The Wiggle series are support vector machine–based predictors that identify regions of functional flexibility using only protein sequence information. Functionally flexible regions are defined as regions that can adopt different conformational states and are assumed to be necessary for bioactivity. Many advances have been made in understanding the relationship between protein sequence and structure. This work contributes to those efforts by making strides to understand the relationship between protein sequence and flexibility. A coarse-grained protein dynamic modeling approach was used to generate the dataset required for support vector machine training. We define our regions of interest based on the participation of residues in correlated large-scale fluctuations. Even with this structure-based approach to computationally define regions of functional flexibility, predictors successfully extract sequence-flexibility relationships that have been experimentally confirmed to be functionally important. Thus, a sequence-based tool to identify flexible regions important for protein function has been created. The ability to identify functional flexibility using a sequence based approach complements structure-based definitions and will be especially useful for the large majority of proteins with unknown structures. The methodology offers promise to identify structural genomics targets amenable to crystallization and the possibility to engineer more flexible or rigid regions within proteins to modify their bioactivity. Proteins are not static entities in biology and are constantly changing their shape and form to perform their necessary biological roles. While we are intuitively aware of their constantly changing nature, we have little understanding of how their flexibility is encoded in the protein sequence. To address this knowledge gap, predictors were created to identify sequence patterns that dictate local regions to be flexible and serve a functional purpose. By combining protein dynamic modeling and machine learning techniques, the Wiggle predictor series were able to generalize the sequence-flexibility relationship for all proteins. With these predictors we are able to identify flexible regions of functional importance such as hinges, recognition loops, and catalytic loops using only sequence information. This work has important contributions to our understanding of the sequence-flexibility relationship and paves the road to identifying local sequence modulations that impact protein function without necessarily changing the structure. Protein structures are not rigid bodies, as suggested by time-independent solid-state crystal structures. Rather, proteins are selected by nature to balance between stability and flexibility in order to traverse the funnels of the protein energy landscape that characterize the conformational states needed to achieve a specific bioactivity. In part because of the way protein structures are traditionally represented and visualized in the crystallographic structure, the dynamics of protein motion is poorly conveyed and often neglected as the protein is treated as a static entity, although intuitively we know otherwise. Furthermore, protein sequence-structure relationships have been heavily focused on creating the most stable structure that may not necessarily be optimal for the execution or regulation of protein function. If the sequence is deterministic of the adopted protein fold, then the flexibility and dynamics of proteins should also be encoded by the sequence. Support for this notion comes from the previous demonstration that large amplitude fluctuations are mostly related to the overall protein shape ,2, whichThe flexibility of proteins is a necessary property to allow for conformational changes observed in allosteric interactions. The classic definition of allostery is the regulation of enzymes through the binding of effector molecules. This definition is now expanded to define allostery as the consequence of the redistribution of conformational states in the protein in response to a given external stimulus . We are The Cooper-Dryden model of allostery is a theory that addresses the contribution of entropy to the allosteric free energy. In extreme cases, this theory suggests that allostery can be achieved in the absence of structural change by simply shifting the internal vibrational modes when reacting to an external stimulus such as ligand binding . AssociaEntropy compensation has been observed using both computational and experimental approaches in many different proteins. Here we consider five examples to make the point. First, molecular dynamic (MD) simulations of lysozyme show differences between the dynamics of substrate bound and free states. When lysozyme is in complex with the substrate, a distant loop (residues 67 to 88) increases in fluctuation to compensate for the decreasing fluctuation observed for the substrate-contacting loop residues 101 to 107) . Fourth,1 to 107 functionally flexible regions (FFRs).In each of these cases, local regions of protein structure serve to accommodate the redistribution of vibrational modes and provide an energy reserve of allosteric free energy as proposed by the Cooper-Dryden model. The relaxation and tensing of regions of local structure is a transition from an ordered to disordered state and vice versa. Such local regions include hinges, recognition loops, and certain catalytic loops whose vibrational states change in the presence of an external stimulus such as substrate binding. While structurally dissimilar, hinges, recognition loops, and catalytic loops all exhibit characteristic fluctuations that differ from the mean fluctuation. Hinges are relatively immobile at the hinge point compared to surrounding fluctuations about the hinge, whereas recognition loops and, in certain examples, catalytic loops show minimal fluctuations at the extremities and maximal fluctuations at the center of the loop. We attempt to identify these regions based on the scale and cooperativeness of fluctuations that often define protein function and refer to them as In this paper, we begin with a structure-based definition of an FFR to obtain our training dataset and describe prediction tools created as a result to identify these regions using only protein sequence information. With the growth of protein structures fueled by structural genomics , it is pThe long-term goal of work such as this is to provide a generalized relationship between sequence and FF for all proteins. An immediate benefit would be in facilitating the structure solution process such that proteins less tractable for crystallization could be identified. Further, by our definition, FFRs border on forming an ordered structure; therefore, if such regions can be identified, it may be possible to introduce a few mutations to stabilize local regions that are not located on the ends of the polypeptide chain. This strategy has been utilized to successfully create a soluble analog of erythropoietin . We alsoWe define FFRs to have the property of coordinated participation in large amplitude fluctuations that are different from the mean vibrational fluctuation of the protein. The Gaussian network model (GNM) , a coarsThere are two reasons for using protein dynamic modeling results instead of experimental temperature factors to define our target regions. First, by using protein dynamic modeling simulation, we are able to investigate protein flexibility with the added dimensionality of having functional importance. Second, by using modes of motion to define our target regions, we are able to focus specifically on large-amplitude fluctuations without including contributions from higher frequency fluctuations. These two features are the distinguishing qualities that set our predictors apart from other disorder predictors. The advantages of using this approach will be highlighted in the subsequent discussion and reflected in comparisons made to other disorder predictors.To identify FFRs, we focus on the first two vibrational modes of protein fluctuation because these modes have been shown to sufficiently describe important contributions to global fluctuations necessary for protein function –24. FlexCorrelation values were used to weight mode information to create an FF score and empirically define a threshold to objectively identify FFRs see . The FF The FF score is used for definition purposes only. With this definition procedure we are able to obtain an objectively defined dataset needed for SVM training. The dominant motions in the lowest amplitude modes correspond to rigid domain motions ,27. The The FF score was first tested on HIV protease. While the recognition loop (residues 36 to 42) is identified without incorporating correlated movement information to weight normalized GNM fluctuations, the flap region (residues 46 to 56) important for dimerization was not identified because fluctuation is suppressed in the dimerized state . We subsImprovements in defining FFRs using the FF score were also observed for calmodulin and bovine pancreatic trypsin inhibitor (BPTI) . CalmoduThe binding affinity of BPTI is influenced by mutations in the active loops (residues 11 to 19 and 35 to 42) that are inserted into the active site of the proteolytic enzymes. Mutations Y35G  and G37ABased on the FF score, each residue in a nonredundant training set was classified as FFR or non-FFR. Residues were separated into a binary classification with FFRs assigned a value of 1 and non-FFRs were assigned a value of −1. Examining the distribution of residues in the two classes shows that an FFR averages 9 ± 11 residues in length and comprises about 20% of all residues. Residues identified as hinges comprise about 0.75% of all residues in the training set. The average maximum length of an FFR for each protein increases with increasing protein length A. This iWe examined the classification preference for each amino acid and secondary structure type using the same assignment values (1 and −1) . ResidueWindow scanning for particular patterns reveals that FFRs occupy a smaller sequence space than their non-FFR counterparts B. For a Z-scores and p-values for association with FFRs  because they additionally capture insertion and deletion probabilities that may occur within the sequence ,37. As sExploring the performances of various SVM architectures have shown that a two-layered architecture yields the best performing predictor to identify residues in FFRs. The first-layer SVM makes an initial classification based on sequence and evolutionary information contained in the HMM states. The second-layer SVM serves to smooth the prediction from the first-layer SVM and uses results obtained from the modified bootstrap analysis to make better predictions. Incorporating information regarding tripeptide classification preferences was instrumental to improving the performance of our final predictor despite having a weak statistical value. Compared to a predictor that does not include tripeptide classification preferences, the performance of the SVM showed an additional 5% increase in accuracy and precision with an additional 3% improvement in recall.The predictive performance of the SVMs was found to be a function of protein length. High false-positive rates were observed for shorter proteins A. This hTo account for protein length, the original training set was partitioned into two sets: A, 760 proteins up to 200 residues in length; and B, 574 proteins longer than 200 residues. SVMs trained on the partitioned training sets both showed an improvement in performance B. TrainiOur final predictors, Wiggle and Wiggle200, use the radial basis kernel function in the first layer and a linear kernel in the second layer. Wiggle is the product of training on all proteins and Wiggle200 was trained on subset A containing proteins up to 200 residues. Since minor improvements were observed for the predictor trained on the subset containing larger proteins, we use Wiggle to conduct our predictions. In the following discussion, we will first revisit the dependency of the predictors on protein size in regard to domain boundary detection. Then we will discuss the performance of the predictors on three examples with experimentally verified FFRs.Flexible linkers between domains, sometimes acting as a hinge, are examples of FFRs and we evaluate the performance of Wiggle and Wiggle200 in the detection of these regions. We use a comprehensive domain boundary benchmark set (BENCH) that was curated to reflect the consensus of experts  . Because the boundary is defined between two residue positions, we expand the definition up to a window size of 15 residues, with the boundary in the center, to evaluate the performance of the predictors. We also partitioned BENCH based on protein size into BENCHA (200 residues or fewer) and BENCHB (more than 200 residues).The general trend in predictor performance for Wiggle and Wiggle200 observed for all datasets  is that precision increases with the size of domain boundary expansion, whereas recall increases up to window