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PubMed1998

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.

Computer Simulation↗

PubMed2004

Database searching by flexible protein structure alignment.

We have recently developed a flexible protein structure alignment program (FATCAT) that identifies structural similarity, at the same time accounting for flexibility of protein structures. One of the most important applications of a structure alignment method is to aid in functional annotations by identifying similar structures in large structural databases. However, none of the flexible structure alignment methods were applied in this task because of a lack of significance estimation of flexible alignments. In this paper, we developed an estimate of the statistical significance of FATCAT alignment score, allowing us to use it as a database-searching tool. The results reported here show that (1) the distribution of the similarity score of FATCAT alignment between two unrelated protein structures follows the extreme value distribution (EVD), adding one more example to the current collection of EVDs of sequence and structure similarities; (2) introducing flexibility into structure comparison only slightly influences the sensitivity and specificity of identifying similar structures; and (3) the overall performance of FATCAT as a database searching tool is comparable to that of the widely used rigid-body structure comparison programs DALI and CE. Two examples illustrating the advantages of using flexible structure alignments in database searching are also presented. The conformational flexibilities that were detected in the first example may be involved with substrate specificity, and the conformational flexibilities detected in the second example may reflect the evolution of structures by block building.

Databases, Protein↗

PubMed1997

Comparison of protein structures using 3D profile alignment.

A novel method for protein structure comparison using 3D profile alignment is presented. The 3D profile is a position-dependent scoring matrix derived from three-dimensional structures and is basically used to estimate sequence-structure compatibility for prediction of protein structure. Our idea is to compare two 3D profiles using a dynamic programming algorithm to obtain optimal alignment and a similarity score between them. When the 3D profile of hemoglobin was compared with each of the profiles in the library, which contained 325 profiles of representative structures, all the profiles of other globins were detected with relatively high scores, and proteins in the same structural class followed the globins. Exhaustive comparison of 3D profiles in the library was also performed to depict protein relatedness in the structure space. Using multidimensional scaling, a planar projection of points in the protein structure space revealed an overall grouping in terms of structural classes, i.e., all-alpha, all-beta, alpha/beta, and alpha+beta. These results differ in implication from those obtained by the conventional structure-structure comparison method. Differences are discussed with respect to the structural divergence of proteins in the course of molecular evolution.

Algorithms↗

PubMed1998

Dictionary of recurrent domains in protein structures.

The rapid growth in the number of experimentally determined three-dimensional protein structures has sharpened the need for comprehensive and up-to-date surveys of known structures. Classic work on protein structure classification has made it clear that a structural survey is best carried out at the level of domains, i.e., substructures that recur in evolution as functional units in different protein contexts. We present a method for automated domain identification from protein structure atomic coordinates based on quantitative measures of compactness and, as the new element, recurrence. Compactness criteria are used to recursively divide a protein into a series of successively smaller and smaller substructures. Recurrence criteria are used to select an optimal size level of these substructures, so that many of the chosen substructures are common to different proteins at a high level of statistical significance. The joint application of these criteria automatically yields consistent domain definitions between remote homologs, a result difficult to achieve using compactness criteria alone. The method is applied to a representative set of 1,137 sequence-unique protein families covering 6,500 known structures. Clustering of the resulting set of domains (substructures) yields 594 distinct fold classes (types of substructures). The Dali Domain Dictionary (http://www.embl-ebi.ac.uk/dali/) not only provides a global structural classification, but also a comprehensive description of families of protein sequences grouped around representative proteins of known structure. The classification will be continuously updated and can serve as a basis for improving our understanding of protein evolution and function and for evolving optimal strategies to complete the map of all natural protein structures.

Amino Acid Sequence↗

PubMed1999

The armadillo family of structural proteins.

The armadillo gene is a segment polarity gene of Drosophila involved in signal transduction through wingless. Since the mid-1980s, a growing number of related proteins have been identified based on sequence homologies. These proteins share a central domain that is composed of a series of imperfect 45 amino acid repeats. Armadillo family members reveal diverse cellular locations reflecting their diverse functions. A single protein exerts several functions through interactions of its armadillo repeat domain with diverse binding partners. The proteins combine structural roles as cell-contact and cytoskeleton-associated proteins and signaling functions by generating and transducing signals affecting gene expression. The study of armadillo family members has made it increasingly clear that a distinction between structural proteins on the one hand and signaling molecules on the other is rather artificial. Instead armadillo family members exert both functions by interacting with a number of distinct cellular-binding partners.

Adenomatous Polyposis Coli Protein↗

PubMed2003

A computational pipeline for protein structure prediction and analysis at genome scale.

