Structural and evolutionary classification of Type II restriction enzymes based on theoretical and experimental analyses

For a very long time, Type II restriction enzymes (REases) have been a paradigm of ORFans: proteins with no detectable similarity to each other and to any other protein in the database, despite common cellular and biochemical function. Crystallographic analyses published until January 2008 provided high-resolution structures for only 28 of 1637 Type II REase sequences available in the Restriction Enzyme database (REBASE). Among these structures, all but two possess catalytic domains with the common PD-(D/E)XK nuclease fold. Two structures are unrelated to the others: R.BfiI exhibits the phospholipase D (PLD) fold, while R.PabI has a new fold termed 'half-pipe'. Thus far, bioinformatic studies supported by site-directed mutagenesis have extended the number of tentatively assigned REase folds to five (now including also GIY-YIG and HNH folds identified earlier in homing endonucleases) and provided structural predictions for dozens of REase sequences without experimentally solved structures. Here, we present a comprehensive study of all Type II REase sequences available in REBASE together with their homologs detectable in the nonredundant and environmental samples databases at the NCBI. We present the summary and critical evaluation of structural assignments and predictions reported earlier, new classification of all REase sequences into families, domain architecture analysis and new predictions of three-dimensional folds. Among 289 experimentally characterized (not putative) Type II REases, whose apparently full-length sequences are available in REBASE, we assign 199 (69%) to contain the PD-(D/E)XK domain. The HNH domain is the second most common, with 24 (8%) members. When putative REases are taken into account, the fraction of PD-(D/E)XK and HNH folds changes to 48% and 30%, respectively. Fifty-six characterized (and 521 predicted) REases remain unassigned to any of the five REase folds identified so far, and may exhibit new architectures. These enzymes are proposed as the most interesting targets for structure determination by high-resolution experimental methods. Our analysis provides the first comprehensive map of sequence-structure relationships among Type II REases and will help to focus the efforts of structural and functional genomics of this large and biotechnologically important class of enzymes.     

A model of restriction endonuclease MvaI in complex with DNA: a template for interpretation of experimental data and a guide for specificity engineering

R.MvaI is a Type II restriction enzyme (REase), which specifically recognizes the pentanucleotide DNA sequence 5'-CCWGG-3' (W indicates A or T). It belongs to a family of enzymes, which recognize related sequences, including 5'-CCSGG-3' (S indicates G or C) in the case of R.BcnI, or 5'-CCNGG-3' (where N indicates any nucleoside) in the case of R.ScrFI. REases from this family hydrolyze the phosphodiester bond in the DNA between the 2nd and 3rd base in both strands, thereby generating a double strand break with 5'-protruding single nucleotides. So far, no crystal structures of REases with similar cleavage patterns have been solved. Characterization of sequence-structure-function relationships in this family would facilitate understanding of evolution of sequence specificity among REases and could aid in engineering of enzymes with new specificities. However, sequences of R.MvaI or its homologs show no significant similarity to any proteins with known structures, thus precluding straightforward comparative modeling. We used a fold recognition approach to identify a remote relationship between R.MvaI and the structure of DNA repair enzyme MutH, which belongs to the PD-(D/E)XK superfamily together with many other REases. We constructed a homology model of R.MvaI and used it to predict functionally important amino acid residues and the mode of interaction with the DNA. In particular, we predict that only one active site of R.MvaI interacts with the DNA target at a time, and the cleavage of both strands (5'-CCAGG-3' and 5'-CCTGG-3') is achieved by two independent catalytic events. The model is in good agreement with the available experimental data and will serve as a template for further analyses of R.MvaI, R.BcnI, R.ScrFI and other related enzymes.     

Inference of relationships in the 'twilight zone' of homology using a combination of bioinformatics and site-directed mutagenesis: a case study of restriction endonucleases Bsp6I and PvuII

Thus far, identification of functionally important residues in Type II restriction endonucleases (REases) has been difficult using conventional methods. Even though known REase structures share a fold and marginally recognizable active site, the overall sequence similarities are statistically insignificant, unless compared among proteins that recognize identical or very similar sequences. Bsp6I is a Type II REase, which recognizes the palindromic DNA sequence 5′GCNGC and cleaves between the cytosine and the unspecified nucleotide in both strands, generating a double-strand break with 5′-protruding single nucleotides. There are no solved structures of REases that recognize similar DNA targets or generate cleavage products with similar characteristics. In straightforward comparisons, the Bsp6I sequence shows no significant similarity to REases with known structures. However, using a fold-recognition approach, we have identified a remote relationship between Bsp6I and the structure of PvuII. Starting from the sequence–structure alignment between Bsp6I and PvuII, we constructed a homology model of Bsp6I and used it to predict functionally significant regions in Bsp6I. The homology model was supported by site-directed mutagenesis of residues predicted to be important for dimerization, DNA binding and catalysis. Completing the picture of sequence–structure–function relationships in protein superfamilies becomes an essential task in the age of structural genomics and our study may serve as a paradigm for future analyses of superfamilies comprising strongly diverged members with little or no sequence similarity.     

