Predicting gene ontology functions from protein's regional surface structures
- Zhi-Ping Liu†1, 2,
- Ling-Yun Wu†1Email author,
- Yong Wang1,
- Luonan Chen3, 4, 5, 6 and
- Xiang-Sun Zhang1
© Liu et al; licensee BioMed Central Ltd. 2007
Received: 27 February 2007
Accepted: 11 December 2007
Published: 11 December 2007
Annotation of protein functions is an important task in the post-genomic era. Most early approaches for this task exploit only the sequence or global structure information. However, protein surfaces are believed to be crucial to protein functions because they are the main interfaces to facilitate biological interactions. Recently, several databases related to structural surfaces, such as pockets and cavities, have been constructed with a comprehensive library of identified surface structures. For example, CASTp provides identification and measurements of surface accessible pockets as well as interior inaccessible cavities.
A novel method was proposed to predict the Gene Ontology (GO) functions of proteins from the pocket similarity network, which is constructed according to the structure similarities of pockets. The statistics of the networks were presented to explore the relationship between the similar pockets and GO functions of proteins. Cross-validation experiments were conducted to evaluate the performance of the proposed method. Results and codes are available at: http://zhangroup.aporc.org/bioinfo/PSN/.
The computational results demonstrate that the proposed method based on the pocket similarity network is effective and efficient for predicting GO functions of proteins in terms of both computational complexity and prediction accuracy. The proposed method revealed strong relationship between small surface patterns (or pockets) and GO functions, which can be further used to identify active sites or functional motifs. The high quality performance of the prediction method together with the statistics also indicates that pockets play essential roles in biological interactions or the GO functions. Moreover, in addition to pockets, the proposed network framework can also be used for adopting other protein spatial surface patterns to predict the protein functions.
It becomes an increasingly important task to annotate protein functions when more and more protein sequences and three dimensional structures become available . A fundamental axiom of biology is the cascade that an amino-acid sequence determines a protein structure, and in turn a protein structure determines protein function . Traditionally, functional relationships among proteins are inferred based on the similarities of conserved sequences. However, sequence-based methods have their limits for those proteins with similar structures but distant sequences, in particular they are generally unable to detect protein similarities with convergence evolution or distinguish distant relationships with divergence evolution . High-throughput technologies on structural genomics have produced a large amount of three dimensional protein structure data, which provide valuable complementary information for analyzing ancient relatives situation such as the case that protein folds remain similar after all traces of sequence similarity disappear during evolution [4–7]. However, similarity measure of primary backbone or global folding analysis also fails sometimes, especially when only overall structure information is considered [8, 6]. Therefore, methods purely relying on sequence and/or global structure comparison may lead to inaccurate function-related annotation in cases where few residues are responsible for the specificity of functions [9, 10].
It is well known that protein functions are mostly determined by physical, chemical and geometric properties of protein surfaces , because the surfaces are places where proteins interact with other biological molecules, protein binding and catalytic activities take place. Surface patterns and local spatial distribution of residues are the key to facilitate the function of a protein although they seem to be unrelated with total sequence and global structure of the protein [12–15]. Structural information of protein surface regions enables detailed studies of relationship between a protein structure and its function [16–18]. Identifying similar surfaces between proteins can be useful for understanding biological roles and annotating protein functions. So far there are a number of studies on the computational analysis of protein surface, such as SURFNET , LIGSITE , CASTp [13, 21], eF-Site , Cavbase , PINTS , SURFACE , Q-SiteFinder , and SCREEN . Some of them have been already used to build comprehensive databases of the identified surface structures.
In this paper, to develop a new method to predict protein functions, we aim to exploit the surface structure information by constructing a pocket similarity network. We choose the CASTp  database as our initial source of protein surface patterns. CASTp provides identification and measurements of surface accessible pockets as well as interior inaccessible cavities. In CASTp, a pocket, which is a local spatial surface pattern, is regarded as an empty concavity on a protein surface into which solvent can gain access. The pockets are obtained by a geometric computation method, which can capture the physicochemical texture and the shape of a surface around functional residues, from the protein structures in PDB . As geometrically defined surface patterns, the pockets are believed to have rather strongly clue to protein functions [27, 28], and thereby are adopted as basic elements to construct the structure similarity network in this paper.
