Using GO-PseAA predictor to identify membrane proteins and their types

Cell membranes are crucial to the life of a cell. Although the basic structure of biological membrane is provided by the lipid bilayer, most of the specific functions are carried out by membrane proteins. Knowledge of membrane protein type often offers important clues toward determining the function of an uncharacterized protein. Therefore, predicting the type of a membrane protein from its primary sequence, or even just identifying whether the uncharacterized protein belongs to a membrane protein or not, is an important and challenging problem in bioinformatics and proteomics. To deal with these problems, the GO-PseAA predictor is introduced that is operated in a hybridization space by combining the gene ontology and pseudo amino acid composition. Meanwhile, to test the prediction quality, a dataset was constructed that contains 6476 non-membrane proteins and 5122 membrane proteins classified into five different types. To avoid redundancy and bias, none of the proteins included has > or = 40% sequence identity to any other. It has been observed that the overall success rate by the jackknife cross-validation test in identifying non-membrane proteins and membrane proteins was 94.76%, and that in identifying the five membrane protein types was 95.84%. The high success rates suggest that the GO-PseAA predictor can catch the core feature of the statistical samples concerned and may become an automated high throughput toll in molecular and cell biology.     

Predicting protease types by hybridizing gene ontology and pseudo amino acid composition

Proteases play a vitally important role in regulating most physiological processes. Different types of proteases perform different functions with different biological processes. Therefore, it is highly desired to develop a fast and reliable means to identify the types of proteases according to their sequences, or even just identify whether they are proteases or nonproteases. The avalanche of protein sequences generated in the postgenomic era has made such a challenge become even more critical and urgent. By hybridizing the gene ontology approach and pseudo amino acid composition approach, a powerful predictor called GO-PseAA predictor was introduced to address the problems. To avoid redundancy and bias, demonstrations were performed on a dataset where none of proteins has >/= 25% sequence identity to any other. The overall success rates thus obtained by the jackknife cross-validation test in identifying protease and nonprotease was 91.82%, and that in identifying the protease type was 85.49% among the following five types: (1) aspartic, (2) cysteine, (3) metallo, (4) serine, and (5) threonine. The high jackknife success rates yielded for such a stringent dataset indicate the GO-PseAA predictor is very powerful and might become a useful tool in bioinformatics and proteomics.     

Predicting protein-protein interactions from sequences in a hybridization space

To understand the networks in living cells, it is indispensably important to identify protein-protein interactions on a genomic scale. Unfortunately, it is both time-consuming and expensive to do so solely based on experiments due to the nature of the problem whose complexity is obviously overwhelming, just like the fact that "life is complicated". Therefore, developing computational techniques for predicting protein-protein interactions would be of significant value in this regard. By fusing the approach based on the gene ontology and the approach of pseudo-amino acid composition, a predictor called "GO-PseAA" predictor was established to deal with this problem. As a showcase, prediction was performed on 6323 protein pairs from yeast. To avoid redundancy and homology bias, none of the protein pairs investigated has > or = 40% sequence identity with any other. The overall success rate obtained by jackknife cross-validation was 81.6%, indicating the GO-PseAA predictor is very promising for predicting protein-protein interactions from protein sequences, and might become a useful vehicle for studying the network biology in the postgenomic era.     

Enzyme function less conserved than anticipated

The level of sequence similarity that implies similarity in protein structure is well established. Recently, many groups proposed thresholds for similarity in sequence implying similarity in enzymatic function. All previous results suggest the strong conservation of enzymatic function above levels of 50% pairwise sequence identity. Here, I argue that all groups substantially overestimated the conservation of enzyme function because their data sets were either too biased, or too small. An unbiased analysis suggested that less than 30% of the pair fragments above 50% sequence identity have entirely identical EC numbers. Another surprising finding was that even BLAST E-values below 10(-50) did not suffice to automatically transfer enzyme function without errors. As expected, most misclassifications originated from similarities in relatively short regions and/or from transferring annotations for different domains. Both problems cannot be corrected easily by adjusting the thresholds for automatic transfer of genome annotations. A score relating sequence identity to alignment length (distance from HSSP-threshold) outperformed statistical BLAST scores for high sequence similarity. In particular, the distance score allowed error-free transfer of enzyme function for the 10% most similar enzyme pairs. The results illustrated how difficult it is to assess the conservation of protein function and to guarantee error-free genome annotations, in general: sets with millions of pair comparisons might not suffice to arrive at statistically significant conclusions. In practice, the revised detailed estimates for the sequence conservation of enzyme function may provide important benchmarks for everyday sequence analysis and for more cautious automatic genome annotations.     

