Protein structure: what is it possible to predict now?

Comparative modelling techniques: where are we?

The enormous increase in data availability brought about by genomic projects is paralleled by an equally unprecedented increase in the expectations for new medical, pharmacological, environmental and biotechnological discoveries. Whether or not we will be able to meet (at least partially) these expectations will depend on how well we will be able to interpret the data and translate the mono-dimensional information encrypted in genomes into a detailed understanding of its biological meaning at the phenotypic level. The process is far from being trivial, and the obstacles along the road are formidable: even the problem of identifying coding regions in eukaryotic genomes is not completely solved. Far more complex is identification of the function of the encoded proteins, and this will probably represent the most challenging problem for the next generations of scientists.

Accessibility and partner number of protein residues, their relationship and a webserver, ContPlot for their display

Background

Depending on chemical features residues have preferred locations – interior or exterior – in protein structures, which also determine how many other residues are found around them. The close packing of residues is the hallmark of protein interior and protein-protein interaction sites.

Results

The average values of accessible surface area (ASA) and partner number (PN, the number of other residues within a distance of 4.5 Å from any atom of a given residue) of different residues have been determined and a webserver, ContPlot has been designed to display these values (relative to the average values) along the protein sequence. This would be useful to visually identify residues that are densely packed, or those involved in protein-protein interactions. The skewness observed in the distribution of PNs is indicative of the hydrophobic or hydrophilic nature of the residue. The variation of ASA with PN can be analytically expressed in terms of a cubic equation. These equations (one for each residue) can be used to estimate the ASA of a polypeptide chain using the PNs of the individual residues in the structure.

Conclusion

The atom-based PNs (obtained by counting surrounding atoms) are highly correlated to the residue-based PN, indicating that the latter can adequately capture the atomic details of packing. The average values of ASA and PN associated with each residue should be useful in protein structure prediction or fold-recognition algorithm. ContPlot would provide a handy tool to assess the importance of a residue in the protein structure or interaction site.

Prediction of protein thermostability with a direction- and distance-dependent knowledge-based potential

The increasing use of enzymes in industrial processes and the importance of understanding protein folding and stability have led to several attempts to predict and quantify the effect of every possible amino acid exchange (mutation) on the thermostability of proteins. In this article we describe a knowledge-based discrimination function that acts as a fast and reliable guide in protein engineering and optimization. The function used consists of two parts, a pairwise energy function based on a distance- and direction-dependent atomic description of the amino acid environment, and a torsion angle energy function. In a first step a training set of 11 proteins including 646 mutant proteins with experimentally determined thermostability was used to optimize the knowledge-based energy functions. The resulting potential function was then tested using a test mutant database consisting of 918 various point mutations introduced in 27 proteins. The best correlation coefficient obtained for the experimental data and the predicted thermostability for the training set is r = 0.81 (561 data points). A total of 76% of the mutations could be predicted correctly as being either stabilizing or destabilizing. The results for the test set are r = 0.74 (747 data points) and 72%, respectively. The global correlation over the combined data (1308 mutants) obtained is 0.78.

Exploiting evolutionary relationships for predicting protein structures

In the last few years there have been many developments in computational biology, particularly with regard to novel, imaginative exploitation of genomic data. Disappointingly, there has been a lack of progress in the methodology for prediction of protein structures. In the last several years, however, promising new methods have finally begun to emerge. These methods are increasing the power and scope of the methodology, but, most importantly, they are generating new areas of investigation that we believe will accelerate progress in the field. In this review we describe recent developments and highlight the implications of their success as well as areas where efforts should be focused.

