Analysis of topological and nontopological structural similarities in the PDB: New examples with old structures

We have developed a new method and program, SARF2, for fast comparison of protein structures, which can detect topological as well as nontopological similarities. The method searches for large ensembles of secondary structure elements, which are mutually compatible in two proteins. These ensembles consist of small fragments of C^α-trace, similarly arranged in three-dimensional space in two proteins, but not necessarily equally-ordered along the polypeptide chains. The program SARF2 is available for everyone through the World-Wide Web (WWW). We have performed an exhaustive pairwise comparison of all the entries from a recent issue of the Protein Data Bank (PDB) and report here on the results of an automated hierarchical cluster analysis. In addition, we report on several new cases of significant structural resemblance between proteins. To this end, a new definition of the significance of structural similarity is introduced, which effectively distinguishes the biologically meaningful equivalences from those occurring by chance. Analyzing the distribution of sequence similarity in significant structural matches, we show that sequence similarity as low as 20% in structurally-prealigned proteins can be a strong indication for the biological relevance of structural similarity. © 1996 Wiley-Liss, Inc.     

Classification and annotation of the relationship between protein structural change and ligand binding

The causal relationship between protein structural change and ligand binding was classified and annotated for 839 nonredundant pairs of crystal structures in the Protein Data Bank—one with and the other without a bound low-molecular-weight ligand molecule. Protein structural changes were first classified into either domain or local motions depending on the size of the moving protein segments. Whether the protein motion was coupled with ligand binding was then evaluated based on the location of the ligand binding site and by application of the linear response theory of protein structural change. Protein motions coupled with ligand binding were further classified into either closure or opening motions. This classification revealed the following: (i) domain motions coupled with ligand binding are dominated by closure motions, which can be described by the linear response theory; (ii) local motions frequently accompany order-disorder or α-helix-coil conformational transitions; and (iii) transferase activity (Enzyme Commission   number 2) is the predominant function among coupled domain closure motions. This could be explained by the closure motion acting to insulate the reaction site of these enzymes from environmental water.     

PRI-Modeler: extracting RNA structural elements from PDB files of protein-RNA complexes

A complete understanding of protein and RNA structures and their interactions is important for determining the binding sites in protein-RNA complexes. Computational approaches exist for identifying secondary structural elements in proteins from atomic coordinates. However, similar methods have not been developed for RNA, due in part to the very limited structural data so far available. We have developed a set of algorithms for extracting and visualizing secondary and tertiary structures of RNA and for analyzing protein-RNA complexes. These algorithms have been implemented in a web-based program called PRI-Modeler (protein-RNA interaction modeler). Given one or more protein data bank files of protein-RNA complexes, PRI-Modeler analyzes the conformation of the RNA, calculates the hydrogen bond (H bond) and van der Waals interactions between amino acids and nucleotides, extracts secondary and tertiary RNA structure elements, and identifies the patterns of interactions between the proteins and RNAs. This paper presents PRI-Modeler and its application to the hydrogen bond and van der Waals interactions in the most representative set of protein-RNA complexes. The analysis reveals several interesting interaction patterns at various levels. The information provided by PRI-Modeler should prove useful for determining the binding sites in protein-RNA complexes. PRI-Modeler is accessible at http://wilab.inha.ac.kr/primodeler/, and supplementary materials are available in the analysis results section at http://wilab.inha.ac.kr/primodeler/.     

Identification and classification of ncRNA molecules using graph properties

The study of non-coding RNA genes has received increased attention in recent years fuelled by accumulating evidence that larger portions of genomes than previously acknowledged are transcribed into RNA molecules of mostly unknown function, as well as the discovery of novel non-coding RNA types and functional RNA elements. Here, we demonstrate that specific properties of graphs that represent the predicted RNA secondary structure reflect functional information. We introduce a computational algorithm and an associated web-based tool (GraPPLE) for classifying non-coding RNA molecules as functional and, furthermore, into Rfam families based on their graph properties. Unlike sequence-similarity-based methods and covariance models, GraPPLE is demonstrated to be more robust with regard to increasing sequence divergence, and when combined with existing methods, leads to a significant improvement of prediction accuracy. Furthermore, graph properties identified as most informative are shown to provide an understanding as to what particular structural features render RNA molecules functional. Thus, GraPPLE may offer a valuable computational filtering tool to identify potentially interesting RNA molecules among large candidate datasets.     

