Protein fold recognition by prediction-based threading

SPARKS 2 and SP3 servers in CASP6

Two single-method servers, SPARKS 2 and SP3, participated in automatic-server predictions in CASP6. The overall results for all as well as detailed performance in comparative modeling targets are presented. It is shown that both SPARKS 2 and SP3 are able to recognize their corresponding best templates for all easy comparative modeling targets. The alignment accuracy, however, is not always the best among all the servers. Possible factors are discussed. SPARKS 2 and SP3 fold recognition servers, as well as their executables, are freely available for all academic users on http://theory.med.buffalo.edu.

SSALN: an alignment algorithm using structure-dependent substitution matrices and gap penalties learned from structurally aligned protein pairs

In template-based modeling of protein structures, the generation of the alignment between the target and the template is a critical step that significantly affects the accuracy of the final model. This paper proposes an alignment algorithm SSALN that learns substitution matrices and position-specific gap penalties from a database of structurally aligned protein pairs. In addition to the amino acid sequence information, secondary structure and solvent accessibility information of a position are used to derive substitution scores and position-specific gap penalties. In a test set of CASP5 targets, SSALN outperforms sequence alignment methods such as a Smith-Waterman algorithm with BLOSUM50 and PSI_BLAST. SSALN also generates better alignments than PSI_BLAST in the CASP6 test set. LOOPP server prediction based on an SSALN alignment is ranked the best for target T0280_1 in CASP6. SSALN is also compared with several threading methods and sequence alignment methods on the ProSup benchmark. SSALN has the highest alignment accuracy among the methods compared. On the Fischer's benchmark, SSALN performs better than CLUSTALW and GenTHREADER, and generates more alignments with accuracy >50%, >60% or >70% than FUGUE, but fewer alignments with accuracy >80% than FUGUE. All the supplemental materials can be found at http://www.cs.cornell.edu/ approximately jianq/research.htm.

A machine learning information retrieval approach to protein fold recognition

MOTIVATION

Recognizing proteins that have similar tertiary structure is the key step of template-based protein structure prediction methods. Traditionally, a variety of alignment methods are used to identify similar folds, based on sequence similarity and sequence-structure compatibility. Although these methods are complementary, their integration has not been thoroughly exploited. Statistical machine learning methods provide tools for integrating multiple features, but so far these methods have been used primarily for protein and fold classification, rather than addressing the retrieval problem of fold recognition-finding a proper template for a given query protein.

RESULTS

Here we present a two-stage machine learning, information retrieval, approach to fold recognition. First, we use alignment methods to derive pairwise similarity features for query-template protein pairs. We also use global profile-profile alignments in combination with predicted secondary structure, relative solvent accessibility, contact map and beta-strand pairing to extract pairwise structural compatibility features. Second, we apply support vector machines to these features to predict the structural relevance (i.e. in the same fold or not) of the query-template pairs. For each query, the continuous relevance scores are used to rank the templates. The FOLDpro approach is modular, scalable and effective. Compared with 11 other fold recognition methods, FOLDpro yields the best results in almost all standard categories on a comprehensive benchmark dataset. Using predictions of the top-ranked template, the sensitivity is approximately 85, 56, and 27% at the family, superfamily and fold levels respectively. Using the 5 top-ranked templates, the sensitivity increases to 90, 70, and 48%.

Nearest-neighbor effects and structural preferences in dipeptides are a function of the electronic properties of amino acid side-chains

The electronic properties of amino acid side-chains are emerging as an important factor in the preference for secondary structure in proteins. These properties have not been fully characterized, nor has their role in the behavior of peptides been explored in any detail. The present studies sought to evaluate several possibilities: 1) that hydrophilicity can be expressed solely in electronic terms, 2) that substituent effects of side-chains extend across the peptide bond, and (3) nearest-neighbor effects in dipeptides correlate with secondary structural preferences. Quantum mechanics (QM) calculations were used to define the electronic properties of individual amino acids and dipeptides. It was found that the hydrophilicity of an amino acid side-chain can be accurately represented as a function of the electron densities of its component atoms. In addition, the nature of an amino acid in the second position of a dipeptide affects the electronic properties (Mulliken populations and electron densities) of the main-chain atoms of the first residue. Certain electronic features of the dipeptides strongly correlated with propensity for secondary structure. Specifically, Mulliken population data at the Calpha atom and N atom predicted preference for alpha-helices versus coil and strand conformations, respectively. Analysis of dipeptides arrayed in either helical or extended structures revealed lengthening of main-chain bonds in the alpha-helical conformations. A thorough characterization of the electronic properties of amino acids and short peptide segments may provide a better understanding of the forces that determine secondary structure in proteins.

A hybrid genetic-neural system for predicting protein secondary structure

BACKGROUND

Due to the strict relation between protein function and structure, the prediction of protein 3D-structure has become one of the most important tasks in bioinformatics and proteomics. In fact, notwithstanding the increase of experimental data on protein structures available in public databases, the gap between known sequences and known tertiary structures is constantly increasing. The need for automatic methods has brought the development of several prediction and modelling tools, but a general methodology able to solve the problem has not yet been devised, and most methodologies concentrate on the simplified task of predicting secondary structure.

RESULTS

In this paper we concentrate on the problem of predicting secondary structures by adopting a technology based on multiple experts. The system performs an overall processing based on two main steps: first, a "sequence-to-structure" prediction is enforced by resorting to a population of hybrid (genetic-neural) experts, and then a "structure-to-structure" prediction is performed by resorting to an artificial neural network. Experiments, performed on sequences taken from well-known protein databases, allowed to reach an accuracy of about 76%, which is comparable to those obtained by state-of-the-art predictors.

CONCLUSION

The adoption of a hybrid technique, which encompasses genetic and neural technologies, has demonstrated to be a promising approach in the task of protein secondary structure prediction.