For the protein functions, we focus on the prediction of Gene Ontology (GO) terms . GO is a functional classification system of gene products and was developed to address the need for consistent descriptions of gene products in different databases. GO organizes biological terms assigned to one of the three controlled vocabularies (ontologies): molecular functions, biological processes and cellular components. GO also includes relationships between terms such as specialization or past-whole relations. GOA is a database to provide high-quality GO annotations to proteins . In this paper, the GO terms of a protein is predicted, based on the pocket similarity network constituted from a set of annotated proteins in GOA database. A pocket similarity network is a network with its nodes representing pockets and edges indicating the similarities of each pair of pockets. We assume that the functions of a protein are mainly determined by the pockets it contains. If two pockets are similar enough, the corresponding proteins maybe share some common functions. In other words, if one pocket in a protein is similar to many pockets in different proteins which share some GO terms, the protein is related to the same GO terms with a high probability. Based on the pocket similarity network, the proposed procedure of the prediction is implemented in the following way. That is, every protein is simply considered as a set of pockets, and the GO terms annotated to a protein are attributed to each of its pockets. Then based on the correspondence scores between pockets and GO terms in the annotated proteins, the scores between a new protein and GO terms are obtained by comparing all pockets of this new protein with other pockets.
Cross-validation experiments are conducted to evaluate the performance of the proposed method. Numerical results are demonstrated in the recall-precision graphs, which verify high effectiveness of the proposed method for both prediction accuracy and computation efficiency. Such computational results together with statistical analysis also reveal that similar surface pockets in proteins are essential to biological activities and are strong clues to similar GO functions. In contrast to the existing approaches which are mainly suitable for homologous proteins, the proposed method is not only effective for the proteins with distant relationships or with convergent evolution, but also can further reveal direct links between small surface patterns and GO functions, which actually can be explored to identify active sites or functional motifs.
As well known, there are many redundant (due to different experiments) or similar (due to same protein family) structures in PDB, which result in biased statistics. In order to eliminate the unexpected side effects, the PDB_SELECT 25  was chosen in this study. The PDB_SELECT database is a subset of the structures in the PDB that does not contain highly homologous sequences. In the PDB_SELECT 25, no two proteins have more than 25% sequence identity for alignments of length 80 or more residues. Note that PDB_SELECT 25 only contains individual chains of proteins, while all chains of each protein are used in the experiments.
For each protein in the dataset, we retrieved all of its pockets from CASTp  when available. Then the surface pocket similarity network was constructed according to the given parameters (see Methods for details). Each node in the network represents a pocket. The edge between two nodes means that the similarity of the corresponding two pockets are beyond a given threshold.
The similarity of two pockets was evaluated by pvSOAR server . An example of searching results is illustrated in Additional File 1. Some proteins that not covered by pvSOAR database are discarded. According to the different similarity thresholds, several pocket similarity networks were obtained and used in the experiments. Obviously, the tighter the threshold is, the less the edges in the network are. Some nodes become isolated when the threshold is tight enough, which means that no similar pocket can be found for the given threshold. The isolated nodes were deleted from the network since they are useless for the following study. An example of the pocket similarity network with detail descriptions is also given in Additional File 1.
In the pocket similarity network, a node or pocket is called GO annotated node if the pocket is from a GO annotated protein. Similarly, an edge is called GO annotated edge if two endpoints of the edge are both annotated. An annotated edge is called a GO related edge if two endpoints of the edge are annotated by at least one common GO term.
All data used in the experiments were retrieved from the related websites, and the versions of databases are as follows: CASTp (2006-01-20), pvSOAR (2006-07-13), GO (2006-09-01) and GOA (2006-05-31).
The percentage of the edges (pocket pairs) related to similar GO terms in pocket similarity network which is constructed by using the E-value of sequence similarity between two pockets as the threshold.
1.0 × 10-1
1.0 × 10-2
1.0 × 10-3
1.0 × 10-4
1.0 × 10-5
GO annotated pairs
The percentage of the edges (pocket pairs) related to similar GO terms in pocket similarity network which is constructed by using the p-value of structure cRMSD similarity between two pockets as the threshold.
