Survey of Natural Language Processing Techniques in Bioinformatics <s> Predicting Protein Structure. <s> A tractor having two internal combustion engines which are spaced apart on either side of the longitudinal axis of the tractor and are covered with separate spaced apart cowlings, the driveable rear wheels of the tractor numbering four or six and being mounted on an axle extending laterally from a narrow part of the tractor's body, an enclosed operator's seat and controls spaced above the rear wheels, the seat and controls being selectively rotatable to positions 180 degrees apart whereby the operator optionally faces forward or to the rear, the tractor having forward and rear lifting devices substantially fully visible from the operator's seat between, in front, the spaced-apart engines, the engines, which are identical, being connected through clutches and a gear train to the rear wheels via a torque converter and to a power take-off shaft extending forward and aft, the tractor's frame being a central horizontal beam and cross-beams rigidly connected thereto. <s> BIB001 </s> Survey of Natural Language Processing Techniques in Bioinformatics <s> Predicting Protein Structure. <s> 1. ::: (1) Co-operation between a laboratory interested in developing the theory for protein secondary structure prediction methods and a laboratory interested in applying and comparing such methods has led to the development of a simple predictive algorithm. ::: ::: 2. ::: (2) Four-state predictions, in which each residue is unambiguously assigned one conformational state of α-helix, extended chain, reverse turn or coil, predict 49% of residue states correctly (in a sample of 26 proteins) when the overall helix and extended-chain content is not taken into account. ::: ::: 3. ::: (3) When the relative abundances of helix, extended chain, reverse turn and coil observed by X-ray crystallography are taken into account, a single constant for each protein and type of conformation can be used to bias the prediction. When predictions are optimized in this way, 63% of all residue states are unambiguously and correctly assigned. ::: ::: 4. ::: (4) By analysing the nature of the bias required, proteins can be classified into helix-rich types, pleated-sheet-rich types, and so on. It is shown that, if the type of protein can be determined even approximately by circular dichroism, 57% of residue states can be correctly predicted without taking into account the X-ray structure. Further, comparable predictions can be obtained if, instead of circular dichroism, preliminary predictions are made to assess the protein type. ::: ::: 5. ::: (5) It is emphasized that the numbers quoted here depend on the method used to assess accuracy, and the algorithm is shown to be at least as good as, and usually superior to, the reported prediction methods assessed in the same way. ::: ::: 6. ::: (6) Ways of further enhancing predictions by the use of additional information from hydrophobic triplets and homologous sequences are also explored. Hydro-phobic triplet information does not significantly improve predictive power and it is concluded that this information is used by proteins in the next stage of folding. On the other hand, the use of homologous sequences appears to be very promising. ::: ::: 7. ::: (7) The implication of these results in protein folding is discussed. <s> BIB002 </s> Survey of Natural Language Processing Techniques in Bioinformatics <s> Predicting Protein Structure. <s> We have trained a two-layered feed-forward neural network on a non-redundant data base of 130 protein chains to predict the secondary structure of water-soluble proteins. A new key aspect is the use of evolutionary information in the form of multiple sequence alignments that are used as input in place of single sequences. The inclusion of protein family information in this form increases the prediction accuracy by six to eight percentage points. A combination of three levels of networks results in an overall three-state accuracy of 70.8% for globular proteins (sustained performance). If four membrane protein chains are included in the evaluation, the overall accuracy drops to 70.2%. The prediction is well balanced between alpha-helix, beta-strand and loop: 65% of the observed strand residues are predicted correctly. The accuracy in predicting the content of three secondary structure types is comparable to that of circular dichroism spectroscopy. The performance accuracy is verified by a sevenfold cross-validation test, and an additional test on 26 recently solved proteins. Of particular practical importance is the definition of a position-specific reliability index. For half of the residues predicted with a high level of reliability the overall accuracy increases to better than 82%. A further strength of the method is the more realistic prediction of segment length. The protein family prediction method is available for testing by academic researchers via an electronic mail server. <s> BIB003 </s> Survey of Natural Language Processing Techniques in Bioinformatics <s> Predicting Protein Structure. <s> Intrinsic disorder refers to segments or to whole proteins that fail to self-fold into fixed 3D structure, with such disorder sometimes existing in the native state. Here we report data on the relationships among intrinsic disorder, sequence complexity as measured by Shannon's entropy, and amino acid