I-Ssp6803I: the first homing endonuclease from the PD-(D/E)XK superfamily exhibits an unusual mode of DNA recognition

MOTIVATION: Restriction endonucleases (REases) and homing endonucleases (HEases) are biotechnologically important enzymes. Nearly all structurally characterized REases belong to the PD-(D/E)XK superfamily of nucleases, while most HEases belong to an unrelated LAGLIDADG superfamily. These two protein folds are typically associated with very different modes of protein-DNA recognition, consistent with the different mechanisms of action required to achieve high specificity. REases recognize short DNA sequences using multiple contacts per base pair, while HEases recognize very long sites using a few contacts per base pair, thereby allowing for partial degeneracy of the target sequence. Thus far, neither REases with the LAGLIDADG fold, nor HEases with the PD-(D/E)XK fold, have been found. RESULTS: Using protein fold recognition, we have identified the first member of the PD-(D/E)XK superfamily among homing endonucleases, a cyanobacterial enzyme I-Ssp6803I. We present a model of the I-Ssp6803I-DNA complex based on the structure of Type II restriction endonuclease R.BglI and predict the active site and residues involved in specific DNA sequence recognition by I-Ssp6803I. Our finding reveals a new unexpected evolutionary link between HEases and REases and suggests how PD-(D/E)XK nucleases may develop a 'HEase-like' way of interacting with the extended DNA sequence. This in turn may be exploited to study the evolution of DNA sequence specificity and to engineer nucleases with new substrate specificities.     

Identification of GATC- and CCGG-recognizing Type II REases and their putative specificity-determining positions using Scan2S--a novel motif scan algorithm with optional secondary structure constraints

Restriction endonucleases (REases) are DNA-cleaving enzymes that have become indispensable tools in molecular biology. Type II REases are highly divergent in sequence despite their common structural core, function and, in some cases, common specificities towards DNA sequences. This makes it difficult to identify and classify them functionally based on sequence, and has hampered the efforts of specificity-engineering. Here, we define novel REase sequence motifs, which extend beyond the PD-(D/E)XK hallmark, and incorporate secondary structure information. The automated search using these motifs is carried out with a newly developed fast regular expression matching algorithm that accommodates long patterns with optional secondary structure constraints. Using this new tool, named Scan2S, motifs derived from REases with specificity towards GATC- and CGGG-containing DNA sequences successfully identify REases of the same specificity. Notably, some of these sequences are not identified by standard sequence detection tools. The new motifs highlight potential specificity-determining positions that do not fully overlap for the GATC- and the CCGG-recognizing REases and are candidates for specificity re-engineering.     

Type II restriction endonuclease R.Hpy188I belongs to the GIY-YIG nuclease superfamily,but exhibits an unusual active site

Background  

Catalytic domains of Type II restriction endonucleases (REases) belong to a few unrelated three-dimensional folds. While the PD-(D/E)XK fold is most common among these enzymes, crystal structures have been also determined for single representatives of two other folds: PLD (R.BfiI) and half-pipe (R.PabI). Bioinformatics analyses supported by mutagenesis experiments suggested that some REases belong to the HNH fold (e.g. R.KpnI), and that a small group represented by R.Eco29kI belongs to the GIY-YIG fold. However, for a large fraction of REases with known sequences, the three-dimensional fold and the architecture of the active site remain unknown, mostly due to extreme sequence divergence that hampers detection of homology to enzymes with known folds.     

A homology model of restriction endonuclease SfiI in complex with DNA

Background

Restriction enzymes (REases) are commercial reagents commonly used in recombinant DNA technologies. They are attractive models for studying protein-DNA interactions and valuable targets for protein engineering. They are, however, extremely divergent: the amino acid sequence of a typical REase usually shows no detectable similarities to any other proteins, with rare exceptions of other REases that recognize identical or very similar sequences. From structural analyses and bioinformatics studies it has been learned that some REases belong to at least four unrelated and structurally distinct superfamilies of nucleases, PD-DxK, PLD, HNH, and GIY-YIG. Hence, they are extremely hard targets for structure prediction and homology-based inference of sequence-function relationships and the great majority of REases remain structurally and evolutionarily unclassified.

