Highly sensitive molecular diagnosis of prostate cancer using surplus material washed off from biopsy needles.

INTRODUCTION: Currently, final diagnosis of prostate cancer (PCa) is based on histopathological analysis of needle biopsies, but this process often bears uncertainties due to small sample size, tumour focality and pathologist's subjective assessment. METHODS: Prostate cancer diagnostic signatures were generated by applying linear discriminant analysis to microarray and real-time RT-PCR (qRT-PCR) data from normal and tumoural prostate tissue samples. Additionally, after removal of biopsy tissues, material washed off from transrectal biopsy needles was used for molecular profiling and discriminant analysis. RESULTS: Linear discriminant analysis applied to microarray data for a set of 318 genes differentially expressed between non-tumoural and tumoural prostate samples produced 26 gene signatures, which classified the 84 samples used with 100% accuracy. To identify signatures potentially useful for the diagnosis of prostate biopsies, surplus material washed off from routine biopsy needles from 53 patients was used to generate qRT-PCR data for a subset of 11 genes. This analysis identified a six-gene signature that correctly assigned the biopsies as benign or tumoural in 92.6% of the cases, with 88.8% sensitivity and 96.1% specificity. CONCLUSION: Surplus material from prostate needle biopsies can be used for minimal-size gene signature analysis for sensitive and accurate discrimination between non-tumoural and tumoural prostates, without interference with current diagnostic procedures. This approach could be a useful adjunct to current procedures in PCa diagnosis.

Evaluation of docking functions for protein-ligand docking.

Docking functions are believed to be the essential component of docking algorithms. Both physically and statistically based functions have been proposed, but there is no consensus about their relative performances. Here, we propose an evaluation approach based on exhaustive enumeration of all possible docking solutions obtained with a discretized description of a rigid docking process. We apply the approach to study both molecular mechanics and statistical potentials. It is found that the statistical potential evaluated is less effective than the AMBER molecular mechanics function to provide an accurate description of the docking process when the exact experimental coordinates are used. However, when coordinates of crystal structures obtained with analogous ligands are used, similar performances are obtained in both cases. Possible reasons for the successes and failures of both docking schemes have been uncovered using linear discriminant analysis, on the basis of a set of physicochemical descriptors capturing the main physical effects at play during protein-ligand docking. In both types of potentials steric effects appear critical to obtain a successful docking. Our results also indicate that neglecting desolvation effects and the explicit treatment of hydrogen bonds are the main source of the failures observed with the molecular mechanics potential. On the other hand, detailed consideration of steric interactions, with a careful treatment of dispersive forces, seems to be needed when using statistical potentials derived from a structural database. The possibility of filtering combinatorial libraries in order to maximize the probability of correct docking is discussed.

Comparative binding energy analysis of the substrate specificity of haloalkane dehalogenase from Xanthobacter autotrophicus GJ10.

Comparative binding energy (COMBINE) analysis was conducted for 18 substrates of the haloalkane dehalogenase from Xanthobacter autotrophicus GJ10 (DhlA): 1-chlorobutane, 1-chlorohexane, dichloromethane, 1,2-dichloroethane, 1,2-dichloropropane, 2-chloroethanol, epichlorohydrine, 2-chloroacetonitrile, 2-chloroacetamide, and their brominated analogues. The purpose of the COMBINE analysis was to identify the amino acid residues determining the substrate specificity of the haloalkane dehalogenase. This knowledge is essential for the tailoring of this enzyme for biotechnological applications. Complexes of the enzyme with these substrates were modeled and then refined by molecular mechanics energy minimization. The intermolecular enzyme-substrate energy was decomposed into residue-wise van der Waals and electrostatic contributions and complemented by surface area dependent and electrostatic desolvation terms. Partial least-squares projection to latent structures analysis was then used to establish relationships between the energy contributions and the experimental apparent dissociation constants. A model containing van der Waals and electrostatic intermolecular interaction energy contributions calculated using the AMBER force field explained 91% (73% cross-validated) of the quantitative variance in the apparent dissociation constants. A model based on van der Waals intermolecular contributions from AMBER and electrostatic interactions derived from the Poisson-Boltzmann equation explained 93% (74% cross-validated) of the quantitative variance. COMBINE models predicted correctly the change in apparent dissociation constants upon single-point mutation of DhlA for six enzyme-substrate complexes. The amino acid residues contributing most significantly to the substrate specificity of DhlA were identified; they include Asp124, Trp125, Phe164, Phe172, Trp175, Phe222, Pro223, and Leu263. These residues are suitable targets for modification by site-directed mutagenesis.

