library for dynamic selection and circulating combination based on clustering (LibD3C) and employed the new training set to realize cytokine classification. Experiments showed that the geometric mean of sensitivity and specificity obtained through our approach is as high as 93.3%, which proves that our approach is effective for identifying cytokines. <s> BIB028 </s> Survey of Natural Language Processing Techniques in Bioinformatics <s> Predicting Protein Function. <s> High-throughput experimental techniques produce several kinds of heterogeneous proteomic and genomic data sets. To computationally annotate proteins, it is necessary and promising to integrate these heterogeneous data sources. Some methods transform these data sources into different kernels or feature representations. Next, these kernels are linearly (or nonlinearly) combined into a composite kernel. The composite kernel is utilized to develop a predictive model to infer the function of proteins. A protein can have multiple roles and functions (or labels). Therefore, multilabel learning methods are also adapted for protein function prediction. We develop a transductive multilabel classifier (TMC) to predict multiple functions of proteins using several unlabeled proteins. We also propose a method called transductive multilabel ensemble classifier (TMEC) for integrating the different data sources using an ensemble approach. The TMEC trains a graph-based multilabel classifier on each single data source, and then combines the predictions of the individual classifiers. We use a directed birelational graph to capture the relationships between pairs of proteins, between pairs of functions, and between proteins and functions. We evaluate the effectiveness of the TMC and TMEC to predict the functions of proteins on three benchmarks. We show that our approaches perform better than recently proposed protein function prediction methods on composite and multiple kernels. The code, data sets used in this paper and supplemental material are available at https://sites.google.com/site/guoxian85/tmec. <s> BIB029 </s> Survey of Natural Language Processing Techniques in Bioinformatics <s> Predicting Protein Function. <s> Conotoxins are small disulfide-rich peptide toxins, which have the exceptional diversity of sequences. Because conotoxins are able to specifically bind to ion channels and interfere with neurotransmission, they are considered as the excellent pharmacological candidates in drug design. Appropriate type assignment of newly sequenced mature ion channel-targeted conotoxins with computational method is conducive to explore the biological and pharmacological functions of conotoxins. In this paper, we developed a novel method based on binomial distribution and radial basis function network to predict the types of ion-channel targeted conotoxins. We achieved the overall accuracy of 89.3% with average accuracy of 89.7% in the prediction of three types of ion channel-targeted conotoxins in jackknife cross-validation test, indicating that the method is superior to other state-of-the-art methods. In addition, we evaluated the proposed model with an independent dataset including 77 conotoxins. The overall accuracy of 85.7% was achieved, validating that our model is reliable. Moreover, we used the proposed method to annotate 336 function-undefined mature conotoxins in the UniProt Database. The model provides the valuable instructions for theoretical and experimental research on conotoxins. <s> BIB030 </s> Survey of Natural Language Processing Techniques in Bioinformatics <s> Predicting Protein Function. <s> The structure and activity of enzymes are influenced by pH value of their surroundings. Although many enzymes work well in the pH range from 6 to 8, some specific enzymes have good efficiencies only in acidic (pH<5) or alkaline (pH>9) solution. Studies have demonstrated that the activities of enzymes correlate with their primary sequences. It is crucial to judge enzyme adaptation to acidic or alkaline environment from its amino acid sequence in molecular mechanism clarification and the design of high efficient enzymes. In this study, we developed a sequence-based method to discriminate acidic enzymes from alkaline enzymes. The analysis of variance was used to choose the optimized discriminating features derived from g-gap dipeptide compositions. And support vector machine was utilized to establish the prediction model. In the rigorous jackknife cross-validation, the overall accuracy of 96.7% was achieved. The method can correctly predict 96.3% acidic and 97.1% alkaline enzymes. Through the comparison between the proposed method and previous methods, it is demonstrated that the proposed method is more accurate. On the basis of this proposed method, we have built an online web-server called AcalPred which can be freely accessed from the website (http://lin.uestc.edu.cn/server/AcalPred). We believe that the AcalPred will become a powerful tool to study enzyme adaptation to acidic or alkaline environment. <s> BIB031 </s> Survey of Natural Language Processing Techniques in Bioinformatics <s> Predicting Protein Function. <s> Natural