Site-directed genome modification: derivatives of DNA-modifying enzymes as targeting tools

The modification of mammalian genomes is an important goal in gene therapy and animal transgenesis. To generate stable genetic and biochemical changes, the therapeutic genes or transgenes need to be incorporated into the host genome. Ideally, the integration of the foreign gene should occur at sites that ensure their continual expression in the absence of any unwanted side effects on cellular metabolism. In this article, we discuss the opportunities provided by natural DNA-modifying enzymes, such as transposases, recombinases and integrases, to mediate the stable integration of foreign genes into host genomes. In addition, we discuss the approaches that have been taken to improve the efficiency and to modify the site-specificity of these enzymes.

Comparative modelling of protein structure and its impact on microbial cell factories

Comparative modeling is becoming an increasingly helpful technique in microbial cell factories as the knowledge of the three-dimensional structure of a protein would be an invaluable aid to solve problems on protein production. For this reason, an introduction to comparative modeling is presented, with special emphasis on the basic concepts, opportunities and challenges of protein structure prediction. This review is intended to serve as a guide for the biologist who has no special expertise and who is not involved in the determination of protein structure. Selected applications of comparative modeling in microbial cell factories are outlined, and the role of microbial cell factories in the structural genomics initiative is discussed.

Improved prediction for N-termini of alpha-helices using empirical information

The prediction of the secondary structure of proteins from their amino acid sequences remains a key component of many approaches to the protein folding problem. The most abundant form of regular secondary structure in proteins is the alpha-helix, in which specific residue preferences exist at the N-terminal locations. Propensities derived from these observed amino acid frequencies in the Protein Data Bank (PDB) database correlate well with experimental free energies measured for residues at different N-terminal positions in alanine-based peptides. We report a novel method to exploit this data to improve protein secondary structure prediction through identification of the correct N-terminal sequences in alpha-helices, based on existing popular methods for secondary structure prediction. With this algorithm, the number of correctly predicted alpha-helix start positions was improved from 30% to 38%, while the overall prediction accuracy (Q3) remained the same, using cross-validated testing. Although the algorithm was developed and tested on multiple sequence alignment-based secondary structure predictions, it was also able to improve the predictions of start locations by methods that use single sequences to make their predictions. Furthermore, the residue frequencies at N-terminal positions of the improved predictions better reflect those seen at the N-terminal positions of alpha-helices in proteins. This has implications for areas such as comparative modeling, where a more accurate prediction of the N-terminal regions of alpha-helices should benefit attempts to model adjacent loop regions. The algorithm is available as a Web tool, located at http://rocky.bms.umist.ac.uk/elephant.

Fold recognition aided by constraints from small angle X-ray scattering data

We performed a systematic exploration of the use of structural information derived from small angle X-ray scattering (SAXS) measurements to improve fold recognition. SAXS data provide the Fourier transform of the histogram of atomic pair distances (pair distribution function) for a given protein and hence can serve as a structural constraint on methods used to determine the native conformational fold of the protein. Here we used it to construct a similarity-based fitness score with which to evaluate candidate structures generated by a threading procedure. In order to combine the SAXS scores with the standard energy scores and other 1D profile-based scores used in threading, we made use both of a linear regression method and of a neural network-based technique to obtain optimal combined fitness scores and applied them to the ranking of candidate structures. Our results show that the use of SAXS data with gapless threading significantly improves the performance of fold recognition.

Prediction of protein solvent accessibility using fuzzy k-nearest neighbor method

MOTIVATION

The solvent accessibility of amino acid residues plays an important role in tertiary structure prediction, especially in the absence of significant sequence similarity of a query protein to those with known structures. The prediction of solvent accessibility is less accurate than secondary structure prediction in spite of improvements in recent researches. The k-nearest neighbor method, a simple but powerful classification algorithm, has never been applied to the prediction of solvent accessibility, although it has been used frequently for the classification of biological and medical data.

RESULTS

We applied the fuzzy k-nearest neighbor method to the solvent accessibility prediction, using PSI-BLAST profiles as feature vectors, and achieved high prediction accuracies. With leave-one-out cross-validation on the ASTRAL SCOP reference dataset constructed by sequence clustering, our method achieved 64.1% accuracy for a 3-state (buried/intermediate/exposed) prediction (thresholds of 9% for buried/intermediate and 36% for intermediate/exposed) and 86.7, 82.0, 79.0 and 78.5% accuracies for 2-state (buried/exposed) predictions (thresholds of each 0, 5, 16 and 25% for buried/exposed), respectively. Our method also showed slightly better accuracies than other methods by about 2-5% on the RS126 dataset and a benchmarking dataset with 229 proteins.

AVAILABILITY

Program and datasets are available at http://biocom1.ssu.ac.kr/FKNNacc/

CONTACT

jul@ssu.ac.kr.

Analysis of the "thermodynamic information content" of a Homo sapiens structural database reveals hierarchical thermodynamic organization

Classification of the amounts and types of lower order structural elements in proteins is a prerequisite to effective comparisons between protein folds. In an effort to provide an additional vehicle for fold comparison, we present an alternative classification scheme whereby protein folds are represented in statistical thermodynamic terms in such a way as to illuminate the energetic building blocks within protein structures. The thermodynamic relationship is examined between amino acid sequences and the conformational ensembles for a database of 159 Homo sapiens protein structures ranging from 50 to 250 amino acids. Using hierarchical clustering, it is shown through fold-recognition experiments that (1) eight thermodynamic environmental descriptors sufficiently accounts for the energetic variation within the native state ensembles of the H. sapiens structural database, (2) an amino acid library of only six residue types is sufficient to encode >90% of the thermodynamic information required for fold specificity in the entire database, and (3) structural resolution of the statistically derived environments reveals sequential cooperative segments throughout the protein, which are independent of secondary structure. As the first level of thermodynamic organization in proteins, these segments represent the thermodynamic counterpart to secondary structure.

