Assigning secondary structure from protein coordinate data.

We have developed a program to convert the three dimensional coordinates describing protein structure in the Brookhaven Data Bank into an assignment of secondary structure. The program assigns secondary structure in the same way a person assigns structure visually. It uses two angles and three distances to assign alpha-helix, 3(10)-helix, beta-strand, hydrogen-bonded beta-turn, non-hydrogen-bonded beta-turn, and poly (L-proline) II type 3(1)-helix. The program is concerned with amide-amide interactions and should be particularly useful to spectroscopists.     

RNA secondary structure and in vitro translation efficiency

Cell-free protein synthesis is a promising technology featuring many advantages compared to in vivo expression techniques. However, most proteins are still synthesized in vivo due to relatively low protein yields commonly achieved in vitro, especially in the batch mode of reaction. In Escherichia coli S30 extract-based cell-free systems protein yields are supposed to be partially limited by a secondary structure formation of the mRNA. In this study we checked promising members of various classes of RNA chaperones and several different RNA helicases on their ability to enhance in vitro translation. The data clearly show that the addition of none of these factors provides a general solution to the problem. However, protein yields can be increased in presence of a microRNA hybridizing with the 5′ untranslated region of mRNAs, possibly by inducing structural changes improving accessibility of the Shine Dalgarno sequence for the ribosomes.     

Improved efficiency of protein structure calculations from NMR data using the program DIANA with redundant dihedral angle constraints

Summary A new strategy for NMR structure calculations of proteins with the variable target function method (Braun, W. and Go, N. (1985)J. Mol. Biol.,186, 611) is described, which makes use of redundant dihedral angle constraints (REDAC) derived from preliminary calculations of the complete structure. The REDAC approach reduces the computation time for obtaining a group of acceptable conformers with the program DIANA 5-100-fold, depending on the complexity of the protein structure, and retains good sampling of conformation space.Dedicated to the memory of Professor V.F. Bystrov     

Improved prediction of RNA secondary structure by integrating the free energy model with restraints derived from experimental probing data

Recently, several experimental techniques have emerged for probing RNA structures based on high-throughput sequencing. However, most secondary structure prediction tools that incorporate probing data are designed and optimized for particular types of experiments. For example, RNAstructure-Fold is optimized for SHAPE data, while SeqFold is optimized for PARS data. Here, we report a new RNA secondary structure prediction method, restrained MaxExpect (RME), which can incorporate multiple types of experimental probing data and is based on a free energy model and an MEA (maximizing expected accuracy) algorithm. We first demonstrated that RME substantially improved secondary structure prediction with perfect restraints (base pair information of known structures). Next, we collected structure-probing data from diverse experiments (e.g. SHAPE, PARS and DMS-seq) and transformed them into a unified set of pairing probabilities with a posterior probabilistic model. By using the probability scores as restraints in RME, we compared its secondary structure prediction performance with two other well-known tools, RNAstructure-Fold (based on a free energy minimization algorithm) and SeqFold (based on a sampling algorithm). For SHAPE data, RME and RNAstructure-Fold performed better than SeqFold, because they markedly altered the energy model with the experimental restraints. For high-throughput data (e.g. PARS and DMS-seq) with lower probing efficiency, the secondary structure prediction performances of the tested tools were comparable, with performance improvements for only a portion of the tested RNAs. However, when the effects of tertiary structure and protein interactions were removed, RME showed the highest prediction accuracy in the DMS-accessible regions by incorporating in vivo DMS-seq data.     

Determining protein topology from skeletons of secondary structures

We report a novel computational procedure for determining protein native topology, or fold, by defining loop connectivity based on skeletons of secondary structures that can usually be obtained from low to intermediate-resolution density maps. The procedure primarily involves a knowledge-based geometry filter followed by an energetics-based evaluation. It was tested on a large set of skeletons covering a wide range of protein architecture, including one modeled from an experimentally determined 7.6A cryo-electron microscopy (cryo-EM) density map. The results showed that the new procedure could effectively deduce protein folds without high-resolution structural data, a feature that could also be used to recognize native fold in structure prediction and to interpret data in fields like structure genomics. Most importantly, in the energetics-based evaluation, it was revealed that, despite the inevitable errors in the artificially constructed structures and limited accuracy of knowledge-based potential functions, the average energy of an ensemble of structures with slightly different configurations around the native skeleton is a much more robust parameter for marking native topology than the energy of individual structures in the ensemble. This result implies that, among all the possible topology candidates for a given skeleton, evolution has selected the native topology as the one that can accommodate the largest structural variations, not the one rigidly trapped in a deep, but narrow, conformational energy well.     

