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The secondary structure peptidomimetic approach is a rational way to develop novel nonpeptide pharmaceutical agents based upon biologically significant proteinaceous leads. A part of this approach elaborated in this laboratory over the past ten years is reviewed along with the recent developments in this field.
Secondary structure of the mRNA in the translational initiation region is an important determinant of translation efficiency. However, the secondary structures that enhance or facilitate translation initiation are rare. We have previously proposed that such structure may exist in the case of bacteriophage T4 gene 25 translational initiation region, which contains three potential Shine-Dalgarno sequences (SD1, SD2, and SD3) with a spacing of 8, 17, and 27 nucleotides from the initiation codon of this gene, respectively. We now present results that clearly demonstrate the existence of a hairpin structure that includes SD1 and SD2 sequences and brings the SD3, the most typical of these Shine-Dalgarno sequences, to a favourable spacing with the initiation codon of gene 25. Using a phage T7 expression system, we show that mutations that prevent the formation of hairpin structure or eliminate the SD3 sequence result in a decreased level of gp25 synthesis. Double mutation in base-pair V restores the level of gene 25 expression that was decreased by either of the two mutations (C-to-G and G-to-C) alone, as predicted by an effect attributable to mRNA secondary structure. We introduced the mutations into the bacteriophage T4 by plasmid-phage recombination. Changes in the plaque and burst sizes of T4 mutants, carrying single and double mutations in the translational initiation region of gene 25, strongly suggest that the predicted mRNA secondary structure controls (enhances) the level of gene 25 expression in vivo. Hybridization of total cellular RNA with a gene 25 specific probe indicated that secondary structure or mutations in the translational initiation region do not notably affect the 25 mRNA stability. Immunoblot analysis of gp25 in Escherichia coli cells infected by T4 mutants showed that mRNA secondary structure increases the level of gp25 synthesis by three- to fourfold. Since the secondary structure increases the level of gp25 synthesis and does not affect mRNA stability, we conclude that this structure enhances translation initiation. We discuss some features of two secondary structures in the translational initiation regions of T4 genes 25 and 38.
CD spectra of reduced and S-3-(trimethylated amino) propylated lysozyme (TMAP lysozyme) have been measured in various solutions containing guanidine hydrochloride or trifluoroethanol (TFE). The CD spectra indicate that there remain residual secondary structures in protein in aqueous solution. The addition of TFE further promotes the formation of secondary structures. In order to examine whether secondary structures are evenly induced over all the polypeptide chain, or locally at particular segments, the limited proteolysis of TMAP lysozyme by trypsin has been performed, and the CD spectra of all the final and intermediate products have been observed in solutions containing TFE. As a result, the fragments vary in a helix-forming propensity. The CD spectra of peptide fragments T5, T7, T9T10, T12T13, T14T15T16, and T17T18 are not significantly affected by the addition of TFE, where T refers to the nomenclature of R.E. Canfield [(1963), Journal of Biological Chemistry, Vol. 238, pp. 2691-2697]. They are fragments of a helix-breaking propensity. On the other hand, fragment I2 composed of T1-T4, and fragments T6T7, T8, and T11, attain secondary structures with the addition of TFE. They are fragments of a helix-forming propensity. Further, it is found that the fragments of a helix-forming propensity just correspond to the helical segments in native lysozyme. We examine the interactions between neighboring fragments, which contribute to the stabilization of local structures along the polypeptide chain.
The secondary structure of the P5abc subdomain (a 56-nt RNA) of the Tetrahymena thermophila group I intron ribozyme has been determined by NMR. Its base pairing in aqueous solution in the absence of magnesium ions is significantly different from the RNA in a crystal but is consistent with thermodynamic predictions. On addition of magnesium ions, the RNA folds into a tertiary structure with greatly changed base pairing consistent with the crystal structure: three Watson-Crick base pairs, three G.U base pairs, and an extra-stable tetraloop are lost. The common assumption that RNA folds by first forming secondary structure and then forming tertiary interactions from the unpaired bases is not always correct.