Fold recognition with minimal gaps

Here we present a simplified form of threading that uses only a 20 x 20 two-body residue-based potential and restricted number of gaps. Despite its simplicity and transparency the Monte Carlo-based threading algorithm performs very well in a rigorous test of fold recognition. The results suggest that by simplifying and constraining the decoy space, one can achieve better fold recognition. Fold recognition results are compared with and supplemented by a PSI-BLAST search. The statistical significance of threading results is rigorously evaluated from statistics of extremes by comparison with optimal alignments of a large set of randomly shuffled sequences. The statistical theory, based on the Random Energy Model, yields a cumulative statistical parameter, epsilon, that attests to the likelihood of correct fold recognition. A large epsilon indicates a significant energy gap between the optimal alignment and decoy alignments and, consequently, a high probability that the fold is correctly recognized. For a particular number of gaps, the epsilon parameter reaches its maximal value, and the fold is recognized. As the number of gaps further increases, the likelihood of correct fold recognition drops off. This is because the decoy space is small when gaps are restricted to a small number, but the native alignment is still well approximated, whereas unrestricted increase of the number of gaps leads to rapid growth of the number of decoys and their statistical dominance over the correct alignment. It is shown that best results are obtained when a combination of one-, two-, and three-gap threading is used. To this end, use of the epsilon parameter is crucial for rigorous comparison of results across the different decoy spaces belonging to a different number of gaps.

Nonatomic solvent-driven Voronoi tessellation of proteins: an open tool to analyze protein folds

A three-dimensional Voronoi tessellation of folded proteins is used to analyze geometrical and topological properties of a set of proteins. To each amino acid is associated a central point surrounded by a Voronoi cell. Voronoi cells describe the packing of the amino acids. Special attention is given to reproduction of the protein surface. Once the Voronoi cells are built, a lot of tools from geometrical analysis can be applied to investigate the protein structure; volume of cells, number of faces per cell, and number of sides per face are the usual signatures of the protein structure. A distinct difference between faces related to primary, secondary, and tertiary structures has been observed. Faces threaded by the main-chain have on average more than six edges, whereas those related to helical packing of the amino acid chain have less than five edges. The faces on the protein surface have on average five edges within 1% error. The average number of faces on the protein surface for a given type of amino acid brings a new point of view in the characterization of the exposition to the solvent and the classification of amino acid as hydrophilic or hydrophobic. It may be a convenient tool for model validation.

Enzyme function less conserved than anticipated

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

Automated assignment of SCOP and CATH protein structure classifications from FSSP scores

We present an automated procedure to assign CATH and SCOP classifications to proteins whose FSSP score is available. CATH classification is assigned down to the topology level, and SCOP classification is assigned to the fold level. Because the FSSP database is updated weekly, this method makes it possible to update also CATH and SCOP with the same frequency. Our predictions have a nearly perfect success rate when ambiguous cases are discarded. These ambiguous cases are intrinsic in any protein structure classification that relies on structural information alone. Hence, we introduce the "twilight zone for structure classification." We further suggest that to resolve these ambiguous cases, other criteria of classification, based also on information about sequence and function, must be used.

Use of structure comparison methods for the refinement of protein structure predictions. I. Identifying the structural family of a protein from low-resolution models

Predicting the three-dimensional structure of proteins is still one of the most challenging problems in molecular biology. Despite its difficulty, several investigators have started to produce consistently low-resolution predictions for small proteins. However, in most of these cases, the prediction accuracy is still too low to make them useful. In the present article, we address the problem of obtaining better-quality predictions, starting from low-resolution models. To this end, we have devised a new procedure that uses these models, together with structure comparison methods, to identify the structural family of the target protein. This would allow, in a second step not described in the present work, to refine the predictions using conserved features of the identified family. In our approach, the structure database is investigated using predictions, at different accuracy levels, for a given protein. As query structures, we used both low-resolution versions of the native structures, as well as different sets of low accuracy predictions. In general, we found that for predictions with a resolution of > or =5-7 A, structure comparison methods were able to identify the fold of a protein in the top positions.

The prediction of the structural class of protein: application of the measure of diversity

Based on the concept that the structural class of a protein is mainly determined by its secondary structure sequence, a new algorithm for prediction of the structural class of a protein is proposed. By use of the number of alpha -helices, beta -strands, and betaalphabeta fragments, the structural class of a protein can be predicted by an algorithm based on the increment of diversity (ID), in which the sole prediction parameter-the increment of diversity is used as the index of prediction of structural class of a protein. The results indicate that the high rates of correct prediction are obtained for complete set (standard set) from Brookhaven Protein Data Bank-CD ROM (PDB) published in October 1995 and the test set newly released from Brookhaven Protein Data Bank-CD ROM (PDB) before July 1998, respectively.

