A quantitative atlas of polyadenylation in five mammals

We developed PolyA-seq, a strand-specific and quantitative method for high-throughput sequencing of 3′ ends of polyadenylated transcripts, and used it to globally map polyadenylation (polyA) sites in 24 matched tissues in human, rhesus, dog, mouse, and rat. We show that PolyA-seq is as accurate as existing RNA sequencing (RNA-seq) approaches for digital gene expression (DGE), enabling simultaneous mapping of polyA sites and quantitative measurement of their usage. In human, we confirmed 158,533 known sites and discovered 280,857 novel sites (FDR < 2.5%). On average 10% of novel human sites were also detected in matched tissues in other species. Most novel sites represent uncharacterized alternative polyA events and extensions of known transcripts in human and mouse, but primarily delineate novel transcripts in the other three species. A total of 69.1% of known human genes that we detected have multiple polyA sites in their 3′UTRs, with 49.3% having three or more. We also detected polyadenylation of noncoding and antisense transcripts, including constitutive and tissue-specific primary microRNAs. The canonical polyA signal was strongly enriched and positionally conserved in all species. In general, usage of polyA sites is more similar within the same tissues across different species than within a species. These quantitative maps of polyA usage in evolutionarily and functionally related samples constitute a resource for understanding the regulatory mechanisms underlying alternative polyadenylation.

Genetic validation of whole-transcriptome sequencing for mapping expression affected by cis-regulatory variation

Background

Identifying associations between genotypes and gene expression levels using microarrays has enabled systematic interrogation of regulatory variation underlying complex phenotypes. This approach has vast potential for functional characterization of disease states, but its prohibitive cost, given hundreds to thousands of individual samples from populations have to be genotyped and expression profiled, has limited its widespread application.

Results

Here we demonstrate that genomic regions with allele-specific expression (ASE) detected by sequencing cDNA are highly enriched for cis-acting expression quantitative trait loci (cis-eQTL) identified by profiling of 500 animals in parallel, with up to 90% agreement on the allele that is preferentially expressed. We also observed widespread noncoding and antisense ASE and identified several allele-specific alternative splicing variants.

Conclusion

Monitoring ASE by sequencing cDNA from as little as one sample is a practical alternative to expression genetics for mapping cis-acting variation that regulates RNA transcription and processing.

Digital genome-wide ncRNA expression, including SnoRNAs, across 11 human tissues using polyA-neutral amplification

Non-coding RNAs (ncRNAs) are an essential class of molecular species that have been difficult to monitor on high throughput platforms due to frequent lack of polyadenylation. Using a polyadenylation-neutral amplification protocol and next-generation sequencing, we explore ncRNA expression in eleven human tissues. ncRNAs 7SL, U2, 7SK, and HBII-52 are expressed at levels far exceeding mRNAs. C/D and H/ACA box snoRNAs are associated with rRNA methylation and pseudouridylation, respectively: spleen expresses both, hypothalamus expresses mainly C/D box snoRNAs, and testes show enriched expression of both H/ACA box snoRNAs and RNA telomerase TERC. Within the snoRNA 14q cluster, 14q(I-6) is expressed at much higher levels than other cluster members. More reads align to mitochondrial than nuclear tRNAs. Many lincRNAs are actively transcribed, particularly those overlapping known ncRNAs. Within the Prader-Willi syndrome loci, the snoRNA HBII-85 (group I) cluster is highly expressed in hypothalamus, greater than in other tissues and greater than group II or III. Additionally, within the disease locus we find novel transcription across a 400,000 nt span in ovaries. This genome-wide polyA-neutral expression compendium demonstrates the richness of ncRNA expression, their high expression patterns, their function-specific expression patterns, and is publicly available.

Definition, conservation and epigenetics of housekeeping and tissue-enriched genes

BACKGROUND

Housekeeping genes (HKG) are constitutively expressed in all tissues while tissue-enriched genes (TEG) are expressed at a much higher level in a single tissue type than in others. HKGs serve as valuable experimental controls in gene and protein expression experiments, while TEGs tend to represent distinct physiological processes and are frequently candidates for biomarkers or drug targets. The genomic features of these two groups of genes expressed in opposing patterns may shed light on the mechanisms by which cells maintain basic and tissue-specific functions.