Results

SfiI is a REase which recognizes the interrupted palindromic sequence 5'GGCCNNNN^NGGCC3' and generates 3 nt long 3' overhangs upon cleavage. SfiI is an archetypal Type IIF enzyme, which functions as a tetramer and cleaves two copies of the recognition site in a concerted manner. Its sequence shows no similarity to other proteins and nothing is known about the localization of its active site or residues important for oligomerization. Using the threading approach for protein fold-recognition, we identified a remote relationship between SfiI and BglI, a dimeric Type IIP restriction enzyme from the PD-DxK superfamily of nucleases, which recognizes the 5'GCCNNNN^NGGC3' sequence and whose structure in complex with the substrate DNA is available. We constructed a homology model of SfiI in complex with its target sequence and used it to predict residues important for dimerization, tetramerization, DNA binding and catalysis.

Conclusions

The bioinformatics analysis suggest that SfiI, a Type IIF enzyme, is more closely related to BglI, an "orthodox" Type IIP restriction enzyme, than to any other REase, including other Type IIF REases with known structures, such as NgoMIV. NgoMIV and BglI belong to two different, very remotely related branches of the PD-DxK superfamily: the α-class (EcoRI-like), and the β-class (EcoRV-like), respectively. Thus, our analysis provides evidence that the ability to tetramerize and cut the two DNA sequences in a concerted manner was developed independently at least two times in the evolution of the PD-DxK superfamily of REases. The model of SfiI will also serve as a convenient platform for further experimental analyses.     

Identification of related proteins on family, superfamily and fold level

Proteins might have considerable structural similarities even when no evolutionary relationship of their sequences can be detected. This property is often referred to as the proteins sharing only a "fold". Of course, there are also sequences of common origin in each fold, called a "superfamily", and in them groups of sequences with clear similarities, designated "family". Developing algorithms to reliably identify proteins related at any level is one of the most important challenges in the fast growing field of bioinformatics today. However, it is not at all certain that a method proficient at finding sequence similarities performs well at the other levels, or vice versa.Here, we have compared the performance of various search methods on these different levels of similarity. As expected, we show that it becomes much harder to detect proteins as their sequences diverge. For family related sequences the best method gets 75% of the top hits correct. When the sequences differ but the proteins belong to the same superfamily this drops to 29%, and in the case of proteins with only fold similarity it is as low as 15%. We have made a more complete analysis of the performance of different algorithms than earlier studies, also including threading methods in the comparison. Using this method a more detailed picture emerges, showing multiple sequence information to improve detection on the two closer levels of relationship. We have also compared the different methods of including this information in prediction algorithms.For lower specificities, the best scheme to use is a linking method connecting proteins through an intermediate hit. For higher specificities, better performance is obtained by PSI-BLAST and some procedures using hidden Markov models. We also show that a threading method, THREADER, performs significantly better than any other method at fold recognition.     

Classification and phylogeny for the annotation of novel eukaryotic GNAT acetyltransferases

The enzymes of the GCN5-related N-acetyltransferase (GNAT) superfamily count more than 870 000 members through all kingdoms of life and share the same structural fold. GNAT enzymes transfer an acyl moiety from acyl coenzyme A to a wide range of substrates including aminoglycosides, serotonin, glucosamine-6-phosphate, protein N-termini and lysine residues of histones and other proteins. The GNAT subtype of protein N-terminal acetyltransferases (NATs) alone targets a majority of all eukaryotic proteins stressing the omnipresence of the GNAT enzymes. Despite the highly conserved GNAT fold, sequence similarity is quite low between members of this superfamily even when substrates are similar. Furthermore, this superfamily is phylogenetically not well characterized. Thus functional annotation based on sequence similarity is unreliable and strongly hampered for thousands of GNAT members that remain biochemically uncharacterized. Here we used sequence similarity networks to map the sequence space and propose a new classification for eukaryotic GNAT acetyltransferases. Using the new classification, we built a phylogenetic tree, representing the entire GNAT acetyltransferase superfamily. Our results show that protein NATs have evolved more than once on the GNAT acetylation scaffold. We use our classification to predict the function of uncharacterized sequences and verify by in vitro protein assays that two fungal genes encode NAT enzymes targeting specific protein N-terminal sequences, showing that even slight changes on the GNAT fold can lead to change in substrate specificity. In addition to providing a new map of the relationship between eukaryotic acetyltransferases the classification proposed constitutes a tool to improve functional annotation of GNAT acetyltransferases.     