PRED-CLASS: cascading neural networks for generalized protein classification and genome-wide applications

A cascading system of hierarchical, artificial neural networks (named PRED-CLASS) is presented for the generalized classification of proteins into four distinct classes-transmembrane, fibrous, globular, and mixed-from information solely encoded in their amino acid sequences. The architecture of the individual component networks is kept very simple, reducing the number of free parameters (network synaptic weights) for faster training, improved generalization, and the avoidance of data overfitting. Capturing information from as few as 50 protein sequences spread among the four target classes (6 transmembrane, 10 fibrous, 13 globular, and 17 mixed), PRED-CLASS was able to obtain 371 correct predictions out of a set of 387 proteins (success rate approximately 96%) unambiguously assigned into one of the target classes. The application of PRED-CLASS to several test sets and complete proteomes of several organisms demonstrates that such a method could serve as a valuable tool in the annotation of genomic open reading frames with no functional assignment or as a preliminary step in fold recognition and ab initio structure prediction methods. Detailed results obtained for various data sets and completed genomes, along with a web sever running the PRED-CLASS algorithm, can be accessed over the World Wide Web at http://o2.biol.uoa.gr/PRED-CLASS.

Fold recognition and accurate query-template alignment by a combination of PSI-BLAST and threading

A homology-based structure prediction method ideally gives both a correct fold assignment and an accurate query-template alignment. In this article we show that the combination of two existing methods, PSI-BLAST and threading, leads to significant enhancement in the success rate of fold recognition. The combined approach, termed COBLATH, also yields much higher alignment accuracy than found in previous studies. It consists of two-way searches both by PSI-BLAST and by threading. In the PSI-BLAST portion, a query is used to search for hits in a library of potential templates and, conversely, each potential template is used to search for hits in a library of queries. In the threading portion, the scoring function is the sum of a sequence profile and a 6x6 substitution matrix between predicted query and known template secondary structure and solvent exposure. "Two-way" in threading means that the query's sequence profile is used to match the sequences of all potential templates and the sequence profiles of all potential templates are used to match the query's sequence. When tested on a set of 533 nonhomologous proteins, COBLATH was able to assign folds for 390 (73%). Among these 390 queries, 265 (68%) had root-mean-square deviations (RMSDs) of less than 8 A between predicted and actual structures. Such high success rate and accuracy make COBLATH an ideal tool for structural genomics.

A structural model for the rolA protein and its interaction with DNA

The study of the plant oncogene rolA has been hampered by a lack of structural information. Here we show that, despite a lack of significant sequence similarity to proteins of known structure, the rolA sequence adopts a known fold; that of the papillomavirus E2 DNA-binding domain. This fold is reliably identified by modern threading programs, which consider predicted secondary structure, but not by others. Although the rolA sequence is only around 16% identical to those of the available template structures, a structural model could be built that performed well against protein structure verification programs. The adopted strategy involved alignment corrections, justified by multiple model building and evaluation, with particular attention paid to the hydrophobic core residues. We find that rolA protein is predicted to resemble the template proteins in two key aspects; existence as a dimer and ability to bind DNA. rolA protein has recently been shown experimentally to possess DNA binding ability. This model predicts Lys 24 and Arg 27 to be involved in sequence-specific interactions and eight other residues to hydrogen-bond phosphate groups of the DNA.

Folding Lennard-Jones proteins by a contact potential

We studied the possibility to approximate a Lennard-Jones interaction by a pairwise contact potential. First we used a Lennard-Jones potential to design off-lattice, protein-like heteropolymer sequences, whose lowest energy (native) conformations were then identified by molecular dynamics. Then we turned to investigate whether one can find a pairwise contact potential, whose ground states are the contact maps associated with these native conformations. We show that such a requirement cannot be satisfied exactly, i.e., no such contact parameters exist. Nevertheless, we found that one can find contact energy parameters for which an energy minimization procedure, acting in the space of contact maps, yields maps whose corresponding structures are close to the native ones. Finally, we show that when these structures are used as the initial point of a molecular dynamics energy minimization process, the correct native folds are recovered with high probability.