Theoretical model of the three-dimensional structure of a sugar-binding protein from Pyrococcus horikoshii: structural analysis and sugar-binding simulations

The three-dimensional structure of a sugar-binding protein from the thermophilic archaea Pyrococcus horikoshii has been predicted by a homology modelling procedure and investigated for its stability and its ability to bind different sugars. The model was created by using as templates the three-dimensional structures of a maltodextrin-binding protein from Pyrococcus furiosus, a trehalose-maltose-binding protein from Thermococcus litoralis and a maltodextrin-binding protein from Escherichia coli. According to the suggestions from the CASP (Critical Assessment of Structure Prediction) meetings, the homology modelling strategy was applied by assessing an accurate multiple sequence alignment, based on the high structural conservation in the family of ATP-binding cassette transporters to which all these proteins belong. The model has been deposited in the Protein Data Bank with the code 1R25. According to the origin of the protein, several characteristics in the organization of the secondary-structure elements and in the distribution of polar and non-polar amino acids are very similar to those of thermophilic proteins, compared with proteins from mesophilic organisms, and are analysed in detail. Finally, a simulation of the binding of several sugars in the binding site of this protein is presented, and interactions with amino acids are highlighted in detail.

CooPPS: a system for the cooperative prediction of protein structures

Predicting the three-dimensional structure of proteins is a difficult task. In the last few years several approaches have been proposed for performing this task taking into account different protein chemical and physical properties. As a result, a growing number of protein structure prediction tools is becoming available, some of them specialized to work on either some aspects of the predictions or on some categories of proteins; however, they are still not sufficiently accurate and reliable for predicting all kinds of proteins. In this context, it is useful to jointly apply different prediction tools and combine their results in order to improve the quality of the predictions. However, several problems have to be solved in order to make this a viable possibility. In this paper a framework and a tool is proposed which allows: (i) definition of a common reference applicative domain for different prediction tools; (ii) characterization of prediction tools through evaluating some quality parameters; (iii) characterization of the performances of a team of predictors jointly applied over a prediction problem; (iv) the singling out of the best team for a prediction problem; and (v) the integration of predictor results in the team in order to obtain a unique prediction. A system implementing the various steps of the proposed framework (CooPPS) has been developed and several experiments for testing the effectiveness of the proposed approach have been carried out.

On the properties and sequence context of structurally ambivalent fragments in proteins

The goal of this work is to characterize structurally ambivalent fragments in proteins. We have searched the Protein Data Bank and identified all structurally ambivalent peptides (SAPs) of length five or greater that exist in two different backbone conformations. The SAPs were classified in five distinct categories based on their structure. We propose a novel index that provides a quantitative measure of conformational variability of a sequence fragment. It measures the context-dependent width of the distribution of (phi,xi) dihedral angles associated with each amino acid type. This index was used to analyze the local structural propensity of both SAPs and the sequence fragments contiguous to them. We also analyzed type-specific amino acid composition, solvent accessibility, and overall structural properties of SAPs and their sequence context. We show that each type of SAP has an unusual, type-specific amino acid composition and, as a result, simultaneous intrinsic preferences for two distinct types of backbone conformation. All types of SAPs have lower sequence complexity than average. Fragments that adopt helical conformation in one protein and sheet conformation in another have the lowest sequence complexity and are sampled from a relatively limited repertoire of possible residue combinations. A statistically significant difference between two distinct conformations of the same SAP is observed not only in the overall structural properties of proteins harboring the SAP but also in the properties of its flanking regions and in the pattern of solvent accessibility. These results have implications for protein design and structure prediction.

Single-body residue-level knowledge-based energy score combined with sequence-profile and secondary structure information for fold recognition

An elaborate knowledge-based energy function is designed for fold recognition. It is a residue-level single-body potential so that highly efficient dynamic programming method can be used for alignment optimization. It contains a backbone torsion term, a buried surface term, and a contact-energy term. The energy score combined with sequence profile and secondary structure information leads to an algorithm called SPARKS (Sequence, secondary structure Profiles and Residue-level Knowledge-based energy Score) for fold recognition. Compared with the popular PSI-BLAST, SPARKS is 21% more accurate in sequence-sequence alignment in ProSup benchmark and 10%, 25%, and 20% more sensitive in detecting the family, superfamily, fold similarities in the Lindahl benchmark, respectively. Moreover, it is one of the best methods for sensitivity (the number of correctly recognized proteins), alignment accuracy (based on the MaxSub score), and specificity (the average number of correctly recognized proteins whose scores are higher than the first false positives) in LiveBench 7 among more than twenty servers of non-consensus methods. The simple algorithm used in SPARKS has the potential for further improvement. This highly efficient method can be used for fold recognition on genomic scales. A web server is established for academic users on http://theory.med.buffalo.edu.

Improving Fold Recognition Without Folds

The most reliable way to align two proteins of unknown structure is through sequence-profile and profile-profile alignment methods. If the structure for one of the two is known, fold recognition methods outperform purely sequence-based alignments. Here, we introduced a novel method that aligns generalised sequence and predicted structure profiles. Using predicted 1D structure (secondary structure and solvent accessibility) significantly improved over sequence-only methods, both in terms of correctly recognising pairs of proteins with different sequences and similar structures and in terms of correctly aligning the pairs. The scores obtained by our generalised scoring matrix followed an extreme value distribution; this yielded accurate estimates of the statistical significance of our alignments. We found that mistakes in 1D structure predictions correlated between proteins from different sequence-structure families. The impact of this surprising result was that our method succeeded in significantly out-performing sequence-only methods even without explicitly using structural information from any of the two. Since AGAPE also outperformed established methods that rely on 3D information, we made it available through. If we solved the problem of CPU-time required to apply AGAPE on millions of proteins, our results could also impact everyday database searches.

The PredictProtein server

PredictProtein (http://www.predictprotein.org) is an Internet service for sequence analysis and the prediction of protein structure and function. Users submit protein sequences or alignments; PredictProtein returns multiple sequence alignments, PROSITE sequence motifs, low-complexity regions (SEG), nuclear localization signals, regions lacking regular structure (NORS) and predictions of secondary structure, solvent accessibility, globular regions, transmembrane helices, coiled-coil regions, structural switch regions, disulfide-bonds, sub-cellular localization and functional annotations. Upon request fold recognition by prediction-based threading, CHOP domain assignments, predictions of transmembrane strands and inter-residue contacts are also available. For all services, users can submit their query either by electronic mail or interactively via the World Wide Web.

