Strategic protein target analysis for developing drugs to stop dental caries.

Dental caries is the most common disease to cause irreversible damage in humans. Several therapeutic agents are available to treat or prevent dental caries, but none besides fluoride has significantly influenced the disease burden globally. Etiologic mechanisms of the mutans group streptococci and specific Lactobacillus species have been characterized to various degrees of detail, from identification of physiologic processes to specific proteins. Here, we analyze the entire Streptococcus mutans proteome for potential drug targets by investigating their uniqueness with respect to non-cariogenic dental plaque bacteria, quality of protein structure models, and the likelihood of finding a drug for the active site. Our results suggest specific targets for rational drug discovery, including 15 known virulence factors, 16 proteins for which crystallographic structures are available, and 84 previously uncharacterized proteins, with various levels of similarity to homologs in dental plaque bacteria. This analysis provides a map to streamline the process of clinical development of effective multispecies pharmacologic interventions for dental caries.

Decoys 'R' Us: a database of incorrect conformations to improve protein structure prediction.

The development of an energy or scoring function for protein structure prediction is greatly enhanced by testing the function on a set of computer-generated conformations (decoys) to determine whether it can readily distinguish native-like conformations from nonnative ones. We have created "Decoys 'R' Us," a database containing many such sets of conformations, to provide a resource that allows scoring functions to be improved.

Constructing side chains on near-native main chains for ab initio protein structure prediction.

Is there value in constructing side chains while searching protein conformational space during an ab initio simulation? If so, what is the most computationally efficient method for constructing these side chains? To answer these questions, four published approaches were used to construct side chain conformations on a range of near-native main chains generated by ab initio protein structure prediction methods. The accuracy of these approaches was compared with a naive approach that selects the most frequently observed rotamer for a given amino acid to construct side chains. An all-atom conditional probability discriminatory function is useful at selecting conformations with overall low all-atom root mean square deviation (r.m.s.d.) and the discrimination improves on sets that are closer to the native conformation. In addition, the naive approach performs as well as more sophisticated methods in terms of the percentage of chi(1) angles built accurately and the all-atom r. m.s.d., between the native and near-native conformations. The results suggest that the naive method would be extremely useful for fast and efficient side chain construction on vast numbers of conformations for ab initio prediction of protein structure.

Probing structure-function relationships of the DNA polymerase alpha-associated zinc-finger protein using computational approaches.

We present the application of a method for protein structure prediction to aid the determination of structure-function relationships by experiment. The structure prediction method was rigourously tested by making blind predictions at the third meeting on the Critical Assessment of Protein Structure methods (CASP3). The method is a combined hierarchical approach involving exhaustive enumeration of all possible folds of a small protein sequence on a tetrahedral lattice. A set of filters, primarily in the form of discriminatory functions, are applied to these conformations. As the filters are applied, greater detail is added to the models resulting in a handful of all-atom "final" conformations. Encouraged by the results at CASP3, we used our approach to help solve a practical biological problem: the prediction of the structure and function of the 67-residue C-terminal zinc-finger region of the DNA polymerase alpha-associated zinc-finger (PAZ) protein. We discuss how the prediction points to a novel function relative to the sequence homologs, in conjunction with evidence from experiment, and how the predicted structure is guiding further experimental studies. This work represents a move from the theoretical realm to actual application of structure prediction methods for gaining unique insight to guide experimental biologists.

Ab initio construction of protein tertiary structures using a hierarchical approach.

We present a hierarchical method to predict protein tertiary structure models from sequence. We start with complete enumeration of conformations using a simple tetrahedral lattice model. We then build conformations with increasing detail, and at each step select a subset of conformations using empirical energy functions with increasing complexity. After enumeration on lattice, we select a subset of low energy conformations using a statistical residue-residue contact energy function, and generate all-atom models using predicted secondary structure. A combined knowledge-based atomic level energy function is then used to select subsets of the all-atom models. The final predictions are generated using a consensus distance geometry procedure. We test the feasibility of the procedure on a set of 12 small proteins covering a wide range of protein topologies. A rigorous double-blind test of our method was made under the auspices of the CASP3 experiment, where we did ab initio structure predictions for 12 proteins using this approach. The performance of our methodology at CASP3 is reasonably good and completely consistent with our initial tests.

Ab initio protein structure prediction using a combined hierarchical approach.

