The cytidylyltransferase superfamily: Identification of the nucleotide-binding site and fold prediction

The crystal structure of glycerol-3-phosphate cytidylyltransferase from B. subtilis (TagD) is about to be solved. Here, we report a testable structure prediction based on the identification by sequence analysis of a superfamily of functionally diverse but structurally similar nucleotide-binding enzymes. We predict that TagD is a member of this family. The most conserved region in this superfamily resembles the ATP-binding HiGH motif of class I aminoacyI-tRNA synthetases. The predicted secondary structure of cytidylyltransferase and its homologues is compatible with the α/β topography of the class I aminoacyl-tRNA synthetases. The hypothesis of similarity of fold is strengthened by sequence-structure alignment and 3D model building using the known structure of tyrosyl tRNA synthetase as template. The proposed 3D model of TagD is plausible both structurally, with a well packed hydrophobic core, and functionally, as the most conserved residues cluster around the putative nucleotide binding site. If correct, the model would imply a very ancient evolutionary link between class I tRNA synthetases and the novel cytidylyltransferase superfamily. © 1995 Wiley-Liss, Inc.     

Progress of 1D protein structure prediction at last

Accuracy of predicting protein secondary structure and solvent accessibility from sequence information has been improved significantly by using information contained in multiple sequence alignments as input to a neural 'network system. For the Asilomar meeting, predictions for 13 proteins were generated automatically using the publicly available prediction method PHD. The results confirm the estimate of 72% three-state prediction accuracy. The fairly accurate predictions of secondary structure segments made the tool useful as a starting point for modeling of higher dimensional aspects of protein structure. © 1995 Wiley-Liss, Inc.     

Progress in fold recognition

The prediction experiment reveals that fold recognition has become a powerful tool in structural biology. We applied our fold recognition technique to 13 target sequences. In two cases, replication terminating protein and prosequence of subtilisin, the predicted structures are very similar to the experimentally determined folds. For the first time, in a public blind test, the unknown structures of proteins have been predicted ahead of experiment to an accuracy approaching molecular detail. In two other cases the approximate folds have been predicted correctly. According to the assessors there were 12 recognizable folds among the target proteins. In our postprediction analysis we find that in 7 cases our fold recognition technique is successful. In several of the remaining cases the predicted folds have interesting features in common with the experimental results. We present our procedure, discuss the results, and comment on several fundamental and technical problems encountered in fold recognition. © 1995 Wiley-Liss, Inc.     

Homology modeling by the ICM method

Five models have been built by the ICM method for the Comparative Modeling section of the Meeting on the Critical Assessment of Techniques for Protein Structure Prediction. The targets have homologous proteins with known three-dimensional structure with sequence identity ranging from 25 to 77%. After alignment of the target sequence with the related three-dimensional structure, the modeling procedure consists of two subproblems: side-chain prediction and loop prediction. The ICM method approaches these problems with the following steps: (1) a starting model is created based on the homologous structure with the conserved portion fixed and the noncon-served portion having standard covalent geometry and free torsion angles; (2) the Biased Probability Monte Carlo (BPMC) procedure is applied to search the subspaces of either all the nonconservative side-chain torsion angles or torsion angles in a loop backbone and surrounding side chains. A special algorithm was designed to generate low-energy loop deformations. The BPMC procedure globally optimizes the energy function consisting of ECEPP/3 and solvation energy terms. Comparison of the predictions with the NMR or crystallographic solutions reveals a high proportion of correctly predicted side chains. The loops were not correctly predicted because imprinted distortions of the backbone increased the energy of the near-native conformation and thus made the solution unrecognizable. Interestingly, the energy terms were found to be reliable and the sampling of conformational space sufficient. The implications of this finding for the strategies of future comparative modeling are discussed. © 1995 Wiley-Liss, Inc.     

Chemical shifts and three-dimensional protein structures

Summary During the past three years it has become possible to compute ab initio the ¹³C, ¹⁵N and ¹⁹F NMR chemical shifts of many sites in native proteins. Chemical shifts are beginning to become a useful supplement to more established methods of solution structure determination, and may find utility in solid-state analysis as well. From ¹³C NMR, information on , and torsions can be obtained, permitting both assignment verification, and structure refinement and prediction. For ¹⁵N, both torsional and hydrogen-bonding effects are important, while for ¹⁹F, chemical shifts are primarily indicators of the local charge field. Chemical shift calculations are still slow, but shielding hypersurfaces — the shift as a function of the dihedral angles that define the molecular conformation — are becoming accessible. Over the next few years, theoretical and computer hardware improvements will enable more routine use of chemical shifts in structural studies, including the study of metal-ligand interactions, the analysis of drug and substrate binding and catalysis, the study of folding/unfolding pathways, as well as the characterization of conformational substates. Rather than simply being a necessary prerequisite for multidimensional NMR, chemical shifts and chemical shift non-equivalence due to folding are now beginning to be useful for structural characterization.     