size 5 and begins to decline afterward . The oveFor BENCH, we find that Wiggle outperforms Wiggle200 in recall by an additional +12.99% with little improvement in precision (+0.31%) and a decrease in accuracy (−6.44%). Wiggle identifies domain boundaries in BENCH at an accuracy of 62.55% with a precision of 6% and recall of 54.15%. We are not surprised to see a poor precision value since both predictors will identify other flexible regions that are not linkers between domains. However, the results here show that our predictors are identifying linkers between domain boundaries, for example, possibly serving a functional purpose as a hinge.For the partitioned benchmark set (BENCHA and BENCHB), we find that Wiggle again outperforms Wiggle200 in domain boundary recall with an additional +14.34% and +12.51%, respectively. Again, minor improvements were observed in precision  and a slight decrease in accuracy  was observed for Wiggle compared to Wiggle200. For the partitioned datasets, BENCHA and BENCHB, Wiggle predicts boundaries at  accuracy,  precision, and  recall, respectively. This clearly indicates that Wiggle, trained on the entire training dataset which includes larger multidomain proteins, has picked up sequence patterns associated with linker regions and is the better predictor for domain boundaries compared to Wiggle200.Although the GNM provides a fast approach to identifying FFRs, there are limitations to the model. Dynamic modeling results are largely dependent on protein conformation, particularly that defined by bound and unbound conformations as discussed earlier for the observed higher false-positive error rate for smaller proteins. Therefore, the FF score does not always correctly define the regions of interest. We examined a few case studies where residues were largely misclassified by the FF score and compared the results to our SVM predictions. While it is ideal to have a precisely classified training dataset, we concluded that the classification made by the FF score provides a sufficient training set for the SVM to detect correct signals in sequence patterns for FFRs. In short, SVMs are powerful enough to generalize the relationship between protein sequence and FFRs as illustrated in the following examples.The arc repressor is stable as a dimer, unfolded as a monomer –41, and Structurally, several flexible regions having important roles for protein function have been detected in the arc repressor using various experimental techniques. Despite being highly disordered in solution, according to an NMR structure determination , the N-tWiggle identifies residues 5 to 8, 23 to 35, 38, and 40 to 53 as FFRs, and Wiggle200 identifies residues 5, 23 to 29, and 43 to 53. The FF score only identifies residues 45 to 53 located at the C-terminus A. Residu++ ion coordination  [PVUII endonucleases (156 amino acids) are homodimerizing proteins that catalyze highly specific DNA cleavage. No regions of flexibility were identified with the FF score A. Wiggle and 94) , and cat and 94) ,50  and correspond to mutations introduced for the creation of a soluble analog  to obtaiGlycosylation of erythropoietin is necessary for its biosynthesis and bioactivity and plays a critical role in its stability ,53. RemoG151 plays an important structural role by introducing a kink in the αD helix. This enables K152 to come in contact with residues in the protein core to form one of the two interaction sites for erythropoietin receptors. Alanine replacement in either position 151 or 152 resulted in a substantial loss of bioactivity . Both ofMutagenesis performed to identify erythropoietin receptor binding sites revealed four regions  important for the activation of receptor signaling . With thSeveral protein disorder predictors were compared to Wiggle and Wiggle200 predictions  to illusSome overlaps are expected with disorder predictions because FFRs may be disordered depending on the conformational state of the protein. Otherwise, we expect little correlation since disorder predictors generally aim to identify structural disorder and regions with a low propensity to form an ordered unit. Potential functional roles were not considered in their design, although these regions are suggested to be important for protein-protein recognition after examining positively classified sequences ,69. WithFor arc repressor (1BAZ), disorder predictors positively classified terminal ends, although some failed to identify it altogether. The hinge region connecting the two helices is not fully identified by most disorder predictors. While Wiggle predictors did not identify all residues involved in recognition at the major groove for PVUII endonuclease (3PVI), it identified the minor groove recognition loop, catalytic loop, and magnesium ion coordinating residues. Current disorder predicting tools failed to identify these regions. Disorder predictors that successfully identified at least one of these regions are based on an index separating hydrophobicity and net charge (FoldIndex and GlobPlot) or the use of homology information (RONN).Most disorder predictors failed to identify all glycosylation sites on erythropoietin (1EER) with the exception of DisEMBL, having the most overlap in predictions with Wiggle. The structure of erythropoietin is entirely helical, and DisEMBL has been designed to predict coils with high B factors. The glycine kink was also missed by most disorder predictors except for DisEMBL and FoldIndex.We also compare the performance of predictors in identifying FFRs as defined by the FF score . Two tesWe report the performance of Wiggle on TESTALL and Wiggle200 on TEST200. Wiggle predictors outperformed the other disorder predictors in overall performance for both test sets when comparing precision and recall values . These rThe motivation for this work is to advance our understanding of protein sequence and FF through easily applied in silico methods. Protein fold and disorder properties are encoded in the amino acid sequence. We believe that functionally important protein flexibility is also encoded in the primary sequence and have successfully created tools to identify these regions. We created two predictors; one specialized for proteins shorter than 200 residues and another for all proteins regardless of size. Between the two predictors, we correctly identified flexible regions of functional importance in several test cases where structure-based classification had difficulties. Our targets include hinges, recognition loops, and localized regions that may serve to accommodate entropy dislocation necessary for allostery.We focused on regional motion important for protein function based on residue participation in correlated low-frequency fluctuations that correspond to large global changes as modeled by the GNM. Our predictors differ from other predictors by including an additional functional consideration in our targets used for training our SVMs. Secondary structure predictors are trained against well-ordered regions of proteins to identify regular secondary structural elements and disorder predictors have been trained using various definitions that include regions missing electron density in X-ray structures or have high temperature factors. Both focus on a subset of sequence space important for structural features but do not address patterns involved in modulated protein flexibility that switch between ordered and disordered states.With the Wiggle predictors, we were able to show detection of domain boundary and experimentally confirmed FFR in specific examples. Comparison to disorder predictors shows that, while there are expected overlaps, different regions are identified. The difference between predictors is that Wiggle predictors are trained to select for residues participating in the two largest modes of global motion, whereas disorder predictors were trained on the propensity to form ordered structures or lack thereof.While false prediction error rates are approximately 30%, this may largely be attributed to the difficulties of defining our regions of interest with misclassifications occurring in both directions when using the FF score. SVMs trained on partitioned datasets showed improved performance, suggesting that the characteristics of FFRs are related to protein size. The Wiggle predictors are especially useful for proteins where no structural data are available. Localizing regions of FF in the absence of structural information will help identify mutational hot spots that may modulate bioactivity and these regions can be targeted in protein engineering experiments. The identification of FFRs by sequence-based methods complements and reduces the limitations in structure-based definitions of flexible regions.A nonredundant training set of protein chains with percent sequence identity of less than or equal to 10%, resolution better than 2.0 Å, and an R-factor less than 0.30 were retrieved from the PDB  using PIThe final training set contained 1,277 sequences with 56.6% of the chains existing in the monomeric state. Multiple copies of a protein found in the asymmetric unit were eliminated. Complexes were manually inspected using the protein quaternary structure file server (PQS)  and liteSAM-2tk  was usedα atoms serving as nodes. The connectivity within the protein structure is represented as a Kirchhoff matrix Γ where R is the distance between the Cα atoms of residues i and j with rc denoting the distance radius threshold (7 Å).The GNM ,77 combibk is the Boltzmann constant, T is the absolute temperature, and γ is a single-parameter harmonic potential that accounts for the fluctuations of a residue about a mean axis.The equilibrium-correlated fluctuations between two sites can be obtained by finding the inverse of the Kirchhoff matrix and is represented as:i and j are defined as:Cross-correlated fluctuations between residues Participation in correlated movements was used to define flexible regions that are functionally important. Readers are referred to the original papers for details.i, the maximum and minimum values, corresponding to residues m and n, respectively, are extracted from the cross-correlation matrix C. These values, C and C, are used to scale the weighted average of the top two modes j of protein fluctuation where μ is the eigenmode and λ is the corresponding eigenvalue.Operationally, FFRs are defined using normalized FF scores. For each residue (mad), taken between FF scores and the median of FF scores 1), to another from the previous state. For a window size of 9, a total of 261 (9 × 29) input features were used for each residue. Values are set to 0 when the window extends beyond terminal ends.Z-scores and p-values obtained for triplets from the modified bootstrap analysis as input features into a second-layer SVM. We find that using the radial basis kernel function to model input features for the first-layer SVM  and the linear kernel function for the second-layer SVM to yield the best performing predictors.The prediction results from this first layer SVM is then included along with calculated With this two-layer architecture and optimized parameters, two different predictors were developed defined by their training sets. Wiggle was trained on the entire training set, while Wiggle200 is a more specialized predictor trained on proteins up to 200 amino acids in length.Wiggle prediction results were compared to a benchmark dataset (BENCH) reflecting the consensus of domain boundaries among CATH, SCOP, and authors of the three-dimensional structures .This dataset contains 312 chains, of which 66% are multidomain proteins, covering 30 distinct architectures and 211 distinct topologies as defined by CATH.The prediction performance was measured based on accuracy, precision, and recall values. Domain boundaries in the dataset were defined between two adjacent positions. We therefore investigated the performance of predictors for a variety of window sizes, up to 15 residues, with the boundary resting in the middle of the expanse. Performance evaluations were also tested on a partitioned benchmark set based on protein sizes up to 200 residues (BENCHA) and longer (BENCHB).To compare residue classification of Wiggle predictors to different disorder predictors for the three specific protein comparisons, we set VSL1 version of PONDR to predict with a 10% false-positive rate, and DisEMBL to predict hot coils defined as coils with high B factors. Recommended defaults for a window size of 9 when requested were used for remaining predictors.We also compare the performances of disorder predictors with two different test sets (TEST200 and TESTALL) containing randomly selected chains used during the training of Wiggle predictors. TEST200 contains 144 chains up to 200 residues and TESTALL contains 256 chains regardless of length. For disorder predictors, we used the same default values and settings as the specific case example comparisons with the exception of PONDR. The default predictor for PONDR (VLXT) was used to accommodate larger proteins in the test sets. Wiggle was used for TESTALL and Wiggle200 for TEST200.