An aminoacyl tRNA synthetase whose sequence fits into neither of the two known classes.

Aminoacyl transfer RNA synthetases catalyse the first step of protein synthesis and establish the rules of the genetic code through the aminoacylation of tRNAs. There is a distinct synthetase for each of the 20 amino acids and throughout evolution these enzymes have been divided into two classes of ten enzymes each. These classes are defined by the distinct architectures of their active sites, which are associated with specific and universal sequence motifs. Because the synthesis of aminoacyl-tRNAs containing each of the twenty amino acids is a universally conserved, essential reaction, the absence of a recognizable gene for cysteinyl tRNA synthetase in the genomes of Archae such as Methanococcus jannaschii and Methanobacterium thermoautotrophicum has been difficult to interpret. Here we describe a different cysteinyl-tRNA synthetase from M. jannaschii and Deinococcus radiodurans and its characterization in vitro and in vivo. This protein lacks the characteristic sequence motifs seen in the more than 700 known members of the two canonical classes of tRNA synthetase and may be of ancient origin. The existence of this protein contrasts with proposals that aminoacylation with cysteine in M. jannaschii is an auxiliary function of a canonical prolyl-tRNA synthetase.

CAFASP2: the second critical assessment of fully automated structure prediction methods.

The results of the second Critical Assessment of Fully Automated Structure Prediction (CAFASP2) are presented. The goals of CAFASP are to (i) assess the performance of fully automatic web servers for structure prediction, by using the same blind prediction targets as those used at CASP4, (ii) inform the community of users about the capabilities of the servers, (iii) allow human groups participating in CASP to use and analyze the results of the servers while preparing their nonautomated predictions for CASP, and (iv) compare the performance of the automated servers to that of the human-expert groups of CASP. More than 30 servers from around the world participated in CAFASP2, covering all categories of structure prediction. The category with the largest participation was fold recognition, where 24 CAFASP servers filed predictions along with 103 other CASP human groups. The CAFASP evaluation indicated that it is difficult to establish an exact ranking of the servers because the number of prediction targets was relatively small and the differences among many servers were also small. However, roughly a group of five "best" fold recognition servers could be identified. The CASP evaluation identified the same group of top servers albeit with a slightly different relative order. Both evaluations ranked a semiautomated method named CAFASP-CONSENSUS, that filed predictions using the CAFASP results of the servers, above any of the individual servers. Although the predictions of the CAFASP servers were available to human CASP predictors before the CASP submission deadline, the CASP assessment identified only 11 human groups that performed better than the best server. Furthermore, about one fourth of the top 30 performing groups corresponded to automated servers. At least half of the top 11 groups corresponded to human groups that also had a server in CAFASP or to human groups that used the CAFASP results to prepare their predictions. In particular, the CAFASP-CONSENSUS group was ranked 7. This shows that the automated predictions of the servers can be very helpful to human predictors. We conclude that as servers continue to improve, they will become increasingly important in any prediction process, especially when dealing with genome-scale prediction tasks. We expect that in the near future, the performance difference between humans and machines will continue to narrow and that fully automated structure prediction will become an effective companion and complement to experimental structural genomics.

Crystal structure of trbp111: a structure-specific tRNA-binding protein.