Structure-lipophilicity relationships of peptides and peptidomimetics

Summary Understanding the physicochemical and structural properties of peptides are important prerequisites for the rational design of bioactive peptides and peptidomimetics. The present contribution reviews methods used for the assessment and prediction of lipophilicity (or hydrophobicity) and their correlation with structural elements of peptides and closely related peptidomimetics.     

A simple and fast approach to prediction of protein secondary structure from multiply aligned sequences with accuracy above 70%.

To improve secondary structure predictions in protein sequences, the information residing in multiple sequence alignments of substituted but structurally related proteins is exploited. A database comprised of 70 protein families and a total of 2,500 sequences, some of which were aligned by tertiary structural superpositions, was used to calculate residue exchange weight matrices within alpha-helical, beta-strand, and coil substructures, respectively. Secondary structure predictions were made based on the observed residue substitutions in local regions of the multiple alignments and the largest possible associated exchange weights in each of the three matrix types. Comparison of the observed and predicted secondary structure on a per-residue basis yielded a mean accuracy of 72.2%. Individual alpha-helix, beta-strand, and coil states were respectively predicted at 66.7, and 75.8% correctness, representing a well-balanced three-state prediction. The accuracy level, verified by cross-validation through jack-knife tests on all protein families, dropped, on average, to only 70.9%, indicating the rigor of the prediction procedure. On the basis of robustness, conceptual clarity, accuracy, and executable efficiency, the method has considerable advantage, especially with its sole reliance on amino acid substitutions within structurally related proteins.     

Exhaustive enumeration of protein conformations using experimental restraints.

We present an efficient new algorithm that enumerates all possible conformations of a protein that satisfy a given set of distance restraints. Rapid growth of all possible self-avoiding conformations on the diamond lattice provides construction of alpha-carbon representations of a protein fold. We investigated the dependence of the number of conformations on pairwise distance restraints for the proteins crambin, pancreatic trypsin inhibitor, and ubiquitin. Knowledge of between one and two contacts per monomer is shown to be sufficient to restrict the number of candidate structures to approximately 1,000 conformations. Pairwise RMS deviations of atomic position comparisons between pairs of these 1,000 structures revealed that these conformations can be grouped into about 25 families of structures. These results suggest a new approach to assessing alternative protein folds given a very limited number of distance restraints. Such restraints are available from several experimental techniques such as NMR, NOESY, energy transfer fluorescence spectroscopy, and crosslinking experiments. This work focuses on exhaustive enumeration of protein structures with emphasis on the possible use of NOESY-determined distance restraints.     

Crystallization of alpha 1-acid glycoprotein

A possible link between cellular cyclic AMP content and Na+K+ATPase activity was investigated in homogenates of rat kidney. Enzyme kinetics of Mg2+ and Na+K+ATPase were run in the presence of cyclic AMP, dibutyryl cAMP and compounds expected to elevate cyclic AMP levels such as forskolin, a potent adenylate cyclase activator, IBMX, an inhibitor of phosphodiesterases, and the beta-agonist isoproterenol. Medullary Na+K+ATPase is strongly inhibited by cyclic AMP whereas cortical Na+K+ATPase was stimulated in the same conditions. The correlation between ATPase activity and cellular cyclic AMP content supports the concept of a possible regulation of the enzyme by cyclic AMP.     

MIXED MODEL APPROACHES FOR ESTIMATING GENETIC VARIANCES AND COVARIANCES

The limitations of methods for analysis of variance(ANOVA)in estimating genetic variances are discussed. Among the three methods(maximum likelihood ML, restricted maximum likelihood REML, and minimum norm quadratic unbiased estimation MINQUE)for mixed linear models, MINQUE method is presented with formulae for estimating variance components and covariances components and for predicting genetic effects. Several genetic models, which cannot be appropriately analyzed by ANOVA methods, are introduced in forms of mixed linear models. Genetic models with independent random effects can be analyzed by MINQUE(1)method whieh is a MINQUE method with all prior values setting 1. MINQUE(1)method can give unbiased estimation for variance components and covariance components, and linear unbiased prediction (LUP) for genetic effects. There are more complicate genetic models for plant seeds which involve correlated random effects. MINQUE(0/1)method, which is a MINQUE method with all prior covariances setting 0 and all prior variances setting 1, is suitable for estimating variance and covariance components in these models. Mixed model approaches have advantage over ANOVA methods for the capacity of analyzing unbalanced data and complicated models. Some problems about estimation and hypothesis test by MINQUE method are discussed.