The majority of information in the biological literature resides in full text articles, instead of abstracts. Yet, abstracts remain the focus of many publicly available literature data mining tools. Most literature mining tools rely on pre-existing lexicons of biological names, often extracted from curated gene or protein databases. This is a limitation, because such databases have low coverage of the many name variants which are used to refer to biological entities in the literature.We present an approach to recognize named entities in full text. The approach collects high frequency terms in an article, and uses support vector machines (SVM) to identify biological entity names. It is also computationally efficient and robust to noise commonly found in full text material. We use the method to create a protein name dictionary from a set of 80,528 full text articles. Only 8.3% of the names in this dictionary match SwissProt description lines. We assess the quality of the dictionary by studying its protein name recognition performance in full text.This dictionary term lookup method compares favourably to other published methods, supporting the significance of our direct extraction approach. The method is strong in recognizing name variants not found in SwissProt. Knowledge discovery and data mining in the biological literature have been attracting more and more interest ,2. AutomMost text mining applications require the ability to identify and classify words, or multi-word terms, that authors use in an article to refer to biological entities . This task is called named entity recognition and has been well studied in computer science for problems such as recognizing names of places, people and organizations in news articles . EffortsSeveral strategies have been tried to recognize biological entity names in articles. Some methods rely on protein and gene databases to assemble dictionaries of protein names. A key disadvantage of these methods is that they critically depend on the database used as source of names, and will not recognize new protein names before the name is entered in the database. Another problem is that databases may not contain each name variant used to refer to a protein, so that partial or fuzzy matching of the names in the database to the text in the article is needed . Fuzzy mMost of these methods were developed for abstracts, because abstracts are readily available for millions of articles . However, it is clear that information content in text from full length articles is much greater than in abstracts, even if the information density (information content divided by document length) is higher in abstracts ,16. SpecCompared to abstracts, however, full length articles are a complex source of text. They usually require pre-processing to convert the document from the HTML or PDF format to plain text streams. The result of the conversion will contain all the sections of the article and separating sections is a hard problem in itself because formatting styles and conventions vary across journals, and change periodically even for a given journal. In addition, journal presentation styles are interleaved with the text of the article and require specialized processing so that the tokenization of the document matches the way the document looks when rendered in a web browser. For instance, removing all HTML tags when converting an HTML document to text is inappropriate, as this may create tokens that do not exist in the article . Although some measures can be taken to limit these problems, in practice, conversion procedures are difficult to fine tune for each style and journal, and converted full length articles are noisy sources of text. Therefore, methods designed to extract named entities from full text must be robust to types and amounts of noise which are not found in abstracts.In this article, we present a method that extracts biological named entities directly from full length articles. This method is used to process a corpus of 80,528 full text articles and assemble a catalog of protein name references indexed by an article PubMed identifier (PMID) with high precision. Based on this catalog, we construct a dictionary of 59,990 protein names. We also present a method that uses this dictionary to identify the set of protein names a full text article refers to. We evaluated the performance of this method with a set of full text articles that was not used for the construction of the catalog. The method evaluated on a test set of 14 articles achieves an average precision of 75%. Most other published methods have been trained and tested on abstracts only . .We have implemented the approaches described here in the Textractor framework Many biological research journal articles focus on one or a few genes or gene product(s). Typical examples are articles that describe the cloning of a particular gene. Other articles that describe the discovery of a new interaction will often focus on the two gene products involved in the interaction. Thus, the high frequency terms in these articles often refer to a gene/protein, the cell line in which the gene/protein is being studied, or the biological process in which the gene/protein is involved. Setting a threshold on the frequency of terms in an article allow us to define the set of terms that are most frequent in the article. In this study, we used a threshold of 30, and thus we considered "most frequent" a term that occurred at least 30 times in a given article  a protein versus a process (3) a protein versus an interaction .Three support vector machine (SVM) models were constructed to predict if a term occurs in a context that indicates (1) a protein The exact [stoichiometry of this > multienzyme complex < is unknown but] its molecular mass in insect cells...", the context included in the window offers little clue to decide if the term refers to a protein or not (The occurrence of the term under inspection is marked by > < and terms considered as features in the window around the occurrence are enclosed in ). In such cases, the SVM score is expected to be small. In comparison, occurrences like "...is restricted to skeletal muscle and does not affect [expression of the >Glut4< isoform].", are very informative since the features "expression" and "isoform" are present together in the local context .Since each of the most frequent terms occurs at least 30 times in a given article, we can make at least 30 predictions for one term in one article. We expect that some occurrences of a term will occur in contexts that do not clearly indicate if the term is a protein or not. For instance, in a sentence such as "We used a simple heuristic expression to combine the classification results of these three models  and created a protein name catalog for all the articles in the JBC2000 dataset.For the JBC2000 dataset, we predicted a catalog of 21,501 protein names and ranked them by their combined scores (Sc). The Sc distribution was evenly divided into 10 buckets. One continuous score range was then randomly selected in each of the 10 buckets, so that each range includes 50 protein names. We evaluated the quality of this catalog by manually verifying this random sample of 500 names in the corresponding articles. Figure Nature Cell Biology (NCB14). The tool used to lookup protein names is provided in Additional File To address this question, we created a protein dictionary from a more comprehensive protein catalog, which is based on 80,528 full text articles  " which was part of the NPG Javascript header that our pre-processing protocol failed to remove). These terms were mistaken for gene/protein names. To rule out any possible effect due to the selection of the articles in NCB14 , we performed a second evaluation on 15 randomly selected articles from PubMedCentral (published in 2003 and not part of our training set). Data in Table Table Inspection of the validation data suggests that YAGI is very good at identifying protein names variants that are longer (word length) than the ones that Textractor typically detects . This point also illustrates a difficulty of comparing protein name extraction methods, since the total set of correct names may include redundancy.In our evaluation, we have chosen to count as correct names those that refer to proteins by themselves, even if such names are shorter versions of longer names. These shorter versions are not partial names, however, because their occurrence in the text is sufficient to recognize a reference to a specific protein.Since YAGI and NLProt use similar sources of information, including protein names derived from biological databases, but differ in their machine learning approach, these results also suggest that Conditional Random Fields , as described for matching terms in the dictionary to full text articles. We found that only 8.3% of the terms in our dictionary are included in SwissProt descriptions. Examples of terms present in the dictionary that do not match SwissProt description lines include: "collagen XI", "Collagen alpha1 I", "TRHRs", "TRIM8 GERP". This result indicates that our method is strong at identifying protein name variants that are not found in SwissProt descriptions. SwissProt is a main source of protein names used for building protein name recognition methods ,13,17. TWe implemented the methods described in this manuscript in the textractor framework . Briefly, this framework provides support to parse full length articles into sentences; create inverted indices where sentences are indexed by words; store sentences, articles and other information in a database; calculate features from sentences, articles or part of the above; and import results of predictions made with the features into the database for integration with other types of information. The Textractor framework uses JDO  for data.We distribute the implementation of our method and other relevant information, e.g., the list of regular expressions used to extract the terms for SVM training, under the Gnu General Public License to maximize their use in the biomedical community per se, but the details, such as how the issue was studied in that article, are very unlikely to be fully described in an abstract. For an experimentalist, the question is more often "how" than "what" and is always about details. Thus, automatic text mining applications that target experimentalists should ideally also be able to extract relevant information from full text articles.Compared to full text, abstracts have the advantages of convenient access and uniformity of format. Based on recent studies, however, the information content in abstracts is less than half the information content in full text ,16. In aThis study addresses an initial step in this direction – to collect and classify several categories of biological entity names from full text that are essential for a detailed understanding of an issue, including how it was studied, from a biological article. These categories include the terms that refer to protein/gene, cell type, biological process, and interactions. Specifically, we compared the recognition of the protein/gene names with other systems and emphasized the strength of our approach in identifying term variants.Our method draws on several concepts introduced in , but difWe have described that our method may fail to detect long protein names (see Results). Such names appear with a lower frequency in articles and are often abbreviated in full text. Approaches to extract acronym definitions from full-text may complement our approach and help extract long protein names  Full texts refer to a large number of protein names in all the sections of the paper . (2) It is difficult for human annotators to identify all spelling variants of protein names that automated methods may identify. To alleviate the impact of these factors on the evaluation, we presented annotators with names identified by each prediction method. In practice, this procedure guarantees that annotators consider each name variant predicted by one of the methods and determine if the name refers to a protein. The protocol thus helps avoid omissions that can occur when annotators