1.0 × 10-1
1.0 × 10-2
1.0 × 10-3
1.0 × 10-4
1.0 × 10-5
GO annotated pairs
The percentage of the edges (pocket pairs) related to similar GO terms in pocket similarity network which is constructed by using the p-value of structure oRMSD similarity between two pockets as the threshold.
1.0 × 10-1
1.0 × 10-2
1.0 × 10-3
1.0 × 10-4
1.0 × 10-5
GO annotated pairs
The percentage of the edges (pocket pairs) related to similar GO terms in pocket similarity network which is constructed by using the combination of E-value of sequence similarity and p-value of structure cRMSD similarity between two pockets as the threshold.
1.0 × 10-1
1.0 × 10-2
1.0 × 10-3
1.0 × 10-4
1.0 × 10-5
GO annotated pairs
In these tables, the first row gives the similarity thresholds, which are ranged from 1.0 × 10-1 (loosest) to 1.0 × 10-5 (tightest) from left to right. The second row indicates the numbers of pocket pairs that satisfy the given threshold, i.e. the numbers of edges in the pocket similarity network. The third row lists the GO annotated edges, i.e. the pocket pairs which are both from GO annotated proteins. Some of the edges are not GO annotated edges, and therefore removed in the next experiments. The fourth row shows how many GO annotated edges in the given pocket similarity network are GO related, i.e. the corresponding proteins of such an edge have at least one identical GO function. The last row is the percentages of GO related edges, which are obtained by dividing the corresponding numbers in the fourth row by the numbers in the third row. The percentage is the statistical significance of the pocket pairs with at least one identical GO function in all GO annotated pocket pairs.
Obviously, when the thresholds become tighter, i.e. two pockets in an edge become more similar, the probability of the corresponding proteins with at least one identical GO term increases. The tendency becomes more significant when the structure similarities such as cRMSD and oRMSD are used instead of the sequence similarity as the threshold, which are illustrated in Tables 2 and 3.
We also computed the frequencies of GO functions in the GO related edges. The GO functions in a GO related edge are the common GO functions of two proteins corresponding to the edge. The top 15 functions found from a pocket similarity network (see Additional File 1) are mostly binding and catalytic activity functions, which are consistent with the existing results in the literature that the pockets or clefts on protein surface play important roles, such as binding [32, 17].
We also analyzed the relationships between functions annotated to a node and the most frequent GO functions in its closest neighbors. The results (see Additional File 1) show that the more frequently a GO term occurs in the closest neighbors of a pocket, the higher probability of this GO term the protein containing this pocket has. This motivates a straightforward prediction method to predict protein GO functions.
These statistics results give the strong implications that proteins with similar pockets may share some common GO terms and the high correlation between the pocket similarity and GO annotations. The pocket similarity can be used to predict protein functions, even when no global sequence or structure similarity is available. The tendency that more similar pockets would have more similar functions is also a clue, that is, protein surface pockets play key roles in facilitating protein functions.
According to the statistics results (see Tables 1, 2, 3, 4), the pocket similarity network constructed by using p-value 10-2 of the structure similarity cRMSD as the threshold was used in prediction experiments. This network consists of 831 nodes and 602 edges, in which 501 edges are annotated. In the network, the pockets belong to 397 proteins from different families, in which 316 proteins are GO annotated.
The leave-one-out cross-validations were conducted to evaluate the performance of the proposed method. That is, in each validation a protein was selected as a target protein from 316 GO annotated proteins. The correspondence scores between each pocket of the target protein and GO terms were calculated from the annotated closest neighbors (not in the target protein) according to the score scheme presented in section Methods. Then the correspondence scores between the target protein and GO terms were inferred based on the scores of its pockets.
The original (i.e. unfiltered) predicted result has recall value 0.774 and precision value 0.706. In the 316 testing proteins, there are 267 proteins, each of which has at least one GO term predicted in the experiments. The prediction method does not hit any GO term of the rest 49 proteins. The coverage is 84.50%, which is considerably high and quite efficient for predicting protein functions. The result illustrates that the presented method can rather correctly predict GO functions for most proteins, even if they come from different protein families, or there is no homologous information available.
The prediction results of proteins by the three GO ontologies individually. These results are similar to the integrated version, and show that the performance of the proposed prediction method does not heavily depend on the considered GO terms. For Gene Ontologies, C means cellular component, F means molecular function, and P means biological process. The proteins with R & P 100% mean that these proteins can be predicted with recall value 1 and precision value 1.