Predicting enzyme subclass by functional domain composition and pseudo amino acid composition

As a continuous effort to use the sequence approach to identify enzymatic function at a deeper level, investigations are extended from the main enzyme classes (Protein Sci. 2004, 13, 2857-2863) to their subclasses. This is indispensable if we wish to understand the molecular mechanism of an enzyme at a deeper level. For each of the 6 main enzyme classes (i.e., oxidoreductase, transferase, hydrolase, lyase, isomerase, and ligase), a subclass training dataset is constructed. To reduce homologous bias, a stringent cutoff was imposed that all the entries included in the datasets have less than 40% sequence identity to each other. To catch the core feature that is intimately related to the biological function, the sample of a protein is represented by hybridizing the functional domain composition and pseudo amino acid composition. On the basis of such a hybridization representation, the FunD-PseAA predictor is established. It is demonstrated by the jackknife cross-validation tests that the overall success rate in identifying the 21 subclasses of oxidoreductases is above 86%, and the corresponding rates in identifying the subclasses of the other 5 main enzyme classes are 94-97%. The high success rates imply that the FunD-PseAA predictor may become a useful tool in bioinformatics and proteomics of the post-genomic era.     

How well is enzyme function conserved as a function of pairwise sequence identity?

Enzyme function conservation has been used to derive the threshold of sequence identity necessary to transfer function from a protein of known function to an unknown protein. Using pairwise sequence comparison, several studies suggested that when the sequence identity is above 40%, enzyme function is well conserved. In contrast, Rost argued that because of database bias, the results from such simple pairwise comparisons might be misleading. Thus, by grouping enzyme sequences into families based on sequence similarity and selecting representative sequences for comparison, he showed that enzyme function starts to diverge quickly when the sequence identity is below 70%. Here, we employ a strategy similar to Rost's to reduce the database bias; however, we classify enzyme families based not only on sequence similarity, but also on functional similarity, i.e. sequences in each family must have the same four digits or the same first three digits of the enzyme commission (EC) number. Furthermore, instead of selecting representative sequences for comparison, we calculate the function conservation of each enzyme family and then average the degree of enzyme function conservation across all enzyme families. Our analysis suggests that for functional transferability, 40% sequence identity can still be used as a confident threshold to transfer the first three digits of an EC number; however, to transfer all four digits of an EC number, above 60% sequence identity is needed to have at least 90% accuracy. Moreover, when PSI-BLAST is used, the magnitude of the E-value is found to be weakly correlated with the extent of enzyme function conservation in the third iteration of PSI-BLAST. As a result, functional annotation based on the E-values from PSI-BLAST should be used with caution. We also show that by employing an enzyme family-specific sequence identity threshold above which 100% functional conservation is required, functional inference of unknown sequences can be accurately accomplished. However, this comes at a cost: those true positive sequences below this threshold cannot be uniquely identified.     

Molecular cloning, expression and characterization of a novel feruloyl esterase enzyme from the symbionts of termite(Coptotermes formosanus) gut

Termites play an important role in the degradation of dead plant materials and have acquired endogenous and symbiotic cellulose digestion capabilities. The feruloyl esterase enzyme (FAE) gene amplified from the metagenomic DNA of Coptotermes formosanus gut was cloned in the TA cloning vector and subcloned into a pET32a expression vector. The Ft3-7 gene has 84% sequence identity with Clostridium saccharolyticum and shows amino acid sequence identity with predicted xylanase/chitin deacetylase and endo-1,4-beta-xylanase. The sequence analysis reveals that probably Ft3-7 could be a new gene and that its molecular mass was 18.5 kDa. The activity of the recombinant enzyme (Ft3-7) produced in Escherichia coli (E.coli) was 21.4 U with substrate ethyl ferulate and its specific activity was 24.6 U/mg protein. The optimum pH and temperature for enzyme activity were 7.0 and 37oC, respectively. The substrate utilization preferences and sequence similarity of the Ft3-7 place it in the type-D sub-class of FAE.     