RESULTS

Here, we generate gene expression profiles of 42 normal human tissues on custom high-density microarrays to systematically identify 1,522 HKGs and 975 TEGs and compile a small subset of 20 housekeeping genes which are highly expressed in all tissues with lower variance than many commonly used HKGs. Cross-species comparison shows that both the functions and expression patterns of HKGs are conserved. TEGs are enriched with respect to both segmental duplication and copy number variation, while no such enrichment is observed for HKGs, suggesting the high expression of HKGs are not due to high copy numbers. Analysis of genomic and epigenetic features of HKGs and TEGs reveals that the high expression of HKGs across different tissues is associated with decreased nucleosome occupancy at the transcription start site as indicated by enhanced DNase hypersensitivity. Additionally, we systematically and quantitatively demonstrated that the CpG islands' enrichment in HKGs transcription start sites (TSS) and their depletion in TEGs TSS. Histone methylation patterns differ significantly between HKGs and TEGs, suggesting that methylation contributes to the differential expression patterns as well.

CONCLUSION

We have compiled a set of high quality HKGs that should provide higher and more consistent expression when used as references in laboratory experiments than currently used HKGs. The comparison of genomic features between HKGs and TEGs shows that HKGs are more conserved than TEGs in terms of functions, expression pattern and polymorphisms. In addition, our results identify chromatin structure and epigenetic features of HKGs and TEGs that are likely to play an important role in regulating their strikingly different expression patterns.

Pokefind: a novel topological filter for use with protein structure prediction

MOTIVATION

Our focus has been on detecting topological properties that are rare in real proteins, but occur more frequently in models generated by protein structure prediction methods such as Rosetta. We previously created the Knotfind algorithm, successfully decreasing the frequency of knotted Rosetta models during CASP6. We observed an additional class of knot-like loops that appeared to be equally un-protein-like and yet do not contain a mathematical knot. These topological features are commonly referred to as slip-knots and are caused by the same mechanisms that result in knotted models. Slip-knots are undetectable by the original Knotfind algorithm. We have generalized our algorithm to detect them, and analyzed CASP6 models built using the Rosetta loop modeling method.

RESULTS

After analyzing known protein structures in the PDB, we found that slip-knots do occur in certain proteins, but are rare and fall into a small number of specific classes. Our group used this new Pokefind algorithm to distinguish between these rare real slip-knots and the numerous classes of slip-knots that we discovered in Rosetta models and models submitted by the various CASP7 servers. The goal of this work is to improve future models created by protein structure prediction methods. Both algorithms are able to detect un-protein-like features that current metrics such as GDT are unable to identify, so these topological filters can also be used as additional assessment tools.

Mapping the genetic architecture of gene expression in human liver

Genetic variants that are associated with common human diseases do not lead directly to disease, but instead act on intermediate, molecular phenotypes that in turn induce changes in higher-order disease traits. Therefore, identifying the molecular phenotypes that vary in response to changes in DNA and that also associate with changes in disease traits has the potential to provide the functional information required to not only identify and validate the susceptibility genes that are directly affected by changes in DNA, but also to understand the molecular networks in which such genes operate and how changes in these networks lead to changes in disease traits. Toward that end, we profiled more than 39,000 transcripts and we genotyped 782,476 unique single nucleotide polymorphisms (SNPs) in more than 400 human liver samples to characterize the genetic architecture of gene expression in the human liver, a metabolically active tissue that is important in a number of common human diseases, including obesity, diabetes, and atherosclerosis. This genome-wide association study of gene expression resulted in the detection of more than 6,000 associations between SNP genotypes and liver gene expression traits, where many of the corresponding genes identified have already been implicated in a number of human diseases. The utility of these data for elucidating the causes of common human diseases is demonstrated by integrating them with genotypic and expression data from other human and mouse populations. This provides much-needed functional support for the candidate susceptibility genes being identified at a growing number of genetic loci that have been identified as key drivers of disease from genome-wide association studies of disease. By using an integrative genomics approach, we highlight how the gene RPS26 and not ERBB3 is supported by our data as the most likely susceptibility gene for a novel type 1 diabetes locus recently identified in a large-scale, genome-wide association study. We also identify SORT1 and CELSR2 as candidate susceptibility genes for a locus recently associated with coronary artery disease and plasma low-density lipoprotein cholesterol levels in the process.

