Dynamic domain threading

Direct correlation analysis improves fold recognition

The extraction of correlated mutations through the method of direct information (DI) provides predicted contact residue pairs that can be used to constrain the three dimensional structures of proteins. We apply this method to a large set of decoy protein folds consisting of many thousand well-constructed models, only tens of which have the correct fold. We find that DI is able to greatly improve the ranking of the true (native) fold but others still remain high scoring that would be difficult to discard due to small shifts in the core beta sheets.

Exploring the limits of fold discrimination by structural alignment: a large scale benchmark using decoys of known fold

Protein structure comparison by pairwise alignment is commonly used to identify highly similar substructures in pairs of proteins and provide a measure of structural similarity based on the size and geometric similarity of the match. These scores are routinely applied in analyses of protein fold space under the assumption that high statistical significance is equivalent to a meaningful relationship, however the truth of this assumption has previously been difficult to test since there is a lack of automated methods which do not rely on the same underlying principles. As a resolution to this we present a method based on the use of topological descriptions of global protein structure, providing an independent means to assess the ability of structural alignment to maintain meaningful structural correspondances on a large scale.

Using a large set of decoys of specified global fold we benchmark three widely used methods for structure comparison, SAP, TM-align and DALI, and test the degree to which this assumption is justified for these methods. Application of a topological edit distance measure to provide a scale of the degree of fold change shows that while there is a broad correlation between high structural alignment scores and low edit distances there remain many pairs of highly significant score which differ by core strand swaps and therefore are structurally different on a global level. Possible causes of this problem and its meaning for present assessments of protein fold space are discussed.

Functional site prediction selects correct protein models

BACKGROUND

The prediction of protein structure can be facilitated by the use of constraints based on a knowledge of functional sites. Without this information it is still possible to predict which residues are likely to be part of a functional site and this information can be used to select model structures from a variety of alternatives that would correspond to a functional protein.

RESULTS

Using a large collection of protein-like decoy models, a score was devised that selected those with predicted functional site residues that formed a cluster. When tested on a variety of small alpha/beta/alpha type proteins, including enzymes and non-enzymes, those that corresponded to the native fold were ranked highly. This performance held also for a selection of larger alpha/beta/alpha proteins that played no part in the development of the method.

CONCLUSION

The use of predicted site positions provides a useful filter to discriminate native-like protein models from non-native models. The method can be applied to any collection of models and should provide a useful aid to all modelling methods from ab initio to homology based approaches.

Development and benchmarking of TASSER(iter) for the iterative improvement of protein structure predictions

To improve the accuracy of TASSER models especially in the limit where threading provided template alignments are of poor quality, we have developed the TASSER(iter) algorithm which uses the templates and contact restraints from TASSER generated models for iterative structure refinement. We apply TASSER(iter) to a large benchmark set of 2,773 nonhomologous single domain proteins that are < or = 200 in length and that cover the PDB at the level of 35% pairwise sequence identity. Overall, TASSER(iter) models have a smaller global average RMSD of 5.48 A compared to 5.81 A RMSD of the original TASSER models. Classifying the targets by the level of prediction difficulty (where Easy targets have a good template with a corresponding good threading alignment, Medium targets have a good template but a poor alignment, and Hard targets have an incorrectly identified template), TASSER(iter) (TASSER) models have an average RMSD of 4.15 A (4.35 A) for the Easy set and 9.05 A (9.52 A) for the Hard set. The largest reduction of average RMSD is for the Medium set where the TASSER(iter) models have an average global RMSD of 5.67 A compared to 6.72 A of the TASSER models. Seventy percent of the Medium set TASSER(iter) models have a smaller RMSD than the TASSER models, while 63% of the Easy and 60% of the Hard TASSER models are improved by TASSER(iter). For the foldable cases, where the targets have a RMSD to the native <6.5 A, TASSER(iter) shows obvious improvement over TASSER models: For the Medium set, it improves the success rate from 57.0 to 67.2%, followed by the Hard targets where the success rate improves from 32.0 to 34.8%, with the smallest improvement in the Easy targets from 82.6 to 84.0%. These results suggest that TASSER(iter) can provide more reliable predictions for targets of Medium difficulty, a range that had resisted improvement in the quality of protein structure predictions.

Evolutionary transitions in protein fold space

With the number of known protein folds potentially approaching completion, the problems associated with their systematic classification are evaluated. It is argued that it will be difficult, if not impossible, to find a general metric based on pairwise comparison that will provide a satisfactory classification. It is suggested that some progress may be made through comparison against a library of idealised 'template' folds, but a proper solution can only be attained if this includes a model of the underlying evolutionary processes. These processes are considered with examples of some unexpected relationships among folds, including circular permutations. The problem is finally set in the wider context of the genetic environment, introducing complications relating to introns, gene fixation and population size.

Protein knots and fold complexity: Some new twists

The current knowledge on topological knots in protein structure is reviewed, considering in turn, knots with three, four and five strand crossings. The latter is the most recent to be identified and has two distinct topological forms. The knot observed in the protein structure is the form that requires the least number of strand crossings to become un-knotted. The position of the chain termini must also correspond to a position that allows (un) knotting in one move. This is postulated as a general property of protein knots and other more complex knots with this property are proposed as the next most likely knots that might be found in a protein. It is also noted that the "Jelly-roll" fold found in some all-beta proteins would provide likely candidates. Alternative measures of knottedness and entanglement are reviewed, including the occurrence of slip-knots. These measures are related to the complexity of the protein fold and may provide useful filters for selecting predicted model structures.

Protein model refinement using structural fragment tessellation

A method is described for the refinement of rough protein models based on finding a selection of structural fragments that match the model. Unlike most fragment-based methods, these are not necessarily contiguous in the sequence and form a tiling (tessellation) that covers most of the structure. The residue positions of the fragments are then used as a target for the model atoms to generate a revised model which is used as the basis of a subsequent pattern definition and search. The method was shown to improve the recognition of the native fold in a series of decoys largely as a result of improved secondary structure representation.