A collective secondary structure prediction for the human erythrocyte spectrin 106-residue repeat segment is developed, based on the sequences of nine segments that have been reported in the literature, utilizing a consensus of several secondary structure prediction methods for locating turn regions. The analysis predicts a five-fold structure, with three alpha-helices and two beta-strand regions, and differs from previous models on the lengths of the helices and the existence of beta-strand structure. We also demonstrate that this structural motif can be folded into tertiary structures that satisfy the experimental spectrin data and several general principles of protein organization.
RNA structure formation is hierarchical and, therefore, secondary structure, the sum of canonical base-pairs, can generally be predicted without knowledge of the three-dimensional structure. Secondary structure prediction algorithms evolved from predicting a single, lowest free energy structure to their current state where statistics can be determined from the thermodynamic ensemble. This article reviews the free energy minimization technique and the salient revolutions in the dynamic programming algorithm methods for secondary structure prediction. Emphasis is placed on highlighting the recently developed method, which statistically samples structures from the complete Boltzmann ensemble.
Prediction of RNA secondary structure is important in the functional analysis of RNA molecules. The RDfolder web server described in this paper provides two methods for prediction of RNA secondary structure: random stacking of helical regions and helical regions distribution. The random stacking method predicts secondary structure by Monte Carlo simulations. The method of helical regions distribution predicts secondary structure based on the helices that appear most frequently in the set of structures, which are generated by the random stacking method. The RDfolder web server can be accessed at http://rna.cbi.pku.edu.cn.
There are two secondary structure models for the eukaryotic selenocysteine (Sec) tRNA(Sec). One model, the 9/4 structure, was experimentally tested and possesses acceptor and T-stems with 9 and 4 bp, respectively [Sturchler et al., 1993; Hubert et al., 1998]. The other one, the 7/5 secondary structure with a bulge in the T-stem, was derived from theoretical calculation [Ioudovitch and Steinberg, 19991. In this report, we show more experimental results supporting the 9/4 secondary structure. Several tRNA(Sec) mutants, whose secondary structure can adopt only the 9/4 structure, were active for serylation and selenylation. Some mutants that cannot base-pair between positions 26 and 44 to provide the 6 bp anticodon stem were still active, inconsistent with the model by Steinberg. We also show that the orientation of the V-arm directly or indirectly influences the selenylation activity, and that the rigid 6 bp D-stem is important. Finally, we conclude that all tRNA(Sec) possess the 13 bp domain II made by the stacking of the colinear AA and T-stems, whether they present the 9/4 structure in Eukarya and Archaea or the 8/5 structure in bacteria.
The heat shock response in Escherichia coli depends primarily on the increased synthesis and stabilization of otherwise scarce and unstable sigma32 (rpoH gene product), which is required for the transcription of heat shock genes. The heat-induced synthesis of sigma32 occurs at the level of translation, and genetic evidence has suggested the involvement of a secondary structure at the 5' portion (nucleotides -19 to +247) of rpoH mRNA in regulation. We now present evidence for the mRNA secondary structure model by means of structure probing of RNA with chemical and enzymatic probes. A similar analysis of several mutant RNAs with a mutation predicted to alter a base pairing or with two compensatory mutations revealed altered secondary structures consistent with the expression and heat inducibility of the corresponding fusion constructs observed in vivo. These findings led us to assess the possible roles of each of the stem-loop structures by analyzing an additional set of deletions and base substitutions. The results indicated not only the primary importance of base pairings between the translation initiation region of ca. 20 nucleotides (the AUG initiation codon plus the "downstream box") and the internal region of rpoH mRNA but also the requirement of appropriate stability of mRNA secondary structures for characteristic thermoregulation, i.e., repression at a low temperature and induction upon a temperature upshift.