Using least median of squares for structural superposition of flexible proteins

Background

The conventional superposition methods use an ordinary least squares (LS) fit for structural comparison of two different conformations of the same protein. The main problem of the LS fit that it is sensitive to outliers, i.e. large displacements of the original structures superimposed.

Results

To overcome this problem, we present a new algorithm to overlap two protein conformations by their atomic coordinates using a robust statistics technique: least median of squares (LMS). In order to effectively approximate the LMS optimization, the forward search technique is utilized. Our algorithm can automatically detect and superimpose the rigid core regions of two conformations with small or large displacements. In contrast, most existing superposition techniques strongly depend on the initial LS estimating for the entire atom sets of proteins. They may fail on structural superposition of two conformations with large displacements. The presented LMS fit can be considered as an alternative and complementary tool for structural superposition.

Conclusion

The proposed algorithm is robust and does not require any prior knowledge of the flexible regions. Furthermore, we show that the LMS fit can be extended to multiple level superposition between two conformations with several rigid domains. Our fit tool has produced successful superpositions when applied to proteins for which two conformations are known. The binary executable program for Windows platform, tested examples, and database are available from https://engineering.purdue.edu/PRECISE/LMSfit.     

Emerging structural themes in large RNA molecules

Extensive networks of tertiary interactions give rise to unique, highly organized domain architectures that characterize the three-dimensional structure of large RNA molecules. Formed by stacked layers of a near-planar arrangement of contiguous coaxial helices, large RNA molecules are relatively flat in overall shape. The functional core of these molecules is stabilized by a diverse set of tertiary interaction motifs that often bring together distant regions of conserved nucleotides. Although homologous RNAs from different organisms can be structurally diverse, they adopt a structurally conserved functional core that includes preassembled active and/or substrate binding sites. These findings broaden our understanding of RNA folding and tertiary structure stabilization, illustrating how large, complex RNAs assemble into unique structures to perform recognition and catalysis.     

Identification of structural domains in proteins by a graph heuristic.

A novel automatic procedure for identifying domains from protein atomic coordinates is presented. The procedure, termed STRUDL (STRUctural Domain Limits), does not take into account information on secondary structures and handles any number of domains made up of contiguous or non-contiguous chain segments. The core algorithm uses the Kernighan-Lin graph heuristic to partition the protein into residue sets which display minimum interactions between them. These interactions are deduced from the weighted Voronoi diagram. The generated partitions are accepted or rejected on the basis of optimized criteria, representing basic expected physical properties of structural domains. The graph heuristic approach is shown to be very effective, it approximates closely the exact solution provided by a branch and bound algorithm for a number of test proteins. In addition, the overall performance of STRUDL is assessed on a set of 787 representative proteins from the Protein Data Bank by comparison to domain definitions in the CATH protein classification. The domains assigned by STRUDL agree with the CATH assignments in at least 81% of the tested proteins. This result is comparable to that obtained previously using PUU (Holm and Sander, Proteins 1994;9:256-268), the only other available algorithm designed to identify domains with any number of non-contiguous chain segments. A detailed discussion of the structures for which our assignments differ from those in CATH brings to light some clear inconsistencies between the concept of structural domains based on minimizing inter-domain interactions and that of delimiting structural motifs that represent acceptable folding topologies or architectures. Considering both concepts as complementary and combining them in a layered approach might be the way forward.     

Direct electrochemistry of engineered cytochrome b562 molecules with a ligand binding pocket

The rapid and reversible electron transfer reaction of cytochrome b562 was observed at an In2O3 electrode. The estimated heterogeneous electron transfer rate constant (k0') was k0' > or = 5.0 x 10(-3) cm s(-1) at pH 6.5. When the methionine-7 (Met-7) residue, which coordinates to the heme iron as an axial ligand, of the wild-type cytochrome b562 was replaced by an Ala or Gly residue, a water molecule bound to the heme iron and the electron transfer rate constants decreased to 1.3 x 10(-3) and 1.8 x 10(-3) cm s(-1), respectively. This decrease in the electron transfer rate would be due to the larger reorganization energy for the structural change at the redox site. The midpoint potential of cytochrome b562 was shifted negatively by approximately 135 mV by replacing Met-7 with Ala or Gly. Similar dissociation kinetics of cyanide for the mutated molecules as compared to native myoglobin was obtained.     