An antigenic HIV-1 peptide sequence engineered into the surface structure of transferrin does not elicit an antibody response

One novel approach for the biological delivery of peptide drugs is to incorporate the sequence of the peptide into the structure of a natural transport protein such as human serum transferrin (HST). However, a potential drawback is that the HST may increase the immunoreactivity of the peptide, in the same way that carrier proteins can be used to generate highly immunogenic peptide hapten conjugates. In this study we have generated a recombinant HST carrier protein that contains a peptide substrate of HIV-1 protease (VSQNYPIVL). The protein retained native HST function, and the peptide was surface exposed since it was immunoreactive in native dot blots, and was cleaved by HIV-1 protease. Immunisation of rabbits with the recombinant protein elicited only a very poor anti-peptide immune response. In contrast, strong anti-peptide immune responses were raised against both the peptide alone, and a chemical conjugate of the peptide with HST. These data demonstrate that it is possible to attenuate the immune response normally directed against an immunogenic peptide sequence by engineering into a surface exposed loop of HST. These findings may have an important impact on the future design of peptide delivery systems.

Transferrin trojan horses as a rational approach for the biological delivery of therapeutic peptide domains

One novel approach for the biological delivery of peptide drugs is to incorporate the sequence of the peptide into the structure of a natural transport protein, such as human serum transferrin. To examine whether this is feasible, a peptide sequence cleavable by the human immunodeficiency virus type 1 protease (VSQNYPIVL) was inserted into various regions of human serum transferrin, and the resultant proteins were tested for function. Experimentally, molecular modeling was used to identify five candidate insertion sites in surface exposed loops of human serum transferrin that were distant from biologically active domains. These insertions were cloned using polymerase chain reaction mutagenesis, and the proteins were expressed using a baculovirus expression vector system. Analysis of the mutant proteins provided a number of important findings: (a) they retained native human serum transferrin function, (b) the inserted peptide sequence was surface exposed, and most importantly, (c) two of these mutants could be cleaved by human immunodeficiency virus-1 protease. In conclusion, this investigation has validated the use of human serum transferrin as a carrier protein for functional peptide domains introduced into its structure using protein engineering. These findings will be useful for developing a novel class of therapeutic agents for a broad spectrum of diseases.

Discrimination of the native from misfolded protein models with an energy function including implicit solvation

An essential requirement for theoretical protein structure prediction is an energy function that can discriminate the native from non-native protein conformations. To date most of the energy functions used for this purpose have been extracted from a statistical analysis of the protein structure database, without explicit reference to the physical interactions responsible for protein stability. The use of the statistical functions has been supported by the widespread belief that they are superior for such discrimination to physics-based energy functions. An effective energy function which combined the CHARMM vacuum potential with a Gaussian model for the solvation free energy is tested for its ability to discriminate the native structure of a protein from misfolded conformations; the results are compared with those obtained with the vacuum CHARMM potential. The test is performed on several sets of misfolded structures prepared by others, including sets of about 650 good decoys for six proteins, as well as on misfolded structures of chymotrypsin inhibitor 2. The vacuum CHARMM potential is successful in most cases when energy minimized conformations are considered, but fails when applied to structures relaxed by molecular dynamics. With the effective energy function the native state is always more stable than grossly misfolded conformations both in energy minimized and molecular dynamics-relaxed structures. The present results suggest that molecular mechanics (physics-based) energy functions, complemented by a simple model for the solvation free energy, should be tested for use in the inverse folding problem, and supports their use in studies of the effective energy surface of proteins in solution. Moreover, the study suggests that the belief in the superiority of statistical functions for these purposes may be ill founded.