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Secondary structure in RNA targets is a significant barrier to short DNA probes. However, when such targets are the end product of an in vitro amplification scheme, it is possible to carry out transcription in the presence of nucleoside triphosphate analogues that reduce secondary structure of the RNA without impairing subsequent hybridization. Here we show that nucleoside triphosphates of 2-aminoadenine (nA) and 2-thiouracil (sU) are taken up by T7 RNA polymerase and that the resulting RNA possesses reduced secondary structure and improved accessibility to DNA probes. The hybridization properties of short RNA transcripts were studied using a new gel mobility shift assay from which melting temperatures were determined. RNA hairpins that contained nA and sU were able to hybridize to DNA probes under conditions where the unmodified hairpins did not. DNA-RNA hybrids that contained nA and sU in the RNA strand exhibited enhanced specificity, increased stability, and greater equality of base pairing strength than the same hybrids without modifications. Substitution of guanine (G) with inosine (I) further reduced secondary structure, but RNA with this base hybridized nonselectively. The high stability of nA-T and A-sU base pairs in DNA-RNA hybrids, combined with the destabilizing effect of the nA-sU couple in RNA targets, accounts for the improved hybridization properties. These results suggest that incorporation of nA and sU during in vitro transcription is a promising strategy for enhancing the performance of oligomeric DNA probes with an RNA target.
We describe a web server, which provides easy access to the SLoop database of loop conformations connecting elements of protein secondary structure. The loops are classified according to their length, the type of bounding secondary structures and the conformation of the mainchain. The current release of the database consists of over 8000 loops of up to 20 residues in length. A loop prediction method, which selects conformers on the basis of the sequence and the positions of the elements of secondary structure, is also implemented. These web pages are freely accessible over the internet at http://www-cryst.bioc.cam.ac.uk/ approximately sloop.
A protein secondary structure database (PSS) has been designed to correlate the Protein Sequence Database of the PIR-International with the atomic coordinates and bond connectivities database of the Protein Data Bank in the Brookhaven National Laboratory. The present database includes secondary structures determined by X-ray diffraction analysis, but not predicted structures. The database currently contains data from both the Protein Sequence Database and the Protein Data Bank Database, and will encompass the NMR database in the future. The main characteristics of the database are as follows: (1) the secondary structures, sites, regions and domains of structural interest are displayed together with protein primary structures; and (2) the secondary structure of a desired length of peptide fragment is displayed upon request, as are the peptide fragment(s) that correspond to a defined secondary structure. This database also has software to indicate amino acid pairs having hydrogen bonds and to count the occurrence frequency of each pair as well as the conformational parameters widely used in semi-empirical methods of secondary structure prediction.
Algorithms of secondary structure prediction have undergone the developments of nearly 30 years. However, the problem of how to appropriately evaluate and compare algorithms has not yet completely solved. A graphic method to evaluate algorithms of secondary structure prediction has been proposed here. Traditionally, the performance of an algorithm is evaluated by a number, i.e., accuracy of various definitions. Instead of a number, we use a graph to completely evaluate an algorithm, in which the mapping points are distributed in a three-dimensional space. Each point represents the predictive result of the secondary structure of a protein. Because the distribution of mapping points in the 3D space generally contains more information than a number or a set of numbers, it is expected that algorithms may be evaluated and compared by the proposed graphic method more objectively. Based on the point distribution, six evaluation parameters are proposed, which describe the overall performance of the algorithm evaluated. Furthermore, the graphic method is simple and intuitive. As an example of application, two advanced algorithms, i.e., the PHD and NNpredict methods, are evaluated and compared. It is shown that there is still much room for further improvement for both algorithms. It is pointed out that the accuracy for predicting either the alpha-helix or beta-strand in proteins with higher alpha-helix or beta-strand content, respectively, should be greatly improved for both algorithms.