are not familiar with the subject of the article, and directly provides the annotation counts (Ci) required to calculate the relative recall measure (see methods).Our method creates a catalog of protein and gene name references directly from full length noisy article materials. Tanabe and Wilbur recently described a method to assemble a gene/protein lexicon from the text of abstracts . While tOur results show that a pure dictionary-based lookup method can outperform NLProt  on full Our method is robust to various sources of noise found in full length articles and achieves a state of the art level of precision on this material. A key element that contributes to the robustness of our method seems to be that we never extract a name from an article as a protein name based only on the morphology of the term, but instead require that the term is predicted several times as a protein name in other articles. This robustness may be the result of considering an array of evidence, found in the sentences from several articles, to determine if a term is likely to refer to a gene or gene product, and reusing this knowledge again and again. In its current state, an outstanding limitation of our approach is its inability to deal with certain types of term ambiguity . This is an area for improvement that will require further research.word as a contiguous sequence of letter, digits, dashes and dot characters . We split documents into sentences of at least m characters, with heuristics that consider the types or identity of characters in a window of 6 characters around potential sentence terminators . Parameter m was set to 40 in this experiment, a value that rejects sentence splits if they would create a sentence shorter than about a quarter of a column of this article. Full details of the sentence splitting procedure are given in the source code in the supplementary material (see class SentenceSplitterIterator).We represent all documents with the Unicode standard . We defiterm as a word or a contiguous sequence of words, which appears in an article, but does not span sentence boundaries. The term may or may not refer to a protein name or to any grammatical class. We call n-gram a term that contains n words. "The" is thus considered a term, and a one-gram, while "The method" can be called a term, a 2-gram. The frequency of a term in a document or set of documents is the number of times the term occurs in the document or set of documents. Here, we identify n-grams in an article by finding all the unique word sequences of length 1 to 5 words that have a frequency greater than one. We define a In an approach similar to , we convfeature a real-valued number that is used as input to a machine learning algorithm. Let p denote the position of one occurrence o of term t in article a, and i(w) be a function that maps each word of the corpus to an integer (a number that uniquely identifies a feature). Such a function could map word "the" to 12 and word "The" to 105, or map both words to the same integer, to wrap cases. The features for term t at p are the set of words W) that exist in a window of words from p - l to p + length(t) + l -1, excluding the term word(s), with l the length of the window on either side of the term occurrence and length(t) the number of words of term t. We define one feature, identified by its index: i(w) for each word w that occurs in the training corpus. For each word w that occurs in this window, and does not belong to the term we set the value of feature i(w) to 1. The value of feature i(w) is set to zero if the word does not occur within the window. The feature thus gets the same value if the term occurs once or multiple times in the window around a term occurrence. For experiments described in this article, we used a window size of 3.We call Our training data set (JBC99) consisted of 1,814 articles  published in the Journal of Biological Chemistry (JBC) during the last quarter of year 1999. Full length articles were obtained as HTML files and converted to text using the HtmlParser package . Image tPG), cell names (C), process names (Pr), and interaction keywords (IK). We made sure that the names in these lists were non-ambiguous, that is, that the name, in any sentence context would be a true instance of its class. To construct PG, for instance, we included n-grams that match the regular expressions ".+ receptor" or ".+ kinase" , because n-grams that end in "receptor" or "kinase" are very unlikely to be used in a context where they do not refer to proteins. Other terms that could be ambiguous – e.g., SNF, which could be a gene/protein name, or a funding agency  – were not used for training. Regular expressions were used to facilitate the assembly of drafts of these lists, but the lists were carefully inspected and edited manually before training.We trained three support vector machines  , C = 19.0005 (PG+/IK-), C = 19.1587(PG+/Pr -).Table Binary SVM classifiers assign a predicted class to a term by considering the sign of the output of a trained support vector machine. The output of the SVM is un-calibrated and is not the prior probability of the class given the features . HoweverPG+/C-, SumPG+/IK- and SumPG+/Pr-. We used a simple heuristic expression to combine the three scores into the final Sc score (we summed the three Sum scores and adversely weighted a negative sum in any of the three classifications by multiplying each negative Sum by 50 before adding them). The parameter 50 was chosen empirically to give more weight to negative individual Sum scores. The combined score, Sc, is such that greater, positive values have a higher possibility of referring to protein names, and negative values are unlikely to refer to protein names.For a SVM model, the individual classification scores for each occurrence of a term in an article were summed, so as to produce three values: SumWe assessed the quality of the biological entity name disambiguation with a test set derived from articles published in JBC during the year 2000. The test set is mostly independent from the training set, as the only common points between the articles in the two sets is that they were published in the same journal and formatted with the same conventions.Not each term in the protein catalog can be used directly to lookup protein names in articles. A key problem is ambiguity, terms that refer to proteins in certain articles, but refer to other concepts in other articles. In an attempt to reduce ambiguity, we filtered the protein catalog with several heuristics. These heuristics are presented in Figure We built the NCB14 set with 14 articles selected randomly from articles published in Nature Cell Biology in 2003. Since this journal was not used for the construction of the protein catalog, the style, formatting and names present in these test articles had never been used to develop our method. Furthermore, we did not refine or tune any of the parameters of our method after we started this evaluation. Therefore, the performance values that we report here should be representative of what can be expected when the method is used on new, unseen, but similar full length article materials.Names found in the dictionary were matched to the text of each article in the test sets. When matching n-grams, we match letters and digits and ignore punctuation and special characters except dashes ('-') and dots ('.'). Using this strategy, if A, B and C are words, an "A B-C" in the dictionary will match "A,B-C" and "A#B-C" in the text of an article, but not "A B,C". Using this matching procedure, the name "PKC-zeta lambda" will match "PKC-zeta/lambda" in article PMID 10749857. Matching of words shorter than 6 characters is case-sensitive , while matching of longer words is case insensitive "human topoisomerase II" will match "human TOPOISOMERASE II"). In contrast to other dictionary-based approaches , our maThe definition of protein that we used to train Textractor differs from the definition used by the human annotators who created the YAPEX corpus . For TexArticles in the HTML format were converted to Unicode text as described under document preprocessing. Both NLProt and the Textractor lookup tool were given the same text material as input. The output produced by NLProt was parsed to extract the protein names recognized by the method. We sorted names to be unique (following the term matching criteria described above), and produced a tab delimited file . This file was merged with the Textractor predictions using the term as unique key. The numbers of occurrences found by each method are also listed. These files were annotated by the authors during the evaluation and are provided in supplementary material to allow comparison with future methods.Various performance measures can be used when comparing prediction or extraction methods. Accuracy of a prediction is defined as the percentage of correct predictions, over all the classes of terms predicted. For instance, when predicting protein and non-protein names, accuracy measures how well both protein and non-protein names are predicted. However, accuracy can be misleading when the test set contains one class in a larger proportion than the other. For instance, if the test set contains 10% of terms that are proteins and no proteins names are predicted, the prediction has an accuracy of 90%. For this reason, we prefer the precision and recall measures. The precision is the accuracy measured over one class, for instance measured over proteins. It is calculated as the ratio of correct predictions over the number of predictions made. Recall measures how many elements of one class are predicted in this class. Recall is calculated as the ratio of correct predictions in one class over the total number of instances of this class that could have been predicted. Intuitively, "precision" measures how specific a prediction is when a class is predicted, and "recall" measures how much the prediction method has missed in a class.To combine and summarize performance values obtained for several articles, we use micro- and macro-evaluation ,25. Macrrelative recall of method i (among n possible methods)as:When comparing several prediction methods, we define the where Ci represent the set of all the correct predictions made by method i, Card(set) represents the number of elements in a set, and Union represents the set that is the union of several sets. It can be seen that if any of the methods has perfect recall, the relative recall of each method matches the traditional recall. Furthermore, when the measure is applied with more than two methods, the values of the relative recall will converge towards the recall as the number of methods increases . The advantage of the relative recall is that only predicted terms need to be evaluated for each prediction method, so that reading the whole article is not required.Given the observed variability of precision in the articles of our test sets, we assume that the recall of an extraction method will also vary from one article to another. Therefore, it is clear that a measure of recall, however consistent and careful the evaluation, will not be informative if evaluated for a single article. Measuring relative recall lowers the cost of evaluation and makes it possible to compare the recall of several methods over larger article samples.LS and FC designed, conducted this study, and wrote the manuscript.Full-text lookup tool with dictionary. This tool implements the lookup approach that is used for evaluation of the protein dictionary in the manuscript. The Java Archive (Zip format) contains a copy of the protein dictionary that is also available from our web site. Click here for file