Number of proteins
Proteins with recall 100%
Proteins with precision 100%
Proteins with R & P 100%
Prediction results in the pocket similarity network by using different cRMSD p-values as thresholds. The detailed prediction results of thresholds from 10-3 to 10-5 are listed in the Additional Files.
Number of proteins
Some predicted GO terms of the un-annotated proteins. The predicted results are almost consistent with the existing functional knowledge in databases and literature. The full predicted results of all un-annotated proteins in the considered pocket similarity network can be found at our website.
Predicted GO terms
PDB Classification: Hydrolase/dna
DNA N-glycosylase activity
DNA-(apurinic or apyrimidinic site) lyase activity
light-harvesting complex (sensu Viridiplantae)
PDB Classification: Light Harvesting Protein
PDB Classification: Oxidoreductase
pyridoxamine-phosphate oxidase activity [source: EC:184.108.40.206]
EC no.: 220.127.116.11
signal transducer activity
PDB Classification: Signaling Protein
two-component signal transduction system (phosphorelay)
calcium ion binding
PDB Classification: Protein Binding
serine-type endopeptidase activity
PDB Classification: Oxidoreductase
ferredoxin-NADP+ reductase activity [source: EC:18.104.22.168]
EC no.: 22.214.171.124
heterotrimeric G-protein complex
PDB Classification: Complex (gtp Binding/transducer)
signal transducer activity
G-protein coupled receptor protein signaling pathway
PDB Classification: DNA Binding Protein/dna
telomeric DNA binding
MHC class I protein complex
PDB Classification: Immune System
MHC class I receptor activity
antigen processing and presentation
zinc ion binding
PDB Classification: Lyase
carbonate dehydratase activity [source: EC:126.96.36.199]
EC no.: 188.8.131.52
one-carbon compound metabolic process
Chemical Component: ZINC ION
Results by Protein Similarity
In order to evaluate and justify the significance of pocket similarity, we constructed the protein similarity network by using a similar method as that used in the construction of the pocket similarity network. The proteins in the same dataset were compared all-against-all using CE algorithm . In the protein similarity network, a node represents a protein, while the edge is created if the CE Z-Score of two linked proteins exceeds a given threshold. Then the leave-one-out prediction tests were conducted in the protein similarity networks with different Z-Score thresholds. The prediction results of the Z-Score thresholds 3.8, 4.8 and 5.8 can be found in Additional File 1, with the statistics of prediction status.
Results by GO Relevance Information
The GO organizes the terms as directed acyclic graphs (DAG), where the child term is more specific and informative than its ancestors. Generic terms do have less relevance for comparing the functional similarity between proteins. Therefore the presence of unspecific GO terms may bias the statistics and prediction results. Two experiments are conducted to evaluate the influence of the unspecific GO terms. In the first experiment, the GO semantic similarity [34–36] of each GO annotated edge is calculated by the relevance similarity proposed in , which takes into account both the relevance information of GO terms and the functional similarity between GO terms. The distributions of GO semantic similarity in different pocket similarity networks are illustrated in Additional File 1. Most of the pairs of proteins with similar pockets in the pocket similarity network have significant semantic similarity. This implies that the similar pockets may correspond to similar functions in the semantic similarity measurement.
The protein surface patterns such as pockets have been shown to be important to protein functions. Some functions of pockets on protein surfaces were already confirmed [32, 17]. The statistics in this paper also show that the proteins containing similar pockets may have similar functions. There are many approaches to predict protein functions by global or local structure similarity [23, 37]. Most of the existing methods are based on structure and/or sequence alignments. In this paper, we proposed a novel approach, which predicts protein functions based on comparison of predefined local surface patterns instead of using structure alignments to find function-related structure motifs. It is an advantage to use the information of multiple surface patterns instead of annotating precisely protein functions to a single pocket because the proposed prediction method can exploit local surface similarity information. But the information provided by pockets may not be in the same level, for example, some pockets may be more closely related to particular protein functions than others, and such difference is not considered in the current work. The introduction of informative structure motifs such as in [23, 37] to the proposed method can also improve the results. In addition, the proposed method can be straightforwardly extended to prediction models with other definitions of locally structure surface patterns as introduced in the first section.