Trbp111 is a 111 amino acid Aquifex aeolicus structure-specific tRNA-binding protein that has homologous counterparts distributed throughout evolution. A dimer is the functional unit for binding a single tRNA. Here we report the 3D structures of the A.aeolicus protein and its Escherichia coli homolog at resolutions of 2.50 and 1.87 A, respectively. The structure shows a symmetrical dimer of two core domains and a central dimerization domain where the N- and C-terminal regions of Trbp111 form an extensive dimer interface. The core of the monomer is a classical oligonucleotide/oligosaccharide-binding (OB) fold with a five-stranded ss-barrel and a small capping helix. This structure is similar to that seen in the anticodon-binding domain of three class II tRNA synthetases and several other proteins. Mutational analysis identified sites important for interactions with tRNA. These residues line the inner surfaces of two clefts formed between the ss-barrel of each monomer and the dimer interface. The results are consistent with a proposed model for asymmetrical docking of the convex side of tRNA to the dimer.

Sequence evolution and the mechanism of protein folding.

The impact on protein evolution of the physical laws that govern folding remains obscure. Here, by analyzing in silico-evolved sequences subjected to evolutionary pressure for fast folding, it is shown that: First, a subset of residues in the thermodynamic folding nucleus is mainly responsible for modulating the protein folding rate. Second and most important, the protein topology itself is of paramount importance in determining the location of these residues in the structure. Further stabilization of the interactions in this nucleus leads to fast folding sequences. Third, these nucleation points restrict the sequence space available to the protein during evolution. Correlated mutations between positions around these hot spots arise in a statistically significant manner, and most involve contacting residues. When a similar analysis is carried out on real proteins, qualitatively similar results are obtained.

Ab initio folding of proteins using restraints derived from evolutionary information.

We present our predictions in the ab initio structure prediction category of CASP3. Eleven targets were folded, using a method based on a Monte Carlo search driven by secondary and tertiary restraints derived from multiple sequence alignments. Our results can be qualitatively summarized as follows: The global fold can be considered "correct" for targets 65 and 74, "almost correct" for targets 64, 75, and 77, "half-correct" for target 79, and "wrong" for targets 52, 56, 59, and 63. Target 72 has not yet been solved experimentally. On average, for small helical and alpha/beta proteins (on the order of 110 residues or smaller), the method predicted low resolution structures with a reasonably good prediction of the global topology. Most encouraging is that in some situations, such as with target 75 and, particularly, target 77, the method can predict a substantial portion of a rare or even a novel fold. However, the current method still fails on some beta proteins, proteins over the 110-residue threshold, and sequences in which only a poor multiple sequence alignment can be built. On the other hand, for small proteins, the method gives results of quality at least similar to that of threading, with the advantage of not being restricted to known folds in the protein database. Overall, these results indicate that some progress has been made on the ab initio protein folding problem. Detailed information about our results can be obtained by connecting to http:/(/)www.bioinformatics.danforthcenter.org/+ ++CASP3.

Reduced protein models and their application to the protein folding problem.

One of the most important unsolved problems of computational biology is prediction of the three-dimensional structure of a protein from its amino acid sequence. In practice, the solution to the protein folding problem demands that two interrelated problems be simultaneously addressed. Potentials that recognize the native state from the myriad of misfolded conformations are required, and the multiple minima conformational search problem must be solved. A means of partly surmounting both problems is to use reduced protein models and knowledge-based potentials. Such models have been employed to elucidate a number of general features of protein folding, including the nature of the energy landscape, the factors responsible for the uniqueness of the native state and the origin of the two-state thermodynamic behavior of globular proteins. Reduced models have also been used to predict protein tertiary and quaternary structure. When combined with a limited amount of experimental information about secondary and tertiary structure, molecules of substantial complexity can be assembled. If predicted secondary structure and tertiary restraints are employed, low resolution models of single domain proteins can be successfully predicted. Thus, simplified protein models have played an important role in furthering the understanding of the physical properties of proteins.