Genome-wide association studies seek to identify regions of the genome in which changes in DNA in a given population are correlated with disease, drug response, or other phenotypes of interest. However, changes in DNA that associate with traits like common human diseases do not lead directly to disease, but instead act on intermediate, molecular phenotypes that in turn induce changes in the higher-order disease traits. Therefore, identifying molecular phenotypes that vary in response to changes in DNA that also associate with changes in disease traits can provide the functional information necessary to not only identify and validate the susceptibility genes directly affected by changes in DNA, but to understand as well the molecular networks in which such genes operate and how changes in these networks lead to changes in disease traits. To enable this type of approach we profiled the expression levels of 39,280 transcripts and genotyped 782,476 SNPs in 427 human liver samples, identifying thousands of DNA variants that strongly associated with liver gene expression. These relationships were then leveraged by integrating them with genotypic and expression data from other human and mouse populations, leading to the direct identification of candidate susceptibility genes corresponding to genetic loci identified as key drivers of disease. Our analysis is able to provide much needed functional support for these candidate susceptibility genes.

Identifying changes in DNA that associate with changes in gene expression in human tissues elucidates the genetic architecture of gene expression in human populations and enables the direct identification of functionally supported candidate susceptibility genes in genomic regions associated with disease.

Rapid knot detection and application to protein structure prediction

MOTIVATION

Knots in polypeptide chains have been found in very few proteins, and consequently should be generally avoided in protein structure prediction methods. Most effective structure prediction methods do not model the protein folding process itself, but rather seek only to correctly obtain the final native state. Consequently, the mechanisms that prevent knots from occurring in native proteins are not relevant to the modeling process, and as a result, knots can occur with significantly higher frequency in protein models. Here we describe Knotfind, a simple algorithm for knot detection that is fast enough for structure prediction, where tens or hundreds of thousands of conformations may be sampled during the course of a prediction. We have used this algorithm to characterize knots in large populations of model structures generated for targets in CASP 5 and CASP 6 using the Rosetta homology-based modeling method.

RESULTS

Analysis of CASP5 models suggested several possible avenues for introduction of knots into these models, and these insights were applied to structure prediction in CASP 6, resulting in a significant decrease in the proportion of knotted models generated. Additionally, using the knot detection algorithm on structures in the Protein Data Bank, a previously unreported deep trefoil knot was found in acetylornithine transcarbamylase.

AVAILABILITY

The Knotfind algorithm is available in the Rosetta structure prediction program at http://www.rosettacommons.org.

Prediction of CASP6 structures using automated Robetta protocols

The Robetta server and revised automatic protocols were used to predict structures for CASP6 targets. Robetta is a publicly available protein structure prediction server (http://robetta.bakerlab.org/ that uses the Rosetta de novo and homology modeling structure prediction methods. We incorporated some of the lessons learned in the CASP5 experiment into the server prior to participating in CASP6. We additionally tested new ideas that were amenable to full-automation with an eye toward improving the server. We find that the Robetta server shows the greatest promise for the more challenging targets. The most significant finding from CASP5, that automated protocols can be roughly comparable in ability with the better human-intervention predictors, is repeated here in CASP6.

Physically realistic homology models built with ROSETTA can be more accurate than their templates

We have developed a method that combines the ROSETTA de novo protein folding and refinement protocol with distance constraints derived from homologous structures to build homology models that are frequently more accurate than their templates. We test this method by building complete-chain models for a benchmark set of 22 proteins, each with 1 or 2 candidate templates, for a total of 39 test cases. We use structure-based and sequence-based alignments for each of the test cases. All atoms, including hydrogens, are represented explicitly. The resulting models contain approximately the same number of atomic overlaps as experimentally determined crystal structures and maintain good stereochemistry. The most accurate models can be identified by their energies, and in 22 of 39 cases a model that is more accurate than the template over aligned regions is one of the 10 lowest-energy models.

Protein structure estimation from minimal restraints using Rosetta

RosettaNMR combines the Rosetta de novo structure prediction method with limited NMR experimental data for rapid estimation of protein structure. The de novo Rosetta algorithm predicts protein three-dimensional structures using only sequence information by combining short fragments selected from known protein structures on the basis of local sequence similarity. These fragments are assembled using a Monte Carlo strategy to generate models that reproduce empirical statistics describing nonlocal protein structure such as overall compactness, hydrophobic burial, and beta-strand pairing. By incorporating chemical shift, nuclear Overhauser enhancement, and?or residual dipolar coupling restraints that are insufficient on their own to determine the protein global fold, the RosettaNMR method correctly estimates the global fold of a variety of different proteins, generating models that are that are generally 4?A or better Calpha root-mean-square deviation to the high-resolution experimental structures. Here we review the capabilities of the RosettaNMR approach, describe the underlying methods, and provide practical tips for applying the technique to structure estimation problems.

