A geometrical constraint approach for reproducing the native backbone conformation of a protein

It is known that the backbone conformation of a protein can be reproduced with precision once a correct contact map (two-dimensional representation showing residue pairs in contact) is given as geometrical constraints. There is, however, no way to infer the correct contact map for a protein of unknown structure. We started with one-dimensional constraints using the quantity N14 (the number of neighboring residues within the radius of 14 Å). Since the plot of N14 along a chain shows a good correlation with the corresponding amino acid sequence, the N14 profile obtained from the X-ray structure is predictable from the sequence. Construction of backbone conformations under a given N14 profile was carried out in the following two steps: (1) a contact map from the N14 profile was produced by taking the product of N14 values of every two residues; (2) backbone conformations were generated by applying the distance geometry technique to distance constraints given by the contact map. If present, disulfide bonds in a protein, as well as the secondary structure, were treated as additional constraints, and both cases with or without the additional information were examined. The method was tested for 11 proteins of known structure, and the results indicated that the reproduced conformation was fairly good, using an X-ray structure for comparison, for small proteins of less than 80 residues long. The basic assumption and effectiveness of the present method were compared with those of previous studies employing the geometrical constraint approach. It has become clear that the specific, one-dimensional information (e.g., N14 profile) is more effective than nonspecific, two-dimensional constraints, such as average interresidue distances between particular types of amino acids. © 1993 Wiley-Liss, Inc.     

Residue-residue contact substitution probabilities derived from aligned three-dimensional structures and the identification of common folds.

We report the derivation of scores that are based on the analysis of residue-residue contact matrices from 443 3-dimensional structures aligned structurally as 96 families, which can be used to evaluate sequence-structure matches. Residue-residue contacts and the more than 3 x 10(6) amino acid substitutions that take place between pairs of these contacts at aligned positions within each family of structures have been tabulated and segregated according to the solvent accessibility of the residues involved. Contact maps within a family of structures are shown to be highly conserved (approximately 75%) even when the sequence identity is approaching 10%. In a comparison involving a globin structure and the search of a sequence databank (> 21,000 sequences), the contact probability scores are shown to provide a very powerful secondary screen for the top scoring sequence-structure matches, where between 69% and 84% of the unrelated matches are eliminated. The search of an aligned set of 2 globins against a sequence databank and the subsequent residue contact-based evaluation of matches locates all 618 globin sequences before the first non-globin match. From a single bacterial serine proteinase structure, the structural template approach coupled with residue-residue contact substitution data lead to the detection of the mammalian serine proteinase family among the top matches in the search of a sequence databank.     

Defrosting the frozen approximation: PROSPECTOR--a new approach to threading

PROSPECTOR (PROtein Structure Predictor Employing Combined Threading to Optimize Results) is a new threading approach that uses sequence profiles to generate an initial probe-template alignment and then uses this "partly thawed" alignment in the evaluation of pair interactions. Two types of sequence profiles are used: the close set, composed of sequences in which sequence identity lies between 35% and 90%; and the distant set, composed of sequences with a FASTA E-score less than 10. Thus, a total of four scoring functions are used in a hierarchical method: the close (distant) sequence profiles screen a structural database to provide an initial alignment of the probe sequence in each of the templates. The same database is then screened with a scoring function composed of sequence plus secondary structure plus pair interaction profiles. This combined hierarchical threading method is called PROSPECTOR1. For the original Fischer database, 59 of 68 pairs are correctly identified in the top position. Next, the set of the top 20 scoring sequences (four scoring functions times the top five structures) is used to construct a protein-specific pair potential based on consensus side-chain contacts occurring in 25% of the structures. In subsequent threading iterations, this protein-specific pair potential, when combined in a composite manner, is found to be more sensitive in identifying the correct pairs than when the original statistical potential is used, and it increases the number of recognized structures for the combined scoring functions, termed PROSPECTOR2, to a total of 61 Fischer pairs identified in the top position. Application to a second, smaller Fischer database of 27 probe-template pairs places 18 (17) structures in the top position for PROSPECTOR1 (PROSPECTOR2). Overall, these studies show that the use of pair interactions as assessed by the improved Z-score enhances the specificity of probe-template matches. Thus, when the hierarchy of scoring functions is combined, the ability to identify correct probe-template pairs is significantly enhanced. Finally, a web server has been established for use by the academic community (http://bioinformatics.danforthcenter.org/services/threading.html).     