composition. Intrinsic disorder identified in protein crystal structures, and by nuclear magnetic resonance, circular dichroism, and prediction from amino acid sequence, all exhibit similar complexity distributions that are shifted to lower values compared to, but significantly overlapping with, the distribution for ordered proteins. Compared to sequences from ordered proteins, these variously characterized intrinsically disordered segments and proteins, and also a collection of low-complexity sequences, typically have obviously higher levels of protein-specific subsets of the following amino acids: R, K, E, P, and S, and lower levels of subsets of the following: C, W, Y, I, and V. The Swiss Protein database of sequences exhibits significantly higher amounts of both low-complexity and predicted-to-be-disordered segments as compared to a non-redundant set of sequences from the Protein Data Bank, providing additional data that nature is richer in disordered and low-complexity segments compared to the commonness of these features in the set of structurally characterized proteins. Proteins 2001;42:38–48. © 2000 Wiley-Liss, Inc. <s> BIB004 </s> Survey of Natural Language Processing Techniques in Bioinformatics <s> Predicting Protein Structure. <s> Motivation: Protein secondary structure prediction is an important step towards understanding how proteins fold in three dimensions. Recent analysis by information theory indicates that the correlation between neighboring secondary structures are much stronger than that of neighboring amino acids. In this article, we focus on the combination problem for sequences, i.e. combining the scores or assignments from single or multiple prediction systems under the constraint of a whole sequence, as a target for improvement in protein secondary structure prediction. ::: ::: Results: We apply several graphical chain models to solve the combination problem and show that they are consistently more effective than the traditional window-based methods. In particular, conditional random fields (CRFs) moderately improve the predictions for helices and, more importantly, for beta sheets, which are the major bottleneck for protein secondary structure prediction. <s> BIB005 </s> Survey of Natural Language Processing Techniques in Bioinformatics <s> Predicting Protein Structure. <s> An automatic method for recognizing natively disordered regions from amino acid sequence is described and benchmarked against predictors that were assessed at the latest critical assessment of techniques for protein structure prediction (CASP) experiment. The method attains a Wilcoxon score of 90.0, which represents a statistically significant improvement on the methods evaluated on the same targets at CASP. The classifier, DISOPRED2, was used to estimate the frequency of native disorder in several representative genomes from the three kingdoms of life. Putative, long (>30 residue) disordered segments are found to occur in 2.0% of archaean, 4.2% of eubacterial and 33.0% of eukaryotic proteins. The function of proteins with long predicted regions of disorder was investigated using the gene ontology annotations supplied with the Saccharomyces genome database. The analysis of the yeast proteome suggests that proteins containing disorder are often located in the cell nucleus and are involved in the regulation of transcription and cell signalling. The results also indicate that native disorder is associated with the molecular functions of kinase activity and nucleic acid binding. <s> BIB006 </s> Survey of Natural Language Processing Techniques in Bioinformatics <s> Predicting Protein Structure. <s> BackgroundMore and more disordered regions have been discovered in protein sequences, and many of them are found to be functionally significant. Previous studies reveal that disordered regions of a protein can be predicted by its primary structure, the amino acid sequence. One observation that has been widely accepted is that ordered regions usually have compositional bias toward hydrophobic amino acids, and disordered regions are toward charged amino acids. Recent studies further show that employing evolutionary information such as position specific scoring matrices (PSSMs) improves the prediction accuracy of protein disorder. As more and more machine learning techniques have been introduced to protein disorder detection, extracting more useful features with biological insights attracts more attention.ResultsThis paper first studies the effect of a condensed position specific scoring matrix with respect to physicochemical properties (PSSMP) on the prediction accuracy, where the PSSMP is derived by merging several amino acid columns of a PSSM belonging to a certain property into a single column. Next, we decompose each conventional physicochemical property of amino acids into two disjoint groups which have a propensity for order and disorder respectively, and show by experiments that some of the new properties perform better than their parent properties in predicting protein disorder. In order to get an effective and compact feature set on this problem, we propose a hybrid feature selection method that inherits the efficiency of uni-variant analysis and the effectiveness of the stepwise feature selection that explores combinations of multiple features. The experimental results show that the selected feature set improves the performance of a