Efficient recognition of protein fold at low sequence identity by conservative application of Psi-BLAST: application

Based on a study involving structural comparisons of proteins sharing 25% or less sequence identity, three rounds of Psi-BLAST appear capable of identifying remote evolutionary homologs with greater than 95% confidence provided that more than 50% of the query sequence can be aligned with the target sequence. Since it seems that more than 80% of all homologous protein pairs may be characterized by a lack of significant sequence similarity, the experimental biologist is often confronted with a lack of guidance from conventional homology searches involving pair-wise sequence comparisons. The ability to disregard levels of sequence identity and expect value in Psi-BLAST if at least 50% of the query sequence has been aligned allows for generation of new hypotheses by consideration of matches that are conventionally disregarded. In one example, we suggest a possible evolutionary linkage between the cupredoxin and immunoglobulin fold families. A thermostable hypothetical protein of unknown function may be a circularly permuted homolog to phosphotriesterase, an enzyme capable of detoxifying organophosphate nerve agents. In a third example, the amino acid sequence of another hypothetical protein of unknown function reveals the ATP binding-site, metal binding site, and catalytic sidechain consistent with kinase activity of unknown specificity. This approach significantly expands the utility of existing sequence data to define the primary structure degeneracy of binding sites for substrates, cofactors and other proteins.     

Predicting enzyme family class in a hybridization space

Given the sequence of a protein, how can we predict whether it is an enzyme or a non‐enzyme? If it is, what enzyme family class it belongs to? Because these questions are closely relevant to the biological function of a protein and its acting object, their importance is self‐evident. Particularly with the explosion of protein sequences entering into data banks and the relatively much slower progress in using biochemical experiments to determine their functions, it is highly desired to develop an automated method that can be used to give fast answers to these questions. By hybridizing the gene ontology and pseudo‐amino‐acid composition, we have introduced a new method that is called GO‐PseAA predictor and operate it in a hybridization space. To avoid redundancy and bias, demonstrations were performed on a data set in which none of the proteins in an individual class has ≥40% sequence identity to any other. The overall success rate thus obtained by the jackknife cross‐validation test in identifying enzyme and non‐enzyme was 93%, and that in identifying the enzyme family was 94% for the following six main Enzyme Commission (EC) classes: (1) oxidoreductase, (2) transferase, (3) hydrolase, (4) lyase, (5) isomerase, and (6) ligase. The corresponding rates by the independent data set test were 98% and 97%, respectively.     

Sequence of a cDNA for mouse thymidylate synthase reveals striking similarity with the prokaryotic enzyme

We report the nucleotide sequence of a cloned cDNA, pMTS-3, that contains a 1-kb insert corresponding to mouse thymidylate synthase (E.C. 2.1.1.45). The open reading frame of 921 nucleotides from the first AUG to the termination codon specifies a protein with a molecular mass of 34,962 daltons. The predicted amino acid sequence is 90% identical with that of the human enzyme. The mouse sequence also has an extremely high degree of similarity (as much as 55% identity) with prokaryotic thymidylate synthase sequences, indicating that thymidylate synthase is among the most highly conserved proteins studied to date. The similarity is especially pronounced (as much as 80% identity) in the 44-amino-acid region encompassing the binding site for deoxyuridylic acid. The cDNA sequence also suggests that mouse thymidylate synthase mRNA lacks a 3' untranslated region, since the termination codon, UAA, is followed immediately by a poly(A) segment.      

Predicting membrane protein type by functional domain composition and pseudo-amino acid composition

Given the sequence of a protein, how can we predict whether it is a membrane protein or non-membrane protein? If it is, what membrane protein type it belongs to? Since these questions are closely relevant to the function of an uncharacterized protein, their importance is self-evident. Particularly, with the explosion of protein sequences entering into databanks and the relatively much slower progress in using biochemical experiments to determine their functions, it is highly desired to develop an automated method that can be used to give a fast answers to these questions. By hybridizing the functional domain (FunD) and pseudo-amino acid composition (PseAA), a new strategy called FunD-PseAA predictor was introduced. To test the power of the predictor, a highly non-homologous data set was constructed where none of proteins has 25% sequence identity to any other. The overall success rates obtained with the FunD-PseAA predictor on such a data set by the jackknife cross-validation test was 85% for the case in identifying membrane protein and non-membrane protein, and 91% in identifying the membrane protein type among the following 5 categories: (1) type-1 membrane protein, (2) type-2 membrane protein, (3) multipass transmembrane protein, (4) lipid chain-anchored membrane protein, and (5) GPI-anchored membrane protein. These rates are much higher than those obtained by the other methods on the same stringent data set, indicating that the FunD-PseAA predictor may become a useful high throughput tool in bioinformatics and proteomics.     