Substitution Matching Similarity, is especially designed for application to non-aligned protein sequences. It allows us to develop a new alignment-free algorithm, named CLUSS, for clustering protein families. To the best of our knowledge, this is the first alignment-free algorithm for clustering protein sequences. Unlike other clustering algorithms, CLUSS is effective on both alignable and non-alignable protein families. In the rest of the paper, we use the term "phylogenetic" in the sense of "relatedness of biological functions".The rapid burgeoning of available protein data makes the use of clustering within families of proteins increasingly important. The challenge is to identify subfamilies of evolutionarily related sequences. This identification reveals phylogenetic relationships, which provide prior knowledge to help researchers understand biological phenomena. A good evolutionary model is essential to achieve a clustering that reflects the biological reality, and an accurate estimate of protein sequence similarity is crucial to the building of such a model. Most existing algorithms estimate this similarity using techniques that are not necessarily biologically plausible, especially for hard-to-align sequences such as proteins with different domain structures, which cause many difficulties for the alignment-dependent algorithms. In this paper, we propose a novel similarity measure based on matching amino acid subsequences. This measure, named SMS for To show the effectiveness of CLUSS, we performed an extensive clustering on COG database. To demonstrate its ability to deal with hard-to-align sequences, we tested it on the GH2 family. In addition, we carried out experimental comparisons of CLUSS with a variety of mainstream algorithms. These comparisons were made on hard-to-align and easy-to-align protein sequences. The results of these experiments show the superiority of CLUSS in yielding clusters of proteins with similar functional activity.We have developed an effective method and tool for clustering protein sequences to meet the needs of biologists in terms of phylogenetic analysis and prediction of biological functions. Compared to existing clustering methods, CLUSS more accurately highlights the functional characteristics of the clustered families. It provides biologists with a new and plausible instrument for the analysis of protein sequences, especially those that cause problems for the alignment-dependent algorithms. With the rapid burgeoning of protein sequence data, the number of proteins for which no experimental data are available greatly exceeds the number of functionally characterized proteins. To predict a function for an uncharacterized protein, it is necessary not only to detect its similarities to proteins of known biochemical properties , but also to adequately assess the differences in cases where similar proteins have different functions . One solution is to cluster each family into distinct subfamilies composed of functionally related proteins. Subfamilies resulting from clustering are easier to analyze experimentally. A subfamily member that attracts particular interest need be compared only with the members of the same subfamily. A biological function can be attributed with high confidence to an uncharacterized protein, if a well-characterized protein within the same cluster is already known. Conversely, a biological function discovered for a newly characterized protein can be extended over all members of the same subfamily. In the rest of the paper, we use the terms subfamily and cluster interchangeably.et al.  = Y [j])      then Add the amino acid X[k] to Γ      else If (|Γ| ≥ l) Add the Γ to E      Γ         Empty end else            Increment k, Increment jend while   if (|Γ| ≥ l) Add Γ to E   Γ   Empty    k = i, j = 0while (k < |X| and j < |Y|)   if (X[k] = Y[j])      then Add the amino acid X[k] to Γ      else if (|Γ| ≥ l) Add Γ to E      Γ         Empty end else            Increment k, Increment jend while   if (|Γ| ≥ l) Add Γ to E   end forElX, Y requires a CPU time proportional to |X|*|Y|. In practice, however, several optimizations are possible in the implementation, using encoding techniques to speed up this process. In our implementation of SMS, we used a technique that improved considerably the speed of the algorithm; we can summarize it as follows:This algorithm for the construction of Γx, y|≥l, we know that each Γx, y in ElX, Y is an expansion of a matched subsequence of length l. Thus, we first collect all the matched subsequences of length l, which takes linear time. Secondly, we expand each of the matched subsequences as much as possible on the both left and right sides. Finally, we select all the expanded matched sequences that are maximal according to the inclusion criterion. This technique is very efficient for reducing the execution time in practice. However, due to the variable lengths of the matched sequences, it may not be possible to reduce the worst-case complexity to a linear time. In the Results section, we provide a time comparison between our algorithm and several existing ones.By the property that all possible matched subsequences satisfy |M be a substitution matrix, and Γ a matched subsequence belonging to the matching set ElX, Y. We define a weight W(Γ) for the matched subsequence Γ, to quantify its importance compared to all the other subsequences of ElX, Y, as follows:Let Γ[i] is the ith amino acid of the matched subsequence Γ, and W[Γ[i], Γ[i]] is the substitution score of this amino acid with itself. Here, in order to make our measure biologically plausible, we use the substitution concept to emphasize the relation that binds one amino acid with itself. The value of M[Γ[i], Γ[i]]  estimate the rate at which each possible amino acid in a sequence keep unchanged over time. For the pair of sequences X and Y, we define the matching score sX, Y, understood as representing the substitution relation of the conserved regions in both sequences, as follows:Where sX, Y. Let smax be the matching score of the longest sequence belonging to the protein family F with itself, defined as follows:To define our similarity measure, we need to scale down X and Y, SX, Y is obtained by dividing the matching score by the value of smax:Finally, the similarity measure between the two sequences l, the minimum length of the matched subsequences in SMS, is set to 4. l = 4 yields good results in all our experiments. Here we will attempt to provide an explanation of this choice.In the CLUSS algorithm described in the following section, et al. in [l. According to theorem 1 in [Our aim is to detect and make use of the significant motifs best conserved during evolution and to minimize the influence of those motifs that occur by chance. This motivates one of the major biological features of our similarity measure, the inclusion of all long conserved subsequences in the matching , since it is well known that the longer the subsequences, the smaller the chance of their being identical by chance, and vice-versa. Here we make use of the theory developed by Karlin t al. in -30 to jurem 1 in  we have:whereandKr, N, the expected length of the longest common word present in at least r out of N sequences [Seq1,...,SeqN), where piν)   pairwise similarity matrix, where N is the number of sequences of the protein family F to be clustered, and Si, j is the similarity measure between the ith and the jth protein sequences belonging to F. The construction of S takes CPU time proportional to N(N-1)T2/2, with T the typical sequence length of the N sequences.Using one of the known substitution score matrices, such as BLOSUM62  or PAM25S. This process ends when there remains only one (sub)tree, which is the phylogenetic tree.To build the phylogenetic tree, we have adopted the classical hierarchical approach. Starting from the protein sequences, each of which is considered as the root node of a (sub)tree containing only one node, we iteratively join a pair of root nodes in order to build a bigger subtree. At each iteration, a pair of root nodes is selected if they are the most similar root nodes in terms of a similarity measure derived from the above similarity matrix Stage 1 . Let L and R be two nearest root nodes at a given iteration step; they are joined together to form a new subtree. Let P be the root node of the new subtree. P thus has two children, L and R. We assign a "length" value DL, P = DR, P = /2 to each of the two branches connecting L and R to P. This value is the estimate of the phylogenetic distance from either node L or R to their parent P in the tree. This distance has no strict mathematical sense; it is merely a measure of the evolutionary distance between the nodes. It is closer to the notion of dissimilarity. The similarity between the new root node P and any other root node K is defined as a weighted average of the similarity between the children of P and the node K:The similarity between two root nodes referred to above is computed in the following way. At the beginning of the iteration, the similarity between any pair of nodes is initialized by the similarity matrix computed in SL, K and SR, K are in that order the similarity values between the nodes L and R with the node K before the joining, and dL and dR are the numbers of leaves in the subtree rooted at L and R, respectively. Note that in order to keep the notation simple, SP, K is retained here to represent the similarity between any pair of nodes that do not have any descendant relationships in the phylogenetic tree.Where F, a family of N protein sequences, after computing their similarity matrix and phylogenetic tree, CLUSS locates subfamily nodes in this tree using [Given ee using ,14 Ward'ee using . The difL  a weight WL according to its importance in F. WL depends on the number and closeness of the protein sequences that are in fact similar to L, and is thus intended to measure how well F is represented by this particular sequence. For this purpose, we make use of the Thompson [WL:First, each leaf node is considered as a subtree in the phylogenetic tree. We assign to each subtree Thompson  method iP is the root of the phylogenetic tree, L a leaf in this tree, branch(L→P)-{P} the subset of nodes on the branch from L to P excluding P, Parent(i) the parent of the node i, DParent(i), i is the length of the branch connecting the node i to its parent (as defined in the previous phase), and dParent(i) the number of leaves in the subtree rooted at the parent of i. According to this definition, the value of WL is small if L is very representative and is large if L is not very representative. Iteratively, we assign to each internal subtree P the weight value WP equal to the sum of the weights of its children WL + WR.Where P the co-similarity value CP (between its two child nodes), which is calculated according to the generalized Ward dissimilarity formula [Iteratively, until the root of the phylogenetic tree is reached, we assign to the subtree rooted at each non-leaf node  formula ,14 intro formula , as follWL and WR are the weights of L and R, respectively, and SL, R is the similarity between L and R computed in Stage 2.Where L and R, the concept of co-similarity reflects the cluster compactness of all the sequences (leaf nodes) in the subtree. In fact, its value is inversely proportional to the within-cluster variance. When the subtree becomes larger, the co-similarity tends to become smaller, which means that the sequences within the subtree become less similar and the difference (separation) between sequences in different clusters becomes less significant.By taking into account information about the neighbourhood around each of the nodes The CLUSS algorithm makes use of a systematic method for deciding which subtrees to retain as a trade-off between searching for the highest co-similarity values and searching for the largest possible clusters. We first separate all the subtrees into two groups, one being the group of high co-similarity subtrees and the other the low co-similarity subtrees. This is done by sorting all possible subtrees in increasing order of co-similarity and computing a separation threshold according to the method based on the maximum interclass inertia .From the group of high co-similarity subtrees, we extract those that are largest. A high co-similarity subtree is largest if the following two conditions are satisfied: 1) it does not contain any low co-similarity subtree; 2) if it is included in another high co-similarity subtree, the latter contains at least one low co-similarity subtree. Each of these (largest) subtrees corresponds to a cluster and its leaves are then