Recognizing the fold of a protein structure

This paper reports a graph-theoretic program, GRATH, that rapidly, and accurately, matches a novel structure against a library of domain structures to find the most similar ones. GRATH generates distributions of scores by comparing the novel domain against the different types of folds that have been classified previously in the CATH database of structural domains. GRATH uses a measure of similarity that details the geometric information, number of secondary structures and number of residues within secondary structures, that any two protein structures share. Although GRATH builds on well established approaches for secondary structure comparison, a novel scoring scheme has been introduced to allow ranking of any matches identified by the algorithm. More importantly, we have benchmarked the algorithm using a large dataset of 1702 non-redundant structures from the CATH database which have already been classified into fold groups, with manual validation. This has facilitated introduction of further constraints, optimization of parameters and identification of reliable thresholds for fold identification. Following these benchmarking trials, the correct fold can be identified with the top score with a frequency of 90%. It is identified within the ten most likely assignments with a frequency of 98%. GRATH has been implemented to use via a server (http://www.biochem.ucl.ac.uk/cgi-bin/cath/Grath.pl). GRATH's speed and accuracy means that it can be used as a reliable front-end filter for the more accurate, but computationally expensive, residue based structure comparison algorithm SSAP, currently used to classify domain structures in the CATH database. With an increasing number of structures being solved by the structural genomics initiatives, the GRATH server also provides an essential resource for determining whether newly determined structures are related to any known structures from which functional properties may be inferred.     

Structural constraints identified with covariation analysis in ribosomal RNA

Covariation analysis is used to identify those positions with similar patterns of sequence variation in an alignment of RNA sequences. These constraints on the evolution of two positions are usually associated with a base pair in a helix. While mutual information (MI) has been used to accurately predict an RNA secondary structure and a few of its tertiary interactions, early studies revealed that phylogenetic event counting methods are more sensitive and provide extra confidence in the prediction of base pairs. We developed a novel and powerful phylogenetic events counting method (PEC) for quantifying positional covariation with the Gutell lab's new RNA Comparative Analysis Database (rCAD). The PEC and MI-based methods each identify unique base pairs, and jointly identify many other base pairs. In total, both methods in combination with an N-best and helix-extension strategy identify the maximal number of base pairs. While covariation methods have effectively and accurately predicted RNAs secondary structure, only a few tertiary structure base pairs have been identified. Analysis presented herein and at the Gutell lab's Comparative RNA Web (CRW) Site reveal that the majority of these latter base pairs do not covary with one another. However, covariation analysis does reveal a weaker although significant covariation between sets of nucleotides that are in proximity in the three-dimensional RNA structure. This reveals that covariation analysis identifies other types of structural constraints beyond the two nucleotides that form a base pair.     

Enhanced protein fold recognition using secondary structure information from NMR.

NMR offers the possibility of accurate secondary structure for proteins that would be too large for structure determination. In the absence of an X-ray crystal structure, this information should be useful as an adjunct to protein fold recognition methods based on low resolution force fields. The value of this information has been tested by adding varying amounts of artificial secondary structure data and threading a sequence through a library of candidate folds. Using a literature test set, the threading method alone has only a one-third chance of producing a correct answer among the top ten guesses. With realistic secondary structure information, one can expect a 60-80% chance of finding a homologous structure. The method has then been applied to examples with published estimates of secondary structure. This implementation is completely independent of sequence homology, and sequences are optimally aligned to candidate structures with gaps and insertions allowed. Unlike work using predicted secondary structure, we test the effect of differing amounts of relatively reliable data.     