Modeling structurally variable regions in homologous proteins with rosetta

A major limitation of current comparative modeling methods is the accuracy with which regions that are structurally divergent from homologues of known structure can be modeled. Because structural differences between homologous proteins are responsible for variations in protein function and specificity, the ability to model these differences has important functional consequences. Although existing methods can provide reasonably accurate models of short loop regions, modeling longer structurally divergent regions is an unsolved problem. Here we describe a method based on the de novo structure prediction algorithm, Rosetta, for predicting conformations of structurally divergent regions in comparative models. Initial conformations for short segments are selected from the protein structure database, whereas longer segments are built up by using three- and nine-residue fragments drawn from the database and combined by using the Rosetta algorithm. A gap closure term in the potential in combination with modified Newton's method for gradient descent minimization is used to ensure continuity of the peptide backbone. Conformations of variable regions are refined in the context of a fixed template structure using Monte Carlo minimization together with rapid repacking of side-chains to iteratively optimize backbone torsion angles and side-chain rotamers. For short loops, mean accuracies of 0.69, 1.45, and 3.62 A are obtained for 4, 8, and 12 residue loops, respectively. In addition, the method can provide reasonable models of conformations of longer protein segments: predicted conformations of 3A root-mean-square deviation or better were obtained for 5 of 10 examples of segments ranging from 13 to 34 residues. In combination with a sequence alignment algorithm, this method generates complete, ungapped models of protein structures, including regions both similar to and divergent from a homologous structure. This combined method was used to make predictions for 28 protein domains in the Critical Assessment of Protein Structure 4 (CASP 4) and 59 domains in CASP 5, where the method ranked highly among comparative modeling and fold recognition methods. Model accuracy in these blind predictions is dominated by alignment quality, but in the context of accurate alignments, long protein segments can be accurately modeled. Notably, the method correctly predicted the local structure of a 39-residue insertion into a TIM barrel in CASP 5 target T0186.

Automated prediction of CASP-5 structures using the Robetta server

Robetta is a fully automated protein structure prediction server that uses the Rosetta fragment-insertion method. It combines template-based and de novo structure prediction methods in an attempt to produce high quality models that cover every residue of a submitted sequence. The first step in the procedure is the automatic detection of the locations of domains and selection of the appropriate modeling protocol for each domain. For domains matched to a homolog with an experimentally characterized structure by PSI-BLAST or Pcons2, Robetta uses a new alignment method, called K*Sync, to align the query sequence onto the parent structure. It then models the variable regions by allowing them to explore conformational space with fragments in fashion similar to the de novo protocol, but in the context of the template. When no structural homolog is available, domains are modeled with the Rosetta de novo protocol, which allows the full length of the domain to explore conformational space via fragment-insertion, producing a large decoy ensemble from which the final models are selected. The Robetta server produced quite reasonable predictions for targets in the recent CASP-5 and CAFASP-3 experiments, some of which were at the level of the best human predictions.

Rosetta predictions in CASP5: successes, failures, and prospects for complete automation

We describe predictions of the structures of CASP5 targets using Rosetta. The Rosetta fragment insertion protocol was used to generate models for entire target domains without detectable sequence similarity to a protein of known structure and to build long loop insertions (and N-and C-terminal extensions) in cases where a structural template was available. Encouraging results were obtained both for the de novo predictions and for the long loop insertions; we describe here the successes as well as the failures in the context of current efforts to improve the Rosetta method. In particular, de novo predictions failed for large proteins that were incorrectly parsed into domains and for topologically complex (high contact order) proteins with swapping of segments between domains. However, for the remaining targets, at least one of the five submitted models had a long fragment with significant similarity to the native structure. A fully automated version of the CASP5 protocol produced results that were comparable to the human-assisted predictions for most of the targets, suggesting that automated genomic-scale, de novo protein structure prediction may soon be worthwhile. For the three targets where the human-assisted predictions were significantly closer to the native structure, we identify the steps that remain to be automated.