Using functional domain composition to predict enzyme family classes

According to their main EC (Enzyme Commission) numbers, enzymes are classified into the following 6 main classes: oxidoreductases, transferases, hydrolases, lyases, isomerases, and ligases. A new method has been developed to predict the enzymatic attribute of proteins by introducing the functional domain composition to formulate a given protein sequence. The advantage by doing so is that both the sequence-order-related features and the function-related features are naturally incorporated in the predictor. As a demonstration, the jackknife cross-validation test was performed on a dataset that consists of proteins with only less than 20% sequence identity to each other in order to get rid of any homologous bias. The overall success rate thus obtained was 85% in identifying the enzyme family classes (including the identification of nonenzyme protein sequences as well). The success rate is significantly higher than those obtained by the other methods on such a stringent dataset. This indicates that using the functional domain composition to represent protein samples for statistical prediction is indeed very promising, and will become a powerful tool in bioinformatics and proteomics.     

Evolution of function in protein superfamilies, from a structural perspective

The recent growth in protein databases has revealed the functional diversity of many protein superfamilies. We have assessed the functional variation of homologous enzyme superfamilies containing two or more enzymes, as defined by the CATH protein structure classification, by way of the Enzyme Commission (EC) scheme. Combining sequence and structure information to identify relatives, the majority of superfamilies display variation in enzyme function, with 25 % of superfamilies in the PDB having members of different enzyme types. We determined the extent of functional similarity at different levels of sequence identity for 486,000 homologous pairs (enzyme/enzyme and enzyme/non-enzyme), with structural and sequence relatives included. For single and multi-domain proteins, variation in EC number is rare above 40 % sequence identity, and above 30 %, the first three digits may be predicted with an accuracy of at least 90 %. For more distantly related proteins sharing less than 30 % sequence identity, functional variation is significant, and below this threshold, structural data are essential for understanding the molecular basis of observed functional differences. To explore the mechanisms for generating functional diversity during evolution, we have studied in detail 31 diverse structural enzyme superfamilies for which structural data are available. A large number of variations and peculiarities are observed, at the atomic level through to gross structural rearrangements. Almost all superfamilies exhibit functional diversity generated by local sequence variation and domain shuffling. Commonly, substrate specificity is diverse across a superfamily, whilst the reaction chemistry is maintained. In many superfamilies, the position of catalytic residues may vary despite playing equivalent functional roles in related proteins. The implications of functional diversity within supefamilies for the structural genomics projects are discussed. More detailed information on these superfamilies is available at http://www.biochem.ucl.ac.uk/bsm/FAM-EC/.     

Assessing annotation transfer for genomics: quantifying the relations between protein sequence, structure and function through traditional and probabilistic scores