Combined multiple sequence reduced protein model approach to predict the tertiary structure of small proteins.

By incorporating predicted secondary and tertiary restraints into ab initio folding simulations, low resolution tertiary structures of a test set of 20 nonhomologous proteins have been predicted. These proteins, which represent all secondary structural classes, contain from 37 to 100 residues. Secondary structural restraints are provided by the PHD secondary structure prediction algorithm that incorporates multiple sequence information. Predicted tertiary restraints are obtained from multiple sequence alignments via a two-step process: First, "seed" side chain contacts are identified from a correlated mutation analysis, and then, the seed contacts are "expanded" by an inverse folding algorithm. These predicted restraints are then incorporated into a lattice based, reduced protein model. Depending upon fold complexity, the resulting nativelike topologies exhibit a coordinate root-mean-square deviation, cRMSD, from native between 3.1 and 6.7 A. Overall, this study suggests that the use of restraints derived from multiple sequence alignments combined with a fold assembly algorithm is a promising approach to the prediction of the global topology of small proteins.

Fold assembly of small proteins using monte carlo simulations driven by restraints derived from multiple sequence alignments.

The feasibility of predicting the global fold of small proteins by incorporating predicted secondary and tertiary restraints into ab initio folding simulations has been demonstrated on a test set comprised of 20 non-homologous proteins, of which one was a blind prediction of target 42 in the recent CASP2 contest. These proteins contain from 37 to 100 residues and represent all secondary structural classes and a representative variety of global topologies. Secondary structure restraints are provided by the PHD secondary structure prediction algorithm that incorporates multiple sequence information. Predicted tertiary restraints are derived from multiple sequence alignments via a two-step process. First, seed side-chain contacts are identified from correlated mutation analysis, and then a threading-based algorithm is used to expand the number of these seed contacts. A lattice-based reduced protein model and a folding algorithm designed to incorporate these predicted restraints is described. Depending upon fold complexity, it is possible to assemble native-like topologies whose coordinate root-mean-square deviation from native is between 3.0 A and 6.5 A. The requisite level of accuracy in side-chain contact map prediction can be roughly 25% on average, provided that about 60% of the contact predictions are correct within +/-1 residue and 95% of the predictions are correct within +/-4 residues. Precision in tertiary contact prediction is more critical than absolute accuracy. Furthermore, only a subset of the tertiary contacts, on the order of 25% of the total, is sufficient for successful topology assembly. Overall, this study suggests that the use of restraints derived from multiple sequence alignments combined with a fold assembly algorithm holds considerable promise for the prediction of the global topology of small proteins.

Comparative binding energy analysis of HIV-1 protease inhibitors: incorporation of solvent effects and validation as a powerful tool in receptor-based drug design.