Although the statistics have shown that proteins with similar pockets have high probabilities to have similar GO functions, we do not assume that a particular pocket determines protein functions when calculating the correspondence scores between pockets and GO terms. In other words, the score is evaluated based on the effect of multiple pockets. The score of a pocket corresponding to a particular GO term is the normalized frequency of the GO term annotated to its closest neighbors. The more frequently the GO term appears in the closest neighbors, the larger the score is. This scoring scheme is very simple and does not explicitly consider the influence of the size of neighborhood. For example, a pocket with many closest neighbors often has smaller scores than the pockets with a very few neighbors. It maybe affect the final prediction results. The scores will also be affected by the total number of GO terms belonging to the neighboring proteins. If some neighboring proteins have many GO functions, the scores of the pocket will be smaller than average. The sensitivity of prediction results to the scoring scheme is important for the application of prediction method, and needs to be further studied in the future. The prediction accuracy may be improved if more elaborated scoring methods are used.
Properties of Pocket Similarity Networks
In the proposed method, only the information of the closest neighbors are exploited. In fact, we found that the pocket similarity networks have many interesting properties. For example, the pocket similarity networks are almost sparse and have some modularity. We used the pockets in the same connected components to infer the correspondence scores between a pocket and GO functions. The prediction results are very close to those of the closest-neighbor-based method (see Additional File 1). Furthermore, if the edges are dense in each group and sparse between any two groups, the pocket similarity network can be partitioned into several groups (modules), where each group can be regarded as a candidate of a pocket family, i.e. a classification of pockets. Then each pocket family could be annotated with functions and used to predict protein functions. The pocket classification will be a further research topic of our study.
Sources of Pocket Similarity
The pvSOAR database is an important but not the only source of pocket similarity. For example, we can compare each pair of pockets by structure alignment tools such as DALI , CE  and SAMO , and use the alignment results as an independent or additional threshold. In the future, we will identify tenser similar pockets by this method and detect the physicochemical and geometric characteristics of these functionally important surface motifs. The works on additional pocket similarity information and identification of biochemical features on these spatial motifs are still ongoing and will be presented in another paper.
Some proteins are not annotated in current GOA database, and their prediction results are partly shown in Table 7. The results reveal direct links between small structural pockets and biological functions. Such information can be further exploited to identify active sites and functional motifs by combining with other biological datasets. We can also predict the GO functions of un-annotated proteins by using the proposed method, and then use the predicted GO terms to predict other protein with unknown functions, as if they are already annotated. Whether or not this method can really improve the prediction accuracy and coverage needs further study in the future. Another important research direction is to exploit the GO hierarchical structure and the semantic similarity in the prediction method to improve the accuracy .
Detection of functional sites
The main point of this paper is utilizing local structure information to improve the effectiveness and accuracy of protein function prediction. It is also very interesting and important to check those functionally similar proteins which have local structure similarity instead of global one to find the functional sites in detail and identify their functions. Our main concern is current data are far from complete to do this. Comprehensive experiments on complete local structure library and larger structure databases such as whole PDB may be necessary to archive interesting and convincible results for detecting functional sites. Also the prediction accuracy may be further improved. Our future attempts will eventually take into account the detailed local structural properties related to protein function.
In conclusion, a novel prediction method was proposed in this paper to predict protein GO functions from the surface pocket similarity, by directly linking structural patterns with biological functions. In addition to the high coverage and accuracy of the predictions, the prediction method is also simple and computationally efficient, and therefore can be applied to large-scale problems. The statistics and computational experiments show the effectiveness of the method. It is a supplement to the existing prediction methods based on sequence and/or global structure similarity. In contrast to the existing methods which are mainly effective for homologous proteins in divergent evolution, the method in this paper is also suitable for the proteins with distant relationship or with convergent evolution.