An improved protein decoy set for testing energy functions for protein structure prediction

We have improved the original Rosetta centroid/backbone decoy set by increasing the number of proteins and frequency of near native models and by building on sidechains and minimizing clashes. The new set consists of 1,400 model structures for 78 different and diverse protein targets and provides a challenging set for the testing and evaluation of scoring functions. We evaluated the extent to which a variety of all-atom energy functions could identify the native and close-to-native structures in the new decoy sets. Of various implicit solvent models, we found that a solvent-accessible surface area-based solvation provided the best enrichment and discrimination of close-to-native decoys. The combination of this solvation treatment with Lennard Jones terms and the original Rosetta energy provided better enrichment and discrimination than any of the individual terms. The results also highlight the differences in accuracy of NMR and X-ray crystal structures: a large energy gap was observed between native and non-native conformations for X-ray structures but not for NMR structures.

Protein-protein docking with simultaneous optimization of rigid-body displacement and side-chain conformations

Protein-protein docking algorithms provide a means to elucidate structural details for presently unknown complexes. Here, we present and evaluate a new method to predict protein-protein complexes from the coordinates of the unbound monomer components. The method employs a low-resolution, rigid-body, Monte Carlo search followed by simultaneous optimization of backbone displacement and side-chain conformations using Monte Carlo minimization. Up to 10(5) independent simulations are carried out, and the resulting "decoys" are ranked using an energy function dominated by van der Waals interactions, an implicit solvation model, and an orientation-dependent hydrogen bonding potential. Top-ranking decoys are clustered to select the final predictions. Small-perturbation studies reveal the formation of binding funnels in 42 of 54 cases using coordinates derived from the bound complexes and in 32 of 54 cases using independently determined coordinates of one or both monomers. Experimental binding affinities correlate with the calculated score function and explain the predictive success or failure of many targets. Global searches using one or both unbound components predict at least 25% of the native residue-residue contacts in 28 of the 32 cases where binding funnels exist. The results suggest that the method may soon be useful for generating models of biologically important complexes from the structures of the isolated components, but they also highlight the challenges that must be met to achieve consistent and accurate prediction of protein-protein interactions.

Circular dichroism spectra of short, fixed-nucleus alanine helices

Very short alanine peptide helices can be studied in a fixed-nucleus, helix-forming system [Siedlicka, M., Goch, G., Ejchart, A., Sticht, H. & Bierzynski, A. (1999) Proc. Natl. Acad. Sci. USA 96, 903-908]. In a 12-residue sequence taken from an EF-hand protein, the four C-terminal peptide units become helical when the peptide binds La(3+), and somewhat longer helices may be made by adding alanine residues at the C terminus. The helices studied here contain 4, 8, or 11 peptide units. Surprisingly, these short fixed-nucleus helices remain almost fully helical from 4 to 65 degrees C, according to circular dichroism results reported here, and in agreement with titration calorimetry results reported recently. These peptides are used here to define the circular dichroism properties of short helices, which are needed for accurate measurement of helix propensities. Two striking properties are: (i) the temperature coefficient of mean peptide ellipticity depends strongly on helix length; and (ii) the intensity of the signal decreases much less rapidly with helix length, for very short helices, than supposed in the past. The circular dichroism spectra of the short helices are compared with new theoretical calculations, based on the experimentally determined direction of the NV(1) transition moment.

De novo prediction of three-dimensional structures for major protein families

We use the Rosetta de novo structure prediction method to produce three-dimensional structure models for all Pfam-A sequence families with average length under 150 residues and no link to any protein of known structure. To estimate the reliability of the predictions, the method was calibrated on 131 proteins of known structure. For approximately 60% of the proteins one of the top five models was correctly predicted for 50 or more residues, and for approximately 35%, the correct SCOP superfamily was identified in a structure-based search of the Protein Data Bank using one of the models. This performance is consistent with results from the fourth critical assessment of structure prediction (CASP4). Correct and incorrect predictions could be partially distinguished using a confidence function based on a combination of simulation convergence, protein length and the similarity of a given structure prediction to known protein structures. While the limited accuracy and reliability of the method precludes definitive conclusions, the Pfam models provide the only tertiary structure information available for the 12% of publicly available sequences represented by these large protein families.