Science Foundation of China [61001013, 61102136, 60971085]; Natural Science Foundation of Fujian Province of China [2011J05158, 2010J01350]; Base Research Project of Shenzhen Bureau of Science, Technology, and Information [JC201006030858A] <s> BIB032 </s> Survey of Natural Language Processing Techniques in Bioinformatics <s> Predicting Protein Function. <s> Identification of protein binding sites is critical for studying the function of the proteins. In this paper, we proposed a method for protein binding site prediction, which combined the order profile propensities and hidden Markov support vector machine (HM-SVM). This method employed the sequential labeling technique to the field of protein binding site prediction. The input features of HM-SVM include the profile-based propensities, the Position-Specific Score Matrix (PSSM), and Accessible Surface Area (ASA). When tested on different data sets, the proposed method showed promising results, and outperformed some closely relative methods by more than 10% in terms of AUC. <s> BIB033 </s> Survey of Natural Language Processing Techniques in Bioinformatics <s> Predicting Protein Function. <s> Protein subcellular location prediction can promote scientists to determinate protein functions,to reveal how and in what kind of cellular environments proteins interact with each other and with other molecules,to understand the intricate pathways that regulate biological processes at the cellular level,and to discover the drug targets. With the exponential increase of protein sequences in the post genomic age,the machine learning based protein subcellular location predicting methods are becoming more and more important. In order to grasp the research focuses on this field,this paper reviewed the research status of this problem from the following five aspects,dataset construction,protein features representation,predicting algorithm design, algorithm testing and Web server building. It pointed out the core and difficult parts in solving this problem,analyzed the problems that had recently arisen,and prospected the research focuses in this field. <s> BIB034 </s> Survey of Natural Language Processing Techniques in Bioinformatics <s> Predicting Protein Function. <s> Automated protein function prediction is one of the grand challenges in computational biology. Multi-label learning is widely used to predict functions of proteins. Most of multi-label learning methods make prediction for unlabeled proteins under the assumption that the labeled proteins are completely annotated, i.e., without any missing functions. However, in practice, we may have a subset of the ground-truth functions for a protein, and whether the protein has other functions is unknown. To predict protein functions with incomplete annotations, we propose a Protein Function Prediction method with Weak-label Learning (ProWL) and its variant ProWL-IF. Both ProWL and ProWL-IF can replenish the missing functions of proteins. In addition, ProWL-IF makes use of the knowledge that a protein cannot have certain functions, which can further boost the performance of protein function prediction. Our experimental results on protein-protein interaction networks and gene expression benchmarks validate the effectiveness of both ProWL and ProWL-IF. <s> BIB035 </s> Survey of Natural Language Processing Techniques in Bioinformatics <s> Predicting Protein Function. <s> Voltage-gated K+ channel (VKC) plays important roles in biology procession, especially in nervous system. Different subfamilies of VKCs have different biological functions. Thus, knowing VKCs' subfamilies has become a meaningful job because it can guide the direction for the disease diagnosis and drug design. However, the traditional wet-experimental methods were costly and time-consuming. It is highly desirable to develop an effective and powerful computational tool for identifying different subfamilies of VKCs. In this study, a predictor, called iVKC-OTC, has been developed by incorporating the optimized tripeptide composition (OTC) generated by feature selection technique into the general form of pseudo-amino acid composition to identify six subfamilies of VKCs. One of the remarkable advantages of introducing the optimized tripeptide composition is being able to avoid the notorious dimension disaster or over fitting problems in statistical predictions. It was observed on a benchmark dataset, by using a jackknife test, that the overall accuracy achieved by iVKC-OTC reaches to 96.77% in identifying the six subfamilies of VKCs, indicating that the new predictor is promising or at least may become a complementary tool to the existing methods in this area. It has not escaped our notice that the optimized tripeptide composition can also be used to investigate other protein classification problems. <s> BIB036 </s> Survey of Natural Language Processing Techniques in Bioinformatics <s> Predicting Protein Function. <s> As a parasitic protozoan, Plasmodium falciparum (P. falciparum) can cause malaria. The mitochondrial proteins of malaria parasite play important roles in the discovery of anti-malarial drug targets. Thus, accurate