An approach to large scale identification of non-obvious structural similarities between proteins

Background

A new sequence independent bioinformatics approach allowing genome-wide search for proteins with similar three dimensional structures has been developed. By utilizing the numerical output of the sequence threading it establishes putative non-obvious structural similarities between proteins. When applied to the testing set of proteins with known three dimensional structures the developed approach was able to recognize structurally similar proteins with high accuracy.

Results

The method has been developed to identify pathogenic proteins with low sequence identity and high structural similarity to host analogues. Such protein structure relationships would be hypothesized to arise through convergent evolution or through ancient horizontal gene transfer events, now undetectable using current sequence alignment techniques. The pathogen proteins, which could mimic or interfere with host activities, would represent candidate virulence factors.

The developed approach utilizes the numerical outputs from the sequence-structure threading. It identifies the potential structural similarity between a pair of proteins by correlating the threading scores of the corresponding two primary sequences against the library of the standard folds. This approach allowed up to 64% sensitivity and 99.9% specificity in distinguishing protein pairs with high structural similarity.

Conclusion

Preliminary results obtained by comparison of the genomes of Homo sapiens and several strains of Chlamydia trachomatis have demonstrated the potential usefulness of the method in the identification of bacterial proteins with known or potential roles in virulence.

Structural characterization of genomes by large scale sequence-structure threading

Background

Using sequence-structure threading we have conducted structural characterization of complete proteomes of 37 archaeal, bacterial and eukaryotic organisms (including worm, fly, mouse and human) totaling 167,888 genes.

Results

The reported data represent first rather general evaluation of performance of full sequence-structure threading on multiple genomes providing opportunity to evaluate its general applicability for large scale studies.

According to the estimated results the sequence-structure threading has assigned protein folds to more then 60% of eukaryotic, 68% of archaeal and 70% of bacterial proteomes.

The repertoires of protein classes, architectures, topologies and homologous superfamilies (according to the CATH 2.4 classification) have been established for distant organisms and superkingdoms. It has been found that the average abundance of CATH classes decreases from "alpha and beta" to "mainly beta", followed by "mainly alpha" and "few secondary structures".

3-Layer (aba) Sandwich has been characterized as the most abundant protein architecture and Rossman fold as the most common topology.

Conclusion

The analysis of genomic occurrences of CATH 2.4 protein homologous superfamilies and topologies has revealed the power-law character of their distributions. The corresponding double logarithmic "frequency – genomic occurrence" dependences characteristic of scale-free systems have been established for individual organisms and for three superkingdoms.

Supplementary materials to this works are available at [1].

Mimicking the action of folding chaperones in molecular dynamics simulations: Application to the refinement of homology-based protein structures

A novel method for the refinement of misfolded protein structures is proposed in which the properties of the solvent environment are oscillated in order to mimic some aspects of the role of molecular chaperones play in protein folding in vivo. Specifically, the hydrophobicity of the solvent is cycled by repetitively altering the partial charges on solvent molecules (water) during a molecular dynamics simulation. During periods when the hydrophobicity of the solvent is increased, intramolecular hydrogen bonding and secondary structure formation are promoted. During periods of increased solvent polarity, poorly packed regions of secondary structures are destabilized, promoting structural rearrangement. By cycling between these two extremes, the aim is to minimize the formation of long-lived intermediates. The approach has been applied to the refinement of structural models of three proteins generated by using the ROSETTA procedure for ab initio structure prediction. A significant improvement in the deviation of the model structures from the corresponding experimental structures was observed. Although preliminary, the results indicate computationally mimicking some functions of molecular chaperones in molecular dynamics simulations can promote the correct formation of secondary structure and thus be of general use in protein folding simulations and in the refinement of structural models of small- to medium-size proteins.

Features of the plasmid pMV158-encoded MobM, a protein involved in its mobilization

The streptococcal promiscuous plasmid pMV158 can be mobilized between a number of bacterial species by means of three elements: (i) the plasmid-encoded nicking-closing protein MobM, involved in the initiation and termination of the conjugative transfer; (ii) the DNA sequence where the MobM-mediated nick takes place (the oriT(pMV158)); and (iii) the function(s) provided by auxiliary plasmids. MobM belongs to the Pre/Mob family of plasmid-encoded DNA-relaxing proteins (relaxases). Purified MobM protein has been used to assay cleavage conditions on plasmid supercoiled DNA. Some structural features of MobM have been addressed by analytical ultracentrifugation, circular dichroism, thermal denaturation, and fluorescence emission. The protein behaved as a dimer of identical subunits with an ellipsoidal shape. MobM showed a high (about 60%) alpha-helical content and a midpoint denaturation of about 40 degrees C. Cell fractionation assays showed that MobM was associated to the cell membrane. This association was abolished when a great alteration was introduced within a putative coiled-coil located at the C-terminal region of the protein. Emission fluorescence suggested that the three Trp residues of MobM are located within a hydrophobic environment. A molecular model of MobM on the known structure of colicin Ia has been built.

Protein flexibility and intrinsic disorder

Comparisons were made among four categories of protein flexibility: (1) low-B-factor ordered regions, (2) high-B-factor ordered regions, (3) short disordered regions, and (4) long disordered regions. Amino acid compositions of the four categories were found to be significantly different from each other, with high-B-factor ordered and short disordered regions being the most similar pair. The high-B-factor (flexible) ordered regions are characterized by a higher average flexibility index, higher average hydrophilicity, higher average absolute net charge, and higher total charge than disordered regions. The low-B-factor regions are significantly enriched in hydrophobic residues and depleted in the total number of charged residues compared to the other three categories. We examined the predictability of the high-B-factor regions and developed a predictor that discriminates between regions of low and high B-factors. This predictor achieved an accuracy of 70% and a correlation of 0.43 with experimental data, outperforming the 64% accuracy and 0.32 correlation of predictors based solely on flexibility indices. To further clarify the differences between short disordered regions and ordered regions, a predictor of short disordered regions was developed. Its relatively high accuracy of 81% indicates considerable differences between ordered and disordered regions. The distinctive amino acid biases of high-B-factor ordered regions, short disordered regions, and long disordered regions indicate that the sequence determinants for these flexibility categories differ from one another, whereas the significantly-greater-than-chance predictability of these categories from sequence suggest that flexible ordered regions, short disorder, and long disorder are, to a significant degree, encoded at the primary structure level.