De novo determination of protein backbone structure from residual dipolar couplings using Rosetta

As genome-sequencing projects rapidly increase the database of protein sequences, the gap between known sequences and known structures continues to grow exponentially, increasing the demand to accelerate structure determination methods. Residual dipolar couplings (RDCs) are an attractive source of experimental restraints for NMR structure determination, particularly rapid, high-throughput methods, because they yield both local and long-range orientational information and can be easily measured and assigned once the backbone resonances of a protein have been assigned. While very extensive RDC data sets have been used to determine the structure of ubiquitin, it is unclear to what extent such methods will generalize to larger proteins with less complete data sets. Here we incorporate experimental RDC restraints into Rosetta, an ab initio structure prediction method, and demonstrate that the combined algorithm provides a general method for de novo determination of a variety of protein folds from RDC data. Backbone structures for multiple proteins up to approximately 125 residues in length and spanning a range of topological complexities are rapidly and reproducibly generated using data sets that are insufficient in isolation to uniquely determine the protein fold de novo, although ambiguities and errors are observed for proteins with symmetry about an axis of the alignment tensor. The models generated are not high-resolution structures completely defined by experimental data but are sufficiently accurate to accelerate traditional high-resolution NMR structure determination and provide structure-based functional insights.

Exact solutions for chemical bond orientations from residual dipolar couplings

New methods for determining chemical structures from residual dipolar couplings are presented. The fundamental dipolar coupling equation is converted to an elliptical equation in the principal alignment frame. This elliptical equation is then combined with other angular or dipolar coupling constraints to form simple polynomial equations that define discrete solutions for the unit vector(s). The methods are illustrated with residual dipolar coupling data on ubiquitin taken in a single anisotropic medium. The protein backbone is divided into its rigid groups (namely, its peptide planes and Calpha frames), which may be solved for independently. A simple procedure for recombining these independent solutions results in backbone dihedral angles phi and psi that resemble those of the known native structure. Subsequent refinement of these phi-psi angles by the ROSETTA program produces a structure of ubiquitin that agrees with the known native structure to 1.1 A Calpha rmsd.

Alanine is helix-stabilizing in both template-nucleated and standard peptide helices

Alanine-based peptides of defined sequence and length show measurable helix contents, allowing them to be used as a model system both for analyzing the mechanism of helix formation and for investigating the contributions of side-chain interactions to protein stability. Extensive characterization of many peptide sequences with varying amino acid contents indicates that the favorable helicity of alanine-based peptides can be attributed to the large helix-stabilizing propensity of alanine. Based on their analysis of alanine-rich sequences N-terminally linked to a synthetic helix-inducing template, Kemp and coworkers [Kemp, D. S., Boyd, J. G. & Muendel, C. C. (1991) Nature (London) 352, 451-454; Kemp, D. S., Oslick, S. L. & Allen, T. J. (1996) J. Am. Chem. Soc. 118, 4249-4255] argue that alanine is helix-indifferent, however, and that the favorable helix contents of alanine-based peptides must have some other explanation. Here, we show that the helix contents of template-nucleated sequences are influenced strongly by properties of the template-helix junction. A model in which the helix propensities of residues at the template-peptide junction are treated separately brings the results from alanine-based peptides and template-nucleated helices into agreement. The resulting model provides a physically plausible resolution of the discrepancies between the two systems and allows the helix contents of both template-nucleated and standard peptide helices to be predicted by using a single set of helix propensities. Helix formation in both standard peptides and template-peptide conjugates can be attributed to the large intrinsic helix-forming tendency of alanine.

Solution structure of the sodium channel inactivation gate

The sodium channel initiates action potentials by opening in response to membrane depolarization. Fast channel inactivation, which is required for proper physiological function, is mediated by a cytoplasmic loop proposed to occlude the ion pore via a hinged lid mechanism with the triad IFM serving as a hydrophobic "latch". The NMR solution structure of the isolated inactivation gate reveals a stably folded core comprised of an alpha-helix capped by an N-terminal turn, supporting a model in which the tightly folded core containing the latch motif pivots on a more flexible hinge region to occlude the pore during inactivation. The structure, in combination with substituted cysteine mutagenesis experiments, indicates that the IFM triad and adjacent Thr are essential components of the latch and suggests differing roles for the residues of the IFMT motif in fast inactivation.