Protein structural change upon ligand binding correlates with enzymatic reaction mechanism

Overall structural changes of enzymes in response to ligand binding were investigated by database analysis of 62 non-redundant enzymes whose ligand-unbound and ligand-bound forms were available in the Protein Data Bank. The results of analysis indicate that transferases often undergo large rigid-body domain motions upon ligand binding, while other enzymes, most typically, hydrolases, change their structures to a small extent. It was also found that the solvent accessibility of the substrate molecule was low in transferases but high in hydrolases. These differences are explained by the enzymatic reaction mechanisms. The transferase reaction requires the catalytic groups to be insulated from the water environment, and thus transferases bury the ligand molecule inside the protein by closing the cleft. On the other hand, the hydrolase reaction involves the surrounding water molecules and occurs at the protein surface, requiring only a small structural change.     

A structural analysis of G-quadruplex/ligand interactions

This focused review article discusses in detail, all available high-resolution small molecule ligand/G-quadruplex structural data derived from crystallographic and NMR based techniques, in an attempt to understand key factors in ligand binding and to highlight the biological importance of these complexes. In contrast to duplex DNA, G-quadruplexes are four-stranded nucleic acid structures folded from guanine rich repeat sequences stabilized by the stacking of guanine G-quartets and extensive Watson-Crick/Hoogsteen hydrogen bonding. Thermally stable, these topologies can play a role in telomere regulation and gene expression. The core structures of G-quadruplexes form stable scaffolds while the loops have been shown, by the addition of small molecule ligands, to be sufficiently adaptable to generate new and extended binding platforms for ligands to associate, either by extending G-quartet surfaces or by forming additional planar dinucleotide pairings. Many of these structurally characterised loop rearrangements were totally unexpected opening up new opportunities for the design of selective ligands. However these rearrangements do significantly complicate attempts to rationally design ligands against well defined but unbound topologies, as seen for the series of napthalene diimides complexes. Drawing together previous findings and with the introduction of two new crystallographic quadruplex/ligand structures we aim to expand the understanding of possible structural adaptations available to quadruplexes in the presence of ligands, thereby aiding in the design of new selective entities.     

Indel PDB: A database of structural insertions and deletions derived from sequence alignments of closely related proteins

Background  

Insertions and deletions (indels) represent a common type of sequence variations, which are less studied and pose many important biological questions. Recent research has shown that the presence of sizable indels in protein sequences may be indicative of protein essentiality and their role in protein interaction networks. Examples of utilization of indels for structure-based drug design have also been recently demonstrated. Nonetheless many structural and functional characteristics of indels remain less researched or unknown.     

Coarse-grained modeling of large RNA molecules with knowledge-based potentials and structural filters

Understanding the function of complex RNA molecules depends critically on understanding their structure. However, creating three-dimensional (3D) structural models of RNA remains a significant challenge. We present a protocol (the nucleic acid simulation tool [NAST]) for RNA modeling that uses an RNA-specific knowledge-based potential in a coarse-grained molecular dynamics engine to generate plausible 3D structures. We demonstrate NAST's capabilities by using only secondary structure and tertiary contact predictions to generate, cluster, and rank structures. Representative structures in the best ranking clusters averaged 8.0 ± 0.3 Å and 16.3 ± 1.0 Å RMSD for the yeast phenylalanine tRNA and the P4-P6 domain of the Tetrahymena thermophila group I intron, respectively. The coarse-grained resolution allows us to model large molecules such as the 158-residue P4-P6 or the 388-residue T. thermophila group I intron. One advantage of NAST is the ability to rank clusters of structurally similar decoys based on their compatibility with experimental data. We successfully used ideal small-angle X-ray scattering data and both ideal and experimental solvent accessibility data to select the best cluster of structures for both tRNA and P4-P6. Finally, we used NAST to build in missing loops in the crystal structures of the Azoarcus and Twort ribozymes, and to incorporate crystallographic data into the Michel–Westhof model of the T. thermophila group I intron, creating an integrated model of the entire molecule. Our software package is freely available at https://simtk.org/home/nast.     