Improvement of RNA secondary structure prediction using RNase H cleavage and randomized oligonucleotides

RNA secondary structure prediction using free energy minimization is one method to gain an approximation of structure. Constraints generated by enzymatic mapping or chemical modification can improve the accuracy of secondary structure prediction. We report a facile method that identifies single-stranded regions in RNA using short, randomized DNA oligonucleotides and RNase H cleavage. These regions are then used as constraints in secondary structure prediction. This method was used to improve the secondary structure prediction of Escherichia coli 5S rRNA. The lowest free energy structure without constraints has only 27% of the base pairs present in the phylogenetic structure. The addition of constraints from RNase H cleavage improves the prediction to 100% of base pairs. The same method was used to generate secondary structure constraints for yeast tRNA^Phe, which is accurately predicted in the absence of constraints (95%). Although RNase H mapping does not improve secondary structure prediction, it does eliminate all other suboptimal structures predicted within 10% of the lowest free energy structure. The method is advantageous over other single-stranded nucleases since RNase H is functional in physiological conditions. Moreover, it can be used for any RNA to identify accessible binding sites for oligonucleotides or small molecules.     

Exploring the effects of sparse restraints on protein structure prediction

One of the main barriers to accurate computational protein structure prediction is searching the vast space of protein conformations. Distance restraints or inter‐residue contacts have been used to reduce this search space, easing the discovery of the correct folded state. It has been suggested that about 1 contact for every 12 residues may be sufficient to predict structure at fold level accuracy. Here, we use coarse‐grained structure‐based models in conjunction with molecular dynamics simulations to examine this empirical prediction. We generate sparse contact maps for 15 proteins of varying sequence lengths and topologies and find that given perfect secondary‐structural information, a small fraction of the native contact map (5%‐10%) suffices to fold proteins to their correct native states. We also find that different sparse maps are not equivalent and we make several observations about the type of maps that are successful at such structure prediction. Long range contacts are found to encode more information than shorter range ones, especially for α and αβ‐proteins. However, this distinction reduces for β‐proteins. Choosing contacts that are a consensus from successful maps gives predictive sparse maps as does choosing contacts that are well spread out over the protein structure. Additionally, the folding of proteins can also be used to choose predictive sparse maps. Overall, we conclude that structure‐based models can be used to understand the efficacy of structure‐prediction restraints and could, in future, be tuned to include specific force‐field interactions, secondary structure errors and noise in the sparse maps.     

Identifying sequence-structure pairs undetected by sequence alignments

We examine how effectively simple potential functions previously developed can identify compatibilities between sequences and structures of proteins for database searches. The potential function consists of pairwise contact energies, repulsive packing potentials of residues for overly dense arrangement and short-range potentials for secondary structures, all of which were estimated from statistical preferences observed in known protein structures. Each potential energy term was modified to represent compatibilities between sequences and structures for globular proteins. Pairwise contact interactions in a sequence-structure alignment are evaluated in a mean field approximation on the basis of probabilities of site pairs to be aligned. Gap penalties are assumed to be proportional to the number of contacts at each residue position, and as a result gaps will be more frequently placed on protein surfaces than in cores. In addition to minimum energy alignments, we use probability alignments made by successively aligning site pairs in order by pairwise alignment probabilities. The results show that the present energy function and alignment method can detect well both folds compatible with a given sequence and, inversely, sequences compatible with a given fold, and yield mostly similar alignments for these two types of sequence and structure pairs. Probability alignments consisting of most reliable site pairs only can yield extremely small root mean square deviations, and including less reliable pairs increases the deviations. Also, it is observed that secondary structure potentials are usefully complementary to yield improved alignments with this method. Remarkably, by this method some individual sequence-structure pairs are detected having only 5-20% sequence identity.     