Thermodynamics of interactions between amino acid side chains: experimental differentiation of aromatic-aromatic, aromatic-aliphatic, and aliphatic-aliphatic side-chain interactions in water

A stationary phase for high-pressure liquid chromatography has been prepared by derivatizing microparticulate silica gel with functionality mimicking the side chain of isoleucine. The chromatographic retentions of a series of hydrophobic and amphiphilic amino acid analytes on this stationary phase (Ile MSP) using an aqueous mobile phase were measured as a function of temperature from 273 K to 323 K. Observed temperature dependencies are consistent with a constant change in heat capacity, DeltaC degrees P, upon binding of the analyte to the stationary phase. The curvatures of plots of retention data versus temperature (related to the magnitude of DeltaC degrees P) are distinctly different for retention of aromatic and aliphatic analytes, with retention of aliphatic analytes Val, Ile, and Leu exhibiting the characteristic signature of the hydrophobic effect, i.e., a large negative DeltaC degrees P upon desolvation from water and a maximum of retention around room temperature. Retention of aromatic analytes (Trp, Phe, and Tyr) involves smaller heat capacity changes and pronounced negative enthalpies of interaction with the stationary phase. Estimates of DeltaC degrees P for the interactions of analyte side chains with the Ile side chain were obtained by fitting the temperature dependence of retention to an expression derived from thermodynamic considerations and chromatographic theory. Similar estimates were made for interactions with the Phe side chain, using previously published data for a phenylalanine mimic stationary phase (Phe MSP) (. Protein Sci. 1:786-795). As with the Ile MSP, the retentions of aliphatic analytes show temperature dependencies markedly different from those of aromatic analytes. Data from both phases indicate that a realistic differentiation can be made between the interactions of various types of amino acid side chains tested (i.e., aliphatic/aliphatic, aliphatic/aromatic, and aromatic/aromatic) by comparison of the corresponding thermodynamic functions for pairwise interactions. The retention of leucine on the Phe MSP and that of phenylalanine on the Ile MSP showed similar DeltaC degrees P values, suggesting that the aromatic-aliphatic interaction is reasonably independent of the residue attached to the stationary phase. This result is consistent with a one-to-one interaction and suggests a simple way to estimate the column-dependent phase factor, making it possible to compare entropies and free energies of interaction obtained using different MSPs. The possibilities for using MSP-derived interaction potentials in folding simulations are discussed.

Disordered C-terminal domain of tyrosyl-tRNA synthetase: secondary structure prediction

The C-terminal domain (residues 320-419) of tyrosyl-tRNA synthetase (TyrRS) from Bacillus stearothermophilus is disordered in the crystal structure and involved in the binding of the anticodon arm of tRNA(Tyr). The sequences of 11 TyrRSs of prokaryotic or mitochondrial origins were aligned and the alignment showed the existence of conserved residues in the sequences of the C-terminal domains. A consensus could be deduced from the application of five programs of secondary structure prediction to the 11 sequences of the query set. These results suggested that the sequences of the C-terminal domains determined a precise and conserved secondary structure. They predicted that the C-terminal domain would have a mixed fold (alpha/beta or alpha+beta), with the alpha-helices in the first half of the sequence and the beta-strands mainly in its second half. Several programs of fold recognition from sequence alone, by threading onto known structures, were applied but none of them identified a type of fold that would be common to the different sequences of the query set. Therefore, the fold of the C-terminal, anticodon binding domain might be novel.

Protein structure prediction and design

Proteins have a unique native conformation, which can be proven in many instances to be determined by the amino acid sequence alone. The folding problem, that is the understanding of how the amino acid sequence directs folding, is still unsolved, despite more than 30 years of effort. However, many new methods have appeared in the past few years. This chapter describes the different principles underlying them and tries to give an overview of their successes and pitfalls.

Protein structure prediction by threading. Why it works and why it does not

We developed a novel Monte Carlo threading algorithm which allows gaps and insertions both in the template structure and threaded sequence. The algorithm is able to find the optimal sequence-structure alignment and sample suboptimal alignments. Using our algorithm we performed sequence-structure alignments for a number of examples for three protein folds (ubiquitin, immunoglobulin and globin) using both "ideal" set of potentials (optimized to provide the best Z-score for a given protein) and more realistic knowledge-based potentials. Two physically different scenarios emerged. If a template structure is similar to the native one (within 2 A RMS), then (i) the optimal threading alignment is correct and robust with respect to deviations of the potential from the "ideal" one; (ii) suboptimal alignments are very similar to the optimal one; (iii) as Monte Carlo temperature decreases a sharp cooperative transition to the optimal alignment is observed. In contrast, if the template structure is only moderately close to the native structure (RMS greater than 3.5 A), then (i) the optimal alignment changes dramatically when an "ideal" potential is substituted by the real one; (ii) the structures of suboptimal alignments are very different from the optimal one, reducing the reliability of the alignment; (iii) the transition to the apparently optimal alignment is non-cooperative. In the intermediate cases when the RMS between the template and the native conformations is in the range between 2 A and 3.5 A, the success of threading alignment may depend on the quality of potentials used. These results are rationalized in terms of a threading free energy landscape. Possible ways to overcome the fundamental limitations of threading are discussed briefly.