Constructing Pocket Similarity Network
The pocket similarity network is a network in which each node represents a pocket and each edge connects two similar pockets. For each considered protein, all its pockets can be obtained from CASTp database. Then an edge is linked between a pair of nodes, if their similarity measure satisfies the given threshold. The pvSOAR is an all-against-all comparison database of pockets in CASTp. For each similar pocket pair, the pvSOAR database provides three statistical significant measurements: E-value of sequence similarity, p-value of structure similarity cRMSD, p-value of structure similarity oRMSD. These three measurements are explained in [21, 28] and the documentation in pvSOAR server. We can select one of these measurements or their combination as the threshold to build the network. In detail, we query each pocket in the pvSOAR database to find the similar pockets which satisfy the given threshold, and then link an edge between the queried pocket and the hitting pocket. After all edges are added, the isolated nodes, i.e. nodes without any linking edge, are removed from the network.
Scoring Pockets and Proteins for Function Prediction
Evaluating Results of Function Prediction
The prediction performance of the proposed method is evaluated by some widely used measurements in information retrieval research, such as recall, precision and F-measure. The evaluation is usually displayed in a recall-precision graph. And the F-measure can be used as a single measure of performance of the test, which is the harmonic mean of precision and recall. Mathematically, these measurements are defined as follows.
Precision P = TP/(TP+FP)
Recall R = TP/(TP+FN)
F-measure = 2 × P × R/(P+R)
where the TP, FP and N are abbreviations of the number of true positive, number of false positive, and number of false negative respectively. The global performance is evaluated by using leave-one-out cross-validation experiments. The specific evaluation of prediction performance on each individual protein is also calculated in the similar way.
protein data bank
coordinate root mean square distance
orientation root mean square distance
computed atlas of surface topography of proteins
pocket and void surfaces of amino acid residues
gene ontology annotation
- R & P:
recall and precision
This work was supported by the National Natural Science Foundation of China (NSFC) under Grant No. 10631070 and No. 60503004. Part of the authors are also supported by the Grant No. 2006CB503905 from the Ministry of Science and Technology, China, and by JSPS and NSFC under JSPS-NSFC collaboration project. LYW is supported by the Knowledge Innovation Program of the Chinese Academy of Sciences. The authors wish to thank Dr. Jie Liang for providing the data of CASTp and pvSOAR databases, and the helpful discussions and comments on this study. Thanks also to Dr. Dong Xu and members of Prof. Zhang's research group in AMSS of Chinese Academy of Sciences for helpful discussions. Many thanks are also to the three anonymous referees for their helpful comments and suggestions.
- Eisenberg D, Marcotte EM, Xenarios I, Yeates TO: Protein function in the post-genomic era. Nature. 2000, 405: 823-826. 10.1038/35015694.View ArticlePubMedGoogle Scholar
- Whisstock JC, Lesk AM: Prediction of protein function from protein sequence and structure. Q Rev Biophys. 2003, 36 (3): 307-340. 10.1017/S0033583503003901.View ArticlePubMedGoogle Scholar
- Todd AE, Orengo CA, Thornton JM: Evolution of function in protein superfamilies: from a structural perspective. J Mol Biol. 2001, 307: 1113-1143. 10.1006/jmbi.2001.4513.View ArticlePubMedGoogle Scholar
- Holm L, Sander C: Mapping the protein universe. Science. 1996, 273: 595-602. 10.1126/science.273.5275.595.View ArticlePubMedGoogle Scholar
- Devos D, Valencia A: Practical limits of function prediction. Proteins. 2000, 41: 98-107. 10.1002/1097-0134(20001001)41:1<98::AID-PROT120>3.0.CO;2-S.View ArticlePubMedGoogle Scholar