Identification of c-myc coding region determinant RNA sequences and structures cleaved by an RNase1-like endoribonuclease

The coding region of c-myc mRNA encompassing the coding region determinant (CRD) nucleotides (nts) 1705-1792 is critical in regulating c-myc mRNA stability. This is in part due to the susceptibility of c-myc CRD RNA to attack by an endoribonuclease. We have previously purified and characterized a mammalian endoribonuclease that cleaves c-myc CRD RNA in vitro. This enzyme is tentatively identified as a 35 kDa RNase1-like endonuclease. In an effort to understand the sequence and secondary structure requirements for RNA cleavage by this enzyme, we have determined the secondary structure of the c-myc CRD RNA nts 1705-1792 using RNase probing technique. The secondary structure of c-myc CRD RNA possesses five stems; two of which contain 4 base pairs (stems I and V) and three consisting of 3 base pairs (stems II, III, and IV). Endonucleolytic assays using the c-myc CRD and several c-myc CRD mutants as substrates led to the following conclusions: (i) the enzyme prefers to cleave in between the dinucleotides UA, CA, and UG in single-stranded regions; (ii) the enzyme is more specific towards UA dinucleotides. These properties further distinguish the enzyme from previously described mammalian endonuclease that cleaves c-myc mRNA in vitro.     

Structural constraints for the Crh protein from solid-state NMR experiments

We demonstrate that short, medium and long-range constraints can be extracted from proton mediated, rare-spin detected correlation solid-state NMR experiments for the microcrystalline 10.4 × 2 kDa dimeric model protein Crh. Magnetization build-up curves from cross signals in NHHC and CHHC spectra deliver detailed information on side chain conformers and secondary structure for interactions between spin pairs. A large number of medium and long-range correlations can be observed in the spectra, and an analysis of the resolved signals reveals that the constraints cover the entire sequence, also including inter-monomer contacts between the two molecules forming the domain-swapped Crh dimer. Dynamic behavior is shown to have an impact on cross signals intensities, as indicated for mobile residues or regions by contacts predicted from the crystal structure, but absent in the spectra. Our work validates strategies involving proton distance measurements for large and complex proteins as the Crh dimer, and confirms the magnetization transfer properties previously described for small molecules in solid protein samples. Electronic supplementary material The online version of this article (doi:) contains supplementary material, which is available to authorized users.     

Collapse transitions in protein-like lattice polymers: The effect of sequence patterns

The collapse transition of lattice protein-like heteropolymers has been studied by means of the Monte Carlo method. The protein model has been reduced to the α-carbon trace restricted to a high coordination lattice. The sequences of model heteropolymers contain two types of mers: hydrophobic/nonpolar (H) and hydrophilic/polar (P). Interactions of HH and PP pairs were assumed to be negative (weaker attractions of PP pairs) while the contact energy for HP pairs was equal to zero. All sequence-specific short-range interactions have been neglected in the present studies. It has been found that homopolymeric chains undergo a smooth collapse transition to a dense globular state. The globule lacks any signatures of local ordering that could be interpreted as a model of protein secondary structure. Hetero-polymers with the sequences of hydrophilic and hydrophobic residues characteristic for α- and β-type proteins undergo a somewhat sharper (though continuous) collapse transition to a dense globular state with elements of local ordering controlled by the sequence. The helical pattern induces more secondary structure than the β-type pattern. For all examined sequences the level of local ordering was lower than the average secondary structure content of globular proteins. The results are compared with other theoretical work and with known experimental facts. The implications for the reduced modeling of protein systems are briefly discussed. © 1997 John Wiley & Sons, Inc. Biopoly 42: 537–548, 1997     

NMR study of a synthetic DNA hairpin

The secondary structure of the synthetic oligodeoxyribonucleotide d(CGCGCGTTTTCGCGCG) (I) has been demonstrated to be a unimolecular hairpin structure (hairpin I) over a wide range of oligonucleotide concentrations (2 X 10(-5) to 1.6 X 10(-3) M) and temperature (0-87 degrees C). The assignments of the resonances to specific protons were carried out by use of two-dimensional nuclear Overhauser effect and COSY spectra and by comparison with the spectra of the duplex formed by d(CG)3. Comparison of hairpin I and the hairpin of d(ATCCTATTTTTAGGAT) (II) reveals that the exchange of imino protons in stem base pairs with solvent is much slower in I than in II. However, the exchange of thymine imino protons in the loop region is much faster in I than in II even though both hairpins contain four unpaired thymine residues. The secondary structure of hairpin I contains only six G X C base pairs, yet it is more stable than the d(CG)8 duplex containing 16 G X C base pairs at all concentrations of duplex lower than 10(-3) M. These observations suggest that intramolecular hairpin formation may effectively compete with bimolecular duplex formations when the appropriate intramolecular base pairs can form.     