A comparative binding energy (COMBINE) analysis (Ortiz et al. J. Med. Chem. 1995, 38, 2681-2691) has been performed on a training set of 33 HIV-1 protease inhibitors, and the resulting regression models have been validated using an additional external set of 16 inhibitors. This data set was originally reported by Holloway et al. (J. Med. Chem. 1995, 38, 305-317), who showed the usefulness of molecular mechanics interaction energies for predicting the activity of novel HIV-1 protease inhibitors within the framework of the MM2X force field and linear regression techniques. We first used the AMBER force field on the same set of three-dimensional structures to check up on any possible force-field dependencies. In agreement with the previous findings, the calculated raw ligand-receptor interaction energies were highly correlated with the inhibitory activities (r2 = 0.81), and the linear regression model relating both magnitudes had an acceptable predictive ability both in internal validation tests (q2 = 0.79, SDEPcv = 0.61) and when applied to the external set of 16 different inhibitors (SDEPex = 1.08). When the interaction energies were further analyzed using the COMBINE formalism, the resulting PLS model showed improved fitting properties (r2 = 0.89) and provided better estimations for the activity of the compounds in the external data set (SDEPex = 0.83). Computation of the electrostatic part of the ligand-receptor interactions by numerically solving the Poisson-Boltzmann equation did not improve the quality of the linear regression model. On the contrary, incorporation of the solvent-screened residue-based electrostatic interactions and two additional descriptors representing the electrostatic energy contributions to the partial desolvation of both the ligands and the receptor resulted in a COMBINE model that achieved a remarkable predictive ability, as assessed by both internal (q2 = 0.73, SDEPcv = 0.69) and external validation tests (SDEPex = 0.59). Finally, when all the inhibitors studied were merged into a single expanded set, a new model was obtained that explained 91% of the variance in biological activity (r2 = 0.91), with very high predictive ability (q2 = 0.81, SDEPcv = 0.66). In addition, the COMBINE analysis provided valuable information about the relative importance of the contributions to the activity of individual residues that can be fruitfully used to design better inhibitors. All in all, COMBINE analysis is validated as a powerful methodology for predicting binding affinities and pharmacological activities of congeneric ligands that bind to a common receptor.

Tertiary structure prediction of the KIX domain of CBP using Monte Carlo simulations driven by restraints derived from multiple sequence alignments.

Using a recently developed protein folding algorithm, a prediction of the tertiary structure of the KIX domain of the CREB binding protein is described. The method incorporates predicted secondary and tertiary restraints derived from multiple sequence alignments in a reduced protein model whose conformational space is explored by Monte Carlo dynamics. Secondary structure restraints are provided by the PHD secondary structure prediction algorithm that was modified for the presence of predicted U-turns, i.e., regions where the chain reverses global direction. Tertiary restraints are obtained via a two-step process: First, seed side-chain contacts are identified from a correlated mutation analysis, and then, a threading-based algorithm expands the number of these seed contacts. Blind predictions indicate that the KIX domain is a putative three-helix bundle, although the chirality of the bundle could not be uniquely determined. The expected root-mean-square deviation for the correct chirality of the KIX domain is between 5.0 and 6.2 A. This is to be compared with the estimate of 12.9 A that would be expected by a random prediction, using the model of F. Cohen and M. Sternberg (J. Mol. Biol. 138:321-333, 1980).

Nativelike topology assembly of small proteins using predicted restraints in Monte Carlo folding simulations.

By incorporating predicted secondary and tertiary restraints derived from multiple sequence alignments into ab initio folding simulations, it has been possible to assemble native-like tertiary structures for a test set of 19 nonhomologous proteins ranging from 29 to 100 residues in length and representing all secondary structural classes. Secondary structural restraints are provided by the PHD secondary structure prediction algorithm that incorporates multiple sequence information. Multiple sequence alignments also provide predicted tertiary restraints via a two-step process: First, seed side chain contacts are selected from a correlated mutation analysis, and then an inverse folding algorithm expands these seed contacts. The predicted secondary and tertiary restraints are incorporated into a lattice-based, reduced protein model for structure assembly and refinement. The resulting native-like topologies exhibit a coordinate root-mean-square deviation from native for the whole chain between 3.1 and 6.7 A, with values ranging from 2.6 to 4.1 A over approximately 80% of the structure. Overall, this study suggests that the use of restraints derived from multiple sequence alignments combined with a fold assembly algorithm is a promising approach to the prediction of the global topology of small proteins.

Assessment of solvation effects on calculated binding affinity differences: trypsin inhibition by flavonoids as a model system for congeneric series.