Strand-loop-strand motifs: Prediction of hairpins and diverging turns in proteins

Beta-sheet proteins have been particularly challenging for de novo structure prediction methods, which tend to pair adjacent beta-strands into beta-hairpins and produce overly local topologies. To remedy this problem and facilitate de novo prediction of beta-sheet protein structures, we have developed a neural network that classifies strand-loop-strand motifs by local hairpins and nonlocal diverging turns by using the amino acid sequence as input. The neural network is trained with a representative subset of the Protein Data Bank and achieves a prediction accuracy of 75.9 +/- 4.4% compared to a baseline prediction rate of 59.1%. Hairpins are predicted with an accuracy of 77.3 +/- 6.1%, diverging turns with an accuracy of 73.9 +/- 6.0%. Incorporation of the beta-hairpin/diverging turn classification into the ROSETTA de novo structure prediction method led to higher contact order models and somewhat improved tertiary structure predictions for a test set of 11 all-beta-proteins and 3 alphabeta-proteins. The beta-hairpin/diverging turn classification from amino acid sequences is available online for academic use (Meiler and Kuhn, 2003; www.jens-meiler.de/turnpred.html).

On the Role of Structural Information in Remote Homology Detection and Sequence Alignment: New Methods Using Hybrid Sequence Profiles

Structural alignments often reveal relationships between proteins that cannot be detected using sequence alignment alone. However, profile search methods based entirely on structural alignments alone have not been found to be effective in finding remote homologs. Here, we explore the role of structural information in remote homolog detection and sequence alignment. To this end, we develop a series of hybrid multidimensional alignment profiles that combine sequence, secondary and tertiary structure information into hybrid profiles. Sequence-based profiles are profiles whose position-specific scoring matrix is derived from sequence alignment alone; structure-based profiles are those derived from multiple structure alignments. We compare pure sequence-based profiles to pure structure-based profiles, as well as to hybrid profiles that use combined sequence-and-structure-based profiles, where sequence-based profiles are used in loop/motif regions and structural information is used in core structural regions. All of the hybrid methods offer significant improvement over simple profile-to-profile alignment. We demonstrate that both sequence-based and structure-based profiles contribute to remote homology detection and alignment accuracy, and that each contains some unique information. We discuss the implications of these results for further improvements in amino acid sequence and structural analysis.

New local potential useful for genome annotation and 3D modeling

A new potential energy function representing the conformational preferences of sequentially local regions of a protein backbone is presented. This potential is derived from secondary structure probabilities such as those produced by neural network-based prediction methods. The potential is applied to the problem of remote homolog identification, in combination with a distance-dependent inter-residue potential and position-based scoring matrices. This fold recognition jury is implemented in a Java application called JThread. These methods are benchmarked on several test sets, including one released entirely after development and parameterization of JThread. In benchmark tests to identify known folds structurally similar to (but not identical with) the native structure of a sequence, JThread performs significantly better than PSI-BLAST, with 10% more structures identified correctly as the most likely structural match in a fold library, and 20% more structures correctly narrowed down to a set of five possible candidates. JThread also improves the average sequence alignment accuracy significantly, from 53% to 62% of residues aligned correctly. Reliable fold assignments and alignments are identified, making the method useful for genome annotation. JThread is applied to predicted open reading frames (ORFs) from the genomes of Mycoplasma genitalium and Drosophila melanogaster, identifying 20 new structural annotations in the former and 801 in the latter.

Structural genomics: computational methods for structure analysis

The success of structural genomics initiatives requires the development and application of tools for structure analysis, prediction, and annotation. In this paper we review recent developments in these areas; specifically structure alignment, the detection of remote homologs and analogs, homology modeling and the use of structures to predict function. We also discuss various rationales for structural genomics initiatives. These include the structure-based clustering of sequence space and genome-wide function assignment. It is also argued that structural genomics can be integrated into more traditional biological research if specific biological questions are included in target selection strategies.

Target selection for structural genomics: a single genome approach

We describe our strategy for selecting targets for protein structure determination in context of structural genomics of a single genome. In the course of target selection, we have studied two of the smallest microbial genomes, Mycoplasma genitalium and Mycoplasma pneumoniae. To our surprise, we found that only 71 Mycoplasma genes or their orthologues can be considered as easy targets for high-throughput structural studies--far fewer than expected. We discuss the methods and criteria used for target selection and the reasons explaining rarity of easy targets. First, despite the common opinion that protein folds can be predicted for only 30-50% of genes, the number of "truly unknown" structures is less than one-third. Second, due to the different codon usage, two thirds of Mycoplasma proteins cannot be directly expressed in E. coli in high-throughput manner and require substitution by their homologues from other organisms. Third, membrane or large multi-domain proteins are difficult targets because of solubility and size issues and often require identification and structure determination of protein domains. Finally, we propose different approaches to address the difficult targets.

Structure-based prediction of potential binding and nonbinding peptides to HIV-1 protease

HIV-1 protease is a major drug target against AIDS as it permits viral maturation by processing the gag and pol polyproteins of the virus. The cleavage sites in these polyproteins do not have obvious sequence homology or a binding motif and the specificity of the protease is not easily determined. We used various threading approaches, together with the crystal structures of substrate complexes which served as template structures, to study the substrate specificity of HIV-1 protease with the aim of obtaining a better differentiation between binding and nonbinding sequences. The predictions from threading improved when distance-dependent interaction energy functions were used instead of contact matrices. To rank the peptides and properly account for the peptide's conformation in the total energy, the results from using short-range potentials on multiple template structures were averaged. Finally, a dynamic threading approach is introduced which is potentially useful for cases when there is only one template structure available. The conformational energy of the peptide-especially the term accounting for the side chains-was found to be important in differentiating between binding and nonbinding sequences. Hence, the substrate specificity, and thus the ability of the virus to mature, is affected by the compatibility of the substrate peptide to fit within the limited conformational space of the active site groove.

