Progress in fold recognition

Relationship between protein structure and geometrical constraints

We evaluate to what extent the structure of proteins can be deduced from incomplete knowledge of disulfide bridges, surface assignments, secondary structure assignments, and additional distance constraints. A cost function taking such constraints into account was used to obtain protein structures using a simple minimization algorithm. For small proteins, the approximate structure could be obtained using one additional distance constraint for each amino acid in the protein. We also studied the effect of using predicted secondary structure and surface assignments. The constraints used in this approach typically may be obtained from low-resolution experimental data. When using a cost function based on distances, half of the resulting structures will be mirrored, because the resulting structure and its mirror image will have the same cost. The secondary structure assignments were therefore divided into chirality constraints and distance constraints. Here we report that the problem of mirrored structures, in some cases, can be solved by using a chirality term in the cost function.

Evaluation of threading specificity and accuracy

Threading experiments with proteins from the globin family provide an indication of the nature of the structural similarity required for successful fold recognition and accurate sequence-structure alignment. Threading scores are found to rise above the noise of false positives whenever roughly 60% of residues from a sequence can be aligned with analogous sites in the structure of a remote homolog. Fold recognition specificity thus appears to be limited by the extent of structural similarity, regardless of the degree of sequence similarity. Threading alignment accuracy is found to depend more critically on the degree of structural similarity. Alignments are accurate, placing the majority of residues exactly as in structural alignment, only when superposition residuals are less than 2.5 A. These criteria for successful recognition and sequence-structure alignment appear to be consistent with the successes and failures of threading methods in blind structure prediction. They also suggest a direct assay for improved threading methods: Potentials and alignment models should be tested for their ability to detect less extensive structural similarities, and to produce accurate alignments when superposition residuals for this conserved "core" fall in the range characteristic of remote homologs.

The current state of the art in protein structure prediction

The capabilities of current protein structure prediction methods have been assessed from the outcome of a set of blind tests. In comparative modeling, many of the numerical methods did not perform as well as expected, although the resulting structures are still of great practical use. The new methods of fold identification ('threading') were partially successful, and show considerable promise for the future. Except for secondary structure data, results from traditional ab initio methods were poor. A second blind prediction experiment is underway, and progress in all areas is expected.

The lipocalin Xlcpl1 expressed in the neural plate of Xenopus laevis embryos is a secreted retinaldehyde binding protein

The cellular and structural properties and binding capabilities of a lipocalin expressed in the early neural plate of Xenopus laevis embryos and the adult choroid plexus have been investigated. It was found that this lipocalin, termed Xlcpl1, binds retinal at a nanomolar concentration, retinoic acid in the micromolar range, but does not show binding to retinol. Furthermore, this protein also binds D/L thyroxine. The Xlcpl1 cDNA was expressed in cell culture using the vaccinia virus expression system. In AtT20 cells, Xlcpl1 was secreted via the constitutive secretory pathway. We therefore assume that cpl1 binds retinaldehyde during the transport through the compartments of the secretory pathway that are considered to be the storage compartments of retinoids. Therefore, cpl1-expressing cells will secrete the precursors of active retinoids such as retinoic acid isomers. These retinoids may enter the cytosol by diffusion or receptor-controlled mechanisms, as has been shown for exogenously applied retinoids. Based on these data, it is suggested that cpl1 is an integral member of the retinoid signaling pathway and, therefore, it plays a key role in pattern formation in early embryonic development.

Structure-derived potentials and protein simulations

There has recently been an explosion in the number of structure-derived potential functions that are based on the increasing number of high-resolution protein crystal structures. These functions differ principally in their reference states; the usual two classes correspond either to initial solvent exposure or to residue exposure of residues. Reference states are critically important for applications of these potentials functions. Inspection of the potential functions and their derivation can tell us not only about protein interaction strengths themselves, but can also provide suggestions for the design of better folding simulations. An appropriate goal in this field is achieving self-consistency between the details in the derivation of potentials and the applied simulations.