As part of the third Critical Assessment of Structure Prediction meeting (CASP3), we predict the three-dimensional structures for 13 proteins using a hierarchical approach. First, all possible compact conformations of a protein sequence are enumerated using a highly simplified tetrahedral lattice model. We select a large subset of these conformations using a lattice-based scoring function and build detailed all-atom models incorporating predicted secondary structure. A combined all-atom knowledge-based scoring function is then used to select three smaller subsets from these all-atom models. Finally, a consensus-based distance geometry procedure is used to generate the best conformations from each of the all-atom subsets. With this method, we are able to predict the global topology/shape for all or a large part of the sequence for six out of the thirteen proteins. For two other proteins, the topology/shape for shorter fragments are predicted. This represents a marked improvement in ab initio prediction since CASP was first instigated in 1994.

Ab initio fold prediction of small helical proteins using distance geometry and knowledge-based scoring functions.

The problem of protein tertiary structure prediction from primary sequence can be separated into two subproblems: generation of a library of possible folds and specification of a best fold given the library. A distance geometry procedure based on random pairwise metrization with good sampling properties was used to generate a library of 500 possible structures for each of 11 small helical proteins. The input to distance geometry consisted of sets of restraints to enforce predicted helical secondary structure and a generic range of 5 to 11 A between predicted contact residues on all pairs of helices. For each of the 11 targets, the resulting library contained structures with low RMSD versus the native structure. Near-native sampling was enhanced by at least three orders of magnitude compared to a random sampling of compact folds. All library members were scored with a combination of an all-atom distance-dependent function, a residue pair-potential, and a hydrophobicity function. In six of the 11 cases, the best-ranking fold was considered to be near native. Each library was also reduced to a final ab initio prediction via consensus distance geometry performed over the 50 best-ranking structures from the full set of 500. The consensus results were of generally higher quality, yielding six predictions within 6.5 A of the native fold. These favorable predictions corresponded to those for which the correlation between the RMSD and the scoring function were highest. The advantage of the reported methodology is its extreme simplicity and potential for including other types of structural restraints.

A combined approach for ab initio construction of low resolution protein tertiary structures from sequence.

An approach to construct low resolution models of protein structure from sequence information using a combination of different methodologies is described. All possible compact self-avoiding C alpha conformations (approximately 10 million) of a small protein chain were exhaustively enumerated on a tetrahedral lattice. The best scoring 10,000 conformations were selected using a lattice-based scoring function. All-atom structures were then generated by fitting an off-lattice four-state phi/psi model to the lattice conformations, using idealised helix and sheet values based on predicted secondary structure. The all-atom conformations were minimised using ENCAD and scored using a second hybrid scoring function. The best scoring 50, 100, and 500 conformations were input to a consensus-based distance geometry routine that used constraints from each the conformation sets and produced a single structure for each set (total of three). Secondary structures were again fitted to the three structures, and the resulting structures were minimised and scored. The lowest scoring conformation was taken to be the "correct" answer. The results of application of this method to twelve proteins are presented.

Distance geometry generates native-like folds for small helical proteins using the consensus distances of predicted protein structures.

For successful ab initio protein structure prediction, a method is needed to identify native-like structures from a set containing both native and non-native protein-like conformations. In this regard, the use of distance geometry has shown promise when accurate inter-residue distances are available. We describe a method by which distance geometry restraints are culled from sets of 500 protein-like conformations for four small helical proteins generated by the method of Simons et al. (1997). A consensus-based approach was applied in which every inter-Calpha distance was measured, and the most frequently occurring distances were used as input restraints for distance geometry. For each protein, a structure with lower coordinate root-mean-square (RMS) error than the mean of the original set was constructed; in three cases the topology of the fold resembled that of the native protein. When the fold sets were filtered for the best scoring conformations with respect to an all-atom knowledge-based scoring function, the remaining subset of 50 structures yielded restraints of higher accuracy. A second round of distance geometry using these restraints resulted in an average coordinate RMS error of 4.38 A.

A graph-theoretic algorithm for comparative modeling of protein structure.

The interconnected nature of interactions in protein structures appears to be the major hurdle in preventing the construction of accurate comparative models. We present an algorithm that uses graph theory to handle this problem. Each possible conformation of a residue in an amino acid sequence is represented using the notion of a node in a graph. Each node is given a weight based on the degree of the interaction between its side-chain atoms and the local main-chain atoms. Edges are then drawn between pairs of residue conformations/nodes that are consistent with each other (i.e. clash-free and satisfying geometrical constraints). The edges are weighted based on the interactions between the atoms of the two nodes. Once the entire graph is constructed, all the maximal sets of completely connected nodes (cliques) are found using a clique-finding algorithm. The cliques with the best weights represent the optimal combinations of the various main-chain and side-chain possibilities, taking the respective environments into account. The algorithm is used in a comparative modeling scenario to build side-chains, regions of main chain, and mix and match between different homologs in a context-sensitive manner. The predictive power of this method is assessed by applying it to cases where the experimental structure is not known in advance.