The Wnt signaling inhibitor dickkopf-1 is required for reentry into the cell cycle of human adult stem cells from bone marrow

Adult human mesenchymal stem cells from bone marrow stroma (hMSCs) differentiate into numerous mesenchymal tissue lineages and are attractive candidates for cell and gene therapy. When early passage hMSCs are plated or replated at low density, the cultures display a lag phase of 3-5 days, a phase of rapid exponential growth, and then enter a stationary phase without the cultures reaching confluence. We found that as the cultures leave the lag phase, they secrete high levels of dickkopf-1 (Dkk-1), an inhibitor of the canonical Wnt signaling pathway. The addition of recombinant Dkk-1 toward the end of the lag period increased proliferation and decreased the cellular concentration of beta-catenin. The addition of antibodies to Dkk-1 in the early log phase decreased proliferation. Also, expression of Dkk-1 in hMSCs decreased during cell cycle arrest induced by serum starvation. The results indicated that high levels of Dkk-1 allow the cells to reenter the cell cycle by inhibiting the canonical Wnt/beta-catenin signaling pathway. Since antibodies to Dkk-1 also increased the lag phase of an osteosarcoma line that expressed the gene, Dkk-1 may have a similar role in some other cell systems.

DSSPcont: Continuous secondary structure assignments for proteins

The DSSP program automatically assigns the secondary structure for each residue from the three-dimensional co-ordinates of a protein structure to one of eight states. However, discrete assignments are incomplete in that they cannot capture the continuum of thermal fluctuations. Therefore, DSSPcont (http://cubic.bioc.columbia.edu/services/DSSPcont) introduces a continuous assignment of secondary structure that replaces 'static' by 'dynamic' states. Technically, the continuum results from calculating weighted averages over 10 discrete DSSP assignments with different hydrogen bond thresholds. A DSSPcont assignment for a particular residue is a percentage likelihood of eight secondary structure states, derived from a weighted average of the ten DSSP assignments. The continuous assignments have two important features: (i) they reflect the structural variations due to thermal fluctuations as detected by NMR spectroscopy; and (ii) they reproduce the structural variation between many NMR models from one single model. Therefore, functionally important variation can be extracted from a single X-ray structure using the continuous assignment procedure.

The PredictProtein server

PredictProtein (PP, http://cubic.bioc.columbia.edu/pp/) is an internet service for sequence analysis and the prediction of aspects of protein structure and function. Users submit protein sequence or alignments; the server returns a multiple sequence alignment, PROSITE sequence motifs, low-complexity regions (SEG), ProDom domain assignments, nuclear localisation signals, regions lacking regular structure and predictions of secondary structure, solvent accessibility, globular regions, transmembrane helices, coiled-coil regions, structural switch regions and disulfide-bonds. Upon request, fold recognition by prediction-based threading is available. For all services, users can submit their query either by electronic mail or interactively from World Wide Web.

Hidden Markov models that use predicted local structure for fold recognition: alphabets of backbone geometry

An important problem in computational biology is predicting the structure of the large number of putative proteins discovered by genome sequencing projects. Fold-recognition methods attempt to solve the problem by relating the target proteins to known structures, searching for template proteins homologous to the target. Remote homologs that may have significant structural similarity are often not detectable by sequence similarities alone. To address this, we incorporated predicted local structure, a generalization of secondary structure, into two-track profile hidden Markov models (HMMs). We did not rely on a simple helix-strand-coil definition of secondary structure, but experimented with a variety of local structure descriptions, following a principled protocol to establish which descriptions are most useful for improving fold recognition and alignment quality. On a test set of 1298 nonhomologous proteins, HMMs incorporating a 3-letter STRIDE alphabet improved fold recognition accuracy by 15% over amino-acid-only HMMs and 23% over PSI-BLAST, measured by ROC-65 numbers. We compared two-track HMMs to amino-acid-only HMMs on a difficult alignment test set of 200 protein pairs (structurally similar with 3-24% sequence identity). HMMs with a 6-letter STRIDE secondary track improved alignment quality by 62%, relative to DALI structural alignments, while HMMs with an STR track (an expanded DSSP alphabet that subdivides strands into six states) improved by 40% relative to CE.

TRASH: a novel metal-binding domain predicted to be involved in heavy-metal sensing, trafficking and resistance

We describe a previously undetected domain - TRASH - containing a well-conserved cysteine motif that we anticipate to be involved in metal coordination. TRASH is encoded by multiple prokaryotic genomes and is present in transcriptional regulators, cation-transporting ATPases and hydrogenases, and is also present as a stand-alone module. The observed domain associations and conserved genome context of TRASH-encoding genes in prokaryotic genomes suggest that TRASH constitutes a novel component in metal trafficking and heavy-metal resistance. The role of the multiple copies of TRASH that are present in vertebrate proteins remains to be elucidated.

Ancient conserved domains shared by animal soluble guanylyl cyclases and bacterial signaling proteins

BACKGROUND

Soluble guanylyl cyclases (SGCs) are dimeric enzymes that transduce signals downstream of nitric oxide (NO) in animals. They sense NO by means of a heme moiety that is bound to their N-terminal extensions.

RESULTS

Using sequence profile searches we show that the N-terminal extensions of the SGCs contain two globular domains. The first of these, the HNOB (Heme NO Binding) domain, is a predominantly alpha-helical domain and binds heme via a covalent linkage to histidine. Versions lacking this conserved histidine and are likely to interact with heme non-covalently. We detected HNOB domains in several bacterial lineages, where they occur fused to methyl accepting domains of chemotaxis receptors or as standalone proteins. The standalone forms are encoded by predicted operons that also contain genes for two component signaling systems and GGDEF-type nucleotide cyclases. The second domain, the HNOB associated (HNOBA) domain occurs between the HNOB and the cyclase domains in the animal SGCs. The HNOBA domain is also detected in bacteria and is always encoded by a gene, which occurs in the neighborhood of a gene for a HNOB domain.

CONCLUSION

The HNOB domain is predicted to function as a heme-dependent sensor for gaseous ligands, and transduce diverse downstream signals, in both bacteria and animals. The HNOBA domain functionally interacts with the HNOB domain, and possibly binds a ligand, either in cooperation, or independently of the latter domain. Phyletic profiles and phylogenetic analysis suggest that the HNOB and HNOBA domains were acquired by the animal lineage via lateral transfer from a bacterial source.