A protein secondary structure prediction method from multiply aligned homologous sequences is presented with an overall per residue three-state accuracy of 70.1%. There are two aims: to obtain high accuracy by identification of a set of concepts important for prediction followed by use of linear statistics; and to provide insight into the folding process. The important concepts in secondary structure prediction are identified as: residue conformational propensities, sequence edge effects, moments of hydrophobicity, position of insertions and deletions in aligned homologous sequence, moments of conservation, auto-correlation, residue ratios, secondary structure feedback effects, and filtering. Explicit use of edge effects, moments of conservation, and auto-correlation are new to this paper. The relative importance of the concepts used in prediction was analyzed by stepwise addition of information and examination of weights in the discrimination function. The simple and explicit structure of the prediction allows the method to be reimplemented easily. The accuracy of a prediction is predictable a priori. This permits evaluation of the utility of the prediction: 10% of the chains predicted were identified correctly as having a mean accuracy of > 80%. Existing high-accuracy prediction methods are "black-box" predictors based on complex nonlinear statistics (e.g., neural networks in PHD: Rost & Sander, 1993a). For medium- to short-length chains (> or = 90 residues and < 170 residues), the prediction method is significantly more accurate (P < 0.01) than the PHD algorithm (probably the most commonly used algorithm). In combination with the PHD, an algorithm is formed that is significantly more accurate than either method, with an estimated overall three-state accuracy of 72.4%, the highest accuracy reported for any prediction method.
Here we propose an approach to include global structural information in the secondary structure prediction procedure based on hidden Markov models (HMMs) of protein folds. We first identify the correct fold or 'topology' of a protein by means of the HMMs of topology families of proteins. Then the most likely structural model for that protein is used to modify the sequence of secondary structure states previously obtained with a prediction algorithm. Our goal is to investigate the effect on the prediction accuracy of including global structural information in the secondary structure prediction scheme, by means of the HMMs. We find that when the HMM of the predicted topology of a protein is used to adjust the secondary structure sequence predicted originally with the Quadratic-Logistic method, the cross-validated prediction accuracy (Q3) improves by 3%. The topology is correctly predicted in 68% of the cases. We conclude that this HMM based approach is a promising tool for effectively incorporating global structural information in the secondary structure prediction scheme.
To study local structures in proteins, we previously developed an autoassociative artificial neural network (autoANN) and clustering tool to discover intrinsic features of macromolecular structures. The hidden unit activations computed by the trained autoANN are a convenient low-dimensional encoding of the local protein backbone structure. Clustering these activation vectors results in a unique classification of protein local structural features called Structural Building Blocks (SBBs). Here we describe application of this method to a larger database of proteins, verification of the applicability of this method to structure classification, and subsequent analysis of amino acid frequencies and several commonly occurring patterns of SBBs. The SBB classification method has several interesting properties: 1) it identifies the regular secondary structures, alpha helix and beta strand; 2) it consistently identifies other local structure features (e.g., helix caps and strand caps); 3) strong amino acid preferences are revealed at some positions in some SBBs; and 4) distinct patterns of SBBs occur in the "random coil" regions of proteins. Analysis of these patterns identifies interesting structural motifs in the protein backbone structure, indicating that SBBs can be used as "building blocks" in the analysis of protein structure. This type of pattern analysis should increase our understanding of the relationship between protein sequence and local structure, especially in the prediction of protein structures.
If secondary structure predictions are to be incorporated into fold recognition methods, an assessment of the effect of specific types of errors in predicted secondary structures on the sensitivity of fold recognition should be carried out. Here, we present a systematic comparison of different secondary structure prediction methods by measuring frequencies of specific types of error. We carry out an evaluation of the effect of specific types of error on secondary structure element alignment (SSEA), a baseline fold recognition method. The results of this evaluation indicate that missing out whole helix or strand elements, or predicting the wrong type of element, is more detrimental than predicting the wrong lengths of elements or overpredicting helix or strand. We also suggest that SSEA scoring is an effective method for assessing accuracy of secondary structure prediction and perhaps may also provide a more appropriate assessment of the "usefulness" and quality of predicted secondary structure, if secondary structure alignments are to be used in fold recognition.