Potential energy functions for threading

Despite little progress in ab initio solutions to the problem of predicting a protein's tertiary structure, over the past four years or so the development of fold-recognition methods for tertiary structure prediction has been the source of some encouragement in this difficult field. Despite promising initial results, these methods are clearly not yet fully mature and many groups are now working on different aspects of the methods involved in the hope of increasing the reliability and sensitivity of these tools.

Optimum superimposition of protein structures: ambiguities and implications

BACKGROUND

Techniques for comparison and optimum superimposition of protein structures are indispensable tools, providing the basis for statistical analysis, modeling, prediction and classification of protein folds. Observed similarity of structures is frequently interpreted as an indication of evolutionary relatedness. A variety of advanced techniques are available, but so far the important issue of uniqueness of structural superimposition has been largely neglected. We set out to investigate this issue by implementing an efficient algorithm for structure superimposition enabling routine searches for alternative alignments.

RESULTS

The algorithm is based on optimum superimposition of structures and dynamic programming. The implementation is tested and validated using published results. In particular, an automatic classification of all protein folds in a recent release of the protein data bank is performed. The results obtained are closely related to published data. Surprisingly, for many protein pairs alternative alignments are obtained. These alignments are indistinguishable in terms of number of equivalent residues and root mean square error of superimposition, but the respective sets of equivalent residue pairs are completely distinct. Alternative alignments are observed for all protein architectures, including mixed alpha/beta folds.

CONCLUSIONS

Superimposition of protein folds is frequently ambiguous. This has several implications on the interpretation of structural similarity with respect to evolutionary relatedness and it restricts the range of applicability of superimposed structures in statistical analysis. In particular, studies based on the implicit assumption that optimum superimposition of structures is unique are bound to be misleading.

Helmholtz free energies of atom pair interactions in proteins

BACKGROUND

Proteins fold to unique three-dimensional structures, but how they achieve this transition and how they maintain their native folds is controversial. Information on the functional form of molecular interactions is required to address these issues. The basic building blocks are the free energies of atom pair interactions in dense protein solvent systems. In a dense medium, entropic effects often dominate over internal energies but free energy estimates are notoriously difficult to obtain. A prominent example is the peptide hydrogen bond (H-bond). It is still unclear to what extent H-bonds contribute to protein folding and stability of native structures.

RESULTS

Radial distribution functions of atom pair interactions are compiled from a database of known protein folds. The functions are transformed to Helmholtz free energies using a recipe from the statistical mechanics of dense interacting systems. In particular we concentrate on the features of the free energy functions of peptide H-bonds. Differences in Helmholtz free energies correspond to the reversible work required or gained when the distance between two particles is changed. Consequently, the functions directly display the energetic features of the respective thermodynamic process, such as H-bond formation or disruption.

CONCLUSIONS

In the H-bond potential, a high barrier isolates a deep narrow minimum at H-bond contact from large distances, but the free energy difference between H-bond contact and large distances is close to zero. The energy barrier plays an intriguing role in H-bond formation and disruption: both processes require activation energy in the order of 2kT. H-bond formation opposes folding to compact states, but once formed, H-bonds act as molecular locks and a network of such bonds keeps polypeptide chains in a precise spatial configuration. On the other hand, peptide H-bonds do not contribute to the thermodynamic stability of native folds, because the energy balance of H-bond formation is close to zero.

A study of combined structure/sequence profiles

BACKGROUND

For genome sequencing projects to achieve their full impact on biology and medicine, each protein sequence must be identified with its three-dimensional structure. Fold assignment methods (also called profile and threading methods) attempt to assign sequences to known protein folds by computing the compatibility of sequence to fold.

RESULTS

We have extended profile methods for the detection of protein folds having structural similarity but low sequence similarity to sequence probes. Our extension combines sequence substitution tables with structural properties to form a combined profile. The structural properties used in this study include distances between residues, exposed areas, areas buried by polar atoms, and properties of the original three-dimensional profile method. We compared the performance of these combined profiles with different sequence matrices and with the original three-dimensional profile method. To determine the optimal gap penalties and weights used with these profiles, we employed a genetic algorithm. The performance of these combined profiles was tested by cross validation using independent test and training sets.

CONCLUSIONS

These studies show that the combined profiles perform better than profiles based on either structural or sequence information alone.