- Watson JD, Laskowski RA, Thornton JM: Predicting protein function from sequence and structural data. Curr Opin Struct Biol. 2005, 15 (3): 275-284. 10.1016/j.sbi.2005.04.003.View ArticlePubMedGoogle Scholar
- Zhang C, Kim SH: Overview of structural genomics: from structure to function. Curr Opin Chem Biol. 2003, 7: 28-32. 10.1016/S1367-5931(02)00015-7.View ArticlePubMedGoogle Scholar
- Thornton JM, Todd AE, Milburn D, Borkakoti N, Orengo CA: From structure to function: approaches and limitations. Nat Struct Biol. 2000, 7 (Suppl): 991-994. 10.1038/80784.View ArticlePubMedGoogle Scholar
- Ferre F, Ausiello G, Zanzoni A, Helmer-Citterich M: SURFACE: a database of protein surface regions for functional annotation. Nucleic Acids Res. 2004, D240-244. 10.1093/nar/gkh054. 32 Database
- Ferre F, Ausiello G, Zanzoni A, Helmer-Citterich M: Functional annotation by identification of local surface similarities: a novel tool for structural genomics. BMC Bioinformatics. 2005, 6: 194-10.1186/1471-2105-6-194.PubMed CentralView ArticlePubMedGoogle Scholar
- Schmitt S, Kuhn D, Klebe G: A new method to detect related function among proteins independent of sequence and fold homology. J Mol Biol. 2002, 323: 387-406. 10.1016/S0022-2836(02)00811-2.View ArticlePubMedGoogle Scholar
- Fischer D, Norel R, Wolfson H, Nussinov R: Surface motifs by a computer vision technique: searches, detection, and implications for protein-ligand recognition. Proteins. 1993, 16 (3): 278-292. 10.1002/prot.340160306.View ArticlePubMedGoogle Scholar
- Liang J, Edelsbrunner H, Woodward C: Anatomy of protein pockets and cavities: measurement of binding site geometry and implications for ligand design. Protein Sci. 1998, 7: 1884-1897.PubMed CentralView ArticlePubMedGoogle Scholar
- Liang J, Edelsbrunner H, Fu P, Sudhakar P, Subramaniam S: Analytic shape computation of macromolecules: II. Identification and computation of inaccessible cavities inside proteins. Proteins. 1998, 33: 18-29. 10.1002/(SICI)1097-0134(19981001)33:1<18::AID-PROT2>3.0.CO;2-H.View ArticlePubMedGoogle Scholar
- Goldsmith-Fischman S, Honig B: Structural genomics: computational methods for structure analysis. Protein Sci. 2003, 12: 1813-1821. 10.1110/ps.0242903.PubMed CentralView ArticlePubMedGoogle Scholar
- Holm L, Sander C: Protein structure comparison by alignment of distance matrices. J Mol Biol. 1993, 233: 123-138. 10.1006/jmbi.1993.1489.View ArticlePubMedGoogle Scholar
- Laskowski RA, Luscombe NM, Swindells MB, Thornton JM: Protein clefts in molecular recognition and function. Protein Sci. 1996, 5 (12): 2438-2452.PubMed CentralPubMedGoogle Scholar
- Jones S, Thornton JM: Analysis of protein-protein interaction sites using surface patches. J Mol Biol. 1997, 272: 121-132. 10.1006/jmbi.1997.1234.View ArticlePubMedGoogle Scholar
- Laskowski RA: SURFNET: a program for visualizing molecular surfaces, cavities and intermolecular interactions. J Mol Graph. 1995, 13: 323-330. 10.1016/0263-7855(95)00073-9.View ArticlePubMedGoogle Scholar
- Hendlich M, Rippmann F, Barnickel G: LIGSITE: automatic and efficient detection of potential small molecule-binding sites in proteins. J Mol Graph Model. 1997, 15: 359-363. 10.1016/S1093-3263(98)00002-3.View ArticlePubMedGoogle Scholar
- Binkowski TA, Naghibzadeh S, Liang J: CASTp: Computed Atlas of Surface Topography of proteins. Nucleic Acids Res. 2003, 31: 3352-3355. 10.1093/nar/gkg512.PubMed CentralView ArticlePubMedGoogle Scholar
- Kinoshita K, Furui J, Nakamura H: Identification of protein functions from a molecular surface database, eF-site. J Struct Funct Genomics. 2002, 2: 9-22. 10.1023/A:1011318527094.View ArticlePubMedGoogle Scholar
- Stark A, Sunyaev S, Russell RB: A model for statistical significance of local similarity in structure. J Mol Biol. 2003, 326: 1307-1316. 10.1016/S0022-2836(03)00045-7.View ArticlePubMedGoogle Scholar