A novel approach to fold recognition using sequence-derived properties from sets of structurally similar local fragments of proteins

Comparative modeling methods can consistently produce reliable structural models for protein sequences with more than 25% sequence identity to proteins with known structure. However, there is a good chance that also sequences with lower sequence identity have their structural components represented in structural databases. To this end, we present a novel fragment-based method using sets of structurally similar local fragments of proteins. The approach differs from other fragment-based methods that use only single backbone fragments. Instead, we use a library of groups containing sets of sequence fragments with geometrically similar local structures and extract sequence related properties to assign these specific geometrical conformations to target sequences. We test the ability of the approach to recognize correct SCOP folds for 273 sequences from the 49 most popular folds. 49% of these sequences have the correct fold as their top prediction, while 82% have the correct fold in one of the top five predictions. Moreover, the approach shows no performance reduction on a subset of sequence targets with less than 10% sequence identity to any protein used to build the library.     

Restriction endonucleases: natural and directed evolution

Type II restriction endonucleases (REs) are highly sequence-specific compared with other classes of nucleases. PD-(D/E)XK nucleases, initially represented by only type II REs, now comprise a large and extremely diverse superfamily of proteins and, although sharing a structurally conserved core, typically display little or no detectable sequence similarity except for the active site motifs. Sequence similarity can only be observed in methylases and few isoschizomers. As a consequence, REs are classified according to combinations of functional properties rather than on the basis of genetic relatedness. New alignment matrices and classification systems based on structural core connectivity and cleavage mechanisms have been developed to characterize new REs and related proteins. REs recognizing more than 300 distinct specificities have been identified in RE database (REBASE: ) but still the need for newer specificities is increasing due to the advancement in molecular biology and applications. The enzymes have undergone constant evolution through structural changes in protein scaffolds which include random mutations, homologous recombinations, insertions, and deletions of coding DNA sequences but rational mutagenesis or directed evolution delivers protein variants with new functions in accordance with defined biochemical or environmental pressures. Redesigning through random mutation, addition or deletion of amino acids, methylation-based selection, synthetic molecules, combining recognition and cleavage domains from different enzymes, or combination with domains of additional functions change the cleavage specificity or substrate preference and stability. There is a growing number of patents awarded for the creation of engineered REs with new and enhanced properties.     

Topology of Type II REases revisited; structural classes and the common conserved core

Type II restriction endonucleases (REases) are deoxyribonucleases that cleave DNA sequences with remarkable specificity. Type II REases are highly divergent in sequence as well as in topology, i.e. the connectivity of secondary structure elements. A widely held assumption is that a structural core of five beta-strands flanked by two alpha-helices is common to these enzymes. We introduce a systematic procedure to enumerate secondary structure elements in an unambiguous and reproducible way, and use it to analyze the currently available X-ray structures of Type II REases. Based on this analysis, we propose an alternative definition of the core, which we term the alphabetaalpha-core. The alphabetaalpha-core includes the most frequently observed secondary structure elements and is not a sandwich, as it consists of a five-strand beta-sheet and two alpha-helices on the same face of the beta-sheet. We use the alphabetaalpha-core connectivity as a basis for grouping the Type II REases into distinct structural classes. In these new structural classes, the connectivity correlates with the angles between the secondary structure elements and with the cleavage patterns of the REases. We show that there exists a substructure of the alphabetaalpha-core, namely a common conserved core, ccc, defined here as one alpha-helix and four beta-strands common to all Type II REase of known structure.     

Fold independent structural comparisons of protein-ligand binding sites for exploring functional relationships

The rapid growth in protein structural data and the emergence of structural genomics projects have increased the need for automatic structure analysis and tools for function prediction. Small molecule recognition is critical to the function of many proteins; therefore, determination of ligand binding site similarity is important for understanding ligand interactions and may allow their functional classification. Here, we present a binding sites database (SitesBase) that given a known protein-ligand binding site allows rapid retrieval of other binding sites with similar structure independent of overall sequence or fold similarity. However, each match is also annotated with sequence similarity and fold information to aid interpretation of structure and functional similarity. Similarity in ligand binding sites can indicate common binding modes and recognition of similar molecules, allowing potential inference of function for an uncharacterised protein or providing additional evidence of common function where sequence or fold similarity is already known. Alternatively, the resource can provide valuable information for detailed studies of molecular recognition including structure-based ligand design and in understanding ligand cross-reactivity. Here, we show examples of atomic similarity between superfamily or more distant fold relatives as well as between seemingly unrelated proteins. Assignment of unclassified proteins to structural superfamiles is also undertaken and in most cases substantiates assignments made using sequence similarity. Correct assignment is also possible where sequence similarity fails to find significant matches, illustrating the potential use of binding site comparisons for newly determined proteins.     