How homologs can help to predict protein folds even though they cannot be predicted for individual sequences

At present, one cannot predict the 3D structure of a protein directly from its sequence alone mainly because of errors in the energy estimates. However, a recently developed simple analytical theory (Finkelstein, 1998) shows that using a set of homologs (i.e., chains with numerous amino acid mutations but with equal 3D folds) one can average the interaction energies over the homologs and predict their common 3D fold even when predictions for individual sequences are wrong because the energy parameters are known only approximately. In this work we verify this theoretical conclusion by computer simulations performed with simplified models of protein chains.

Thinking about a nuclear matrix

The possible existence in eukaryotic cells of an internal, non-chromatin nuclear structural framework that facilitates gene readout as a set of spatially concerted reactions has become a popular but controversial theater of investigation. This article endeavors to present a circumspect review of the nuclear matrix concept as we presently know it, framed around two contrasting hypotheses: (1) that an internal nuclear framework actively enhances gene expression (in much the same way the cytoskeleton mediates cell locomotion, mitosis and intracellular vesicular traffic) versus (2) that the interphase chromosomes have fixed, inherited positions and that the DNA replication, transcripton and RNA processing machinery diffusionally arrives at sites of gene readout, with some aspects of nuclear structure thus being more a result than a cause of gene expression. On balance, the available information suggests that interactions among various gene expression machines may contribute to isolated nuclear matrix preparations. Some components of isolated nuclear matrix preparations may also reflect induced or reconfigured protein-protein associations. The protein characterization and ultrastructural analysis of the isolated nuclear matrix has advanced significantly in recent years, although controversies remain. Important new clues are now coming in from promising contemporary lines of research that report on nuclear structure in living cells.

A free energy analysis by unfolding applied to 125-mers on a cubic lattice

BACKGROUND

A common approach to the protein folding problem involves computer simulation of folding using lattice models of amino acid sequences. Key factors for good performance in such models are the correct choice of the temperature and the average interaction energy between residues. In order to push the lattice approach to its limit it is important to have a method to adjust these parameters for optimal folding that is not limited by our ability to successfully simulate folding in a reasonable time.

RESULTS

In this study, we adopt a simple cubic-lattice model and present a method for calculating the free energy of a chain as a function of the number of native contacts. This does not require that we are able to fold the sequence by simulation and it provides a method of estimating the folding transition temperature. For a given set of parameters, the free energy analysis also allows an estimate of foldability. By applying the method to sequences with 27 and 125 residues, we show that optimal folding occurs near the folding transition temperature and at either zero or small negative average interaction energy. We find ourselves able to fold only 125-mers that have significant short-range native contacts.

CONCLUSIONS

A free energy analysis during unfolding is a useful tool for the study of foldability and should be applicable to a variety of folding models. In this way we are able to fold some 125-mer designed sequences and our results confirm the finding that short-range contacts contribute to foldability.

A measure of success in fold recognition

Prediction of protein structure by fold recognition, or threading, was recently put to the test in a 'blind' structure prediction experiment, CASP2. Thirty-two teams from around the world participated, preparing predictions for 22 different 'target' proteins whose structures were soon to be determined. As experimental structures became available, we, as organizers of the threading competition, computed objective measures of fold-recognition specificity and model accuracy, to identify and characterize successful predictions. Here, we present a brief summary of these prediction evaluations, a tally of 'correct' predictions and a discussion of factors associated with correct predictions. We find that threading produced specific recognition and accurate models whenever the structural database contained a template spanning a large fraction of target sequence. Presence of conserved sequence motifs was helpful, but not required, and it would appear that threading can succeed whenever similarity to a known structure is sufficiently extensive.