On the basis of molecular models of the interaction between trypsin and a series of seven structurally congeneric bioflavonoid inhibitors, the influence of solvation effects in the calculation of binding free energy differences in congeneric series has been assessed. The models were derived by making use of the X-ray crystal structure of bovine trypsin and the DOCK program, and the complementarity of the interactions between the functional groups of the docked molecules and the binding site region was corroborated independently with the GRID program. Interaction energies calculated for the complexes using molecular mechanics were found to correlate with the experimental inhibitory activities, although the quality of the correlation was dependent on the molecular modeling protocol. To understand why such correlations could be obtained in the absence of an explicit description of solvent effects, the in vitro activities were transformed into binding free energies, and continuum electrostatic theory was used to incorporate solvent effects by approximating them to the electrostatic contribution to the binding free energies. The results of our calculations show that, within this congeneric series, the cost in electrostatic free energy of desolvating both the enzyme binding site and the buried part of the inhibitors (delta Gdesolv) is roughly constant within the series. On the other hand, the electrostatic interaction energy (EeleLR) varies only slightly along the series in comparison with the van der Waals interaction (EVDWLR), and this variation is mostly solvent-independent, i.e., the reaction field energy of the solvent in the bound state (EsrfLR) makes almost a negligible contribution to the binding free energy differences. As a result, differences in binding free energy are dominated by the van der Waals term, while the electrostatic contribution is, to a good approximation, solvent-independent. A similar scenario may account for the good correlations frequently found between ligand activities and ligand-receptor interaction energies derived using plain molecular mechanics, although generality remains to be determined.

1H and 15N NMR assignment and solution structure of the SH3 domain of spectrin: comparison of unrefined and refined structure sets with the crystal structure.

The assignment of the 1H and 15N nuclear magnetic resonance spectra of the Src-homology region 3 domain of chicken brain alpha-spectrin has been obtained. A set of solution structures has been determined from distance and dihedral angle restraints, which provide a reasonable representation of the protein structure in solution, as evaluated by a principal component analysis of the global pairwise root-mean-square deviation (rmsd) in a large set of structures consisting of the refined and unrefined solution structures and the crystal structure. The solution structure is well defined, with a lower degree of convergence between the structures in the loop regions than in the secondary structure elements. The average pairwise rmsd between the 15 refined solution structures is 0.71 +/- 0.13 A for the backbone atoms and 1.43 +/- 0.14 A for all heavy atoms. The solution structure is basically the same as the crystal structure. The average rmsd between the 15 refined solution structures and the crystal structure is 0.76 A for the backbone atoms and 1.45 +/- 0.09 A for all heavy atoms. There are, however, small differences probably caused by intermolecular contacts in the crystal structure.

Reliability of comparative molecular field analysis models: effects of data scaling and variable selection using a set of human synovial fluid phospholipase A2 inhibitors.

The effects of data pretreatment, data scaling, and variable selection on three-dimensional quantitative structure-activity relationships derived by comparative molecular field analysis (CoMFA) using the GRID energy function were studied in detail for a set of inhibitors of the human synovial fluid phospholipase A2 (HSF-PLA2). The quality of the models was evaluated for predictive power and ability to map the receptor binding site by (a) comparison of predicted and experimental activities using cross-validation and external validation sets and (b) comparison of the regions selected in space in the CoMFA models with a crystal structure of a HSF-PLA2-inhibitor complex, with optimized comparative binding energy analysis (COMBINE) models (Ortiz et al., 1995) and with structure-activity relationships derived previously for different sets of compounds. It is found that (1) data scaling and dielectric modeling strongly influence CoMFA results. Unscaled data and a uniform dielectric constant of 4 are well suited to GRID-CoMFA studies for the present compound set. (2) The GOLPE and Q2-GRS variable selection methods select variables in roughly the same regions in Cartesian space, but they produce different models in chemometric space and differ in their sensitivity to data scaling and pretreatment and their tendency to overfitting. (3) CoMFA models are consistent with COMBINE models in that they identify approximately the same intermolecular interactions as relevant for activity. Our study provides support for the qualitative receptor-mapping properties of CoMFA models and for the validity of variable selection when applied with care and also provides guidelines for how to evaluate the quality of CoMFA models.