classifier built with Radial Basis Function Networks (RBFN) in comparison with the feature set constructed with PSSMs or PSSMPs that adopt simply the conventional physicochemical properties.ConclusionDistinguishing disordered regions from ordered regions in protein sequences facilitates the exploration of protein structures and functions. Results based on independent testing data reveal that the proposed predicting model DisPSSMP performs the best among several of the existing packages doing similar tasks, without either under-predicting or over-predicting the disordered regions. Furthermore, the selected properties are demonstrated to be useful in finding discriminating patterns for order/disorder classification. <s> BIB007 </s> Survey of Natural Language Processing Techniques in Bioinformatics <s> Predicting Protein Structure. <s> This article presents a web server iPDA, which aims at identifying the disordered regions of a query protein. Automatic prediction of disordered regions from protein sequences is an important problem in the study of structural biology. The proposed classifier DisPSSMP2 is different from several existing disorder predictors by its employment of position-specific scoring matrices with respect to physicochemical properties (PSSMP), where the physicochemical properties adopted here especially take the disorder propensity of amino acids into account. The web server iPDA integrates DisPSSMP2 with several other sequence predictors in order to investigate the functional role of the detected disordered region. The predicted information includes sequence conservation, secondary structure, sequence complexity and hydrophobic clusters. According to the proportion of the secondary structure elements predicted, iPDA dynamically adjusts the cutting threshold of determining protein disorder. Furthermore, a pattern mining package for detecting sequence conservation is embedded in iPDA for discovering potential binding regions of the query protein, which is really helpful to uncovering the relationship between protein function and its primary sequence. The web service is available at http://biominer.bime.ntu.edu.tw/ipda and mirrored at http://biominer.cse.yzu.edu.tw/ipda. <s> BIB008 </s> Survey of Natural Language Processing Techniques in Bioinformatics <s> Predicting Protein Structure. <s> In recent years, protein structure prediction using local structure information has made great progress. Many fragment libraries or structure alphabets have been developed. In this study, the entropies and correlations of local structures are first calculated. The results show that neighboring local structures are strongly correlated. Then, a dual-layer model has been designed for protein local structure prediction. The position-specific score matrix, generated by PSI-BLAST, is inputted to the first-layer classifier, whose output is further enhanced by a second-layer classifier. The neural network is selected as the classifier. Two structure alphabets are explored, which are represented in Cartesian coordinate space and in torsion angles space respectively. Testing on the nonredundant dataset shows that the dual-layer model is an efficient method for protein local structure prediction. The Q-scores are 0.456 and 0.585 for the two structure alphabets, which is a significant improvement in comparison with related works. Proteins 2008. © 2008 Wiley-Liss, Inc. <s> BIB009 </s> Survey of Natural Language Processing Techniques in Bioinformatics <s> Predicting Protein Structure. <s> In recent years, protein structure prediction using local structure information has made great progress. In this study, a novel and effective method is developed to predict the local structure and the folding fragments of proteins. First, the proteins with known structures are split into fragments. Second, these fragments, represented by dihedrals, are clustered to produce the building blocks (BBs). Third, an efficient machine learning method is used to predict the local structures of proteins from sequence profiles. Finally, a bi-gram model, trained by an iterated algorithm, is introduced to simulate the interactions of these BBs. For test proteins, the building-block lattice is constructed, which contains all the folding fragments of the proteins. The local structures and the optimal fragments are then obtained by the dynamic programming algorithm. The experiment is performed on a subset of the PDB database with sequence identity less than 25%. The results show that the performance of the method is better than the method that uses only sequence information. When multiple paths are returned, the average classification accuracy of local structures is 72.27% and the average prediction accuracy of local structures is 67.72%, which is a significant improvement in comparison with previous studies. The method can predict not only the local structures but also the folding fragments of proteins. This work is helpful for the ab initio protein structure prediction and especially, the understanding of the folding process of proteins. Proteins 2008. © 2008 Wiley-Liss, Inc. <s> BIB010 </s> Survey of Natural Language Processing Techniques in Bioinformatics <s> Predicting Protein Structure. <s> Motivation: Order and Disorder prediction using Conditional Random Fields, OnD-CRF, is a new method for accurately predicting the transition between structured and mobile or disordered