collected to form the corresponding cluster  . As mentQ-measure value of the clusterings obtained is superior to 92% with a standard deviation of 3.57%  groups enzymes with "β-mannosidase" activity, while the fourth branch (subfamilies 23 to 28) includes "β-glucuronidases".To show the performances of CLUSS with multi-domain protein families which are known to be hard to align and have not yet been definitively aligned, experimental tests were performed on 316 proteins belonging to the Glycoside Hydrolases family 2 from the CAZy database (version of January 2006), the FASTA file is provided as supplementary material [see Additional file β-galactosidases" in databases. While the branch 1 "β-galactosidases" are composed of five modules, known as the "sugar binding domain", the "immunoglobulin-like β-sandwich", the "(αβ)8-barrel", the "β-gal small_N domain" and the "β-gal small_C domain", the members of subfamily 17 lack the last two of these domains, which makes them more similar to "β-mannosidases" and "β-glucuronidases". These enzymes are distinct from those of branch 1 [The clustering scheme obtained warrants further comment. The "orphan" subfamily 17 includes nineteen sequences labelled as "branch 1  and theiputative β-galactosidases" in databases. To the best of our knowledge, none of these proteins, identified through genome sequencing projects, have been characterized by biochemical techniques, so their enzymatic activity remains hypothetical. At the beginning of this branch, subfamily 8  group enzymes that are annotated as "nidases" ,42 and "sidases" . Again, et al. [et al. [Subfamily 22 8-barrel proteins are a group within the GH2 family, studied recently by Côté et al. [et al. [α/β)8-barrel" makes these sequences hard-to-align using classical alignment approaches. The difficulties in aligning these modules are comparable to the problems encountered with the alignments of tandem-repeats, which have been exhaustively discussed [β-galactosidase", "β-mannosidase", "β-glucuronidase" and "exo-β-D-glucosaminidase" enzymatic activities, all of them extensively studied at the biochemical level. These sequences are multi-modular, with various types of modules, which complicate their alignment. Thus, the clustering of such protein sequences using the alignment-dependent algorithms becomes problematic. In our experiments, we tested quite a few known algorithms to align the 33 protein sequences, such as MUSCLE [et al. [et al. [The 33 8-barrel proteins were subdivided by CLUSS into five subfamilies, organized in four main branches 8-barrel proteins were subdivided into five subfamilies. Almost all the enzymes were clustered in the appropriate clusters, except for seven proteins that were unclustered, among which we find the following well-classified enzymes: "β-galactosidase" enzymes: GenBank: AAA69907, GenBank: AAA35265 and GenBank: AAA23216; "β-mannosidase" enzyme: NCBI:ZP_00425692; "exo-β-D-glucosaminidase" enzyme: GenBank: AAX62629.The 33 (α/β)8-barrel proteins were subdivided by TRIBE-MCL into two mixed subfamilies. We find the "β-mannosidase" enzymes EMBL: CAB63902, GenBank: AAD42775 and EMBL: CAD33708 grouped in the "β-galactosidase"subfamily. Furthermore, the "exo-β-D-glucosaminidase" enzymes and the "β-glucuronidases" enzymes are grouped in the same subfamily.The 33 (α/β)8-barrel proteins were subdivided by gSPC into three subfamilies. Almost all the enzymes were grouped in the appropriate subfamily, except for the "β-galactosidases" and the "β-glucuronidases" which were grouped in the same subfamily.The 33 (α/β)8-barrel proteins generated by CLUSS corresponds better to the known characteristics of their biochemical activities and modular structures than do those yielded by the other algorithms tested. The results obtained with our new algorithm were highly comparable with those of the more complex, structure-based analysis performed by Côté et al. [et al. [Globally, the clustering of the 33 , we compared the execution times of SMS and ClustalW ; these rThe alignment of protein sequences often provides information on conserved and mutated motifs, which is a good approach to measure the similarity between two protein sequences. The problem with this approach is that the result depends primarily on the alignability of the protein sequences, also on the algorithm selected and the parameters set by the user depending on the alignment algorithm used , which implies several different alignments with each algorithm. Such variations may create difficulties in measuring similarity between sequences and consequently complicate the clustering task. For the case of easy-to-align protein families, such as the G-protein family, almost all alignment algorithms find the same alignment for the conserved regions; however, the alignments of the less conserved regions are significantly different. On the other hand, for the case of hard-to-align protein families, such as the GH2 family, each alignment algorithm tends to diverge to its own, distinct results. Thus, in all cases, there is a significant need to develop efficient and robust alignment-independent approaches to clustering protein sequences.The SMS developed in this paper makes it possible to measure the similarity between protein sequences based solely on the conserved motifs. The major advantage of SMS compared to the alignment-dependent approaches is that it gives significant results with protein sequences independent of their alignability, which allows SMS to be effective on both easy-to-align and hard-to-align protein families. This property is inherited by CLUSS, our new clustering algorithm, which uses SMS as its similarity measure. CLUSS used jointly with SMS is an effective clustering algorithm when used on protein sets with a restricted number of functions, which is the case of almost all protein families. It more accurately highlights the characteristics of the biochemical activities and modular structures of the clustered protein sequences than do the alignment-dependent algorithms.So far, our similarity measure has been based on pre-determined substitution matrices. A possible future development is to propose an approach to automatically compute the weights of the conserved motifs instead of relying on pre-calculated substitution scores. There is also a need to speed up the extraction of the conserved motifs and the clustering of the phylogenetic tree, to scale the algorithm on datasets that are much larger in size with many more biological functions.Clustering of protein families into phylogenetically correct groups is a difficult problem, especially for those whose alignment is not biologically validated and not definitively performed. In this paper, we have proposed a new similarity measure, SMS, based on which we develop the new clustering algorithm CLUSS. CLUSS is applied directly to non-aligned sequences. Compared to existing clustering methods, CLUSS more accurately reflects the functional characteristics of the clustered families. It provides biologists with a new and plausible instrument for the analysis of protein sequences, especially those that cause problems for the alignment-dependent algorithms.We believe that CLUSS can become an effective method and tool for clustering protein sequences to meet the needs of biologists in terms of phylogenetic analysis and function prediction. In fact, CLUSS gives an efficient evolutionary representation of the phylogenetic relationships between protein sequences. This algorithm constitutes a significant new tool for the study of protein families, the annotation of newly sequenced genomes and the prediction of protein functions, especially for proteins with multi-domain structures whose alignment is not definitively established. Finally, the tool can also be easily adapted to cluster other types of genomic data. The application server and the implementation are available at CLUSS website.Project name: CLUSSProject home page: Operating system(s): MS WindowsProgramming language: C++Other requirements: /License: Freely offeredAny restrictions to use by non-academics: /GH2: Glycoside Hydrolase family 2GH8: Glycoside Hydrolase family 8COG: Clusters of Orthologous Groups of proteinsROK: Repressor, ORF, KinasesAK designed, programmed and executed all experimentations with CLUSS and SMS, created the CLUSS web site, and wrote most of the manuscript. SW supervised the whole project, provided resources and wrote part of the manuscript. RB helped to design SMS and improve CLUSS through links with biological aspects, analyzed the results of clustering methods and wrote part of the manuscript. AF analyzed some results of the clustering method and helped in writing the manuscript. All authors read and approved the final manuscript.Members of the SS1 subset from the COG familyClick here for fileMembers of the SS2 subset from the COG familyClick here for fileMembers of the SS3 subset from the COG familyClick here for fileMembers of the SS4 subset from the COG familyClick here for fileMembers of the SS5 subset from the COG familyClick here for fileMembers of the SS6 subset from the COG familyClick here for fileClustering result for the SS1 subset using CLUSS.Click here for fileClustering result for the SS2 subset using CLUSSClick here for fileClustering result for the SS3 subset using CLUSS.Click here for fileClustering result for the SS4 subset using CLUSS.Click here for fileClustering result for the SS5 subset using CLUSS.Click here for fileClustering result for the SS6 subset using CLUSS.Click here for fileMembers of the G-protein familyClick here for fileClustering result of the G-Proteins family using CLUSSClick here for fileMembers of GH2 familyClick here for fileClustering result for the GH2 family using CLUSSClick here for fileα/β)8-barrel group from the GH2 familyMembers of the 33 (Click here for fileα/β)8-barrel group using CLUSSClustering result for the 33 (Click here for fileComparison between the execution times of SMS and ClustalWClick here for file


Finding a multidimensional potential landscape is the key for addressing important global issues, such as the robustness of cellular networks. We have uncovered the underlying potential energy landscape of a simple gene regulatory network: a toggle switch. This was realized by explicitly constructing the steady state probability of the gene switch in the protein concentration space in the presence of the intrinsic statistical fluctuations due to the small number of proteins in the cell. We explored the global phase space for the system. We found that the protein synthesis rate and the unbinding rate of proteins to the gene were small relative to the protein degradation rate; the gene switch is monostable with only one stable basin of attraction. When both the protein synthesis rate and the unbinding rate of proteins to the gene are large compared with the protein degradation rate, two global basins of attraction emerge for a toggle switch. These basins correspond to the biologically stable functional states. The potential energy barrier between the two basins determines the time scale of conversion from one to the other. We found as the protein synthesis rate and protein unbinding rate to the gene relative to the protein degradation rate became larger, the potential energy barrier became larger. This also corresponded to systems with less noise or the fluctuations on the protein numbers. It leads to the robustness of the biological basins of the gene switches. The technique used here is general and can be applied to explore the potential energy landscape of the gene networks. Cellular networks are at the heart of systems biology at present. To understand how cellular networks function in these highly fluctuating environments, a global approach is needed. Here we provide a global framework, in terms of potential landscapes, for studying the gene regulatory networks in the presence of the intrinsic statistical fluctuations. We uncovered the underlying landscape for the network. We identified the basins of attraction of the landscape as the biological functional states. The potential barrier between the two basins determines the time scale of conversion