Protein homology detection and fold inference through multiple alignment entropy profiles

Homology detection and protein structure prediction are central themes in bioinformatics. Establishment of relationship between protein sequences or prediction of their structure by sequence comparison methods finds limitations when there is low sequence similarity. Recent works demonstrate that the use of profiles improves homology detection and protein structure prediction. Profiles can be inferred from protein multiple alignments using different approaches. The "Conservatism-of-Conservatism" is an effective profile analysis method to identify structural features between proteins having the same fold but no detectable sequence similarity. The information obtained from protein multiple alignments varies according to the amino acid classification employed to calculate the profile. In this work, we calculated entropy profiles from PSI-BLAST-derived multiple alignments and used different amino acid classifications summarizing almost 500 different attributes. These entropy profiles were converted into pseudocodes which were compared using the FASTA program with an ad-hoc matrix. We tested the performance of our method to identify relationships between proteins with similar fold using a nonredundant subset of sequences having less than 40% of identity. We then compared our results using Coverage Versus Error per query curves, to those obtained by methods like PSI-BLAST, COMPASS and HHSEARCH. Our method, named HIP (Homology Identification with Profiles) presented higher accuracy detecting relationships between proteins with the same fold. The use of different amino acid classifications reflecting a large number of amino acid attributes, improved the recognition of distantly related folds. We propose the use of pseudocodes representing profile information as a fast and powerful tool for homology detection, fold assignment and analysis of evolutionary information enclosed in protein profiles.     

cDNA sequence of adrenodoxin reductase. Identification of NADP-binding sites in oxidoreductases

Adrenodoxin reductase is an NADP dependent flavoenzyme which functions as the reductase of mitochondrial P 450 systems. We sequenced two adrenodoxin reductase cDNAs isolated from a bovine adrenal cortex cDNA library. The deduced amino acid sequence shows no similarity to the sequence of the microsomal P 450 systems or other known protein sequences. Nonetheless, by sequence analysis and c comparisons with known sequences of dinucleotide-binding folds of two NADP-binding flavoenzymes, two regions of adrenodoxin reductase sequence were identified as the FAD- and NADP-binding sites. These analyses revealed a consensus sequence for the NADP-binding dinucleotide fold (GXGXXAXXXAXXXXXXG, in one-letter amino acid code) that differs from FAD and NAD-binding dinucleotide-fold sequences. In the data base of protein sequences, the NADP-binding-site sequence appears solely in NADP-dependent enzymes, the binding sites of which were not known to date. Thus, this sequence may be used for identification of a certain type of NADP-binding site of enzymes that show no significant sequence similarity.     

Annotating nucleic acid-binding function based on protein structure

Many of the targets of structural genomics will be proteins with little or no structural similarity to those currently in the database. Therefore, novel function prediction methods that do not rely on sequence or fold similarity to other known proteins are needed. We present an automated approach to predict nucleic-acid-binding (NA-binding) proteins, specifically DNA-binding proteins. The method is based on characterizing the structural and sequence properties of large, positively charged electrostatic patches on DNA-binding protein surfaces, which typically coincide with the DNA-binding-sites. Using an ensemble of features extracted from these electrostatic patches, we predict DNA-binding proteins with high accuracy. We show that our method does not rely on sequence or structure homology and is capable of predicting proteins of novel-binding motifs and protein structures solved in an unbound state. Our method can also distinguish NA-binding proteins from other proteins that have similar, large positive electrostatic patches on their surfaces, but that do not bind nucleic acids.     