Comparison of NH exchange and circular dichroism as techniques for measuring the parameters of the helix-coil transition in peptides

Circular dichroism and NH exchange are compared directly as techniques for measuring helix content in peptides and the parameters of the helix-coil transition. To cover a broad range of helix contents, alanine-based peptides with chain lengths varying from 12 to 22 residues are examined over the temperature range from 0.6 to 26.9 degrees C in 1 M sodium chloride, 2H2O. Helix-coil transition theory independently fits both circular dichroism and exchange data, but the helix contents measured by exchange are larger than those measured by circular dichroism. The two techniques are brought into agreement by removing the assumption that the intrinsic chemical exchange rate in the helix is the same as the exchange rate measured for short unstructured model peptides. This modification allows the circular dichroism and NH exchange data to be described by the same set of helix parameters and indicates that the intrinsic exchange rate in the presence of helical structure is reduced approximately 17% relative to the rates measured in unstructured models. To test the possibility that this effect is electrostatic in origin, the sensitivity of the exchange reaction to ionic strength is determined. A substantial dependence of exchange rate on ionic strength is found, but the form of the dependence is complex. In studies of the exchange rates of native proteins, the exchange-competent form of the protein is assumed to exchange with the same rate constant as a blocked dipeptide with the identical amino acid sequences. Our result suggests that this assumption will be seriously in error in some cases because of charge effects in the protein.

Helix propagation and N-cap propensities of the amino acids measured in alanine-based peptides in 40 volume percent trifluoroethanol

The helix propagation and N-cap propensities of the amino acids have been measured in alanine-based peptides in 40 volume percent trifluoroethanol (40% TFE) to determine if this helix-stabilizing solvent uniformly affects all amino acids. The propensities in 40% TFE are compared with revised values of the helix parameters of alanine-based peptides in water. Revision of the propensities in water is the result of redefining the capping statistical weights and evaluating the helix nucleation constant with N-capping explicitly included in the helix-coil model. The propagation propensities of all amino acids increase in 40% TFE relative to water, but the increases are highly variable. In water, all beta-branched and beta-substituted amino acids are helix breakers. In 40% TFE, the propagation propensities of the nonpolar amino acids increase greatly, leaving charged and neutral polar, beta-substituted amino acids as helix breakers. Glycine and proline are strong helix breakers in both solvents. Free energy differences for helix propagation (delta delta G) between alanine and other nonpolar amino acids are twice as large in water as predicted from side-chain conformational entropies, but delta delta G values in 40% TFE are close to those predicted from side-chain entropies. This dependence of delta delta G on the solvent points to a specific role of water in determining the relative helix propensities of the nonpolar amino acids. The N-cap propensities converge toward a common value in 40% TFE, suggesting that differential solvation by water contributes to the diversity of N-cap values shown by the amino acids.

Models for the 3(10)-helix/coil, pi-helix/coil, and alpha-helix/3(10)-helix/coil transitions in isolated peptides

Models for the 3(10)-helix/coil and pi-helix/coil equilibria have been derived. The theory is based on classifying residues into helical or nonhelical (coil) conformations. Statistical weights are assigned to residues in a helical conformation with an associated helical hydrogen bond, a helical conformation with no hydrogen bond, an N-cap position, a C-cap position, or the reference coil conformation. The models for alpha-helix formation and 3(10)-helix formation have also been combined to describe a three-state equilibrium in which alpha-helical, 3(10)-helical, and coil conformations are populated. The results are compared with the modified Lifson-Roig theory for the alpha-helix/coil equilibrium. The comparison accounts for the experimental observations that 3(10)-helices tend to be short and pi-helices are not favored for any length. This work may provide a framework for quantitatively rationalizing experimental work on isolated 3(10)-helices and mixed 3(10)-/alpha-helices.

A general two-process model describes the hydrogen exchange behavior of RNase A in unfolding conditions

When NMR hydrogen exchange was used previously to monitor the kinetics of RNase A unfolding, some peptide NH protons were found to show EX2 exchange (detected by base catalysis) in addition to the expected EX1 exchange, whose rate is limited by the kinetic unfolding process. In earlier work, two groups showed independently that a restricted two-process model successfully fits published hydrogen exchange rates of native RNase A in the range 0-0.7 M guanidinium chloride. We find that this model predicts properties that are very different from the observed properties of the EX2 exchange reactions of RNase A in conditions where guanidine-induced unfolding takes place. The model predicts that EX2 exchange should be too fast to measure by the technique used, whereas it is readily measurable. Possible explanations for the contradiction are considered here, and we show that removing the restriction from the earlier two-process model is sufficient to resolve the contradiction; instead of specifying that exchange caused by global unfolding occurs by the EX2 mechanism, we allow it to occur by the general mechanism, which includes both the EX1 and EX2 cases. It is logical to remove this restriction because global unfolding of RNase A is known to give rise to EX1 exchange in these unfolding conditions. Resolving the contradiction makes it possible to determine whether populated unfolding intermediates contribute to the EX2 exchange, and this question is considered elsewhere. The results and simulations indicate that moderate or high denaturant concentrations readily give rise to EX1 exchange in native proteins. Earlier studies showed that hydrogen exchange in native proteins typically occurs by the EX2 mechanism but that high temperatures or pH values above 7 may give rise to EX1 exchange. High denaturant concentrations should be added to the list of variables likely to cause EX1 exchange.