Bioinformatics methods to predict protein structure and function. A practical approach

Protein structure prediction by using bioinformatics can involve sequence similarity searches, multiple sequence alignments, identification and characterization of domains, secondary structure prediction, solvent accessibility prediction, automatic protein fold recognition, constructing three-dimensional models to atomic detail, and model validation. Not all protein structure prediction projects involve the use of all these techniques. A central part of a typical protein structure prediction is the identification of a suitable structural target from which to extrapolate three-dimensional information for a query sequence. The way in which this is done defines three types of projects. The first involves the use of standard and well-understood techniques. If a structural template remains elusive, a second approach using nontrivial methods is required. If a target fold cannot be reliably identified because inconsistent results have been obtained from nontrivial data analyses, the project falls into the third type of project and will be virtually impossible to complete with any degree of reliability. In this article, a set of protocols to predict protein structure from sequence is presented and distinctions among the three types of project are given. These methods, if used appropriately, can provide valuable indicators of protein structure and function.

The directional atomic solvation energy: an atom-based potential for the assignment of protein sequences to known folds

The Directional Atomic Solvation EnergY (DASEY) is an atom-based description of the environment of an amino acid position within a known 3D protein structure. The DASEY has been developed to align and score a probe amino acid sequence to a library of template protein structures for fold assignment. DASEY is computed by summing the atomic solvation parameters of atoms falling within a tetrahedral sector, or petal, extending 16 A along each of the four bond axes of each alpha-carbon atom of the protein. The DASEY discriminates between pairs of structurally equivalent positions and random pairs in protein structures sharing a fold but belonging to different superfamilies, unlike some previous descriptors of protein environments, such as buried area. Furthermore, the DASEY values have characteristic patterns of residue replacement, an essential feature of a successful fold assignment method. Benchmarking fold assignment with DASEY achieves coverage of 56% of sequences with 90% accuracy when probe sequences are matched to protein structural templates belonging to the same fold but to a different superfamily, an improvement of greater than 200% over a previous method.

Toward predicting protein topology: an approach to identifying beta hairpins

Although secondary structure prediction methods have recently improved, progress from secondary to tertiary structure prediction has been limited. A promising but largely unexplored route to this goal is to predict structure motifs from secondary structure knowledge. Here we present a novel method for the recognition of beta hairpins that combines secondary structure predictions and threading methods by using a database search and a neural network approach. The method successfully predicts 48 and 77%, respectively, of all of hairpin and nonhairpin beta-coil-beta motifs in a protein database. We find that the main contributors to motif recognition are predicted accessibility and turn propensities.

Structural Modelling of the Sm-like Protein Hfq from Escherichia coli

The Hfq polypeptide of Escherichia coli is a nucleic acid-binding protein involved in the expression of many proteins. Derivation of its three-dimensional structure is important for our understanding of its role in gene regulation at the molecular level. In this study, we combined computational and biophysical analysis to derive a possible structure for Hfq. As a first step towards determining the structure, we searched for possible sequence-structure compatibility, using secondary structure prediction and protein domain and fold-recognition methods available on the WEB. One fold, essentially beta sheet in character, the Sm motif of small nuclear ribonucleoproteins, even though it initially fell well below the confidence thresholds, was proposed and further validated by a series of biophysical and biochemical studies. The Hfq hexamer structure was modelled on the human Sm D3B structure using optimised sequence alignments and molecular mechanics methods. This structure accounts for the physico-chemical properties of Hfq and highlights amino acid residues that could interact with RNA.

A critical assessment of the secondary structure alpha-helices and their termini in proteins

Secondary structure prediction from amino acid sequence is a key component of protein structure prediction, with current accuracy at approximately 75%. We analysed two state-of-the-art secondary structure prediction methods, PHD and JPRED, comparing predictions with secondary structure assigned by the algorithms DSSP and STRIDE. The specific focus of our study was alpha-helix N-termini, as empirical free energy scales are available for residue preferences at N-terminal positions. Although these prediction methods perform well in general at predicting the alpha-helical locations and length distributions in proteins, they perform less well at predicting the correct helical termini. For example, although most predicted alpha-helices overlap a real alpha-helix (with relatively few completely missed or extra predicted helices), only one-third of JPRED and PHD predictions correctly identify the N-terminus. Analysis of neighbouring N-terminal sequences to predicted helical N-termini shows that the correct N-terminus is often within one or two residues. More importantly, the true N-terminal motif is, on average, more favourable as judged by our experimentally measured free energies. This suggests a simple, but powerful, strategy to improve secondary structure prediction using empirically derived energies to adjust the predicted output to a more favourable N-terminal sequence.

Monophyly of class I aminoacyl tRNA synthetase, USPA, ETFP, photolyase, and PP-ATPase nucleotide-binding domains: implications for protein evolution in the RNA

Protein sequence and structure comparisons show that the catalytic domains of Class I aminoacyl-tRNA synthetases, a related family of nucleotidyltransferases involved primarily in coenzyme biosynthesis, nucleotide-binding domains related to the UspA protein (USPA domains), photolyases, electron transport flavoproteins, and PP-loop-containing ATPases together comprise a distinct class of alpha/beta domains designated the HUP domain after HIGH-signature proteins, UspA, and PP-ATPase. Several lines of evidence are presented to support the monophyly of the HUP domains, to the exclusion of other three-layered alpha/beta folds with the generic "Rossmann-like" topology. Cladistic analysis, with patterns of structural and sequence similarity used as discrete characters, identified three major evolutionary lineages within the HUP domain class: the PP-ATPases; the HIGH superfamily, which includes class I aaRS and related nucleotidyltransferases containing the HIGH signature in their nucleotide-binding loop; and a previously unrecognized USPA-like group, which includes USPA domains, electron transport flavoproteins, and photolyases. Examination of the patterns of phyletic distribution of distinct families within these three major lineages suggests that the Last Universal Common Ancestor of all modern life forms encoded 15-18 distinct alpha/beta ATPases and nucleotide-binding proteins of the HUP class. This points to an extensive radiation of HUP domains before the last universal common ancestor (LUCA), during which the multiple class I aminoacyl-tRNA synthetases emerged only at a late stage. Thus, substantial evolutionary diversification of protein domains occurred well before the modern version of the protein-dependent translation machinery was established, i.e., still in the RNA world.