Prediction algorithm for amino acid types with their secondary structure in proteins (PLATON) using chemical shifts

The algorithm PLATON is able to assign sets of chemical shifts derived from a single residue to amino acid types with its secondary structure (amino acid species). A subsequent ranking procedure using optionally two different penalty functions yields predictions for possible amino acid species for the given set of chemical shifts. This was demonstrated in the case of the -spectrin SH3 domain and applied to 9 further protein data sets taken from the BioMagRes database. A database consisting of reference chemical shift patterns (reference CSPs) was generated from assigned chemical shifts of proteins with known 3D-structure. This reference CSP database is used in our approach for extracting distributions of amino acid types with their most likely secondary structure elements (namely -helix, -sheet, and coil) for single amino acids by comparison with query CSPs. Results obtained for the 10 investigated proteins indicates that the percentage of correct amino acid species in the first three positions in the ranking list, ranges from 71.4% to 93.2% for the more favorable penalty function. Where only the top result of the ranking list for these 10 proteins is considered, 36.5% to 83.1% of the amino acid species are correctly predicted. The main advantage of our approach, over other methods that rely on average chemical shift values is the ability to increase database content by incorporating newly derived CSPs, and therefore to improve PLATON's performance over time.     

Determining the three-dimensional fold of a protein from approximate constraints

We propose a new approach for calculating the three-dimensional (3D) structure of a protein from distance and dihedral angle constraints derived from experimental data. We suggest that such constraints can be obtained from experiments such as tritium planigraphy, chemical or enzymatic cleavage of the polypeptide chain, paramagnetic perturbation of nuclear magnetic resonance (NMR) spectra, measurement of hydrogen-exchange rates, mutational studies, mass spectrometry, and electron paramagnetic resonance. These can be supplemented with constraints from theoretical prediction of secondary structures and of buried/exposed residues. We report here distance geometry calculations to generate the structures of a test protein Staphylococcal nuclease (STN), and the HIV-1 rev protein (REV) of unknown structure. From the available 3D atomic coordinates of STN, we set up simulated data sets consisting of varying number and quality of constraints, and used our group's Self Correcting Distance Geometry (SECODG) program DIAMOD to generate structures. We could generate the correct tertiary fold from qualitative (approximate) as well as precise distance constraints. The root mean square deviations of backbone atoms from the native structure were in the range of 2.0 A to 8.3 A, depending on the number of constraints used. We could also generate the correct fold starting from a subset of atoms that are on the surface and those that are buried. When we used data sets containing a small fraction of incorrect distance constraints, the SECODG technique was able to detect and correct them. In the case of REV, we used a combination of constraints obtained from mutagenic data and structure predictions. DIAMOD generated helix-loop-helix models, which, after four self-correcting cycles, populated one family exclusively. The features of the energy-minimized model are consistent with the available data on REV-RNA interaction. Our method could thus be an attractive alternative for calculating protein 3D structures, especially in cases where the traditional methods of X-ray crystallography and multidimensional NMR spectroscopy have been unsuccessful.     

Assessment and Challenges of Ligand Docking into Comparative Models of G-Protein Coupled Receptors

The rapidly increasing number of high-resolution X-ray structures of G-protein coupled receptors (GPCRs) creates a unique opportunity to employ comparative modeling and docking to provide valuable insight into the function and ligand binding determinants of novel receptors, to assist in virtual screening and to design and optimize drug candidates. However, low sequence identity between receptors, conformational flexibility, and chemical diversity of ligands present an enormous challenge to molecular modeling approaches. It is our hypothesis that rapid Monte-Carlo sampling of protein backbone and side-chain conformational space with Rosetta can be leveraged to meet this challenge. This study performs unbiased comparative modeling and docking methodologies using 14 distinct high-resolution GPCRs and proposes knowledge-based filtering methods for improvement of sampling performance and identification of correct ligand-receptor interactions. On average, top ranked receptor models built on template structures over 50% sequence identity are within 2.9 Å of the experimental structure, with an average root mean square deviation (RMSD) of 2.2 Å for the transmembrane region and 5 Å for the second extracellular loop. Furthermore, these models are consistently correlated with low Rosetta energy score. To predict their binding modes, ligand conformers of the 14 ligands co-crystalized with the GPCRs were docked against the top ranked comparative models. In contrast to the comparative models themselves, however, it remains difficult to unambiguously identify correct binding modes by score alone. On average, sampling performance was improved by 10³ fold over random using knowledge-based and energy-based filters. In assessing the applicability of experimental constraints, we found that sampling performance is increased by one order of magnitude for every 10 residues known to contact the ligand. Additionally, in the case of DOR, knowledge of a single specific ligand-protein contact improved sampling efficiency 7 fold. These findings offer specific guidelines which may lead to increased success in determining receptor-ligand complexes.     