Expanding the nitrogen regulatory protein superfamily: Homology detection at below random sequence identity

Nitrogen regulatory (PII) proteins are signal transduction molecules involved in controlling nitrogen metabolism in prokaryots. PII proteins integrate the signals of intracellular nitrogen and carbon status into the control of enzymes involved in nitrogen assimilation. Using elaborate sequence similarity detection schemes, we show that five clusters of orthologs (COGs) and several small divergent protein groups belong to the PII superfamily and predict their structure to be a (betaalphabeta)(2) ferredoxin-like fold. Proteins from the newly emerged PII superfamily are present in all major phylogenetic lineages. The PII homologs are quite diverse, with below random (as low as 1%) pairwise sequence identities between some members of distant groups. Despite this sequence diversity, evidence suggests that the different subfamilies retain the PII trimeric structure important for ligand-binding site formation and maintain a conservation of conservations at residue positions important for PII function. Because most of the orthologous groups within the PII superfamily are composed entirely of hypothetical proteins, our remote homology-based structure prediction provides the only information about them. Analogous to structural genomics efforts, such prediction gives clues to the biological roles of these proteins and allows us to hypothesize about locations of functional sites on model structures or rationalize about available experimental information. For instance, conserved residues in one of the families map in close proximity to each other on PII structure, allowing for a possible metal-binding site in the proteins coded by the locus known to affect sensitivity to divalent metal ions. Presented analysis pushes the limits of sequence similarity searches and exemplifies one of the extreme cases of reliable sequence-based structure prediction. In conjunction with structural genomics efforts to shed light on protein function, our strategies make it possible to detect homology between highly diverse sequences and are aimed at understanding the most remote evolutionary connections in the protein world.     

Evolution and similarity evaluation of protein structures in contact map space

Prediction of fold from amino acid sequence of a protein has been an active area of research in the past few years, but the limited accuracy of existing techniques emphasizes the need to develop newer approaches to tackle this task. In this study, we use contact map prediction as an intermediate step in fold prediction from sequence. Contact map is a reduced graph-theoretic representation of proteins that models the local and global inter-residue contacts in the structure. We start with a population of random contact maps for the protein sequence and "evolve" the population to a "high-feasibility" configuration using a genetic algorithm. A neural network is employed to assess the feasibility of contact maps based on their 4 physically relevant properties. We also introduce 5 parameters, based on algebraic graph theory and physical considerations, that can be used to judge the structural similarity between proteins through contact maps. To predict the fold of a given amino acid sequence, we predict a contact map that will sufficiently approximate the structure of the corresponding protein. Then we assess the similarity of this contact map with the representative contact map of each fold; the fold that corresponds to the closest match is our predicted fold for the input sequence. We have found that our feasibility measure is able to differentiate between feasible and infeasible contact maps. Further, this novel approach is able to predict the folds from sequences significantly better than a random predictor.     

Structure-based prediction of DNA target sites by regulatory proteins

Regulatory proteins play a critical role in controlling complex spatial and temporal patterns of gene expression in higher organism, by recognizing multiple DNA sequences and regulating multiple target genes. Increasing amounts of structural data on the protein-DNA complex provides clues for the mechanism of target recognition by regulatory proteins. The analyses of the propensities of base-amino acid interactions observed in those structural data show that there is no one-to-one correspondence in the interaction, but clear preferences exist. On the other hand, the analysis of spatial distribution of amino acids around bases shows that even those amino acids with strong base preference such as Arg with G are distributed in a wide space around bases. Thus, amino acids with many different geometries can form a similar type of interaction with bases. The redundancy and structural flexibility in the interaction suggest that there are no simple rules in the sequence recognition, and its prediction is not straightforward. However, the spatial distributions of amino acids around bases indicate a possibility that the structural data can be used to derive empirical interaction potentials between amino acids and bases. Such information extracted from structural databases has been successfully used to predict amino acid sequences that fold into particular protein structures. We surmised that the structures of protein-DNA complexes could be used to predict DNA target sites for regulatory proteins, because determining DNA sequences that bind to a particular protein structure should be similar to finding amino acid sequences that fold into a particular structure. Here we demonstrate that the structural data can be used to predict DNA target sequences for regulatory proteins. Pairwise potentials that determine the interaction between bases and amino acids were empirically derived from the structural data. These potentials were then used to examine the compatibility between DNA sequences and the protein-DNA complex structure in a combinatorial "threading" procedure. We applied this strategy to the structures of protein-DNA complexes to predict DNA binding sites recognized by regulatory proteins. To test the applicability of this method in target-site prediction, we examined the effects of cognate and noncognate binding, cooperative binding, and DNA deformation on the binding specificity, and predicted binding sites in real promoters and compared with experimental data. These results show that target binding sites for several regulatory proteins are successfully predicted, and our data suggest that this method can serve as a powerful tool for predicting multiple target sites and target genes for regulatory proteins.