identification of mitochondrial proteins of malaria parasite is a key step for understanding their functions and finding potential drug targets. In this work, we developed a sequence-based method to identify the mitochondrial proteins of malaria parasite. At first, we extended adjoining dipeptide composition to g-gap dipeptide composition for discretely formulating the protein sequences. Subsequently, the analysis of variance (ANOVA) combined with incremental feature selection (IFS) was used to pick out the optimal features. Finally, the jackknife cross-validation was used to evaluate the performance of the proposed model. Evaluation results showed that the maximum accuracy of 97.1% could be achieved by using 101 optimal 5-gap dipeptides. The comparison with previous methods demonstrated that our method was accurate and efficient. <s> BIB037 </s> Survey of Natural Language Processing Techniques in Bioinformatics <s> Predicting Protein Function. <s> The bacteriophage virion proteins play extremely important roles in the fate of host bacterial cells. Accurate identification of bacteriophage virion proteins is very important for understanding their functions and clarifying the lysis mechanism of bacterial cells. In this study, a new sequence-based method was developed to identify phage virion proteins. In the new method, the protein sequences were initially formulated by the g-gap dipeptide compositions. Subsequently, the analysis of variance (ANOVA) with incremental feature selection (IFS) was used to search for the optimal feature set. It was observed that, in jackknife cross-validation, the optimal feature set including 160 optimized features can produce the maximum accuracy of 85.02%. By performing feature analysis, we found that the correlation between two amino acids with one gap was more important than other correlations for phage virion protein prediction and that some of the 1-gap dipeptides were important and mainly contributed to the virion protein prediction. This analysis will provide novel insights into the function of phage virion proteins. On the basis of the proposed method, an online web-server, PVPred, was established and can be freely accessed from the website (http://lin.uestc.edu.cn/server/PVPred). We believe that the PVPred will become a powerful tool to study phage virion proteins and to guide the related experimental validations. <s> BIB038 </s> Survey of Natural Language Processing Techniques in Bioinformatics <s> Predicting Protein Function. <s> Playing crucial roles in various cellular processes, such as recognition of specific nucleotide sequences, regulation of transcription, and regulation of gene expression, DNA-binding proteins are essential ingredients for both eukaryotic and prokaryotic proteomes. With the avalanche of protein sequences generated in the postgenomic age, it is a critical challenge to develop automated methods for accurate and rapidly identifying DNA-binding proteins based on their sequence information alone. Here, a novel predictor, called "iDNA-Prot|dis", was established by incorporating the amino acid distance-pair coupling information and the amino acid reduced alphabet profile into the general pseudo amino acid composition (PseAAC) vector. The former can capture the characteristics of DNA-binding proteins so as to enhance its prediction quality, while the latter can reduce the dimension of PseAAC vector so as to speed up its prediction process. It was observed by the rigorous jackknife and independent dataset tests that the new predictor outperformed the existing predictors for the same purpose. As a user-friendly web-server, iDNA-Prot|dis is accessible to the public at http://bioinformatics.hitsz.edu.cn/iDNA-Prot_dis/. Moreover, for the convenience of the vast majority of experimental scientists, a step-by-step protocol guide is provided on how to use the web-server to get their desired results without the need to follow the complicated mathematic equations that are presented in this paper just for the integrity of its developing process. It is anticipated that the iDNA-Prot|dis predictor may become a useful high throughput tool for large-scale analysis of DNA-binding proteins, or at the very least, play a complementary role to the existing predictors in this regard. <s> BIB039 </s> Survey of Natural Language Processing Techniques in Bioinformatics <s> Predicting Protein Function. <s> Mycobacterium tuberculosis is a bacterium that causes tuberculosis, one of the most prevalent infectious diseases. Predicting the subcellular localization of mycobacterial proteins in this bacterium may provide vital clues for the prediction of protein function as well as for drug discovery and design. Therefore, a computational method that can predict the subcellular localization of mycobacterial proteins with high precision is highly desirable. We propose a computational method to predict the subcellular localization of mycobacterial proteins. An objective and strict benchmark dataset was constructed after collecting 272 non-redundant proteins from the universal protein resource (the UniProt database). Subsequently, a novel feature