MOTIVATION: Experimental techniques alone cannot keep up with the production rate of protein sequences, while computational techniques for protein structure predictions have matured to such a level to provide reliable structural characterization of proteins at large scale. Integration of multiple computational tools for protein structure prediction can complement experimental techniques. RESULTS: We present an automated pipeline for protein structure prediction. The centerpiece of the pipeline is our threading-based protein structure prediction system PROSPECT. The pipeline consists of a dozen tools for identification of protein domains and signal peptide, protein triage to determine the protein type (membrane or globular), protein fold recognition, generation of atomic structural models, prediction result validation, etc. Different processing and prediction branches are determined automatically by a prediction pipeline manager based on identified characteristics of the protein. The pipeline has been implemented to run in a heterogeneous computational environment as a client/server system with a web interface. Genome-scale applications on Caenorhabditis elegans, Pyrococcus furiosus and three cyanobacterial genomes are presented. AVAILABILITY: The pipeline is available at http://compbio.ornl.gov/proteinpipeline/

Algorithms↗

PubMed2005

Refinement of NMR-determined protein structures with database derived distance constraints.

The protein structures determined by NMR (Nuclear Magnetic Resonance Spectroscopy) are not as detailed and accurate as those by X-ray crystallography and are often underdetermined due to the inadequate distance data available from NMR experiments. The uses of NMR-determined structures in such important applications as homology modeling and rational drug design have thus been severely limited. Here we show that with the increasing numbers of high quality protein structures being determined, a computational approach to enhancing the accuracy of the NMR-determined structures becomes possible by deriving additional distance constraints from the distributions of the distances in databases of known protein structures. We show through a survey on 462 NMR structures that, in fact, many inter-atomic distances in these structures deviate considerably from their database distributions and based on the refinement results on 10 selected NMR structures that these structures can actually be improved significantly when a selected set of distances are constrained within their high probability ranges in their database distributions.

Algorithms↗

PubMed2004

Identification and characterization of viral structural proteins of infectious salmon anemia virus.

Infectious salmon anemia virus (ISAV) is an unclassified Orthomyxovirus that has been shown to contain a segmented genome with eight single-stranded RNA species coding for 10 viral proteins. Four major structural proteins were characterized in the present study: two glycosylated proteins with estimated molecular masses of 42 and 50 kDa, one 66-kDa phosphoprotein, and one 22-kDa protein. Examination of lysed virions revealed the two glycoproteins and the 22-kDa protein in the soluble fraction, while the 66-kDa phosphoprotein and a minor part of the 22-kDa protein were found in the pelleted fraction. Immunofluorescence staining of infected cells demonstrated that the 22-kDa protein was a late protein accumulating in the nucleus. We conclude that the 66-kDa protein is the nucleoprotein, the 22-kDa protein is the matrix protein, and the 42- and 50-kDa proteins are the surface proteins. Radioimmunoprecipitation analysis of the 42-kDa glycoprotein, which was previously shown to represent the ISAV hemagglutinin, indicated that this protein exists at least as dimers. Further, by labeling of purified ISAV with [1,3-(3)H]diisopropyl fluorophosphate, it was also demonstrated that the viral esterase is located with the hemagglutinin. This finding was confirmed by demonstration of acetylesterase activity in affinity-purified hemagglutinin preparations. Finally, the active-site serine residue could be tentatively identified at position 32 within the amino acid sequence of the hemagglutinin of ISAV strain Glesvaer/2/90. It is proposed that the ISAV vp66 protein be termed nucleoprotein, the gp42 protein be termed HE protein, and the vp22 protein be termed matrix protein.

Amino Acid Sequence↗

PubMed2006

The fragment transformation method to detect the protein structural motifs.

To identify functional structural motifs from protein structures of unknown function becomes increasingly important in recent years due to the progress of the structural genomics initiatives. Although certain structural patterns such as the Asp-His-Ser catalytic triad are easy to detect because of their conserved residues and stringently constrained geometry, it is usually more challenging to detect a general structural motifs like, for example, the betabetaalpha-metal binding motif, which has a much more variable conformation and sequence. At present, the identification of these motifs usually relies on manual procedures based on different structure and sequence analysis tools. In this study, we develop a structural alignment algorithm combining both structural and sequence information to identify the local structure motifs. We applied our method to the following examples: the betabetaalpha-metal binding motif and the treble clef motif. The betabetaalpha-metal binding motif plays an important role in nonspecific DNA interactions and cleavage in host defense and apoptosis. The treble clef motif is a zinc-binding motif adaptable to diverse functions such as the binding of nucleic acid and hydrolysis of phosphodiester bonds. Our results are encouraging, indicating that we can effectively identify these structural motifs in an automatic fashion. Our method may provide a useful means for automatic functional annotation through detecting structural motifs associated with particular functions.