Addition of side chain interactions to modified Lifson-Roig helix-coil theory: application to energetics of phenylalanine-methionine interactions

We introduce here i, i + 3 and i, i + 4 side chain interactions into the modified Lifson-Roig helix-coil theory of Doig et al. (1994, Biochemistry 33:3396-3403). The helix/coil equilibrium is a function of initiation, propagation, capping, and side chain interaction parameters. If each of these parameters is known, the helix content of any isolated peptide can be predicted. The model considers every possible conformation of a peptide, is not limited to peptides with only a single helical segment, and has physically meaningful parameters. We apply the theory to measure the i, i + 4 interaction energies between Phe and Met side chains. Peptides with these residues spaced i, i + 4 are significantly more helical than controls where they are spaced i, i + 5. Application of the model yields delta G for the Phe-Met orientation to be -0.75 kcal.mol-1, whereas that for the Met-Phe orientation is -0.54 kcal.mol-1. These orientational preferences can be explained, in part, by rotamer preferences for the interacting side chains. We place Phe-Met i, i + 4 at the N-terminus, the C-terminus, and in the center of the host peptide. The model quantitatively predicts the observed helix contents using a single parameter for the side chain-side chain interaction energy. This result indicates that the model works well even when the interaction is at different locations in the helix.

Exchange kinetics of individual amide protons in 15N-labeled helical peptides measured by isotope-edited NMR

Amide proton exchange measured by one-dimensional 15N-edited proton NMR has been used to probe helical structure in an alanine-based peptide. This study is the first report of individual peptide NH exchange rates determined in a simple, repeating sequence peptide whose helical structure can be predicted by helix-coil theory. Measured protection factors directly demonstrate that the ends of the helix are frayed. The protection factors are compared to the Lifson-Roig theory, modified to include N-capping, using known values for helix propensities and N-cap propensities. Base-catalyzed exchange rates are shown to measure the extent of hydrogen bonding of the peptide NHs, and the results are fitted by a simple model in which hydrogen bonding of the peptide NH group provides protection and no exchange occurs from the hydrogen-bonded state. Protection from acid-catalyzed exchange correlates with hydrogen bonding by both the NH and CO groups of a peptide unit: the data are fitted by a model in which exchange occurs only when both hydrogen bonds formed by a peptide unit are broken. This result indicates that acid-catalyzed exchange occurs by the O-protonation mechanism, in agreement with earlier work [Perrin & Arrhenius (1982) J. Am. Chem. Soc. 104, 6693-6696; Perrin et al. (1984) J. Am. Chem. Soc. 106, 2749-2753; Tüchsen & Woodward (1985) J. Mol. Biol. 185, 421-430].

Kinetics of amide proton exchange in helical peptides of varying chain lengths. Interpretation by the Lifson-Roig equation

The kinetics of amide proton exchange (1H----2H) have been measured by proton nuclear magnetic resonance spectroscopy for a set of helical peptides with the generic formula Ac-(AAKAA)m Y-NH2 and with chain lengths varying from 6 to 51 residues. The integrated intensity of the amide resonances has been measured as a function of time in 2H2O at pH* 2.50. Exchange kinetics for these peptides can be modeled by applying the Lifson-Roig treatment for the helix-to-coil transition. The Lifson-Roig equation is used to compute the probability that each residue is helical, as defined by its backbone (phi, psi) angles. A recursion formula then is used to find the probability that the backbone amide proton of each residue is hydrogen bonded. The peptide helix can be treated as a homopolymer, and direct exchange from the helix can be neglected. The expression for the exchange kinetics contains only three unknown parameters: the rate constant for exchange of a non-hydrogen-bonded (random coil) backbone amide proton and the nucleation (v2) and propagation (w) parameters of the Lifson-Roig theory. The fit of the exchange curves to these three parameters is very good, and the values for v2 and w agree with those derived from circular dichroism studies of the thermally-induced unfolding of related peptides [Scholtz, J.M., Qian, H., York, E.J., Stewart, J.M., & Baldwin, R.L. (1991) Biopolymers (in press]).