selection strategy based on binomial distribution was used to optimize the feature vector. Finally, a subset containing 219 chosen tripeptide features was imported into a support vector machine-based method to estimate the performance of the dataset in accurately and sensitively identifying these proteins. We found that the proposed method gave a maximum overall accuracy of 89.71% with an average accuracy of 81.12% in the jackknife cross-validation. The results indicate that our prediction method gave an efficient and powerful performance when compared with other published methods. We made the proposed method available on a purpose built Web server called MycoSub that is freely accessible at . We anticipate that MycoSub will become a useful tool for studying the functions of mycobacterial proteins and for designing and developing anti-mycobacterium drugs. <s> BIB040 </s> Survey of Natural Language Processing Techniques in Bioinformatics <s> Predicting Protein Function. <s> Identification of DNA-binding proteins is an important problem in biomedical research as DNA-binding proteins are crucial for various cellular processes. Currently, the machine learning methods achieve the-state-of-the-art performance with different features. A key step to improve the performance of these methods is to find a suitable representation of proteins. In this study, we proposed a feature vector composed of three kinds of sequence-based features, including overall amino acid composition, pseudo amino acid composition (PseAAC) proposed by Chou and physicochemical distance transformation. These features not only consider the sequence composition of proteins, but also incorporate the sequence-order information of amino acids in proteins. The feature vectors were fed into Support Vector Machine (SVM) for DNA-binding protein identification. The proposed method is called PseDNA-Pro. Experiments on stringent benchmark datasets and independent test datasets by using the Jackknife test showed that PseDNA-Pro can achieve an accuracy of higher than 80 %, outperforming several state-of-the-art methods, including DNAbinder, DNA-Prot, and iDNA-Prot. These results indicate that the combination of various features for DNA-binding protein prediction is a suitable approach, and the sequence-order information among residues in proteins is relative for discrimination. For practical applications, a web-server of PseDNA-Pro was established, which is available from http://bioinformatics.hitsz.edu.cn/PseDNA-Pro/. <s> BIB041
Predicting protein function is one of the most basic research topics in bioinformatics. It involves predicting protein-protein interactions and interaction sites BIB014 BIB033 , localizing subcellular protein BIB034 BIB012 BIB015 BIB016 BIB024 BIB025 BIB026 BIB040 BIB019 , predicting and classifying transmembrane protein  BIB013 BIB021 BIB020 , protein remote homology detection BIB027 BIB022 , classifying protein functions BIB035 BIB028 BIB029  BIB036 BIB037 BIB038 BIB030  , recognizing multifunctional enzymes BIB023 BIB031 BIB032 , and DNA binding protein identification BIB041 BIB039 . The protein sequence is easy to determine. Similar to natural language, the protein sequence has many complicated rules. However, summarizing and understanding the rules of protein sequences are difficult. Therefore, analyzing and predicting the "protein language" expressed by amino acid sequences by using computational linguistics and machine learning methods are necessary. Through these procedures, we may be able to understand the functions of protein sequences. Predicting protein-protein interactions is one of the most basic research topics in protein functions. Many researchers are committed to predicting whether two protein sequences exhibit interactions. To date, many machine learning methods have been applied, including SVM BIB001 , kernel method BIB006 BIB007 , decision-making tree BIB003 BIB010 , random forest  , Bayesian network  , and the autoregressive model BIB004 . Several text processing methods, such as ontology annotation and sample weighting  , are used to detect features and process training data. When predicting protein-protein interactions, researchers also aim to analyze the region of protein-protein interactions, which is used to predict protein-protein interaction sites. Information approaches commonly used in grammatical analyses, such as condition random fields  and a hidden Markov model (HMM) BIB008 , have been used to analyze interaction sites and have achieved good results. Moreover, random forest BIB017 , SVM BIB005 , ANN BIB002 , Bayesian network BIB009 , linear regression BIB011 , and other machine learning methods are used to predict protein-protein interaction sites. Nevertheless, some researchers doubt that determining the protein sequence alone is inadequate to provide sufficient information for predicting interactions BIB018 . Text mining and machine learning researchers should develop new features and classification methods to solve this problem. The websites of existing common software used to predict protein-protein interactions and interaction sites are provided in Table 2 .