Amino Acid Motifs↗

PubMed1992

Protein structures in SDS micelle-protein complexes.

Sodium dodecyl sulfate (SDS) is used more often than any other detergent as an excellent denaturing or "unfolding" detergent. However, formation of ordered structure (alpha-helix or beta-sheet) in certain peptides is known to be induced by interaction with SDS micelles. The SDS-induced structures formed by these peptides are amphiphilic, having both a hydrophobic and a hydrophilic face. Previous work in this area has revealed that SDS induces helical folding in a wide variety of non-helical proteins. Here, we describe the interaction of several structurally unrelated proteins with SDS micelles and the correlation of these structures to helical amphiphilic regions present in the primary sequence. It is likely that the ability of native nonordered protein structures to form induced amphiphilic ordered structures is rather common.

Animals↗

PubMed2004

Clustering of protein structural fragments reveals modular building block approach of nature.

Structures of peptide fragments drawn from a protein can potentially occupy a vast conformational continuum. We co-ordinatize this conformational space with the help of geometric invariants and demonstrate that the peptide conformations of the currently available protein structures are heavily biased in favor of a finite number of conformational types or structural building blocks. This is achieved by representing a peptides' backbone structure with geometric invariants and then clustering peptides based on closeness of the geometric invariants. This results in 12,903 clusters, of which 2207 are made up of peptides drawn from functionally and/or structurally related proteins. These are termed "functional" clusters and provide clues about potential functional sites. The rest of the clusters, including the largest few, are made up of peptides drawn from unrelated proteins and are termed "structural" clusters. The largest clusters are of regular secondary structures such as helices and beta strands as well as of beta hairpins. Several categories of helices and strands are discovered based on geometric differences. In addition to the known classes of loops, we discover several new classes, which will be useful in protein structure modeling. Our algorithm does not require assignment of secondary structure and, therefore, overcomes the limitations in loop classification due to ambiguity in secondary structure assignment at loop boundaries.

Algorithms↗

PubMed2000

Cloning, sequencing, expression and promoter analysis of a structural protein of bacteriophage MB78.

Bacteriophage MB78, a virulent phage of Salmonella typhimurium isolated in our laboratory. It is different from the well-known temperate phage P22 and 9NA. A detailed physical map has been constructed. To understand more about the physiology and genetics of this interesting phage it has become necessary to fragment the phage genome, clone the fragments and analyze in depth. A number of promoters of bacteriophage MB78 have been cloned and characterized recently. As a part of this program, in this investigation, we report cloning, sequencing and expression and promoter analysis of the ClaI G fragment. We identified the expressed protein as phage structural. Phage structural proteins play a vital role in forming the core head of the phage particle.

Amino Acid Sequence↗

PubMed2002

Protein structure resources.

The Protein Data Bank (PDB) is the primary source of macromolecular structure data for a worldwide community of users. A subset of those users then process these data to derive secondary information which is also available on the WWW. This process includes validation, some form of reductionism, via sequence or structure, or visualization. The result, a set of further web-accessible resources on protein structure and functional classification, links to primary genomic information, protein-protein and protein-ligand interactions, protein dynamics and protein-modeling resources. This paper reports on these processes and a subset of the web resources that result.

Crystallography, X-Ray↗

PubMedDate not supplied

Protein structure prediction and biomolecular recognition: from protein sequence to peptidomimetic design with the human beta3 integrin.

Computational tools can bridge the gap between sequence and protein 3D structure based on the notion that information is to be retrieved from the databases and that knowledge-based methods can help in approaching a solution of the protein-folding problem. To this aim our group has implemented neural network-based predictors capable of performing with some success in different tasks, including predictions of the secondary structure of globular and membrane proteins, the topology of membrane proteins and porins and stable alpha-helical segments suited for protein design. Moreover we have developed methods for predicting contact maps in proteins and the probability of finding a cysteine in a disulfide bridge, tools which can contribute to the goal of predicting the 3D structure starting from the sequence (the so called ab initio prediction). All our predictors take advantage of evolution information derived from the structural alignments of homologous (evolutionary related) proteins and taken from the sequence and structure databases. When it is necessary to build models for proteins of unknown spatial structure, which have very little homology with other proteins of known structure, non-standard techniques need to be developed and the tools for protein structure predictions may help in protein modeling. The results of a recent simulation performed in our lab highlights the role of high performing computing technology and of tools of computational biology in protein modeling and peptidomimetic design.