An all-atom distance-dependent conditional probability discriminatory function for protein structure prediction.

We present a formalism to compute the probability of an amino acid sequence conformation being native-like, given a set of pairwise atom-atom distances. The formalism is used to derive three discriminatory functions with different types of representations for the atom-atom contacts observed in a database of protein structures. These functions include two virtual atom representations and one all-heavy atom representation. When applied to six different decoy sets containing a range of correct and incorrect conformations of amino acid sequences, the all-atom distance-dependent discriminatory function is able to identify correct from incorrect more often than the discriminatory functions using approximate representations. We illustrate the importance of using a detailed atomic description for obtaining the most accurate discrimination, and the necessity for testing discriminatory functions against a wide variety of decoys. The discriminatory function is also shown to be capable of capturing the fine details of atom-atom preferences. These results suggest that the all-atom distance-dependent discriminatory function will be useful for protein structure prediction and model refinement.

Determinants of side chain conformational preferences in protein structures.

A discriminatory function based on a statistical analysis of atomic contacts in protein structures is used for selecting side chain rotamers given a peptide main chain. The function allows us to rank different possible side chain conformations on the basis of contacts between side chain atoms and atoms in the environment. We compare the differences in constructing side chain conformations using contacts with only the local main chain, using the entire main chain, and by building pairs of side chains simultaneously with local main chain information. Using only the local main chain allows us to construct side chains with approximately 75% of the chi1 angles within 30 degrees of the experimental value, and an average side chain atom r.m.s.d. of 1.72 A in a set of 10 proteins. The results of constructing side chains for the 10 proteins are compared with the results of other side chain building methods previously published. The comparison shows similar accuracies. An advantage of the present method is that it can be used to select a small number of likely side chain conformations for each residue, thus permitting limited combinatorial searches for building multiple protein side chains simultaneously.

Handling context-sensitivity in protein structures using graph theory: bona fide prediction.

We constructed five comparative models in a blind manner for the second meeting on the Critical Assessment of protein Structure Prediction methods (CASP2). The method used is based on a novel graph-theoretic clique-finding approach, and attempts to address the problem of interconnected structural changes in the comparative modeling of protein structures. We discuss briefly how the method is used for protein structure prediction, and detail how it performs in the blind tests. We find that compared to CASP1, significant improvements in building insertions and deletions and sidechain conformations have been achieved.

Confronting the problem of interconnected structural changes in the comparative modeling of proteins.

Comparative models of three proteins have been built using a variety of computational methods, heavily supplemented by visual inspection. We consider the accuracy obtained to be worse than expected. A careful analysis of the models shows that a major reason for the poor results is the interconnectedness of the structural differences between the target proteins and the template structures they were modeled from. Side chain conformations are often determined by details of the structure remote in the sequence, and can be influenced by relatively small main chain changes. Almost all of the regions of substantial main chain conformational change interact with at least one other such region, so that they often cannot be modeled independently. Visual inspection is sometimes effective in correcting errors in sequence alignment and in spotting when an alternative template structure is more appropriate. We expect some improvements in the near future through the development of structure-based sequence alignment tools, side chain interconnectedness rotamer choice algorithms, and a better understanding of the context sensitivity of conformational features.

Congenital neuroblastoma: evaluation with multimodality imaging.

The authors retrospectively evaluated 12 patients with congenital (neonatal) neuroblastoma to assess the utility of newer imaging modalities. Findings at prenatal ultrasound (US), performed in four patients, were nonspecific (hydramnios and hydrops fetalis) in two and consistent with a suprarenal mass (one solid, one cystic) in the other two. Postnatal US helped accurately detect adrenal tumors (solid or complex, with one exception) but was less accurate in the diagnosis of metastatic disease to the liver. Computed tomography accurately depicted all primary tumors and liver metastases. Magnetic resonance (MR) imaging helped establish the correct diagnosis in three patients. This study again confirmed the benign course of neonatal neuroblastoma, with 50% of the patients classified with stage IV-S disease and two deaths occurring in the series, both due to complications. Therefore, aggressive diagnostic imaging is less desirable, and US is therefore very useful, despite its limitations. The prenatal detection and solid appearance of a suprarenal mass makes the diagnosis of neuroblastoma very likely, as does the presence of liver lesions. In the absence of these characteristic findings, US should be repeated to exclude adrenal hemorrhage. MR imaging seems to be a good alternative in some instances.