Structure-affinity relationships for the binding of actinomycin D to DNA.

Molecular models of the complexes between actinomycin D and 14 different DNA hexamers were built based on the X-ray crystal structure of the actinomycin-d(GAAGCTTC)2 complex. The DNA sequences included the canonical GpC binding step flanked by different base pairs, nonclassical binding sites such as GpG and GpT, and sites containing 2,6-diamino-purine. A good correlation was found between the intermolecular interaction energies calculated for the refined complexes and the relative preferences of actinomycin binding to standard and modified DNA. A detailed energy decomposition into van der Waals and electrostatic components for the interactions between the DNA base pairs and either the chromophore or the peptidic part of the antibiotic was performed for each complex. The resulting energy matrix was then subjected to principal component analysis, which showed that actinomycin D discriminates among different DNA sequences by an interplay of hydrogen bonding and stacking interactions. The structure-affinity relationships for this important antitumor drug are thus rationalized and may be used to advantage in design of novel sequence-specific DNA-binding agents.

Method for low resolution prediction of small protein tertiary structure.

A new method for the de novo prediction of protein structures at low resolution has been developed. Starting from a multiple sequence alignment, protein secondary structure is predicted, and only those topological elements with high reliability are selected. Then, the multiple sequence alignment and the secondary structure prediction are combined to predict side chain contacts. Such contact map prediction is carried out in two stages. First, an analysis of correlated mutations is carried out to identify pairs of topological elements of secondary structure which are in contact. Then, inverse folding is used to select compatible fragments in contact, thereby enriching the number and identity of predicted side chain contacts. The final outcome of the procedure is a set of noisy secondary and tertiary restraints. These are used as a restrained potential in a Monte Carlo simulation of simplified protein models driven by statistical potentials. Low energy structures are then searched for by using simulated annealing techniques. Implementation of the restraints is carried out so as to take into account of their low resolution. Using this procedure, it has been possible to predict de novo the structure of three very different protein topologies: an alpha/beta protein, the bovine pancreatic trypsin inhibitor (6pti), an alpha-helical protein, calbindin (3icb), and an all beta- protein, the SH3 domain of spectrin (1shg). In all cases, low resolution folds have been obtained with a root mean square deviation (RMSD) of 4.5-5.5 A with respect to the native structure. Some misfolded topologies appear in the simulations, but it is possible to select the native one on energetic grounds. Thus, it is demonstrated that the methodology is general for all protein motifs. Work is in progress in order to test the methodology on a larger set of protein structures.

Role of a nonnative interaction in the folding of the protein G B1 domain as inferred from the conformational analysis of the alpha-helix fragment.

BACKGROUND: The role of local interactions in protein folding and stability can be investigated by the conformational analysis of protein fragments. The hydrophobic staple and Schellman motifs have been described at the N and C terminus, respectively, of protein alpha-helices. These motifs are characterized by an interaction between two hydrophobic residues, one outside the helix and one within the helix, and their importance for helix stability has been analyzed in model peptides. In the alpha-helix of the protein G B1 domain, only the Schellman motif is formed--the hydrophobic staple motif is absent despite the favourable sequence pattern. We have experimentally analyzed the solution conformation of the 19-41 fragment of protein G. This peptide comprises the helical residues and contains both the hydrophobic staple and Schellman motif sequences. RESULTS: In the isolated peptide in water, the hydrophobic staple motif is formed and stabilizes the helical structure as compared with a shorter peptide lacking it, but the Schellman motif is not formed. In 30% aqueous TFE, the helix is more stable than in pure water and both motifs are formed. CONCLUSIONS: The results suggest that the importance of each motif for the folding and stability of protein G is different. The nonnative hydrophobic staple interaction can help to nucleate the helix at the beginning of folding but has later to be disrupted. The Schellman motif, while not providing enough energy for substantial helix stabilization in the unfolded state, could be important for determining the local fold of the sequence in the context of the rest of the protein.