Performance of a neural-network-based determination of amino acid class and secondary structure from 1H-15N NMR data

A neural network which can determine both amino acid class andsecondary structure using NMR data from ¹⁵N-labeled proteinsis described. We have included nitrogen chemical shifts,³J_HN_H coupling constants, -protonchemical shifts, and side-chain proton chemical shifts as input to athree-layer feed-forward network. The network was trained with 456 spinsystems from several proteins containing various types of secondarystructure, and tested on human ubiquitin, which has no sequence homologywith any of the proteins in the training set. A very limited set of data,representative of those from a TOCSY-HSQC and HNHA experiment, was used.Nevertheless, in 60% of the spin systems the correct amino acid classwas among the top two choices given by the network, while in 96% ofthe spin systems the secondary structure was correctly identified. Theperformance of this network clearly shows the potential of the neuralnetwork algorithm in the automation of NMR spectral analysis.     

Effect of target secondary structure on RNAi efficiency

RNA interference (RNAi) mediated by small interfering RNAs (siRNAs) or short hairpin RNAs (shRNAs) has become a powerful tool for gene knockdown studies. However, the levels of knockdown vary greatly. Here, we examine the effect of target disruption energy, a novel measure of target accessibility, along with other parameters that may affect RNAi efficiency. Based on target secondary structures predicted by the Sfold program, the target disruption energy represents the free energy cost for local alteration of the target structure to allow target binding by the siRNA guide strand. In analyses of 100 siRNAs and 101 shRNAs targeted to 103 endogenous human genes, we find that the disruption energy is an important determinant of RNAi activity and the asymmetry of siRNA duplex asymmetry is important for facilitating the assembly of the RNA-induced silencing complex (RISC). We estimate that target accessibility and duplex asymmetry can improve the target knockdown level significantly by nearly 40% and 26%, respectively. In the RNAi pathway, RISC assembly precedes target binding by the siRNA guide strand. Thus, our findings suggest that duplex asymmetry has significant upstream effect on RISC assembly and target accessibility has strong downstream effect on target recognition. The results of the analyses suggest criteria for improving the design of siRNAs and shRNAs.     

Predicted secondary structure and membrane topology of the scrapie prion protein

The integral membrane sialoglycoprotein PrPSc is the only identifiable component of the scrapie prion. Scrapie in animals and Creutzfeldt-Jakob disease in humans are transmissible, degenerative neurological diseases caused by prions. Standard predictive strategies have been used to analyze the secondary structure of the prion protein in conjunction with Fourier analysis of the primary sequence hydrophobicities to detect potential amphipathic regions. Several hydrophobic segments, a proline- and glycine-rich repeat region and putative glycosylation sites are incorporated into a model for the integral membrane topology of PrP. The complete amino acid sequences of the hamster, human and mouse prion proteins are compared and the effects of residue substitutions upon the predicted conformation of the polypeptide chain are discussed. While PrP has a unique primary structure, its predicted secondary structure shares some interesting features with the serum amyloid A proteins. These proteins undergo a post-translational modification to yield amyloid A, molecules that share with PrP the ability to polymerize into birefringent filaments. Our analyses may explain some experimental observations on PrP, and suggest further studies on the properties of the scrapie and cellular PrP isoforms.     

Improved strategies for the determination of protein structures from NMR data: the solution structure of acyl carrier protein

The hybrid method that combines the early stages of a distance geometry program with simulated annealing in the presence of NMR constraints was optimized to obtain structures fully consistent with the observed NMR data. This was achieved by using more restrictive bounds of the NOE constraints than those usually used in the literature and by grouping the NOEs into classes dependent on the quality of the experimental NOE data. The 'floating' stereospecific assignment introduced at the simulated annealing stage of the calculations further improved the definition of the local conformation. An improved sampling and convergence property of the hybrid method was obtained by means of fitting the substructure obtained from the distance geometry program to different conformations. Compared to the standard hybrid methods, this procedure gave superior structures for a 77 amino acid protein, acyl carrier protein from Escherichia coli.     

Resonance assignments, secondary structure and topology of leukaemia inhibitory factor in solution

The chemical shift assignments and secondary structure of a murine–human chimera,MH35, of leukaemia inhibitory factor (LIF), a 180-residue protein of molecular mass 20 kDa,have been determined from multidimensional heteronuclear NMR spectra acquired on auniformly 13C,15N-labelled sample. Secondary structure elements were defined on the basisof chemical shifts, NH-CH coupling constants, medium-range NOEs and the location ofslowly exchanging amide protons. The protein contains four -helices, the relativeorientations of which were determined on the basis of long-range, interhelical NOEs. The fourhelices are arranged in an up-up-down-down orientation, as found in other four-helical bundlecytokines. The overall topology of MH35-LIF is similar to that of the X-ray crystallographicstructure for murine LIF [Robinson et al. (1994) Cell, 77, 1101–1116]. Differencesbetween the X-ray structure and the solution structure are evident in the N-terminal tail, wherethe solution structure has a trans-Pro17 compared with the cis-Pro17 found in the crystalstructure and the small antiparallel -sheet encompassing residues in the N-terminus andCD loop in the crystal structure is less stable.     