Databases, Factual↗

PubMed1990

An object-oriented database for protein structure analysis.

An object-oriented database system has been developed which is being used to store protein structure data. The database can be queried using the logic programming language Prolog or the query language Daplex. Queries retrieve information by navigating through a network of objects which represent the primary, secondary and tertiary structures of proteins. Routines written in both Prolog and Daplex can integrate complex calculations with the retrieval of data from the database, and can also be stored in the database for sharing among users. Thus object-oriented databases are better suited to prototyping applications and answering complex queries about protein structure than relational databases. This system has been used to find loops of varying length and anchor positions when modelling homologous protein structures.

Amino Acid Sequence↗

PubMed2006

The transthyretin-related protein: structural investigation of a novel protein family.

The transthyretin-related protein (TRP) family comprises proteins predicted to be structurally related to the homotetrameric transport protein transthyretin (TTR). The function of TRPs is not yet fully established, but recent data suggest that they are involved in purine catabolism. We have determined the three-dimensional structure of the Escherichia coli TRP in two crystal forms; one at 1.65 A resolution in the presence of zinc, and the other at 2.1 A resolution in the presence of zinc and bromide. The structures revealed five zinc-ion-binding sites per monomer. Of these, the zinc ions bound at sites I and II are coordinated in tetrahedral geometries to the side chains of residues His9, His96, His98, Ser114, and three water molecules at the putative ligand-binding site. Of these four residues, His9, His98, and Ser114 are conserved. His9 and His98 bind the central zinc (site I) together with two water molecules. The side chain of His98 also binds to the zinc ion at site II. Bromide ions bind at site I only, replacing one of the water molecules coordinated to the zinc ion. The C-terminal four amino acid sequence motif Y-[RK]-G-[ST] constitutes the signature sequence of the TRP family. Two Tyr111 residues form direct hydrogen bonds to each other over the tetramer interface at the area, which in TTR constitutes the rear part of its thyroxine-binding channel. The putative substrate/ligand-binding channel of TRP is consequently shallower and broader than its counterpart in TTR.

Amino Acid Sequence↗

PubMed2002

Structural proteomics: methods in deriving protein structural information and issues in data management.

Structural proteomics is an emerging paradigm that is gaining importance in the post-genomic era as a valuable discipline to process the protein target information being deciphered. The field plays a crucial role in assigning function to sequenced proteins, defining pathways in which the targets are involved, and understanding structure-function relationships of the protein targets. A key component of this research sector is accessing the three-dimensional structures of protein targets by both experimental and theoretical methods. This then leads to the question of how to store, retrieve, and manipulate vast amounts of sequence (1-D) and structural (3-D) information in a relational format so that extensive data analysis can be achieved. We at SBI have addressed both of these fundamental requirements of structural proteomics. We have developed an extensive collection of three-dimensional protein structures from sequence data and have implemented a relational architecture for data management. In this article we will discuss our approaches to structural proteomics and the tools that life science researchers can use in their discovery efforts.

Computational Biology↗

PubMed2004

On the use of DXMS to produce more crystallizable proteins: structures of the T. maritima proteins TM0160 and TM1171.

The structure of two Thermotoga maritima proteins, a conserved hypothetical protein (TM0160) and a transcriptional regulator (TM1171), have now been determined at 1.9 A and 2.3 A resolution, respectively, as part of a large-scale structural genomics project. Our first efforts to crystallize full-length versions of these targets were unsuccessful. However, analysis of the recombinant purified proteins using the technique of enhanced amide hydrogen/deuterium exchange mass spectroscopy (DXMS) revealed substantial regions of rapid amide deuterium hydrogen exchange, consistent with flexible regions of the structures. Based on these exchange data, truncations were designed to selectively remove the disordered C-terminal regions, and the resulting daughter proteins showed greatly enhanced crystallizability. Comparative DXMS analysis of full-length protein versus truncated forms demonstrated complete and exact preservation of the exchange rate profiles in the retained sequence, indicative of conservation of the native folded structure. This study presents the first structures produced with the aid of the DXMS method for salvaging intractable crystallization targets. The structure of TM0160 represents a new fold and highlights the use of this approach where any prior structural knowledge is absent. The structure of TM1171 represents an example where the lack of a substrate/cofactor may impair crystallization. The details of both structures are presented and discussed.

Amino Acid Sequence↗