- Laurie AT, Jackson RM: Q-SiteFinder: an energy-based method for the prediction of protein-ligand binding sites. Bioinformatics. 2005, 21 (9): 1908-1916. 10.1093/bioinformatics/bti315.View ArticlePubMedGoogle Scholar
- Nayal M, Honig B: On the nature of cavities on protein surfaces: application to the identification of drug-binding sites. Proteins. 2006, 63: 892-906. 10.1002/prot.20897.View ArticlePubMedGoogle Scholar
- Berman HM, Westbrook J, Feng Z, Gilliland G, Bhat TN, Weissig H, Shindyalov IN, Bourne PE: The Protein Data Bank. Nucleic Acids Res. 2000, 28: 235-242. 10.1093/nar/28.1.235.PubMed CentralView ArticlePubMedGoogle Scholar
- Binkowski TA, Adamian L, Liang J: Inferring functional relationships of proteins from local sequence and spatial surface patterns. J Mol Biol. 2003, 332 (2): 505-526. 10.1016/S0022-2836(03)00882-9.View ArticlePubMedGoogle Scholar
- Binkowski TA, Freeman P, Liang J: pvSOAR: detecting similar surface patterns of pocket and void surfaces of amino acid residues on proteins. Nucleic Acids Res. 2004, 32: W555-W558. 10.1093/nar/gkh390.PubMed CentralView ArticlePubMedGoogle Scholar
- Consortium TGO: Gene Ontology: tool for the unification of biology. Nature Genet. 2000, 25: 25-29. 10.1038/75556.View ArticleGoogle Scholar
- Camon E, Magrane M, Barrell D, Lee V, Dimmer E, Maslen J, Binns D, Harte N, Lopez R, Apweiler R: The Gene Ontology Annotation(GOA) Database: sharing knowledge in Uniprot with Gene Ontology. Nucleic Acids Res. 2004, D262-D266. 10.1093/nar/gkh021. 32 Database
- Hobohm U, Sander C: Selection of a representative set of structures from the Brookhaven Protein Data Bank. Protein Sci. 1992, 1: 409-417.PubMed CentralView ArticlePubMedGoogle Scholar
- Binkowski TA, Andrzej J, Liang J: Protein surface analysis for function annotation in high-throughput structural genomics pipeline. Protein Sci. 2005, 14: 2972-2981. 10.1110/ps.051759005.PubMed CentralView ArticlePubMedGoogle Scholar
- Shindyalov IN, Bourne PE: Protein structure alignment by incremental combinatorial extension (CE) of the optimal path. Protein Engineering. 1998, 11: 739-747. 10.1093/protein/11.9.739.View ArticlePubMedGoogle Scholar
- Lord PW, Stevens RD, Brass A, Goble CA: Investigating semantic similarity measures across the Gene Ontology: the relationship between sequence and annotation. Bioinformatics. 2003, 19: 1275-1283. 10.1093/bioinformatics/btg153.View ArticlePubMedGoogle Scholar
- Carroll S, Pavlovic V: Protein classification using probabilistic chain graphs and the Gene Ontology structure. Bioinformatics. 2006, 22: 1871-1878. 10.1093/bioinformatics/btl187.View ArticlePubMedGoogle Scholar
- Schlicker A, Domingues FS, Rahnenfuhrer J, Lengauer T: A new measure for functional similarity of gene products based on Gene Ontology. BMC Bioinformatics. 2006, 7: 302-10.1186/1471-2105-7-302.PubMed CentralView ArticlePubMedGoogle Scholar
- Pazos F, Sternberg MJE: Automated prediction of protein function and detection of functional sites from structure. Proc Natl Acad Sci USA. 2004, 101: 14754-14759. 10.1073/pnas.0404569101.PubMed CentralView ArticlePubMedGoogle Scholar
- Holm L, Sander C: Dali: a network tool for protein structure comparison. Trends in Biochemical Sciences. 1995, 20: 478-480. 10.1016/S0968-0004(00)89105-7.View ArticlePubMedGoogle Scholar
- Chen L, Wu LY, Wang Y, Zhang S, Zhang XS: Revealing divergent evolution, identifying circular permutations and detecting active-sites by protein structure comparison. BMC Structural Biology. 2006, 6: 18-10.1186/1472-6807-6-18.PubMed CentralView ArticlePubMedGoogle Scholar
- Shug J, Diskin S, Mazzarelli J, Brunk B, Stoeckert C: Predicting gene ontology functions from ProDom and CDD protein domains. Genome Res. 2002, 12 (4): 648-655. 10.1101/gr.222902.View ArticleGoogle Scholar
This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.