Conserved water molecules in MHC class-I molecules and their putative structural and functional roles

A set of conserved water positions making direct contacts with the alpha1 and alpha2 domains of the MHC class-I protein was identified by a cluster analysis in 12 high-resolution crystal structures of proteins from different allele types and different species, comprising human, mouse and rat. The analysis revealed a total of 63 clusters, corresponding to water molecules, whose positions are conserved in half or more of the analyzed structures. Analysis of these clusters shows that the most conserved water positions-those appearing in the largest fraction of the structures-were also the most accurately defined, as measured by their normalized crystallographic B-factor. Not too surprisingly, these positions displayed better overlap and formed more H-bonds with the protein. In a second part of this work, a detailed analysis is presented of three of the most conserved water positions and their putative structural and functional roles are discussed. The most highly conserved of the three appears to play an important role in stabilizing the conformation of a twisted beta-turn between residues 118 and 122 (numbering of HLA-B3501, PDB code 1A1N). An equivalent water molecule was found to be associated with a similar beta-turn in 43 unrelated structures surveyed in the PDB, leading to the suggestion that this water molecule plays an important structural role in this type of turn. The second water molecule makes hydrogen bonds with residues lining pocket B in the peptide-binding groove and is suggested to play a role in modulating peptide recognition. The third highly conserved water molecule is located at the first kink of the alpha2 helix, possibly playing a role in determining the position of the N-terminal segment of that helix, which also carries side chains in contact with the bound peptide. This information on conserved water positions in MHC class-I molecules should be helpful in modeling interactions with bound peptide antigens and in designing new peptides with tailor-made affinities.     

Direct observation of ligand colocalization on individual receptor molecules.

We have exploited the novel methodology of far-field fluorescence microscopy at the single molecule level to study colocalization of two different ligand molecules on an individual receptor. The use of dual-wavelength single molecule imaging allows discrimination between isolated and colocalized ligands with an accuracy of 40 nm. In the case of very close proximity of the two ligands, below 7 nm, single pair Forster energy-transfer was observed. The latter finding unequivocally demonstrates colocalization of two ligands on an individual receptor.     

Selecting targets for structural determination by navigating in a graph of protein families

MOTIVATION: A major goal in structural genomics is to enrich the catalogue of proteins whose 3D structures are known. In an attempt to address this problem we mapped over 10 000 proteins with solved structures onto a graph of all Swissprot protein sequences (release 36, approximately 73 000 proteins) provided by ProtoMap, with the goal of sorting proteins according to their likelihood of belonging to new superfamilies. We hypothesized that proteins within neighbouring clusters tend to share common structural superfamilies or folds. If true, the likelihood of finding new superfamilies increases in clusters that are distal from other solved structures within the graph. RESULTS: We defined an order relation between unsolved proteins according to their 'distance' from solved structures in the graph, and sorted approximately 48 000 proteins. Our list can be partitioned into three groups: approximately 35 000 proteins sharing a cluster with at least one known structure; approximately 6500 proteins in clusters with no solved structure but with neighbouring clusters containing known structures; and a third group contains the rest of the proteins, approximately 6100 (in 1274 clusters). We tested the quality of the order relation using thousands of recently solved structures that were not included when the order was defined. The tests show that our order is significantly better (P-value approximately 10(5)) than a random order. More interestingly, the order within the union of the second and third groups, and the order within the third group alone, perform better than random (P-values: 0.0008 and 0.15, respectively) and are better than alternative orders created using PSI-BLAST. Herein, we present a method for selecting targets to be used in structural genomics projects. AVAILABILITY: List of proteins to be used for targets selection combined with a set of biological filters for narrowing down potential targets is in http://www.protarget.cs.huji.ac.il.