from one to the other. The robustness of the biological functional states of the network, the gene switches in this case, can be guaranteed if the conversions among the basins of attraction are not frequent, or, in other words, the barriers among the basins are relatively large. More detailed features of the network, such as the key genes or regulating links relevant to diseases , can be uncovered from the underlying landscape. Our technique is general and can be applied to explore the potential landscape of more realistic gene networks. Furthermore, our approach can also be helpful in guiding the network optimal design for synthetic biology. In the post-genome era, with a wealth of data on genomic sequences, the crucial question becomes how to understand the organization of these sequences in nature and how genes function –4. This The underlying nature of cellular networks has been explored by many experimental techniques . It has Theoretical models of cellular networks have often been formulated with a set of chemical reaction equations in bulk. These averaged dynamical descriptions are inherently local. To probe the global properties, one often has to explore different parameters. Since the parameter space is huge, the issue of global robustness is hard to address directly from these approaches.Here we will explore the nature of the network from another angle: we formulate the problem in terms of the potential energy function or potential energy landscape. If the potential landscape of the cellular network is known, the global properties can be explored ,22. ThisThere is another intriguing factor controlling the gene expression patterns. In the cell, there are a finite number of molecules . The intrinsic statistical fluctuations, usually not encountered in bulk due to the large-number averaging, can be significant and play an important role in the dynamics of gene expression. This gives the source of intrinsic statistical fluctuations or noise. On the other hand, the fluctuations from highly dynamical and nonhomogeneous environments of the interior of the cell give the source of the external noise for the networks –30. It iIn general, instead of studying the averaged chemical reaction network equations in bulk, we should use statistical descriptions to model the cellular process. This can be realized by constructing a master equation for the evolution of probability instead of average concentration for the corresponding chemical reaction network equations ,31–35. OThere are three aims of this paper. Our first aim is to develop a time-dependent Hartree approximation scheme  to solveAs our goal is to uncover the potential energy landscape, we first studied the chemical reaction network involved in gene regulations. In particular we need to take into account the intrinsic statistical fluctuations due to the finite number of molecules in the cells. The statistical nature of the chemical reactions can be captured by the corresponding master equations. We established master equations for the gene regulations that describe the evolution of the networks probabilistically. The master equation is almost impossible to solve due to its inherent huge dimensions. We therefore used the Hartree approximation to reduce the dimensionality ,12,36. IGene expression is regulated in various and complex ways, and can be represented by many coupled biochemical reactions. In this report, our goal was not just to explain some specific gene network system as accurately as possible, but to illustrate mathematical tools for exploring the general mechanisms of transcriptional regulatory gene networks. We therefore took abstractions of some essential biochemical reactions from complicated reactions of diverse systems.βα, M represents the regulatory protein synthesized or produced by gene β, and αβq is for the multimer-type of proteins. For example, if ABq = 2(3), dimer (tetramer) proteins produced from gene B repress the expression of gene A, Let us start with the explanation of some terminologies used in this manuscript: “activator” is a regulatory protein that increases the level of transcription, “repressor” is a regulatory protein that decreases the level of transcription. By “operator” we mean the DNA site or the gene where regulatory proteins (either an activator or a repressor) bind. First we are interested in the effect of “operator fluctuation” by which we mean the biochemical reactions that change the state of the operator. The operator is said to be in an occupied state if a regulatory protein is bound to it, and in an unoccupied state if the protein is not bound to it. For the repressor we include the following reaction.Notice that the superscript 1(0) in αb stands for the burst size of produced proteins (αM), g1(0) is a protein synthesis probability per unit time, and αk is the degradation probability per unit time.Next we include the transcription and translation steps. Here we ignore mRNA and consider only one step combining transcription and translation:We can say that P:An, Bn, Cn, … is the number of proteins expressed by gene A,B,C, … , respectively. AS, BS, CS, … is 1 or 0, and represents the activity state of the operator. The number of states, N, is An × 2 × Bn × 2 × Cn × 2 × … . We expected to have N-coupled differential equations, which are not feasible to solve. Following a mean field approach  = 1. The generalization to include activating proteins is straightforward. While the state vector is a simple product of individual genes, the operator product form of Ω is chosen deliberately to reproduce the original master i   . Here, τThis illustrates how biological robustness is realized for the toggle switch. As the protein synthesis rate and unbinding rate of protein to DNA increase relative to the degradation rate, more proteins are synthesized. These proteins are strong repressors. This leads to smaller fluctuations. Furthermore, the associated barrier height between the two basins of attraction becomes large, and the two basins of attraction become more stable since it is harder to go from one well to another. So, small fluctuations and large barrier heights both serve as the source for the robustness and stability of the gene toggle switch. In other words, it is more unlikely for the system to change from one basin of attraction to the other. Therefore, the system becomes robust. The robustness issue is not yet well-understood for cellular networks in general. Here we explored the robustness of the switches against the intrinsic statistical fluctuations coming from the finite number of protein and DNA molecules. This is clearly very important and has potential applications to the robustness problem of lambda phage in bacteria.We also studied the time evolution of the probability and the potential energy landscape with dynamic equations. We chose the specific parameters and initial conditions to illustrate the idea. The results are shown in In Finding the multidimensional potential energy landscape is the key to addressing important global issues such as the robustness of cellular networks. We have uncovered the underlying potential energy landscape of a simple gene network: toggle switch. We found that as the protein synthesis rate and the unbinding of protein to DNA rate relative to degradation change from small to large, the underlying potential energy landscape changes from having monostable to bistable basins of attraction. These basins correspond to stable, biologically functional states. The potential barrier between the two basins determines the time scale of conversion from one to the other. We found that as the protein synthesis rate and unbinding of protein with DNA rate relative to degradation became greater, the potential energy barrier became greater and the statistical fluctuations were effectively more severely suppressed. This leads to the robustness of the biological basins of the gene switches.In principle, our approach can be generalized to more realistic networks involving multiple genes as well as additional levels of regulations. This could be realized by averaging the interactions among genes in the corresponding master equations. It effectively reduces the dimensionality of the problem from exponential to polynomial number of degrees of freedom. It is worthwhile to note the limitation of this approach. When the interactions among genes are very strong, our approach is less effective.Recently, synthetic biology became an important part of systems biology –45. TherThe adaptive landscape idea was first introduced into biology by S. Wright in the 1930s –49. LandThis model can be modified to include more biochemical reactions. To investigate the role of mRNA, we can consider the transcription and translation process separately. To focus on the statistical fluctuations of genes turning on and off, it is possible to generalize the formalism to compute the statistical fluctuations quantitatively. We also can take into account the spatial variation of the state variables, such as the number of proteins.


The accomplishment of the various genome sequencing projects resulted in accumulation of massive amount of gene sequence information. This calls for a large-scale computational method for predicting protein localization from sequence. The protein localization can provide valuable information about its molecular function, as well as the biological pathway in which it participates. The prediction of localization of a protein at subnuclear level is a challenging task. In our previous work we proposed an SVM-based system using protein sequence information for this prediction task. In this work, we assess protein similarity with Gene Ontology (GO) and then improve the performance of the system by adding a module of nearest neighbor classifier using a similarity measure derived from the GO annotation terms for protein sequences..The performance of the new system proposed here was compared with our previous system using a set of proteins resided within 6 localizations collected from the Nuclear Protein Database (NPD). The overall MCC (accuracy) is elevated from 0.284 (50.0%) to 0.519 (66.5%) for single-localization proteins in leave-one-out cross-validation; and from 0.420 (65.2%) to 0.541 (65.2%) for an independent set of multi-localization proteins. The new system is available at The prediction of protein subnuclear localizations can be largely influenced by various definitions of similarity for a pair of proteins based on different similarity measures of GO terms. Using the sum of similarity scores over the matched GO term pairs for two proteins as the similarity definition produced the best predictive outcome. Substantial improvement in predicting protein subnuclear localizations has been achieved by combining Gene Ontology with sequence information. With the completion of genomic sequencing projects, the need for automated prediction of protein subcellular or subnuclear localizations becomes increasingly important. The localization of a protein can provide valuable information about its molecular function, as well as the biological pathway in which it participates ,2. The bde facto standard tool to annotate gene products for various databases. GO is a controlled vocabulary of terms split into three related ontologies consisting of Molecular Function (MF), Biological Processes (BP) and Cellular Components (CC). Molecular function describes activities, such as catalytic or binding activities, at the molecular level. Molecular functions generally correspond to activities that can be performed by individual gene products, but some activities are performed by assembled complexes of gene products. A biological process is series of events accomplished by one or more ordered assemblies of molecular functions. A cellular component is a component of a cell, but with the proviso that it is part of some larger object such as an anatomical structure, a gene product group. A gene product might be associated with or located in one or more cellular components [GO has been developed to help manage the overwhelming mass of current biological data that are difficult to tie together into a cohesive