regions in proteins. OnD-CRF applies CRFs relying on features which are generated from the amino acids sequence and from secondary structure prediction. Benchmarking results based on CASP7 targets, and evaluation with respect to several CASP criteria, rank the OnDCRF model highest among the fully automatic server group. Availability: http://babel.ucmp.umu.se/ond-crf/ <s> BIB011 </s> Survey of Natural Language Processing Techniques in Bioinformatics <s> Predicting Protein Structure. <s> BackgroundMany proteins contain disordered regions that lack fixed three-dimensional (3D) structure under physiological conditions but have important biological functions. Prediction of disordered regions in protein sequences is important for understanding protein function and in high-throughput determination of protein structures. Machine learning techniques, including neural networks and support vector machines have been widely used in such predictions. Predictors designed for long disordered regions are usually less successful in predicting short disordered regions. Combining prediction of short and long disordered regions will dramatically increase the complexity of the prediction algorithm and make the predictor unsuitable for large-scale applications. Efficient batch prediction of long disordered regions alone is of greater interest in large-scale proteome studies.ResultsA new algorithm, IUPforest-L, for predicting long disordered regions using the random forest learning model is proposed in this paper. IUPforest-L is based on the Moreau-Broto auto-correlation function of amino acid indices (AAIs) and other physicochemical features of the primary sequences. In 10-fold cross validation tests, IUPforest-L can achieve an area of 89.5% under the receiver operating characteristic (ROC) curve. Compared with existing disorder predictors, IUPforest-L has high prediction accuracy and is efficient for predicting long disordered regions in large-scale proteomes.ConclusionThe random forest model based on the auto-correlation functions of the AAIs within a protein fragment and other physicochemical features could effectively detect long disordered regions in proteins. A new predictor, IUPforest-L, was developed to batch predict long disordered regions in proteins, and the server can be accessed from http://dmg.cs.rmit.edu.au/IUPforest/IUPforest-L.php <s> BIB012 </s> Survey of Natural Language Processing Techniques in Bioinformatics <s> Predicting Protein Structure. <s> Knowledge of protein structural class can provide important information about its folding patterns. Many approaches have been developed for the prediction of protein structural classes. However, the information used by these approaches is primarily based on amino acid sequences. In this study, a novel method is presented to predict protein structural classes by use of chemical shift (CS) information derived from nuclear magnetic resonance spectra. Firstly, 399 non-homologue (about 15% identity) proteins were constructed to investigate the distribution of averaged CS values of six nuclei ((13)CO, (13)Cα, (13)Cβ, (1)HN, (1)Hα and (15)N) in three protein structural classes. Subsequently, support vector machine was proposed to predict three protein structural classes by using averaged CS information of six nuclei. Overall accuracy of jackknife cross-validation achieves 87.0%. Finally, the feature selection technique is applied to exclude redundant information and find out an optimized feature set. Results show that the overall accuracy increased to 88.0% by using the averaged CSs of (13)CO, (1)Hα and (15)N. The proposed approach outperformed other state-of-the-art methods in terms of predictive accuracy in particular for low-similarity protein data. We expect that our proposed approach will be an excellent alternative to traditional methods for protein structural class prediction. <s> BIB013 </s> Survey of Natural Language Processing Techniques in Bioinformatics <s> Predicting Protein Structure. <s> Protein folding is recognized as a critical problem in the field of biophysics in the 21st century. Predicting protein-folding patterns is challenging due to the complex structure of proteins. In an attempt to solve this problem, we employed ensemble classifiers to improve prediction accuracy. In our experiments, 188-dimensional features were extracted based on the composition and physical-chemical property of proteins and 20-dimensional features were selected using a coupled position-specific scoring matrix. Compared with traditional prediction methods, these methods were superior in terms of prediction accuracy. The 188-dimensional feature-based method achieved 71.2% accuracy in five cross-validations. The accuracy rose to 77% when we used a 20-dimensional feature vector. These methods were used on recent data, with 54.2% accuracy. Source codes and dataset, together with web server and software tools for prediction, are available at: http://datamining.xmu.edu.cn/main/~cwc/ProteinPredict.html. <s> BIB014 </s> Survey of Natural Language Processing Techniques in Bioinformatics <s> Predicting Protein Structure. <s> The prediction of protein structural classes is beneficial to understanding folding patterns, functions and interactions of proteins. In this study, we proposed a feature selection-based method to accurately predict protein structural classes. Three datasets with sequence identity lower than 