Evolutionary Analyses of DNA Sequences Subject to Constraints on Secondary Structure

Evolutionary models appropriate for analyzing nucleotide sequences that are subject to constraints on secondary structure are developed. The models consider the evolution of pairs of nucleotides, and they incorporate the effects of base-pairing constraints on nucleotide substitution rates by introducing a new parameter to extensions of standard models of sequence evolution. To illustrate some potential uses of the models, a likelihood-ratio test is constructed for the null hypothesis that two (prespecified) regions of DNA evolve independently of each other. The sampling properties of the test are explored via simulation. The test is then incorporated into a heuristic method for identifying the location of unknown stems. The test and related procedures are applied to data from ribonuclease P RNA sequences of bacteria.     

Recent advances in human quantitative-trait-locus mapping: comparison of methods for selected sibling pairs

During the past few years, there has been a great deal of new work on methods for mapping quantitative-trait loci by use of sibling pairs and sibships. There are several new methods based on linear regression, as well as several more that are based on score statistics. In theory, most of the new methods should be relatively robust to violations of distributional assumptions and to selected sampling, but, in practice, there has been little evaluation of how the methods perform on selected samples. We survey most of the new regression-based statistics and score statistics and propose a few minor variations on the score statistics. We use simulation to evaluate the type I error and the power of all of the statistics, considering (a) population samples of sibling pairs and (b) sibling pairs ascertained on the basis of at least one sibling with a trait value in the top 10% of the distribution. Most of the statistics have correct type I error for selected samples. The statistics proposed by Xu et al. and by Sham and Purcell are generally the most powerful, along with one of our score statistic variants. Even among the methods that are most powerful for "nice" data, some are more robust than others to non-Gaussian trait models and/or misspecified trait parameters.     

Contextual constraints on codon pair usage: structural and biological implications

Complementary DNA sequence data of 278 protein coding genes from prokaryotic systems have been analysed at the level of near neighbour codon pairs. Our analysis points out that constraints exist even at the level of near neighbour codon pairs. These constraints are in addition to those which arise due to relative levels of tRNA. Codon pairs, which in the data base have different occurrence values from their expected values, neither have common secondary structure nor do have better stabilization due to high base stacking. Our study points out that there are strong interaction between constituent codons in these codon pairs. These strongly interacting codon pairs, we suggest, are involved in the formation of three dimensional structural elements of cDNA/mRNA and interact with ribosome and thus modulate translation.     

Protein threading using PROSPECT: design and evaluation

The computer system PROSPECT for the protein fold recognition using the threading method is described and evaluated in this article. For a given target protein sequence and a template structure, PROSPECT guarantees to find a globally optimal threading alignment between the two. The scoring function for a threading alignment employed in PROSPECT consists of four additive terms: i) a mutation term, ii) a singleton fitness term, iii) a pairwise-contact potential term, and iv) alignment gap penalties. The current version of PROSPECT considers pair contacts only between core (alpha-helix or beta-strand) residues and alignment gaps only in loop regions. PROSPECT finds a globally optimal threading efficiently when pairwise contacts are considered only between residues that are spatially close (7 A or less between the C(beta) atoms in the current implementation). On a test set consisting of 137 pairs of target-template proteins, each pair being from the same superfamily and having sequence identity 相似文献