De novo high-resolution protein structure determination from sparse spin-labeling EPR data

As many key proteins evade crystallization and remain too large for nuclear magnetic resonance spectroscopy, electron paramagnetic resonance (EPR) spectroscopy combined with site-directed spin labeling offers an alternative approach for obtaining structural information. Such information must be translated into geometric restraints to be used in computer simulations. Here, distances between spin labels are converted into distance ranges between beta carbons by using a "motion-on-a-cone" model, and a linear-correlation model links spin-label accessibility to the number of neighboring residues. This approach was tested on T4-lysozyme and alphaA-crystallin with the de novo structure prediction algorithm Rosetta. The results demonstrate the feasibility of obtaining highly accurate, atomic-detail models from EPR data by yielding 1.0 A and 2.6 A full-atom models, respectively. Distance restraints between amino acids far apart in sequence but close in space are most valuable for structure determination. The approach can be extended to other experimental techniques such as fluorescence spectroscopy, substituted cysteine accessibility method, or mutational studies.     

Simultaneous determination of fluctuating age structure and mortality from field data

The use of a Leslie matrix for analysis of a population normally implies that the age structure of the population is known. However, this restriction can be overcome if the population can be partitioned into recognisably different stages, and some information on stage duration and fecundity is available, in which case the age structure may be determined by the analysis itself. As an example of this approach we consider the estimation of the mortality rate applying to a population from a sequence of observed stage frequency vectors. The technique does not require that the population has attained a stable age structure nor that distinct cohorts can be recognised.     

Novel methods for secondary structure determination using low wavelength (VUV) circular dichroism spectroscopic data

Background  

Circular Dichroism (CD) spectroscopy is a widely used method for studying protein structures in solution. Modern synchrotron radiation CD (SRCD) instruments have considerably higher photon fluxes than do conventional lab-based CD instruments, and hence have the ability to routinely measure CD data to much lower wavelengths. Recently a new reference dataset of SRCD spectra of proteins of known structure, designed to cover secondary structure and fold space, has been produced which includes low wavelength (vacuum ultraviolet – VUV) data. However, the existing algorithms used to calculate protein secondary structures from CD data have not been designed to take optimal advantage of the additional information in these low wavelength data.     

Synthesizing secondary data into survival analysis to improve estimation efficiency

The accelerated failure time (AFT) model and Cox proportional hazards (PH) model are broadly used for survival endpoints of primary interest. However, the estimation efficiency from those models can be further enhanced by incorporating the information from secondary outcomes that are increasingly available and highly correlated with primary outcomes. Those secondary outcomes could be longitudinal laboratory measures collected from doctor visits or cross-sectional disease-relevant variables, which are believed to contain extra information related to primary survival endpoints to a certain extent. In this paper, we develop a two-stage estimation framework to combine a survival model with a secondary model that contains secondary outcomes, named as the empirical-likelihood-based weighting (ELW), which comprises two weighting schemes accommodated to the AFT model (ELW-AFT) and the Cox PH model (ELW-Cox), respectively. This innovative framework is flexibly adaptive to secondary outcomes with complex data features, and it leads to more efficient parameter estimation in the survival model even if the secondary model is misspecified. Extensive simulation studies showcase more efficiency gain from ELW compared to conventional approaches, and an application in the Atherosclerosis Risk in Communities study also demonstrates the superiority of ELW by successfully detecting risk factors at the time of hospitalization for acute myocardial infarction.     

Protein secondary structure from Fourier transform infrared spectroscopy: a data base analysis

An infrared (ir) method to determine the secondary structure of proteins in solution using the amide I region of the spectrum has been devised. The method is based on the circular dichroism (CD) matrix method for secondary structure analysis given by Compton and Johnson (L. A. Compton and W. C. Johnson, 1986, Anal. Biochem. 155, 155-167). The infrared data matrix was constructed from the normalized Fourier transform infrared spectra from 1700 to 1600 cm-1 of 17 commercially available proteins. The secondary structure matrix was constructed from the X-ray data of the seventeen proteins with secondary structure elements of helix, beta-sheet, beta-turn, and other (random). The CD and ir methods were compared by analyzing the proteins of the CD and ir databases as unknowns. Both methods produce similar results compared to structures obtained by X-ray crystallographic means with the CD slightly better for helix conformation, and the ir slightly better for beta-sheet. The relatively good ir analysis for concanavalin A and alpha-chymotrypsin indicate that the ir method is less affected by the presence of aromatic groups. The concentration of the protein and the cell path length need not be known for the ir analysis since the spectra can be normalized to the total ir intensity in the amide I region. The ir spectra for helix, beta-sheet, beta-turn, and other, as extracted from the data-base, agree with the literature band assignments. The ir data matrix and the inverse matrix necessary to analyze unknown proteins are presented.     

Improved performance in protein secondary structure prediction by combining multiple predictions

In this paper(1) we present a novel framework for protein secondary structure prediction. In this prediction framework, firstly we propose a novel parameterized semi-probability profile, which combines single sequence with evolutionary information effectively. Secondly, different semi-probability profiles are respectively applied as network input to predict protein secondary structure. Then a comparison among these different predictions is discussed in this article. Finally, na?ve Bayes approaches are used to combine these predictions in order to obtain a better prediction performance than individual prediction. The experimental results show that our proposed framework can indeed improve the prediction accuracy.