25% were used to test the prediction performance of the method. Through jackknife cross-validation, we have verified that the overall accuracies of these three datasets are 92.1%, 89.7% and 84.0%, respectively. The proposed method is more efficient and accurate than other existing methods. The present study will offer an excellent alternative to other methods for predicting protein structural classes. <s> BIB015 </s> Survey of Natural Language Processing Techniques in Bioinformatics <s> Predicting Protein Structure. <s> The analysis of biological information from protein sequences is important for the study of cellular functions and interactions, and protein fold recognition plays a key role in the prediction of protein structures. Unfortunately, the prediction of protein fold patterns is challenging due to the existence of compound protein structures. Here, we processed the latest release of the Structural Classification of Proteins (SCOP, version 1.75) database and exploited novel techniques to impressively increase the accuracy of protein fold classification. The techniques proposed in this paper include ensemble classifying and a hierarchical framework, in the first layer of which similar or redundant sequences were deleted in two manners; a set of base classifiers, fused by various selection strategies, divides the input into seven classes; in the second layer of which, an analogous ensemble method is adopted to predict all protein folds. To our knowledge, it is the first time all protein folds can be intelligently detected hierarchically. Compared with prior studies, our experimental results demonstrated the efficiency and effectiveness of our proposed method, which achieved a success rate of 74.21%, which is much higher than results obtained with previous methods (ranging from 45.6% to 70.5%). When applied to the second layer of classification, the prediction accuracy was in the range between 23.13% and 46.05%. This value, which may not be remarkably high, is scientifically admirable and encouraging as compared to the relatively low counts of proteins from most fold recognition programs. The web server Hierarchical Protein Fold Prediction (HPFP) is available at http://datamining.xmu.edu.cn/software/hpfp. <s> BIB016 </s> Survey of Natural Language Processing Techniques in Bioinformatics <s> Predicting Protein Structure. <s> Motivation: Owing to its importance in both basic research (such as molecular evolution and protein attribute prediction) and practical application (such as timely modeling the 3D structures of proteins targeted for drug development), protein remote homology detection has attracted a great deal of interest. It is intriguing to note that the profilebased approach is promising and holds high potential in this regard. To further improve protein remote homology detection, a key step is how to find an optimal means to extract the evolutionary information into the profiles. Results: Here, we propose a novel approach, the so-called profilebased protein representation, to extract the evolutionary information via the frequency profiles. The latter can be calculated from the multiple sequence alignments generated by PSI-BLAST. Three top performing sequence-based kernels (SVM-Ngram, SVM-pairwise and SVM-LA) were combined with the profile-based protein representation. Various tests were conducted on a SCOP benchmark dataset that contains 54 families and 23 superfamilies. The results showed that the new approach is promising, and can obviously improve the performance of the three kernels. Furthermore, our approach can also provide useful insights for studying the features of proteins in various families. It has not escaped our notice that the current approach can be easily combined with the existing sequence-based methods so as to improve their performance as well. Availability and implementation: For users’ convenience, the source code of generating the profile-based proteins and the multiple kernel learning was also provided at <s> BIB017
Protein structure determines function BIB017 . Hence, it should be analyzed to determine protein function. The structural analysis of protein mainly focuses on certain protein sequences and classifies regions into the -helix, -lamella, and protein disordered regions. Predicting the -helix and -lamella regions is the same as predicting the secondary protein structure. If a protein sequence is regarded as a natural language, then analyzing the type of protein in a region is similar to calibrating grammar in natural language processing. First, the secondary protein structure is predicted by combining rules and statistics BIB001 BIB002 BIB009 BIB010 . However, faced with the bottleneck of statistical prediction, some researchers have proposed using machine learning prediction methods, including methods based on artificial neural network (ANN) BIB003 , support vector machine (SVM) BIB015 BIB013 , random forest BIB016 BIB014  , and maximum entropy BIB005 . Predicting the protein disordered region is also conducted. This region refers to the area without a stable or unique 3D structure in the protein space structure. Many text mining and machine learning methods, including ANN BIB004 BIB007 BIB008 , SVM BIB006   , conditional random field BIB011 , and random forest BIB012 , have been used to predict the protein disordered region. Common existing server addresses are listed in Table 1 .