whole from a computational perspective ,18. It hmponents . It is aet al. [et al. [et al. [Each category of GO terms is structured as a directed acyclic graph (DAG). Currently there are over 20,000 GO terms . The relet al. ,20 propo [et al.  used a r [et al.  proposed [et al.  predicteAlthough the semantic similarity between two GO terms has been extensively investigated, how to define similarity between two gene products based on GO annotations for a specific application remains unclear. Suppose that each gene product is annotated by a set of GO terms. Each GO term from one set will be paired with all GO terms in the other set. There are three general ways of defining similarity for two gene products from those GO term pairs: (1) to take the maximum value from the similarity scores of GO term pairs ,24, (2) To provide a valid comparison with our previous system, the same dataset as in  was usedGiven a test protein with GO annotations, the similarity scores between this protein and all the other proteins in the training set are calculated from the similarity scores of GO term pairs (see Methods). The protein with the highest similarity score is designated as the nearest neighbor of the testing protein and its class label will be assigned to the test protein. If multiple proteins in various localizations attain the same highest score or the test protein does not have GO annotation, then the test protein will be assigned as "unpredicted". The unpredicted proteins will be passed on to the SVM module, which uses sequence information , for a fSince the numbers of proteins for the six localizations are unbalanced, the Matthew's correlation coefficient (MCC) was employed for the optimization of parameters and evaluation of performance . The oveThree different similarity measures for GO term pairs were compared: (1) Lord's method , 2) Sim Sim2, noVery few studies have focused on exploring similarity definition of proteins based on GO terms. Two simple ways are usually employed in defining the similarity between two proteins annotated by GO terms. One is to take the maximum value from the similarity scores of GO term pairs. The other is to take average over all the similarity scores of GO term pairs. However, the above two methods produced poor results especially when the proteins were annotated by many GO terms for the prediction of protein subnuclear localization. Consequently, an extensive investigation on various similarity definitions obtained from similarity scores of GO terms was warranted. As shown in Table et al. [pms (probability of the minimum subsumer) of shared parents of the query terms, meaning that the semantic distances of many different GO term pairs are identical. In order to alleviate this problem, GO terms of homologs retrieved by BLAST were used for the representation of a query protein. The parameter E-value in BLAST is crucial for the quality of homologs, as well as the number of candidate homologs. If E-value is too large, then homologs of low quality may be retrieved. On the other hand, if E-value is too small, then the number of candidate homologs retrieved becomes small. We tested the following E-value parameters: 100, 10-1, 10-2, ..., 10-10, 10-15, 10-20, 10-30, 10-50, 10-100, 10-200, and found that E-value = 10-9 was a good trade-off value. Even with this threshold the BLAST could retrieve different numbers of hits for different query proteins. We found that up to 5 homologs were suitable to represent the query protein . The reason may be considered as follows. If two GO terms are remotely related, but sharing a common ancestor, they still have a positive score which contributes to the similarity of two proteins. However, the similarity for protein pairs based on the matched GO terms has zero contribution from those unmatched GO terms. It seems that the unmatched terms add noise to the data and thus weaken the discriminative ability of the nearest neighbour module in our system. In our study, the best performance was attained when the similarity measure of two protein sequences is defined as SUM_Match. The similarity scores of ~20,000 matched GO term pairs can be pre-computed and stored in a hash table to effectively reduce the computation time.A question that needs to be clarified in the GO-based approach is whether the prediction accuracy could be artificially inflated if the proteins in training or testing sets have their specific subnuclear class annotated in GO. We examined this issue as follows. In this study, there are six GO terms associated with the subnuclear compartments: PML body (GO:0016605), Nuclear lamina (GO:0005652), Nuclear speck , Chromatin (GO:0000785), Nucleoplasm (GO:0005654), and Nucleolus (GO:0005730). All proteins annotated by any of the above six GO terms are listed in the supplementary file see . It is oTo assess if these specific GO terms are influential in the prediction, the performance of the GO module was compared before and after the removal of the six GO terms from the annotation list. As shown in Table The incorporation of the GO module has substantially improved the system performance. However, the module still makes relatively high number of incorrect predictions. This error can not be corrected by the next SVM module. Therefore, it would be desirable if the system can integrate the outcomes from two modules whenever two predictions are available. We are investigating the possibility on this aspect.Our system can be combined with other subcellular localization predictors, e.g. WoLF PSORT , PA-SUB Gene Ontology terms have been effectively incorporated into our previous SVM-based system for the prediction of protein subnuclear localization with the use of a nearest neighbour classification module. The improvement on performance of the new system is substantial. Various similarity definitions for a pair of proteins from different similarity measures of GO terms have been examined for their effect on prediction. The use of the sum of similarity scores over the matched GO term pairs for two proteins as the similarity definition produced the best predictive outcome in our study. The extensive investigation conducted in this work may provide some guidance on the determination of similarity definition for protein pairs based on GO terms in other applications.-9. We selected up to 5 homologs, and submitted the Swiss-Prot accession numbers of the homologs to the QuickGO server [Given a protein sequence, we first BLASTed it against the Swiss-Prot database with a threshold E-value = 10O server  for the First we define the depth for each GO term as follows.gi) = the distance of the longest path from GO term gi to the root of Gene_Ontology, i.e., GO:0003673.Depth:The similarity of two GO terms P is the set of ancestral GO terms shared by both g1 and g2 including themselves. When g1 = g2, Depth(gc) = Depth(g1) = Depth(g2). For two GO terms from different ontologies , their MRCA is the root GO:0003673, whose depth is zero. That means that there is no similarity between two GO terms from different ontologies.where The GO term similarity described here is the same as the method simLP implemented by Gentleman  in BiocoG1 and G2. The similarity Sim_Pro between the two proteins can be defined as a function of Sim_GO.Consider two proteins that are represented respectively by the sets of GO terms For example, consider protein A (Entrez protein accession number: CAC84554) and protein B (SwissProt accession number:P46055), annotated by 3 GO terms  and 4 GO terms , respectively. The simLP score for each GO term pair is listed in Table (a) MAX: take the maximum similarity score from the similarity scores of all pairs of GO terms. Sim_Pro = 7.(b) AVG: take the average similarity score over all pairs of GO terms. Sim_Pro = (7+7+2+2)/12 = 1.5.(c) SUM: take the sum over all pairs of GO terms, Sim_Pro = 7+7+2+2 = 18.(d) MAX_Match: same as (a), except that only the matched GO term pairs are considered, e.g. GO:0006412. Sim_Pro = 7.(e) AVG_Match: same as (b), except that only the matched GO term pairs are considered, e.g. GO:0006412. Sim_Pro = 7/1 = 7.(f) SUM_Match: same as (c), except that only the matched GO term pairs are considered, e.g. GO:0006412. Sim_Pro = 7.(g) AVG_BestPairs: Average similarity between the best paired GO terms calculated with the following pseudo codes:1G|, |2G|}NumofBestPairs ← min {|Sim_Pro ← 01G|>0 and |2G|>0)While }, gi ∈ G1, gj ∈ G2Max_sim_GO ← max{Sim_GO/3 = 3.(h) SUM_BestPairs: same as (g), except that we do not divide Sim_Pro by NumofBestPairs, i.e., remove the last line in the pseudo codes in (g). Sim_Pro = 7+2+0 = 9.In this work, the similarity Sim_Pro of two proteins employed in the final system is based on function (f) SUM_Match:where Sim_GO is defined in (1). Alternatively, if Sim_GO is defined as a constant 1, the Sim_Pro is exactly the Inner Product of two GO vectors (see below).The Inner Product of two GO term vectors has been used in previous study for the prediction of protein subcellular localization ,25. A veOur system includes a K-Nearest Neighbor (KNN) model. The best result was achieved with K = 1. A protein is assigned with a localization label of its nearest neighbor that has the highest similarity score Sim_Pro. If the protein does not have associated GO terms or has multiple nearest neighbors in various classes, then the second SVM module built on sequence information  will be In our previous work , we builSince the numbers of proteins for various localizations are unbalanced, the Matthew's correlation coefficient (MCC) was employed for the optimization of parameters and evaluation of performance :pn is the number of correctly predicted proteins of the location n, sn is the number of correctly predicted proteins not in the location n, un is the number of under-predicted proteins, and on the number of over-predicted proteins.where m = m1 + m2 + … + mN test proteins, where mn is the number of proteins belonging to class n. Suppose further that out of the proteins considered, pn proteins are predicted to belong to class n. Then p = p1 + p2 + … + pN is the number of correctly predicted proteins. The accuracy for class n isAlso, the overall accuracy for the multi-class classification proposed by Rost  was usedacc, is defined asand the overall accuracy, denoted by QProject name: Subnuclear Compartments Prediction System (Version 2.0)Project home page: Operating system(s): LinuxProgramming language: PerlLicense: NoneAny restrictions to use by non-academics: NoneLZ designed the system, implemented programs and carried out the detail study. YD conceived the idea of this work, supervised project and participated in manuscript preparation. All authors have read and approved the final manuscript.AA : SVM module based on protein sequence informationGO-AA: Combination of Gene Ontology module and sequence information moduleet al. [Lord: The GO term similarity is defined on information content by Lord et al. SimLP: The GO term similarity is defined as the longest path shared by two GO terms Exact Match: The GO term similarity is defined as 1 if two GO terms are identical, 0 otherwise.MAX: The similarity of two proteins is defined as the maximum of the similarity scores of all GO term pairsAVG: The similarity of two proteins is defined as the average of the similarity scores of all GO term pairsSUM: The similarity of two proteins is defined as the sum of similarity scores over all GO term pairsMAX_Match: The similarity of two proteins is defined as the maximum of similarity scores of all matched GO term pairsAVG_Match: The similarity of two proteins is defined as the average of similarity scores of all matched GO term pairsSUM_Match: The similarity of two proteins is defined as the sum of similarity scores over all matched GO term pairsAVG_BestPairs: The similarity of two proteins is defined as the average of similarity scores of the best paired GO termsSUM_BestPairs: The similarity of two proteins is defined as the sum of similarity scores over all best paired GO termsGO annotation for single-localization proteins. The data provides single-localization proteins annotated by six subnuclear compartment GO terms.Click here for file