Measuring in a quantitative, statistical sense the degree to which structural and functional information can be "transferred" between pairs of related protein sequences at various levels of similarity is an essential prerequisite for robust genome annotation. To this end, we performed pairwise sequence, structure and function comparisons on approximately 30,000 pairs of protein domains with known structure and function. Our domain pairs, which are constructed according to the SCOP fold classification, range in similarity from just sharing a fold, to being nearly identical. Our results show that traditional scores for sequence and structure similarity have the same basic exponential relationship as observed previously, with structural divergence, measured in RMS, being exponentially related to sequence divergence, measured in percent identity. However, as the scale of our survey is much larger than any previous investigations, our results have greater statistical weight and precision. We have been able to express the relationship of sequence and structure similarity using more "modern scores," such as Smith-Waterman alignment scores and probabilistic P-values for both sequence and structure comparison. These modern scores address some of the problems with traditional scores, such as determining a conserved core and correcting for length dependency; they enable us to phrase the sequence-structure relationship in more precise and accurate terms. We found that the basic exponential sequence-structure relationship is very general: the same essential relationship is found in the different secondary-structure classes and is evident in all the scoring schemes. To relate function to sequence and structure we assigned various levels of functional similarity to the domain pairs, based on a simple functional classification scheme. This scheme was constructed by combining and augmenting annotations in the enzyme and fly functional classifications and comparing subsets of these to the Escherichia coli and yeast classifications. We found sigmoidal relationships between similarity in function and sequence, with clear thresholds for different levels of functional conservation. For pairs of domains that share the same fold, precise function appears to be conserved down to approximately 40 % sequence identity, whereas broad functional class is conserved to approximately 25 %. Interestingly, percent identity is more effective at quantifying functional conservation than the more modern scores (e.g. P-values). Results of all the pairwise comparisons and our combined functional classification scheme for protein structures can be accessed from a web database at http://bioinfo.mbb.yale.edu/alignCopyright 2000 Academic Press.     

Chicken poly(ADP-ribose) synthetase: complete deduced amino acid sequence and comparison with mammalian enzyme sequences.

The complete nucleotide (nt) sequence of the cDNA encoding the chicken poly(ADP-ribose) synthetase has been determined. Positive clones overlapping the 5' region or the 3' region of the cDNA have been isolated from a lambda gt 10 hen oviduct cDNA library using two human cDNA probes. The missing middle portion has been obtained by the polymerase chain reaction procedure. A single 3033-nt open reading frame from start codon to stop codon encodes a sequence of 1011 amino acid residues. The alignment of this sequence with those from human and mouse reveals overall identities of 79% and 77%, respectively. However, an identity of about 82% is obtained in the DNA-binding domain within the two zinc fingers, and an even higher similarity (85-87%) is observed in the NAD-binding domain. The isolated clones consistently hybridize on chicken Northern blots to an mRNA species of about 4 kb, whereas they do not cross-hybridize with RNA blots of Drosophila melanogaster. Thus, it appears that, even if the functional properties of the enzyme are maintained, the cDNA identity will be much decreased in nonvertebrate organisms.     

Sequence assessment of comigrating AFLPTM bands in Echinacea -- implications for comparative biological studies.

The extent of sequence identity among clones derived from monomorphic and polymorphic AFLPTM polymorphism bands was quantified. A total of 79 fragments from a monomorphic band of 273 bp and 48 fragments from a polymorphic band of 159 bp, isolated from individuals belonging to different populations, varieties, and species of Echinacea, were cloned and sequenced. The monomorphic fragments exhibited above 90% sequence identity among clones within samples. Sequence identity within variety ranged from 82.78% to 94.87% and within species from 75.82% to 98.9% and was 57.97% in the genus. The polymorphic fragments exhibited much less sequence identity. In some instances, even two clones from the same fragment were different in their size and sequence. Within sample, clone sequence identity ranged from 100% to 51.57%, within variety from 33.33% to 100% in one variety, and from 23.66% to 45% within species and was as low as 1.25% within the genus. In addition, sequences of the same size were aligned to verify the nature of their sequence dissimilarity/similarity. Within each size group, identical sequences were found across species and varieties. In general, comigrating bands cannot be considered homologous. Thus, the use of AFLPTM band data for comparative studies is appropriate only if the results emanating from such analyses are considered as approximations and are interpreted as phenotypic but not genotypic.     

Leucine aminopeptidase from Arabidopsis thaliana. Molecular evidence for a phylogenetically conserved enzyme of protein turnover in higher plants.