Protein sequence threading: Averaging over structures

Multiple sequence alignments are a routine tool in protein fold recognition, but multiple structure alignments are computationally less cooperative. This work describes a method for protein sequence threading and sequence-to-structure alignments that uses multiple aligned structures, the aim being to improve models from protein threading calculations. Sequences are aligned into a field due to corresponding sites in homologous proteins. On the basis of a test set of more than 570 protein pairs, the procedure does improve alignment quality, although no more than averaging over sequences. For the force field tested, the benefit of structure averaging is smaller than that of adding sequence similarity terms or a contribution from secondary structure predictions. Although there is a significant improvement in the quality of sequence-to-structure alignments, this does not directly translate to an immediate improvement in fold recognition capability.

Fold recognition without folds

Fold recognition predicts protein three-dimensional structure by establishing relationships between a protein sequence and known protein structures. Most methods explicitly use information derived from the secondary and tertiary structure of the templates. Here we show that rigorous application of a sequence search method (PSI-BLAST) with no reference to secondary or tertiary structure information is able to perform as well as traditional fold recognition methods. Since the method, SENSER, does not require knowledge of the three-dimensional structure, it can be used to infer relationships that are not tractable by methods dependent on structural templates.

C-terminal domain of gyrase A is predicted to have a beta-propeller structure

Two different type II topoisomerases are known in bacteria. DNA gyrase (Gyr) introduces negative supercoils into DNA. Topoisomerase IV (Par) relaxes DNA supercoils. GyrA and ParC subunits of bacterial type II topoisomerases are involved in breakage and reunion of DNA. The spatial structure of the C-terminal fragment in GyrA/ParC is not available. We infer homology between the C-terminal domain of GyrA/ParC and a regulator of chromosome condensation (RCC1), a eukaryotic protein that functions as a guanine-nucleotide-exchange factor for the nuclear G protein Ran. This homology, complemented by detection of 6 sequence repeats with 4 predicted beta-strands each in GyrA/ParC sequences, allows us to predict that the GyrA/ParC C-terminal domain folds into a 6-bladed beta-propeller. The prediction rationalizes available experimental data and sheds light on the spatial properties of the largest topoisomerase domain that lacks structural information.

Classification and evolutionary history of the single-strand annealing proteins, RecT, Redbeta, ERF and RAD52

BACKGROUND

The DNA single-strand annealing proteins (SSAPs), such as RecT, Redbeta, ERF and Rad52, function in RecA-dependent and RecA-independent DNA recombination pathways. Recently, they have been shown to form similar helical quaternary superstructures. However, despite the functional similarities between these diverse SSAPs, their actual evolutionary affinities are poorly understood.

RESULTS

Using sensitive computational sequence analysis, we show that the RecT and Redbeta proteins, along with several other bacterial proteins, form a distinct superfamily. The ERF and Rad52 families show no direct evolutionary relationship to these proteins and define novel superfamilies of their own. We identify several previously unknown members of each of these superfamilies and also report, for the first time, bacterial and viral homologs of Rad52. Additionally, we predict the presence of aberrant HhH modules in RAD52 that are likely to be involved in DNA-binding. Using the contextual information obtained from the analysis of gene neighborhoods, we provide evidence of the interaction of the bacterial members of each of these SSAP superfamilies with a similar set of DNA repair/recombination protein. These include different nucleases or Holliday junction resolvases, the ABC ATPase SbcC and the single-strand-binding protein. We also present evidence of independent assembly of some of the predicted operons encoding SSAPs and in situ displacement of functionally similar genes.

CONCLUSIONS

There are three evolutionarily distinct superfamilies of SSAPs, namely the RecT/Redbeta, ERF, and RAD52, that have different sequence conservation patterns and predicted folds. All these SSAPs appear to be primarily of bacteriophage origin and have been acquired by numerous phylogenetically distant cellular genomes. They generally occur in predicted operons encoding one or more of a set of conserved DNA recombination proteins that appear to be the principal functional partners of the SSAPs.

Improved detection of homologous membrane proteins by inclusion of information from topology predictions

A total of 20%-25% of the proteins in a typical genome are helical membrane proteins. The transmembrane regions of these proteins have markedly different properties when compared with globular proteins. This presents a problem when homology search algorithms optimized for globular proteins are applied to membrane proteins. Here we present modifications of the standard Smith-Waterman and profile search algorithms that significantly improve the detection of related membrane proteins. The improvement is based on the inclusion of information about predicted transmembrane segments in the alignment algorithm. This is done by simply increasing the alignment score if two residues predicted to belong to transmembrane segments are aligned with each other. Benchmarking over a test set of G-protein-coupled receptor sequences shows that the number of false positives is significantly reduced in this way, both when closely related and distantly related proteins are searched for.

Studies in the assessment of folding quality for protein modeling and structure prediction

A diagnostic for assessing the quality of a fold has been developed to which further criteria can be progressively added. The goal is to create a measure that can follow the status of a protein structure in a simulation or modeling process, when the answer (the experimental structure) is not known in advance, rather than simply reject deliberate misfolds. This places greater emphasis on the need to study, and calibrate against, marginal cases, i.e., unusual native structures, incomplete structures, partially erroneous X-ray structures, good models, poor models, and the effect of cofactors. The first three terms introduced in the diagnostic are appropriate core-forming properties or noncore properties of residues in relation to tertiary structure, appropriate neighboring structure density for each residue in relation to tertiary structure, and secondary structure consistency. While the method emerges as a useful simulation analysis tool, we find a need for further fine-tuning to diminish sensitivity to minor conformational changes that retain essential features of the fold, balanced against the need to obtain a more sensitive response when a conformational change involves less physically meaningful interatomic interactions. This dual utility is difficult to obtain: the investigation highlights some of the issues. Initial attempts to obtain it have led to terms in the diagnostic that are admittedly complex: simplifications must also be explored.