Leucine aminopeptidases are exopeptidases which are presumably involved in the processing and regular turnover of intracellular proteins; however, their precise function in cellular metabolism remains to be established. Towards this goal, a full-length complementary DNA encoding a plant leucine aminopeptidase was isolated from a cDNA library of Arabidopsis thaliana and sequenced. The nucleotide sequence showed 49.5% identity to the Escherichia coli xerB-encoded leucine aminopeptidase. Sequence analysis revealed that the cDNA encodes a polypeptide of 520 amino acids with a calculated molecular mass of 54,506 Da. The C-terminal part (amino acids 200-520) of the deduced amino acid sequence showed 43.8% sequence identity to the xerB-encoded leucine aminopeptidase and 42.6% sequence identity to the amino acid sequence of bovine lens leucine aminopeptidase (EC 3.4.11.1). No sequence similarity (not even over short sequence elements) was observed with any other known peptidase or proteinase sequence. The cDNA was expressed as a fusion protein from the lacZ promoter in E. coli. Enzymatic analysis proved that the cloned cDNA encoded an active leucine aminopeptidase. The properties of this enzyme, including metal requirements, inhibitor sensitivity, pH optimum and the remarkable temperature stability, are very similar to those reported for leucine aminopeptidases from other tissues. Amino acids involved in metal and substrate binding in bovine lens aminopeptidase are completely conserved in the plant enzyme as well as in the XerB protein. Our results show that leucine aminopeptidases form a superfamily of highly conserved enzymes, spanning the evolutionary period from the bacteria to animals and higher plants. This is the first aminopeptidase cloned from a plant.     

Testing the ortholog conjecture with comparative functional genomic data from mammals

A common assumption in comparative genomics is that orthologous genes share greater functional similarity than do paralogous genes (the "ortholog conjecture"). Many methods used to computationally predict protein function are based on this assumption, even though it is largely untested. Here we present the first large-scale test of the ortholog conjecture using comparative functional genomic data from human and mouse. We use the experimentally derived functions of more than 8,900 genes, as well as an independent microarray dataset, to directly assess our ability to predict function using both orthologs and paralogs. Both datasets show that paralogs are often a much better predictor of function than are orthologs, even at lower sequence identities. Among paralogs, those found within the same species are consistently more functionally similar than those found in a different species. We also find that paralogous pairs residing on the same chromosome are more functionally similar than those on different chromosomes, perhaps due to higher levels of interlocus gene conversion between these pairs. In addition to offering implications for the computational prediction of protein function, our results shed light on the relationship between sequence divergence and functional divergence. We conclude that the most important factor in the evolution of function is not amino acid sequence, but rather the cellular context in which proteins act.     

Combination of threading potentials and sequence profiles improves fold recognition

Using a benchmark set of structurally similar proteins, we conduct a series of threading experiments intended to identify a scoring function with an optimal combination of contact-potential and sequence-profile terms. The benchmark set is selected to include many medium-difficulty fold recognition targets, where sequence similarity is undetectable by BLAST but structural similarity is extensive. The contact potential is based on the log-odds of non-local contacts involving different amino acid pairs, in native as opposed to randomly compacted structures. The sequence profile term is that used in PSI-BLAST. We find that combination of these terms significantly improves the success rate of fold recognition over use of either term alone, with respect to both recognition sensitivity and the accuracy of threading models. Improvement is greatest for targets between 10 % and 20 % sequence identity and 60 % to 80 % superimposable residues, where the number of models crossing critical accuracy and significance thresholds more than doubles. We suggest that these improvements account for the successful performance of the combined scoring function at CASP3. We discuss possible explanations as to why sequence-profile and contact-potential terms appear complementary.     

A top-down approach to classify enzyme functional classes and sub-classes using random forest

Advancements in sequencing technologies have witnessed an exponential rise in the number of newly found enzymes. Enzymes are proteins that catalyze bio-chemical reactions and play an important role in metabolic pathways. Commonly, function of such enzymes is determined by experiments that can be time consuming and costly. Hence, a need for a computing method is felt that can distinguish protein enzyme sequences from those of non-enzymes and reliably predict the function of the former. To address this problem, approaches that cluster enzymes based on their sequence and structural similarity have been presented. But, these approaches are known to fail for proteins that perform the same function and are dissimilar in their sequence and structure. In this article, we present a supervised machine learning model to predict the function class and sub-class of enzymes based on a set of 73 sequence-derived features. The functional classes are as defined by International Union of Biochemistry and Molecular Biology. Using an efficient data mining algorithm called random forest, we construct a top-down three layer model where the top layer classifies a query protein sequence as an enzyme or non-enzyme, the second layer predicts the main function class and bottom layer further predicts the sub-function class. The model reported overall classification accuracy of 94.87% for the first level, 87.7% for the second, and 84.25% for the bottom level. Our results compare very well with existing methods, and in many cases report better performance. Using feature selection methods, we have shown the biological relevance of a few of the top rank attributes.